diff --git a/llvm/projects/gpu_profiler/.gitignore b/llvm/projects/gpu_profiler/.gitignore
deleted file mode 100644
index dd2c293453382269c150c372d926f287a74edea5..0000000000000000000000000000000000000000
--- a/llvm/projects/gpu_profiler/.gitignore
+++ /dev/null
@@ -1,3 +0,0 @@
-*.swp
-jetsonTX2Power
-pp
diff --git a/llvm/projects/gpu_profiler/CMakeLists.txt b/llvm/projects/gpu_profiler/CMakeLists.txt
deleted file mode 100644
index c6cf3041eee354609b3999e5a8dcd424990f75ec..0000000000000000000000000000000000000000
--- a/llvm/projects/gpu_profiler/CMakeLists.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-cmake_minimum_required(VERSION 3.5)
-set(libsrc src/profiler.cpp)
-set (CMAKE_CXX_STANDARD 11)
-add_library(gpu_profiler STATIC ${libsrc})
-target_include_directories(gpu_profiler PRIVATE include)
diff --git a/llvm/projects/gpu_profiler/Makefile b/llvm/projects/gpu_profiler/Makefile
deleted file mode 100644
index 412d38265ab5c9408d4ac444ded9d6bd8b72f1b7..0000000000000000000000000000000000000000
--- a/llvm/projects/gpu_profiler/Makefile
+++ /dev/null
@@ -1,5 +0,0 @@
-all:
- g++ -std=c++11 -O3 profiler.cpp -o pp -lpthread
-
-clean:
- rm -rf pp
diff --git a/llvm/projects/gpu_profiler/include/profiler.h b/llvm/projects/gpu_profiler/include/profiler.h
deleted file mode 100644
index 9468554833fa82fd3b3c8c2ab85a393e21e92c80..0000000000000000000000000000000000000000
--- a/llvm/projects/gpu_profiler/include/profiler.h
+++ /dev/null
@@ -1,123 +0,0 @@
-#include <atomic>
-#include <chrono>
-#include <cmath>
-#include <condition_variable>
-#include <fstream>
-#include <iostream>
-#include <string>
-#include <thread>
-#include <vector>
-
-// Reads power rails at runtime and computes the GPU and DDR energy within a window
-// of time, which is delimitered by the calls to resume_profiler() and pause_profiler()
-//
-// IMPORTANT: Must call pause_profiler() to kill the profiler thread
-//
-// Public interface methods:
-// void start_profiler();
-// void resume_profiler();
-// void pause_profiler();
-// std::pair<double, double> get_time_energy() const;
-// void reset()
-// void pause_profiler();
-class Profiler {
-public:
- using clock_type = std::chrono::high_resolution_clock;
-
- Profiler();
-
- ~Profiler();
-
- // Reinitializes boolean vars used for control flow and launches the profiler
- // thread. DOES NOT reset other internal data structures.
- void start_profiler();
-
- // Resumes the profiling of whatever executable's currently running
- // DOES NOT reset any data
- void resume_profiler();
-
- // Stops profiler by putting profiler thread to sleep
- void pause_profiler();
-
- // Gets the delta time and total GPU and DDR energy between the last two
- // calls to resume_profiler and pause_profiler
- //
- // Returns this as a pair of <delta time in milliseconds, energy>
- std::pair<double, double> get_time_energy() const;
-
- // Resets all internal data structures, including the vector storing all power_readings.
- void reset();
-
- // Exit the profiler and kill the thread
- // Must call start_profiler() to reuse this object after calling pause_profiler()
- void stop_profiler();
-
-private:
- // Jetson's ARM cores' physical IDs. The two Denver cores are 1 and 2, and
- // we can't use them.
- const unsigned core0 = 0;
- const unsigned core1 = 3;
- const unsigned core2 = 4;
- const unsigned core3 = 5;
-
- // Power rails are mounted as files. Keeping the old power rail file names for possible future
- // integrations
- const std::string cpu_power_rail = "/sys/devices/3160000.i2c/i2c-0/0-0041/iio_device/in_power1_input";
- const std::string gpu_power_rail = "/sys/devices/3160000.i2c/i2c-0/0-0040/iio_device/in_power0_input";
- const std::string ddr_power_rail = "/sys/devices/3160000.i2c/i2c-0/0-0041/iio_device/in_power2_input";
- const std::string soc_power_rail = "/sys/devices/3160000.i2c/i2c-0/0-0040/iio_device/in_power1_input";
- const std::string sys_power_rail = "/sys/devices/3160000.i2c/i2c-0/0-0041/iio_device/in_power0_input";
- // Critical assumption: If this file doesn't exist, then the board isn't a Jetson
- const std::string jetson_chip_id = "/sys/module/tegra_fuse/parameters/tegra_chip_id";
-
- // True if running on Jetson, else false
- bool on_jetson_;
-
- // An individual power reading
- struct PowerReading {
- std::chrono::time_point<clock_type> time_;
- double cpu_;
- double gpu_;
- double ddr_;
- double soc_;
- double sys_;
- };
-
- // Stores all power readings and is cleared only when reset() is called
- std::vector<PowerReading> power_readings_;
-
- // For reading the i2c buses via sysfs
- std::ifstream cpu_stream_;
- std::ifstream gpu_stream_;
- std::ifstream ddr_stream_;
- std::ifstream soc_stream_;
- std::ifstream sys_stream_;
-
- double run_profiler_overhead = 0.0;
-
- mutable std::mutex vector_mutex_;
-
- std::mutex mutex_;
-
- std::condition_variable cond_var_;
-
- std::chrono::time_point<clock_type> start_time_;
-
- std::chrono::time_point<clock_type> stop_time_;
-
- std::atomic_bool should_run_profiler_; // True if we want to resume the profiling thread
-
- std::atomic_bool should_stop_profiler_; // Quit profiling
-
- std::thread profiler_thread_;
-
- // Obtain's a single power reading from the GPU and DDR rails
- void obtain_power_reading();
-
- // Pins the given thread to the specified core
- void pin_thread(std::thread &t, const unsigned core) const;
-
- // Runs the profiler thread, keeping it alive by wrapping the functionality
- // in an infinite loop
- void run_profiler();
-};
diff --git a/llvm/projects/gpu_profiler/offline_profiler.cpp b/llvm/projects/gpu_profiler/offline_profiler.cpp
deleted file mode 100644
index 6b9f37ef62cc2c8600d11474100f27873bc36d7a..0000000000000000000000000000000000000000
--- a/llvm/projects/gpu_profiler/offline_profiler.cpp
+++ /dev/null
@@ -1,595 +0,0 @@
-#include <cmath>
-#include <chrono>
-
-#include <iostream>
-#include <fstream>
-#include <string>
-#include <boost/algorithm/string.hpp>
-
-#include <vector>
-#include <map>
-
-#include <thread>
-#include <atomic>
-#include <sched.h>
-
-#define NUM_ARGS 4
-
-// This is a simple power profiler that can sample the power of the various
-// components in a Jetson TX2. The usage is simple: profile() measures power
-// for the specified program, and then dumpOutput() prints the readings to a
-// file. profile() can be called as many times as desired - the internal state
-// is reset each time and thus the measurements are not cumulative.
-class Profiler {
-private:
- // Jetson's ARM cores' physical IDs. The two Denver cores are 1 and 2, and
- // we can't use them.
- const unsigned core0 = 0;
- const unsigned core1 = 3;
- const unsigned core2 = 4;
- const unsigned core3 = 5;
-
- // sysfs paths for i2c buses of various components
- const char * const cpu_power_rail = "/sys/devices/3160000.i2c/i2c-0/0-0041/iio_device/in_power1_input";
- const char * const gpu_power_rail = "/sys/devices/3160000.i2c/i2c-0/0-0040/iio_device/in_power0_input";
- const char * const ddr_power_rail = "/sys/devices/3160000.i2c/i2c-0/0-0041/iio_device/in_power2_input";
- const char * const soc_power_rail = "/sys/devices/3160000.i2c/i2c-0/0-0040/iio_device/in_power1_input";
- const char * const sys_power_rail = "/sys/devices/3160000.i2c/i2c-0/0-0041/iio_device/in_power0_input";
-
- // It takes some time for the GPU's power to return to idle (ms)
- const unsigned gpu_idle_time = 0;
-
- // An individual power reading
- struct PowerReading {
- std::chrono::time_point<std::chrono::high_resolution_clock> time_;
- double cpu_;
- double gpu_;
- double ddr_;
- double soc_;
- double sys_;
- };
-
- // Individual tensor op
- struct TensorOp {
- std::string name_;
-
- double start_;
- double finish_;
- double time_;
-
- double energy_;
- double gpu_energy_;
- double ddr_energy_;
-
- double power_;
- double gpu_power_;
- double ddr_power_;
-
- TensorOp(std::string name, double start, double finish)
- : name_(name), start_(start), finish_(finish), time_(finish - start),
- energy_(0.0), gpu_energy_(0.0), ddr_energy_(0.0),
- power_(0.0), gpu_power_(0.0), ddr_power_(0.0) {
- }
- };
-
- // Aggregate tensor info
- struct AggTensorInfo {
- // Op name
- std::string name_;
-
- // Averages
- double average_time_;
-
- double average_energy_;
- double average_gpu_energy_;
- double average_ddr_energy_;
-
- double average_power_;
- double average_gpu_power_;
- double average_ddr_power_;
-
- // Standard deviations
- double time_std_;
-
- double energy_std_;
- double gpu_energy_std_;
- double ddr_energy_std_;
-
- double power_std_;
- double gpu_power_std_;
- double ddr_power_std_;
- };
-
- // Total time, energy, and power
- struct TotalInfo {
- double time_;
-
- double energy_;
- double gpu_energy_;
- double ddr_energy_;
-
- double power_;
- double gpu_power_;
- double ddr_power_;
-
- void clear() {
- time_ = 0.0;
-
- energy_ = 0.0;
- gpu_energy_ = 0.0;
- ddr_energy_ = 0.0;
-
- power_ = 0.0;
- gpu_power_ = 0.0;
- ddr_power_ = 0.0;
- }
- };
-
- // For reading the i2c buses via sysfs
- std::ifstream cpu_stream_;
- std::ifstream gpu_stream_;
- std::ifstream ddr_stream_;
- std::ifstream soc_stream_;
- std::ifstream sys_stream_;
-
- // Start time (so graph begins from t=0)
- std::chrono::time_point<std::chrono::high_resolution_clock> start_time_;
-
- // Per-run info
- std::vector<PowerReading> power_readings_;
-
- // Aggregate (across all runs) info
- std::map<std::string, std::vector<TensorOp>> tensor_info_;
- std::vector<AggTensorInfo> agg_tensor_info_;
- TotalInfo total_info_;
- unsigned iterations_;
-
- // Start and stop flags to synchronize the program and profiling threads
- std::atomic_bool start_;
- std::atomic_bool stop_;
-
-private:
- // Resets tensor info and total time and energy
- void resetGlobal() {
- tensor_info_.clear();
- agg_tensor_info_.clear();
- total_info_.clear();
- }
-
- // Resets power readings and flags
- void resetLocal() {
- power_readings_.clear();
- start_ = false;
- stop_ = false;
- }
-
- // Pins the given thread to the specified core
- void pinThread(std::thread &t, const unsigned core) const {
- cpu_set_t cpuset;
- CPU_ZERO(&cpuset);
- CPU_SET(core, &cpuset);
- if (pthread_setaffinity_np(t.native_handle(), sizeof(cpu_set_t), &cpuset) != 0)
- std::cout << "Couldn't set thread affinity\n";
- }
-
- // Adds a tensor op to the map
- void addTensorOp(std::string &op_name, TensorOp &top) {
- // Create a vector if this is the first entry
- auto it = tensor_info_.find(op_name);
- if (it == tensor_info_.end()) {
- tensor_info_.insert(std::pair<std::string, std::vector<TensorOp>>(op_name, std::vector<TensorOp>()));
- }
- tensor_info_[op_name].push_back(top);
- }
-
- // Obtain's a single power reading from the GPU and DDR rails
- void getPowerReading() {
- PowerReading reading;
-
- // The order matters here. All the reads have to happen together first
- // and then all the seeks have to happen together at the end, otherwise
- // there will be a significant time difference between the readings of
- // the different rails.
- reading.time_ = std::chrono::high_resolution_clock::now();
- gpu_stream_ >> reading.gpu_;
- ddr_stream_ >> reading.ddr_;
- power_readings_.push_back(reading);
-
- // Reset the input position of the files
- gpu_stream_.seekg(0);
- ddr_stream_.seekg(0);
- }
-
- // Executes the program to be profiled
- void runProgram(const std::string& program) {
- // Tell the profiling thread to start, execute the program that needs
- // to be profiled, and then tell the profiling thread to stop.
- start_ = true;
- const auto result = std::system(program.c_str());
- stop_ = true;
- }
-
- // Records power while the program is running
- void recordPower() {
- // Obtain the new start time, wait for the start signal, and keep
- // profiling until the stop flag is set.
- start_time_ = std::chrono::high_resolution_clock::now();
- while (!start_);
- while (!stop_)
- getPowerReading();
- }
-
- // Calculates stats for the entire execution (CPU+GPU phase)
- void updateTotalStats() {
- double energy = 0.0;
- double gpu_energy = 0.0;
- double ddr_energy = 0.0;
-
- std::chrono::time_point<std::chrono::high_resolution_clock> prev_time = start_time_;
- for (auto reading : power_readings_) {
- std::chrono::duration<double> duration = reading.time_ - prev_time;
- gpu_energy += reading.gpu_ * duration.count();
- ddr_energy += reading.ddr_ * duration.count();
- prev_time = reading.time_;
- }
- energy = gpu_energy + ddr_energy;
- auto time = std::chrono::duration<double>(prev_time - start_time_).count();
-
- total_info_.time_ += time;
- total_info_.energy_ += (gpu_energy + ddr_energy);
- total_info_.gpu_energy_ += gpu_energy;
- total_info_.ddr_energy_ += ddr_energy;
-
- total_info_.power_ += (energy / time);
- total_info_.gpu_power_ += (gpu_energy / time);
- total_info_.ddr_power_ += (ddr_energy / time);
- }
-
- // Calculates energy and power usage of the given tensor operation
- void calculateTensorEP(TensorOp &top) const {
- auto prev_time = top.start_;
- unsigned i = 0;
-
- // Skip until we hit the start time of the operation
- for (; std::chrono::duration<double>(power_readings_[i].time_.time_since_epoch()).count() < top.start_; i++);
-
- // Keep going until we hit the finish time of the operation or we run out of readings
- for (double curr_time; ((curr_time = std::chrono::duration<double>(power_readings_[i].time_.time_since_epoch()).count()) <= top.finish_)
- && (i < power_readings_.size()); i++) {
- auto duration = curr_time - prev_time;
- prev_time = curr_time;
-
- top.gpu_energy_ += power_readings_[i].gpu_ * duration;
- top.ddr_energy_ += power_readings_[i].ddr_ * duration;
- }
- top.energy_ = top.gpu_energy_ + top.ddr_energy_;
-
- top.power_ = top.energy_ / top.time_;
- top.gpu_power_ = top.gpu_energy_ / top.time_;
- top.ddr_power_ = top.ddr_energy_ / top.time_;
- }
-
- // Calculates stats for all the tensors in the timestamp file
- void updatePerOpStats() {
- const char * const op_file = "profile_data.txt";
- std::string line;
- std::ifstream ifs(op_file, std::ios::in);
-
- // Calculate time and energy for each tensor operation. There are two
- // possibilities for the file format:
- // If the line doesn't begin with #, we are looking at FP32 code
- // without any conversions to/from FP16, and each operation occupies
- // two consecutive lines in the timestamp file.
- // If the line does begin with #, we are looking at FP16 code with
- // conversion routines in the middle. In this case, *after* the current
- // line, there will be two lines for F2H, two lines for H2F, and then
- // one line for the end of the operation.
- while (std::getline(ifs, line)) {
- std::vector<std::string> tokens;
- boost::split(tokens, line, boost::is_any_of("\t"));
- std::string op_name = tokens[0];
-
- // FP32
- if (tokens[0][0] != '#') {
- // First line with tensor op name and start time
- std::string op_name = tokens[0];
- const auto start = std::stod(tokens[1]);
-
- // Second line with tensor op end time
- std::getline(ifs, line);
- tokens.clear();
- boost::split(tokens, line, boost::is_any_of("\t"));
- const auto finish = std::stod(tokens[1]);
-
- TensorOp top(op_name, start, finish);
- calculateTensorEP(top);
- addTensorOp(op_name, top);
- } else {
- // First line with tensor op name and start time
- std::string op_name = tokens[0].substr(1);
- const auto start = std::stod(tokens[1]);
-
- // Second line with f2h
- std::getline(ifs, line);
- tokens.clear();
- boost::split(tokens, line, boost::is_any_of("\t"));
- std::string f2h_name = op_name + "_f2h";
- const auto f2h_start = std::stod(tokens[1]);
-
- // Third line with f2h
- std::getline(ifs, line);
- tokens.clear();
- boost::split(tokens, line, boost::is_any_of("\t"));
- const auto f2h_finish = std::stod(tokens[1]);
-
- // Add f2h
- TensorOp f2h(f2h_name, f2h_start, f2h_finish);
- calculateTensorEP(f2h);
- addTensorOp(f2h_name, f2h);
-
- // Fourth line with h2f
- std::getline(ifs, line);
- tokens.clear();
- boost::split(tokens, line, boost::is_any_of("\t"));
- std::string h2f_name = op_name + "_h2f";
- const auto h2f_start = std::stod(tokens[1]);
-
- // Fifth line with h2f
- std::getline(ifs, line);
- tokens.clear();
- boost::split(tokens, line, boost::is_any_of("\t"));
- const auto h2f_finish = std::stod(tokens[1]);
-
- // Add h2f
- TensorOp h2f(h2f_name, h2f_start, h2f_finish);
- calculateTensorEP(h2f);
- addTensorOp(h2f_name, h2f);
-
- // Sixth and final line with tensor op end time
- std::getline(ifs, line);
- tokens.clear();
- boost::split(tokens, line, boost::is_any_of("\t"));
- const auto finish = std::stod(tokens[1]);
-
- // Subtract f2h's and h2f's time and energy to get just the computation's info
- TensorOp top(op_name, start, finish);
- calculateTensorEP(top);
-
- top.time_ -= (f2h.time_ + h2f.time_);
- top.energy_ -= (f2h.energy_ + h2f.energy_);
- top.gpu_energy_ -= (f2h.gpu_energy_ + h2f.gpu_energy_);
- top.ddr_energy_ -= (f2h.ddr_energy_ + h2f.ddr_energy_);
- top.power_ = top.energy_ / top.time_;
- top.gpu_power_ = top.gpu_energy_ / top.time_;
- top.ddr_power_ = top.ddr_energy_ / top.time_;
-
- addTensorOp(op_name, top);
- }
- }
- ifs.close();
- }
-
- void updateStats() {
- updatePerOpStats();
- updateTotalStats();
- }
-
- // Calculates the average and standard deviation of each metric of each tensor op
- void calculateAggregateStats() {
- for (auto it = tensor_info_.begin(); it != tensor_info_.end(); it++) {
- AggTensorInfo ati;
- ati.name_ = it->first;
- auto topv = it->second;
-
- double total_time = 0.0;
- double total_energy = 0.0;
- double total_gpu_energy = 0.0;
- double total_ddr_energy = 0.0;
- double total_power = 0.0;
- double total_gpu_power = 0.0;
- double total_ddr_power = 0.0;
-
- double time_sum = 0.0;
- double energy_sum = 0.0;
- double gpu_energy_sum = 0.0;
- double ddr_energy_sum = 0.0;
- double power_sum = 0.0;
- double gpu_power_sum = 0.0;
- double ddr_power_sum = 0.0;
-
- // Calculate average
- for (const auto &top : topv) {
- total_time += top.time_;
- total_energy += top.energy_;
- total_gpu_energy += top.gpu_energy_;
- total_ddr_energy += top.ddr_energy_;
- total_power += top.power_;
- total_gpu_power += top.gpu_power_;
- total_ddr_power += top.ddr_power_;
- }
-
- ati.average_time_ = total_time / iterations_;
- ati.average_energy_ = total_energy / iterations_;
- ati.average_gpu_energy_ = total_gpu_energy / iterations_;
- ati.average_ddr_energy_ = total_ddr_energy / iterations_;
- ati.average_power_ = total_power / iterations_;
- ati.average_gpu_power_ = total_gpu_power / iterations_;
- ati.average_ddr_power_ = total_ddr_power / iterations_;
-
- // Calculate standard deviation
- for (const auto &top : topv) {
- auto time_diff = top.time_ - ati.average_time_;
- time_sum += time_diff * time_diff;
-
- auto energy_diff = top.energy_ - ati.average_energy_;
- energy_sum += energy_diff * energy_diff;
- auto gpu_energy_diff = top.gpu_energy_ - ati.average_gpu_energy_;
- gpu_energy_sum += gpu_energy_diff * gpu_energy_diff;
- auto ddr_energy_diff = top.ddr_energy_ - ati.average_ddr_energy_;
- ddr_energy_sum += ddr_energy_diff * ddr_energy_diff;
-
- auto power_diff = top.power_ - ati.average_power_;
- power_sum += power_diff * power_diff;
- auto gpu_power_diff = top.gpu_power_ - ati.average_gpu_power_;
- gpu_power_sum += gpu_power_diff * gpu_power_diff;
- auto ddr_power_diff = top.ddr_power_ - ati.average_ddr_power_;
- ddr_power_sum += ddr_power_diff * ddr_power_diff;
- }
-
- ati.time_std_ = std::sqrt(time_sum / iterations_);
- ati.energy_std_ = std::sqrt(energy_sum / iterations_);
- ati.gpu_energy_std_ = std::sqrt(gpu_energy_sum / iterations_);
- ati.ddr_energy_std_ = std::sqrt(ddr_energy_sum / iterations_);
- ati.power_std_ = std::sqrt(power_sum / iterations_);
- ati.gpu_power_std_ = std::sqrt(gpu_power_sum / iterations_);
- ati.ddr_power_std_ = std::sqrt(ddr_power_sum / iterations_);
-
- agg_tensor_info_.push_back(ati);
- }
- }
-
-public:
- Profiler() {
- cpu_stream_.open(cpu_power_rail, std::ifstream::in);
- gpu_stream_.open(gpu_power_rail, std::ifstream::in);
- ddr_stream_.open(ddr_power_rail, std::ifstream::in);
- soc_stream_.open(soc_power_rail, std::ifstream::in);
- sys_stream_.open(sys_power_rail, std::ifstream::in);
-
- if (!cpu_stream_.is_open() or !gpu_stream_.is_open() or !ddr_stream_.is_open()
- or !soc_stream_.is_open() or !sys_stream_.is_open()) {
- std::cout << "Failed to open one of the power rails for reading\n";
- exit(1);
- }
- }
-
- ~Profiler() {
- cpu_stream_.close();
- gpu_stream_.close();
- ddr_stream_.close();
- soc_stream_.close();
- sys_stream_.close();
- }
-
- void profile(const std::string& program, const int iterations) {
- iterations_ = iterations;
- resetGlobal();
-
- for (unsigned i = 0; i < iterations_; i++) {
- resetLocal();
-
- // Launch two threads: one for running the program and one for
- // profiling it. Pin the threads to specific cores to remove migration
- // overhead. Profiling showed that the sampling rate increases slightly
- // with pinning.
- std::thread prog(&Profiler::runProgram, this, program);
- std::thread power(&Profiler::recordPower, this);
- pinThread(prog, core1);
- pinThread(power, core2);
- prog.join();
- power.join();
-
- updateStats();
-
- // Sleep for some time to bring the GPU back to idle
- std::this_thread::sleep_for(std::chrono::milliseconds(gpu_idle_time));
- }
-
- calculateAggregateStats();
- }
-
- void dumpTensorInfo(const char * const filename) const {
- std::cout<<"dumping to"<<filename<<'\n';
- const std::string header = "Op,Time (ms),Energy (mJ),GPU Energy (mJ),DDR Energy (mJ),Power (mW),GPU Power (mW),DDR Power (mW),Time std,Energy std,GPU Energy std,DDR Energy std,Power std,GPU Power std,DDR Power std\n";
- std::ofstream ofs;
- ofs.open(filename);
- //ofs << header;
- for (const auto &ati : agg_tensor_info_) {
- ofs << ati.name_
- << "," << ati.average_time_ * 1e3
- << "," << ati.average_energy_
- /*
- << "," << ati.average_gpu_energy_
- << "," << ati.average_ddr_energy_
- << "," << ati.average_power_
- << "," << ati.average_gpu_power_
- << "," << ati.average_ddr_power_
- << "," << ati.time_std_ * 1e3
- << "," << ati.energy_std_
- << "," << ati.gpu_energy_std_
- << "," << ati.ddr_energy_std_
- << "," << ati.power_std_
- << "," << ati.gpu_power_std_
- << "," << ati.ddr_power_std_*/
- << "\n";
-
- std::cout << ati.average_time_ * 1e3 << "," << ati.average_energy_ << "\n";
- }
- ofs.close();
- }
-
- void dumpPowerReadings(const char * const filename) const {
- std::ofstream ofs;
- ofs.open(filename);
- for (const auto &reading : power_readings_) {
- std::chrono::duration<double> duration = reading.time_ - start_time_;
- //std::chrono::duration<double> duration = reading.time_.time_since_epoch();
- ofs << std::to_string(duration.count())
- << " " << reading.gpu_
- << " " << reading.ddr_
- << "\n";
- }
- ofs.close();
- }
-
- void dumpTotalInfo() const {
- auto total_time = total_info_.time_ / iterations_;
-
- auto total_energy = total_info_.energy_ / iterations_;
- auto gpu_energy = total_info_.gpu_energy_ / iterations_;
- auto ddr_energy = total_info_.ddr_energy_ / iterations_;
-
- auto power = total_info_.power_ / iterations_;
- auto gpu_power = total_info_.gpu_power_ / iterations_;
- auto ddr_power = total_info_.ddr_power_ / iterations_;
-
- std::cout << "-----------------------------------------------------\n";
- std::cout << "Program info (average)\n";
- std::cout << "-----------------------------------------------------\n";
- std::cout << "\tExecution time: " << total_time << " seconds\n";
- std::cout << "\tTotal energy: " << total_energy << " mJ\n";
- std::cout << "\t GPU: " << gpu_energy << " mJ\n";
- std::cout << "\t DDR: " << ddr_energy << " mJ\n";
- std::cout << "\tPower: " << power << " mW\n";
- std::cout << "\t GPU: " << gpu_power << " mW\n";
- std::cout << "\t DDR: " << ddr_power << " mW\n";
- std::cout << "-----------------------------------------------------\n";
- }
-};
-
-int main(int argc, char *argv[]) {
- if (argc < NUM_ARGS) {
- std::cout << "Usage: " << argv[0] << " <program> <params> END_PARAM <iterations> <tensor output file> [power output file]\n";
- exit(1);
- }
-
- std::string program(argv[1]);
- size_t i = 2;
- for (; i < argc; i++){
- if (std::string(argv[i]) == "END_PARAM"){
- break;
- }
- program += " " + std::string(argv[i]);
- }
- i += 1;
-
- Profiler pp;
- pp.profile(program, std::stoi(argv[i]));
- pp.dumpTensorInfo(argv[i + 1]);
-
- if (argc > NUM_ARGS)
- pp.dumpPowerReadings(argv[i + 2]);
-
- return 0;
-}
-
diff --git a/llvm/projects/gpu_profiler/plot.sh b/llvm/projects/gpu_profiler/plot.sh
deleted file mode 100755
index 8e4573b10c2fab993b4998d2040d10b0f7e9f9c5..0000000000000000000000000000000000000000
--- a/llvm/projects/gpu_profiler/plot.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/bin/sh
-input=$1
-gnuplot -p << EOF
- #set terminal png
- #set output "$input.png"
- set xlabel "Time (s)"
- set ylabel "Power (mW)"
- set title "Power usage of GPU and DDR over time"
- plot "$input" using 1:2 title 'GPU' with lines,"$input" using 1:3 title 'DDR' with lines
-EOF
diff --git a/llvm/projects/gpu_profiler/results/lenet/lenet-m3fullCLK-1.pdf b/llvm/projects/gpu_profiler/results/lenet/lenet-m3fullCLK-1.pdf
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diff --git a/llvm/projects/gpu_profiler/run.pl b/llvm/projects/gpu_profiler/run.pl
deleted file mode 100755
index 8674e63d9453fbb1e07371d99cf22c4745f234b3..0000000000000000000000000000000000000000
--- a/llvm/projects/gpu_profiler/run.pl
+++ /dev/null
@@ -1,64 +0,0 @@
-#!/usr/bin/perl
-
-use strict;
-use warnings;
-
-my $time;
-my $iterations = 100;
-
-# FP32
-print "############### FP32 ##############\n";
-
-print "Running Lenet\n";
-$time = `date`;
-print $time;
-`~/awesome_profiler/pp ./lenet_tanh $iterations lenet-fp32.csv`;
-
-print "Running FC2\n";
-$time = `date`;
-print $time;
-`~/awesome_profiler/pp ./fc2_clipped $iterations fc2-fp32.csv`;
-
-print "Running FC3\n";
-$time = `date`;
-print $time;
-`~/awesome_profiler/pp ./fc3_clipped $iterations fc3-fp32.csv`;
-
-print "Running FC4\n";
-$time = `date`;
-print $time;
-`~/awesome_profiler/pp ./fc4_clipped $iterations fc4-fp32.csv`;
-
-print "Running CIFAR\n";
-$time = `date`;
-print $time;
-`~/awesome_profiler/pp ./cifar_keras $iterations cifar-fp32.csv`;
-
-# FP16
-print "############### FP16 ##############\n";
-
-print "Running Lenet\n";
-$time = `date`;
-print $time;
-`~/awesome_profiler/pp ./lenet_tanh_half $iterations lenet-fp16.csv`;
-
-print "Running FC2\n";
-$time = `date`;
-print $time;
-`~/awesome_profiler/pp ./fc2_half $iterations fc2-fp16.csv`;
-
-print "Running FC3\n";
-$time = `date`;
-print $time;
-`~/awesome_profiler/pp ./fc3_half $iterations fc3-fp16.csv`;
-
-print "Running FC4\n";
-$time = `date`;
-print $time;
-`~/awesome_profiler/pp ./fc4_half $iterations fc4-fp16.csv`;
-
-print "Running CIFAR\n";
-$time = `date`;
-print $time;
-`~/awesome_profiler/pp ./cifar_keras_half $iterations cifar-fp16.csv`;
-
diff --git a/llvm/projects/gpu_profiler/run_dnns.pl b/llvm/projects/gpu_profiler/run_dnns.pl
deleted file mode 100755
index 041f3e3cae8598d34ac8d38f65cd37d51e8aa0ba..0000000000000000000000000000000000000000
--- a/llvm/projects/gpu_profiler/run_dnns.pl
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/usr/bin/perl
-
-use strict;
-use warnings;
-
-my $time;
-my $iterations = 100;
-my @networks = ("alexnet", "alexnet2", "resnet18", "vgg16");
-
-# FP32
-print "############### FP32 ##############\n";
-
-foreach my $network (@networks) {
- print "Running $network\n";
- $time = `date`;
- print $time;
- `~/awesome_profiler/pp ./${network}_cifar10 $iterations ${network}_fp32.csv`;
-}
-
-# FP16
-print "############### FP16 ##############\n";
-
-foreach my $network (@networks) {
- print "Running $network\n";
- $time = `date`;
- print $time;
- `~/awesome_profiler/pp ./${network}_cifar10_half $iterations ${network}_fp16.csv`;
-}
-
diff --git a/llvm/projects/gpu_profiler/run_image_pipelines.pl b/llvm/projects/gpu_profiler/run_image_pipelines.pl
deleted file mode 100755
index 8e6df67d2e96d343cff3cc6a324693c14abaa3f3..0000000000000000000000000000000000000000
--- a/llvm/projects/gpu_profiler/run_image_pipelines.pl
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/usr/bin/perl
-
-use strict;
-use warnings;
-
-my $time;
-my $iterations = 100;
-my @pipelines = ("pipeline_GEMO", "pipeline_GEO", "pipeline_GEOM", "pipeline_GSM", "pipeline_GSME");
-
-# FP32
-print "############### FP32 ##############\n";
-
-foreach my $pipeline (@pipelines) {
- print "Running $pipeline\n";
- $time = `date`;
- print $time;
- `~/awesome_profiler/pp ./${pipeline} $iterations ${pipeline}_fp32.csv`;
-}
-
-# FP16
-print "############### FP16 ##############\n";
-
-foreach my $pipeline (@pipelines) {
- print "Running $pipeline\n";
- $time = `date`;
- print $time;
- `~/awesome_profiler/pp ./${pipeline}_half $iterations ${pipeline}_fp16.csv`;
-}
-
diff --git a/llvm/projects/gpu_profiler/src/offline_profiler.cpp b/llvm/projects/gpu_profiler/src/offline_profiler.cpp
deleted file mode 100644
index 25ca45241c29e7a0f8edb0518d8347a185caf5a4..0000000000000000000000000000000000000000
--- a/llvm/projects/gpu_profiler/src/offline_profiler.cpp
+++ /dev/null
@@ -1,584 +0,0 @@
-#include <cmath>
-#include <chrono>
-
-#include <iostream>
-#include <fstream>
-#include <string>
-#include <boost/algorithm/string.hpp>
-
-#include <vector>
-#include <map>
-
-#include <thread>
-#include <atomic>
-#include <sched.h>
-
-#define NUM_ARGS 4
-
-// This is a simple power profiler that can sample the power of the various
-// components in a Jetson TX2. The usage is simple: profile() measures power
-// for the specified program, and then dumpOutput() prints the readings to a
-// file. profile() can be called as many times as desired - the internal state
-// is reset each time and thus the measurements are not cumulative.
-class Profiler {
-private:
- // Jetson's ARM cores' physical IDs. The two Denver cores are 1 and 2, and
- // we can't use them.
- const unsigned core0 = 0;
- const unsigned core1 = 3;
- const unsigned core2 = 4;
- const unsigned core3 = 5;
-
- // sysfs paths for i2c buses of various components
- const char * const cpu_power_rail = "/sys/devices/3160000.i2c/i2c-0/0-0041/iio_device/in_power1_input";
- const char * const gpu_power_rail = "/sys/devices/3160000.i2c/i2c-0/0-0040/iio_device/in_power0_input";
- const char * const ddr_power_rail = "/sys/devices/3160000.i2c/i2c-0/0-0041/iio_device/in_power2_input";
- const char * const soc_power_rail = "/sys/devices/3160000.i2c/i2c-0/0-0040/iio_device/in_power1_input";
- const char * const sys_power_rail = "/sys/devices/3160000.i2c/i2c-0/0-0041/iio_device/in_power0_input";
-
- // It takes some time for the GPU's power to return to idle (ms)
- const unsigned gpu_idle_time = 0;
-
- // An individual power reading
- struct PowerReading {
- std::chrono::time_point<std::chrono::high_resolution_clock> time_;
- double cpu_;
- double gpu_;
- double ddr_;
- double soc_;
- double sys_;
- };
-
- // Individual tensor op
- struct TensorOp {
- std::string name_;
-
- double start_;
- double finish_;
- double time_;
-
- double energy_;
- double gpu_energy_;
- double ddr_energy_;
-
- double power_;
- double gpu_power_;
- double ddr_power_;
-
- TensorOp(std::string name, double start, double finish)
- : name_(name), start_(start), finish_(finish), time_(finish - start),
- energy_(0.0), gpu_energy_(0.0), ddr_energy_(0.0),
- power_(0.0), gpu_power_(0.0), ddr_power_(0.0) {
- }
- };
-
- // Aggregate tensor info
- struct AggTensorInfo {
- // Op name
- std::string name_;
-
- // Averages
- double average_time_;
-
- double average_energy_;
- double average_gpu_energy_;
- double average_ddr_energy_;
-
- double average_power_;
- double average_gpu_power_;
- double average_ddr_power_;
-
- // Standard deviations
- double time_std_;
-
- double energy_std_;
- double gpu_energy_std_;
- double ddr_energy_std_;
-
- double power_std_;
- double gpu_power_std_;
- double ddr_power_std_;
- };
-
- // Total time, energy, and power
- struct TotalInfo {
- double time_;
-
- double energy_;
- double gpu_energy_;
- double ddr_energy_;
-
- double power_;
- double gpu_power_;
- double ddr_power_;
-
- void clear() {
- time_ = 0.0;
-
- energy_ = 0.0;
- gpu_energy_ = 0.0;
- ddr_energy_ = 0.0;
-
- power_ = 0.0;
- gpu_power_ = 0.0;
- ddr_power_ = 0.0;
- }
- };
-
- // For reading the i2c buses via sysfs
- std::ifstream cpu_stream_;
- std::ifstream gpu_stream_;
- std::ifstream ddr_stream_;
- std::ifstream soc_stream_;
- std::ifstream sys_stream_;
-
- // Start time (so graph begins from t=0)
- std::chrono::time_point<std::chrono::high_resolution_clock> start_time_;
-
- // Per-run info
- std::vector<PowerReading> power_readings_;
-
- // Aggregate (across all runs) info
- std::map<std::string, std::vector<TensorOp>> tensor_info_;
- std::vector<AggTensorInfo> agg_tensor_info_;
- TotalInfo total_info_;
- unsigned iterations_;
-
- // Start and stop flags to synchronize the program and profiling threads
- std::atomic_bool start_;
- std::atomic_bool stop_;
-
-private:
- // Resets tensor info and total time and energy
- void resetGlobal() {
- tensor_info_.clear();
- agg_tensor_info_.clear();
- total_info_.clear();
- }
-
- // Resets power readings and flags
- void resetLocal() {
- power_readings_.clear();
- start_ = false;
- stop_ = false;
- }
-
- // Pins the given thread to the specified core
- void pinThread(std::thread &t, const unsigned core) const {
- cpu_set_t cpuset;
- CPU_ZERO(&cpuset);
- CPU_SET(core, &cpuset);
- if (pthread_setaffinity_np(t.native_handle(), sizeof(cpu_set_t), &cpuset) != 0)
- std::cout << "Couldn't set thread affinity\n";
- }
-
- // Adds a tensor op to the map
- void addTensorOp(std::string &op_name, TensorOp &top) {
- // Create a vector if this is the first entry
- auto it = tensor_info_.find(op_name);
- if (it == tensor_info_.end()) {
- tensor_info_.insert(std::pair<std::string, std::vector<TensorOp>>(op_name, std::vector<TensorOp>()));
- }
- tensor_info_[op_name].push_back(top);
- }
-
- // Obtain's a single power reading from the GPU and DDR rails
- void getPowerReading() {
- PowerReading reading;
-
- // The order matters here. All the reads have to happen together first
- // and then all the seeks have to happen together at the end, otherwise
- // there will be a significant time difference between the readings of
- // the different rails.
- reading.time_ = std::chrono::high_resolution_clock::now();
- gpu_stream_ >> reading.gpu_;
- ddr_stream_ >> reading.ddr_;
- power_readings_.push_back(reading);
-
- // Reset the input position of the files
- gpu_stream_.seekg(0);
- ddr_stream_.seekg(0);
- }
-
- // Executes the program to be profiled
- void runProgram(const char * const program) {
- // Tell the profiling thread to start, execute the program that needs
- // to be profiled, and then tell the profiling thread to stop.
- start_ = true;
- const auto result = std::system(program);
- stop_ = true;
- }
-
- // Records power while the program is running
- void recordPower() {
- // Obtain the new start time, wait for the start signal, and keep
- // profiling until the stop flag is set.
- start_time_ = std::chrono::high_resolution_clock::now();
- while (!start_);
- while (!stop_)
- getPowerReading();
- }
-
- // Calculates stats for the entire execution (CPU+GPU phase)
- void updateTotalStats() {
- double energy = 0.0;
- double gpu_energy = 0.0;
- double ddr_energy = 0.0;
-
- std::chrono::time_point<std::chrono::high_resolution_clock> prev_time = start_time_;
- for (auto reading : power_readings_) {
- std::chrono::duration<double> duration = reading.time_ - prev_time;
- gpu_energy += reading.gpu_ * duration.count();
- ddr_energy += reading.ddr_ * duration.count();
- prev_time = reading.time_;
- }
- energy = gpu_energy + ddr_energy;
- auto time = std::chrono::duration<double>(prev_time - start_time_).count();
-
- total_info_.time_ += time;
- total_info_.energy_ += (gpu_energy + ddr_energy);
- total_info_.gpu_energy_ += gpu_energy;
- total_info_.ddr_energy_ += ddr_energy;
-
- total_info_.power_ += (energy / time);
- total_info_.gpu_power_ += (gpu_energy / time);
- total_info_.ddr_power_ += (ddr_energy / time);
- }
-
- // Calculates energy and power usage of the given tensor operation
- void calculateTensorEP(TensorOp &top) const {
- auto prev_time = top.start_;
- unsigned i = 0;
-
- // Skip until we hit the start time of the operation
- for (; std::chrono::duration<double>(power_readings_[i].time_.time_since_epoch()).count() < top.start_; i++);
-
- // Keep going until we hit the finish time of the operation or we run out of readings
- for (double curr_time; ((curr_time = std::chrono::duration<double>(power_readings_[i].time_.time_since_epoch()).count()) <= top.finish_)
- && (i < power_readings_.size()); i++) {
- auto duration = curr_time - prev_time;
- prev_time = curr_time;
-
- top.gpu_energy_ += power_readings_[i].gpu_ * duration;
- top.ddr_energy_ += power_readings_[i].ddr_ * duration;
- }
- top.energy_ = top.gpu_energy_ + top.ddr_energy_;
-
- top.power_ = top.energy_ / top.time_;
- top.gpu_power_ = top.gpu_energy_ / top.time_;
- top.ddr_power_ = top.ddr_energy_ / top.time_;
- }
-
- // Calculates stats for all the tensors in the timestamp file
- void updatePerOpStats() {
- const char * const op_file = "profile_data.txt";
- std::string line;
- std::ifstream ifs(op_file, std::ios::in);
-
- // Calculate time and energy for each tensor operation. There are two
- // possibilities for the file format:
- // If the line doesn't begin with #, we are looking at FP32 code
- // without any conversions to/from FP16, and each operation occupies
- // two consecutive lines in the timestamp file.
- // If the line does begin with #, we are looking at FP16 code with
- // conversion routines in the middle. In this case, *after* the current
- // line, there will be two lines for F2H, two lines for H2F, and then
- // one line for the end of the operation.
- while (std::getline(ifs, line)) {
- std::vector<std::string> tokens;
- boost::split(tokens, line, boost::is_any_of("\t"));
- std::string op_name = tokens[0];
-
- // FP32
- if (tokens[0][0] != '#') {
- // First line with tensor op name and start time
- std::string op_name = tokens[0];
- const auto start = std::stod(tokens[1]);
-
- // Second line with tensor op end time
- std::getline(ifs, line);
- tokens.clear();
- boost::split(tokens, line, boost::is_any_of("\t"));
- const auto finish = std::stod(tokens[1]);
-
- TensorOp top(op_name, start, finish);
- calculateTensorEP(top);
- addTensorOp(op_name, top);
- } else {
- // First line with tensor op name and start time
- std::string op_name = tokens[0].substr(1);
- const auto start = std::stod(tokens[1]);
-
- // Second line with f2h
- std::getline(ifs, line);
- tokens.clear();
- boost::split(tokens, line, boost::is_any_of("\t"));
- std::string f2h_name = op_name + "_f2h";
- const auto f2h_start = std::stod(tokens[1]);
-
- // Third line with f2h
- std::getline(ifs, line);
- tokens.clear();
- boost::split(tokens, line, boost::is_any_of("\t"));
- const auto f2h_finish = std::stod(tokens[1]);
-
- // Add f2h
- TensorOp f2h(f2h_name, f2h_start, f2h_finish);
- calculateTensorEP(f2h);
- addTensorOp(f2h_name, f2h);
-
- // Fourth line with h2f
- std::getline(ifs, line);
- tokens.clear();
- boost::split(tokens, line, boost::is_any_of("\t"));
- std::string h2f_name = op_name + "_h2f";
- const auto h2f_start = std::stod(tokens[1]);
-
- // Fifth line with h2f
- std::getline(ifs, line);
- tokens.clear();
- boost::split(tokens, line, boost::is_any_of("\t"));
- const auto h2f_finish = std::stod(tokens[1]);
-
- // Add h2f
- TensorOp h2f(h2f_name, h2f_start, h2f_finish);
- calculateTensorEP(h2f);
- addTensorOp(h2f_name, h2f);
-
- // Sixth and final line with tensor op end time
- std::getline(ifs, line);
- tokens.clear();
- boost::split(tokens, line, boost::is_any_of("\t"));
- const auto finish = std::stod(tokens[1]);
-
- // Subtract f2h's and h2f's time and energy to get just the computation's info
- TensorOp top(op_name, start, finish);
- calculateTensorEP(top);
-
- top.time_ -= (f2h.time_ + h2f.time_);
- top.energy_ -= (f2h.energy_ + h2f.energy_);
- top.gpu_energy_ -= (f2h.gpu_energy_ + h2f.gpu_energy_);
- top.ddr_energy_ -= (f2h.ddr_energy_ + h2f.ddr_energy_);
- top.power_ = top.energy_ / top.time_;
- top.gpu_power_ = top.gpu_energy_ / top.time_;
- top.ddr_power_ = top.ddr_energy_ / top.time_;
-
- addTensorOp(op_name, top);
- }
- }
- ifs.close();
- }
-
- void updateStats() {
- updatePerOpStats();
- updateTotalStats();
- }
-
- // Calculates the average and standard deviation of each metric of each tensor op
- void calculateAggregateStats() {
- for (auto it = tensor_info_.begin(); it != tensor_info_.end(); it++) {
- AggTensorInfo ati;
- ati.name_ = it->first;
- auto topv = it->second;
-
- double total_time = 0.0;
- double total_energy = 0.0;
- double total_gpu_energy = 0.0;
- double total_ddr_energy = 0.0;
- double total_power = 0.0;
- double total_gpu_power = 0.0;
- double total_ddr_power = 0.0;
-
- double time_sum = 0.0;
- double energy_sum = 0.0;
- double gpu_energy_sum = 0.0;
- double ddr_energy_sum = 0.0;
- double power_sum = 0.0;
- double gpu_power_sum = 0.0;
- double ddr_power_sum = 0.0;
-
- // Calculate average
- for (const auto &top : topv) {
- total_time += top.time_;
- total_energy += top.energy_;
- total_gpu_energy += top.gpu_energy_;
- total_ddr_energy += top.ddr_energy_;
- total_power += top.power_;
- total_gpu_power += top.gpu_power_;
- total_ddr_power += top.ddr_power_;
- }
-
- ati.average_time_ = total_time / iterations_;
- ati.average_energy_ = total_energy / iterations_;
- ati.average_gpu_energy_ = total_gpu_energy / iterations_;
- ati.average_ddr_energy_ = total_ddr_energy / iterations_;
- ati.average_power_ = total_power / iterations_;
- ati.average_gpu_power_ = total_gpu_power / iterations_;
- ati.average_ddr_power_ = total_ddr_power / iterations_;
-
- // Calculate standard deviation
- for (const auto &top : topv) {
- auto time_diff = top.time_ - ati.average_time_;
- time_sum += time_diff * time_diff;
-
- auto energy_diff = top.energy_ - ati.average_energy_;
- energy_sum += energy_diff * energy_diff;
- auto gpu_energy_diff = top.gpu_energy_ - ati.average_gpu_energy_;
- gpu_energy_sum += gpu_energy_diff * gpu_energy_diff;
- auto ddr_energy_diff = top.ddr_energy_ - ati.average_ddr_energy_;
- ddr_energy_sum += ddr_energy_diff * ddr_energy_diff;
-
- auto power_diff = top.power_ - ati.average_power_;
- power_sum += power_diff * power_diff;
- auto gpu_power_diff = top.gpu_power_ - ati.average_gpu_power_;
- gpu_power_sum += gpu_power_diff * gpu_power_diff;
- auto ddr_power_diff = top.ddr_power_ - ati.average_ddr_power_;
- ddr_power_sum += ddr_power_diff * ddr_power_diff;
- }
-
- ati.time_std_ = std::sqrt(time_sum / iterations_);
- ati.energy_std_ = std::sqrt(energy_sum / iterations_);
- ati.gpu_energy_std_ = std::sqrt(gpu_energy_sum / iterations_);
- ati.ddr_energy_std_ = std::sqrt(ddr_energy_sum / iterations_);
- ati.power_std_ = std::sqrt(power_sum / iterations_);
- ati.gpu_power_std_ = std::sqrt(gpu_power_sum / iterations_);
- ati.ddr_power_std_ = std::sqrt(ddr_power_sum / iterations_);
-
- agg_tensor_info_.push_back(ati);
- }
- }
-
-public:
- Profiler() {
- cpu_stream_.open(cpu_power_rail, std::ifstream::in);
- gpu_stream_.open(gpu_power_rail, std::ifstream::in);
- ddr_stream_.open(ddr_power_rail, std::ifstream::in);
- soc_stream_.open(soc_power_rail, std::ifstream::in);
- sys_stream_.open(sys_power_rail, std::ifstream::in);
-
- if (!cpu_stream_.is_open() or !gpu_stream_.is_open() or !ddr_stream_.is_open()
- or !soc_stream_.is_open() or !sys_stream_.is_open()) {
- std::cout << "Failed to open one of the power rails for reading\n";
- exit(1);
- }
- }
-
- ~Profiler() {
- cpu_stream_.close();
- gpu_stream_.close();
- ddr_stream_.close();
- soc_stream_.close();
- sys_stream_.close();
- }
-
- void profile(const char * const program, const int iterations) {
- iterations_ = iterations;
- resetGlobal();
-
- for (unsigned i = 0; i < iterations_; i++) {
- resetLocal();
-
- // Launch two threads: one for running the program and one for
- // profiling it. Pin the threads to specific cores to remove migration
- // overhead. Profiling showed that the sampling rate increases slightly
- // with pinning.
- std::thread prog(&Profiler::runProgram, this, program);
- std::thread power(&Profiler::recordPower, this);
- pinThread(prog, core1);
- pinThread(power, core2);
- prog.join();
- power.join();
-
- updateStats();
-
- // Sleep for some time to bring the GPU back to idle
- std::this_thread::sleep_for(std::chrono::milliseconds(gpu_idle_time));
- }
-
- calculateAggregateStats();
- }
-
- void dumpTensorInfo(const char * const filename) const {
- const std::string header = "Op,Time (ms),Energy (mJ),GPU Energy (mJ),DDR Energy (mJ),Power (mW),GPU Power (mW),DDR Power (mW),Time std,Energy std,GPU Energy std,DDR Energy std,Power std,GPU Power std,DDR Power std\n";
- std::ofstream ofs;
- ofs.open(filename);
- //ofs << header;
- for (const auto &ati : agg_tensor_info_) {
- ofs << ati.name_
- << "," << ati.average_time_ * 1e3
- << "," << ati.average_energy_
- /*
- << "," << ati.average_gpu_energy_
- << "," << ati.average_ddr_energy_
- << "," << ati.average_power_
- << "," << ati.average_gpu_power_
- << "," << ati.average_ddr_power_
- << "," << ati.time_std_ * 1e3
- << "," << ati.energy_std_
- << "," << ati.gpu_energy_std_
- << "," << ati.ddr_energy_std_
- << "," << ati.power_std_
- << "," << ati.gpu_power_std_
- << "," << ati.ddr_power_std_*/
- << "\n";
-
- std::cout << ati.average_time_ * 1e3 << "," << ati.average_energy_ << "\n";
- }
- ofs.close();
- }
-
- void dumpPowerReadings(const char * const filename) const {
- std::ofstream ofs;
- ofs.open(filename);
- for (const auto &reading : power_readings_) {
- std::chrono::duration<double> duration = reading.time_ - start_time_;
- //std::chrono::duration<double> duration = reading.time_.time_since_epoch();
- ofs << std::to_string(duration.count())
- << " " << reading.gpu_
- << " " << reading.ddr_
- << "\n";
- }
- ofs.close();
- }
-
- void dumpTotalInfo() const {
- auto total_time = total_info_.time_ / iterations_;
-
- auto total_energy = total_info_.energy_ / iterations_;
- auto gpu_energy = total_info_.gpu_energy_ / iterations_;
- auto ddr_energy = total_info_.ddr_energy_ / iterations_;
-
- auto power = total_info_.power_ / iterations_;
- auto gpu_power = total_info_.gpu_power_ / iterations_;
- auto ddr_power = total_info_.ddr_power_ / iterations_;
-
- std::cout << "-----------------------------------------------------\n";
- std::cout << "Program info (average)\n";
- std::cout << "-----------------------------------------------------\n";
- std::cout << "\tExecution time: " << total_time << " seconds\n";
- std::cout << "\tTotal energy: " << total_energy << " mJ\n";
- std::cout << "\t GPU: " << gpu_energy << " mJ\n";
- std::cout << "\t DDR: " << ddr_energy << " mJ\n";
- std::cout << "\tPower: " << power << " mW\n";
- std::cout << "\t GPU: " << gpu_power << " mW\n";
- std::cout << "\t DDR: " << ddr_power << " mW\n";
- std::cout << "-----------------------------------------------------\n";
- }
-};
-
-int main(int argc, char *argv[]) {
- if (argc < NUM_ARGS) {
- std::cout << "Usage: " << argv[0] << " <program> <iterations> <tensor output file> [power output file]\n";
- exit(1);
- }
-
- Profiler pp;
- pp.profile(argv[1], std::stoi(argv[2]));
- pp.dumpTensorInfo(argv[3]);
-
- if (argc > NUM_ARGS)
- pp.dumpPowerReadings(argv[4]);
-
- return 0;
-}
-
diff --git a/llvm/projects/gpu_profiler/src/profiler.cpp b/llvm/projects/gpu_profiler/src/profiler.cpp
deleted file mode 100644
index cae8823fa23056d44973f3b23d16c4c2f28e89f7..0000000000000000000000000000000000000000
--- a/llvm/projects/gpu_profiler/src/profiler.cpp
+++ /dev/null
@@ -1,200 +0,0 @@
-#include <cassert>
-#include "profiler.h"
-
-Profiler::Profiler() : should_run_profiler_(false), should_stop_profiler_(false) {
- // Open all streams. Not done in start_profiler() function bc the streams
- // should be strictly opened once
- cpu_stream_.open(cpu_power_rail, std::ifstream::in);
- gpu_stream_.open(gpu_power_rail, std::ifstream::in);
- ddr_stream_.open(ddr_power_rail, std::ifstream::in);
- soc_stream_.open(soc_power_rail, std::ifstream::in);
- sys_stream_.open(sys_power_rail, std::ifstream::in);
-
- // Check if the jetson file id file exists to indirectly check architecture
- std::ifstream jetson_file(jetson_chip_id);
- on_jetson_ = jetson_file.good();
- if (on_jetson_ &&
- (!cpu_stream_.is_open() || !gpu_stream_.is_open()
- || !ddr_stream_.is_open() || !soc_stream_.is_open()
- || !sys_stream_.is_open())) {
- std::cout << "Failed to open one of the power rails for reading\n";
- exit(1);
- }
-}
-
-Profiler::~Profiler() {
- cpu_stream_.close();
- gpu_stream_.close();
- ddr_stream_.close();
- soc_stream_.close();
- sys_stream_.close();
-}
-
-// Reinitializes boolean vars used for control flow and launches the profiler
-// thread. DOES NOT reset other internal data structures.
-void Profiler::start_profiler(){
- // Reinitialize in case the profiler object has been used before
- should_run_profiler_ = false;
- should_stop_profiler_ = false;
- profiler_thread_ = std::thread(&Profiler::run_profiler, this);
- pin_thread(profiler_thread_, core1);
-}
-
-// Resumes the profiling of whatever executable's currently running
-// DOES NOT reset any data
-void Profiler::resume_profiler() {
- {
- std::unique_lock<std::mutex> mutex_lock(mutex_);
- if (should_run_profiler_){
- std::cout << "WARNING: resume_profiler was already called\n";
- }
- //std::cout<<"RESUME RESUME RESUME RESUME\n";
- should_run_profiler_ = true;
- start_time_ = clock_type::now();
- }
- cond_var_.notify_one();
-}
-
-// Stops profiler by putting profiler thread to sleep
-void Profiler::pause_profiler() {
- {
- std::unique_lock<std::mutex> mutex_lock(mutex_);
- if (!should_run_profiler_){
- std::cout << "WARNING: pause_profiler was already called\n";
- }
- //std::cout<<"PAUSE PAUSE PAUSE PAUSE\n";
- should_run_profiler_ = false;
- stop_time_ = clock_type::now();
- }
- cond_var_.notify_one();
-}
-
-// Gets the delta time and total GPU and DDR energy between the last two
-// calls to resume_profiler and pause_profiler
-//
-// Returns this as a pair of <delta time in milliseconds, energy>
-std::pair<double, double> Profiler::get_time_energy() const {
- std::unique_lock<std::mutex> mutex_lock(vector_mutex_); // MUST use a mutex
-
- double total_energy = 0.0;
- if (on_jetson_) {
- //std::cout<<"power readings size"<<power_readings_.size()<<'\n';
- auto prev_time = start_time_;
- for (size_t i = 0; i < power_readings_.size(); i++){
- const auto& reading = power_readings_[i];
- std::chrono::duration<double> duration_secs = reading.time_ - prev_time;
- total_energy += (reading.gpu_ + reading.ddr_);// * duration_secs.count();
- prev_time = reading.time_;
- }
- }
- std::chrono::duration<double, std::milli> duration_milli = stop_time_ - start_time_;
- double delta_time = duration_milli.count();
- return std::make_pair(delta_time, total_energy);
-}
-
-void Profiler::reset() {
- std::unique_lock<std::mutex> bool_var_lock(mutex_);
- std::unique_lock<std::mutex> vector_lock(vector_mutex_);
- should_stop_profiler_ = false; // Can call reset after calling pause_profiler()
- should_run_profiler_ = false; // Can call reset after calling resume
- power_readings_.clear();
- run_profiler_overhead = 0.0;
-}
-
-// Exit the profiler and kill the thread
-// Must call start_profiler() to reuse this object after calling pause_profiler()
-void Profiler::stop_profiler() {
- std::cout << "Exiting profiler\n";
- should_stop_profiler_ = true;
- cond_var_.notify_one();
- profiler_thread_.join();
-}
-
-// Obtain's a single power reading from the GPU and DDR rails
-void Profiler::obtain_power_reading() {
- std::unique_lock<std::mutex> mutex_lock(vector_mutex_); // MUST use a mutex
-
- PowerReading reading;
-
- // The order matters here. All the reads have to happen together first
- // and then all the seeks have to happen together at the end, otherwise
- // there will be a significant time difference between the readings of
- // the different rails.
- reading.time_ = clock_type::now();
- if (on_jetson_){
- gpu_stream_ >> reading.gpu_;
- ddr_stream_ >> reading.ddr_;
-
- gpu_stream_.seekg(0);
- ddr_stream_.seekg(0);
-
- } else {
- reading.gpu_ = 0.0;
- reading.ddr_ = 0.0;
- }
- power_readings_.push_back(reading);
-}
-
-// Pins the given thread to the specified core
-void Profiler::pin_thread(std::thread &t, const unsigned core) const {
- cpu_set_t cpuset;
- CPU_ZERO(&cpuset);
- CPU_SET(core, &cpuset);
- if (pthread_setaffinity_np(t.native_handle(), sizeof(cpu_set_t), &cpuset) != 0)
- std::cout << "Couldn't set thread affinity\n";
-}
-
-// Runs the profiler thread, keeping it alive by wrapping the functionality
-// in an infinite loop
-void Profiler::run_profiler(){
- while (true){
- if (should_stop_profiler_) {
- break;
- }
- // Need to lock the mutex and check the condition var
- {
- std::unique_lock<std::mutex> mutex_lock(mutex_);
- if (should_stop_profiler_) {
- break;
- }
- // Wake the thread up when it's time to run the profiler or exit
- // the profiler
- cond_var_.wait(mutex_lock, [this]{return should_run_profiler_
- || should_stop_profiler_; });
- }
- if (should_stop_profiler_) {
- break;
- }
- obtain_power_reading();
- }
-}
-
-/*
-// TESTS
-void resume_pause_profiler(Profiler& profile_wrapper, unsigned long sleep_millis){
- profile_wrapper.resume_profiler();
- std::this_thread::sleep_for(std::chrono::milliseconds(sleep_millis));
- profile_wrapper.pause_profiler();
-
- auto time_energy_pair = profile_wrapper.get_time_energy();
- profile_wrapper.reset();
- //if (time_energy_pair.first > sleep_millis + 1 || time_energy_pair.first < sleep_millis - 1){
- printf("WARNING: time: %f, energy: %f\n", time_energy_pair.first, time_energy_pair.second);
- //}
- std::this_thread::sleep_for(std::chrono::milliseconds(sleep_millis));
- //std::cout<<"\n\n";
-}
-
-int main(){
- Profiler profile_wrapper;
- profile_wrapper.start_profiler();
-
- unsigned long sleep_millis = 25;
- for (size_t i = 0; i < 50; i++){
- resume_pause_profiler(profile_wrapper, sleep_millis);
- }
- // IMPORTANT
- profile_wrapper.stop_profiler();
- return 0;
-}
-*/
diff --git a/llvm/projects/hpvm-tensor-rt/.gitignore b/llvm/projects/hpvm-tensor-rt/.gitignore
deleted file mode 100644
index 9581f1d5b06aaafaaae77ef6175bc243707e4685..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-./build
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/CMakeLists.txt b/llvm/projects/hpvm-tensor-rt/CMakeLists.txt
deleted file mode 100644
index 0eab861beac699407114d0cae4e063f41dbdab95..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/CMakeLists.txt
+++ /dev/null
@@ -1,259 +0,0 @@
-cmake_minimum_required (VERSION 2.6)
-project (cudnn-training)
-
-find_package(CUDA 6.5 REQUIRED)
-
-
-if (CMAKE_BUILD_TYPE STREQUAL "Debug")
- message("Debug mode")
- set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_60,code=compute_60;-std=c++11;-g;-lineinfo;-Xcompiler;-ggdb;-lcurand)
-else()
- set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_60,code=compute_60;-std=c++11;-DNDEBUG;-Xcompiler;-DNDEBUG;-lcurand)
-endif()
-
-set(CUDA_PROPAGATE_HOST_FLAGS OFF)
-
-# Addresses a bug where code is not compiled as C++11 in non-CUDA code and older g++ versions
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -I/ " )
-
-add_definitions(-DNO_INJECTION)
-add_definitions(-DPROMISE_TUNER_ENABLED)
-if(USE_GFLAGS)
- add_definitions(-DUSE_GFLAGS)
-endif()
-
-if(USE_AUTOTUNER)
- remove_definitions(-DNO_INJECTION)
-endif()
-
-
-
-include_directories($ENV{CUDNN_PATH} $ENV{CUDNN_PATH}/include)
-include_directories(./tensor_runtime/include)
-include_directories(../gpu_profiler/include)
-include_directories(../soc_simulator/include)
-link_directories($ENV{CUDNN_PATH} $ENV{CUDNN_PATH}/lib $ENV{CUDNN_PATH}/lib64)
-
-
-# Adding new rule for building a cuDNN runtime library
-cuda_add_library(tensor_runtime tensor_runtime/src/tensor_runtime.cu)
-cuda_add_cublas_to_target(tensor_runtime)
-
-# Adding new rule for building a cuDNN runtime library
-cuda_add_library(tensor_cpu_runtime tensor_runtime/src/tensor_cpu_runtime.cc)
-
-find_library(GPU_PROFILER_LIB
- NAMES libgpu_profiler.a
- HINTS ../gpu_profiler/lib
-)
-
-find_library(SOC_SIMULATOR_LIB
- NAMES libpromise_profiler.a
- HINTS ../soc_simulator/lib
-)
-
-
-if(USE_GFLAGS)
- target_link_libraries(tensor_runtime gflags cudnn -lcurand)
-else()
- target_link_libraries(tensor_runtime cudnn -lcurand)
-endif()
-
-target_link_libraries(tensor_cpu_runtime)
-
-# Adding rule for the debugging source
-add_executable(test_ops dnn_sources/src/test_ops.cc)
-target_link_libraries(test_ops tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-#**** CPU sources
-add_executable(fc2_cpu dnn_sources/src/fc2_cpu.cc)
-target_link_libraries(fc2_cpu tensor_cpu_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-
-
-# Full-Precision versions
-add_executable(lenet_tanh dnn_sources/src/lenet2_tanh.cc)
-target_link_libraries(lenet_tanh tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(lenet_keras dnn_sources/src/lenet_keras.cc)
-target_link_libraries(lenet_keras tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(lenet_int32 dnn_sources/src/lenet_int32.cc)
-target_link_libraries(lenet_int32 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-
-add_executable(alexnet_cifar10 dnn_sources/src/alexnet_cifar10_front.cc)
-target_link_libraries(alexnet_cifar10 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(alexnet_cifar10_tuner dnn_sources/src/alexnet_cifar10_tuner.cc)
-target_link_libraries(alexnet_cifar10_tuner tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(alexnet_cifar10_approx dnn_sources/src/alexnet_cifar10_approx.cc)
-target_link_libraries(alexnet_cifar10_approx tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(alexnet2_cifar10 dnn_sources/src/alexnet2_cifar10.cc)
-target_link_libraries(alexnet2_cifar10 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(alexnet2_cifar10_tuner dnn_sources/src/alexnet2_cifar10_tuner.cc)
-target_link_libraries(alexnet2_cifar10_tuner tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(vgg16_cifar10 dnn_sources/src/vgg16_cifar10.cc)
-target_link_libraries(vgg16_cifar10 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(vgg16_cifar10_tuner dnn_sources/src/vgg16_cifar10_tuner.cc)
-target_link_libraries(vgg16_cifar10_tuner tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(resnet18_cifar10 dnn_sources/src/resnet18_cifar10.cc)
-target_link_libraries(resnet18_cifar10 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(resnet18_cifar10_approx dnn_sources/src/resnet18_cifar10_approx.cc)
-target_link_libraries(resnet18_cifar10_approx tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(resnet18_cifar10_inputapprox dnn_sources/src/resnet18_cifar10_inputapprox.cc)
-target_link_libraries(resnet18_cifar10_inputapprox tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(resnet18_cifar10_tuner dnn_sources/src/resnet18_cifar10_tuner.cc)
-target_link_libraries(resnet18_cifar10_tuner tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(vgg16_cifar100 dnn_sources/src/vgg16_cifar100.cc)
-target_link_libraries(vgg16_cifar100 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(vgg16_cifar100_tuner dnn_sources/src/vgg16_cifar100_tuner.cc)
-target_link_libraries(vgg16_cifar100_tuner tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(vgg16_cifar100_top5 dnn_sources/src/vgg16_cifar100_5.cc)
-target_link_libraries(vgg16_cifar100_top5 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-
-# REF binaries
-add_executable(mobilenet_cifar10 dnn_sources/src/mobilenet_cifar10.cc)
-target_link_libraries(mobilenet_cifar10 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-
-add_executable(mobilenet_cifar10_shallow dnn_sources/src/mobilenet_cifar10_shallow.cc)
-target_link_libraries(mobilenet_cifar10_shallow tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-
-
-#### Image Pipeline Tuning sources
-
-add_executable(pipeline_GEMO dnn_sources/src/pipeline_GEMO.cc)
-target_link_libraries(pipeline_GEMO tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(pipeline_GEO dnn_sources/src/pipeline_GEO.cc)
-target_link_libraries(pipeline_GEO tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(pipeline_GEOM dnn_sources/src/pipeline_GEOM.cc)
-target_link_libraries(pipeline_GEOM tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(pipeline_GSM dnn_sources/src/pipeline_GSM.cc)
-target_link_libraries(pipeline_GSM tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(pipeline_GSME dnn_sources/src/pipeline_GSME.cc)
-target_link_libraries(pipeline_GSME tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-
-
-
-#*** Half precision networks
-
-
-#add_executable(fc4_half dnn_sources/src/half/fc4_half.cc)
-#target_link_libraries(fc4_half tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-#add_executable(lenet_tanh_half dnn_sources/src/half/lenet_tanh_half.cc)
-#target_link_libraries(lenet_tanh_half tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-#add_executable(lenet_keras_half dnn_sources/src/half/lenet_keras_half.cc)
-#target_link_libraries(lenet_keras_half tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-
-#********* Promise API sources
-add_executable(lenet_promise dnn_sources/src/promise/lenet_promise.cc)
-target_link_libraries(lenet_promise tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-# Quantized PROMISE sources
-add_executable(alexnet_promise dnn_sources/src/promise/alexnet_promise.cc)
-target_link_libraries(alexnet_promise tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(alexnet2_promise dnn_sources/src/promise/alexnet2_promise.cc)
-target_link_libraries(alexnet2_promise tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(resnet18_promise dnn_sources/src/promise/resnet18_promise.cc)
-target_link_libraries(resnet18_promise tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(vgg16_cifar100_promise dnn_sources/src/promise/vgg16_cifar100_promise.cc)
-target_link_libraries(vgg16_cifar100_promise tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(vgg16_cifar10_promise dnn_sources/src/promise/vgg16_cifar10_promise.cc)
-target_link_libraries(vgg16_cifar10_promise tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-
-# REF Source - BUILT After Support for SMART QUANTIZATION
-add_executable(mobilenet_promise dnn_sources/src/promise/mobilenet_promise.cc)
-target_link_libraries(mobilenet_promise tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(mobilenet_shallow_promise dnn_sources/src/promise/mobilenet_shallow_promise.cc)
-target_link_libraries(mobilenet_shallow_promise tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-
-
-#### Image Pipeline PROMISE sources
-add_executable(pipeline_GEMO_promise dnn_sources/src/promise/pipeline_GEMO_promise.cc)
-target_link_libraries(pipeline_GEMO_promise tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(pipeline_GEO_promise dnn_sources/src/promise/pipeline_GEO_promise.cc)
-target_link_libraries(pipeline_GEO_promise tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(pipeline_GEOM_promise dnn_sources/src/promise/pipeline_GEOM_promise.cc)
-target_link_libraries(pipeline_GEOM_promise tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(pipeline_GSM_promise dnn_sources/src/promise/pipeline_GSM_promise.cc)
-target_link_libraries(pipeline_GSM_promise tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(pipeline_GSME_promise dnn_sources/src/promise/pipeline_GSME_promise.cc)
-target_link_libraries(pipeline_GSME_promise tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-
-
-############# Promise Validation Sources #############
-
-add_executable(alexnet_valid dnn_sources/src/promise/alexnet_valid.cc)
-target_link_libraries(alexnet_valid tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(alexnet2_valid dnn_sources/src/promise/alexnet2_valid.cc)
-target_link_libraries(alexnet2_valid tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(resnet18_valid dnn_sources/src/promise/resnet18_valid.cc)
-target_link_libraries(resnet18_valid tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(vgg16_cifar100_valid dnn_sources/src/promise/vgg16_cifar100_valid.cc)
-target_link_libraries(vgg16_cifar100_valid tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(vgg16_cifar10_valid dnn_sources/src/promise/vgg16_cifar10_valid.cc)
-target_link_libraries(vgg16_cifar10_valid tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-
-add_executable(mobilenet_valid dnn_sources/src/promise/mobilenet_valid.cc)
-target_link_libraries(mobilenet_valid tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(mobilenet_shallow_valid dnn_sources/src/promise/mobilenet_shallow_valid.cc)
-target_link_libraries(mobilenet_shallow_valid tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-
-##### Image pipeline validation sources
-add_executable(pipeline_GEMO_valid dnn_sources/src/promise/pipeline_GEMO_valid.cc)
-target_link_libraries(pipeline_GEMO_valid tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(pipeline_GEO_valid dnn_sources/src/promise/pipeline_GEO_valid.cc)
-target_link_libraries(pipeline_GEO_valid tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(pipeline_GEOM_valid dnn_sources/src/promise/pipeline_GEOM_valid.cc)
-target_link_libraries(pipeline_GEOM_valid tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(pipeline_GSM_valid dnn_sources/src/promise/pipeline_GSM_valid.cc)
-target_link_libraries(pipeline_GSM_valid tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(pipeline_GSME_valid dnn_sources/src/promise/pipeline_GSME_valid.cc)
-target_link_libraries(pipeline_GSME_valid tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
diff --git a/llvm/projects/hpvm-tensor-rt/CMakeLists_cpu.txt b/llvm/projects/hpvm-tensor-rt/CMakeLists_cpu.txt
deleted file mode 100644
index cff0129c2aa02b9776ed7bba8e92029d2c2560e8..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/CMakeLists_cpu.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-cmake_minimum_required (VERSION 2.6)
-project (approxhpvm-tensorRt-cpu)
-
-
-# Addresses a bug where code is not compiled as C++11 in non-CUDA code and older g++ versions
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 " )
-
-
-# Adding new rule for building a cuDNN runtime library
-add_library(tensor_cpu_runtime tensor_runtime/src/tensor_cpu_runtime.cc)
-target_link_libraries(tensor_cpu_runtime)
-
-
-#**** CPU sources
-add_executable(fc2_cpu dnn_sources/src/fc2_cpu.cc)
-target_link_libraries(fc2_cpu tensor_cpu_runtime)
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/README.md b/llvm/projects/hpvm-tensor-rt/README.md
deleted file mode 100644
index 5b3e5f99d39cf5c697051fa2580eb74f207bb031..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/README.md
+++ /dev/null
@@ -1,68 +0,0 @@
-# AppproxHPVM Tensor Runtime
-
-## Dependencies
-
-* CUDNN-7.0 or above
-* CUDA-9.0 or above
-* CUBLAS-9.0 or above - often included with cuda-toolkit
-
-## Dependent Library Builds
-
-```shell
-cd ../gpu_profiler
-mkdir lib
-cmake ../
-make
-
-cd ../soc_simulator
-mkdir lib
-cmake ../
-make
-```
-
-
-## BUILD
-
-```shell
-source bin/setup_cuda_llvm_paths.sh
-mkdir build
-cd build
-cmake ../
-make
-```
-
-
-## Directory Structure
-
-* ./tensor_runtime:
- * ./tensor_runtime/include/: Include files for Tensor Runtime
- * ./tensor_runtime/include/tensor_signatures.cc: Include file with Tensor RT signatures
- * NOTE: UPDATE this with updated API
- * ./tensor_runtime/src/: HPVM Tensor RT sources
-
-* ./dnn_sources:
- * ./dnn_sources/src/${BENCH}.cc: Per Bench FULL-precision source
- * ./dnn_sources/src/half/${BENCH}.cc: Per Bench HALF-precision source
- * ./dnn_sources/src/promise/${BENCH}.cc: Per Bench PROMISE-API source
-
-* ./lib:
- * ./lib/tensor_runtime.ll
- * NOTE: generated from ./tensor_runtime/include/tensor_signatures.cc
- * ./lib/libtensor_runtime.a
- * NOTE: Linked against HPVM benchmarks
- * ./lib/libtensor_autotuner.a
- * NOTE: error-injection library linked with benchmarks
-
-* ./bin:
- * ./bin/install_runtime.sh: Script for moving Tensor RT files to ./lib
- * ./bin/run_autotuner.py: Python script for running Autotuner experiments
- * ./bin/setup_tyler_paths.sh: Tyler-specific path setup for Tensor RT
- * ./bin/setup_jetson.sh: Jetson board specific path setup for Tensor RT
- * ./bin/swing_selection.py: Script for hardware mapping
- * NOTE: Includes the L2,L1 norm mapping to hardware knobs
-
-* ./opentuner:
- * ./opentuner/autotuner/: Autotuner scripts
- * ./opentuner/autotuner/approxhpvm_tuner.py: Tuner script for ApproxHPVM binaries
- * ./opentuner/autotuner/promise_tuner.py: Tuner script for tuning PROMISE voltage levels
-
diff --git a/llvm/projects/hpvm-tensor-rt/bin/error_sensitivity.py b/llvm/projects/hpvm-tensor-rt/bin/error_sensitivity.py
deleted file mode 100644
index 9f2ffb3eacd3cb81bcefb4b44a48f1d0a8a8356d..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/error_sensitivity.py
+++ /dev/null
@@ -1,139 +0,0 @@
-
-
-import subprocess
-import os
-import operator
-
-
-def constructTunerFile(num_flags, tensor_id, error_level, default_error):
-
- f = open("opentuner_flags", "w+")
-
- for i in range(num_flags):
- if i == tensor_id:
- f.write(str(error_level) + "\n")
- else:
- f.write(str(default_error) + "\n")
-
- f.close()
-
-
-
-def runAndTestError(binary_name, gold_acc):
-
- num_runs = 20
-
- binary_name = "./" + binary_name
- FNULL = open(os.devnull, 'wb')
- p = subprocess.Popen([binary_name, str(num_runs)], stdout = FNULL)
- p.wait()
-
- f = open("run_accuracies.txt")
-
- total_err = 0.0
- for x in f:
- acc = float(x.strip())
- total_err += (gold_acc - acc)
-
- avg_err = total_err / num_runs
-
- return avg_err
-
-
-
-
-def test_sensitivity(Bench):
-
- tensor_errors = []
-
- error_levels = [6, 9, 12, 15]
- num_flags = Bench.num_flags
-
- for tensor_id in range(num_flags):
- total_error = 0
- for error_level in error_levels:
- constructTunerFile(num_flags, tensor_id, error_level, 0)
- error = runAndTestError(Bench.tuner_binary, Bench.tuner_accuracy)
- print (tensor_id, error_level, error)
- total_error += error
-
- avg_error = total_error / len(error_levels)
-
- tensor_errors.append([tensor_id, avg_error])
-
-
- print ("\n\n*** Per-Tensor Avg Errors \n\n")
-
- f_name = Bench.base_dir + "/tensor_errors_1000.txt"
- f = open(f_name, "w+")
- for i in range(len(tensor_errors)):
- print (i, tensor_errors[i][1])
- f.write(str(i) + "\t" + str(tensor_errors[i][1]) + "\n")
-
- f.close()
-
- f_name = Bench.base_dir + "/tensor_errors_ranked_1000.txt"
- f2 = open(f_name, "w+")
- tensor_errors.sort(key=operator.itemgetter(1))
-
-
- for i in range(len(tensor_errors)):
- print (i, tensor_errors[i][1])
-
- f2.write(str(tensor_errors[i][0]) + "\t" + str(tensor_errors[i][1]) + "\n")
-
-
- f2.close()
-
-
-
-def test_sensitivity2(Bench):
-
- num_flags = Bench.num_flags
-
- constructTunerFile(num_flags, 0, 3, 3)
- error = runAndTestError(Bench.tuner_binary, Bench.tuner_accuracy)
-
- ref_acc = Bench.tuner_accuracy - error
- print ("*** Gold accuracy = ", Bench.tuner_accuracy, " Ref accuracy = ", ref_acc, " *** \n\n")
-
-
- tensor_errors = []
-
- error_levels = [6, 9, 12, 15]
-
- for tensor_id in range(num_flags):
- total_error = 0
- for error_level in error_levels:
- constructTunerFile(num_flags, tensor_id, error_level, 3)
- error = runAndTestError(Bench.tuner_binary, ref_acc)
- print (tensor_id, error_level, error)
- total_error += error
-
- avg_error = total_error / len(error_levels)
-
- tensor_errors.append([tensor_id, avg_error])
-
-
- print ("\n\n*** Per-Tensor Avg Errors \n\n")
-
- f_name = Bench.base_dir + "/tensor_composite_errors.txt"
- f = open(f_name, "w+")
- for i in range(len(tensor_errors)):
- print (i, tensor_errors[i][1])
- f.write(str(i) + "\t" + str(tensor_errors[i][1]) + "\n")
-
- f.close()
-
- f_name = Bench.base_dir + "/tensor_composite_errors_ranked.txt"
- f2 = open(f_name, "w+")
- tensor_errors.sort(key=operator.itemgetter(1))
-
-
- for i in range(len(tensor_errors)):
- print (i, tensor_errors[i][1])
-
- f2.write(str(tensor_errors[i][0]) + "\t" + str(tensor_errors[i][1]) + "\n")
-
-
- f2.close()
diff --git a/llvm/projects/hpvm-tensor-rt/bin/exhaustive.py b/llvm/projects/hpvm-tensor-rt/bin/exhaustive.py
deleted file mode 100644
index bae38bf7e497897ae3db4e12dce48914903739fb..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/exhaustive.py
+++ /dev/null
@@ -1,140 +0,0 @@
-
-import os
-import sys
-import shutil
-import subprocess
-import shutil
-
-
-
-class Benchmark:
- def __init__(self):
- self.binary = ""
- self.num_flags = 4
-
-
-
-Alexnet1 = Benchmark()
-Alexnet1.binary = "./lenet_keras_promise"
-Alexnet1.accuracy = 98.8
-Alexnet1.flags = [[8], [1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4] ]
-
-
-Alexnet2 = Benchmark()
-Alexnet2.binary = "./fc4_clipped_promise"
-Alexnet2.accuracy = 93.72
-Alexnet2.flags = [[3, 4, 5, 6, 7], [2, 3, 4, 5, 6, 7], [2, 3, 4, 5, 6, 7], [2, 3, 4, 5, 6, 7] ]
-
-
-
-def dumpConfig(conf_flags, dir_prefix, file_id):
-
- shutil.copy("promise_flags", dir_prefix + "/" + str(file_id) + ".txt")
-
-
-def dumpFinalConfigs(final_confs, dir_prefix):
-
- f = open(dir_prefix + "/final_confs.txt", "w+")
- for conf in final_confs:
- ind = 0
- for flag in conf:
- f.write(str(flag))
- if ind < len(conf) - 1:
- f.write(",")
-
- ind += 1
- f.write("\n")
-
- f.close()
-
-
-def getAccuracy():
-
- file = open("final_accuracy", "r")
- acc_str = file.read()
- file.close()
- accuracy = float(acc_str)
-
- try:
- accuracy = float(acc_str)
- except:
- return 20
-
- print accuracy
- return accuracy
-
-
-
-
-def testConfidence(binary, target_acc, total_runs):
-
- for i in range(total_runs):
- p = subprocess.Popen("./" + binary, shell=False)
- p.wait()
- acc = getAccuracy()
- if acc < target_acc:
- return False
-
- return True
-
-
-def singleRun(binary):
-
- p = subprocess.Popen("./" + binary, shell=False)
- p.wait()
-
- return getAccuracy()
-
-
-
-def createPromiseFile(conf_flags):
-
- f = open("promise_flags", "w+")
- for flag in conf_flags:
- f.write(str(flag) + "\n")
- f.close()
-
-
-
-def runExhaustive(Bench, threshold, dir_prefix):
-
- flags = Bench.flags
-
- accepted_confs = []
- ind = 0
- for flag1 in flags[0]:
- for flag2 in flags[1]:
- for flag3 in flags[2]:
- for flag4 in flags[3]:
- print (flag1, flag2, flag3, flag4)
- conf_flags = []
- conf_flags.append(flag1)
- conf_flags.append(flag2)
- conf_flags.append(flag3)
- conf_flags.append(flag4)
-
- createPromiseFile(conf_flags)
-
- accuracy = singleRun(Bench.binary)
- target_acc = Bench.accuracy - threshold
-
- if accuracy > target_acc:
- if testConfidence(Bench.binary, target_acc, 3):
- dumpConfig(conf_flags, dir_prefix, ind)
- accepted_confs.append(conf_flags)
-
- ind += 1
-
- dumpFinalConfigs(accepted_confs, dir_prefix)
-
-
-
-if __name__ == "__main__":
-
- #runExhaustive(Alexnet1, 1.0, "lenet_1")
- #runExhaustive(Alexnet1, 2.0, "lenet_2")
-
- runExhaustive(Alexnet2, 1.0, "fc4_1")
- runExhaustive(Alexnet2, 2.0, "fc4_2")
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/bin/extractQuantRange.py b/llvm/projects/hpvm-tensor-rt/bin/extractQuantRange.py
deleted file mode 100644
index 0b7f09d92e91894d284b40cc0bd2d346c08e36c7..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/extractQuantRange.py
+++ /dev/null
@@ -1,42 +0,0 @@
-
-
-import sys
-
-
-if __name__ == "__main__":
-
- f = open(sys.argv[1], "r")
- f2 = open("quant_ranges.txt", "w+")
-
- layer_line = False
- for x in f:
- if "ConvLayer_PROMISE" in x or "FCLayer_PROMISE" in x or layer_line == True:
- if layer_line == True:
- layer_line = False
- else:
- layer_line = True
-
- print x
- toks = x.split(",")
-
- for tok in toks:
- tok = tok.strip()
- tok_val = ""
- try:
- tok_val = float(tok)
- try:
- tok_val = int(tok)
- except:
- print (tok_val)
- f2.write(str(tok_val) + " ")
- #f2.write("tok_val = ", tok_val + " ")
- except:
- continue
-
- f2.write("\n")
-
-
- f.close()
- f2.close()
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/bin/install_runtime.sh b/llvm/projects/hpvm-tensor-rt/bin/install_runtime.sh
deleted file mode 100644
index 33a54cd0de626113e5cf11e2f6a6928d4fa384eb..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/install_runtime.sh
+++ /dev/null
@@ -1,11 +0,0 @@
-#!/bin/sh
-
-export HPVM_TENSOR_RT_HOME=/home/hsharif3/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/
-export PATH=/home/hsharif3/Gitlab/hpvm/build/bin/:$PATH
-
-clang++ -I/software/cuda-9.1/include -emit-llvm -c ${HPVM_TENSOR_RT_HOME}/tensor_runtime/include/tensor_signatures.cc -o ${HPVM_TENSOR_RT_HOME}/lib/tensor_runtime.bc
-llvm-dis --version
-llvm-dis ${HPVM_TENSOR_RT_HOME}/lib/tensor_runtime.bc
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/bin/mark_depthwise.py b/llvm/projects/hpvm-tensor-rt/bin/mark_depthwise.py
deleted file mode 100644
index c64a9f242fcf80b585c5862ceef16b8fb8ce50a5..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/mark_depthwise.py
+++ /dev/null
@@ -1,48 +0,0 @@
-
-import sys
-
-
-def loadLayerDesc(layer_desc_file):
-
- layer_desc = []
- f = open(layer_desc_file)
- for x in f:
- vals = x.split()
- layer_desc.append(vals)
-
- return layer_desc
-
-
-
-if __name__ == "__main__":
-
- if len(sys.argv) < 4:
- print ("Usage: python mark_depthwise.py $layer_file $input_conf $output_conf")
-
- layer_file_name = sys.argv[1]
- input_file_name = sys.argv[2]
- output_file_name = sys.argv[3]
-
-
- layer_desc = loadLayerDesc(layer_file_name)
-
- f_in = open(input_file_name)
- f_out = open(output_file_name, "w+")
-
- for x in f_in:
- it = 0
- confs = x.split(",")
- print confs
- for conf in confs:
- print (" it = ", it, " layer_desc[it] = ", layer_desc[it], " \n")
- if layer_desc[it][0] == "depthwise_conv":
- f_out.write("9,")
- else:
- f_out.write(conf)
- if it < len(confs) - 1:
- f_out.write(",")
-
- it += 1
-
- f_in.close()
- f_out.close()
diff --git a/llvm/projects/hpvm-tensor-rt/bin/measure_conf_accuracy.py b/llvm/projects/hpvm-tensor-rt/bin/measure_conf_accuracy.py
deleted file mode 100644
index 4ca1f3f52e59498725414f37e56e06e5e74f1953..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/measure_conf_accuracy.py
+++ /dev/null
@@ -1,316 +0,0 @@
-
-import os
-import sys
-import shutil
-import subprocess
-import shutil
-import numpy as np
-
-
-
-class Benchmark:
- def __init__(self):
- self.binary = ""
-
-
-
-benchmarks = {}
-
-Alexnet1 = Benchmark()
-Alexnet1.binary = "./lenet_keras_promise"
-Alexnet1.accuracy = 98.7
-Alexnet1.loss1_conf = "8 8 8 8,4,4,7"
-Alexnet1.loss2_conf = "8 8 8 8,3,4,7"
-
-benchmarks["lenet"] = Alexnet1
-
-
-Alexnet2 = Benchmark()
-Alexnet2.binary = "./fc4_clipped_promise"
-Alexnet2.accuracy = 93.72
-Alexnet2.loss1_conf = "7,7,6,7"
-Alexnet2.loss2_conf = "4,4,4,5"
-
-benchmarks["fc4"] = Alexnet2
-
-
-Alexnet3 = Benchmark()
-Alexnet3.binary = "./alexnet_valid"
-Alexnet3.accuracy = 79.16
-Alexnet3.loss1_conf = "8 8 8 8,6,6,6,7,7"
-Alexnet3.loss2_conf = "8 8 8 8,4,4,6,4,7"
-
-benchmarks["alexnet"] = Alexnet3
-
-
-Alexnet4 = Benchmark()
-Alexnet4.binary = "./alexnet2_valid"
-Alexnet4.accuracy = 85.09
-Alexnet4.loss1_conf = "9 9 9,7,7,7,9 9 9,7,9 9"
-Alexnet4.loss2_conf = "9 9 9,7,7,6,8 8 8,6,9 9"
-
-benchmarks["alexnet2"] = Alexnet4
-
-
-Alexnet5 = Benchmark()
-Alexnet5.binary = "./resnet18_valid"
-Alexnet5.accuracy = 89.44
-Alexnet5.loss1_conf = "9 9 9,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8,8 8"
-Alexnet5.loss2_conf = "9 9 9,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,7,8,8,7,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8,8 8"
-
-benchmarks["resnet"] = Alexnet5
-
-
-
-Alexnet6 = Benchmark()
-Alexnet6.binary = "./vgg16_cifar10_valid"
-Alexnet6.accuracy = 89.41
-Alexnet6.loss1_conf = "9 9 9,7,7,7,9 9 9,8 8 8,7,8 8 8,7,7,8 8 8,8 8 8,7,9 9 9,9 9"
-Alexnet6.loss2_conf = "9 9 9,5,5,8 8 8 8,4,6,4,7,8 8 8,4,4,4,7,8 8 8,8 8"
-
-benchmarks["vgg16_cifar10"] = Alexnet6
-
-
-Alexnet7 = Benchmark()
-Alexnet7.binary = "./vgg16_cifar100_valid"
-Alexnet7.accuracy = 66.19
-Alexnet7.loss1_conf = "9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,7,7,7,8 8 8,8 8 8 8,7,7,8 8 8 8,8 8 8,8 8"
-Alexnet7.loss2_conf = "9 9 9,8 8 8 8,8 8 8,7,8 8 8,8 8 8,8 8 8 8,6,6,7,8 8 8,7,6,8 8 8,8 8"
-
-benchmarks["vgg16_cifar100"] = Alexnet7
-
-
-
-Alexnet8 = Benchmark()
-Alexnet8.binary = "./pipeline_GEOM_valid"
-Alexnet8.loss1_conf = "8 8,8 8 8,8 8,7"
-Alexnet8.loss2_conf = "8 8,8 8 8,8 8,6"
-
-benchmarks["pipeline_GEOM"] = Alexnet8
-
-
-
-Alexnet9 = Benchmark()
-Alexnet9.binary = "./pipeline_GEMO_valid"
-Alexnet9.loss1_conf = "8 8,8 8 8,8 8,8 8"
-Alexnet9.loss2_conf = "7,8 8 8,8 8,8 8"
-
-benchmarks["pipeline_GEMO"] = Alexnet9
-
-
-
-Alexnet10 = Benchmark()
-Alexnet10.binary = "./pipeline_GEO_valid"
-Alexnet10.loss1_conf = "8 8,8 8 8,8 8"
-Alexnet10.loss2_conf = "8 8,8 8 8,8 8"
-
-benchmarks["pipeline_GEO"] = Alexnet10
-
-
-
-Alexnet11 = Benchmark()
-Alexnet11.binary = "./pipeline_GSM_valid"
-Alexnet11.loss1_conf = "8 8,8 8,7"
-Alexnet11.loss2_conf = "7,8 8,6"
-
-benchmarks["pipeline_GSM"] = Alexnet11
-
-
-
-Alexnet12 = Benchmark()
-Alexnet12.binary = "./pipeline_GSME_valid"
-Alexnet12.loss1_conf = "8 8,8 8,8 8,8 8 8"
-Alexnet12.loss2_conf = "7,8 8,8 8,8 8 8"
-
-benchmarks["pipeline_GSME"] = Alexnet12
-
-
-
-def createPromiseFile(conf_flag_str):
-
- conf_flags = conf_flag_str.split(",")
- f = open("promise_flags", "w+")
- for flag_str in conf_flags:
- flags = flag_str.split()
- f.write(str(flags[0]) + "\n")
- f.close()
-
-
-def getRunAccuracies():
-
- run_accuracies = []
- file = open("run_accuracies.txt", "r")
- file_str = file.read()
-
- for flag in file_str.split("\n"):
- print ("*** flag = ", flag)
- flag = flag.strip()
- if flag == "":
- continue
- run_accuracies.append(float(flag))
-
- file.close()
-
- return run_accuracies
-
-
-
-def testConfidence(binary):
-
- p = subprocess.Popen("./" + binary, shell=False)
- p.wait()
- run_accuracies = getRunAccuracies()
-
- return np.mean(run_accuracies), np.std(run_accuracies)
-
-
-
-def getAccuracy():
-
- file = open("final_accuracy", "r")
- acc_str = file.read()
- file.close()
- accuracy = float(acc_str)
-
- try:
- accuracy = float(acc_str)
- except:
- return 20
-
- #print accuracy
- return accuracy
-
-
-def getPSNR():
-
- file = open("avg_psnr", "r")
- acc_str = file.read()
- file.close()
- accuracy = float(acc_str)
-
- try:
- accuracy = float(acc_str)
- except:
- return -100
-
- #print accuracy
- return accuracy
-
-
-
-
-def testPSNRConfidence(binary, total_runs):
-
- run_accuracies = []
- run_psnr = []
- for i in range(total_runs):
- p = subprocess.Popen("./" + binary, shell=False)
- p.wait()
- acc = getAccuracy()
- psnr = getPSNR()
- run_accuracies.append(acc)
- run_psnr.append(psnr)
-
- return np.mean(run_accuracies), np.std(run_accuracies), np.mean(run_psnr), np.std(run_psnr)
-
-
-
-def runBench(bench_name, dir_prefix):
-
- Bench = benchmarks[bench_name]
- binary = Bench.binary
- accuracy = Bench.accuracy
-
- createPromiseFile(Bench.loss1_conf)
- mean, std = testConfidence(binary)
- print ("mean = ", mean, " std = ", std)
-
-
- f = open(dir_prefix + "/" + binary + "_loss1.txt" ,"w+")
- f.write("mean = " + str(mean) + " std = " + str(std))
- f.close()
-
- createPromiseFile(Bench.loss2_conf)
- mean, std = testConfidence(binary)
- print ("mean = ", mean, " std = ", std)
-
-
- f = open(dir_prefix + "/" + binary + "_loss2.txt" ,"w+")
- f.write("mean = " + str(mean) + " std = " + str(std))
- f.close()
-
-
-
-
-
-def gen30dbFile():
-
- f = open("psnr.txt", "w+");
- f.write("30");
- f.close()
-
-
-def gen20dbFile():
-
- f = open("psnr.txt", "w+");
- f.write("20");
- f.close()
-
-
-
-def runPSNRBench(bench_name, dir_prefix):
-
- Bench = benchmarks[bench_name]
- binary = Bench.binary
-
- gen30dbFile()
- createPromiseFile(Bench.loss1_conf)
- mean, std, psnr_mean, psnr_std = testPSNRConfidence(binary, 20)
- print ("mean = ", mean, " std = ", std)
-
-
- f = open(dir_prefix + "/" + binary + "_loss30.txt" ,"w+")
- f.write("mean = " + str(mean) + " std = " + str(std))
- f.write(" psnr_mean = " + str(psnr_mean) + " psnr_std = " + str(psnr_std))
- f.close()
-
-
- gen20dbFile()
- createPromiseFile(Bench.loss2_conf)
- mean, std, psnr_mean, psnr_std = testPSNRConfidence(binary, 20)
- print ("mean = ", mean, " std = ", std)
-
- f = open(dir_prefix + "/" + binary + "_loss20.txt" ,"w+")
- f.write("mean = " + str(mean) + " std = " + str(std))
- f.write(" psnr_mean = " + str(psnr_mean) + " psnr_std = " + str(psnr_std))
- f.close()
-
-
-
-
-
-
-def runDNNs():
-
- #runBench("fc4", "avg_accuracies")
- #runBench("lenet", "avg_accuracies")
- #runBench("alexnet", "avg_accuracies")
- #runBench("alexnet2", "avg_accuracies")
- #runBench("resnet", "avg_accuracies")
- #runBench("vgg16_cifar10", "avg_accuracies")
- #runBench("vgg16_cifar100", "avg_accuracies")
-
- runPSNRBench("pipeline_GEOM", "avg_accuracies")
- runPSNRBench("pipeline_GEMO", "avg_accuracies")
- runPSNRBench("pipeline_GEO", "avg_accuracies")
- runPSNRBench("pipeline_GSM", "avg_accuracies")
- runPSNRBench("pipeline_GSME", "avg_accuracies")
-
-
-
-
-if __name__ == "__main__":
-
- runDNNs()
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/bin/measure_confidence.py b/llvm/projects/hpvm-tensor-rt/bin/measure_confidence.py
deleted file mode 100644
index 74aa23c71aa3e81fc9422a3cc73ba3b69ed98c8a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/measure_confidence.py
+++ /dev/null
@@ -1,125 +0,0 @@
-
-import argparse
-import os
-import subprocess
-import sys
-
-
-def getAccuracy(file_name):
-
- if not os.path.exists(file_name):
- print("final_accuracy file not found ")
- sys.exit(0)
-
- file = open(file_name, "r")
- acc_str = file.read()
- accuracy = float(acc_str)
- print accuracy
- return accuracy
-
-
-total_runs = 12.0
-skip_lines = 0
-
-
-def test_func():
- print "test_func"
- sys.exit(0)
-
-
-def do_multiple_runs(binary_name, accuracy_threshold, confidence_threshold):
-
- #total_runs = 100.0
- successful_runs = 0.0
- total_acc = 0
-
- for i in range(int(total_runs)):
- subprocess.call(binary_name)
- accuracy = getAccuracy("final_accuracy")
- total_acc += accuracy
-
- if accuracy > accuracy_threshold:
- successful_runs += 1
-
- confidence = (successful_runs / total_runs) * 100.0
- print("confidence = ", confidence)
- avg_acc = total_acc / total_runs
- print("average accuracy = ", avg_acc)
-
- return confidence, avg_acc
-
-
-def compute_confidence(binary_name, accuracy, confidence, result_dir, output_dir):
-
- confidence_list = []
-
- if not os.path.exists(result_dir):
- print("Path does not exist")
- sys.exit(0)
-
- file_names = os.listdir(result_dir)
- print file_names
-
- for file_name in file_names:
- # Skip sub-directories
- if os.path.isdir(result_dir + "/" + file_name):
- continue
-
- f = open(result_dir + "/" + file_name)
- tuner_file = open("opentuner_flags", "w+")
-
- index = 0
- results_str = ""
- for x in f:
- if index >= skip_lines:
- error_knob = int(float(x.split()[1]))
- print error_knob
- tuner_file.write(str(error_knob) + "\n")
-
- results_str += x
- index += 1
-
- tuner_file.close()
-
- run_confidence, avg_accuracy = do_multiple_runs(binary, accuracy, confidence)
-
- if run_confidence > 90:
- f2 = open(output_dir + "/" + file_name, "w+")
- f2.write("total_runs=" + str(total_runs) + "\t confidence=" + str(run_confidence) + "\t avg_accuracy=" + str(avg_accuracy) + "\n")
- f2.write(results_str)
- f2.close()
-
- conf_result = (run_confidence, avg_accuracy, file_name)
- confidence_list.append(conf_result)
-
- return confidence_list
-
-
-if __name__ == "__main__":
-
- argparser = argparse.ArgumentParser(description='runs best configs to get high confidence on accuracy')
- argparser.add_argument('--result-dir', help='Directory containing OpenTuner configurations')
- argparser.add_argument('--output-dir', help='Directory for storing output directory')
- argparser.add_argument('--binary', help='Binary name to run')
- argparser.add_argument('--accuracy', type=float, help='Accuracy constraint')
- argparser.add_argument('--confidence', type=float, help='Confidence threshold')
-
-
- args = argparser.parse_args()
- result_dir = args.result_dir
- output_dir = args.output_dir
- binary = args.binary
- accuracy = args.accuracy
- confidence = args.confidence
-
- confidence_list = compute_confidence(binary, accuracy, confidence, result_dir, output_dir)
- #print confidence_list
-
- sorted_list = sorted(confidence_list, key = lambda tup: tup[0], reverse=True)
-
- output_file = open(output_dir + "/confidence_summary.txt", "w+")
- for x in sorted_list:
- output_file.write(str(x[0]) + "\t" + str(x[1]) + "\t" + str(x[2]) + "\n")
-
- output_file.close()
-
diff --git a/llvm/projects/hpvm-tensor-rt/bin/mergeTensorOpAndErrors.py b/llvm/projects/hpvm-tensor-rt/bin/mergeTensorOpAndErrors.py
deleted file mode 100644
index 3c9ea9de2854ed133350950d3995f459120176de..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/mergeTensorOpAndErrors.py
+++ /dev/null
@@ -1,60 +0,0 @@
-
-
-
-if __name__ == "__main__":
-
- dnn_benchs = []
- dnn_benchs.append("fc4")
- dnn_benchs.append("lenet_keras")
- dnn_benchs.append("alexnet_cifar10")
- dnn_benchs.append("alexnet2_cifar10")
- dnn_benchs.append("vgg16_cifar10")
- dnn_benchs.append("vgg16_cifar100")
- dnn_benchs.append("resnet18_cifar10")
- dnn_benchs.append("mobilenet")
- dnn_benchs.append("mobilenet_shallow")
-
-
- for bench in dnn_benchs:
- errors_file1 = "build_tuner/tuner_results/" + bench + "/tensor_errors_1000.txt"
- errors_file2 = "build_test/tuner_results/" + bench + "/tensor_composite_errors.txt"
- ops_file = "build_tuner/tuner_results/" + bench + "/op_names.txt"
-
- f1 = open(errors_file1)
- f2 = open(errors_file2)
- f3 = open(ops_file)
-
- fout = open("build_tuner/tuner_results/" + bench + "/tensor_op_errors.txt", "w+")
-
- bench_data = []
- for x in f3:
- op_name = x.strip()
- bench_data.append([op_name, 0.0, 0.0])
-
- it = 0
- for x in f1:
- if it >= len(bench_data):
- break
- toks = x.split()
- error1 = float(toks[1])
- print error1
- bench_data[it][1] = error1
- it += 1
-
- it = 0
- for x in f2:
- if it >= len(bench_data):
- break
- toks = x.split()
- error2 = float(toks[1])
- bench_data[it][2] = error2
- it += 1
-
- for i in range(len(bench_data)):
- fout.write(str(i) + "\t" + bench_data[i][0] + "\t" + str(bench_data[i][1]) + "\t" + str(bench_data[i][2]) + "\n")
-
- fout.close()
- f1.close()
- f2.close()
- f3.close()
-
diff --git a/llvm/projects/hpvm-tensor-rt/bin/read_weight_ranges.py b/llvm/projects/hpvm-tensor-rt/bin/read_weight_ranges.py
deleted file mode 100644
index c54d7dfcddc161aa20dd8378d2652d32c4905e38..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/read_weight_ranges.py
+++ /dev/null
@@ -1,43 +0,0 @@
-
-
-import numpy as np
-import os
-import struct
-
-
-def read_value_range(file_name):
-
- print file_name
- f = open(file_name, "rb")
-
- bytes = os.stat(file_name).st_size
- elems = bytes/4
-
- data_arr = struct.unpack('f'*elems, f.read(4*elems))
-
- print (np.amin(data_arr))
- print (np.amax(data_arr))
-
-
-
-
-if __name__ == "__main__":
-
- dir_prefix = "model_params/alexnet2_cifar10/"
- print dir_prefix
- read_value_range(dir_prefix + "norm_cifar_input.bin")
- read_value_range(dir_prefix + "conv1.bin")
- read_value_range(dir_prefix + "conv1_bias.bin")
- read_value_range(dir_prefix + "conv2.bin")
- read_value_range(dir_prefix + "conv2_bias.bin")
- read_value_range(dir_prefix + "conv3.bin")
- read_value_range(dir_prefix + "conv3_bias.bin")
- read_value_range(dir_prefix + "conv4.bin")
- read_value_range(dir_prefix + "conv4_bias.bin")
- read_value_range(dir_prefix + "conv5.bin")
- read_value_range(dir_prefix + "conv5_bias.bin")
- read_value_range(dir_prefix + "conv6.bin")
- read_value_range(dir_prefix + "conv6_bias.bin")
- read_value_range(dir_prefix + "fc1.bin")
- read_value_range(dir_prefix + "fc1_bias.bin")
-
diff --git a/llvm/projects/hpvm-tensor-rt/bin/replace_half_calls.py b/llvm/projects/hpvm-tensor-rt/bin/replace_half_calls.py
deleted file mode 100644
index b75a7d4750074cf6234151ae21a8bff5af1050d5..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/replace_half_calls.py
+++ /dev/null
@@ -1,35 +0,0 @@
-
-
-import sys
-
-
-if __name__ == "__main__":
-
- if len(sys.argv) < 3:
- print ("Usage: python replace_half_calls.py in_file.cc half_out_file.cc \n")
- sys.exit(0)
-
- file_name = sys.argv[1]
- out_file_name = sys.argv[2]
-
- f = open(file_name)
- str = f.read()
-
- str = str.replace("tensorConvolution", "tensorHalfConvolution")
- str = str.replace("tensorAdd", "tensorHalfAdd")
- str = str.replace("tensorRelu", "tensorHalfRelu")
- str = str.replace("tensorRelu2", "tensorHalfRelu2")
- str = str.replace("tensorTanh", "tensorHalfTanh")
- str = str.replace("tensorPooling", "tensorHalfPooling")
- str = str.replace("tensorGemmGPU", "tensorHalfGemmGPU")
-
- print (str)
-
- f.close()
-
- f2 = open(out_file_name, "w+")
-
- f2.write(str)
-
- f2.close()
-
diff --git a/llvm/projects/hpvm-tensor-rt/bin/select_top_results.py b/llvm/projects/hpvm-tensor-rt/bin/select_top_results.py
deleted file mode 100644
index 898b4c4f42211e010b1544039cbd4b4125c03b92..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/select_top_results.py
+++ /dev/null
@@ -1,89 +0,0 @@
-
-
-import argparse
-import sys
-import os
-
-
-log_index = 7
-linear_index = 8
-quad_index = 9
-
-top_k = 10
-
-def dump_results(sorted_list, k, result_dir, sub_dir):
-
- ref_dir = result_dir + "/" + sub_dir
- if not os.path.exists(ref_dir):
- os.mkdir(ref_dir)
-
- for i in range(k):
- file_name = sorted_list[i][1]
- file_name = ref_dir + "/" + file_name + "_rank_" + str(i)
- f = open(file_name, "w+")
- f.write(str(sorted_list[i][2]) + "\t")
- f.write(str(sorted_list[i][3]) + "\t")
- f.write(str(sorted_list[i][4]) + "\n")
- f.write(sorted_list[i][0])
- f.close()
-
-
-
-
-def select_top_results(result_dir):
-
- if not os.path.exists(result_dir):
- print("Path does not exist")
- sys.exit(0)
-
- file_names = os.listdir(result_dir)
- print file_names
-
- results_arr = []
-
- for file_name in file_names:
- # Skip sub-directories
- if os.path.isdir(result_dir + "/" + file_name):
- continue
-
- log_result = 0.0
- linear_result = 0.0
- quad_result = 0.0
- file_str = ""
-
- f = open(result_dir + "/" + file_name)
- for x in f:
- words = x.split()
- log_result += float(words[log_index])
- linear_result += float(words[linear_index])
- quad_result += float(words[quad_index])
- file_str += x
-
-
- file_result = (file_str, file_name, log_result, linear_result, quad_result)
- results_arr.append(file_result)
-
-
- sorted_list = sorted(results_arr, key = lambda tup: tup[2])
- dump_results(sorted_list, top_k, result_dir, "log")
-
- sorted_list = sorted(results_arr, key = lambda tup: tup[3])
- dump_results(sorted_list, top_k, result_dir, "linear")
-
- sorted_list = sorted(results_arr, key = lambda tup: tup[4])
- dump_results(sorted_list, top_k, result_dir, "quad")
-
-
-
-if __name__ == "__main__":
-
- argparser = argparse.ArgumentParser(description='runs best configs to get high confidence on accuracy')
- argparser.add_argument('--result-dir', help='Directory containing OpenTuner configurations')
-
- args = argparser.parse_args()
- result_dir = args.result_dir
-
- select_top_results(result_dir)
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/bin/setupEnv.sh b/llvm/projects/hpvm-tensor-rt/bin/setupEnv.sh
deleted file mode 100644
index 58f16f20d0af12f041840b8037ae13e49c214ed4..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/setupEnv.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-#!/bin/bash
-module load cuda-toolkit/8.0
-export CUDNN_PATH=/software/cuda-toolkit-8.0/lib64/
-export LIBRARY_PATH=$LIBRARY_PATH:/software/cuda-toolkit-8.0/lib64/
-
diff --git a/llvm/projects/hpvm-tensor-rt/bin/setup_aws_paths.sh b/llvm/projects/hpvm-tensor-rt/bin/setup_aws_paths.sh
deleted file mode 100644
index d9f092a19f12a91bd588a356fc99744c14deb26a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/setup_aws_paths.sh
+++ /dev/null
@@ -1,14 +0,0 @@
-#!/bin/bash
-
-# CUDNN Path setup
-# module load cuda-toolkit/9.1
-export CUDA_INCLUDE_PATH=/usr/local/cuda/include
-export CUDNN_PATH=/use/local/cuda/lib64/
-export LIBRARY_PATH=/usr/local/cuda/lib64/:$LIBRARY_PATH
-#export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:$LD_LIBRARY_PATH
-
-# HPVM Path setup
-#export CPATH=$CPATH:/home/hsharif3/anaconda2/include/
-#export PATH=/home/hsharif3/Gitlab/hpvm/build/bin/:$PATH
-#export LLVM_BUILD_ROOT=/home/hsharif3/Gitlab/hpvm/build/
-#export LLVM_SRC_ROOT=/home/hsharif3/Gitlab/hpvm/llvm/
diff --git a/llvm/projects/hpvm-tensor-rt/bin/setup_cuda_llvm_paths.sh b/llvm/projects/hpvm-tensor-rt/bin/setup_cuda_llvm_paths.sh
deleted file mode 100644
index 3548f182f198724600aee855b66169a1bdf12a3a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/setup_cuda_llvm_paths.sh
+++ /dev/null
@@ -1,14 +0,0 @@
-#!/bin/bash
-
-# CUDNN Path setup
-module load cuda-toolkit/9.1
-export CUDA_INCLUDE_PATH=/software/cuda-9.1/include
-export CUDNN_PATH=/software/cuda-9.1/lib64/
-export LIBRARY_PATH=/software/cuda-9.1/lib64/:$LIBRARY_PATH
-export LD_LIBRARY_PATH=/software/cuda-9.1/lib64/:$LD_LIBRARY_PATH
-
-# HPVM Path setup
-export CPATH=$CPATH:/home/hsharif3/anaconda2/include/
-export PATH=/home/hsharif3/Gitlab/hpvm/build/bin/:$PATH
-export LLVM_BUILD_ROOT=/home/hsharif3/Gitlab/hpvm/build/
-export LLVM_SRC_ROOT=/home/hsharif3/Gitlab/hpvm/llvm/
diff --git a/llvm/projects/hpvm-tensor-rt/bin/setup_jetson.sh b/llvm/projects/hpvm-tensor-rt/bin/setup_jetson.sh
deleted file mode 100644
index b288ccfe43c577f9ad14c4eb16284539ae5682ea..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/setup_jetson.sh
+++ /dev/null
@@ -1,8 +0,0 @@
-
-export LIBRARY_PATH=$LIBRARY_PATH:/usr/local/cuda-9.0/targets/aarch64-linux/lib/
-export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-9.0/targets/aarch64-linux/lib/
-export CUDNN_PATH=/usr/local/cuda-9.0/
-export CUDA_INCLUDE_PATH=${CUDNN_PATH}/include
-
-export LLVM_BUILD_ROOT=/home/nvidia/Gitlab/hpvm/build/
-export LLVM_SRC_ROOT=/home/nvidia/Gitlab/hpvm/llvm/
diff --git a/llvm/projects/hpvm-tensor-rt/bin/setup_paths.sh b/llvm/projects/hpvm-tensor-rt/bin/setup_paths.sh
deleted file mode 100644
index 446481b79a47827bf47341ce9d14f15f57d26866..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/setup_paths.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/bin/sh
-
-# Setting include path for Anaconda include files
-export CPATH=$CPATH:/home/hsharif3/anaconda2/include/
-# Setting path for llvm/clang-4.0 build
-export PATH=/home/hsharif3/Gitlab/llvm/llvm/build/bin/:$PATH
-
-export LLVM_BUILD_ROOT=/home/hsharif3/Gitlab/hpvm/build/
-
-export LLVM_SRC_ROOT=/home/hsharif3/Gitlab/hpvm/llvm/
diff --git a/llvm/projects/hpvm-tensor-rt/bin/setup_tyler_paths.sh b/llvm/projects/hpvm-tensor-rt/bin/setup_tyler_paths.sh
deleted file mode 100644
index 3548f182f198724600aee855b66169a1bdf12a3a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/setup_tyler_paths.sh
+++ /dev/null
@@ -1,14 +0,0 @@
-#!/bin/bash
-
-# CUDNN Path setup
-module load cuda-toolkit/9.1
-export CUDA_INCLUDE_PATH=/software/cuda-9.1/include
-export CUDNN_PATH=/software/cuda-9.1/lib64/
-export LIBRARY_PATH=/software/cuda-9.1/lib64/:$LIBRARY_PATH
-export LD_LIBRARY_PATH=/software/cuda-9.1/lib64/:$LD_LIBRARY_PATH
-
-# HPVM Path setup
-export CPATH=$CPATH:/home/hsharif3/anaconda2/include/
-export PATH=/home/hsharif3/Gitlab/hpvm/build/bin/:$PATH
-export LLVM_BUILD_ROOT=/home/hsharif3/Gitlab/hpvm/build/
-export LLVM_SRC_ROOT=/home/hsharif3/Gitlab/hpvm/llvm/
diff --git a/llvm/projects/hpvm-tensor-rt/bin/swing_selection.py b/llvm/projects/hpvm-tensor-rt/bin/swing_selection.py
deleted file mode 100644
index b5c484a23029f97218500571ebb8bcafc718d430..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/swing_selection.py
+++ /dev/null
@@ -1,304 +0,0 @@
-
-
-import os
-import warnings
-import matplotlib.pyplot as plt
-import matplotlib.cm as cm
-from matplotlib.ticker import MultipleLocator
-import numpy as np
-from scipy.signal import savgol_filter
-import math
-import struct
-
-
-
-def readDataFromText(textFile):
- results = []
- with open(textFile, "r") as f:
- for line in f:
- token = line.split("\t")
- if (len(token) < 7):
- continue
- record = (token[0], float(token[1]), float(token[5]), float(token[6]))
- results.append(record)
- return results
-
-
-convL1bins = [(0.985901, 1.36474), (0.852871, 1.16982), (0.422283, 0.55701), (0.259752, 0.335259), (0.216577, 0.277843), (0.185812, 0.23733), (0.148996, 0.189171), (0.100007, 0.125816), (0.0003127876261714846, 0.014511194080114365)]
-convL2bins = [(0.995298, 1.3643), (0.861066, 1.16279), (0.426857, 0.547827), (0.262645, 0.330186), (0.218984, 0.273731), (0.187878, 0.233872), (0.150619, 0.186512), (0.10106, 0.124477), (0.00035427528200671077, 0.020199092105031013)]
-
-biasL1bins = [(0.3510325849056244, 0.49078235030174255), (0.30895063281059265, 0.4311973750591278), (0.16023841500282288, 0.22283604741096497), (0.099583700299263, 0.1381179839372635), (0.08340170979499817, 0.11503150314092636), (0.07280077040195465, 0.09948030859231949), (0.05857400223612785, 0.07965542376041412), (0.04044099152088165, 0.054193537682294846), (0.0, 0.0)]
-biasL2bins = [(0.4154910147190094, 0.5820578932762146), (0.3656001389026642, 0.5121639370918274), (0.18930286169052124, 0.2637346684932709), (0.11687946319580078, 0.16306844353675842), (0.09796475619077682, 0.13558265566825867), (0.0848352462053299, 0.11619425565004349), (0.06783176958560944, 0.09277229756116867), (0.046059850603342056, 0.062238890677690506), (0.0, 0.0)]
-
-gemmL1bins= [(0.711203, 0.772211), (0.625894, 0.679601), (0.322665, 0.350383), (0.199646, 0.216727), (0.166556, 0.180781), (0.142945, 0.155132), (0.114662, 0.124399), (0.0771065, 0.0835984), (0.00034660729579627514, 0.008546584285795689)]
-gemmL2bins= [(0.715208, 0.768102), (0.629411, 0.675947), (0.324433, 0.348358), (0.200659, 0.21539), (0.167381, 0.179634), (0.143637, 0.154119), (0.115197, 0.123548), (0.0774642, 0.0829647), (0.0003496285935398191, 0.009841435588896275)]
-
-
-
-def findBinByOp(op):
- if op == 'tensorConv':
- return convL1bins, convL2bins
- if op == 'tensorAdd':
- return biasL1bins, biasL2bins
- if op == 'tensorGemm':
- return gemmL1bins, gemmL2bins
-
- return None, None
-
-
-def getSwing(Lx, opLxbin):
- if opLxbin == None:
- return 0
- for i, (minT, maxT) in enumerate(opLxbin):
- if Lx > minT:
- return i
-
- return 9
-
-
-
-def getConfiguration(L_thresholds):
- configuration = []
- for l in L_thresholds:
- # L0 is op_type
- opL1bin, opL2bin = findBinByOp(l[0])
- # NOTE: L2 is L1 error, L3 is L2 error
- sL1 = getSwing(l[2], opL1bin)
- sL2 = getSwing(l[3], opL2bin)
- if sL1 < 7:
- sL1 = sL1 + 1
- if sL2 < 7:
- sL2 = sL2 + 1
- configuration.append((l[0], l[1], l[2], l[3], sL1, sL2, max(sL1, sL2)))
-
- return configuration
-
-
-def displayConfig(config):
- for c in config:
- print(c)
-
-def displayMultipleConfigurations(configurations):
- for f, c in configurations.items():
- print(f)
- displayConfig(c)
- print()
-
-def getConfigFromFile(filename):
- L_requirements = readDataFromText(filename)
- config = getConfiguration(L_requirements)
- return config
-
-
-def getConfigurationsFromDir(dirname):
- configurations = dict()
- for f in os.listdir(dirname):
- configurations[f] = getConfigFromFile(os.path.join(dirname, f))
-
- return configurations
-
-
-def getLayerWiseTarget(config):
- target = []
- for i, op in enumerate(config):
- if (op[0] == 'tensorGemm') or (op[0] == 'tensorConv'):
- t = op[6]
- for j in range(i+1, len(config)):
- if config[j][0] == 'tensorGemm' or config[j][0] == 'tensorConv':
- break
- t = max(t, config[j][6])
- target.append(t)
- t = 0
-
- return target
-
-
-def dumpLayerWiseTarget(file, targets):
- with open(file, "w") as f:
- for name, t in targets.items():
- f.write(name)
- f.write(" ")
- for i in t:
- f.write(str(i))
- f.write(" ")
- f.write("\n")
-
-
-def getTargetsFromConfigurations(configs):
- targets = dict()
- for f, c in configs.items():
- targets[f] = [d[6] for d in c]
-
- return targets
-
-
-def dumpBenchmarkTargets(name, benchmark_dir):
- benchmark_targets = dict()
- error = ['linear', 'log', 'quad']
- for e in error:
- results_dir = os.path.join(benchmark_dir, e)
- configs = getConfigurationsFromDir(results_dir)
- benchmark_targets[e] = getTargetsFromConfigurations(configs)
-
- return benchmark_targets
-
-
-
-def dumpTargets(filename, targets):
- with open(filename, "w") as f:
- for e, file_configs in targets.items():
- for name, config in file_configs.items():
- for c in config:
- f.write(str(c))
- f.write(" ")
- f.write("\n")
-
-
-
-def getLayerSwings(layer_desc, configurations):
-
- layer_swings = []
- for i in range(len(configurations)):
- config_vals = configurations[i]
- if len(config_vals) == 0:
- continue
-
- layer_index = 0
- index = 0
- swing_vals = []
-
- while layer_index < len(layer_desc):
- if len(layer_desc[layer_index]) == 1:
- promise_swing = config_vals[index]
- layer_type = layer_desc[layer_index][0]
- layer_type = layer_type.strip()
- print ("****layer_type = ", layer_type)
- if layer_type != "conv" and layer_type != "dense":
- promise_swing = -9
- if layer_type == "depthwise_conv":
- promise_swing = 9
- index += 1
- else:
- #print ("index = ", index)
- # FIXIT: Doesn't look right
- print (config_vals[index], config_vals[index+1])
- promise_swing = max(config_vals[index], config_vals[index+1])
- stride = len(layer_desc[layer_index])
- index += stride
-
- swing_vals.append(promise_swing)
- layer_index += 1
-
- layer_swings.append(swing_vals)
-
- return layer_swings
-
-
-
-
-def loadLayerDesc(layer_desc_file):
-
- layer_desc = []
- f = open(layer_desc_file)
- for x in f:
- vals = x.split()
- layer_desc.append(vals)
-
- return layer_desc
-
-
-
-def dumpLayerTargets(targets, tuned_result_dir, layer_desc_file):
-
- layer_desc = loadLayerDesc(layer_desc_file)
- print (layer_desc)
-
- file_names = []
- configurations = []
- for e, file_configs in targets.items():
- for name, config in file_configs.items():
- config_vals = []
- for c in config:
- config_vals.append(c)
- print (config_vals)
-
- configurations.append(config_vals)
-
- rank = e + "_" + "_".join(name.split("_")[-2:])
- file_names.append(rank)
-
-
- # NOTE: get PROMISE swing values corresponding to each layer
- layer_swings = getLayerSwings(layer_desc, configurations)
-
- targets_file_path = tuned_result_dir + "/layer_targets.txt"
- f = open(targets_file_path, "w+")
-
- for config in layer_swings:
- index = 0
- for swing in config:
- swing_str = ""
- if swing == 8 or swing == 9:
- layer_size = len(layer_desc[index])
- for i in range(layer_size):
- swing_str += str(swing)
- if i < layer_size - 1:
- swing_str += " "
- elif swing == -9:
- swing_str += "8"
- else:
- swing_str += str(swing)
-
- if index < len(config) - 1:
- swing_str += ","
-
- f.write(swing_str)
- index += 1
-
- f.write("\n")
-
- f.close()
-
- print(layer_swings)
- return layer_swings, file_names
-
-
-
-def replaceFirstLayer(layer_swings):
-
- # Ensuring first conv on GPU
- for conf in layer_swings:
- conf[0] = 9
-
-
-
-def computeLayerTargets(tuned_result_dir, layer_desc_file):
-
- targets_file_path = tuned_result_dir + "/tensor_targets.txt"
- targets = dumpBenchmarkTargets(targets_file_path, tuned_result_dir)
-
- dumpTargets(targets_file_path, targets)
-
- layer_swings, file_names = dumpLayerTargets(targets, tuned_result_dir, layer_desc_file)
-
- replaceFirstLayer(layer_swings)
-
- return layer_swings, file_names
-
-
-# Externally-called function
-def compute_swing_selection(tuned_result_dir, layer_file):
-
- return computeLayerTargets(tuned_result_dir, layer_file)
-
-
-
-
-if __name__ == "__main__":
-
- tuned_result_dir = "./vgg16_cifar10_tuner_1/high_confidence/"
- layer_file = "layer_composition.txt"
-
- tuned_result_dir = "./resnet18_cifar10_tuner_1/high_confidence/"
- layer_file = "layer_composition2.txt"
- computeLayerTargets(tuned_result_dir, layer_file)
diff --git a/llvm/projects/hpvm-tensor-rt/bin/tensor_inline.sh b/llvm/projects/hpvm-tensor-rt/bin/tensor_inline.sh
deleted file mode 100755
index f67f22ebad5352d99238addd26d9e1b568ee2125..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/tensor_inline.sh
+++ /dev/null
@@ -1,2 +0,0 @@
-clang-4.0 -emit-llvm tensor_cpu_runtime.cc -S -o tensor_cpu_runtime.ll
-opt-4.0 -always-inline tensor_cpu_runtime.ll -S -o tensor_cpu_runtime.ll
diff --git a/llvm/projects/hpvm-tensor-rt/bin/times.py b/llvm/projects/hpvm-tensor-rt/bin/times.py
deleted file mode 100644
index 082b0d91acb19e70a6c217b25f8747f3197b45b7..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/times.py
+++ /dev/null
@@ -1,78 +0,0 @@
-
-
-
-class Config:
- def __init__(self):
- self.runtime = 0
- self.fed_runs = 0
- self.full_runs = 0
-
-
-def computeTimes(bench):
-
- conf_runs = 60
- fed_time = (bench.runtime * 100) + (bench.fed_runs * conf_runs * bench.runtime)
- fed_time_hrs = fed_time / (60*60)
-
- full_time = (bench.runtime * 1000) + (bench.full_runs * conf_runs * bench.runtime)
- full_time_hrs = full_time / (60*60)
-
- print ("fedtime_hrs = ", fed_time_hrs, " full_time_hrs = ", full_time_hrs, "\n")
-
-
-
-if __name__ == "__main__":
-
-
- resnet = Config()
- resnet.runtime = 8
- resnet.fed_runs = 3
- resnet.full_runs = 5
-
- computeTimes(resnet)
-
- alexnet = Config()
- alexnet.runtime = 7.8
- alexnet.fed_runs = 47
- alexnet.full_runs = 274
-
- computeTimes(alexnet)
-
- alexnet2 = Config()
- alexnet2.runtime = 2.3
- alexnet2.fed_runs = 62
- alexnet2.full_runs = 339
-
- computeTimes(alexnet2)
-
- vgg1 = Config()
- vgg1.runtime = 7.4
- vgg1.fed_runs = 15
- vgg1.full_runs = 211
-
- computeTimes(vgg1)
-
-
- vgg2 = Config()
- vgg2.runtime = 15.4
- vgg2.fed_runs = 8
- vgg2.full_runs = 150
-
- computeTimes(vgg2)
-
-
- lenet = Config()
- lenet.runtime = 0.98
- lenet.fed_runs = 64
- lenet.full_runs = 228
-
- computeTimes(lenet)
-
-
- mobilenet = Config()
- mobilenet.runtime = 11
- mobilenet.fed_runs = 32
- mobilenet.full_runs = 267
-
- computeTimes(mobilenet)
-
diff --git a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/benchmarks.py b/llvm/projects/hpvm-tensor-rt/bin/tuner_src/benchmarks.py
deleted file mode 100644
index fada8ddf641068f4622f48cbe816c5a6f9ff7843..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/benchmarks.py
+++ /dev/null
@@ -1,483 +0,0 @@
-
-# NOTES: Batch13 (Baseline) Batch14 - With ErrorSens (10, 25, 35)
-
-# Batch 9: No Error Sens. Min : P3
-# Batch 10: No Error Sens + More Runs for Loss1 and Loss2. Min: P3
-# Batch 11: Error Sens: Skipping 30% elems in each : Min: P3. More runs in Loss1 (4000) and Loss2 (2000)
-# Batch 12: Error Sens: 10, 25, 35, for Loss1, 2, 3, respectively, Min: P3. 1000 Runs for All
-# Batch 13: No Error Sens: Equal Runs (1000) for all. Min: P1
-# Batch 14: Reruning Batch12 with bugFix!
-# Batch 15: MAJOR CHANGE: 3 different skip levels for each Loss1,Loss2,Loss3
-
-# Batch 18: Batch13 (Basline) + ParetoCurve (1500 Runs) - BUGGY IGNORE!!!
-
-# Batch 19: (Basline) + ParetoCurve + 2 runs in Tuning Phase (1500 Runs)
-
-# Batch 20: 3-Skip levels + + 2 runs + 1500 Runs + EnergyBandSize now % of Max (Compare against Batch19
-
-
-# Batch 200: AlgoTuner - 1000 images - 1500 runs (IGNORE)
-# Batch 201: AlgoTuner - 2000 images - 1500 runs
-# Batch 202: AlgoTuner - 2000 images - 500 runs
-# Batch 203: AlgoTuner - 2000 images - 3000 runs
-
-
-#---- CHANGES: i) Reshufled inputs ii) 3K images for tuning
-# Batch 210: 3K images, 1000 runs (1500 resnet), no FP32 used in tuning
-# Batch 211: Same as Batch-210 + uses tensorConvPerfCuda*Half*
-
-
-#batch_id = "batch210"
-#batch_id = "batch211"
-batch_id = "batch210"
-
-
-class Benchmark:
- def __init__(self):
- self.tuner_binary = ""
- self.promise_binary = ""
- self.tuner_accuracy = 0
- self.promise_accuracy = 0
- self.num_flags = 0
- self.num_layers = 0
- self.autotuner_runs = 0
- self.error_range_1 = 0
- self.error_range_2 = 0
- self.result_dir_1 = ""
- self.result_dir_2 = ""
- self.promise_result_dir_1 = ""
- self.promise_result_dir_2 = ""
-
-
-
-bench_tuner_data = {}
-
-# FIXIT: Fix Variable Names below
-Alexnet1 = Benchmark()
-Alexnet1.tuner_binary = "alexnet_cifar10_tuner"
-Alexnet1.promise_binary = "alexnet_promise"
-Alexnet1.validation_binary = "alexnet_valid"
-Alexnet1.num_flags = 21
-Alexnet1.num_layers = 6
-Alexnet1.error_range_1 = 10
-Alexnet1.error_range_2 = 13
-Alexnet1.start_promise_range = 1
-Alexnet1.skip_layers = 0
-#Alexnet1.skip_layer_str = "0"
-Alexnet1.skip_layer_str = "5_0"
-
-Alexnet1.base_dir = "../build_tuner/tuner_results/alexnet_cifar10/"
-Alexnet1.result_dir_1 = "../build_tuner/tuner_results/alexnet_cifar10/loss_1/" + batch_id
-Alexnet1.result_dir_2 = "../build_tuner/tuner_results/alexnet_cifar10/loss_2/" + batch_id
-Alexnet1.result_dir_3 = "../build_tuner/tuner_results/alexnet_cifar10/loss_3/" + batch_id
-
-Alexnet1.tensor_desc_file = "tuner_results/alexnet_cifar10/alexnet_tensors.txt"
-Alexnet1.layer_file = "tuner_results/alexnet_cifar10/alexnet_layers.txt"
-Alexnet1.cost_file = "../build_tuner/tuner_results/alexnet_cifar10/op_cost.txt"
-Alexnet1.layer_knobs = "../opentuner/data/alexnet/knobs.txt"
-
-#Alexnet1.loss1_result_file = "tuner_results/alexnet2_cifar10/alexnet_layers.txt"
-Alexnet1.loss1_result_file = "tuner_results/alexnet_cifar10/loss_1/promise_tuned_confs/promise_confs.txt"
-Alexnet1.loss2_result_file = "tuner_results/alexnet_cifar10/loss_2/promise_tuned_confs/promise_confs.txt"
-
-Alexnet1.autotuner_runs = 1000
-Alexnet1.tuner_accuracy = 79.9
-#Alexnet1.promise_accuracy = 79.9
-Alexnet1.promise_accuracy = 78.86
-Alexnet1.validation_accuracy = 79.19
-
-bench_tuner_data["alexnet_cifar10"] = Alexnet1
-
-
-Alexnet2 = Benchmark()
-Alexnet2.tuner_binary = "alexnet2_cifar10_tuner"
-Alexnet2.promise_binary = "alexnet2_promise"
-Alexnet2.validation_binary = "alexnet2_valid"
-Alexnet2.num_flags = 23
-Alexnet2.num_layers = 7
-Alexnet2.error_range_1 = 10
-Alexnet2.error_range_2 = 13
-Alexnet2.start_promise_range = 1
-#Alexnet2.skip_layer_str = "0"
-Alexnet2.skip_layer_str = "6_1_0"
-
-Alexnet2.base_dir = "../build_tuner/tuner_results/alexnet2_cifar10/"
-Alexnet2.result_dir_1 = "../build_tuner/tuner_results/alexnet2_cifar10/loss_1/" + batch_id
-Alexnet2.result_dir_2 = "../build_tuner/tuner_results/alexnet2_cifar10/loss_2/" + batch_id
-Alexnet2.result_dir_3 = "../build_tuner/tuner_results/alexnet2_cifar10/loss_3/" + batch_id
-Alexnet2.tensor_desc_file = "tuner_results/alexnet2_cifar10/alexnet2_tensors.txt"
-Alexnet2.layer_file = "tuner_results/alexnet2_cifar10/alexnet2_layers.txt"
-Alexnet2.cost_file = "../build_tuner/tuner_results/alexnet2_cifar10/op_cost.txt"
-Alexnet2.layer_knobs = "../opentuner/data/alexnet2/knobs.txt"
-#Alexnet2.loss1_result_file = "tuner_results/alexnet2_cifar10/loss_1/promise_tuned_confs/promise_confs.txt"
-#Alexnet2.loss2_result_file = "tuner_results/alexnet2_cifar10/loss_2/promise_tuned_confs/promise_confs.txt"
-Alexnet2.autotuner_runs = 1000
-Alexnet2.tuner_accuracy = 84.19
-#Alexnet2.promise_accuracy = 84.19
-Alexnet2.promise_accuracy = 84.7
-Alexnet2.validation_accuracy = 85.15
-
-bench_tuner_data["alexnet2_cifar10"] = Alexnet2
-
-
-
-Alexnet3 = Benchmark()
-Alexnet3.tuner_binary = "vgg16_cifar10_tuner"
-Alexnet3.promise_binary = "./vgg16_cifar10_promise"
-Alexnet3.validation_binary = "vgg16_cifar10_valid"
-Alexnet3.num_flags = 50
-Alexnet3.num_layers = 15
-Alexnet3.error_range_1 = 9
-Alexnet3.error_range_2 = 11
-Alexnet3.start_promise_range = 1
-#Alexnet3.skip_layer_str = "0"
-Alexnet3.skip_layer_str = "14_3_4_1_6"
-
-Alexnet3.base_dir = "../build_tuner/tuner_results/vgg16_cifar10/"
-Alexnet3.result_dir_1 = "../build_tuner/tuner_results/vgg16_cifar10/loss_1/" + batch_id
-Alexnet3.result_dir_2 = "../build_tuner/tuner_results/vgg16_cifar10/loss_2/" + batch_id
-Alexnet3.result_dir_3 = "../build_tuner/tuner_results/vgg16_cifar10/loss_3/" + batch_id
-
-Alexnet3.tensor_desc_file = "tuner_results/vgg16_cifar10/vgg16_tensors.txt"
-Alexnet3.layer_file = "tuner_results/vgg16_cifar10/vgg16_layers.txt"
-Alexnet3.cost_file = "../build_tuner/tuner_results/vgg16_cifar10/op_cost.txt"
-Alexnet3.layer_knobs = "../opentuner/data/vgg16_cifar10/knobs.txt"
-
-Alexnet3.loss1_result_file = "tuner_results/vgg16_cifar10/loss_1/promise_tuned_confs/promise_confs.txt"
-Alexnet3.loss2_result_file = "tuner_results/vgg16_cifar10/loss_2/promise_tuned_confs/promise_confs.txt"
-
-Alexnet3.autotuner_runs = 1000
-Alexnet3.tuner_accuracy = 90.19
-#Alexnet3.promise_accuracy = 90.19
-Alexnet3.promise_accuracy = 88.53
-Alexnet3.validation_accuracy = 89.05
-
-bench_tuner_data["vgg16_cifar10"] = Alexnet3
-
-
-
-Alexnet4 = Benchmark()
-Alexnet4.tuner_binary = "resnet18_cifar10_tuner"
-Alexnet4.promise_binary = "resnet18_promise"
-Alexnet4.validation_binary = "resnet18_valid"
-Alexnet4.num_flags = 73
-Alexnet4.num_layers = 22
-Alexnet4.error_range_1 = 7
-Alexnet4.error_range_2 = 9
-Alexnet4.start_promise_range = 1
-#Alexnet4.skip_layer_str = "0"
-Alexnet4.skip_layer_str = "0_1_2_14_15_17_18_21"
-Alexnet4.base_dir = "../build_tuner/tuner_results/resnet18_cifar10/"
-Alexnet4.result_dir_1 = "../build_tuner/tuner_results/resnet18_cifar10/loss_1/" + batch_id
-Alexnet4.result_dir_2 = "../build_tuner/tuner_results/resnet18_cifar10/loss_2/" + batch_id
-Alexnet4.result_dir_3 = "../build_tuner/tuner_results/resnet18_cifar10/loss_3/" + batch_id
-Alexnet4.tensor_desc_file = "tuner_results/resnet18_cifar10/resnet_tensors.txt"
-Alexnet4.layer_file = "tuner_results/resnet18_cifar10/resnet_layers.txt"
-Alexnet4.cost_file = "../build_tuner/tuner_results/resnet18_cifar10/op_cost.txt"
-Alexnet4.layer_knobs = "../opentuner/data/resnet/knobs.txt"
-
-Alexnet4.loss1_result_file = "tuner_results/resnet18_cifar10/loss_1/promise_tuned_confs/promise_confs.txt"
-Alexnet4.loss2_result_file = "tuner_results/resnet18_cifar10/loss_2/promise_tuned_confs/promise_confs.txt"
-
-Alexnet4.autotuner_runs = 1500
-Alexnet4.tuner_accuracy = 89.6
-#Alexnet4.promise_accuracy = 89.59 - 1000 images
-Alexnet4.promise_accuracy = 89.5
-Alexnet4.validation_accuracy = 89.65
-
-bench_tuner_data["resnet18_cifar10"] = Alexnet4
-
-
-
-
-
-Alexnet5 = Benchmark()
-Alexnet5.tuner_binary = "vgg16_cifar100_tuner"
-Alexnet5.promise_binary = "vgg16_cifar100_promise"
-Alexnet5.validation_binary = "vgg16_cifar100_valid"
-Alexnet5.num_flags = 50
-Alexnet5.num_layers = 15
-Alexnet5.error_range_1 = 9
-Alexnet5.error_range_2 = 11
-Alexnet5.start_promise_range = 1
-#Alexnet5.skip_layer_str = "0"
-Alexnet5.skip_layer_str = "0_1_2_3_4"
-Alexnet5.base_dir = "../build_tuner/tuner_results/vgg16_cifar100/"
-Alexnet5.result_dir_1 = "../build_tuner/tuner_results/vgg16_cifar100/loss_1/" + batch_id
-Alexnet5.result_dir_2 = "../build_tuner/tuner_results/vgg16_cifar100/loss_2/" + batch_id
-Alexnet5.result_dir_3 = "../build_tuner/tuner_results/vgg16_cifar100/loss_3/" + batch_id
-
-Alexnet5.tensor_desc_file = "../build_tuner/tuner_results/vgg16_cifar100/vgg16_tensors.txt"
-Alexnet5.layer_file = "../build_tuner/tuner_results/vgg16_cifar100/vgg16_layers.txt"
-Alexnet5.cost_file = "../build_tuner/tuner_results/vgg16_cifar100/op_cost.txt"
-Alexnet5.layer_knobs = "../opentuner/data/vgg16_cifar100/knobs.txt"
-
-Alexnet5.loss1_result_file = "tuner_results/vgg_cifar100/loss_1/promise_tuned_confs/promise_confs.txt"
-Alexnet5.loss2_result_file = "tuner_results/vgg_cifar100/loss_2/promise_tuned_confs/promise_confs.txt"
-Alexnet5.autotuner_runs = 1000
-Alexnet5.tuner_accuracy = 67.95
-#Alexnet5.promise_accuracy = 66.8
-Alexnet5.promise_accuracy = 67.86
-Alexnet5.validation_accuracy = 68.65
-
-bench_tuner_data["vgg16_cifar100"] = Alexnet5
-
-
-
-Alexnet6 = Benchmark()
-Alexnet6.tuner_binary = "lenet_keras"
-Alexnet6.promise_binary = "lenet_promise"
-Alexnet6.validation_binary = "lenet_promise"
-#Alexnet6.validation_binary = "lenet_keras_valid"
-Alexnet6.num_flags = 14
-Alexnet6.num_layers = 4
-Alexnet6.error_range_1 = 16
-Alexnet6.error_range_2 = 20
-Alexnet6.start_promise_range = 1
-Alexnet6.skip_layer_str = "0"
-
-Alexnet6.base_dir = "../build_tuner/tuner_results/lenet_keras/"
-Alexnet6.result_dir_1 = "../build_tuner/tuner_results/lenet_keras/loss_1/" + batch_id
-Alexnet6.result_dir_2 = "../build_tuner/tuner_results/lenet_keras/loss_2/" + batch_id
-Alexnet6.result_dir_3 = "../build_tuner/tuner_results/lenet_keras/loss_3/" + batch_id
-
-Alexnet6.tensor_desc_file = "tuner_results/lenet_keras/lenet_tensors.txt"
-Alexnet6.layer_file = "tuner_results/lenet_keras/lenet_layers.txt"
-Alexnet6.cost_file = "../build_tuner/tuner_results/lenet_keras/op_cost.txt"
-Alexnet6.layer_knobs = "../opentuner/data/lenet/knobs.txt"
-
-#Alexnet6.loss1_result_file = "tuner_results/vgg_cifar100/loss_1/promise_tuned_confs/promise_confs.txt"
-#Alexnet6.loss2_result_file = "tuner_results/vgg_cifar100/loss_2/promise_tuned_confs/promise_confs.txt"
-Alexnet6.autotuner_runs = 1000
-Alexnet6.tuner_accuracy = 98.9
-Alexnet6.promise_accuracy = 99.7
-Alexnet6.validation_accuracy = 99
-
-bench_tuner_data["lenet_keras"] = Alexnet6
-
-
-
-
-Alexnet7 = Benchmark()
-Alexnet7.tuner_binary = "mobilenet_cifar10"
-#Alexnet7.promise_binary = "mobilenet_quant"
-Alexnet7.promise_binary = "mobilenet_promise"
-Alexnet7.validation_binary = "mobilenet_valid"
-Alexnet7.num_flags = 85
-Alexnet7.num_layers = 15
-Alexnet7.error_range_1 = 7
-Alexnet7.error_range_2 = 8
-Alexnet7.start_promise_range = 1
-#Alexnet7.skip_layer_str = "0"
-Alexnet7.skip_layer_str = "1_14_0_6_2"
-Alexnet7.base_dir = "../build_tuner/tuner_results/mobilenet/"
-Alexnet7.result_dir_1 = "../build_tuner/tuner_results/mobilenet/loss_1/" + batch_id
-Alexnet7.result_dir_2 = "../build_tuner/tuner_results/mobilenet/loss_2/" + batch_id
-Alexnet7.result_dir_3 = "../build_tuner/tuner_results/mobilenet/loss_3/" + batch_id
-
-Alexnet7.tensor_desc_file = "tuner_results/mobilenet/mobilenet_ops.txt"
-Alexnet7.layer_file = "tuner_results/mobilenet/mobilenet_layer_comp.txt"
-Alexnet7.cost_file = "../build_tuner/tuner_results/mobilenet/op_cost.txt"
-Alexnet7.layer_knobs = "../opentuner/data/mobilenet/knobs.txt"
-
-#--- Files below needed for VALIDATION experiment
-Alexnet7.loss1_result_file = "tuner_results/mobilenet/loss_1/batch1/promise_tuner/high_confidence/promise_confs.txt"
-Alexnet7.loss2_result_file = "tuner_results/mobilenet/loss_2/batch1/promise_tuner/high_confidence/promise_confs.txt"
-Alexnet7.autotuner_runs = 1000
-Alexnet7.tuner_accuracy = 84.8
-#Alexnet7.promise_accuracy = 84.8
-Alexnet7.promise_accuracy = 83.73
-Alexnet7.validation_accuracy = 84.4
-
-bench_tuner_data["mobilenet_cifar10"] = Alexnet7
-
-
-
-Alexnet8 = Benchmark()
-Alexnet8.tuner_binary = "mobilenet_cifar10_shallow"
-Alexnet8.promise_binary = "mobilenet_shallow_promise"
-Alexnet8.validation_binary = "mobilenet_shallow_valid"
-Alexnet8.num_flags = 42
-Alexnet8.num_layers = 8
-Alexnet8.error_range_1 = 10
-Alexnet8.error_range_2 = 12
-Alexnet8.start_promise_range = 1
-#Alexnet8.skip_layer_str = "0"
-Alexnet8.skip_layer_str = "7_0_1"
-Alexnet8.base_dir = "../build_tuner/tuner_results/mobilenet_shallow/"
-Alexnet8.result_dir_1 = "../build_tuner/tuner_results/mobilenet_shallow/loss_1/" + batch_id
-Alexnet8.result_dir_2 = "../build_tuner/tuner_results/mobilenet_shallow/loss_2/" + batch_id
-Alexnet8.result_dir_3 = "../build_tuner/tuner_results/mobilenet_shallow/loss_3/" + batch_id
-
-Alexnet8.tensor_desc_file = "../build_tuner/tuner_results/mobilenet_shallow/mobilenet_shallow_ops.txt"
-Alexnet8.layer_file = "../build_tuner/tuner_results/mobilenet_shallow/mobilenet_shallow_layer_comp.txt"
-Alexnet8.cost_file = "../build_tuner/tuner_results/mobilenet_shallow/op_cost.txt"
-Alexnet8.layer_knobs = "../opentuner/data/mobilenet_shallow/knobs.txt"
-
-Alexnet8.loss1_result_file = "../build_tuner/tuner_results/mobilenet_shallow/loss_1/batch2/promise_tuner/high_confidence/promise_selected_confs.txt"
-Alexnet8.loss2_result_file = "../build_tuner/tuner_results/mobilenet_shallow/loss_2/batch2/promise_tuner/high_confidence/promise_selected_confs.txt"
-
-Alexnet8.autotuner_runs = 1000
-Alexnet8.tuner_accuracy = 87.6
-#Alexnet8.promise_accuracy = 87.59
-Alexnet8.promise_accuracy = 87.76
-Alexnet8.validation_accuracy = 88.5
-
-bench_tuner_data["mobilenet_shallow"] = Alexnet8
-
-
-
-"""
-Alexnet9 = Benchmark()
-Alexnet9.tuner_binary = "fc4_clipped"
-Alexnet9.promise_binary = ""
-Alexnet9.validation_binary = ""
-Alexnet9.num_flags = 12
-Alexnet9.num_layers = 4
-Alexnet9.error_range_1 = 12
-Alexnet9.error_range_2 = 16
-Alexnet9.start_promise_range = 3
-Alexnet9.skip_layer_str = "0"
-Alexnet9.base_dir = "../build_tuner/tuner_results/fc4/"
-Alexnet9.result_dir_1 = "../build_tuner/tuner_results/fc4/loss1/batch1"
-Alexnet9.result_dir_2 = "../build_tuner/tuner_results/fc4/loss2/batch1"
-Alexnet9.tensor_desc_file = ""
-Alexnet9.layer_file = ""
-
-Alexnet9.loss1_result_file = ""
-Alexnet9.loss2_result_file = ""
-
-Alexnet9.autotuner_runs = 1000
-Alexnet9.tuner_accuracy = 93.8
-Alexnet9.promise_accuracy = 0.0
-Alexnet9.validation_accuracy = 0.0
-
-bench_tuner_data["fc4"] = Alexnet9
-
-
-
-
-Pipeline1 = Benchmark()
-Pipeline1.tuner_binary = "pipeline_GEOM"
-Pipeline1.promise_binary = "pipeline_GEOM_promise"
-Pipeline1.validation_binary = "pipeline_GEOM_valid"
-Pipeline1.num_flags = 9
-Pipeline1.num_layers = 4
-Pipeline1.error_range_1 = 10
-Pipeline1.error_range_2 = 15
-Pipeline1.start_promise_range = 2
-Pipeline1.skip_layer_str = "1_2"
-Pipeline1.result_dir_1 = "tuner_results/pipeline_GEOM/loss_30/batch1"
-Pipeline1.result_dir_2 = "tuner_results/pipeline_GEOM/loss_20/batch1"
-Pipeline1.tensor_desc_file = "tuner_results/pipeline_GEOM/pipeline_GEOM_tensors.txt"
-Pipeline1.layer_file = "tuner_results/pipeline_GEOM/pipeline_GEOM_layers.txt"
-#Pipeline1.loss1_result_file = "tuner_results/vgg_cifar100/loss_1/promise_tuned_confs/promise_confs.txt"
-#Pipeline1.loss2_result_file = "tuner_results/vgg_cifar100/loss_2/promise_tuned_confs/promise_confs.txt"
-Pipeline1.autotuner_runs = 300
-Pipeline1.tuner_accuracy = 95
-Pipeline1.promise_accuracy = 95
-Pipeline1.validation_accuracy = 95
-
-bench_tuner_data["pipeline_GEOM"] = Pipeline1
-
-
-Pipeline2 = Benchmark()
-Pipeline2.tuner_binary = "pipeline_GEMO"
-Pipeline2.promise_binary = "pipeline_GEMO_promise"
-Pipeline2.validation_binary = "pipeline_GEMO_valid"
-Pipeline2.num_flags = 9
-Pipeline2.num_layers = 4
-Pipeline2.error_range_1 = 10
-Pipeline2.error_range_2 = 15
-Pipeline2.start_promise_range = 2
-Pipeline2.skip_layer_str = "1_3"
-Pipeline2.result_dir_1 = "tuner_results/pipeline_GEMO/loss_30/batch1"
-Pipeline2.result_dir_2 = "tuner_results/pipeline_GEMO/loss_20/batch1"
-Pipeline2.tensor_desc_file = "tuner_results/pipeline_GEMO/pipeline_GEMO_tensors.txt"
-Pipeline2.layer_file = "tuner_results/pipeline_GEMO/pipeline_GEMO_layers.txt"
-#Pipeline1.loss1_result_file = "tuner_results/vgg_cifar100/loss_1/promise_tuned_confs/promise_confs.txt"
-#Pipeline1.loss2_result_file = "tuner_results/vgg_cifar100/loss_2/promise_tuned_confs/promise_confs.txt"
-Pipeline2.autotuner_runs = 300
-Pipeline2.tuner_accuracy = 95
-Pipeline2.promise_accuracy = 95
-Pipeline2.validation_accuracy = 95
-
-bench_tuner_data["pipeline_GEMO"] = Pipeline2
-
-
-
-
-Pipeline3 = Benchmark()
-Pipeline3.tuner_binary = "pipeline_GSME"
-Pipeline3.promise_binary = "pipeline_GSME_promise"
-Pipeline3.validation_binary = "pipeline_GSME_valid"
-Pipeline3.num_flags = 9
-Pipeline3.num_layers = 4
-Pipeline3.error_range_1 = 10
-Pipeline3.error_range_2 = 15
-Pipeline3.start_promise_range = 2
-Pipeline3.skip_layer_str = "1_3"
-Pipeline3.result_dir_1 = "tuner_results/pipeline_GSME/loss_30/batch1"
-Pipeline3.result_dir_2 = "tuner_results/pipeline_GSME/loss_20/batch1"
-Pipeline3.tensor_desc_file = "tuner_results/pipeline_GSME/pipeline_GSME_tensors.txt"
-Pipeline3.layer_file = "tuner_results/pipeline_GSME/pipeline_GSME_layers.txt"
-#Pipeline1.loss1_result_file = "tuner_results/vgg_cifar100/loss_1/promise_tuned_confs/promise_confs.txt"
-#Pipeline1.loss2_result_file = "tuner_results/vgg_cifar100/loss_2/promise_tuned_confs/promise_confs.txt"
-Pipeline3.autotuner_runs = 300
-Pipeline3.tuner_accuracy = 95
-Pipeline3.promise_accuracy = 95
-Pipeline3.validation_accuracy = 95
-
-bench_tuner_data["pipeline_GSME"] = Pipeline3
-
-
-Pipeline4 = Benchmark()
-Pipeline4.tuner_binary = "pipeline_GEO"
-Pipeline4.promise_binary = "pipeline_GEO_promise"
-Pipeline4.validation_binary = "pipeline_GEO_valid"
-Pipeline4.num_flags = 7
-Pipeline4.num_layers = 3
-Pipeline4.error_range_1 = 10
-Pipeline4.error_range_2 = 15
-Pipeline4.start_promise_range = 2
-Pipeline4.skip_layer_str = "1_2"
-Pipeline4.result_dir_1 = "tuner_results/pipeline_GEO/loss_30/batch1"
-Pipeline4.result_dir_2 = "tuner_results/pipeline_GEO/loss_20/batch1"
-Pipeline4.tensor_desc_file = "tuner_results/pipeline_GEO/pipeline_GEO_tensors.txt"
-Pipeline4.layer_file = "tuner_results/pipeline_GEO/pipeline_GEO_layers.txt"
-#Pipeline1.loss1_result_file = "tuner_results/vgg_cifar100/loss_1/promise_tuned_confs/promise_confs.txt"
-#Pipeline1.loss2_result_file = "tuner_results/vgg_cifar100/loss_2/promise_tuned_confs/promise_confs.txt"
-Pipeline4.autotuner_runs = 300
-Pipeline4.tuner_accuracy = 95
-Pipeline4.promise_accuracy = 95
-Pipeline4.validation_accuracy = 95
-
-bench_tuner_data["pipeline_GEO"] = Pipeline4
-
-
-Pipeline5 = Benchmark()
-Pipeline5.tuner_binary = "pipeline_GSM"
-Pipeline5.promise_binary = "pipeline_GSM_promise"
-Pipeline5.validation_binary = "pipeline_GSM_valid"
-Pipeline5.num_flags = 6
-Pipeline5.num_layers = 3
-Pipeline5.error_range_1 = 10
-Pipeline5.error_range_2 = 15
-Pipeline5.start_promise_range = 2
-Pipeline5.skip_layer_str = "1_1"
-Pipeline5.result_dir_1 = "tuner_results/pipeline_GSM/loss_30/batch1"
-Pipeline5.result_dir_2 = "tuner_results/pipeline_GSM/loss_20/batch1"
-Pipeline5.tensor_desc_file = "tuner_results/pipeline_GSM/pipeline_GSM_tensors.txt"
-Pipeline5.layer_file = "tuner_results/pipeline_GSM/pipeline_GSM_layers.txt"
-#Pipeline1.loss1_result_file = "tuner_results/vgg_cifar100/loss_1/promise_tuned_confs/promise_confs.txt"
-#Pipeline1.loss2_result_file = "tuner_results/vgg_cifar100/loss_2/promise_tuned_confs/promise_confs.txt"
-Pipeline5.autotuner_runs = 300
-Pipeline5.tuner_accuracy = 95
-Pipeline5.promise_accuracy = 95
-Pipeline5.validation_accuracy = 95
-
-bench_tuner_data["pipeline_GSM"] = Pipeline5
-
-"""
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/buildRtConfig.py b/llvm/projects/hpvm-tensor-rt/bin/tuner_src/buildRtConfig.py
deleted file mode 100644
index 81115b0587b36b68e6acfb92ab14d7424ee117f5..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/buildRtConfig.py
+++ /dev/null
@@ -1,526 +0,0 @@
-
-
-import os
-import sys
-from benchmarks import bench_tuner_data
-from swing_selection import loadLayerDesc
-from benchmarks import batch_id
-
-
-op_mapping = {}
-op_mapping["conv"] = "conv"
-op_mapping["depthwise_conv"] = "group_conv"
-op_mapping["dense"] = "mul"
-op_mapping["batchnorm"] = "batchnorm"
-op_mapping["pool"] = "pool_max"
-op_mapping["pool_mean"] = "pool_mean"
-op_mapping["activation"] = "relu"
-op_mapping["tanh"] = "tanh"
-op_mapping["add"] = "add"
-
-
-
-approx_map = {}
-approx_map["1"] = "swing_level 1"
-approx_map["2"] = "swing_level 2"
-approx_map["3"] = "swing_level 3"
-approx_map["4"] = "swing_level 4"
-approx_map["5"] = "swing_level 5"
-approx_map["6"] = "swing_level 6"
-approx_map["7"] = "swing_level 7"
-
-#approx_map["8"] = "perf 1"
-#approx_map["9"] = "perf 2"
-approx_map["11"] = "fp32 1"
-approx_map["12"] = "fp16 1"
-
-
-approx_map["21"] = "perf 21"
-approx_map["22"] = "perf 22"
-approx_map["23"] = "perf 23"
-approx_map["24"] = "perf 24"
-approx_map["25"] = "perf 25"
-approx_map["26"] = "perf 26"
-approx_map["27"] = "perf 27"
-approx_map["28"] = "perf 28"
-approx_map["29"] = "perf 29"
-approx_map["30"] = "perf 30"
-
-approx_map["31"] = "samp 31"
-approx_map["32"] = "samp 32"
-approx_map["33"] = "samp 33"
-approx_map["34"] = "samp 34"
-approx_map["35"] = "samp 35"
-approx_map["36"] = "samp 36"
-
-
-
-class Config:
- def __init__(self):
- self.avg_accuracy = 0
- self.avg_loss = 0
- self.speedup = 1
- self.fname = ""
- self.flags = []
-
-
-
-
-def isLayer(layer_comp):
- if layer_comp[0] == "dense" or layer_comp[0] == "conv":
- return True
- else:
- return False
-
-
-def getOpMapping(op_name):
-
-
- if op_name not in op_mapping:
- print ("ERROR: OP not found!! = ", op_name, "\n")
- sys.exit(0)
-
- return op_mapping[op_name]
-
-
-
-def getApproxMapping(flag, layer_comp):
-
- flag_str = str(flag)
- if flag_str not in approx_map:
- print ("ERROR: OP not found!! = ", flag_str, "\n")
- sys.exit(0)
-
- if "dense" in layer_comp and flag > 7:
- if flag == 12:
- return "fp16 1"
- else:
- return "fp32 1"
-
-
- return approx_map[flag_str]
-
-
-
-def skipFile(fname):
-
- skip_files = {}
- skip_files["confidence_summary.txt"] = 1
- skip_files["promise_confs.txt"] = 1
-
- if "accuracy" in fname: # *_accuracy files should be skipped
- return True
-
- if "norms" in fname: # *_accuracy files should be skipped
- return True
-
- if ".#" in fname: # *_accuracy files should be skipped
- return True
-
- #if "_promise" in fname: # *_accuracy files should be skipped
- # return True
-
- if not fname[-1].isdigit():
- return True
-
- if fname in skip_files:
- return True
- else:
- return False
-
-
-
-def parseTopLine(x):
-
- toks = x.split()
-
- speedup = 1.0
- accuracy = 0.0
- for tok in toks:
- if "avg_accuracy" in tok:
- avg_accuracy = float(tok.split("=")[1])
- if "speedup" in tok:
- speedup = float(tok.split("=")[1])
-
-
- return avg_accuracy, speedup
-
-
-
-def loadConfigData(result_dir, baseline_accuracy, sub_dir = "high_confidence"):
-
- config_arr = []
-
- #result_dir += "/promise_tuner/high_confidence/"
- #result_dir += "/algo_tuner/high_confidence/"
- result_dir += "/algo_tuner/" + sub_dir + "/"
- file_names = os.listdir(result_dir)
-
-
- for fname in file_names:
- if not skipFile(fname):
-
- fpath = result_dir + fname
- config = Config()
- f = open(fpath, "r")
-
- it = 0
- for x in f:
- if x.strip == "":
- continue
- if it == 0:
- avg_accuracy, speedup = parseTopLine(x)
- config.avg_accuracy = avg_accuracy
- config.avg_loss = baseline_accuracy - avg_accuracy
- config.speedup = speedup
- config.fname = fname
- #print ("acc = " + str(avg_accuracy) + "\n")
- else:
- flag = int(x.strip())
- config.flags.append(flag)
- it += 1
-
- config_arr.append(config)
-
-
- return config_arr
-
-
-
-
-def loadConfigsFromDir(result_dir, baseline_accuracy):
-
- config_arr = []
-
- #result_dir += "/promise_tuner/high_confidence/"
- #result_dir += "/algo_tuner/high_confidence/"
- #result_dir += "/algo_tuner/" + sub_dir + "/"
- file_names = os.listdir(result_dir)
-
-
- for fname in file_names:
- if not skipFile(fname):
-
- fpath = result_dir + fname
- config = Config()
- f = open(fpath, "r")
-
- it = 0
- for x in f:
- if x.strip == "":
- continue
- if it == 0:
- avg_accuracy, speedup = parseTopLine(x)
- config.avg_accuracy = avg_accuracy
- config.avg_loss = baseline_accuracy - avg_accuracy
- config.speedup = speedup
- config.fname = fname
- #print ("acc = " + str(avg_accuracy) + "\n")
- else:
- flag = int(x.strip())
- config.flags.append(flag)
- it += 1
-
- config_arr.append(config)
-
-
- return config_arr
-
-
-
-
-
-
-
-def loadPromiseConfigs(result_dir, baseline_accuracy, sub_dir = "promise_test"):
-
- config_arr = []
- result_dir += "/algo_tuner/" + sub_dir + "/"
- file_names = os.listdir(result_dir)
-
- for fname in file_names:
- if "_promise" in fname:
-
- fpath = result_dir + fname
- config = Config()
- f = open(fpath, "r")
-
- it = 0
- for x in f:
- if x.strip == "":
- continue
-
- if it == 0:
- avg_accuracy, speedup = parseTopLine(x)
- config.avg_accuracy = avg_accuracy
- config.avg_loss = baseline_accuracy - avg_accuracy
- config.speedup = speedup
- config.fname = fname
- #print ("acc = " + str(avg_accuracy) + "\n")
- else:
- flag = int(x.strip())
- config.flags.append(flag)
-
- it += 1
-
- config_arr.append(config)
-
-
- return config_arr
-
-
-
-
-
-def getFP(flag):
-
- if flag < 11:
- return "fp16"
- else:
- return "fp32"
-
-
-
-def getHardwareTarget(flag):
-
- if flag <= 7:
- return "promise"
- else:
- return "gpu"
-
- return "gpu"
-
-
-def handlePromiseConfs(flag, layer_comp):
-
- approx_tech = getApproxMapping(flag, layer_comp)
- config_str = ""
- if flag <= 7:
- config_str += approx_tech + " "
-
- return config_str
-
-
-def handleGPUApproxs(flag, layer_comp):
-
- approx_tech = getApproxMapping(flag, layer_comp)
- config_str = ""
- if flag > 7:
- print ("flag = ", flag)
- config_str += getOpMapping(layer_comp[0]) + " " + approx_tech + " "
- for op in layer_comp[1:]:
- print (layer_comp[1:])
- print (op)
-
- op_name = getOpMapping(op)
- config_str += str(op_name) + " " + getFP(flag) + " 1 "
-
- return config_str
-
-
-def generateBaselineConfig(layer_comp):
-
- config_str = ""
- config_str += "gpu "
- for op in layer_comp:
- op_name = getOpMapping(op)
- config_str += str(op_name) + " fp16 1 "
-
- return config_str
-
-
-
-def buildConfigStr(config, layer_desc):
-
- index = 1
- it = 0
- flags = config.flags
- config_str = ""
-
- for layer_comp in layer_desc:
- config_str += str(index) + " "
-
- print ("laye_comp = ", layer_comp)
-
- if isLayer(layer_comp):
- flag = flags[it]
- it += 1
-
- print ("flag* = ", flag)
- # Add Target Target - GPU, PROMISE
- config_str += getHardwareTarget(flag) + " "
-
- print ("config_str = ", config_str)
-
- config_str += handlePromiseConfs(flag, layer_comp)
- config_str += handleGPUApproxs(flag, layer_comp)
-
- else: # if a non-Layer Operation
- config_str += generateBaselineConfig(layer_comp)
-
-
- config_str += "\n"
- index += 1
-
-
- config_str += str(index) + " gpu softmax fp32 1\n"
-
- return config_str
-
-
-def adjustLoss(loss):
-
- if loss < 0.3:
- loss += 0.4
- else:
- loss = loss * 1.5
-
- return loss
-
-
-
-def dumpConfig(layer_desc, config_arrs, result_dir):
-
- f = open(result_dir + "/tuner_pareto_confs_" + batch_id + ".txt", "w+")
- it = 1
- for config in config_arrs:
- f.write("+++++\n")
- f.write("conf" + str(it) + " " + str(config.speedup) + " 0 " + str(config.avg_accuracy) + " " + str(adjustLoss(config.avg_loss)) + "\n")
-
- config_str = buildConfigStr(config, layer_desc)
-
- f.write(config_str)
-
- f.write("-----\n")
-
- it += 1
-
-
-
-def prependBaseline(Bench):
-
- f1 = open(Bench.base_dir + "/tuner_confs_base.txt", "r")
- baseline_str = f1.read()
- f1.close()
-
- f2 = open(Bench.base_dir + "/tuner_pareto_confs_" + batch_id + ".txt", "r")
- config_str = f2.read()
- f2.close()
-
- f3 = open(Bench.base_dir + "/tuner_pareto_confs_" + batch_id + ".txt", "w+")
- f3.write(baseline_str)
- f3.write(config_str)
- f3.close()
-
-
-
-def generateConf(Bench):
-
- layer_desc = loadLayerDesc(Bench.layer_file)
-
- print ("layer_desc = ", layer_desc)
-
- #config_arr1 = loadConfigData(Bench.result_dir_1, Bench.promise_accuracy)
- #config_arr2 = loadConfigData(Bench.result_dir_2, Bench.promise_accuracy)
- #config_arr3 = loadConfigData(Bench.result_dir_3, Bench.promise_accuracy)
-
- result_dir1 = Bench.result_dir_1 + "/algo_tuner/pareto/"
- result_dir2 = Bench.result_dir_2 + "/algo_tuner/pareto/"
- result_dir3 = Bench.result_dir_3 + "/algo_tuner/pareto/"
-
- config_arr1 = loadConfigsFromDir(result_dir1, Bench.promise_accuracy)
- config_arr2 = loadConfigsFromDir(result_dir2, Bench.promise_accuracy)
- config_arr3 = loadConfigsFromDir(result_dir3, Bench.promise_accuracy)
-
- config_arrs = config_arr1 + config_arr2 + config_arr3
-
- dumpConfig(layer_desc, config_arrs, Bench.base_dir)
-
- prependBaseline(Bench)
-
-
-
-
-def dumpBaselineConfs(Bench):
-
- layer_desc = loadLayerDesc(Bench.layer_file)
-
- print ("layer_desc = ", layer_desc)
-
- f = open(Bench.base_dir + "/tuner_confs_base.txt", "w+")
-
- f.write("+++++\n")
- f.write("conf" + str(1) + " " + str(1) + " 0 " + str(Bench.promise_accuracy) + " " + str(0) + "\n")
-
- config = Config()
- flags = []
- for i in range(Bench.num_layers):
- flags.append(11)
-
- config.flags = flags
- config_str = buildConfigStr(config, layer_desc)
-
- f.write(config_str)
- f.write("-----\n")
-
-
- f.close()
-
-
- #f.write("+++++\n")
- #f.write("conf" + str(2) + " " + str(1.5) + " 0 " + str(Bench.promise_accuracy) + " " + str(0) + "\n")
-
- #config = Config()
- #flags = []
- #for i in range(Bench.num_layers):
- # flags.append(12)
-
- #config.flags = flags
- #config_str = buildConfigStr(config, layer_desc)
-
- #f.write(config_str)
- #f.write("-----\n")
-
-
-
-
-
-
-if __name__ == "__main__":
-
- Bench = bench_tuner_data["alexnet_cifar10"]
- generateConf(Bench)
- dumpBaselineConfs(Bench)
-
- Bench = bench_tuner_data["alexnet2_cifar10"]
- generateConf(Bench)
- dumpBaselineConfs(Bench)
-
- Bench = bench_tuner_data["vgg16_cifar10"]
- generateConf(Bench)
- dumpBaselineConfs(Bench)
-
- Bench = bench_tuner_data["vgg16_cifar100"]
- generateConf(Bench)
- dumpBaselineConfs(Bench)
-
- Bench = bench_tuner_data["resnet18_cifar10"]
- generateConf(Bench)
- dumpBaselineConfs(Bench)
-
- Bench = bench_tuner_data["lenet_keras"]
- generateConf(Bench)
- dumpBaselineConfs(Bench)
-
- Bench = bench_tuner_data["mobilenet_cifar10"]
- generateConf(Bench)
- dumpBaselineConfs(Bench)
-
- Bench = bench_tuner_data["mobilenet_shallow"]
- generateConf(Bench)
- dumpBaselineConfs(Bench)
-
-
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/compareResults.py b/llvm/projects/hpvm-tensor-rt/bin/tuner_src/compareResults.py
deleted file mode 100644
index 6ee7466242d47299d5aa7622f15aef7d35832a2a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/compareResults.py
+++ /dev/null
@@ -1,66 +0,0 @@
-
-
-
-import os
-from benchmarks import bench_tuner_data
-from buildRtConfig import loadConfigData
-from buildRtConfig import loadConfigsFromDir
-
-
-
-def compareBench(batch_ids, Bench):
-
- losses = ["1", "2", "3"]
- for loss in losses:
- print ("\n Loss = ", loss, " % \n")
- for id in batch_ids:
- result_dir = Bench.base_dir + "/loss_" + loss + "/batch" + id
- #config_arr = loadConfigData(result_dir, Bench.promise_accuracy, "high_confidence")
-
- #result_dir += "/algo_tuner/high_confidence/"
- result_dir += "/promise_tuner3/high_confidence/"
-
- config_arr = loadConfigsFromDir(result_dir, Bench.promise_accuracy)
-
- count = len(config_arr)
- if len(config_arr) > 0:
- max_speedup = max(config.speedup for config in config_arr)
- else:
- max_speedup = 1.0
- print ("Bench = ", Bench.promise_binary, " BatchID = ", id, " Loss = ", loss, " Count = ", count, " MaxS = ", max_speedup)
-
-
-
-
-if __name__ == "__main__":
-
-
- batch_ids = []
-
- #batch_ids.append("13") # No Error Sens - baseline
- #batch_ids.append("14") # Ops Skipped 10% for Loss1, 25% Loss2, 40% Loss3
- #batch_ids.append("15") # 3 differnet levels for each of Loss1, Loss2, Loss3
- #batch_ids.append("19") # Baseline + Pareto
- #batch_ids.append("20") # Batch18 + Pareto
-
- #batch_ids.append("101") # Algo-specific tuning
-
- #batch_ids.append("201") # Algo-specific tuning
-
- #---- batch_ids.append("202") # Algo-specific tuning
- #batch_ids.append("212") # Algo-specific tuning
- #batch_ids.append("211") # Algo-specific tuning
-
-
- batch_ids.append("220") # Algo-specific tuning
-
-
- compareBench(batch_ids, bench_tuner_data["lenet_keras"])
- compareBench(batch_ids, bench_tuner_data["alexnet_cifar10"])
- compareBench(batch_ids, bench_tuner_data["mobilenet_cifar10"])
-
- compareBench(batch_ids, bench_tuner_data["alexnet2_cifar10"])
- compareBench(batch_ids, bench_tuner_data["vgg16_cifar10"])
- compareBench(batch_ids, bench_tuner_data["vgg16_cifar100"])
- compareBench(batch_ids, bench_tuner_data["resnet18_cifar10"])
- compareBench(batch_ids, bench_tuner_data["mobilenet_shallow"])
diff --git a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/compute_confs.py b/llvm/projects/hpvm-tensor-rt/bin/tuner_src/compute_confs.py
deleted file mode 100644
index f82c09095ceac24d8ee4a765f1d63be987b625a9..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/compute_confs.py
+++ /dev/null
@@ -1,56 +0,0 @@
-
-
-from swing_selection import compute_swing_selection
-from swing_selection2 import compute_swing_selection2
-
-
-def computeBenchSwings(Bench):
-
- dir_prefix = "../build_tuner/"
-
- loss_confs = []
- conf_ranks = []
- # Swing selection for 1% and 2% results
- #Bench = bench_tuner_data[bench_name]
- tuned_result_dir = dir_prefix + Bench.result_dir_1 + "/high_confidence/"
- layer_file = Bench.layer_file
- layer_swings, file_names = compute_swing_selection(tuned_result_dir, layer_file)
- loss_confs.append(layer_swings)
- conf_ranks.append(file_names)
- print (file_names)
-
- tuned_result_dir = dir_prefix + Bench.result_dir_2 + "/high_confidence/"
- layer_swings, file_names = compute_swing_selection(tuned_result_dir, layer_file)
- loss_confs.append(layer_swings)
- conf_ranks.append(file_names)
- print (file_names)
-
-
- return loss_confs, conf_ranks
-
-
-
-
-
-def computePSNRBenchSwings(Bench):
-
- loss_confs = []
- conf_ranks = []
- # Swing selection for 1% and 2% results
- #Bench = bench_tuner_data[bench_name]
- tuned_result_dir = Bench.result_dir_1 + "/high_confidence/"
- layer_file = Bench.layer_file
- layer_swings, file_names = compute_swing_selection2(tuned_result_dir, layer_file)
- loss_confs.append(layer_swings)
- conf_ranks.append(file_names)
- print (file_names)
-
- tuned_result_dir = Bench.result_dir_2 + "/high_confidence/"
- layer_swings, file_names = compute_swing_selection2(tuned_result_dir, layer_file)
- loss_confs.append(layer_swings)
- conf_ranks.append(file_names)
- print (file_names)
-
-
- return loss_confs, conf_ranks
-
diff --git a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/error_sensitivity.py b/llvm/projects/hpvm-tensor-rt/bin/tuner_src/error_sensitivity.py
deleted file mode 100644
index 186477164240694ebae63f019b7824dc1e12c83b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/error_sensitivity.py
+++ /dev/null
@@ -1,378 +0,0 @@
-
-
-import subprocess
-import os
-import operator
-from benchmarks import bench_tuner_data
-from swing_selection import loadLayerDesc
-import math
-
-
-def constructTunerFile(num_flags, tensor_id, error_level, default_error):
-
- f = open("opentuner_flags", "w+")
-
- for i in range(num_flags):
- if i == tensor_id:
- f.write(str(error_level) + "\n")
- else:
- f.write(str(default_error) + "\n")
-
- f.close()
-
-
-
-def runAndTestError(binary_name, gold_acc):
-
- num_runs = 10
-
- binary_name = "./" + binary_name
- FNULL = open(os.devnull, 'wb')
- p = subprocess.Popen([binary_name, str(num_runs)], stdout = FNULL)
- p.wait()
-
- f = open("run_accuracies.txt")
-
- total_err = 0.0
- for x in f:
- acc = float(x.strip())
- total_err += (gold_acc - acc)
-
- avg_err = total_err / num_runs
-
- return avg_err
-
-
-
-def roundDecimal(val):
-
- new_val = int(val * 10000)
- new_val = float(new_val) / 10000
-
- return new_val
-
-
-
-
-def test_sensitivity(Bench):
-
- tensor_errors = []
-
- error_levels = [6, 9, 12, 15]
- num_flags = Bench.num_flags
-
- for tensor_id in range(num_flags):
- total_error = 0
- for error_level in error_levels:
- constructTunerFile(num_flags, tensor_id, error_level, 0)
- error = runAndTestError(Bench.tuner_binary, Bench.tuner_accuracy)
- #print (tensor_id, error_level, error)
- total_error += error
-
- avg_error = total_error / len(error_levels)
-
- tensor_errors.append([tensor_id, avg_error])
-
-
- print ("\n\n*** Per-Tensor Avg Errors \n\n")
-
- f_name = Bench.base_dir + "/tensor_errors_multiple.txt"
- f = open(f_name, "w+")
- for i in range(len(tensor_errors)):
- print (i, tensor_errors[i][1])
- f.write(str(i) + "\t" + str(tensor_errors[i][1]) + "\n")
-
- f.close()
-
- f_name = Bench.base_dir + "/tensor_errors_ranked_1000.txt"
- f2 = open(f_name, "w+")
- tensor_errors.sort(key=operator.itemgetter(1))
-
-
- for i in range(len(tensor_errors)):
- print (i, tensor_errors[i][1])
-
- f2.write(str(tensor_errors[i][0]) + "\t" + str(tensor_errors[i][1]) + "\n")
-
-
- f2.close()
-
-
-
-def test_sensitivity2(Bench):
-
- num_flags = Bench.num_flags
-
- constructTunerFile(num_flags, 0, 6, 6)
- error = runAndTestError(Bench.tuner_binary, Bench.tuner_accuracy)
-
- ref_acc = Bench.tuner_accuracy - error
- print ("*** Gold accuracy = ", Bench.tuner_accuracy, " Ref accuracy = ", ref_acc, " *** \n\n")
-
-
- tensor_errors = []
-
- error_levels = [6, 9, 12, 15]
-
- for tensor_id in range(num_flags):
- total_error = 0
- for error_level in error_levels:
- constructTunerFile(num_flags, tensor_id, error_level, 6)
- error = runAndTestError(Bench.tuner_binary, ref_acc)
- print (tensor_id, error_level, error)
- total_error += error
-
- avg_error = total_error / len(error_levels)
-
- tensor_errors.append([tensor_id, avg_error])
-
-
- print ("\n\n*** Per-Tensor Avg Errors \n\n")
-
- f_name = Bench.base_dir + "/tensor_composite_errors.txt"
- f = open(f_name, "w+")
- for i in range(len(tensor_errors)):
- print (i, tensor_errors[i][1])
- f.write(str(i) + "\t" + str(tensor_errors[i][1]) + "\n")
-
- f.close()
-
- f_name = Bench.base_dir + "/tensor_composite_errors_ranked.txt"
- f2 = open(f_name, "w+")
- tensor_errors.sort(key=operator.itemgetter(1))
-
-
- for i in range(len(tensor_errors)):
- print (i, tensor_errors[i][1])
-
- f2.write(str(tensor_errors[i][0]) + "\t" + str(tensor_errors[i][1]) + "\n")
-
-
- f2.close()
-
-
-
-def test_sensitivity3(Bench):
-
- tensor_errors = []
-
- error_levels = [2, 5, 8, 11, 14, 17]
- num_flags = Bench.num_flags
-
- for tensor_id in range(num_flags):
- total_error = 0
- errors = []
- for error_level in error_levels:
- constructTunerFile(num_flags, tensor_id, error_level, 0)
- error = runAndTestError(Bench.tuner_binary, Bench.tuner_accuracy)
- print (tensor_id, error_level, error)
- errors.append(error)
-
- tensor_errors.append([tensor_id, errors])
-
-
- print ("\n\n*** Per-Tensor Avg Errors \n\n")
-
- f_name = Bench.base_dir + "/tensor_errors_multiple.txt"
- f = open(f_name, "w+")
- for i in range(len(tensor_errors)):
- print (i, tensor_errors[i][1])
- f.write(str(i))
- for j in range(len(tensor_errors[i][1])):
- val = roundDecimal(tensor_errors[i][1][j])
- f.write("\t" + str(val) )
- f.write("\n")
-
- f.close()
-
-
-
-
-
-def test_sensitivity4(Bench):
-
- num_flags = Bench.num_flags
-
- constructTunerFile(num_flags, 0, 5, 5)
- error = runAndTestError(Bench.tuner_binary, Bench.tuner_accuracy)
-
- ref_acc = Bench.tuner_accuracy - error
- print ("*** Gold accuracy = ", Bench.tuner_accuracy, " Ref accuracy = ", ref_acc, " *** \n\n")
-
-
- tensor_errors = []
- error_levels = [4, 8, 11, 14, 16, 19]
-
- for tensor_id in range(num_flags):
- errors = []
- for error_level in error_levels:
- constructTunerFile(num_flags, tensor_id, error_level, 5)
- error = runAndTestError(Bench.tuner_binary, ref_acc)
- print (tensor_id, error_level, error)
- errors.append(error)
-
- tensor_errors.append([tensor_id, errors])
-
-
-
- print ("\n\n*** Per-Tensor Avg Errors \n\n")
-
- f_name = Bench.base_dir + "/composite_errors.txt"
- f = open(f_name, "w+")
- for i in range(len(tensor_errors)):
- print (i, tensor_errors[i][1])
- f.write(str(i))
- for j in range(len(tensor_errors[i][1])):
- val = roundDecimal(tensor_errors[i][1][j])
- f.write("\t" + str(val) )
- f.write("\n")
-
- f.close()
-
-
-
-
-
-def readTensorErrs(result_dir):
-
- tensor_errs = []
- f = open(result_dir + "/tensor_errors.txt")
-
- for x in f:
- err = float(x.split()[1])
- tensor_errs.append(err)
-
- return tensor_errs
-
-
-
-def readTensorErrs2(result_dir):
-
- tensor_errs = []
- f = open(result_dir + "/tensor_errors_multiple.txt")
-
- for x in f:
- toks = x.split()
- total_err = 0.0
- for tok in toks[2:-1]:
- err = float(tok)
- total_err += err
-
- avg_err = total_err / len(toks[2:-1])
- tensor_errs.append(avg_err)
-
- return tensor_errs
-
-
-def isSkipLayer(layer):
-
- if "dense" in layer or "conv" in layer:
- return False
- else:
- return True
-
-
-def readLayerCosts(cost_file):
-
- f = open(cost_file)
- layer_costs = []
- for x in f:
- cost = float(x.strip())
- layer_costs.append(cost)
-
- return layer_costs
-
-
-
-disable_skipping = False
-
-def select_skip_layers(Bench, percent_to_skip):
-
- if disable_skipping:
- return "0"
-
- result_dir = Bench.base_dir
- layer_file = Bench.layer_file
-
- tensor_errs = readTensorErrs2(result_dir)
- layer_costs = readLayerCosts(Bench.cost_file)
- layer_desc = loadLayerDesc(layer_file)
-
- it = 0
- index = 0
- layer_errs = []
- for layer in layer_desc:
- layer_len = len(layer)
- avg_err = tensor_errs[index]
- index += layer_len
-
- if isSkipLayer(layer):
- continue
-
- cost = (math.sqrt(layer_costs[it])) / 100;
- ERR_IMPACT = avg_err / cost
- #print ("layer, ", it, " avg_err = ", avg_err, " cost = ", cost, " err_impact = ", ERR_IMPACT)
-
- layer_errs.append((ERR_IMPACT, it))
- it += 1
-
- layer_errs.sort(key=operator.itemgetter(0), reverse=True)
-
- to_skip = len(layer_errs)
- to_skip = math.ceil((percent_to_skip / 100.0) * to_skip)
-
- skip_str = ""
- it = 0
- for err in layer_errs:
- if it >= to_skip:
- break
-
- skip_str += str(err[1])
- if it < to_skip - 1:
- skip_str += "_"
-
- it += 1
-
- return skip_str
-
-
-
-
-
-
-if __name__ == "__main__":
-
-
- AlexNet = bench_tuner_data["alexnet_cifar10"]
- skip_str = select_skip_layers(AlexNet, 10)
- print ("AlexNet skip_str = ", skip_str)
-
-
- AlexNet2 = bench_tuner_data["alexnet2_cifar10"]
- skip_str = select_skip_layers(AlexNet2, 15)
- print ("AlexNet2 skip_str = ", skip_str)
-
-
- VGG16 = bench_tuner_data["vgg16_cifar10"]
- skip_str = select_skip_layers(VGG16, 15)
- print ("VGG16 skip_str = ", skip_str)
-
-
- VGG16_100 = bench_tuner_data["vgg16_cifar100"]
- skip_str = select_skip_layers(VGG16_100, 15)
- print ("VGG16_100 skip_str = ", skip_str)
-
-
- ResNet = bench_tuner_data["resnet18_cifar10"]
- skip_str = select_skip_layers(ResNet, 10)
- print ("ResNet skip_str = ", skip_str)
-
-
- MobileNet = bench_tuner_data["mobilenet_cifar10"]
- skip_str = select_skip_layers(MobileNet, 15)
- print ("MobileNet skip_str = ", skip_str)
-
-
- MobileNet_SH = bench_tuner_data["mobilenet_shallow"]
- skip_str = select_skip_layers(MobileNet_SH, 15)
- print ("MobileNet_SH skip_str = ", skip_str)
diff --git a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/pareto_curve.py b/llvm/projects/hpvm-tensor-rt/bin/tuner_src/pareto_curve.py
deleted file mode 100644
index d90403be23fae547fde9e2ac4996f5cca3b0e5fb..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/pareto_curve.py
+++ /dev/null
@@ -1,313 +0,0 @@
-
-
-from buildRtConfig import loadConfigData
-from benchmarks import bench_tuner_data
-import os
-import shutil
-
-
-AL_THRESHOLD = 0.1
-SPEEDUP_BAND_SIZE = 0.1
-ENERGY_BAND_SIZE = 10
-
-class Configuration:
- def __init__(self, name, speedup, energy, accuracy, accuracy_loss, flags):
- self.name = name
- self.speedup = speedup
- self.energy = energy
- self.accuracy = accuracy
- self.accuracy_loss = accuracy_loss
- self.flags = flags
- def __repr__(self):
- return repr((self.name, self.speedup, self.energy, self.accuracy, self.accuracy_loss))
-
-configuration_objects = [
- Configuration('conf1', 1.05, 15, 85, 1.2, []),
- Configuration('conf2', 2.51, 12, 83, 1.4, []),
- Configuration('conf3', 2.05, 10, 84, 0.8, []),
-]
-
-def compute_pareto_points(configurations):
- speedupconfigurations = []
- energyconfigurations = []
- #sort configurations based on speedup
- sorted_configurations = sorted(configurations, key=lambda conf: conf.accuracy_loss)
-
- start_idx = 0
- while start_idx < len(sorted_configurations):
- end_idx = start_idx + 1;
- # find end_idx
- while end_idx < len(sorted_configurations) and (sorted_configurations[end_idx].accuracy_loss - sorted_configurations[start_idx].accuracy_loss < AL_THRESHOLD) :
- end_idx += 1
- # find best speedup end energy in this accuracy loss level
- sp = -1.0
- sp_idx = 0
- en = -1.0
- en_idx = 0
- for i in range(start_idx, end_idx):
- if sorted_configurations[i].speedup > sp:
- sp = sorted_configurations[i].speedup
- sp_idx = i
- if sorted_configurations[i].energy > en:
- en = sorted_configurations[i].energy
- en_idx = i
- sp_not_dominated = True
- # if not empty list of configurations
- if speedupconfigurations:
- if speedupconfigurations[-1].speedup >= sp:
- sp_not_dominated = False
- en_not_dominated = True
- # if not empty list of configurations
- if energyconfigurations:
- if energyconfigurations[-1].energy >= en:
- en_not_dominated = False
- if sp_not_dominated:
- speedupconfigurations.append(sorted_configurations[sp_idx])
- if en_not_dominated:
- energyconfigurations.append(sorted_configurations[en_idx])
- # outer while loop variable increment
- start_idx = end_idx
- return [speedupconfigurations, energyconfigurations]
-
-
-def compute_pareto_points_with_margin(configurations, speedup_band_width, energy_band_width):
- speedupconfigurations = []
- energyconfigurations = []
- #sort configurations based on speedup
- sorted_configurations = sorted(configurations, key=lambda conf: conf.accuracy_loss)
-
- idx_to_sp_conf_dict = {}
- idx_to_en_conf_dict = {}
-
- start_idx = 0
- while start_idx < len(sorted_configurations):
- end_idx = start_idx + 1;
- # find end_idx
- while end_idx < len(sorted_configurations) and (sorted_configurations[end_idx].accuracy_loss - sorted_configurations[start_idx].accuracy_loss < AL_THRESHOLD) :
- end_idx += 1
- # find best speedup end energy in this accuracy loss level
- sp = -1.0
- sp_idx = 0
- en = -1.0
- en_idx = 0
- for i in range(start_idx, end_idx):
- if sorted_configurations[i].speedup > sp:
- sp = sorted_configurations[i].speedup
- sp_idx = i
- if sorted_configurations[i].energy < en:
- en = sorted_configurations[i].energy
- en_idx = i
- sp_not_dominated = True
- # if not empty list of configurations
- if speedupconfigurations:
- if speedupconfigurations[-1].speedup >= sp:
- sp_not_dominated = False
- en_not_dominated = True
- # if not empty list of configurations
- if energyconfigurations:
- if energyconfigurations[-1].energy >= en:
- en_not_dominated = False
- if sp_not_dominated:
- speedupconfigurations.append(sorted_configurations[sp_idx])
- idx_to_sp_conf_dict[start_idx] = len(speedupconfigurations)-1
- if en_not_dominated:
- energyconfigurations.append(sorted_configurations[en_idx])
- idx_to_en_conf_dict[start_idx] = len(energyconfigurations)-1
- # outer while loop variable increment
- start_idx = end_idx
-
- # We want to add configurations in a band of a certain width around the curves
- # not possible to do during contruction, because the quality of the curve would
- # deteriorate quickly
-
- AdjustedSpeedupCurve = []
- AdjustedEnergyCurve = []
-
- start_idx = 0
- while start_idx < len(sorted_configurations):
- end_idx = start_idx + 1;
- # find end_idx
- while end_idx < len(sorted_configurations) and (sorted_configurations[end_idx].accuracy_loss - sorted_configurations[start_idx].accuracy_loss < AL_THRESHOLD) :
- end_idx += 1
- for i in range(start_idx, end_idx):
- if sorted_configurations[i].speedup + speedup_band_width >= speedupconfigurations[idx_to_sp_conf_dict[start_idx]].speedup:
- AdjustedSpeedupCurve.append(sorted_configurations[i])
- if sorted_configurations[i].energy + energy_band_width >= energyconfigurations[idx_to_en_conf_dict[start_idx]].energy:
- AdjustedEnergyCurve.append(sorted_configurations[i])
- # outer while loop variable increment
- start_idx = end_idx
-
- return [AdjustedSpeedupCurve, AdjustedEnergyCurve]
-
-
-
-def findParetoConfigs(base_dir, accuracy):
-
- result_dir = base_dir + "/algo_tuner/pareto/"
- try:
- os.mkdir(result_dir)
- except:
- print ("could not create dir")
-
- input_dir = base_dir
- config_arr = loadConfigData(input_dir, accuracy, "high_confidence")
-
- config_list = []
- it = 0
- for config in config_arr:
- config = Configuration(config.fname , config.speedup, 100, config.avg_accuracy, config.avg_loss, config.flags)
- config_list.append(config)
-
-
- if (len(config_list) > 0):
- max_speedup = max(config.speedup for config in config_list)
- else:
- max_speedup = 1.5
-
- SPEEDUP_BAND_SIZE = 0.05 # max_speedup * 1.0 / 12 # 4 # 20% of the max speedup
- ENERGY_BAND_SIZE = 10
-
- print ("max_speedup = ", max_speedup, " BAND_SIZE = ", SPEEDUP_BAND_SIZE)
-
-
- print ("*SPEEDUP_BAND_SIZE = ", SPEEDUP_BAND_SIZE)
-
- ASC, AEC = compute_pareto_points_with_margin(config_list, SPEEDUP_BAND_SIZE, ENERGY_BAND_SIZE)
-
-
- #print (ASC)
- #print (config_list)
-
- if len(ASC) < 5:
- ASC = config_list
-
-
- if len(ASC) > 50:
- ASC, AEC = compute_pareto_points_with_margin(config_list, SPEEDUP_BAND_SIZE/4, ENERGY_BAND_SIZE)
-
-
- print ("len(config_list) = ", len(config_list))
- print ("len(ASC) = ", len(ASC))
-
- for conf in ASC:
- src_path = base_dir + "/algo_tuner/high_confidence/" + conf.name
- dst_path = base_dir + "/algo_tuner/pareto/" + conf.name
- shutil.copy(src_path, dst_path)
-
- return ASC
-
-
-
-def flagsPerLayer(ASC, num_layers):
-
- layer_flags = []
- for i in range(num_layers):
- layer_map = {}
- layer_flags.append(layer_map)
-
-
- for config in ASC:
- config_flags = config.flags
- for i in range(len(config_flags)):
- layer_flags[i][config_flags[i]] = 1
-
-
- print (layer_flags)
-
- return layer_flags
-
-
-
-
-
-
-def dumpBenchPareto(Bench):
-
- result_dir1 = Bench.result_dir_1
- result_dir2 = Bench.result_dir_2
- result_dir3 = Bench.result_dir_3
-
- acc_thresh = Bench.promise_accuracy
-
- ASC1 = findParetoConfigs(result_dir1, acc_thresh)
- ASC2 = findParetoConfigs(result_dir2, acc_thresh)
- ASC3 = findParetoConfigs(result_dir3, acc_thresh)
-
-
- flags1 = flagsPerLayer(ASC1, Bench.num_layers)
- flags2 = flagsPerLayer(ASC2, Bench.num_layers)
- flags3 = flagsPerLayer(ASC3, Bench.num_layers)
-
- return flags1, flags2, flags3
-
-
-
-
-if __name__ == "__main__":
-
- Bench = bench_tuner_data["alexnet_cifar10"]
- dumpBenchPareto(Bench)
-
- Bench = bench_tuner_data["alexnet2_cifar10"]
- dumpBenchPareto(Bench)
-
- Bench = bench_tuner_data["vgg16_cifar10"]
- dumpBenchPareto(Bench)
-
- Bench = bench_tuner_data["vgg16_cifar100"]
- dumpBenchPareto(Bench)
-
- Bench = bench_tuner_data["resnet18_cifar10"]
- dumpBenchPareto(Bench)
-
- Bench = bench_tuner_data["lenet_keras"]
- dumpBenchPareto(Bench)
-
- Bench = bench_tuner_data["mobilenet_cifar10"]
- dumpBenchPareto(Bench)
-
- Bench = bench_tuner_data["mobilenet_shallow"]
- dumpBenchPareto(Bench)
-
-
- #get_pareto_configs("")
-
- #SC, EC = compute_pareto_points(configuration_objects)
- #ASC, AEC = compute_pareto_points_with_margin(configuration_objects, SPEEDUP_BAND_SIZE, ENERGY_BAND_SIZE)
-
- #print(SC)
- #print(EC)
-
- #print(ASC)
- #print(AEC)
-
-
-
-
-
-
- #result_dir = base_dir + "/pareto/"
- #try:
- # os.mkdir(result_dir)
- #except:
- # print "could not create dir"
-
- #input_dir = base_dir + "/full_results/"
- #result_dir = "../build_tuner/tuner_results/alexnet_cifar10/loss_3/batch15"
- #config_arr = loadConfigData(input_dir, accuracy)
-
- #config_list = []
-
- #it = 0
- #for config in config_arr:
- # config = Configuration(config.fname , config.speedup, 100, config.avg_accuracy, config.avg_loss)
- # config_list.append(config)
-
-
- #ASC, AEC = compute_pareto_points_with_margin(config_list, SPEEDUP_BAND_SIZE, ENERGY_BAND_SIZE)
-
- #for conf in ASC:
- # dst_path = conf.name.replace("full_results", "pareto")
- # shutil.copy(conf.name, dst_path)
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/profiling.py b/llvm/projects/hpvm-tensor-rt/bin/tuner_src/profiling.py
deleted file mode 100644
index 3ed37822a6fa654c16f5c8ce3b41dc8287931b87..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/profiling.py
+++ /dev/null
@@ -1,26 +0,0 @@
-
-import time
-
-profiled_ops = {}
-
-def startProfile(op_id):
- start = time.time()
- return start
-
-
-def stopProfile(op_id, start):
- end = time.time()
- total_time = end - start
-
- profiled_ops[op_id] = total_time
-
-
-def dumpProfiles(file_name):
-
- f = open(file_name, "w+")
- for op_id in profiled_ops:
- f.write(op_id + " : " + str(profiled_ops[op_id]) + "\n")
-
- f.close()
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/remap.py b/llvm/projects/hpvm-tensor-rt/bin/tuner_src/remap.py
deleted file mode 100644
index 8dc69357526b711d563d454f0ce41219dbfe579c..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/remap.py
+++ /dev/null
@@ -1,291 +0,0 @@
-
-import sys
-import os
-import shutil
-from validation import invokeBinary
-from buildRtConfig import loadConfigData, loadPromiseConfigs
-from benchmarks import bench_tuner_data, batch_id
-from swing_selection import convL1bins, convL2bins
-
-
-
-def readKnobConfig(file_path):
-
- knobs_speedup = {}
- f = open(file_path, "r")
- for x in f:
- toks = x.split("\t")
- ID = int(toks[0].split(",")[1])
-
- speedup = float(toks[2])
- knobs_speedup[ID] = speedup
-
- print ("knobs_speedup = ", knobs_speedup)
-
- return knobs_speedup
-
-
-
-def getPromiseSwing(l1, l2, flag):
-
- if l1 < 0.1 or l2 < 0.1:
- return flag
-
- swing = 1
- for i in range(len(convL1bins)):
- l1_t = convL1bins[i][0]
- l2_t = convL2bins[i][0]
-
- if l1 > l1_t and l2 > l2_t:
- break
- swing += 1
-
- return swing
-
-
-
-def replaceWithPromise(layer_flags, norms_file):
-
- num_layers = len(layer_flags)
-
- f = open(norms_file, "r")
- it = 0
- for x in f:
- op_name = x.split()[0]
- print ("op_name = ", op_name)
- if op_name == "tensorMul":
- break;
-
- l1 = float(x.split()[5])
- l2 = float(x.split()[6])
-
- if it > 0:
- flag = getPromiseSwing(l1, l2, layer_flags[it])
- layer_flags[it] = flag
-
- #print ("l1 = ", l1, " l2 = ", l2)
- it += 1
-
- if it == num_layers:
- break
-
- print (layer_flags)
- return layer_flags
-
-
-
-
-def readCostFile(file_path):
-
- layer_costs = []
- f = open(file_path)
- for x in f:
- cost = float(x.strip())
- layer_costs.append(cost)
-
- print ("len(layer_costs) = ", layer_costs)
- f.close()
-
- return layer_costs
-
-
-
-def getSpeedup(flags, knobs_speedup, layer_costs):
-
- orig_cost = 0.0
- total_cost = 0.0
- it = 0
- for flag_value in flags:
- op_cost = layer_costs[it]
- speedup = knobs_speedup[flag_value]
-
- total_cost += (op_cost * 1.0 / speedup * 1.0)
- orig_cost += op_cost
- it += 1
-
- speedup = (orig_cost * 1.0) / (total_cost * 1.0)
-
- return speedup
-
-
-
-def dumpNewFlags(new_flags, orig_file, promise_flags_file, layer_costs, knobs_speedup):
-
- speedup = getSpeedup(new_flags, knobs_speedup, layer_costs)
-
- top_line = ""
- for x in open(orig_file, "r"):
- top_line = x
- break
-
- f = open(promise_flags_file, "w+")
- f.write(top_line.replace("\n", ""))
- f.write("\tnew_speedup=" + str(speedup) + "\n")
-
-
- for flag in new_flags:
- f.write(str(flag) + "\n")
-
- f.close()
-
-
-
-
-def remapLossConfig(configs_arr, result_dir, sub_dir, layer_costs, knobs_speedup):
-
-
- for conf in configs_arr:
- layer_flags = conf.flags
- fname = conf.fname
- norms_file = result_dir + "/algo_tuner/" + sub_dir + "/" + fname + "_norms"
- orig_file = result_dir + "/algo_tuner/" + sub_dir + "/" + fname
- new_flags = replaceWithPromise(layer_flags, norms_file)
-
- promise_test_dir = result_dir + "/algo_tuner/promise_test/"
- if not os.path.exists(promise_test_dir):
- os.mkdir(promise_test_dir)
-
- promise_flags_file = result_dir + "/algo_tuner/promise_test/" + fname + "_promise"
- dumpNewFlags(new_flags, orig_file, promise_flags_file, layer_costs, knobs_speedup)
-
-
-
-def remapConfigs(Bench):
-
-
- loss1_dir = Bench.result_dir_1
- loss2_dir = Bench.result_dir_2
- loss3_dir = Bench.result_dir_3
-
- loss1_configs = loadConfigData(loss1_dir, 100, "validated")
- loss2_configs = loadConfigData(loss2_dir, 100, "validated")
- loss3_configs = loadConfigData(loss3_dir, 100, "validated")
-
- knobs_speedup = readKnobConfig("../opentuner/data/global_knobs.txt")
- layer_costs = readCostFile(Bench.cost_file)
-
- remapLossConfig(loss1_configs, loss1_dir, "validated", layer_costs, knobs_speedup)
- remapLossConfig(loss2_configs, loss2_dir, "validated", layer_costs, knobs_speedup)
- remapLossConfig(loss3_configs, loss3_dir, "validated", layer_costs, knobs_speedup)
-
-
-
-
-def validateRemapConfigs(Bench):
-
- num_layers = Bench.num_layers
- base_conf = getBaselineConfig(num_layers)
- # Path to binary to run
- binary_path = Bench.promise_binary
- # NOTE: 'target_acc' passed 0.0 since unused for baseline run
- invokeBinary(binary_path, base_conf, 1, 2000, 8000, 0.0)
- gold_acc = readAccuracy("final_accuracy")
-
-
- loss1_dir = Bench.result_dir_1
- loss2_dir = Bench.result_dir_2
- loss3_dir = Bench.result_dir_3
-
- loss1_configs = loadPromiseConfigs(loss1_dir, 100, "promise_test")
- loss2_configs = loadPromiseConfigs(loss2_dir, 100, "promise_test")
- loss3_configs = loadPromiseConfigs(loss3_dir, 100, "promise_test")
-
- runs = 30
- validateAlgoConfigs(binary_path, loss1_dir, loss1_configs, gold_acc, 1.0, runs)
- validateAlgoConfigs(binary_path, loss2_dir, loss2_configs, gold_acc, 2.0, runs)
- validateAlgoConfigs(binary_path, loss3_dir, loss3_configs, gold_acc, 3.0, runs)
-
-
-
-
-
-
-
-
-def copyNormFile(fname, result_dir, sub_dir):
-
- target_dir = result_dir + "/algo_tuner/" + sub_dir
- dest_file = target_dir + "/" + fname + "_norms"
-
- shutil.copy("accuracy_summary", dest_file)
-
-
-
-
-def dumpNorms(binary_path, result_dir, configs_arr):
-
- runs = 1
- for conf in configs_arr:
- layer_swings = conf.flags
- invokeBinary(binary_path, layer_swings, runs, 3000, 5000, 100)
-
- #copyNormFile(conf.fname, result_dir, "high_confidence")
- copyNormFile(conf.fname, result_dir, "validated")
-
-
-
-def computeConfigNorms(Bench):
-
- loss1_dir = Bench.result_dir_1
- loss2_dir = Bench.result_dir_2
- loss3_dir = Bench.result_dir_3
-
- loss1_configs = loadConfigData(loss1_dir, 100, "validated")
- loss2_configs = loadConfigData(loss2_dir, 100, "validated")
- loss3_configs = loadConfigData(loss3_dir, 100, "validated")
-
-
- binary_path = Bench.promise_binary
-
- dumpNorms(binary_path, loss1_dir, loss1_configs)
- dumpNorms(binary_path, loss2_dir, loss2_configs)
- dumpNorms(binary_path, loss3_dir, loss3_configs)
-
-
-
-if __name__ == "__main__":
-
- Bench = bench_tuner_data["alexnet_cifar10"]
- computeConfigNorms(Bench)
- remapConfigs(Bench)
- #validateRemapConfigs(Bench)
-
- Bench = bench_tuner_data["alexnet2_cifar10"]
- computeConfigNorms(Bench)
- remapConfigs(Bench)
- #validateRemapConfigs(Bench)
-
- Bench = bench_tuner_data["vgg16_cifar10"]
- computeConfigNorms(Bench)
- remapConfigs(Bench)
- #validateRemapConfigs(Bench)
-
- Bench = bench_tuner_data["vgg16_cifar100"]
- computeConfigNorms(Bench)
- remapConfigs(Bench)
- #validateRemapConfigs(Bench)
-
- Bench = bench_tuner_data["resnet18_cifar10"]
- computeConfigNorms(Bench)
- remapConfigs(Bench)
- #validateRemapConfigs(Bench)
-
- Bench = bench_tuner_data["mobilenet_shallow"]
- computeConfigNorms(Bench)
- remapConfigs(Bench)
- #validateRemapConfigs(Bench)
-
- Bench = bench_tuner_data["mobilenet_cifar10"]
- computeConfigNorms(Bench)
- remapConfigs(Bench)
- #validateRemapConfigs(Bench)
-
- Bench = bench_tuner_data["lenet_keras"]
- computeConfigNorms(Bench)
- remapConfigs(Bench)
- #validateRemapConfigs(Bench)
-
- #computeConfigNorms(Bench)
- #remapConfigs(Bench)
-
- #validateRemapConfigs(Bench)
diff --git a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/run_algo_tuner.py b/llvm/projects/hpvm-tensor-rt/bin/tuner_src/run_algo_tuner.py
deleted file mode 100644
index 2df75fbfc4e7568361747f75f06a4b818a8f99be..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/run_algo_tuner.py
+++ /dev/null
@@ -1,102 +0,0 @@
-
-
-import os
-import subprocess
-from error_sensitivity import select_skip_layers
-
-
-def runAlgoTunerCmd(Bench, dir_prefix, result_dir, acc_threshold, autotuner_runs):
-
- tuner_cmd = "python2 ../opentuner/autotuner/algo_tuner.py "
- tuner_cmd += " --test-limit "
- tuner_cmd += str(autotuner_runs)
- tuner_cmd += " --binary ./"
- tuner_cmd += Bench.promise_binary
- tuner_cmd += " --num-layers "
- tuner_cmd += str(Bench.num_layers)
- tuner_cmd += " --result-dir "
- tuner_cmd += dir_prefix
- tuner_cmd += result_dir + "/algo_tuner/"
- tuner_cmd += " --accuracy "
- tuner_cmd += str(Bench.promise_accuracy - acc_threshold)
- tuner_cmd += " --cost-file "
- tuner_cmd += Bench.cost_file
- tuner_cmd += " --knobs-config "
- tuner_cmd += "../opentuner/data/global_knobs.txt"
- tuner_cmd += " --layer-knobs "
- tuner_cmd += Bench.layer_knobs
-
-
- print (tuner_cmd)
-
- p = subprocess.Popen(tuner_cmd, shell=True)
- p.wait()
-
-
-"""
-
-def promiseTunerLoss1(Bench, dir_prefix):
-
- tuner_runs = int(Bench.autotuner_runs / 3)
-
- skip_layers1 = "0"
- skip_layers2 = "0_" + select_skip_layers(Bench, 30)
- skip_layers3 = "0_" + select_skip_layers(Bench, 50)
-
- runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_1, 0.85, tuner_runs, skip_layers1)
- runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_1, 0.85, tuner_runs, skip_layers2)
- runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_1, 0.85, tuner_runs, skip_layers3)
-
-
-def promiseTunerLoss2(Bench, dir_prefix):
-
- tuner_runs = int(Bench.autotuner_runs / 3)
-
- skip_layers1 = "0"
- skip_layers2 = "0_" + select_skip_layers(Bench, 20)
- skip_layers3 = "0_" + select_skip_layers(Bench, 40)
-
- runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_2, 1.7, tuner_runs, skip_layers1)
- runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_2, 1.7, tuner_runs, skip_layers2)
- runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_2, 1.7, tuner_runs, skip_layers3)
-
-
-
-def promiseTunerLoss3(Bench, dir_prefix):
-
- tuner_runs = int (Bench.autotuner_runs / 3)
-
- skip_layers1 = "0"
- skip_layers2 = "0_" + select_skip_layers(Bench, 10)
- skip_layers3 = "0_" + select_skip_layers(Bench, 30)
-
- runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_3, 2.5, tuner_runs, skip_layers1)
- runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_3, 2.5, tuner_runs, skip_layers2)
- runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_3, 2.5, tuner_runs, skip_layers3)
-
-
-"""
-
-
-BASELINE = True
-
-
-def runAlgoBench(Bench):
-
- # NOTE-IMP: Changing current directory to one with promise binaries
- dir_prefix = "../build_tuner/"
-
-
- if BASELINE:
- tuner_runs = Bench.autotuner_runs
- runAlgoTunerCmd(Bench, dir_prefix, Bench.result_dir_1, 0.85, tuner_runs)
- runAlgoTunerCmd(Bench, dir_prefix, Bench.result_dir_2, 1.7, tuner_runs)
- runAlgoTunerCmd(Bench, dir_prefix, Bench.result_dir_3, 2.5, tuner_runs)
-
- else:
- promiseTunerLoss1(Bench, dir_prefix)
- promiseTunerLoss2(Bench, dir_prefix)
- promiseTunerLoss3(Bench, dir_prefix)
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/run_algo_tuner2.py b/llvm/projects/hpvm-tensor-rt/bin/tuner_src/run_algo_tuner2.py
deleted file mode 100644
index 99867fade3aac75d2fcc4c411e25c2d16595052d..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/run_algo_tuner2.py
+++ /dev/null
@@ -1,186 +0,0 @@
-
-
-import os
-import numpy as np
-import subprocess
-from error_sensitivity import select_skip_layers
-from pareto_curve import dumpBenchPareto
-from remap import readCostFile
-
-
-def runAlgoTunerCmd(Bench, dir_prefix, result_dir, acc_threshold, autotuner_runs):
-
- fixed_runs = 100
-
- tuner_cmd = "python2 ../opentuner/autotuner/algo_tuner2.py "
- tuner_cmd += " --test-limit "
- tuner_cmd += str(fixed_runs)
- tuner_cmd += " --binary ./"
- tuner_cmd += Bench.promise_binary
- tuner_cmd += " --num-layers "
- tuner_cmd += str(Bench.num_layers)
- tuner_cmd += " --result-dir "
- tuner_cmd += dir_prefix
- tuner_cmd += result_dir + "/promise_tuner3/"
- tuner_cmd += " --accuracy "
- tuner_cmd += str(Bench.promise_accuracy - acc_threshold)
- tuner_cmd += " --cost-file "
- tuner_cmd += Bench.cost_file
- tuner_cmd += " --layer-file "
- tuner_cmd += Bench.layer_file
- tuner_cmd += " --knobs-config "
- tuner_cmd += "../opentuner/data/global_knobs.txt"
- tuner_cmd += " --layer-knobs "
- tuner_cmd += " local_knobs.txt "
-
-
- print (tuner_cmd)
-
- p = subprocess.Popen(tuner_cmd, shell=True)
- p.wait()
-
-
-
-def is50Knob(flag):
-
- flags50 = {}
- flags50[21] = 1
- flags50[22] = 1
- flags50[26] = 1
- flags50[27] = 1
- flags50[31] = 1
- flags50[32] = 1
-
- if flag in flags50:
- return True
- else:
- return False
-
-
-
-def is25Knob(flag):
-
- flags25 = {}
- flags25[23] = 1
- flags25[24] = 1
- flags25[25] = 1
- flags25[28] = 1
- flags25[29] = 1
- flags25[30] = 1
- flags25[33] = 1
- flags25[34] = 1
- flags25[35] = 1
- flags25[36] = 1
-
- if flag in flags25:
- return True
- else:
- return False
-
-
-
-def addPromiseFlags(flag_map):
-
- flags = []
-
- has_50_flag = False
- has_25_flag = False
-
- for flag in flag_map:
- if is50Knob(flag):
- has_50_flag = True
- if is25Knob(flag):
- has_25_flag = True
-
-
- if has_50_flag:
- flag_map[7] = 1
- flag_map[5] = 1
- flag_map[3] = 1
-
- if has_25_flag:
- flag_map[7] = 1
-
- return flag_map
-
-
-
-def addCostBasedFlags(flag_map, layer_costs, i):
-
- median = np.median(layer_costs)
- max_cost = np.max(layer_costs)
- sorted_vals = np.sort(layer_costs)
-
- print ("**** Median = ", median)
- print ("**** Max_cost = ", max_cost)
- print ("**** Sorted_vals = ", sorted_vals, "\n\n")
-
-
- if (layer_costs[i] > (median * 1.5)):
- flag_map[7] = 1
-
- if (layer_costs[i] > (median * 3)) or layer_costs[i] == max_cost:
- flag_map[7] = 1
- flag_map[5] = 1
- flag_map[3] = 1
-
-
- if (layer_costs[i] < (median / 10)):
- flag_map = {}
- flag_map[12] = 1
-
- return flag_map
-
-
-
-
-
-def constructKnobsFile(flags, layer_costs):
-
- f = open("local_knobs.txt", "w+")
- for i in range(len(flags)):
- flag_map = flags[i]
-
- if i > 0:
- flag_map = addPromiseFlags(flag_map)
- flag = addCostBasedFlags(flag_map, layer_costs, i)
-
- it = 0
- for flag in flag_map:
- f.write(str(flag))
- if it < len(flag_map) - 1:
- f.write(",")
- it += 1
-
- f.write("\n")
-
- f.close()
-
-
-
-
-
-def runPromiseAlgoBench(Bench):
-
- # NOTE-IMP: Changing current directory to one with promise binaries
- dir_prefix = "../build_tuner/"
-
-
- tuner_runs = Bench.autotuner_runs
-
- layer_costs = readCostFile(Bench.cost_file)
-
- flags1, flags2, flags3 = dumpBenchPareto(Bench)
-
- constructKnobsFile(flags1, layer_costs)
- runAlgoTunerCmd(Bench, dir_prefix, Bench.result_dir_1, 0.8, tuner_runs)
-
- constructKnobsFile(flags2, layer_costs)
- runAlgoTunerCmd(Bench, dir_prefix, Bench.result_dir_2, 1.6, tuner_runs)
-
- constructKnobsFile(flags3, layer_costs)
- runAlgoTunerCmd(Bench, dir_prefix, Bench.result_dir_3, 2.2, tuner_runs)
-
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/run_autotuner.py b/llvm/projects/hpvm-tensor-rt/bin/tuner_src/run_autotuner.py
deleted file mode 100644
index 1e533b8702139966166f860b72a3df1ccae03ee6..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/run_autotuner.py
+++ /dev/null
@@ -1,346 +0,0 @@
-
-import os
-import sys
-import subprocess
-import shutil
-
-from swing_selection import loadLayerDesc
-from error_sensitivity import test_sensitivity, test_sensitivity2, test_sensitivity3, test_sensitivity4
-from benchmarks import bench_tuner_data
-from run_psnr import runPSNRTuner
-from run_ha_tuner import runTunerBench
-from run_hs_tuner import runPromiseBench
-from run_algo_tuner import runAlgoBench
-from compute_confs import computePSNRBenchSwings, computeBenchSwings
-from validation import runPromiseBenchValidation2, runBenchValidation, runAlgoBenchValidate
-from profiling import startProfile, stopProfile, dumpProfiles
-from utils import createResultDirs
-from benchmarks import batch_id
-
-
-
-
-def runTunerValidation():
-
- runBenchValidation(bench_tuner_data["mobilenet_shallow"])
-
- #runBenchValidation("mobilenet_cifar10")
-
- #runBenchValidation("alexnet_cifar10")
- #runBenchValidation("vgg16_cifar10")
- #runBenchValidation("alexnet2_cifar10")
- #runBenchValidation("resnet18_cifar10")
- #runBenchValidation("vgg16_cifar100")
-
-
-def computeLayerSwings():
-
-
- computeBenchSwings(bench_tuner_data["mobilenet_shallow"])
-
- #computeBenchSwings("mobilenet_cifar10")
-
- #computeBenchSwings("mobilenet_cifar10")
-
- #computeBenchSwings("lenet_keras")
- #computeBenchSwings("alexnet_cifar10")
- #computeBenchSwings("alexnet2_cifar10")
- #computePSNRBenchSwings("pipeline_GEOM")
- #computePSNRBenchSwings("pipeline_GEMO")
- #computePSNRBenchSwings("pipeline_GEO")
- #computePSNRBenchSwings("pipeline_GSM")
- #computePSNRBenchSwings("pipeline_GSME")
-
-
-
-
-
-def runPromiseTuner():
-
-
- start = startProfile("MobileNet")
- runPromiseBench(bench_tuner_data["mobilenet_cifar10"])
- stopProfile("MobileNet", start)
-
- start = startProfile("Alexnet")
- runPromiseBench(bench_tuner_data["alexnet_cifar10"])
- stopProfile("Alexnet", start)
-
- start = startProfile("Alexnet2")
- runPromiseBench(bench_tuner_data["alexnet2_cifar10"])
- stopProfile("Alexnet2", start)
-
- start = startProfile("VGG16_10")
- runPromiseBench(bench_tuner_data["vgg16_cifar10"])
- stopProfile("VGG16_10", start)
-
- start = startProfile("VGG16_100")
- runPromiseBench(bench_tuner_data["vgg16_cifar100"])
- stopProfile("VGG16_100", start)
-
- start = startProfile("ResNet")
- runPromiseBench(bench_tuner_data["resnet18_cifar10"])
- stopProfile("ResNet", start)
-
- start = startProfile("MobileNet-SH")
- runPromiseBench(bench_tuner_data["mobilenet_shallow"])
- stopProfile("MobileNet-SH", start)
-
- start = startProfile("LeNet")
- runPromiseBench(bench_tuner_data["lenet_keras"])
- stopProfile("LeNet", start)
-
-
-
- #runPSNRPromiseBench("pipeline_GEOM")
- #runPSNRPromiseBench("pipeline_GEMO")
- #runPSNRPromiseBench("pipeline_GEO")
- #runPSNRPromiseBench("pipeline_GSM")
- #runPSNRPromiseBench("pipeline_GSME")
-
- dumpProfiles("time_profile" + batch_id + ".txt")
-
-
-
-
-def runPromiseValidation():
-
-
- start = startProfile("AlexNet")
- runPromiseBenchValidation2(bench_tuner_data["alexnet_cifar10"])
- stopProfile("AlexNet", start)
-
- start = startProfile("AlexNet2")
- runPromiseBenchValidation2(bench_tuner_data["alexnet2_cifar10"])
- stopProfile("AlexNet2", start)
-
- start = startProfile("VGG16_100")
- runPromiseBenchValidation2(bench_tuner_data["vgg16_cifar100"])
- stopProfile("VGG16_100", start)
-
- start = startProfile("VGG16_10")
- runPromiseBenchValidation2(bench_tuner_data["vgg16_cifar10"])
- stopProfile("VGG16_10", start)
- #runPromiseBenchValidation2(bench_tuner_data["lenet_keras"])
-
- start = startProfile("ResNet")
- runPromiseBenchValidation2(bench_tuner_data["resnet18_cifar10"])
- stopProfile("ResNet", start)
-
- start = startProfile("MobileNet_SH")
- runPromiseBenchValidation2(bench_tuner_data["mobilenet_shallow"])
- stopProfile("MobileNet_SH", start)
-
- start = startProfile("MobileNet")
- runPromiseBenchValidation2(bench_tuner_data["mobilenet_cifar10"])
- stopProfile("MobileNet", start)
-
-
- dumpProfiles("validation_prof" + batch_id + ".txt")
-
-
-
-
-def runAutotuner():
-
- runTunerBench(bench_tuner_data["alexnet_cifar10"])
- runTunerBench(bench_tuner_data["alexnet2_cifar10"])
-
- #runTunerBench("mobilenet_shallow")
- #runTunerBench("mobilenet_cifar10")
-
- #runTunerBench("lenet_keras")
- #runTunerBench("resnet18_cifar10")
- #runTunerBench("vgg16_cifar10")
-
- #runPSNRTuner("pipeline_GEOM")
- #runPSNRTuner("pipeline_GEMO")
- #runPSNRTuner("pipeline_GEO")
- #runPSNRTuner("pipeline_GSM")
- #runPSNRTuner("pipeline_GSME")
-
-
-
-
-def runSensAnalysis():
-
- start = startProfile("LeNet")
- test_sensitivity4(bench_tuner_data["lenet_keras"])
- stopProfile("LeNet", start)
-
- """
- start = startProfile("AlexNet")
- test_sensitivity4(bench_tuner_data["alexnet_cifar10"])
- stopProfile("AlexNet", start)
-
- start = startProfile("AlexNet2")
- test_sensitivity4(bench_tuner_data["alexnet2_cifar10"])
- stopProfile("AlexNet2", start)
-
- start = startProfile("ResNet")
- test_sensitivity4(bench_tuner_data["resnet18_cifar10"])
- stopProfile("ResNet", start)
-
- start = startProfile("MobileNet")
- test_sensitivity4(bench_tuner_data["mobilenet_cifar10"])
- stopProfile("MobileNet", start)
-
- start = startProfile("MobileNet_SH")
- test_sensitivity4(bench_tuner_data["mobilenet_shallow"])
- stopProfile("MobileNet_SH", start)
-
- start = startProfile("VGG_10")
- test_sensitivity4(bench_tuner_data["vgg16_cifar10"])
- stopProfile("VGG16_10", start)
-
- start = startProfile("VGG_100")
- test_sensitivity4(bench_tuner_data["vgg16_cifar100"])
- stopProfile("VGG16_100", start)
-
- dumpProfiles("sens_time_prof.txt")
-
- """
-
- start = startProfile("LeNet")
- test_sensitivity3(bench_tuner_data["lenet_keras"])
- stopProfile("LeNet", start)
-
- start = startProfile("AlexNet")
- test_sensitivity3(bench_tuner_data["alexnet_cifar10"])
- stopProfile("AlexNet", start)
-
- start = startProfile("AlexNet2")
- test_sensitivity3(bench_tuner_data["alexnet2_cifar10"])
- stopProfile("AlexNet2", start)
-
- start = startProfile("ResNet")
- test_sensitivity3(bench_tuner_data["resnet18_cifar10"])
- stopProfile("ResNet", start)
-
-
- start = startProfile("MobileNet")
- test_sensitivity3(bench_tuner_data["mobilenet_cifar10"])
- stopProfile("MobileNet", start)
-
- start = startProfile("MobileNet_SH")
- test_sensitivity3(bench_tuner_data["mobilenet_shallow"])
- stopProfile("MobileNet_SH", start)
-
- start = startProfile("VGG_10")
- test_sensitivity3(bench_tuner_data["vgg16_cifar10"])
- stopProfile("VGG16_10", start)
-
- start = startProfile("VGG_100")
- test_sensitivity3(bench_tuner_data["vgg16_cifar100"])
- stopProfile("VGG16_100", start)
-
- dumpProfiles("sens_time_prof.txt")
-
-
- """
- test_sensitivity2(bench_tuner_data["fc4"])
- test_sensitivity2(bench_tuner_data["lenet_keras"])
- test_sensitivity2(bench_tuner_data["mobilenet_cifar10"])
- test_sensitivity2(bench_tuner_data["mobilenet_shallow"])
- test_sensitivity2(bench_tuner_data["resnet18_cifar10"])
- test_sensitivity2(bench_tuner_data["alexnet_cifar10"])
- test_sensitivity2(bench_tuner_data["alexnet2_cifar10"])
- test_sensitivity2(bench_tuner_data["vgg16_cifar10"])
- test_sensitivity2(bench_tuner_data["vgg16_cifar100"])
-
-
- test_sensitivity(bench_tuner_data["fc4"])
- test_sensitivity(bench_tuner_data["lenet_keras"])
- test_sensitivity(bench_tuner_data["mobilenet_cifar10"])
- test_sensitivity(bench_tuner_data["mobilenet_shallow"])
- test_sensitivity(bench_tuner_data["resnet18_cifar10"])
- test_sensitivity(bench_tuner_data["alexnet_cifar10"])
- test_sensitivity(bench_tuner_data["alexnet2_cifar10"])
- test_sensitivity(bench_tuner_data["vgg16_cifar10"])
- test_sensitivity(bench_tuner_data["vgg16_cifar100"])
- """
-
-
-
-def runAlgoTuner():
-
- Bench = bench_tuner_data["alexnet_cifar10"]
- runAlgoBench(Bench)
-
- Bench = bench_tuner_data["mobilenet_shallow"]
- runAlgoBench(Bench)
-
- Bench = bench_tuner_data["mobilenet_cifar10"]
- runAlgoBench(Bench)
-
- Bench = bench_tuner_data["vgg16_cifar10"]
- runAlgoBench(Bench)
-
- Bench = bench_tuner_data["lenet_keras"]
- runAlgoBench(Bench)
-
- Bench = bench_tuner_data["alexnet2_cifar10"]
- runAlgoBench(Bench)
-
-
- Bench = bench_tuner_data["vgg16_cifar100"]
- runAlgoBench(Bench)
-
- Bench = bench_tuner_data["resnet18_cifar10"]
- runAlgoBench(Bench)
-
-
-
-
-def runAlgoTunerValidation():
-
-
- Bench = bench_tuner_data["alexnet_cifar10"]
- runAlgoBenchValidate(Bench)
-
- Bench = bench_tuner_data["mobilenet_shallow"]
- runAlgoBenchValidate(Bench)
-
- Bench = bench_tuner_data["mobilenet_cifar10"]
- runAlgoBenchValidate(Bench)
-
- Bench = bench_tuner_data["vgg16_cifar10"]
- runAlgoBenchValidate(Bench)
-
- Bench = bench_tuner_data["lenet_keras"]
- runAlgoBenchValidate(Bench)
-
- Bench = bench_tuner_data["alexnet2_cifar10"]
- runAlgoBenchValidate(Bench)
-
- Bench = bench_tuner_data["vgg16_cifar100"]
- runAlgoBenchValidate(Bench)
-
- Bench = bench_tuner_data["resnet18_cifar10"]
- runAlgoBenchValidate(Bench)
-
-
-
-
-
-if __name__ == "__main__":
-
- createResultDirs(bench_tuner_data)
-
- #runAutotuner()
-
- #runTunerValidation()
-
- #computeLayerSwings()
-
- #runPromiseTuner()
-
- #runAlgoTuner()
-
- runAlgoTunerValidation()
-
- #runPromiseValidation()
-
- #runSensAnalysis()
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/run_ha_tuner.py b/llvm/projects/hpvm-tensor-rt/bin/tuner_src/run_ha_tuner.py
deleted file mode 100644
index 055d2c4c1bde6bf02e080c53101f03dc1791fd9e..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/run_ha_tuner.py
+++ /dev/null
@@ -1,52 +0,0 @@
-
-
-
-import subprocess
-
-
-
-#, bench_name
-def runTunerBench(Bench):
-
- #Bench = bench_tuner_data[bench_name]
-
- #FIXIT: Replace approxhpvm_tuner2 with approxhpvm_tuner
- tuner_cmd = "python ../opentuner/autotuner/approxhpvm_tuner.py "
- tuner_cmd += " --test-limit "
- tuner_cmd += str(Bench.autotuner_runs)
- tuner_cmd += " --binary ./"
- tuner_cmd += Bench.tuner_binary
- tuner_cmd += " --num-flags "
- tuner_cmd += str(Bench.num_flags)
- tuner_cmd += " --error-range "
- tuner_cmd += str(Bench.error_range_2)
- tuner_cmd += " --result-dir "
- tuner_cmd += Bench.result_dir_2
- tuner_cmd += " --accuracy "
- tuner_cmd += str(Bench.tuner_accuracy - 1.70)
-
- print (tuner_cmd)
-
- p = subprocess.Popen(tuner_cmd, shell=True)
- p.wait()
-
-
- tuner_cmd = "python ../opentuner/autotuner/approxhpvm_tuner.py "
- tuner_cmd += " --test-limit "
- tuner_cmd += str(Bench.autotuner_runs)
- tuner_cmd += " --binary ./"
- tuner_cmd += Bench.tuner_binary
- tuner_cmd += " --num-flags "
- tuner_cmd += str(Bench.num_flags)
- tuner_cmd += " --error-range "
- tuner_cmd += str(Bench.error_range_1)
- tuner_cmd += " --result-dir "
- tuner_cmd += Bench.result_dir_1
- tuner_cmd += " --accuracy "
- tuner_cmd += str(Bench.tuner_accuracy - 0.85)
-
- print (tuner_cmd)
-
- p = subprocess.Popen(tuner_cmd, shell=True)
- p.wait()
-
diff --git a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/run_hs_tuner.py b/llvm/projects/hpvm-tensor-rt/bin/tuner_src/run_hs_tuner.py
deleted file mode 100644
index f1a9c8f417bafdf4084a687670074101bec3faa0..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/run_hs_tuner.py
+++ /dev/null
@@ -1,185 +0,0 @@
-
-
-import os
-import subprocess
-from error_sensitivity import select_skip_layers
-
-
-def runPromiseTunerCmd(Bench, dir_prefix, result_dir, acc_threshold, autotuner_runs, skip_layers):
-
- tuner_cmd = "python2 ../opentuner/autotuner/promise_tuner3.py "
- tuner_cmd += " --test-limit "
- tuner_cmd += str(autotuner_runs)
- tuner_cmd += " --binary ./"
- tuner_cmd += Bench.promise_binary
- tuner_cmd += " --num-flags "
- tuner_cmd += str(Bench.num_layers)
- tuner_cmd += " --start-range "
- tuner_cmd += str(Bench.start_promise_range)
- tuner_cmd += " --error-range "
- #tuner_cmd += str(10)
- # NOTE: Increasing flags from ApproxTechiqueTuner
- tuner_cmd += str(12)
- tuner_cmd += " --result-dir "
- tuner_cmd += dir_prefix
- tuner_cmd += result_dir + "/promise_tuner/"
- tuner_cmd += " --accuracy "
- tuner_cmd += str(Bench.promise_accuracy - acc_threshold)
- tuner_cmd += " --layer-file "
- tuner_cmd += dir_prefix
- tuner_cmd += Bench.tensor_desc_file
- # NOTE: Cost file is new addition - ***NOT*** present in promisetuner1 and promisetuner2
- tuner_cmd += " --cost-file "
- tuner_cmd += Bench.cost_file
- #tuner_cmd += " --gpu-layers "
- #tuner_cmd += str(Bench.skip_layers)
- tuner_cmd += " --gpu-layers 0 "
- tuner_cmd += " --skip-layers \""
- #tuner_cmd += str(Bench.skip_layer_str) + "\""
- tuner_cmd += str(skip_layers) + "\""
-
- print (tuner_cmd)
-
- p = subprocess.Popen(tuner_cmd, shell=True)
- p.wait()
-
-
-
-
-def promiseTunerLoss1(Bench, dir_prefix):
-
- tuner_runs = Bench.autotuner_runs
-
- skip_layers1 = "0"
- skip_layers2 = "0_" + select_skip_layers(Bench, 30)
- skip_layers3 = "0_" + select_skip_layers(Bench, 50)
-
- runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_1, 0.85, tuner_runs, skip_layers1)
- runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_1, 0.85, tuner_runs, skip_layers2)
- runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_1, 0.85, tuner_runs, skip_layers3)
-
-
-def promiseTunerLoss2(Bench, dir_prefix):
-
- tuner_runs = Bench.autotuner_runs
-
- skip_layers1 = "0"
- skip_layers2 = "0_" + select_skip_layers(Bench, 20)
- skip_layers3 = "0_" + select_skip_layers(Bench, 40)
-
- runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_2, 1.7, tuner_runs, skip_layers1)
- runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_2, 1.7, tuner_runs, skip_layers2)
- runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_2, 1.7, tuner_runs, skip_layers3)
-
-
-
-def promiseTunerLoss3(Bench, dir_prefix):
-
- tuner_runs = Bench.autotuner_runs
-
- skip_layers1 = "0"
- skip_layers2 = "0_" + select_skip_layers(Bench, 10)
- skip_layers3 = "0_" + select_skip_layers(Bench, 30)
-
- runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_3, 2.5, tuner_runs, skip_layers1)
- runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_3, 2.5, tuner_runs, skip_layers2)
- runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_3, 2.5, tuner_runs, skip_layers3)
-
-
-BASELINE = True
-
-
-def runPromiseBench(Bench):
-
- # NOTE-IMP: Changing current directory to one with promise binaries
- dir_prefix = "../build_tuner/"
-
-
- if BASELINE:
- tuner_runs = Bench.autotuner_runs * 2
- skip_layers = "0"
- runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_1, 0.85, tuner_runs, skip_layers)
- runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_2, 1.7, tuner_runs, skip_layers)
- runPromiseTunerCmd(Bench, dir_prefix, Bench.result_dir_3, 2.5, tuner_runs, skip_layers)
-
- else:
-
- promiseTunerLoss1(Bench, dir_prefix)
-
- promiseTunerLoss2(Bench, dir_prefix)
-
- promiseTunerLoss3(Bench, dir_prefix)
-
-
-
-
- """
- #tuner_cmd = "python ../opentuner/autotuner/promise_tuner2.py "
- tuner_cmd = "python ../opentuner/autotuner/promise_tuner3.py "
- tuner_cmd += " --test-limit "
- tuner_cmd += str(Bench.autotuner_runs)
- tuner_cmd += " --binary ./"
- tuner_cmd += Bench.promise_binary
- tuner_cmd += " --num-flags "
- tuner_cmd += str(Bench.num_layers)
- tuner_cmd += " --start-range "
- tuner_cmd += str(Bench.start_promise_range)
- tuner_cmd += " --error-range "
- #tuner_cmd += str(10)
- tuner_cmd += " --result-dir "
- tuner_cmd += result_dir_prefix
- tuner_cmd += Bench.result_dir_2 + "/promise_tuner/"
- tuner_cmd += " --accuracy "
- tuner_cmd += str(Bench.promise_accuracy - 1.90)
- tuner_cmd += " --layer-file "
- tuner_cmd += result_dir_prefix
- tuner_cmd += Bench.tensor_desc_file
- # NOTE: Cost file is new addition - ***NOT*** present in promisetuner1 and promisetuner2
- tuner_cmd += " --cost-file "
- tuner_cmd += Bench.cost_file
- #tuner_cmd += " --gpu-layers "
- #tuner_cmd += str(Bench.skip_layers)
- tuner_cmd += " --gpu-layers 0 "
- tuner_cmd += " --skip-layers \""
- tuner_cmd += str(Bench.skip_layer_str) + "\""
-
- print (tuner_cmd)
-
- p = subprocess.Popen(tuner_cmd, shell=True)
- p.wait()
-
- #tuner_cmd = "python ../opentuner/autotuner/promise_tuner2.py "
- tuner_cmd = "python ../opentuner/autotuner/promise_tuner3.py "
- tuner_cmd += " --test-limit "
- tuner_cmd += str(Bench.autotuner_runs)
- tuner_cmd += " --binary ./"
- tuner_cmd += Bench.promise_binary
- tuner_cmd += " --num-flags "
- tuner_cmd += str(Bench.num_layers)
- tuner_cmd += " --start-range "
- tuner_cmd += str(Bench.start_promise_range)
- tuner_cmd += " --error-range "
- tuner_cmd += str(10)
- tuner_cmd += " --result-dir "
- tuner_cmd += result_dir_prefix
- tuner_cmd += Bench.result_dir_1 + "/promise_tuner/"
- tuner_cmd += " --accuracy "
- tuner_cmd += str(Bench.promise_accuracy - 0.95)
- tuner_cmd += " --layer-file "
- tuner_cmd += result_dir_prefix
- tuner_cmd += Bench.tensor_desc_file
- tuner_cmd += " --cost-file "
- tuner_cmd += Bench.cost_file
- #tuner_cmd += " --gpu-layers "
- #tuner_cmd += str(Bench.skip_layers)
- tuner_cmd += " --gpu-layers 0 "
- tuner_cmd += " --skip-layers \""
- tuner_cmd += str(Bench.skip_layer_str) + "\""
-
-
- print (tuner_cmd)
-
- p = subprocess.Popen(tuner_cmd, shell=True)
- p.wait()
-
- """
diff --git a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/run_psnr.py b/llvm/projects/hpvm-tensor-rt/bin/tuner_src/run_psnr.py
deleted file mode 100644
index 77e70609b89f200e37af1a12348874f9d447d0cd..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/run_psnr.py
+++ /dev/null
@@ -1,143 +0,0 @@
-
-
-import subprocess
-
-
-def gen30dbFile():
-
- f = open("psnr.txt", "w+");
- f.write("30");
- f.close()
-
-
-def gen20dbFile():
-
- f = open("psnr.txt", "w+");
- f.write("20");
- f.close()
-
-
-
-def runPSNRTuner(bench_name):
-
- Bench = bench_tuner_data[bench_name]
-
- # 20DB run
- gen20dbFile()
- tuner_cmd = "python ../opentuner/autotuner/approxhpvm_tuner.py "
- tuner_cmd += " --test-limit "
- tuner_cmd += str(Bench.autotuner_runs)
- tuner_cmd += " --binary ./"
- tuner_cmd += Bench.tuner_binary
- tuner_cmd += " --num-flags "
- tuner_cmd += str(Bench.num_flags)
- tuner_cmd += " --error-range "
- tuner_cmd += str(Bench.error_range_2)
- tuner_cmd += " --result-dir "
- tuner_cmd += Bench.result_dir_2
- tuner_cmd += " --accuracy "
- tuner_cmd += str(Bench.tuner_accuracy)
-
- print (tuner_cmd)
-
- p = subprocess.Popen(tuner_cmd, shell=True)
- p.wait()
-
-
- # 30DB run
- gen30dbFile()
- tuner_cmd = "python ../opentuner/autotuner/approxhpvm_tuner.py "
- tuner_cmd += " --test-limit "
- tuner_cmd += str(Bench.autotuner_runs)
- tuner_cmd += " --binary ./"
- tuner_cmd += Bench.tuner_binary
- tuner_cmd += " --num-flags "
- tuner_cmd += str(Bench.num_flags)
- tuner_cmd += " --error-range "
- tuner_cmd += str(Bench.error_range_1)
- tuner_cmd += " --result-dir "
- tuner_cmd += Bench.result_dir_1
- tuner_cmd += " --accuracy "
- tuner_cmd += str(Bench.tuner_accuracy)
-
- print (tuner_cmd)
-
- p = subprocess.Popen(tuner_cmd, shell=True)
- p.wait()
-
-
-
-def runPSNRPromiseBench(bench_name):
-
- # NOTE-IMP: Changing current directory to one with promise binaries
- #os.chdir("../build_promise/")
- result_dir_prefix = "../build_tuner/"
-
- Bench = bench_tuner_data[bench_name]
-
- # 20db Run
- gen20dbFile()
- tuner_cmd = "python ../opentuner/autotuner/promise_tuner2.py "
- tuner_cmd += " --test-limit "
- tuner_cmd += str(Bench.autotuner_runs)
- tuner_cmd += " --binary ./"
- tuner_cmd += Bench.promise_binary
- tuner_cmd += " --num-flags "
- tuner_cmd += str(Bench.num_layers)
- tuner_cmd += " --start-range "
- tuner_cmd += str(Bench.start_promise_range)
- tuner_cmd += " --error-range "
- tuner_cmd += str(10)
- tuner_cmd += " --result-dir "
- tuner_cmd += result_dir_prefix
- tuner_cmd += Bench.result_dir_2 + "/promise_tuner/"
- tuner_cmd += " --accuracy "
- tuner_cmd += str(Bench.promise_accuracy)
- tuner_cmd += " --layer-file "
- tuner_cmd += result_dir_prefix
- tuner_cmd += Bench.tensor_desc_file
- tuner_cmd += " --gpu-layers 0 "
- tuner_cmd += " --skip-layers \""
- tuner_cmd += str(Bench.skip_layer_str) + "\""
-
-
- print (tuner_cmd)
-
- p = subprocess.Popen(tuner_cmd, shell=True)
- p.wait()
-
- # 30DB run
- gen30dbFile()
- tuner_cmd = "python ../opentuner/autotuner/promise_tuner2.py "
- tuner_cmd += " --test-limit "
- tuner_cmd += str(Bench.autotuner_runs)
- tuner_cmd += " --binary ./"
- tuner_cmd += Bench.promise_binary
- tuner_cmd += " --num-flags "
- tuner_cmd += str(Bench.num_layers)
- tuner_cmd += " --start-range "
- tuner_cmd += str(Bench.start_promise_range)
- tuner_cmd += " --error-range "
- tuner_cmd += str(10)
- tuner_cmd += " --result-dir "
- tuner_cmd += result_dir_prefix
- tuner_cmd += Bench.result_dir_1 + "/promise_tuner/"
- tuner_cmd += " --accuracy "
- tuner_cmd += str(Bench.promise_accuracy)
- tuner_cmd += " --layer-file "
- tuner_cmd += result_dir_prefix
- tuner_cmd += Bench.tensor_desc_file
- tuner_cmd += " --gpu-layers 0 "
- tuner_cmd += " --skip-layers \""
- tuner_cmd += str(Bench.skip_layer_str) + "\""
-
-
- print (tuner_cmd)
-
- p = subprocess.Popen(tuner_cmd, shell=True)
- p.wait()
-
-
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/swing_selection.py b/llvm/projects/hpvm-tensor-rt/bin/tuner_src/swing_selection.py
deleted file mode 100644
index 399143c357c618aeba1665f5f1b8ecda4097d84c..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/swing_selection.py
+++ /dev/null
@@ -1,304 +0,0 @@
-
-
-import os
-import warnings
-import matplotlib.pyplot as plt
-import matplotlib.cm as cm
-from matplotlib.ticker import MultipleLocator
-import numpy as np
-from scipy.signal import savgol_filter
-import math
-import struct
-
-
-
-def readDataFromText(textFile):
- results = []
- with open(textFile, "r") as f:
- for line in f:
- token = line.split("\t")
- if (len(token) < 7):
- continue
- record = (token[0], float(token[1]), float(token[5]), float(token[6]))
- results.append(record)
- return results
-
-
-convL1bins = [(0.985901, 1.36474), (0.852871, 1.16982), (0.422283, 0.55701), (0.259752, 0.335259), (0.216577, 0.277843), (0.185812, 0.23733), (0.148996, 0.189171), (0.100007, 0.125816), (0.0003127876261714846, 0.014511194080114365)]
-convL2bins = [(0.995298, 1.3643), (0.861066, 1.16279), (0.426857, 0.547827), (0.262645, 0.330186), (0.218984, 0.273731), (0.187878, 0.233872), (0.150619, 0.186512), (0.10106, 0.124477), (0.00035427528200671077, 0.020199092105031013)]
-
-biasL1bins = [(0.3510325849056244, 0.49078235030174255), (0.30895063281059265, 0.4311973750591278), (0.16023841500282288, 0.22283604741096497), (0.099583700299263, 0.1381179839372635), (0.08340170979499817, 0.11503150314092636), (0.07280077040195465, 0.09948030859231949), (0.05857400223612785, 0.07965542376041412), (0.04044099152088165, 0.054193537682294846), (0.0, 0.0)]
-biasL2bins = [(0.4154910147190094, 0.5820578932762146), (0.3656001389026642, 0.5121639370918274), (0.18930286169052124, 0.2637346684932709), (0.11687946319580078, 0.16306844353675842), (0.09796475619077682, 0.13558265566825867), (0.0848352462053299, 0.11619425565004349), (0.06783176958560944, 0.09277229756116867), (0.046059850603342056, 0.062238890677690506), (0.0, 0.0)]
-
-gemmL1bins= [(0.711203, 0.772211), (0.625894, 0.679601), (0.322665, 0.350383), (0.199646, 0.216727), (0.166556, 0.180781), (0.142945, 0.155132), (0.114662, 0.124399), (0.0771065, 0.0835984), (0.00034660729579627514, 0.008546584285795689)]
-gemmL2bins= [(0.715208, 0.768102), (0.629411, 0.675947), (0.324433, 0.348358), (0.200659, 0.21539), (0.167381, 0.179634), (0.143637, 0.154119), (0.115197, 0.123548), (0.0774642, 0.0829647), (0.0003496285935398191, 0.009841435588896275)]
-
-
-
-def findBinByOp(op):
- if op == 'tensorConv':
- return convL1bins, convL2bins
- if op == 'tensorAdd':
- return biasL1bins, biasL2bins
- if op == 'tensorGemm':
- return gemmL1bins, gemmL2bins
-
- return None, None
-
-
-def getSwing(Lx, opLxbin):
- if opLxbin == None:
- return 0
- for i, (minT, maxT) in enumerate(opLxbin):
- if Lx > minT:
- return i
-
- return 9
-
-
-
-def getConfiguration(L_thresholds):
- configuration = []
- for l in L_thresholds:
- # L0 is op_type
- opL1bin, opL2bin = findBinByOp(l[0])
- # NOTE: L2 is L1 error, L3 is L2 error
- sL1 = getSwing(l[2], opL1bin)
- sL2 = getSwing(l[3], opL2bin)
- if sL1 < 7:
- sL1 = sL1 + 1
- if sL2 < 7:
- sL2 = sL2 + 1
- configuration.append((l[0], l[1], l[2], l[3], sL1, sL2, max(sL1, sL2)))
-
- return configuration
-
-
-def displayConfig(config):
- for c in config:
- print(c)
-
-def displayMultipleConfigurations(configurations):
- for f, c in configurations.items():
- print(f)
- displayConfig(c)
- print()
-
-def getConfigFromFile(filename):
- L_requirements = readDataFromText(filename)
- config = getConfiguration(L_requirements)
- return config
-
-
-def getConfigurationsFromDir(dirname):
- configurations = dict()
- for f in os.listdir(dirname):
- configurations[f] = getConfigFromFile(os.path.join(dirname, f))
-
- return configurations
-
-
-def getLayerWiseTarget(config):
- target = []
- for i, op in enumerate(config):
- if (op[0] == 'tensorGemm') or (op[0] == 'tensorConv'):
- t = op[6]
- for j in range(i+1, len(config)):
- if config[j][0] == 'tensorGemm' or config[j][0] == 'tensorConv':
- break
- t = max(t, config[j][6])
- target.append(t)
- t = 0
-
- return target
-
-
-def dumpLayerWiseTarget(file, targets):
- with open(file, "w") as f:
- for name, t in targets.items():
- f.write(name)
- f.write(" ")
- for i in t:
- f.write(str(i))
- f.write(" ")
- f.write("\n")
-
-
-def getTargetsFromConfigurations(configs):
- targets = dict()
- for f, c in configs.items():
- targets[f] = [d[6] for d in c]
-
- return targets
-
-
-def dumpBenchmarkTargets(name, benchmark_dir):
- benchmark_targets = dict()
- error = ['linear', 'log', 'quad']
- for e in error:
- results_dir = os.path.join(benchmark_dir, e)
- configs = getConfigurationsFromDir(results_dir)
- benchmark_targets[e] = getTargetsFromConfigurations(configs)
-
- return benchmark_targets
-
-
-
-def dumpTargets(filename, targets):
- with open(filename, "w") as f:
- for e, file_configs in targets.items():
- for name, config in file_configs.items():
- for c in config:
- f.write(str(c))
- f.write(" ")
- f.write("\n")
-
-
-
-def getLayerSwings(layer_desc, configurations):
-
- layer_swings = []
- for i in range(len(configurations)):
- config_vals = configurations[i]
- if len(config_vals) == 0:
- continue
-
- layer_index = 0
- index = 0
- swing_vals = []
-
- while layer_index < len(layer_desc):
- if len(layer_desc[layer_index]) == 1:
- promise_swing = config_vals[index]
- layer_type = layer_desc[layer_index][0]
- layer_type = layer_type.strip()
- print ("****layer_type = ", layer_type)
- if layer_type != "conv" and layer_type != "dense":
- promise_swing = -9
- if layer_type == "depthwise_conv":
- promise_swing = -9
- index += 1
- else:
- #print ("index = ", index)
- # FIXIT: Doesn't look right
- print (config_vals[index], config_vals[index+1])
- promise_swing = max(config_vals[index], config_vals[index+1])
- stride = len(layer_desc[layer_index])
- index += stride
-
- swing_vals.append(promise_swing)
- layer_index += 1
-
- layer_swings.append(swing_vals)
-
- return layer_swings
-
-
-
-
-def loadLayerDesc(layer_desc_file):
-
- layer_desc = []
- f = open(layer_desc_file)
- for x in f:
- vals = x.split()
- layer_desc.append(vals)
-
- return layer_desc
-
-
-
-def dumpLayerTargets(targets, tuned_result_dir, layer_desc_file):
-
- layer_desc = loadLayerDesc(layer_desc_file)
- print (layer_desc)
-
- file_names = []
- configurations = []
- for e, file_configs in targets.items():
- for name, config in file_configs.items():
- config_vals = []
- for c in config:
- config_vals.append(c)
- print (config_vals)
-
- configurations.append(config_vals)
-
- rank = e + "_" + "_".join(name.split("_")[-2:])
- file_names.append(rank)
-
-
- # NOTE: get PROMISE swing values corresponding to each layer
- layer_swings = getLayerSwings(layer_desc, configurations)
-
- targets_file_path = tuned_result_dir + "/layer_targets.txt"
- f = open(targets_file_path, "w+")
-
- for config in layer_swings:
- index = 0
- for swing in config:
- swing_str = ""
- if swing == 8 or swing == 9:
- layer_size = len(layer_desc[index])
- for i in range(layer_size):
- swing_str += str(swing)
- if i < layer_size - 1:
- swing_str += " "
- elif swing == -9:
- swing_str += "8"
- else:
- swing_str += str(swing)
-
- if index < len(config) - 1:
- swing_str += ","
-
- f.write(swing_str)
- index += 1
-
- f.write("\n")
-
- f.close()
-
- print(layer_swings)
- return layer_swings, file_names
-
-
-
-def replaceFirstLayer(layer_swings):
-
- # Ensuring first conv on GPU
- for conf in layer_swings:
- conf[0] = 9
-
-
-
-def computeLayerTargets(tuned_result_dir, layer_desc_file):
-
- targets_file_path = tuned_result_dir + "/tensor_targets.txt"
- targets = dumpBenchmarkTargets(targets_file_path, tuned_result_dir)
-
- dumpTargets(targets_file_path, targets)
-
- layer_swings, file_names = dumpLayerTargets(targets, tuned_result_dir, layer_desc_file)
-
- replaceFirstLayer(layer_swings)
-
- return layer_swings, file_names
-
-
-# Externally-called function
-def compute_swing_selection(tuned_result_dir, layer_file):
-
- return computeLayerTargets(tuned_result_dir, layer_file)
-
-
-
-
-if __name__ == "__main__":
-
- tuned_result_dir = "./vgg16_cifar10_tuner_1/high_confidence/"
- layer_file = "layer_composition.txt"
-
- tuned_result_dir = "./resnet18_cifar10_tuner_1/high_confidence/"
- layer_file = "layer_composition2.txt"
- computeLayerTargets(tuned_result_dir, layer_file)
diff --git a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/swing_selection2.py b/llvm/projects/hpvm-tensor-rt/bin/tuner_src/swing_selection2.py
deleted file mode 100644
index 588edad2a289a67d30c1ade15d4737556327f4fb..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/swing_selection2.py
+++ /dev/null
@@ -1,289 +0,0 @@
-
-
-import os
-import warnings
-import matplotlib.pyplot as plt
-import matplotlib.cm as cm
-from matplotlib.ticker import MultipleLocator
-import numpy as np
-from scipy.signal import savgol_filter
-import math
-import struct
-
-
-
-def readDataFromText(textFile):
- results = []
- with open(textFile, "r") as f:
- for line in f:
- token = line.split("\t")
- if (len(token) < 7):
- continue
- record = (token[0], float(token[1]), float(token[5]), float(token[6]))
- results.append(record)
- return results
-
-
-convL1bins = [(0.985901, 1.36474), (0.852871, 1.16982), (0.422283, 0.55701), (0.259752, 0.335259), (0.216577, 0.277843), (0.185812, 0.23733), (0.148996, 0.189171), (0.100007, 0.125816), (0.0003127876261714846, 0.014511194080114365)]
-convL2bins = [(0.995298, 1.3643), (0.18, 0.19), (0.14, 0.16), (0.11, 0.12), (0.08, 0.09), (0.06, 0.07), (0.04, 0.05), (0.029, 0.035), (0.00031427528200671077, 0.020199092105031013)]
-#convL2bins = [(0.995298, 1.3643), (0.18, 0.19), (0.14, 0.16), (0.11, 0.12), (0.08, 0.09), (0.06, 0.07), (0.04, 0.05), (0.001, 0.004), (0.00031427528200671077, 0.020199092105031013)]
-
-biasL1bins = [(0.3510325849056244, 0.49078235030174255), (0.30895063281059265, 0.4311973750591278), (0.16023841500282288, 0.22283604741096497), (0.099583700299263, 0.1381179839372635), (0.08340170979499817, 0.11503150314092636), (0.07280077040195465, 0.09948030859231949), (0.05857400223612785, 0.07965542376041412), (0.04044099152088165, 0.054193537682294846), (0.0, 0.0)]
-biasL2bins = [(0.4154910147190094, 0.5820578932762146), (0.3656001389026642, 0.5121639370918274), (0.18930286169052124, 0.2637346684932709), (0.11687946319580078, 0.16306844353675842), (0.09796475619077682, 0.13558265566825867), (0.0848352462053299, 0.11619425565004349), (0.06783176958560944, 0.09277229756116867), (0.046059850603342056, 0.062238890677690506), (0.0, 0.0)]
-
-gemmL1bins= [(0.711203, 0.772211), (0.625894, 0.679601), (0.322665, 0.350383), (0.199646, 0.216727), (0.166556, 0.180781), (0.142945, 0.155132), (0.114662, 0.124399), (0.0771065, 0.0835984), (0.00034660729579627514, 0.008546584285795689)]
-gemmL2bins= [(0.715208, 0.768102), (0.629411, 0.675947), (0.324433, 0.348358), (0.200659, 0.21539), (0.167381, 0.179634), (0.143637, 0.154119), (0.115197, 0.123548), (0.0774642, 0.0829647), (0.0003496285935398191, 0.009841435588896275)]
-
-
-
-def findBinByOp(op):
- if op == 'tensorConv':
- return convL1bins, convL2bins
- if op == 'tensorAdd':
- return biasL1bins, biasL2bins
- if op == 'tensorGemm':
- return gemmL1bins, gemmL2bins
-
- return None, None
-
-
-def getSwing(Lx, opLxbin):
- if opLxbin == None:
- return 0
- for i, (minT, maxT) in enumerate(opLxbin):
- if Lx > minT:
- return i
-
- return 9
-
-
-
-def getConfiguration(L_thresholds):
- configuration = []
- for l in L_thresholds:
- # L0 is op_type
- opL1bin, opL2bin = findBinByOp(l[0])
- # NOTE: L2 is L1 error, L3 is L2 error
- # only using L2 for image pipelines
- sL2 = getSwing(l[3], opL2bin)
- if sL2 < 7:
- sL2 = sL2 + 1
- configuration.append((l[0], l[1], l[2], l[3], sL2, sL2, sL2))
-
- return configuration
-
-
-def displayConfig(config):
- for c in config:
- print(c)
-
-def displayMultipleConfigurations(configurations):
- for f, c in configurations.items():
- print(f)
- displayConfig(c)
- print()
-
-def getConfigFromFile(filename):
- L_requirements = readDataFromText(filename)
- config = getConfiguration(L_requirements)
- return config
-
-
-def getConfigurationsFromDir(dirname):
- configurations = dict()
- for f in os.listdir(dirname):
- configurations[f] = getConfigFromFile(os.path.join(dirname, f))
-
- return configurations
-
-
-def getLayerWiseTarget(config):
- target = []
- for i, op in enumerate(config):
- if (op[0] == 'tensorGemm') or (op[0] == 'tensorConv'):
- t = op[6]
- target.append(t)
-
- return target
-
-
-def dumpLayerWiseTarget(file, targets):
- with open(file, "w") as f:
- for name, t in targets.items():
- f.write(name)
- f.write(" ")
- for i in t:
- f.write(str(i))
- f.write(" ")
- f.write("\n")
-
-
-def getTargetsFromConfigurations(configs):
- targets = dict()
- for f, c in configs.items():
- targets[f] = [d[6] for d in c]
-
- return targets
-
-
-def dumpBenchmarkTargets(name, benchmark_dir):
- benchmark_targets = dict()
- error = ['linear', 'log', 'quad']
- for e in error:
- results_dir = os.path.join(benchmark_dir, e)
- configs = getConfigurationsFromDir(results_dir)
- benchmark_targets[e] = getTargetsFromConfigurations(configs)
-
- return benchmark_targets
-
-
-def dumpTargets(filename, targets):
- with open(filename, "w") as f:
- for e, file_configs in targets.items():
- for name, config in file_configs.items():
- for c in config:
- f.write(str(c))
- f.write(" ")
- f.write("\n")
-
-
-
-def getLayerSwings(layer_desc, configurations):
-
- layer_swings = []
- for i in range(len(configurations)):
- config_vals = configurations[i]
- layer_index = 0
- index = 0
- swing_vals = []
-
- while layer_index < len(layer_desc):
- if len(layer_desc[layer_index]) == 1:
- promise_swing = config_vals[index]
- layer_type = layer_desc[layer_index]
- if layer_type != "conv" and layer_type != "dense":
- promise_swing = -9
- index += 1
- else:
- print (config_vals[index], config_vals[index+1])
- promise_swing = max(config_vals[index], config_vals[index+1])
- stride = len(layer_desc[layer_index])
- #print ("*stride = ", stride)
- index += stride
-
- swing_vals.append(promise_swing)
- layer_index += 1
-
- layer_swings.append(swing_vals)
-
- return layer_swings
-
-
-
-
-def loadLayerDesc(layer_desc_file):
-
- layer_desc = []
- f = open(layer_desc_file)
- for x in f:
- vals = x.split()
- layer_desc.append(vals)
-
- return layer_desc
-
-
-
-def dumpLayerTargets(targets, tuned_result_dir, layer_desc_file):
-
- layer_desc = loadLayerDesc(layer_desc_file)
- print (layer_desc)
-
- file_names = []
- configurations = []
- for e, file_configs in targets.items():
- for name, config in file_configs.items():
- config_vals = []
- for c in config:
- config_vals.append(c)
- print (config_vals)
-
- configurations.append(config_vals)
-
- rank = e + "_" + "_".join(name.split("_")[-2:])
- file_names.append(rank)
-
-
- # NOTE: get PROMISE swing values corresponding to each layer
- layer_swings = getLayerSwings(layer_desc, configurations)
-
- targets_file_path = tuned_result_dir + "/layer_targets.txt"
- f = open(targets_file_path, "w+")
-
- for config in layer_swings:
- index = 0
- for swing in config:
- swing_str = ""
- if swing == 8 or swing == 9:
- layer_size = len(layer_desc[index])
- for i in range(layer_size):
- swing_str += str(swing)
- if i < layer_size - 1:
- swing_str += " "
- elif swing == -9:
- swing_str += "8"
- else:
- swing_str += str(swing)
-
- if index < len(config) - 1:
- swing_str += ","
-
- f.write(swing_str)
- index += 1
-
- f.write("\n")
-
- f.close()
-
- print(layer_swings)
- return layer_swings, file_names
-
-
-
-def replaceFirstLayer(layer_swings):
-
- # Ensuring first conv on GPU
- for conf in layer_swings:
- conf[0] = 9
-
-
-
-def computeLayerTargets(tuned_result_dir, layer_desc_file):
-
- targets_file_path = tuned_result_dir + "/tensor_targets.txt"
- targets = dumpBenchmarkTargets(targets_file_path, tuned_result_dir)
-
- dumpTargets(targets_file_path, targets)
-
- layer_swings, file_names = dumpLayerTargets(targets, tuned_result_dir, layer_desc_file)
-
- replaceFirstLayer(layer_swings)
-
- return layer_swings, file_names
-
-
-# Externally-called function
-def compute_swing_selection2(tuned_result_dir, layer_file):
-
- return computeLayerTargets(tuned_result_dir, layer_file)
-
-
-
-
-if __name__ == "__main__":
-
- tuned_result_dir = "./vgg16_cifar10_tuner_1/high_confidence/"
- layer_file = "layer_composition.txt"
-
- tuned_result_dir = "./resnet18_cifar10_tuner_1/high_confidence/"
- layer_file = "layer_composition2.txt"
- computeLayerTargets(tuned_result_dir, layer_file)
diff --git a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/utils.py b/llvm/projects/hpvm-tensor-rt/bin/tuner_src/utils.py
deleted file mode 100644
index 8af0341070ccdaae24fdf537c6d35da27bfa7e5e..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/utils.py
+++ /dev/null
@@ -1,20 +0,0 @@
-
-
-import os
-
-
-def createResultDirs(benchmarks):
-
- for bench_name in benchmarks:
- Bench = benchmarks[bench_name]
-
- try:
- print (Bench.result_dir_1)
- os.mkdir(Bench.result_dir_1)
- os.mkdir(Bench.result_dir_2)
- os.mkdir(Bench.result_dir_3)
- except:
- print ("!ERROR: Could NOT create result directory")
- continue
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/validation.py b/llvm/projects/hpvm-tensor-rt/bin/tuner_src/validation.py
deleted file mode 100644
index 8099bd41e63ebf407fafd7a16cb38e563db37d67..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/bin/tuner_src/validation.py
+++ /dev/null
@@ -1,740 +0,0 @@
-
-
-import os
-import sys
-import subprocess
-import shutil
-from compute_confs import computePSNRBenchSwings, computeBenchSwings
-from buildRtConfig import loadConfigData
-
-
-
-def getLayerString(layer_swings):
-
- index = 0
- layer_string = ""
- for swing in layer_swings:
- layer_string += str(swing)
- if index < len(layer_swings) - 1:
- layer_string += ","
- return layer_string
-
-
-
-def testValidationRun(Bench, validation_dir, layer_swings, threshold, rank_str):
-
- #### FIXME
- #os.chdir("../build_promise/")
-
- validation_acc = Bench.validation_accuracy
- target_acc = validation_acc - threshold
-
- validation_binary = Bench.validation_binary
-
- # Write to promise_flags
- fout = open("promise_flags", "w+")
- for swing in layer_swings:
- int_swing = int(swing)
- if int_swing > 0:
- fout.write(str(swing) + "\n")
- fout.close()
-
- # Execute Validation Run
- p = subprocess.Popen("./" + validation_binary, shell=True)
- p.wait()
-
- f = open("run_accuracies.txt")
- index = 0.0
- unsuccessful = 0.0
- sum_acc = 0.0
- for x in f:
- x = x.strip()
- acc = float(x)
- if acc < target_acc:
- unsuccessful += 1
- index += 1
- sum_acc += acc
-
- f.close()
-
- confidence = ( (index - unsuccessful) / index) * 100.0
- print ("run_confidence = ", confidence)
- avg_acc = sum_acc / index
-
- out_fname = validation_dir + validation_binary + "_" + str(avg_acc)
- shutil.copy("run_accuracies.txt", out_fname + "_" + rank_str)
-
- layer_string = getLayerString(layer_swings)
- f = open(out_fname, "w")
- f.write("config:\t" + layer_string + "\n")
- f.write("confidence:\t" + str(confidence) + "\n")
- f.close()
-
- return confidence
-
-
-
-
-def testPromiseRun(Bench, layer_swings, threshold):
-
- #### FIXME
- #os.chdir("../build_promise/")
-
- validation_acc = Bench.validation_accuracy
- target_acc = validation_acc - threshold
-
- validation_binary = Bench.validation_binary
-
- # Write to promise_flags
- fout = open("promise_flags", "w+")
- for swing in layer_swings:
- int_swing = int(swing)
- if int_swing > 0:
- fout.write(str(swing) + "\n")
- fout.close()
-
- # Execute Validation Run
- p = subprocess.Popen("./" + validation_binary, shell=True)
- p.wait()
-
- f = open("run_accuracies.txt")
- index = 0.0
- unsuccessful = 0.0
- sum_acc = 0.0
- for x in f:
- x = x.strip()
- acc = float(x)
- if acc < target_acc:
- unsuccessful += 1
- index += 1
- sum_acc += acc
-
- f.close()
-
- confidence = ( (index - unsuccessful) / index) * 100.0
- print ("run_confidence = ", confidence)
- avg_acc = sum_acc / index
-
- return confidence
-
-
-
-
-
-def dumpConfigConfidence(configs, confidence_list,
- result_dir, layer_desc_file):
-
- #### FIXME
- #os.chdir("../build_tuner/")
-
- layer_desc = loadLayerDesc(layer_desc_file)
- print (layer_desc)
-
- f = open(result_dir + "/conf_confidences.txt", "w+")
-
- count = 0
- for config in configs:
- index = 0
- for swing in config:
- swing_str = ""
- if swing == 8 or swing == 9:
- layer_size = len(layer_desc[index])
- for i in range(layer_size):
- swing_str += str(swing)
- if i < layer_size - 1:
- swing_str += " "
- elif swing == -9:
- swing_str += "8"
- else:
- swing_str += str(swing)
-
- if index < len(config) - 1:
- swing_str += ","
- f.write(swing_str)
-
- index += 1
-
- f.write("\t" + str(confidence_list[count]))
- f.write("\n")
- count +=1
-
- f.close()
-
-
-
-
-def dumpValidatedConfigs(configs, result_dir, layer_desc_file,
- output_file_name):
-
- os.chdir("../build_tuner/")
-
- layer_desc = loadLayerDesc(layer_desc_file)
- print (layer_desc)
-
- f = open(result_dir + "/" + output_file_name, "w+")
-
- for config in configs:
- index = 0
- for swing in config:
- swing_str = ""
- if swing == 8 or swing == 9:
- layer_size = len(layer_desc[index])
- for i in range(layer_size):
- swing_str += str(swing)
- if i < layer_size - 1:
- swing_str += " "
- elif swing == -9:
- swing_str += "8"
- else:
- swing_str += str(swing)
-
- if index < len(config) - 1:
- swing_str += ","
- f.write(swing_str)
-
- index += 1
- f.write("\n")
- f.close()
-
-
-
-def dumpRankings(validated_ranks, result_dir, rank_file):
-
- os.chdir("../build_tuner/")
- f = open(result_dir + "/" + rank_file, "w+")
- for rank in validated_ranks:
- f.write(rank + "\n")
-
- f.close()
-
-
-
-
-def replaceFP32Configs(loss_confs1, loss_confs2):
-
- for swing_conf in loss_confs1:
- for i in range(0, len(swing_conf)):
- if swing_conf[i] == 9:
- swing_conf[i] = 8
- if i == len(swing_conf) - 1:
- swing_conf[i] = 7
-
- for swing_conf in loss_confs2:
- for i in range(0, len(swing_conf)):
- if swing_conf[i] == 9:
- swing_conf[i] = 8
- if i == len(swing_conf) - 1:
- swing_conf[i] = 7
-
-
- return loss_confs1, loss_confs2
-
-
-
-def replaceGPUConfigs(Bench, loss_confs1, loss_confs2):
-
- skip_layer_str = Bench.skip_layer_str
- layer_ids = skip_layer_str.split("_")
- skip_layers = []
- for layer_id in layer_ids:
- skip_layers.append(int(layer_id))
-
-
- for swing_conf in loss_confs1:
- for i in range(0, len(swing_conf)):
- if i in skip_layers and swing_conf[i] < 8:
- swing_conf[i] = 8
-
- for swing_conf in loss_confs2:
- for i in range(0, len(swing_conf)):
- if i in skip_layers and swing_conf[i] < 8:
- swing_conf[i] = 8
-
-
- return loss_confs1, loss_confs2
-
-
-
-
-def runBenchValidation(Bench):
-
- #Bench = bench_tuner_data[bench_name]
-
- loss_confs, conf_ranks = computeBenchSwings(Bench)
- loss1_confs = loss_confs[0]
- loss2_confs = loss_confs[1]
- conf_ranks1 = conf_ranks[0]
- conf_ranks2 = conf_ranks[1]
-
- #loss1_confs, loss2_confs = replaceFP32Configs(loss1_confs, loss2_confs)
-
-
- validation_dir_1 = "../build_tuner/" + Bench.result_dir_1 + "/validation_runs/"
- if not os.path.exists(validation_dir_1):
- os.mkdir(validation_dir_1)
-
- validation_dir_2 = "../build_tuner/" + Bench.result_dir_2 + "/validation_runs/"
- if not os.path.exists(validation_dir_2):
- os.mkdir(validation_dir_2)
-
-
- ind = 0
- validated_confs1 = []
- validated_ranks1 = []
- failed_confs1 = []
- confidences1 = []
- for layer_swings in loss1_confs:
- print ("len(layer_Swings) = ", len(layer_swings), "\n")
- confidence = testValidationRun(Bench, validation_dir_1,
- layer_swings, 1.0, conf_ranks1[ind])
- if confidence >= 95:
- validated_confs1.append(layer_swings)
- confidences1.append(confidence)
- validated_ranks1.append(conf_ranks1[ind])
- else:
- failed_confs1.append(layer_swings)
- ind += 1
-
-
- ind = 0
- validated_confs2 = []
- validated_ranks2 = []
- failed_confs2 = []
- confidences2 = []
- for layer_swings in loss2_confs:
- confidence = testValidationRun(Bench, validation_dir_2, layer_swings, 2.0, conf_ranks2[ind])
- if confidence >= 92:
- validated_confs2.append(layer_swings)
- confidences2.append(confidence)
- validated_ranks2.append(conf_ranks2[ind])
- else:
- failed_confs2.append(layer_swings)
- ind += 1
-
- dumpValidatedConfigs(validated_confs1, Bench.result_dir_1,
- Bench.layer_file, "validated_confs.txt")
- dumpValidatedConfigs(validated_confs2, Bench.result_dir_2,
- Bench.layer_file, "validated_confs.txt")
-
- dumpValidatedConfigs(failed_confs1, Bench.result_dir_1,
- Bench.layer_file, "failed_confs.txt")
- dumpValidatedConfigs(failed_confs2, Bench.result_dir_2,
- Bench.layer_file, "failed_confs.txt")
-
- dumpRankings(validated_ranks1, Bench.result_dir_1, "validated_ranks.txt")
- dumpRankings(validated_ranks2, Bench.result_dir_2, "validated_ranks.txt")
-
- dumpConfigConfidence(validated_confs1, confidences1,
- Bench.result_dir_1, Bench.layer_file)
-
- dumpConfigConfidence(validated_confs2, confidences2,
- Bench.result_dir_2, Bench.layer_file)
-
-
- print (validated_confs1)
- print (validated_confs2)
-
-
-
-def readPromiseResults(loss1_file, loss2_file):
-
- loss_confs = []
- loss1_confs = []
- f1 = open(loss1_file)
- for x in f1:
- print (x)
- swing_toks = x.split(",")
- swing_list = []
- for swing_str in swing_toks:
- swing_val = int(swing_str.split(" ")[0])
- swing_list.append(swing_val)
- loss1_confs.append(swing_list)
-
- loss_confs.append(loss1_confs)
-
- loss2_confs = []
- f2 = open(loss1_file)
- for x in f2:
- swing_toks = x.split(",")
- swing_list = []
- for swing_str in swing_toks:
- swing_val = int(swing_str.split(" ")[0])
- swing_list.append(swing_val)
- loss2_confs.append(swing_list)
-
- loss_confs.append(loss2_confs)
-
- return loss_confs
-
-
-
-
-
-
-def readPromiseResults2(loss1_file, loss2_file, layer_file):
-
- layer_desc = loadLayerDesc(layer_file)
-
- loss_confs = []
- loss1_confs = []
- f1 = open(loss1_file)
- for x in f1:
- print (x)
- swing_toks = x.split(",")
- swing_list = []
-
- it = 0
- for swing_str in swing_toks:
- swing_val = int(swing_str.split(" ")[0])
- if "conv" in layer_desc[it] or "dense" in layer_desc[it]:
- swing_list.append(swing_val)
-
- it += 1
-
- loss1_confs.append(swing_list)
-
- loss_confs.append(loss1_confs)
-
- loss2_confs = []
- f2 = open(loss1_file)
- for x in f2:
- swing_toks = x.split(",")
- swing_list = []
-
- it = 0
- for swing_str in swing_toks:
- swing_val = int(swing_str.split(" ")[0])
- if "conv" in layer_desc[it] or "dense" in layer_desc[it]:
- swing_list.append(swing_val)
-
- it += 1
-
- loss2_confs.append(swing_list)
-
- loss_confs.append(loss2_confs)
-
- return loss_confs
-
-
-
-
-
-def readPromiseResults3(result_dir):
-
- loss_confs = []
- # NOTE: Second parameter is ignored
- config_arr = loadConfigData(result_dir, 100)
-
- for config in config_arr:
- loss_confs.append(config.flags)
-
- return loss_confs
-
-
-
-
-
-
-
-
-def runPromiseBenchValidation(Bench):
-
-
- dir_prefix = "../build_tuner/"
- #Bench = bench_tuner_data[bench_name]
- #loss_confs = readPromiseResults(dir_prefix + Bench.loss1_result_file, dir_prefix + Bench.loss2_result_file)
- loss_confs = readPromiseResults2(dir_prefix + Bench.loss1_result_file, dir_prefix + Bench.loss2_result_file, Bench.layer_file)
-
- loss1_confs = loss_confs[0]
- loss2_confs = loss_confs[1]
-
- ind = 0
- validated_confs1 = []
- failed_confs1 = []
- for layer_swings in loss1_confs:
- confidence = testPromiseRun(Bench, layer_swings, 1.0)
- if confidence >= 95:
- validated_confs1.append(layer_swings)
- else:
- failed_confs1.append(layer_swings)
- ind += 1
-
-
- ind = 0
- validated_confs2 = []
- failed_confs2 = []
- for layer_swings in loss2_confs:
- confidence = testPromiseRun(Bench, layer_swings, 2.0)
- if confidence >= 95:
- validated_confs2.append(layer_swings)
- else:
- failed_confs2.append(layer_swings)
- ind += 1
-
-
- dumpValidatedConfigs(validated_confs1, Bench.result_dir_1,
- Bench.layer_file, "promise_validated_confs.txt")
- dumpValidatedConfigs(validated_confs2, Bench.result_dir_2,
- Bench.layer_file, "promise_validated_confs.txt")
-
- dumpValidatedConfigs(failed_confs1, Bench.result_dir_1,
- Bench.layer_file, "promise_failed_confs.txt")
- dumpValidatedConfigs(failed_confs2, Bench.result_dir_2,
- Bench.layer_file, "promise_failed_confs.txt")
-
-
-
-
-
-def copyValidatedConf(result_dir, validated_confs):
-
- src_dir = result_dir + "/promise_tuner/high_confidence/"
- dest_dir = result_dir + "/promise_tuner/validated/"
-
- if not os.path.isdir(dest_dir):
- os.mkdir(dest_dir)
-
- for fname in validated_confs:
- shutil.copy(src_dir + fname, dest_dir + fname)
-
-
-
-def copyFailedConf(result_dir, failed_confs):
-
- src_dir = result_dir + "/promise_tuner/high_confidence/"
- dest_dir = result_dir + "/promise_tuner/failed/"
-
- if not os.path.isdir(dest_dir):
- os.mkdir(dest_dir)
-
- for fname in failed_confs:
- shutil.copy(src_dir + fname, dest_dir + fname)
-
-
-
-
-def validateConfigs(Bench, result_dir, configs_arr, acc_thresh):
-
- validated_confs = []
- failed_confs = []
- for conf in configs_arr:
- layer_swings = conf.flags
- confidence = testPromiseRun(Bench, layer_swings, acc_thresh)
- if confidence >= 95:
- validated_confs.append(conf.fname)
- else:
- failed_confs.append(conf.fname)
-
-
- copyValidatedConf(result_dir, validated_confs)
- copyFailedConf(result_dir, failed_confs)
-
-
-
-
-
-
-
-def runPromiseBenchValidation2(Bench):
-
-
- config_arr1 = loadConfigData(Bench.result_dir_1, 100)
- config_arr2 = loadConfigData(Bench.result_dir_2, 100)
- config_arr3 = loadConfigData(Bench.result_dir_3, 100)
-
-
- validateConfigs(Bench, Bench.result_dir_1, config_arr1, 1.0)
- validateConfigs(Bench, Bench.result_dir_2, config_arr2, 2.0)
- validateConfigs(Bench, Bench.result_dir_3, config_arr3, 3.0)
-
-
-
-
-### NOTE: Algo Tuner Validation routines
-
-
-
-
-
-def addAccuracyLoss(dest_file, accuracy_loss):
-
- f = open(dest_file, "r")
- file_str = ""
- ind = 0
- for x in f:
- line_str = x
- if ind == 0:
- line_str = x.replace("\n", "")
- line_str += "\tvalidation_loss=" + str(accuracy_loss) + "\n"
-
- file_str += line_str
- ind += 1
- f.close()
-
-
- f_out = open(dest_file, "w+")
- f_out.write(file_str)
- f_out.close()
-
-
-
-
-def dumpValidConfigs(result_dir, validated_confs):
-
- src_dir = result_dir + "/algo_tuner/high_confidence/"
- dest_dir = result_dir + "/algo_tuner/validated/"
-
- if not os.path.isdir(dest_dir):
- os.mkdir(dest_dir)
-
- for (fname, accuracy_loss) in validated_confs:
- dest_file = dest_dir + fname
- shutil.copy(src_dir + fname, dest_file)
- addAccuracyLoss(dest_file, accuracy_loss)
-
-
-
-def dumpFailedConfigs(result_dir, failed_confs):
-
- src_dir = result_dir + "/algo_tuner/high_confidence/"
- dest_dir = result_dir + "/algo_tuner/failed/"
-
- if not os.path.isdir(dest_dir):
- os.mkdir(dest_dir)
-
- for (fname, accuracy_loss) in failed_confs:
- dest_file = dest_dir + fname
- shutil.copy(src_dir + fname, dest_file)
- addAccuracyLoss(dest_file, accuracy_loss)
-
-
-
-
-def readAccuracy(file_name):
-
- file = open(file_name, "r")
- file_str = file.read()
- file.close()
-
- accuracy = 0.0
- try:
- accuracy = float(file_str)
- except:
- print ("ERROR: Reading accuracy from 'final_accuracy' file")
- sys.exit(0)
-
- print ("accuracy = ", accuracy)
- return accuracy
-
-def getBaselineConfig(num_layers):
-
- fp32_swing = 11
- swings = []
-
- for i in range(num_layers):
- swings.append(str(fp32_swing))
-
- return swings
-
-
-
-def readConfidence(target_acc):
-
- f = open("run_accuracies.txt")
- index = 0.0
- unsuccessful = 0.0
- sum_acc = 0.0
- for x in f:
- x = x.strip()
- acc = float(x)
- if acc < target_acc:
- unsuccessful += 1
- index += 1
- sum_acc += acc
-
- f.close()
-
- confidence = ( (index - unsuccessful) / index) * 100.0
- print ("run_confidence = ", confidence)
- avg_acc = sum_acc / index
-
- return confidence, avg_acc
-
-
-
-def invokeBinary(binary_path, layer_swings, runs, input_size, offset, target_acc): # threshold):
-
- default_skip = 4
- # Write to promise_flags
- fout = open("promise_flags", "w+")
- for swing in layer_swings:
- int_swing = int(swing)
- if int_swing > 0:
- fout.write(str(swing) + "\n")
- fout.close()
-
- run_cmd = "./" + binary_path + " " + str(runs) + " " + str(target_acc) + " " + str(default_skip) + " " + str(input_size) + " " + str(offset)
- # Execute Validation Run
- #p = subprocess.Popen("./" + validation_binary, shell=True)
-
- p = subprocess.Popen(run_cmd, shell=True)
- p.wait()
-
-
-
-
-
-def validateAlgoConfigs(binary_path, result_dir, configs_arr, gold_acc, acc_thresh, runs):
-
- # NOTE: Use confidence target as 95%
- confidence_target = 95
- # NOTE: 1 run sufficient for software approximations
-
- validated_confs = []
- failed_confs = []
-
- #validation_acc = Bench.validation_accuracy
- target_acc = gold_acc - acc_thresh
-
- for conf in configs_arr:
- layer_swings = conf.flags
- invokeBinary(binary_path, layer_swings, runs, 2000, 8000, target_acc)
- confidence, avg_acc = readConfidence(target_acc)
-
- accuracy_loss = gold_acc - avg_acc
- if confidence >= confidence_target:
- validated_confs.append((conf.fname, accuracy_loss))
- else:
- failed_confs.append((conf.fname, accuracy_loss))
-
-
- dumpValidConfigs(result_dir, validated_confs)
- dumpFailedConfigs(result_dir, failed_confs)
-
-
-
-
-def runAlgoBenchValidate(Bench):
-
- num_layers = Bench.num_layers
- base_conf = getBaselineConfig(num_layers)
- # Path to binary to run
- binary_path = Bench.promise_binary
- # NOTE: 'target_acc' passed 0.0 since unused for baseline run
- invokeBinary(binary_path, base_conf, 1, 2000, 8000, 0.0)
- gold_acc = readAccuracy("final_accuracy")
-
-
- loss1_dir = Bench.result_dir_1
- loss2_dir = Bench.result_dir_2
- loss3_dir = Bench.result_dir_3
-
- loss1_configs = loadConfigData(loss1_dir, 100)
- loss2_configs = loadConfigData(loss2_dir, 100)
- loss3_configs = loadConfigData(loss3_dir, 100)
-
- runs = 1
- validateAlgoConfigs(binary_path, loss1_dir, loss1_configs, gold_acc, 1.0, runs)
- validateAlgoConfigs(binary_path, loss2_dir, loss2_configs, gold_acc, 2.0, runs)
- validateAlgoConfigs(binary_path, loss3_dir, loss3_configs, gold_acc, 3.0, runs)
-
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/CMakeLists.txt b/llvm/projects/hpvm-tensor-rt/code_autogenerators/CMakeLists.txt
deleted file mode 100644
index 095e037430dbf1751dddfd047d0cf0157ad9e2e7..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/CMakeLists.txt
+++ /dev/null
@@ -1,119 +0,0 @@
-cmake_minimum_required (VERSION 2.6)
-project (cudnn-training)
-
-find_package(CUDA 6.5 REQUIRED)
-
-
-if (CMAKE_BUILD_TYPE STREQUAL "Debug")
- message("Debug mode")
- set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_60,code=compute_60;-std=c++11;-g;-lineinfo;-Xcompiler;-ggdb;-lcurand)
-else()
- set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_60,code=compute_60;-std=c++11;-DNDEBUG;-Xcompiler;-DNDEBUG;-lcurand)
-endif()
-
-set(CUDA_PROPAGATE_HOST_FLAGS OFF)
-
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -I/ " )
-
-add_definitions(-DNO_INJECTION)
-add_definitions(-DPROMISE_TUNER_ENABLED)
-if(USE_GFLAGS)
- add_definitions(-DUSE_GFLAGS)
-endif()
-
-if(USE_AUTOTUNER)
- remove_definitions(-DNO_INJECTION)
-endif()
-
-
-
-include_directories($ENV{CUDNN_PATH} /home/nvidia/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/$ENV{CUDNN_PATH}/include)
-include_directories(/home/nvidia/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/./tensor_runtime/include)
-include_directories(/home/nvidia/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/../gpu_profiler/include)
-include_directories(/home/nvidia/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/../soc_simulator/include)
-link_directories($ENV{CUDNN_PATH} /home/nvidia/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/$ENV{CUDNN_PATH}/lib /home/nvidia/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/$ENV{CUDNN_PATH}/lib64)
-
-
-cuda_add_library(tensor_runtime /home/nvidia/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/tensor_runtime/src/tensor_runtime.cu)
-cuda_add_cublas_to_target(tensor_runtime)
-
-cuda_add_library(tensor_cpu_runtime /home/nvidia/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/tensor_runtime/src/tensor_cpu_runtime.cc)
-
-find_library(GPU_PROFILER_LIB
- NAMES libgpu_profiler.a
- HINTS /home/nvidia/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/../gpu_profiler/lib
-)
-
-find_library(SOC_SIMULATOR_LIB
- NAMES libpromise_profiler.a
- HINTS /home/nvidia/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/../soc_simulator/lib
-)
-
-
-if(USE_GFLAGS)
- target_link_libraries(tensor_runtime gflags cudnn -lcurand)
-else()
- target_link_libraries(tensor_runtime cudnn -lcurand)
-endif()
-
-target_link_libraries(tensor_cpu_runtime)
-
-# lenet_keras_half_autogenerated_knobs
-add_executable(lenet_keras_fp16_perf20 lenet_keras_half_autogenerated_knobs/lenet_keras_fp16_perf20.cc)
-target_link_libraries(lenet_keras_fp16_perf20 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(lenet_keras_fp16_perf26 lenet_keras_half_autogenerated_knobs/lenet_keras_fp16_perf26.cc)
-target_link_libraries(lenet_keras_fp16_perf26 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(lenet_keras_fp16_perf22 lenet_keras_half_autogenerated_knobs/lenet_keras_fp16_perf22.cc)
-target_link_libraries(lenet_keras_fp16_perf22 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(lenet_keras_fp16_perf25 lenet_keras_half_autogenerated_knobs/lenet_keras_fp16_perf25.cc)
-target_link_libraries(lenet_keras_fp16_perf25 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(lenet_keras_fp16_perf23 lenet_keras_half_autogenerated_knobs/lenet_keras_fp16_perf23.cc)
-target_link_libraries(lenet_keras_fp16_perf23 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(lenet_keras_fp16_samp33 lenet_keras_half_autogenerated_knobs/lenet_keras_fp16_samp33.cc)
-target_link_libraries(lenet_keras_fp16_samp33 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(lenet_keras_fp16_perf24 lenet_keras_half_autogenerated_knobs/lenet_keras_fp16_perf24.cc)
-target_link_libraries(lenet_keras_fp16_perf24 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(lenet_keras_fp16_samp31 lenet_keras_half_autogenerated_knobs/lenet_keras_fp16_samp31.cc)
-target_link_libraries(lenet_keras_fp16_samp31 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(lenet_keras_fp16_perf30 lenet_keras_half_autogenerated_knobs/lenet_keras_fp16_perf30.cc)
-target_link_libraries(lenet_keras_fp16_perf30 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(lenet_keras_fp16_samp36 lenet_keras_half_autogenerated_knobs/lenet_keras_fp16_samp36.cc)
-target_link_libraries(lenet_keras_fp16_samp36 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(lenet_keras_fp16_perf21 lenet_keras_half_autogenerated_knobs/lenet_keras_fp16_perf21.cc)
-target_link_libraries(lenet_keras_fp16_perf21 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(lenet_keras_fp16_samp34 lenet_keras_half_autogenerated_knobs/lenet_keras_fp16_samp34.cc)
-target_link_libraries(lenet_keras_fp16_samp34 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(lenet_keras_fp16_samp32 lenet_keras_half_autogenerated_knobs/lenet_keras_fp16_samp32.cc)
-target_link_libraries(lenet_keras_fp16_samp32 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(lenet_keras_fp16_samp35 lenet_keras_half_autogenerated_knobs/lenet_keras_fp16_samp35.cc)
-target_link_libraries(lenet_keras_fp16_samp35 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(lenet_keras_fp16_perf29 lenet_keras_half_autogenerated_knobs/lenet_keras_fp16_perf29.cc)
-target_link_libraries(lenet_keras_fp16_perf29 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(lenet_keras_fp16_perf27 lenet_keras_half_autogenerated_knobs/lenet_keras_fp16_perf27.cc)
-target_link_libraries(lenet_keras_fp16_perf27 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-add_executable(lenet_keras_fp16_perf28 lenet_keras_half_autogenerated_knobs/lenet_keras_fp16_perf28.cc)
-target_link_libraries(lenet_keras_fp16_perf28 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-
-
-# lenet_keras_autogenerated_knobs
-add_executable(lenet_keras_fp32_perf20 lenet_keras_autogenerated_knobs/lenet_keras_fp32_perf20.cc)
-target_link_libraries(lenet_keras_fp32_perf20 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/benchmark_different_clock_frequencies_testing_automator.py b/llvm/projects/hpvm-tensor-rt/code_autogenerators/benchmark_different_clock_frequencies_testing_automator.py
deleted file mode 100644
index d787af8ec350b7fa2f2eeb2b0ed4c3ae4c015c95..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/benchmark_different_clock_frequencies_testing_automator.py
+++ /dev/null
@@ -1,139 +0,0 @@
-# Automates online benchmark testing with different clock speeds
-# Input: GPU clock speed, DDR clock speed, set of benchmark names to test
-# Set of benchmarks format: (full_bin_name, half_bin_name)
-import os
-import sys
-
-from subprocess import Popen, PIPE
-
-def set_clock_speeds(gpu_speed_mhz, ddr_speed_mhz):
- def find_closest_clock_speed(goal_speed):
- # Reads /sys/devices/17000000.gp10b/devfreq/17000000.gp10b/available_frequencies
- # and finds the closest clock speed
- AVAIL_FREQS = "/sys/devices/17000000.gp10b/devfreq/17000000.gp10b/available_frequencies"
- avail_freqs_file = open(AVAIL_FREQS, "r")
- avail_speeds_lst = avail_freqs_file.read().strip().split()
- avail_freqs_file.close()
-
- min_diff = abs(gpu_speed - int(avail_speeds_lst[0]))
- closest_speed = int(avail_speeds_lst[0])
- for avail_speed in avail_speeds_lst[1:]:
- avail_speed = int(avail_speed)
- curr_diff = abs(gpu_speed - avail_speed)
- if curr_diff < min_diff:
- min_diff = curr_diff
- closest_speed = avail_speed
- return closest_speed
-
- new_conf_filename = 'jetson_clocks_conf%d_%d.txt' % (gpu_speed_mhz, ddr_speed_mhz)
- curr_conf_filename = "jetson_clocks_conf_backup.txt"
- if os.path.isfile(curr_conf_filename):
- os.remove(curr_conf_filename)
-
- # Get the current configurations in a file
- sudo_password = 'nvidia'
- p = Popen(['sudo', '/home/nvidia/jetson_clocks.sh', '--store', curr_conf_filename], \
- stdin=PIPE, universal_newlines=True)
- p.communicate(sudo_password + '\n')
- assert p.returncode == 0
-
- # Read the current config file in
- curr_conf_file = open(curr_conf_filename, "r")
- curr_confs = curr_conf_file.read().strip().split('\n')
- curr_conf_file.close()
-
- GPU_MIN_FREQ = "/sys/devices/17000000.gp10b/devfreq/17000000.gp10b/min_freq"
- GPU_MAX_FREQ = "/sys/devices/17000000.gp10b/devfreq/17000000.gp10b/max_freq"
- GPU_CUR_FREQ = "/sys/devices/17000000.gp10b/devfreq/17000000.gp10b/cur_freq"
-
- DDR_UPDATE_PATH = "/sys/kernel/debug/bpmp/debug/clk/emc/rate"
-
- # Copy everything in the old configuration except for the GPU/DDR lines
- new_conf_file = open(new_conf_filename, "w")
- for line in curr_confs:
- # Write the GPU clock frequencies at the end to configure the clocks even if
- # the current configuration doesn't have one of the lines
- if line.startswith(GPU_MIN_FREQ) or line.startswith(GPU_MAX_FREQ) or \
- line.startswith(GPU_CUR_FREQ) or line.startswith(DDR_UPDATE_PATH):
- continue
- else:
- new_conf_file.write("%s\n" % line)
-
- MHZ_TO_HZ_MULT = 1000000
- gpu_speed = gpu_speed_mhz * MHZ_TO_HZ_MULT
- ddr_speed = ddr_speed_mhz * MHZ_TO_HZ_MULT
-
- # Set GPU
- closest_gpu_speed = find_closest_clock_speed(gpu_speed)
- print("Setting GPU speed to %d" % closest_gpu_speed)
- new_conf_file.write("%s:%d\n" % (GPU_MIN_FREQ, closest_gpu_speed))
- new_conf_file.write("%s:%d\n" % (GPU_MAX_FREQ, closest_gpu_speed))
- #new_conf_file.write("%s:%d\n" % (GPU_CUR_FREQ, closest_gpu_speed))
-
- # Set DDR
- new_conf_file.write("%s:%d\n" % (DDR_UPDATE_PATH, ddr_speed))
- new_conf_file.close()
-
- # Set the new configuration
- p = Popen(['sudo', '/home/nvidia/jetson_clocks.sh', '--restore', new_conf_filename], \
- stdin=PIPE, universal_newlines=True)
- p.communicate(sudo_password + '\n')
- assert p.returncode == 0
- print("SUCCESSFULLY SET CLOCK SPEEDS")
-
-
-def run_benchmark(bin_name, should_print_bin_output):
- print("RUNNING %s" % bin_name)
- proc = Popen("./%s" % bin_name, stdout = PIPE, universal_newlines = True)
- proc_output = proc.communicate()[0]
- assert proc.returncode == 0
-
- if should_print_bin_output:
- print(proc_output)
- print("FINISHED RUNNING %s" % bin_name)
- return proc_output
-
-
-def parse_binary_output(proc_output):
- avg_time_key_ind = proc_output.find("Average time:")
- assert avg_time_key_ind >= 0
- avg_time = proc_output[avg_time_key_ind : proc_output.find("\n", avg_time_key_ind)]
- print(avg_time)
- return avg_time
-
-
-# Input: a list of tuples of benchmark names
-# Can change to input a file containing benchmarks to run
-def run_benchmarks(benchmarks_filename, output_filename, should_print_bin_output):
- benchmarks_file = open(benchmarks_filename, "r")
- output_file = open(output_filename, "w")
-
- def parse_binary_names_tuple(tuple_line):
- tuple_line = tuple_line.replace("(", "").replace(")", "").strip().split(',')
- return tuple_line[0].strip(), tuple_line[1].strip()
-
- for line in benchmarks_file:
- full_bin_name, half_bin_name = parse_binary_names_tuple(line)
- output_file.write("%s: %s\n" % (full_bin_name, \
- parse_binary_output(run_benchmark(full_bin_name, should_print_bin_output))))
- output_file.write("%s: %s\n" % (half_bin_name, \
- parse_binary_output(run_benchmark(half_bin_name, should_print_bin_output))))
-
- benchmarks_file.close()
- output_file.close()
-
-
-if __name__ == "__main__":
- num_args = len(sys.argv)
-
- if num_args != 5 and num_args != 6:
- print("Usage: python online_benchmark_testing_automator.py <gpu freq in MHz> <ddr freq in MHz> <binary_names_file> <output_file> [1 to print binary output]")
- print("Binary names file format: (full_binary_name, half_binary_name)<newline>")
- exit(1)
- print("GPU clock speed: %s" % sys.argv[1])
- print("DDR clock speed: %s" % sys.argv[2])
- print("Benchmarks file name: %s" % sys.argv[3])
- print("Output file name: %s" % sys.argv[4])
-
- set_clock_speeds(int(sys.argv[1]), int(sys.argv[2]))
- run_benchmarks(sys.argv[3], sys.argv[4], num_args == 6 and sys.argv[-1] == "1")
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/benchmark_testing_automator.py b/llvm/projects/hpvm-tensor-rt/code_autogenerators/benchmark_testing_automator.py
deleted file mode 100644
index 197b653d3bf6983a9500badcc4766bac1274fb63..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/benchmark_testing_automator.py
+++ /dev/null
@@ -1,90 +0,0 @@
-# Automates online benchmark testing with different clock speeds
-# Input: set of benchmark names to test
-# Set of benchmarks format: (full_bin_name, half_bin_name)
-import os
-import sys
-
-from collections import defaultdict
-from subprocess import Popen, PIPE
-
-def run_benchmark(bin_name):
- print("RUNNING %s" % bin_name)
- proc = Popen("./%s" % bin_name, stdout = PIPE, universal_newlines = True)
- proc_output = proc.communicate()[0]
- assert proc.returncode == 0
- print("FINISHED RUNNING %s" % bin_name)
- return proc_output
-
-
-def parse_binary_output(proc_output, per_tensor):
- final_acc_key_ind = proc_output.find("**** Final Accuracy")
- assert final_acc_key_ind >= 0
- final_acc = proc_output[final_acc_key_ind : proc_output.find("\n", final_acc_key_ind)]
- print(final_acc)
-
- if per_tensor:
- first_op_ind = proc_output.find("Operation ")
- total_op_ind = proc_output.find('\n', proc_output.find("Total energy"))
- assert first_op_ind >= 0
- assert total_op_ind >= 0
-
- time_energy_output = proc_output[first_op_ind : total_op_ind]
- print(time_energy_output)
- return time_energy_output, final_acc
-
- else:
- avg_time_key_ind = proc_output.find("Average time:")
- assert avg_time_key_ind >= 0
-
- avg_time = proc_output[avg_time_key_ind : proc_output.find("\n", avg_time_key_ind)]
- print(avg_time)
-
- return avg_time, final_acc
-
-
-def get_sorted_binaries(builds_dir):
- # dict of network names to lists of binaries
- # list of binaries should be in sorted order (can do that when we run the benchmarks)
- network_bins = defaultdict(list)
- for bin_name in os.listdir(builds_dir):
- if bin_name.find("profiling") == -1:
- continue
- network_name = bin_name[ : bin_name.rfind("_")]
- network_bins[network_name].append(bin_name)
- return network_bins
-
-
-# Input: a list of tuples of benchmark names
-# Can change to input a file containing benchmarks to run
-def run_benchmarks(sorted_bins, builds_dir, output_filename, per_tensor):
- def get_knob_id(bin_name):
- return int(bin_name[bin_name.rfind("_") + 1 : ])
-
- output_file = open(output_filename, "w", buffering = 0)
- for network_name in sorted(sorted_bins.keys()):
- # Sort the binaries in order by knob id
- sorted_bins[network_name].sort(key = get_knob_id)
- print("--------------------------------------")
- print(network_name)
- output_file.write("--------------------------------------\n%s\n" % network_name)
-
- # Go through all binaries
- for bin_name in sorted_bins[network_name]:
- print(bin_name)
- binary_output = run_benchmark(os.path.join(builds_dir, bin_name))
- time_energy_output, final_acc = parse_binary_output(binary_output, per_tensor)
- output_file.write("%s, %s, %s\n\n" % (bin_name, time_energy_output, final_acc))
- print("--------------------------------------\n")
- output_file.write("--------------------------------------\n\n")
- output_file.close()
-
-if __name__ == "__main__":
- num_args = len(sys.argv)
-
- if num_args != 3 and num_args != 4:
- print("Usage: python online_benchmark_testing_automator.py <builds dir> <outputs_file_name> [per_tensor]")
- print("To delete autogen dirs: python online_benchmark_testing_automator.py clean")
- exit(1)
- print("Output file name: %s" % sys.argv[2])
- sorted_bins = get_sorted_binaries(sys.argv[1])
- run_benchmarks(sorted_bins, sys.argv[1], sys.argv[2], num_args == 4 and sys.argv[3] == "per_tensor")
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/cmakelists_generator.py b/llvm/projects/hpvm-tensor-rt/code_autogenerators/cmakelists_generator.py
deleted file mode 100644
index 04f6c5eec378276cd0c89fcc7013cb6996a90f2f..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/cmakelists_generator.py
+++ /dev/null
@@ -1,109 +0,0 @@
-# Generates a CMakeLists.txt file for all generated files in a specific directory
-# Input: Arbitrarily long list containing names of all generated files directories
-# Ex: alexnet_cifar10_autogenerated_knobs mobilenet_cifar10_autogenerated_knobs
-# If inputted 0 parameters: Generates CMakeLists.txt file for all generated files in CURRENT dir
-
-import sys
-import os
-
-def get_all_generated_directory_names():
- '''
- Returns a list of all generated source code directories (<>_autogenerated_knobs)
- in the current directory. Called when program is run with 0 args
- '''
- generated_dir_names = []
- for dir_name in os.listdir("."):
- print(dir_name)
- if dir_name.endswith("autogenerated_knobs"):
- generated_dir_names.append(dir_name)
- return generated_dir_names
-
-
-def generate_cmakelists_setup(cmakelists_file):
- '''
- Copies over all the setup instructions (ex: finding libraries) from a "base" CMakeLists.txt
- file. Ends copyng when we find the first instance of add_executable
-
- Args:
- cmakelists_file: File object to write cmake instructions to
-
- Assumption: All setup instructions are being any add_executable instructions
- '''
- BASE_CMAKELISTS_PATH = "/home/nvidia/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt"
- base_cmakelists_file = open(os.path.join(BASE_CMAKELISTS_PATH, "CMakeLists.txt"), "r")
-
- find_lib_line = ""
-
- for line in base_cmakelists_file:
- if line.find("add_executable") != -1:
- break
-
- elif line.startswith("#"):
- continue
-
- # Special case: ignore / if -I flag exists
- elif line.find("/") != -1 and line.find("-I") == -1:
- dot_dot_slash_ind = line.find("../")
- dot_slash_ind = line.find("./")
- if dot_dot_slash_ind != -1:
- start_ind = dot_dot_slash_ind
- elif dot_slash_ind != -1:
- start_ind = dot_slash_ind
- else:
- slash_ind = line.find("/")
- prev_space_ind = line[:slash_ind].rfind(" ")
- start_ind = prev_space_ind + 1
-
- old_rel_path = []
- while start_ind < len(line):
- if line[start_ind] == ")" or line[start_ind].isspace():
- break
- old_rel_path.append(line[start_ind])
- start_ind += 1
- old_rel_path = ''.join(old_rel_path)
- if os.path.isabs(old_rel_path):
- cmakelists_file.write(line)
- else:
- new_path = os.path.join(BASE_CMAKELISTS_PATH, old_rel_path)
- cmakelists_file.write(line.replace(old_rel_path, new_path))
- continue
- cmakelists_file.write(line)
- base_cmakelists_file.close()
-
-
-def generate_cmakelists_file(cmakelists_file, source_file_dirs):
- generate_cmakelists_setup(cmakelists_file)
- LIBRARIES = "tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB}"
- cmake_instrs = []
-
- for source_file_dir in source_file_dirs:
- cmake_instrs.append("# %s" % source_file_dir)
- for source_file in os.listdir(source_file_dir):
- # Executable name = name of source code file without file extension
- file_ext_ind = source_file.find(".cc")
- if file_ext_ind == -1:
- print("WARNING: Found file with wrong extension. Skipping. %s" % source_file)
- continue
- exec_name = source_file[ : file_ext_ind]
-
- source_file_path = os.path.join(source_file_dir, source_file)
- cmake_instrs.append("add_executable(%s %s)" % (exec_name, source_file_path))
- cmake_instrs.append("target_link_libraries(%s %s)\n" % (exec_name, LIBRARIES))
- cmake_instrs.append("\n")
- cmakelists_file.write('\n'.join(cmake_instrs))
-
-
-if __name__ == "__main__":
- num_args = len(sys.argv)
-
- if num_args >= 2 and sys.argv[1] == "--usage":
- print("python cmakelists_generator.py <names of all generated files directories>")
- print("If given no parameters: Generates CMakeLists.txt file for all generated files in CURRENT directory")
- exit(1)
-
- cmakelists_file = open("CMakeLists.txt", "w")
- if num_args == 1:
- generate_cmakelists_file(cmakelists_file, get_all_generated_directory_names())
- else:
- generate_cmakelists_file(cmakelists_file, sys.argv[1:])
- cmakelists_file.close()
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_fp16.txt b/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_fp16.txt
deleted file mode 100644
index 563d7f4a03b3b3a50e2c08c76616a88ea7958b5a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_fp16.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-../dnn_sources/src/half/profiling/alexnet2_cifar10_half_profiling.cc
-../dnn_sources/src/half/profiling/alexnet_cifar10_half_profiling.cc
-../dnn_sources/src/half/profiling/mobilenet_depthwise_half_profiling.cc
-../dnn_sources/src/half/profiling/mobilenet_shallow_depthwise_half_profiling.cc
-../dnn_sources/src/half/profiling/resnet18_cifar10_half_profiling.cc
-../dnn_sources/src/half/profiling/vgg16_cifar100_half_profiling.cc
-../dnn_sources/src/half/profiling/vgg16_cifar10_half_profiling.cc
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_fp16_first_three.txt b/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_fp16_first_three.txt
deleted file mode 100644
index 4a0beb250e2241c7523e69b5262cb9ffc977d28d..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_fp16_first_three.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-../dnn_sources/src/half/profiling/alexnet2_cifar10_half_profiling.cc
-../dnn_sources/src/half/profiling/alexnet_cifar10_half_profiling.cc
-../dnn_sources/src/half/profiling/resnet18_cifar10_half_profiling.cc
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_fp16_remainder.txt b/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_fp16_remainder.txt
deleted file mode 100644
index 20ca95abcf1ee1aab337fa391abb5f1a74583fe1..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_fp16_remainder.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-../dnn_sources/src/half/profiling/mobilenet_depthwise_half_profiling.cc
-../dnn_sources/src/half/profiling/mobilenet_shallow_depthwise_half_profiling.cc
-../dnn_sources/src/half/profiling/vgg16_cifar100_half_profiling.cc
-../dnn_sources/src/half/profiling/vgg16_cifar10_half_profiling.cc
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_fp16_sources.txt b/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_fp16_sources.txt
deleted file mode 100644
index 506497e42889dc1d8bb2465912e87f56464e7ecc..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_fp16_sources.txt
+++ /dev/null
@@ -1 +0,0 @@
-../dnn_sources/src/half/lenet_keras_half.cc
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_fp32.txt b/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_fp32.txt
deleted file mode 100644
index 12b87930416c4269a62f2020a06b42cf5cf4dc13..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_fp32.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-../dnn_sources/src/profiling/alexnet2_profiling.cc
-../dnn_sources/src/profiling/alexnet_cifar10_profiling.cc
-../dnn_sources/src/profiling/mobilenet_cifar10_profiling.cc
-../dnn_sources/src/profiling/mobilenet_shallow_profiling.cc
-../dnn_sources/src/profiling/mobilenet_depthwise_profiling.cc
-../dnn_sources/src/profiling/mobilenet_shallow_depthwise_profiling.cc
-../dnn_sources/src/profiling/resnet18_cifar10_profiling.cc
-../dnn_sources/src/profiling/vgg16_cifar100_profiling.cc
-../dnn_sources/src/profiling/vgg16_cifar10_profiling.cc
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_fp32_sources.txt b/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_fp32_sources.txt
deleted file mode 100644
index cd8f03c30712f0162db2cc8bcf563087be05bf64..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_fp32_sources.txt
+++ /dev/null
@@ -1 +0,0 @@
-../dnn_sources/src/lenet_keras.cc
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_fp32_test.txt b/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_fp32_test.txt
deleted file mode 100644
index a59f773cda240a311c0c873c9366494018b87312..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_fp32_test.txt
+++ /dev/null
@@ -1 +0,0 @@
-../dnn_sources/src/profiling/mobilenet_shallow_depthwise_profiling.cc
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_mobilenet_depth.txt b/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_mobilenet_depth.txt
deleted file mode 100644
index 2b7382da3570917c1983ad0c3fe02763d8565635..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_mobilenet_depth.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-../dnn_sources/src/profiling/mobilenet_depthwise_profiling.cc
-../dnn_sources/src/profiling/mobilenet_shallow_depthwise_profiling.cc
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_one_file.txt b/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_one_file.txt
deleted file mode 100644
index 32b18d4ca22672be6b44ecb674ea3ad00e18276d..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/filenames_one_file.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-../dnn_sources/src/half/profiling/vgg16_cifar100_half_profiling.cc
-../dnn_sources/src/half/profiling/vgg16_cifar10_half_profiling.cc
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp16.txt b/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp16.txt
deleted file mode 100644
index 207eb1ed1f45ffde7dad0da4e125aa0ceaa5c5cd..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp16.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-perf,20 1,1,1,1 2.25 tensorHalfConvolution tensorConvApproxHalf
-perf,21 1,2,1,0 2.25 tensorHalfConvolution tensorConvApproxHalf
-perf,22 1,2,1,1 2.25 tensorHalfConvolution tensorConvApproxHalf
-perf,23 1,3,1,0 1.88 tensorHalfConvolution tensorConvApproxHalf
-perf,24 1,3,1,1 1.88 tensorHalfConvolution tensorConvApproxHalf
-perf,25 1,3,1,2 1.88 tensorHalfConvolution tensorConvApproxHalf
-perf,26 2,1,1,0 2.25 tensorHalfConvolution tensorConvApproxHalf
-perf,27 2,1,1,1 2.25 tensorHalfConvolution tensorConvApproxHalf
-perf,28 3,1,1,0 1.88 tensorHalfConvolution tensorConvApproxHalf
-perf,29 3,1,1,1 1.88 tensorHalfConvolution tensorConvApproxHalf
-perf,30 3,1,1,2 1.88 tensorHalfConvolution tensorConvApproxHalf
-samp,31 1,1,2,0 1.88 tensorHalfConvolution tensorConvApproxHalf
-samp,32 1,1,2,1 1.88 tensorHalfConvolution tensorConvApproxHalf
-samp,33 1,1,4,0 1.88 tensorHalfConvolution tensorConvApproxHalf
-samp,34 1,1,4,1 1.88 tensorHalfConvolution tensorConvApproxHalf
-samp,35 1,1,4,2 1.88 tensorHalfConvolution tensorConvApproxHalf
-samp,36 1,1,4,3 1.88 tensorHalfConvolution tensorConvApproxHalf
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp16_knobs_31_36.txt b/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp16_knobs_31_36.txt
deleted file mode 100644
index fc76565110cf34ab57024dd852c1a51b23a8f45e..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp16_knobs_31_36.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-samp,31 1,1,2,0 1.88 tensorHalfConvolution tensorConvApproxHalf
-samp,32 1,1,2,1 1.88 tensorHalfConvolution tensorConvApproxHalf
-samp,33 1,1,4,0 1.88 tensorHalfConvolution tensorConvApproxHalf
-samp,34 1,1,4,1 1.88 tensorHalfConvolution tensorConvApproxHalf
-samp,35 1,1,4,2 1.88 tensorHalfConvolution tensorConvApproxHalf
-samp,36 1,1,4,3 1.88 tensorHalfConvolution tensorConvApproxHalf
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp16_old.txt b/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp16_old.txt
deleted file mode 100644
index 72c43e61288c532feed94f5768357b3113d5de49..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp16_old.txt
+++ /dev/null
@@ -1,18 +0,0 @@
-perf,20 1,1,0 2.25 tensorHalfConvolution tensorConvPerfCudaHalf
-perf,21 1,2,0 2.25 tensorHalfConvolution tensorConvPerfCudaHalf
-perf,22 1,2,1 2.25 tensorHalfConvolution tensorConvPerfCudaHalf
-perf,23 1,3,0 1.88 tensorHalfConvolution tensorConvPerfCudaHalf
-perf,24 1,3,1 1.88 tensorHalfConvolution tensorConvPerfCudaHalf
-perf,25 1,3,2 1.88 tensorHalfConvolution tensorConvPerfCudaHalf
-perf,26 2,1,0 2.25 tensorHalfConvolution tensorConvPerfCudaHalf
-perf,27 2,1,1 2.25 tensorHalfConvolution tensorConvPerfCudaHalf
-perf,28 3,1,0 1.88 tensorHalfConvolution tensorConvPerfCudaHalf
-perf,29 3,1,1 1.88 tensorHalfConvolution tensorConvPerfCudaHalf
-perf,30 3,1,2 1.88 tensorHalfConvolution tensorConvPerfCudaHalf
-samp,31 2,0 1.88 tensorHalfConvolution tensorConvInputHalf
-samp,32 2,1 1.88 tensorHalfConvolution tensorConvInputHalf
-samp,33 4,0 1.88 tensorHalfConvolution tensorConvInputHalf
-samp,34 4,1 1.88 tensorHalfConvolution tensorConvInputHalf
-samp,35 4,2 1.88 tensorHalfConvolution tensorConvInputHalf
-samp,36 4,3 1.88 tensorHalfConvolution tensorConvInputHalf
-samp,37 1,1 1.88 tensorHalfConvolution tensorConvInputHalf
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp16_samp.txt b/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp16_samp.txt
deleted file mode 100644
index 0f0593226f6fbeddda91046e7416fe108bfb6d90..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp16_samp.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-samp,31 2,0 1.88 tensorHalfConvolution tensorConvInputHalf
-samp,32 2,1 1.88 tensorHalfConvolution tensorConvInputHalf
-samp,33 4,0 1.88 tensorHalfConvolution tensorConvInputHalf
-samp,34 4,1 1.88 tensorHalfConvolution tensorConvInputHalf
-samp,35 4,2 1.88 tensorHalfConvolution tensorConvInputHalf
-samp,36 4,3 1.88 tensorHalfConvolution tensorConvInputHalf
-samp,37 1,1 1.88 tensorHalfConvolution tensorConvInputHalf
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp16_vgg16.txt b/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp16_vgg16.txt
deleted file mode 100644
index a172a4e515ebfd24a51267da8bac2cb5f13ce6c0..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp16_vgg16.txt
+++ /dev/null
@@ -1,13 +0,0 @@
-perf,20 1,1,1,1 2.25 tensorHalfConvolution tensorConvApproxHalf
-perf,21 1,2,1,0 2.25 tensorHalfConvolution tensorConvApproxHalf
-perf,22 1,2,1,1 2.25 tensorHalfConvolution tensorConvApproxHalf
-perf,23 1,3,1,0 1.88 tensorHalfConvolution tensorConvApproxHalf
-perf,24 1,3,1,1 1.88 tensorHalfConvolution tensorConvApproxHalf
-perf,25 1,3,1,2 1.88 tensorHalfConvolution tensorConvApproxHalf
-perf,26 2,1,1,0 2.25 tensorHalfConvolution tensorConvApproxHalf
-perf,27 2,1,1,1 2.25 tensorHalfConvolution tensorConvApproxHalf
-perf,28 3,1,1,0 1.88 tensorHalfConvolution tensorConvApproxHalf
-perf,29 3,1,1,1 1.88 tensorHalfConvolution tensorConvApproxHalf
-perf,30 3,1,1,2 1.88 tensorHalfConvolution tensorConvApproxHalf
-samp,32 1,1,2,1 1.88 tensorHalfConvolution tensorConvApproxHalf
-samp,36 1,1,4,3 1.88 tensorHalfConvolution tensorConvApproxHalf
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp32.txt b/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp32.txt
deleted file mode 100644
index 78f3e361ee8a96c6520793b435815210e1fc7117..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp32.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-perf,20 1,1,1,1 2.25 tensorConvolution tensorConvApprox
-perf,21 1,2,1,0 2.25 tensorConvolution tensorConvApprox
-perf,22 1,2,1,1 2.25 tensorConvolution tensorConvApprox
-perf,23 1,3,1,0 1.88 tensorConvolution tensorConvApprox
-perf,24 1,3,1,1 1.88 tensorConvolution tensorConvApprox
-perf,25 1,3,1,2 1.88 tensorConvolution tensorConvApprox
-perf,26 2,1,1,0 2.25 tensorConvolution tensorConvApprox
-perf,27 2,1,1,1 2.25 tensorConvolution tensorConvApprox
-perf,28 3,1,1,0 1.88 tensorConvolution tensorConvApprox
-perf,29 3,1,1,1 1.88 tensorConvolution tensorConvApprox
-perf,30 3,1,1,2 1.88 tensorConvolution tensorConvApprox
-samp,31 1,1,2,0 1.88 tensorConvolution tensorConvApprox
-samp,32 1,1,2,1 1.88 tensorConvolution tensorConvApprox
-samp,33 1,1,4,0 1.88 tensorConvolution tensorConvApprox
-samp,34 1,1,4,1 1.88 tensorConvolution tensorConvApprox
-samp,35 1,1,4,2 1.88 tensorConvolution tensorConvApprox
-samp,36 1,1,4,3 1.88 tensorConvolution tensorConvApprox
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp32_baseline.txt b/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp32_baseline.txt
deleted file mode 100644
index df001ba497d0ed440dd34beead33d607651d3f35..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp32_baseline.txt
+++ /dev/null
@@ -1 +0,0 @@
-perf,20 1,1,1,1 2.25 tensorConvolution tensorConvApprox
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp32_old.txt b/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp32_old.txt
deleted file mode 100644
index 36a7dbca05ef71b6046a91066acf5382f2a5c7a3..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp32_old.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-perf,20 1,1,0 2.25 tensorConvolution tensorConvPerfCuda
-perf,21 1,2,0 2.25 tensorConvolution tensorConvPerfCuda
-perf,22 1,2,1 2.25 tensorConvolution tensorConvPerfCuda
-perf,23 1,3,0 1.88 tensorConvolution tensorConvPerfCuda
-perf,24 1,3,1 1.88 tensorConvolution tensorConvPerfCuda
-perf,25 1,3,2 1.88 tensorConvolution tensorConvPerfCuda
-perf,26 2,1,0 2.25 tensorConvolution tensorConvPerfCuda
-perf,27 2,1,1 2.25 tensorConvolution tensorConvPerfCuda
-perf,28 3,1,0 1.88 tensorConvolution tensorConvPerfCuda
-perf,29 3,1,1 1.88 tensorConvolution tensorConvPerfCuda
-perf,30 3,1,2 1.88 tensorConvolution tensorConvPerfCuda
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp32_to_fp16.txt b/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp32_to_fp16.txt
deleted file mode 100644
index 913397cc4936bf11f3eefa15b5804700865e7b6b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp32_to_fp16.txt
+++ /dev/null
@@ -1 +0,0 @@
-fp16,12 0 1.5 tensorConvolution tensorHalfConvolution
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp32_vgg16.txt b/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp32_vgg16.txt
deleted file mode 100644
index 6fbab7d7b85255cd86748634faea0bf48ed75e42..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_fp32_vgg16.txt
+++ /dev/null
@@ -1,13 +0,0 @@
-perf,20 1,1,1,1 2.25 tensorConvolution tensorConvApprox
-perf,21 1,2,1,0 2.25 tensorConvolution tensorConvApprox
-perf,22 1,2,1,1 2.25 tensorConvolution tensorConvApprox
-perf,23 1,3,1,0 1.88 tensorConvolution tensorConvApprox
-perf,24 1,3,1,1 1.88 tensorConvolution tensorConvApprox
-perf,25 1,3,1,2 1.88 tensorConvolution tensorConvApprox
-perf,26 2,1,1,0 2.25 tensorConvolution tensorConvApprox
-perf,27 2,1,1,1 2.25 tensorConvolution tensorConvApprox
-perf,28 3,1,1,0 1.88 tensorConvolution tensorConvApprox
-perf,29 3,1,1,1 1.88 tensorConvolution tensorConvApprox
-perf,30 3,1,1,2 1.88 tensorConvolution tensorConvApprox
-samp,32 1,1,2,1 1.88 tensorConvolution tensorConvApprox
-samp,36 1,1,4,3 1.88 tensorConvolution tensorConvApprox
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_test.txt b/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_test.txt
deleted file mode 100644
index 68686b25de1c607e34d75044cd7ff19cf0c8890a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/knob_config_test.txt
+++ /dev/null
@@ -1 +0,0 @@
-fp16,12 0 1.5 tensorHalfConvolution tensorHalfConvolution
diff --git a/llvm/projects/hpvm-tensor-rt/code_autogenerators/source_code_autogenerator.py b/llvm/projects/hpvm-tensor-rt/code_autogenerators/source_code_autogenerator.py
deleted file mode 100644
index 589cdd0f4fe05cb8e9844ba9ac3dccd73133f09f..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/code_autogenerators/source_code_autogenerator.py
+++ /dev/null
@@ -1,463 +0,0 @@
-# Input: file of the following table format
-# id knob configurations (arbitrary # of columns) orig_func_name new_func_name
-# Input: file containing list of filenames to generate modified sources for
-# Generates:
-# a new directory called <original_source_nane>_different_knobs
-# files named <original_source_name>_<id>.txt within their respective directories
-
-import glob
-import sys
-import os
-import re
-import shutil
-
-class Approx:
- FP32 = 0
- FP16 = 1
- PERF = 2
- SAMP = 3
-
-class KnobConfiguration:
- '''
- Stores the configurations as well as other useful information for each knob configuration
- Stores: id (may factor out if ids are guaranteed to start at 0/1 and be consecutive)
- original function name
- modified function name
- new function parameters (knobs)
- new function call (modified function name(knobs))
- '''
- def __init__(self, raw_config):
- '''
- Args: raw_config = line of configuration file to parse
- '''
- line_as_lst = raw_config.strip().split()
- # approx,<id> knob1,knob2,etc IGNORE old_fun_name new_fun_name
-
- approx_id_lst = line_as_lst[0].split(',')
- assert len(approx_id_lst) == 2
-
- self.id = int(approx_id_lst[1])
-
- if approx_id_lst[0] == "fp32":
- self.approx = Approx.FP32
- self.filename_ext = "fp32_converted"
- return # special case
- elif approx_id_lst[0] == "fp16":
- self.approx = Approx.FP16
- self.filename_ext = "fp16_converted"
- return # special case
- elif approx_id_lst[0] == "perf":
- self.approx = Approx.PERF
- elif approx_id_lst[0] == "samp":
- self.approx = Approx.SAMP
-
- self.orig_func_name = line_as_lst[-2] # Second to last element
- self.modified_func_name = line_as_lst[-1] # Last element
- self.params = line_as_lst[1].split(",") # First element = knob configuration
- self.filename_ext = approx_id_lst[0] + "_" + "_".join(self.params) # approx_method_knobs
-
-
- # DEBUG
- def __repr__(self):
- if self.approx == Approx.FP32:
- return "FP32"
- elif self.approx == Approx.FP16:
- return "FP16"
-
- approx_type = None
- if self.approx == Approx.PERF:
- approx_type = "PERF"
- elif self.approx == Approx.SAMP:
- approx_type = "SAMP"
- return "Approx: %s, ID: %d, Orig func nane: %s, Modified func nane: %s, Params: %s" \
- % (approx_type, self.id, self.orig_func_name, self.modified_func_name, \
- ', '.join(self.params))
-
-
-def get_new_path(old_path, orig_source_code_dir):
- '''
- Returns a path that's compatible with the location of the generated source code
-
- Args:
- old_path: Original path of file that's being included
- orig_source_code_dir: Path to original source code dir wrt the current dir
- '''
- if os.path.isabs(old_path): # Old path works
- return old_path
- # Adding an extra .. because the path should be wrt the generated directory
- return os.path.join("..", orig_source_code_dir, old_path)
-
-
-# "complete_line" = a valid line of code
-def get_new_function_calls(complete_line, knob_config):
- '''
- Returns a copy of an inputted line of code such that all instances of old
- function calls are replaced with newFunctionCall(old params, knobs)
-
- Note: The old calls aren't completely overriden, as we still need the old parameters but
- insert new parameters as well
-
- Args:
- complete_line: A complete line of code to process
- knob_config: KnobConfiguration object representing current configuration
- '''
- orig_func_ind = complete_line.find(knob_config.orig_func_name)
- new_line = []
- line_start_ind = 0
- last_ind = 0
-
- while orig_func_ind != -1:
- new_line.append(complete_line[line_start_ind : orig_func_ind])
- line_start_ind = complete_line.find(")", orig_func_ind) + 1
-
- old_func_call = complete_line[complete_line.find("(", orig_func_ind): line_start_ind]
- if knob_config.modified_func_name == knob_config.orig_func_name:
- # count the number of new parameters
- num_repl_params = len(knob_config.params)
- old_func_params = old_func_call.strip().split(',')
- new_line.append("%s%s, %s)" % (knob_config.modified_func_name, ', '.join(old_func_params[:-num_repl_params]), ', '.join(knob_config.params)))
-
- else:
- new_line.append("%s%s, %s)" % (knob_config.modified_func_name, old_func_call[:-1], ', '.join(knob_config.params)))
- orig_func_ind = complete_line.find(knob_config.orig_func_name, line_start_ind)
- new_line.append(complete_line[line_start_ind : ])
- return ''.join(new_line)
-
-
-def convert_local_paths(file_contents, orig_source_dir):
- '''
- Converts all local paths wrt the original source file's directory to paths compatible
- with the current source code directory
-
- Args:
- file_contents: String containing source code read from file
- orig_source_dir: Path of original source code dir wrt the current directory
- '''
- last_include_ind = file_contents.rfind("#include")
- last_include_newline_ind = file_contents.find("\n", last_include_ind)
- include_lines = file_contents[ : last_include_newline_ind].split("\n")
-
- new_file_contents = []
- for line in include_lines:
- if line.startswith("#"):
- include_file = line.split()[1]
- if include_file.startswith("\""):
- new_include_path = get_new_path(include_file.replace("\"", ""), orig_source_dir.replace("\"", ""))
- new_file_contents.append("#include \"%s\"\n" % new_include_path)
- else:
- new_file_contents.append(line)
- new_file_contents.append(file_contents[last_include_newline_ind : ])
- return '\n'.join(new_file_contents)
-
-
-def get_tensor_operation(line):
- tensor_func_call = None
- tensor_op_name = None
- start_ind = None
- end_ind = None
-
- start_ind = line.find("tensor")
- if start_ind != -1:
- end_ind = line.find('(')
- tensor_op_name = line[start_ind + len("tensor") : end_ind]
- if tensor_op_name[0].isupper(): # crude way of checking whether we have a camel cased method
- end_ind = line.find('(')
- tensor_func_call = line[start_ind : end_ind]
- return tensor_func_call, tensor_op_name, start_ind, end_ind
-
-
-def generate_fp32_source(new_file, source_file, orig_source_dir):
- # Copy the source code over
- new_file_contents = convert_local_paths(source_file.read(), orig_source_dir)
- new_file.write(new_file_contents)
-
-
-def generate_fp16_source(knob_config, new_file, source_file, orig_source_dir):
- file_contents = convert_local_paths(source_file.read(), orig_source_dir).split('\n')
-
- new_file_contents = []
- for line in file_contents:
- tensor_func_call, tensor_op_name, start_ind, end_ind = get_tensor_operation(line)
- # tensorHalfSoftmax doesn't exist
- if line.find("Softmax") == -1 and tensor_func_call:
- new_file_contents.append(line[ : start_ind] + "tensorHalf" + tensor_op_name \
- + line[end_ind : ])
- else:
- new_file_contents.append(line)
- new_file.write('\n'.join(new_file_contents))
-
-
-def generate_approx_source(knob_config, new_file, source_file, orig_source_dir):
- new_file_contents = []
-
- # Store complete line to handle cases where one line of code is split into two lines
- complete_line = ""
- for line in source_file:
- # Replace the current path of the local include with a path that's compatible
- # with the location of the generated source code
- if line.startswith("#"):
- include_file = line.split()[1]
- if include_file.startswith("\""):
- new_include_path = get_new_path(include_file.replace("\"", ""), orig_source_dir.replace("\"", ""))
- new_file_contents.append("#include \"%s\"\n" % new_include_path)
- else:
- new_file_contents.append(line)
- continue
-
- # Handles case where 1 actual line of code is split into 2 lines
- elif line.find("}") != -1 or line.find("{") != -1:
- complete_line += line
- new_file_contents.append(complete_line)
- complete_line = ""
- continue
-
- elif line.find(";") == -1: # Last char is always \n
- complete_line += line
- continue
-
- complete_line += line
- orig_func_ind = complete_line.find(knob_config.orig_func_name)
- if orig_func_ind != -1:
- new_file_contents.append(get_new_function_calls(complete_line, knob_config))
- else:
- new_file_contents.append(complete_line)
- complete_line = ""
- new_file.write(''.join(new_file_contents))
-
-
-def generate_source_code(table, dir_name, filename, source_name, profile_per_tensor):
- '''
- Generates source code for all configurations in the table for one original source
- Args
- table: List of KnobConfigurations
- dir_name: Directory new sources should be placed in
- filename: Filename of original source
- source_name: Filename without the file extension (ex: foo/blah.cc --> blah)
- '''
- source_file = open(filename, "r")
- orig_source_dir = os.path.dirname(filename)
-
- for knob_config in table:
- source_file.seek(0, 0)
- new_filename = os.path.join(dir_name, "%s_%s.cc" % (source_name, knob_config.id))
- #new_filename = os.path.join(dir_name, "%s_%s.cc" % (source_name, knob_config.filename_ext))
- new_file = open(new_filename, "w")
- if knob_config.approx == Approx.FP16:
- generate_fp16_source(knob_config, new_file, source_file, orig_source_dir)
- elif knob_config.approx == Approx.FP32:
- generate_fp32_source(new_file, source_file, orig_source_dir)
- elif knob_config.approx == Approx.PERF or knob_config.approx == Approx.SAMP:
- generate_approx_source(knob_config, new_file, source_file, orig_source_dir)
-
- new_file.close() # Need to flush
-
- if profile_per_tensor:
- add_profiling_calls_per_tensor(new_filename)
- print("Generated source code as %s" % new_filename)
- source_file.close()
-
-
-def generate_all_sources(table, orig_files_filename, profile_per_tensor):
- '''
- Generates directories and source code for all original sources for all knob configurations
- Args:
- table: List of KnobConfiguration objects
- orig_files_filename: Filename of file containing all original source names to generate new
- sources for
- '''
- orig_files = open(orig_files_filename, "r")
- for orig_filename in orig_files:
- orig_filename = orig_filename.strip()
-
- # Source name = original filename without the .cc
- last_slash_ind = orig_filename.rfind("/")
- file_ext_ind = orig_filename.find(".cc")
- if last_slash_ind == -1:
- source_name = orig_filename[ : file_ext_ind]
- else:
- source_name = orig_filename[last_slash_ind + 1 : file_ext_ind]
- print("Source name: %s" % source_name)
-
- # Start with a clean directory
- dir_name = "%s_autogenerated_knobs" % source_name
- print("Setting up directory: %s" % dir_name)
- if os.path.isdir(dir_name):
- print("Directory exists: clearing everything")
- for old_file in glob.glob(os.path.join(dir_name, "*")):
- os.remove(old_file)
-
- else:
- print("Generating directory: %s" % dir_name)
- os.makedirs(dir_name)
-
- generate_source_code(table, dir_name, orig_filename, source_name, profile_per_tensor)
- print("\n")
- orig_files.close()
-
-
-# This is a lazy approach but it works so ...
-def add_profiling_calls_per_tensor(source_filename):
- source_file = open(source_filename, "r")
- orig_source = source_file.read().split('\n')
- source_file.close()
-
- modified_source = []
-
- init_profiler_cmd = "%sProfiler profiler;"
- start_profiler_call = "%sprofiler.start_profiler();"
- resume_profiler_call = "%sprofiler.resume_profiler();"
- pause_profiler_call = "%sprofiler.pause_profiler();"
- stop_profiler_call = "%sprofiler.stop_profiler();"
- time_energy_profiler_call = "%sauto time_energy_%d = profiler.get_time_energy();"
- reset_profiler_call = "%sprofiler.reset();"
-
- time_var_decl = "%sdouble %s_time = 0.0;"
- time_energy_decl = "%sdouble %s_energy = 0.0;"
-
- time_incr_cmd = "%s%s_time += time_energy_%d.first;"
- energy_incr_cmd = "%s%s_energy += time_energy_%d.second;"
-
- output_per_tensor = "%sstd::cout << \"Operation %s, time: \" << (%s_time) / total_runs <<\", energy: \" << (%s_energy) / total_runs << std::endl; "
- total_output = "%sstd::cout << \"Total %s: \" << (%s) / total_runs << std::endl;"
-
- time_energy_count = 0
-
- tensor_operations = set()
- for line in orig_source:
- line = line.strip()
- tensor_func_call, _, _, _ = get_tensor_operation(line)
- if tensor_func_call: tensor_operations.add(tensor_func_call)
-
- inserted_end_profiler_call = False
- has_seen_for_loop = False
- close_bracket_count = 0
- line_ind = 0
-
- while line_ind < len(orig_source):
- line = orig_source[line_ind]
- num_leading_spaces = len(line) - len(line.lstrip())
- leading_spaces_str = ' '.join(["" for _ in range(num_leading_spaces)])
-
- if line.find("for") != -1:
- has_seen_for_loop = True
-
- if has_seen_for_loop and line.find("}") != -1:
- close_bracket_count += 1
-
- if line.find("#include") != -1:
- modified_source.append(leading_spaces_str + line)
- line_ind += 1
- continue
-
- if line.find("profiler") != -1 or line.find("total_time") != -1 or line.find("total_energy") != -1:
- line_ind += 1
- continue
-
- if line.find("int total_runs") != -1:
- # Now we insert the counters
- for op_name in tensor_operations:
- modified_source.append(time_var_decl % (leading_spaces_str, op_name))
- modified_source.append(time_energy_decl % (leading_spaces_str, op_name))
- modified_source.append(line)
- modified_source.append(init_profiler_cmd % leading_spaces_str)
- modified_source.append(start_profiler_call % leading_spaces_str)
- line_ind += 1
- continue
-
- if close_bracket_count == 2 and not inserted_end_profiler_call: # NOTE this breaks if there are helper methods/scopes
- modified_source.append(line)
- total_time_str = []
- total_energy_str = []
-
- for op_name in tensor_operations:
- modified_source.append(output_per_tensor % (leading_spaces_str, op_name, op_name, op_name))
- total_time_str.append("%s_time" % op_name)
- total_energy_str.append("%s_energy" % op_name)
-
- modified_source.append(total_output % (leading_spaces_str, "time", ' + '.join(total_time_str)))
- modified_source.append(total_output % (leading_spaces_str, "energy", ' + '.join(total_energy_str)))
-
- modified_source.append(stop_profiler_call % leading_spaces_str)
- line_ind += 1
- inserted_end_profiler_call = True
- continue
-
- tensor_ind = line.find("tensor")
- if tensor_ind == -1:
- modified_source.append(line)
- line_ind += 1
- continue
-
- word_after_tensor = line[tensor_ind + len("tensor")]
- if word_after_tensor[0].isupper(): # crude way of checking whether we have a camel cased method
- tensor_op = line[tensor_ind : line.find('(')]
-
- modified_source.append(resume_profiler_call % leading_spaces_str)
- modified_source.append(line)
-
- # Address one line that's split up into 2 lines for readability
- if line.find(")") == -1 and line_ind + 1 < len(orig_source) \
- and orig_source[line_ind + 1].find(")") != -1:
- line_ind += 1
- modified_source.append(orig_source[line_ind])
-
- modified_source.append(pause_profiler_call % leading_spaces_str)
- modified_source.append(time_energy_profiler_call % (leading_spaces_str, time_energy_count))
- modified_source.append(time_incr_cmd % (leading_spaces_str, tensor_op, time_energy_count))
- modified_source.append(energy_incr_cmd % (leading_spaces_str, tensor_op, time_energy_count))
- modified_source.append(reset_profiler_call % leading_spaces_str)
- modified_source.append("")
-
- time_energy_count += 1
- else:
- modified_source.append(line)
- line_ind += 1
-
- source_file = open(source_filename, "w")
- source_file.write('\n'.join(modified_source))
- source_file.close()
-
-
-def parse_table(table_filename):
- '''
- Given the filename of a table, parses the table into a list of KnobConfigurations
- '''
- # Can we assume that the ids always start at 1 --> if so, can index by knobs
- # else: need to use a dict
- table = []
- table_file = open(table_filename, "r")
- for raw_config in table_file:
- table.append(KnobConfiguration(raw_config))
- table_file.close()
- return table
-
-
-def delete_autogenerated_dirs():
- for dir_name in os.listdir("."):
- if dir_name.endswith("profiling_autogenerated_knobs"):
- print("DELETING %s" % dir_name)
- shutil.rmtree(dir_name)
- print("DONE")
-
-if __name__ == "__main__":
- num_args = len(sys.argv)
-
- if num_args == 2 and sys.argv[1] == "clean":
- delete_autogenerated_dirs()
- exit(0)
-
- if num_args != 3 and num_args != 4:
- print("Usage: python source_code_autogenerator.py <table file> <original filenames file> [per_tensor]")
- print("To delete autogen dirs: python source_code_autogenerator.py clean")
-
- if num_args >= 2 and sys.argv[1] == "--usage":
- print("Table file format: <id> <knob configurations separated by spaces> <orig func name> <new func name>")
- print("Original filenames file: <original_filename><newline> etc")
- else:
- print("Run with --usage flag for more detailed information")
- exit(1)
-
- profile_per_tensor = num_args == 4 and sys.argv[3] == "per_tensor"
-
- table = parse_table(sys.argv[1])
- generate_all_sources(table, sys.argv[2], profile_per_tensor)
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/include/op_overheads.h b/llvm/projects/hpvm-tensor-rt/dnn_sources/include/op_overheads.h
deleted file mode 100644
index 4eaf88e6d613c51a5a75ef8ce73b55a3410f1dbd..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/include/op_overheads.h
+++ /dev/null
@@ -1,148 +0,0 @@
-
-
-#ifndef OP_OVERHEADS_HEADER
-#define OP_OVERHEADS_HEADER
-
-
-#include <sstream>
-#include "../../tensor_runtime/include/tensor.h"
-#include "types.h"
-
-
-float scale_down_factor = 10000.0;
-float error_factor = 0.1;
-std::string result_str = "";
-
-
-// TODO: Every routine needs testing
-
-
-// private function
-static float getScaledComps(double total_comps, int error_scale){
-
- total_comps = total_comps / scale_down_factor;
- float comp_scale = 1.0 + (error_factor * error_scale);
- total_comps = total_comps / comp_scale;
-
- return total_comps;
-}
-
-
-static void addNormToResult(float comps){
-
- std::ostringstream ss;
- ss << std::fixed << comps;
-
- result_str.append( std::string(ss.str()) );
- result_str.append("\t");
-}
-
-
-
-static void addCompsToResult(float comps){
-
- std::ostringstream ss;
- ss << std::fixed << comps;
-
- result_str.append( std::string(ss.str()) );
- result_str.append("\n");
-}
-
-
-void add_conv_overheads(void* input_ptr, void* filter_ptr,
- int strideA, int strideB, int error_scale){
-
- Tensor* input = (Tensor*) input_ptr;
- Tensor* filter = (Tensor*) filter_ptr;
-
-}
-
-
-void add_gemm_overheads(void* lhs_ptr, void* rhs_ptr, int error_scale){
-
- Tensor* lhs = (Tensor*) lhs_ptr;
- Tensor* rhs = (Tensor*) rhs_ptr;
-
- int m = lhs->dims.dim_sizes[0];
- // The rhs last dimension must contain the neurons
- int n = rhs->dims.dim_sizes[rhs->dims.num_dims-1]; // output neurons
- int k = 1;
-
- // Flattening the dimensions after the batch dimension
- for (int j = 1 ; j < lhs->dims.num_dims; j++){
- k = k * lhs->dims.dim_sizes[j]; // input neurons
- }
-
- int rhs_k = rhs->dims.dim_sizes[rhs->dims.num_dims-2];
- // Dimension-note: Check if k is same across the two tensors
- printf("m = %d, n = %d, k = %d \n", m, n, k);
-
- if(rhs_k != k){
- printf("rhs=%d and lhs=%d columns/rows don't match", rhs_k, k);
- abort();
- }
-
- double total_comps = m * n * rhs_k * 1.0;
- float scaled_comps = getScaledComps(total_comps, error_scale);
-
- printf("error_scale = %d, total_comps = %f, scaled_comps = %f \n",
- error_scale, total_comps, scaled_comps);
-
- addCompsToResult(scaled_comps);
-
-}
-
-
-void add_bias_overheads(void* input_ptr, int error_scale){
-
- Tensor* input = (Tensor*) input_ptr;
-
- double total_comps = input->num_elems;
- float scaled_comps = getScaledComps(total_comps, error_scale);
-
- printf("error_scale = %d, total_comps = %f, scaled_comps = %f \n",
- error_scale, total_comps, scaled_comps);
-
- addCompsToResult(scaled_comps);
-
-}
-
-
-void add_relu_overheads(void* input_ptr, int error_scale){
-
- Tensor* input = (Tensor*) input_ptr;
-
- double total_comps = input->num_elems;
- float scaled_comps = getScaledComps(total_comps, error_scale);
-
- printf("error_scale = %d, total_comps = %f, scaled_comps = %f \n",
- error_scale, total_comps, scaled_comps);
-
- addCompsToResult(scaled_comps);
-
-}
-
-float add_pool_overheads(void* input_ptr, int kernel_size,
- int stride_size, int error_scale){
-
-}
-
-
-void add_norms(void* norms_ptr){
-
- Norm_t* norms = (Norm_t*) norms_ptr;
-
- addNormToResult(norms->l1_norm);
- addNormToResult(norms->l2_norm);
- addNormToResult(norms->inf_norm);
-
-}
-
-void dump_result(char* file_name){
-
- FILE* fp = fopen(file_name, "w+");
- fwrite(result_str.c_str(), 1, result_str.length(), fp);
- fclose(fp);
-}
-
-#endif
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/include/types.h b/llvm/projects/hpvm-tensor-rt/dnn_sources/include/types.h
deleted file mode 100644
index 3e4f64610da64fb04b6270035da8557e940eb7e2..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/include/types.h
+++ /dev/null
@@ -1,39 +0,0 @@
-
-#ifndef TYPES_HEADER
-#define TYPES_HEADER
-
-/*
-struct Dimension_t{
- int num_dims;
- size_t* dim_sizes;
-};
-
-
-struct Tensor_t{
- int tensor_id; // used for indexing (in the tensor runtime)
- int data_type; // {float_type, double_type, half_type, int_type}
- int data_format; // {nchw, nhwc}
- void* host_data;
- size_t num_elems; // Total elements
- size_t size_in_bytes; // Total size in bytes
- struct Dimension_t dims;
-};
-
-
-
-enum Tensor_type_t{
- float_type,
- double_type,
- half_type,
- int_type
-};
-
-
-// NOTE: Currently only NCHW is supported due to limited cuDNN support
-enum Tensor_format_t{
- nchw,
- nhwc
-};
-*/
-
-#endif
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/include/utils.h b/llvm/projects/hpvm-tensor-rt/dnn_sources/include/utils.h
deleted file mode 100644
index a627f83e6b2aa9f38b09d82ee94ce35da1a6bafe..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/include/utils.h
+++ /dev/null
@@ -1,855 +0,0 @@
-
-// Header guards
-#ifndef UTILS_HEADER
-#define UTILS_HEADER
-
-
-#include <sstream>
-#include <vector>
-#include <bits/stdc++.h>
-#include <tensor_runtime.h>
-#include <tensor.h>
-#include <cmath>
-
-
-std::vector<float> run_accuracies;
-
-
-void printTensorInfo(void* tensor_ptr){
-
- struct Tensor* tensor = (struct Tensor*) tensor_ptr;
-
- if(tensor->gpu_data != NULL){
- printf("Successful cudaMalloc \n");
- }
-
- printf("tensor dims = %d \n", tensor->dims.num_dims);
- printf("dim1_size = %lu \n", tensor->dims.dim_sizes[0]);
- printf("dim2_size = %lu \n", tensor->dims.dim_sizes[1]);
- printf("num_elems = %lu \n", tensor->num_elems);
-}
-
-
-// FIXIT: Move this to debug.h and include in all files
-void dumpWeightsToFile(char* file_name, void* weights_ptr){
-
- struct Tensor* weights = (Tensor*) weights_ptr;
- // Move data back to host
- hpvm_request_tensor(weights, 0);
-
- FILE* fp = fopen(file_name, "wb");
- if(fp == NULL){
- printf("File %s could not be created. Check if directory exists \n", file_name);
- abort();
- }
-
- //printf("size_in_bytes = %lu \n", weights->size_in_bytes);
- size_t bytes_written = fwrite(weights->host_data, 1, weights->size_in_bytes, fp);
- //printf("bytes_written = %lu \n", bytes_written);
- fclose(fp);
-}
-
-
-
-void fillTensorWithOnes(void* tensor_ptr){
-
- struct Tensor* tensor = (struct Tensor*) tensor_ptr;
-
- hpvm_request_tensor(tensor, 0);
-
- // initialization is specific to the floating point type
- if(tensor->data_type == CUDNN_DATA_FLOAT){
- float* data_arr = (float*) tensor->host_data;
- for(unsigned int i = 0; i < tensor->num_elems; i++){
- data_arr[i] = 1.0;
- }
- }
-}
-
-
-void fillWithOnesAndTwos(void* tensor_ptr){
-
- struct Tensor* tensor = (struct Tensor*) tensor_ptr;
-
- hpvm_request_tensor(tensor, 0);
-
- // initialization is specific to the floating point type
- if(tensor->data_type == CUDNN_DATA_FLOAT){
- float* data_arr = (float*) tensor->host_data;
- for(unsigned int i = 0; i < tensor->num_elems/2; i++){
- data_arr[i] = 1.0;
- }
-
- for(unsigned int i = tensor->num_elems/2; i < tensor->num_elems; i++){
- data_arr[i] = 2.0;
- }
-
- }
-}
-
-
-void fillTensorWithVal(void* tensor_ptr, float target_value){
-
- struct Tensor* tensor = (struct Tensor*) tensor_ptr;
-
- hpvm_request_tensor(tensor, 0);
-
- // initialization is specific to the floating point type
- if(tensor->data_type == CUDNN_DATA_FLOAT){
- float* data_arr = (float*) tensor->host_data;
- for(unsigned int i = 0; i < tensor->num_elems; i++){
- data_arr[i] = target_value;
- }
- }
-}
-
-
-void fillTensorWithNegOnes(void* tensor_ptr){
-
- struct Tensor* tensor = (struct Tensor*) tensor_ptr;
-
- hpvm_request_tensor(tensor, 0);
-
- // initialization is specific to the floating point type
- if(tensor->data_type == CUDNN_DATA_FLOAT){
- float* data_arr = (float*) tensor->host_data;
- for(unsigned int i = 0; i < tensor->num_elems; i++){
- data_arr[i] = -1.0;
- }
- }
-}
-
-
-void fillTensorVals(void* tensor_ptr){
-
- struct Tensor* tensor = (struct Tensor*) tensor_ptr;
- // initialization is specific to the floating point type
- if(tensor->data_type == CUDNN_DATA_FLOAT){
- float* data_arr = (float*) tensor->host_data;
- for(unsigned int i = 0; i < tensor->num_elems; i++){
- data_arr[i] = i + 1;
- }
- }
-}
-
-
-void printTensorValues(void* tensor_ptr){
-
- struct Tensor* tensor = (struct Tensor*) tensor_ptr;
-
- hpvm_request_tensor(tensor, 0);
-
- // printing is specific to the floating point type
- if(tensor->data_type == CUDNN_DATA_FLOAT){
- float* data_arr = (float*) tensor->host_data;
- for(unsigned int i = 0; i < tensor->num_elems; i++){
- printf("%f,", data_arr[i]);
- }
- }
-
- printf("\n");
-}
-
-
-void printTensorDims(void* tensor_ptr){
-
- struct Tensor* tensor = (struct Tensor*) tensor_ptr;
-
- printf("Num_elems = %lu \n", tensor->num_elems);
- for (int i = 0; i < tensor->dims.num_dims; i++){
- printf("dim[%d] = %lu \n", i, tensor->dims.dim_sizes[i]);
- }
-}
-
-
-
-void compareTensors(void* tensor1_ptr, void* tensor2_ptr){
-
- struct Tensor* tensor1 = (struct Tensor*) tensor1_ptr;
- struct Tensor* tensor2 = (struct Tensor*) tensor2_ptr;
-
- hpvm_request_tensor(tensor1, 0);
- hpvm_request_tensor(tensor2, 0);
-
- float* tensor_data1 = (float*) tensor1->host_data;
- float* tensor_data2 = (float*) tensor2->host_data;
-
- for(unsigned int i = 0; i < tensor1->num_elems; i++){
- if(tensor_data1[i] != tensor_data2[i]){
- printf("Tensor data mismatch at index %d \n", i);
- abort();
- }
- }
-}
-
-
-
-void compareValues(void* tensor_ptr, float* data, size_t num_elems){
-
- struct Tensor* tensor = (struct Tensor*) tensor_ptr;
-
- hpvm_request_tensor(tensor, 0);
-
- float* tensor_data = (float*) tensor->host_data;
- for(unsigned int i = 0; i < num_elems; i++){
- if(tensor_data[i] != data[i]){
- printf("Tensor data mismatch");
- abort();
- }
- }
-}
-
-
-void* readInputTensor(const char* file_name, int data_type, int dim1_size, int dim2_size,
- int dim3_size, int dim4_size){
-
- int type_size = 4; // NOTE: Assuming floating point tensors
- int num_elems = dim1_size * dim2_size * dim3_size * dim4_size;
- int size_in_bytes = type_size * dim1_size * dim2_size * dim3_size * dim4_size;
- uint8_t* file_data = (uint8_t*) malloc(sizeof(char) * num_elems);
- float* tensor_data = (float*) malloc(sizeof(float) * num_elems);
- int file_header_size = 16;
-
- FILE* file = fopen(file_name, "rb");
- if(file == NULL){
- printf("Data file %s is not found. Aborting... \n", file_name);
- abort();
- }
-
-
- fseek(file, file_header_size, SEEK_CUR); // Skipping the file header
- size_t bytes_read = fread(file_data, 1, sizeof(uint8_t) * num_elems, file);
-
- fclose(file);
-
- for (size_t i = 0; i < num_elems; ++i){
- tensor_data[i] = (float) file_data[i] / 255.0f;
- }
-
- // NOTE: Using NCHW format
- struct Tensor* input = (struct Tensor*) create4DTensor(data_type, nchw, dim1_size, dim2_size,
- dim3_size, dim4_size);
-
- initTensorData(input, tensor_data, size_in_bytes);
- // compareValues(input, tensor_data, num_elems);
-
- return input;
-}
-
-
-//*** FIXIT: Move this to CPU-only
-struct Tensor* readTrainedWeightsCPU(const char* file_name, int data_type,
- int dim1_size, int dim2_size,
- int dim3_size, int dim4_size){
-
- // FIXIT: Don't assume floating point types
- int type_size = 4; // NOTE: Assuming floating point tensors
- long int num_elems = dim1_size * dim2_size * dim3_size * dim4_size;
- long int size_in_bytes = type_size * dim1_size * dim2_size * dim3_size * dim4_size;
- float* tensor_data = (float*) malloc(sizeof(float) * num_elems);
- int file_header_size = 0;
-
- FILE* file = fopen(file_name, "rb");
- if(file == NULL){
- printf("Data file %s is not found. Aborting... \n", file_name);
- abort();
- }
-
- fseek(file, file_header_size, SEEK_CUR); // Skipping the file header
- size_t bytes_read = fread(tensor_data, 1, size_in_bytes, file);
-
- printf("size in bytes = %lu, bytes read = %lu \n", size_in_bytes, bytes_read);
-
- fclose(file);
-
-
- struct Tensor* weights = (struct Tensor*) create4DTensor(data_type, nchw, dim1_size, dim2_size,
- dim3_size, dim4_size);
-
- initTensorData(weights, tensor_data, size_in_bytes);
- //compareValues(weights, tensor_data, num_elems);
- free(tensor_data);
-
- return weights;
-}
-
-
-struct Tensor* readTrainedWeights(const char* file_name, int data_type,
- long int dim1_size, long int dim2_size,
- long int dim3_size, long int dim4_size){
-
- // FIXIT: Don't assume floating point types
- int type_size = 4; // NOTE: Assuming floating point tensors
- long int num_elems = dim1_size * dim2_size * dim3_size * dim4_size;
- long int size_in_bytes = type_size * dim1_size * dim2_size * dim3_size * dim4_size;
- float* tensor_data = (float*) malloc(sizeof(float) * num_elems);
- printf("size_in_bytes = %lu \n", size_in_bytes);
-
- int file_header_size = 0;
-
- FILE* file = fopen(file_name, "rb");
- if(file == NULL){
- printf("Data file %s is not found. Aborting... \n", file_name);
- abort();
- }
-
- fseek(file, file_header_size, SEEK_CUR); // Skipping the file header
- size_t bytes_read = fread(tensor_data, 1, size_in_bytes, file);
-
- // printf("size in bytes = %lu, bytes read = %lu \n", size_in_bytes, bytes_read);
-
- fclose(file);
-
-
- struct Tensor* weights = (struct Tensor*) create4DTensor(data_type, nchw, dim1_size, dim2_size,
- dim3_size, dim4_size);
-
- initTensorData(weights, tensor_data, size_in_bytes);
- //compareValues(weights, tensor_data, num_elems);
- free(tensor_data);
-
- return weights;
-}
-
-
-
-
-struct Tensor* readInputBatch(const char* file_name, int data_type,
- int start, int end,
- int dim2_size, int dim3_size, int dim4_size){
-
- int dim1_size = end - start;
- // FIXIT: Don't assume floating point types
- int type_size = 4; // NOTE: Assuming floating point tensors
- long int num_elems = dim1_size * dim2_size * dim3_size * dim4_size;
- long int size_in_bytes = type_size * dim1_size * dim2_size * dim3_size * dim4_size;
- float* tensor_data = (float*) malloc(sizeof(float) * num_elems);
- int file_header_size = type_size * start * dim2_size * dim3_size * dim4_size;
-
- FILE* file = fopen(file_name, "rb");
- if(file == NULL){
- printf("Data file %s is not found. Aborting... \n", file_name);
- abort();
- }
-
- fseek(file, file_header_size, SEEK_SET); // Skipping the file header
- size_t bytes_read = fread(tensor_data, 1, size_in_bytes, file);
-
-
- fclose(file);
-
-
- struct Tensor* weights = (struct Tensor*) create4DTensor(data_type, nchw, dim1_size, dim2_size,
- dim3_size, dim4_size);
-
- initTensorData(weights, tensor_data, size_in_bytes);
- free(tensor_data);
-
- return weights;
-}
-
-
-
-void* copyInputBatch(const char* file_name,
- int start, int end,
- int dim2_size, int dim3_size, int dim4_size,
- void* inputTensor_ptr){
-
- struct Tensor* inputTensor = (struct Tensor*) inputTensor_ptr;
-
- int dim1_size = end - start;
- // FIXIT: Don't assume floating point types
- int type_size = 4; // NOTE: Assuming floating point tensors
- long int num_elems = dim1_size * dim2_size * dim3_size * dim4_size;
- long int size_in_bytes = type_size * dim1_size * dim2_size * dim3_size * dim4_size;
- float* tensor_data = (float*) malloc(sizeof(float) * num_elems);
- int file_header_size = type_size * start * dim2_size * dim3_size * dim4_size;
-
- FILE* file = fopen(file_name, "rb");
- if(file == NULL){
- printf("Data file %s is not found. Aborting... \n", file_name);
- abort();
- }
-
- fseek(file, file_header_size, SEEK_SET); // Skipping the file header
- size_t bytes_read = fread(tensor_data, 1, size_in_bytes, file);
-
- fclose(file);
-
-
- initTensorData(inputTensor, tensor_data, size_in_bytes);
- free(tensor_data);
-
- printf("******NOTE: tensor Dims = %d \n", inputTensor->dims.num_dims);
- if(inputTensor->host_data == NULL || inputTensor->gpu_data == NULL)
- printf("ERROR: NULL data pointers \n");
-
-
- // Chaning Tensor Placement to HOST
- changeTensorPlacement(inputTensor, HOST);
-
-
- return inputTensor;
-}
-
-
-
-uint8_t* readLabels(const char* labels_file, int num_labels){
-
- uint8_t* labels = (uint8_t*) malloc(sizeof(uint8_t) * num_labels);
- FILE* file = fopen(labels_file, "rb");
- if(file == NULL){
- printf("Data file %s is not found. Aborting...\n", labels_file);
- abort();
- }
-
- size_t bytes_read = fread(labels, 1, sizeof(uint8_t) * num_labels, file);
-
- fclose(file);
-
- return labels;
-}
-
-
-
-uint32_t* readLabels3(const char* labels_file, int num_labels){
-
- uint32_t* labels = (uint32_t*) malloc(sizeof(uint32_t) * num_labels);
- FILE* file = fopen(labels_file, "rb");
- if(file == NULL){
- printf("Data file %s is not found. Aborting...\n", labels_file);
- abort();
- }
-
- size_t bytes_read = fread(labels, 1, sizeof(uint32_t) * num_labels, file);
-
- fclose(file);
-
- return labels;
-}
-
-
-uint8_t* readLabelsBatch(const char* labels_file, int start, int end){
-
- int num_labels = end - start;
- int file_header_size = sizeof(uint8_t) * start;
-
- uint8_t* labels = (uint8_t*) malloc(sizeof(uint8_t) * num_labels);
- FILE* file = fopen(labels_file, "rb");
- if(file == NULL){
- printf("Data file %s is not found. Aborting...\n", labels_file);
- abort();
- }
-
- fseek(file, file_header_size, SEEK_SET); // Skipping the file header
-
- size_t bytes_read = fread(labels, 1, sizeof(uint8_t) * num_labels, file);
-
-
- fclose(file);
-
- // printf("--labels bytes_read = %lu \n", bytes_read);
- return labels;
-}
-
-
-uint32_t* readLabelsBatch3(const char* labels_file, int start, int end){
-
- int num_labels = end - start;
- int file_header_size = sizeof(uint32_t) * start;
-
- uint32_t* labels = (uint32_t*) malloc(sizeof(uint32_t) * num_labels);
- FILE* file = fopen(labels_file, "rb");
- if(file == NULL){
- printf("Data file %s is not found. Aborting...\n", labels_file);
- abort();
- }
-
- fseek(file, file_header_size, SEEK_SET); // Skipping the file header
-
- size_t bytes_read = fread(labels, 1, sizeof(uint32_t) * num_labels, file);
-
-
- fclose(file);
-
- return labels;
-}
-
-
-
-void computeAccuracy(const char* labels_file, int num_labels, void* result_ptr){
-
- struct Tensor* result = (struct Tensor*) result_ptr;
-
- uint8_t* labels = readLabels(labels_file, num_labels);
- size_t batch_dim = result->dims.dim_sizes[0];
- size_t channels = result->dims.dim_sizes[1];
- float* data = (float*) result->host_data;
- int num_errors = 0;
-
- for(int i = 0; i < batch_dim; i++){
- int chosen = 0;
- for (int id = 1; id < 10; ++id){
- if (data[i * channels + chosen] < data[i * channels + id]) chosen = id;
- }
-
- //printf("chosen = %d, label = %d \n", chosen, labels[i]);
- if(chosen != labels[i])
- num_errors++;
- }
-
- float accuracy = ((batch_dim - num_errors) * 1.0 / batch_dim * 1.0) * 100.0;
- printf("****** Accuracy = %f \n\n", accuracy);
-
-
- FILE* fp = fopen("final_accuracy", "w+");
- if(fp != NULL){
-
- std::ostringstream ss;
- ss << std::fixed << accuracy;
- std::string print_str = ss.str();
-
- fwrite(print_str.c_str(), 1, print_str.length(), fp);
- fclose(fp);
- }
-
-}
-
-
-
-
-// NOTE: batch_size and num_classes are Unused arguments
-float computeAccuracy2(uint8_t* labels, int batch_size,
- void* result_ptr, size_t num_classes = 10){
-
- struct Tensor* result = (struct Tensor*) result_ptr;
-
- size_t batch_dim = result->dims.dim_sizes[0];
- num_classes = result->dims.dim_sizes[1];
- float* data = (float*) result->host_data;
- int num_errors = 0;
-
- printf("batch_dim = %lu, channels = %lu \n", batch_dim, num_classes);
-
- for(unsigned int i = 0; i < batch_dim; i++){
-
- int chosen = 0;
- for (int id = 1; id < num_classes; ++id){
- if (data[i * num_classes + chosen] < data[i * num_classes + id]) chosen = id;
- }
-
- if(chosen != labels[i])
- num_errors++;
-
- }
-
- float accuracy = ((batch_dim - num_errors) * 1.0 / batch_dim * 1.0) * 100.0;
- printf("****** Accuracy = %f \n\n", accuracy);
-
- FILE* fp = fopen("final_accuracy", "w+");
- if(fp != NULL){
-
- std::ostringstream ss;
- ss << std::fixed << accuracy;
- std::string print_str = ss.str();
-
- fwrite(print_str.c_str(), 1, print_str.length(), fp);
- }
-
- fclose(fp);
-
- return accuracy;
-}
-
-
-
-float computeAccuracy3(uint32_t* labels, void* result_ptr){
-
- struct Tensor* result = (struct Tensor*) result_ptr;
-
- size_t batch_dim = result->dims.dim_sizes[0];
- size_t num_classes = result->dims.dim_sizes[1];
- float* data = (float*) result->host_data;
- int num_errors = 0;
-
- printf("batch_dim = %lu, num_classes = %lu \n", batch_dim, num_classes);
-
- for(int i = 0; i < batch_dim; i++){
-
- int chosen = 0;
- for (int id = 1; id < num_classes; ++id){
- if (data[i * num_classes + chosen] < data[i * num_classes + id]) chosen = id;
- }
-
- if(chosen != labels[i])
- num_errors++;
- }
-
- float accuracy = ((batch_dim - num_errors) * 1.0 / batch_dim * 1.0) * 100.0;
- printf("****** Accuracy = %f \n\n", accuracy);
-
- FILE* fp = fopen("final_accuracy", "w+");
- if(fp != NULL){
-
- std::ostringstream ss;
- ss << std::fixed << accuracy;
- std::string print_str = ss.str();
-
- fwrite(print_str.c_str(), 1, print_str.length(), fp);
- }
-
- fclose(fp);
-
- return accuracy;
-}
-
-
-
-struct ClassProb{
- float prob;
- int index;
-};
-
-
-bool descendFloatComp(ClassProb obj1, ClassProb obj2){
- return obj1.prob > obj2.prob;
-}
-
-
-float computeTop5Accuracy(uint8_t* labels, int num_labels,
- void* result_ptr, unsigned num_classes = 10){
-
- struct Tensor* result = (struct Tensor*) result_ptr;
-
- size_t batch_dim = result->dims.dim_sizes[0];
- size_t channels = result->dims.dim_sizes[1];
- float* data = (float*) result->host_data;
- int num_errors = 0;
-
- printf("batch_dim = %lu, channels = %lu \n", batch_dim, channels);
-
- for(int i = 0; i < num_labels; i++){
-
- std::vector<ClassProb> elem_probs;
- for (int id = 0; id < num_classes; ++id){
- ClassProb cProb;
- cProb.prob = data[i * channels + id];
- cProb.index = id;
- elem_probs.push_back(cProb);
- }
-
- std:sort(elem_probs.begin(), elem_probs.end(), descendFloatComp);
- // Check if any of top-5 predictions matches
- bool matched = false;
- for(int j = 0; j < 5; j++){
- ClassProb cProb = elem_probs[j];
- if(cProb.index == labels[i])
- matched = true;
- }
-
- if(!matched)
- num_errors +=1;
- }
-
- float accuracy = ((batch_dim - num_errors) * 1.0 / batch_dim * 1.0) * 100.0;
- printf("****** Accuracy = %f \n\n", accuracy);
-
- FILE* fp = fopen("final_accuracy", "w+");
- if(fp != NULL){
-
- std::ostringstream ss;
- ss << std::fixed << accuracy;
- std::string print_str = ss.str();
-
- fwrite(print_str.c_str(), 1, print_str.length(), fp);
- }
-
- fclose(fp);
-
- return accuracy;
-}
-
-
-
-
-void dumpFinalAccuracy(float accuracy){
-
- printf("\n\n **** Final Accuracy = %f \n", accuracy);
-
- FILE* fp = fopen("final_accuracy", "w+");
- if(fp != NULL){
- std::ostringstream ss;
- ss << std::fixed << accuracy;
- std::string print_str = ss.str();
-
- fwrite(print_str.c_str(), 1, print_str.length(), fp);
- }
-
- fclose(fp);
-
- run_accuracies.push_back(accuracy);
-}
-
-
-
-void dumpAvgPSNR(float avg_psnr){
-
- FILE* fp = fopen("avg_psnr", "w+");
- if(fp != NULL){
- std::ostringstream ss;
- ss << std::fixed << avg_psnr;
- std::string print_str = ss.str();
- fwrite(print_str.c_str(), 1, print_str.length(), fp);
- }
-
- fclose(fp);
-}
-
-
-void dumpPSNRStd(float psnr_std){
-
- FILE* fp = fopen("psnr_std.txt", "w+");
- if(fp != NULL){
- std::ostringstream ss;
- ss << std::fixed << psnr_std;
- std::string print_str = ss.str();
- fwrite(print_str.c_str(), 1, print_str.length(), fp);
- }
-
- fclose(fp);
-}
-
-
-
-
-
-void dumpExecutionAccuracies(){
-
- FILE* fp = fopen("run_accuracies.txt", "w+");
- if(fp != NULL){
- for (int i = 0; i < run_accuracies.size(); i++){
- float accuracy = run_accuracies[i];
- std::ostringstream ss;
- ss << std::fixed << accuracy;
- std::string print_str = ss.str();
- fwrite(print_str.c_str(), 1, print_str.length(), fp);
- fwrite("\n", 1, 1, fp);
- }
-
- }
-
- fclose(fp);
-}
-
-
-float readPSNRFromFile(const char* file_name){
-
- float psnr;
- FILE* pFile = fopen(file_name, "r");
- if(pFile == NULL){
- printf("ERROR: psnr.txt not found! \n");
- abort();
- }
-
- fscanf(pFile, "%f", &psnr);
- printf("**** PSNR read = %f \n\n", psnr);
- return psnr;
-}
-
-
-float computePSNRViolation(void* gold_ptr, void* approx_ptr, float PSNR_threshold){
-
-
- PSNR_threshold = readPSNRFromFile("psnr.txt");
- std::vector<float> psnr_list;
-
- struct Tensor* gold_tensor = (struct Tensor*) gold_ptr;
- struct Tensor* approx_tensor = (struct Tensor*) approx_ptr;
-
- size_t* dim_sizes = gold_tensor->dims.dim_sizes;
- size_t batch_dim = dim_sizes[0];
- size_t image_size = dim_sizes[1] * dim_sizes[2] * dim_sizes[3];
-
- printf("batch_dim = %lu, image_size = %lu \n", batch_dim, image_size);
-
- float* gold_data = (float*) gold_tensor->host_data;
- float* approx_data = (float*) approx_tensor->host_data;
-
- FILE* fp = fopen("img_psnr.txt", "w+");
-
- float sum_psnr = 0.0;
- int num_errors = 0;
- for(size_t i = 0; i < batch_dim; i++){
- float mse_sum = 0.0;
- float max_val = -999999;
- size_t offset = i * image_size;
-
- for(size_t j = 0; j < image_size; j++){
- float diff = gold_data[offset + j] - approx_data[offset + j];
- float diff_square = diff * diff;
- mse_sum += diff_square;
-
- if(max_val < gold_data[offset + j]){
- max_val = gold_data[offset + j];
- }
- }
-
- mse_sum = mse_sum / image_size;
- float psnr = 20 * log10(255 / sqrt(mse_sum));
-
- sum_psnr += psnr;
- if (psnr < PSNR_threshold)
- num_errors += 1;
-
- printf("PSNR value = %f \n", psnr);
- psnr_list.push_back(psnr);
-
- std::ostringstream ss;
- ss << std::fixed << psnr;
- std::string print_str = ss.str();
- fwrite(print_str.c_str(), 1, print_str.length(), fp);
- fwrite("\n", 1, 1, fp);
- }
-
- float violation_rate = (num_errors * 1.0) / batch_dim * 100.0;
- printf("*** violation_rate= %f \n\n", violation_rate);
-
- float avg_psnr = sum_psnr / batch_dim;
- printf("*** avg_psnr = %f \n\n", avg_psnr);
- dumpAvgPSNR(avg_psnr);
-
- float success_rate = 100.0 - violation_rate;
- dumpFinalAccuracy(success_rate);
-
- fclose(fp);
-
-
- float var = 0.0;
- for(size_t i = 0; i < batch_dim; i++){
- var = var + (psnr_list[i] - avg_psnr) * (psnr_list[i] - avg_psnr);
- }
-
- var /= batch_dim;
- float std = sqrt(var);
-
- dumpPSNRStd(std);
-
- return violation_rate;
-}
-
-
-void dumpOutput(void* output_ptr, const char* file_name){
-
- struct Tensor* out_tensor = (struct Tensor*) output_ptr;
- size_t size_in_bytes = out_tensor->size_in_bytes;
- printf ("** Output size = %lu \n", size_in_bytes);
-
- float* host_data = (float*) out_tensor->host_data;
- FILE* fd = fopen(file_name, "w+");
- fwrite(host_data, 1, size_in_bytes, fd);
- fclose(fd);
-}
-
-
-
-#endif
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/include/utils_cpu.h b/llvm/projects/hpvm-tensor-rt/dnn_sources/include/utils_cpu.h
deleted file mode 100644
index 45ef7211a4c04f15d1763fde729b4ca550851008..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/include/utils_cpu.h
+++ /dev/null
@@ -1,467 +0,0 @@
-
-// Header guards
-#ifndef UTILS_HEADER
-#define UTILS_HEADER
-
-
-#include <sstream>
-#include <vector>
-#include <bits/stdc++.h>
-#include "../../tensor_runtime/include/tensor_cpu.h"
-#include "../../tensor_runtime/include/tensor_cpu_runtime.h"
-//#include "types.h"
-#include <cmath>
-#include <stdint.h>
-
-
-std::vector<float> run_accuracies;
-
-
-void printTensorInfo(void* tensor_ptr){
-
- struct Tensor* tensor = (struct Tensor*) tensor_ptr;
-
- if(tensor->gpu_data != NULL){
- printf("Successful cudaMalloc \n");
- }
-
- printf("tensor dims = %d \n", tensor->dims.num_dims);
- printf("dim1_size = %lu \n", tensor->dims.dim_sizes[0]);
- printf("dim2_size = %lu \n", tensor->dims.dim_sizes[1]);
- printf("num_elems = %lu \n", tensor->num_elems);
-}
-
-
-
-void printTensorDims(void* tensor_ptr){
-
- struct Tensor* tensor = (struct Tensor*) tensor_ptr;
-
- printf("Num_elems = %lu \n", tensor->num_elems);
- for (int i = 0; i < tensor->dims.num_dims; i++){
- printf("dim[%d] = %lu \n", i, tensor->dims.dim_sizes[i]);
- }
-}
-
-
-
-void compareTensors(void* tensor1_ptr, void* tensor2_ptr){
-
- struct Tensor* tensor1 = (struct Tensor*) tensor1_ptr;
- struct Tensor* tensor2 = (struct Tensor*) tensor2_ptr;
-
- //hpvm_request_tensor(tensor1, 0);
- //hpvm_request_tensor(tensor2, 0);
-
- float* tensor_data1 = (float*) tensor1->host_data;
- float* tensor_data2 = (float*) tensor2->host_data;
-
- for(unsigned int i = 0; i < tensor1->num_elems; i++){
- if(tensor_data1[i] != tensor_data2[i]){
- printf("Tensor data mismatch at index %d \n", i);
- abort();
- }
- }
-}
-
-
-
-//*** FIXIT: Move this to CPU-only
-struct Tensor* readTrainedWeightsCPU(const char* file_name, int data_type,
- int dim1_size, int dim2_size,
- int dim3_size, int dim4_size){
-
- // FIXIT: Don't assume floating point types
- int type_size = 4; // NOTE: Assuming floating point tensors
- long int num_elems = dim1_size * dim2_size * dim3_size * dim4_size;
- long int size_in_bytes = type_size * dim1_size * dim2_size * dim3_size * dim4_size;
- float* tensor_data = (float*) malloc(sizeof(float) * num_elems);
- int file_header_size = 0;
-
- FILE* file = fopen(file_name, "rb");
- if(file == NULL){
- printf("Data file %s is not found. Aborting... \n", file_name);
- abort();
- }
-
- fseek(file, file_header_size, SEEK_CUR); // Skipping the file header
- size_t bytes_read = fread(tensor_data, 1, size_in_bytes, file);
-
- printf("size in bytes = %lu, bytes read = %lu \n", size_in_bytes, bytes_read);
-
- fclose(file);
-
-
- struct Tensor* weights = (struct Tensor*) create4DTensor(data_type, nchw, dim1_size, dim2_size,
- dim3_size, dim4_size);
-
- initTensorData(weights, tensor_data, size_in_bytes);
- //compareValues(weights, tensor_data, num_elems);
- free(tensor_data);
-
- return weights;
-}
-
-
-struct Tensor* readTrainedWeights(const char* file_name, int data_type,
- int dim1_size, int dim2_size,
- int dim3_size, int dim4_size){
-
- return readTrainedWeightsCPU(file_name, data_type, dim1_size, dim2_size, dim3_size, dim4_size);
-}
-
-
-
-uint8_t* readLabels(const char* labels_file, int num_labels){
-
- uint8_t* labels = (uint8_t*) malloc(sizeof(uint8_t) * num_labels);
- FILE* file = fopen(labels_file, "rb");
- if(file == NULL){
- printf("Data file %s is not found. Aborting...\n", labels_file);
- abort();
- }
-
- size_t bytes_read = fread(labels, 1, sizeof(uint8_t) * num_labels, file);
-
- fclose(file);
-
- return labels;
-}
-
-
-uint8_t* readLabelsBatch(const char* labels_file, int start, int end){
-
- int num_labels = end - start;
- int file_header_size = sizeof(uint8_t) * start;
-
- uint8_t* labels = (uint8_t*) malloc(sizeof(uint8_t) * num_labels);
- FILE* file = fopen(labels_file, "rb");
- if(file == NULL){
- printf("Data file %s is not found. Aborting...\n", labels_file);
- abort();
- }
-
- fseek(file, file_header_size, SEEK_SET); // Skipping the file header
-
- size_t bytes_read = fread(labels, 1, sizeof(uint8_t) * num_labels, file);
-
-
- fclose(file);
-
- return labels;
-}
-
-
-
-void computeAccuracy(const char* labels_file, int num_labels, void* result_ptr){
-
- struct Tensor* result = (struct Tensor*) result_ptr;
-
- uint8_t* labels = readLabels(labels_file, num_labels);
- size_t batch_dim = result->dims.dim_sizes[0];
- size_t channels = result->dims.dim_sizes[1];
- float* data = (float*) result->host_data;
- int num_errors = 0;
-
- for(int i = 0; i < batch_dim; i++){
- int chosen = 0;
- for (int id = 1; id < 10; ++id){
- if (data[i * channels + chosen] < data[i * channels + id]) chosen = id;
- }
-
- if(chosen != labels[i])
- num_errors++;
- }
-
- float accuracy = ((batch_dim - num_errors) * 1.0 / batch_dim * 1.0) * 100.0;
- printf("****** Accuracy = %f \n\n", accuracy);
-
-
- FILE* fp = fopen("final_accuracy", "w+");
- if(fp != NULL){
- fprintf(fp, "%f", accuracy);
- fclose(fp);
- }
-
-}
-
-
-
-float computeAccuracy2(uint8_t* labels, int num_labels, void* result_ptr, unsigned num_classes = 10){
-
- unsigned num_zeros = 0;
-
- struct Tensor* result = (struct Tensor*) result_ptr;
-
- size_t batch_dim = result->dims.dim_sizes[0];
- size_t channels = result->dims.dim_sizes[1];
- float* data = (float*) result->host_data;
- int num_errors = 0;
-
- printf("batch_dim = %lu, channels = %lu \n", batch_dim, channels);
-
- for(int i = 0; i < num_labels; i++){
- int chosen = 0;
- for (int id = 1; id < num_classes; ++id){
- if (data[i * channels + chosen] < data[i * channels + id]) chosen = id;
- }
-
- if(labels[i] == 0)
- num_zeros++;
-
- if(chosen != labels[i])
- num_errors++;
- }
-
- float accuracy = ((batch_dim - num_errors) * 1.0 / batch_dim * 1.0) * 100.0;
- printf("****** Accuracy = %f \n\n", accuracy);
-
- FILE* fp = fopen("final_accuracy", "w+");
- if(fp != NULL){
- fprintf(fp, "%f", accuracy);
- }
-
- fclose(fp);
-
- return accuracy;
-}
-
-
-struct ClassProb{
- float prob;
- int index;
-};
-
-
-bool descendFloatComp(ClassProb obj1, ClassProb obj2){
- return obj1.prob > obj2.prob;
-}
-
-
-float computeTop5Accuracy(uint8_t* labels, int num_labels, void* result_ptr, unsigned num_classes = 10){
-
- struct Tensor* result = (struct Tensor*) result_ptr;
-
- size_t batch_dim = result->dims.dim_sizes[0];
- size_t channels = result->dims.dim_sizes[1];
- float* data = (float*) result->host_data;
- int num_errors = 0;
-
- printf("batch_dim = %lu, channels = %lu \n", batch_dim, channels);
-
- for(int i = 0; i < num_labels; i++){
-
- std::vector<ClassProb> elem_probs;
- for (int id = 0; id < num_classes; ++id){
- ClassProb cProb;
- cProb.prob = data[i * channels + id];
- cProb.index = id;
- elem_probs.push_back(cProb);
- }
-
- std:sort(elem_probs.begin(), elem_probs.end(), descendFloatComp);
- // Check if any of top-5 predictions matches
- bool matched = false;
- for(int j = 0; j < 5; j++){
- ClassProb cProb = elem_probs[j];
- if(cProb.index == labels[i])
- matched = true;
- }
-
- if(!matched)
- num_errors +=1;
- }
-
- float accuracy = ((batch_dim - num_errors) * 1.0 / batch_dim * 1.0) * 100.0;
- printf("****** Accuracy = %f \n\n", accuracy);
-
- FILE* fp = fopen("final_accuracy", "w+");
- if(fp != NULL){
- fprintf(fp, "%f", accuracy);
- }
-
- fclose(fp);
-
- return accuracy;
-}
-
-
-
-
-void dumpFinalAccuracy(float accuracy){
-
- printf("\n\n **** Final Accuracy = %f \n", accuracy);
-
- FILE* fp = fopen("final_accuracy", "w+");
- if(fp != NULL){
- fprintf(fp, "%f", accuracy);
- }
-
- fclose(fp);
-
- run_accuracies.push_back(accuracy);
-}
-
-
-
-/*void dumpAvgPSNR(float avg_psnr){
-
- FILE* fp = fopen("avg_psnr", "w+");
- if(fp != NULL){
- std::ostringstream ss;
- ss << std::fixed << avg_psnr;
- std::string print_str = ss.str();
- fwrite(print_str.c_str(), 1, print_str.length(), fp);
- }
-
- fclose(fp);
-}
-*/
-
-/*void dumpPSNRStd(float psnr_std){
-
- FILE* fp = fopen("psnr_std.txt", "w+");
- if(fp != NULL){
- std::ostringstream ss;
- ss << std::fixed << psnr_std;
- std::string print_str = ss.str();
- fwrite(print_str.c_str(), 1, print_str.length(), fp);
- }
-
- fclose(fp);
-}*/
-
-
-
-
-/*
-void dumpExecutionAccuracies(){
-
- FILE* fp = fopen("run_accuracies.txt", "w+");
- if(fp != NULL){
- for (int i = 0; i < run_accuracies.size(); i++){
- float accuracy = run_accuracies[i];
- std::ostringstream ss;
- ss << std::fixed << accuracy;
- std::string print_str = ss.str();
- fwrite(print_str.c_str(), 1, print_str.length(), fp);
- fwrite("\n", 1, 1, fp);
- }
-
- }
-
- fclose(fp);
-}
-*/
-
-float readPSNRFromFile(const char* file_name){
-
- float psnr;
- FILE* pFile = fopen(file_name, "r");
- if(pFile == NULL){
- printf("ERROR: psnr.txt not found! \n");
- abort();
- }
-
- fscanf(pFile, "%f", &psnr);
- printf("**** PSNR read = %f \n\n", psnr);
- return psnr;
-}
-
-
-/*float computePSNRViolation(void* gold_ptr, void* approx_ptr, float PSNR_threshold){
-
-
- PSNR_threshold = readPSNRFromFile("psnr.txt");
- std::vector<float> psnr_list;
-
- struct Tensor* gold_tensor = (struct Tensor*) gold_ptr;
- struct Tensor* approx_tensor = (struct Tensor*) approx_ptr;
-
- size_t* dim_sizes = gold_tensor->dims.dim_sizes;
- size_t batch_dim = dim_sizes[0];
- size_t image_size = dim_sizes[1] * dim_sizes[2] * dim_sizes[3];
-
- printf("batch_dim = %lu, image_size = %lu \n", batch_dim, image_size);
-
- float* gold_data = (float*) gold_tensor->host_data;
- float* approx_data = (float*) approx_tensor->host_data;
-
- FILE* fp = fopen("img_psnr.txt", "w+");
-
- float sum_psnr = 0.0;
- int num_errors = 0;
- for(size_t i = 0; i < batch_dim; i++){
- float mse_sum = 0.0;
- float max_val = -999999;
- size_t offset = i * image_size;
-
- for(size_t j = 0; j < image_size; j++){
- float diff = gold_data[offset + j] - approx_data[offset + j];
- float diff_square = diff * diff;
- mse_sum += diff_square;
-
- if(max_val < gold_data[offset + j]){
- max_val = gold_data[offset + j];
- }
- }
-
- mse_sum = mse_sum / image_size;
- float psnr = 20 * log10(255 / sqrt(mse_sum));
-
- sum_psnr += psnr;
- if (psnr < PSNR_threshold)
- num_errors += 1;
-
- printf("PSNR value = %f \n", psnr);
- psnr_list.push_back(psnr);
-
- std::ostringstream ss;
- ss << std::fixed << psnr;
- std::string print_str = ss.str();
- fwrite(print_str.c_str(), 1, print_str.length(), fp);
- fwrite("\n", 1, 1, fp);
- }
-
- float violation_rate = (num_errors * 1.0) / batch_dim * 100.0;
- printf("*** violation_rate= %f \n\n", violation_rate);
-
- float avg_psnr = sum_psnr / batch_dim;
- printf("*** avg_psnr = %f \n\n", avg_psnr);
- dumpAvgPSNR(avg_psnr);
-
- float success_rate = 100.0 - violation_rate;
- dumpFinalAccuracy(success_rate);
-
- fclose(fp);
-
-
- float var = 0.0;
- for(size_t i = 0; i < batch_dim; i++){
- var = var + (psnr_list[i] - avg_psnr) * (psnr_list[i] - avg_psnr);
- }
-
- var /= batch_dim;
- float std = sqrt(var);
-
- //dumpPSNRStd(std);
-
- return violation_rate;
-}*/
-
-
-void dumpOutput(void* output_ptr, const char* file_name){
-
- struct Tensor* out_tensor = (struct Tensor*) output_ptr;
- size_t size_in_bytes = out_tensor->size_in_bytes;
- printf ("** Output size = %lu \n", size_in_bytes);
-
- float* host_data = (float*) out_tensor->host_data;
- FILE* fd = fopen(file_name, "w+");
- fwrite(host_data, 1, size_in_bytes, fd);
- fclose(fd);
-}
-
-
-
-#endif
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/#test_fc_network2.cc# b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/#test_fc_network2.cc#
deleted file mode 100644
index 88ceb9105059aeb2eca8f18029af674ea8b14a10..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/#test_fc_network2.cc#
+++ /dev/null
@@ -1,92 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-#include "../include/types.h"
-
-
-void test4LayerFC(){
-
- printf("********* 4-layer FC Network ********* \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 10000;
- void* input = readInputTensor("../model_params/lenet_params/datasets/t10k-images-idx3-ubyte",
- float_type,
- test_batch_size, 1, 28, 28);
- void* fc1_weights = readTrainedWeights("../model_params/FC_network1/fc1.bin",
- float_type, 1, 1, 784, 1000);
- void* fc1_bias = readTrainedWeights("../model_params/FC_network1/fc1_bias.bin",
- float_type, 1, 1000, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/FC_network1/fc2.bin",
- float_type, 1, 1, 1000, 500);
- void* fc2_bias = readTrainedWeights("../model_params/FC_network1/fc2_bias.bin",
- float_type, 1, 500, 1, 1);
- void* fc3_weights = readTrainedWeights("../model_params/FC_network1/fc3.bin",
- float_type, 1, 1, 500, 200);
- void* fc3_bias = readTrainedWeights("../model_params/FC_network1/fc3_bias.bin",
- float_type, 1, 200, 1, 1);
- void* fc4_weights = readTrainedWeights("../model_params/FC_network1/fc4.bin",
- float_type, 1, 1, 200, 10);
- void* fc4_bias = readTrainedWeights("../model_params/FC_network1/fc4_bias.bin",
- float_type, 1, 10, 1, 1);
-
- //dumpWeightsToFile("tensors_out/input_fc.out", input);
- //dumpWeightsToFile("tensors_out/fc1_w_fc.out", fc1_weights);
-
- // Start Profiling execution times of Tensor operations
- startProfiling();
-
- // Layer-1
- void* fc1out = tensorGemmGPU(input, fc1_weights);
- printTensorDims(fc1out);
- //dumpWeightsToFile("tensors_out/fc1out_fc.out", fc1out);
-
- void* fc1_bias_out = tensorAdd(fc1out, fc1_bias);
- printTensorDims(fc1_bias_out);
- //dumpWeightsToFile("tensors_out/fc_fc1.out", fc1_bias_out);
-
- // Layer-2
- void* fc2out = tensorGemmGPU(fc1_bias_out, fc2_weights);
- printTensorDims(fc2out);
-
- void* fc2_bias_out = tensorAdd(fc2out, fc2_bias);
- printTensorDims(fc2_bias_out);
-
- // Layer-3
- void* fc3out = tensorGemmGPU(fc2_bias_out, fc3_weights);
- printTensorDims(fc3out);
-
- void* fc3_bias_out = tensorAdd(fc3out, fc3_bias);
- printTensorDims(fc3_bias_out);
-
- // Layer-4
- void* fc4out = tensorGemmGPU(fc3_bias_out, fc4_weights);
- printTensorDims(fc4out);
-
- void* fc4_bias_out = tensorAdd(fc4out, fc4_bias);
- printTensorDims(fc4_bias_out);
-
- void* result = tensorSoftmax(fc4_bias_out);
- printTensorDims(result);
-
- stopProfiling();
-
- computeAccuracy("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size, result);
-}
-
-
-
-
-int main(){
-
- initializeRuntime(0);
-
- test4LayerFC();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet2_cifar10.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet2_cifar10.cc
deleted file mode 100644
index fe71eb14caedba8d5813bbb0fa7feadcf0c72950..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet2_cifar10.cc
+++ /dev/null
@@ -1,146 +0,0 @@
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testCifarNet(){
-
- printf("********* Alexnet2 CIFAR-10 DNN ********** \n");
-
- std::string dir_prefix = std::string("../model_params/alexnet2_cifar10/");
- std::string input_path = dir_prefix + std::string("norm_cifar_input.bin");
- std::string labels_path = dir_prefix + std::string("test_labels.bin");
-
- void* conv1_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv1.bin",
- float_type, 32, 3, 3, 3);
- void* conv1_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv1_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv2.bin",
- float_type, 32, 32, 3, 3);
- void* conv2_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv2_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv3_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv3.bin",
- float_type, 64, 32, 3, 3);
- void* conv3_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv3_bias.bin",
- float_type, 1, 64, 1, 1);
- void* conv4_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv4.bin",
- float_type, 64, 64, 3, 3);
- void* conv4_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv4_bias.bin",
- float_type, 1, 64, 1, 1);
- void* conv5_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv5.bin",
- float_type, 128, 64, 3, 3);
- void* conv5_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv5_bias.bin",
- float_type, 1, 128, 1, 1);
- void* conv6_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv6.bin",
- float_type, 128, 128, 3, 3);
- void* conv6_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv6_bias.bin",
- float_type, 1, 128, 1, 1);
-
- void* fc1_weights = readTrainedWeights("../model_params/alexnet2_cifar10/fc1.bin",
- float_type, 1, 1, 2048, 10);
- void* fc1_bias = readTrainedWeights("../model_params/alexnet2_cifar10/fc1_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
-
- startMemTracking();
-
- int test_input_size = 10000;
- int batch_size = 2500;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- // NOTE: Starting time profiling
- startProfiling();
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
- void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
-
- void* conv1out = tensorConvolution(input, conv1_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv1out, conv1_bias);
- void* conv1_tanh = tensorTanh(conv1out);
-
- // 2nd Layer
- void* conv2out = tensorConvolution(conv1_tanh, conv2_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv2out, conv2_bias);
- void* conv2_tanh = tensorTanh(conv2out);
- void* pool2out = tensorPooling(conv2_tanh, 0, 2, 2, 0, 0, 2, 2);
-
- // 3rd Layer
- void* conv3out = tensorConvolution(pool2out, conv3_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv3out, conv3_bias);
- void* conv3_tanh = tensorTanh(conv3out);
-
- // 4th Layer
- void* conv4out = tensorConvolution(conv3_tanh, conv4_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv4out, conv4_bias);
- void* conv4_tanh = tensorTanh(conv4out);
- void* pool4out = tensorPooling(conv4_tanh, 0, 2, 2, 0, 0, 2, 2);
-
- // 5th Layer
- void* conv5out = tensorConvolution(pool4out, conv5_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv5out, conv5_bias);
- void* conv5_tanh = tensorTanh(conv5out);
-
- // 6th Layer
- void* conv6out = tensorConvolution(conv5_tanh, conv6_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv6out, conv6_bias);
-
- void* conv6_tanh = tensorTanh(conv6out);
- void* pool6out = tensorPooling(conv6_tanh, 0, 2, 2, 0, 0, 2, 2);
-
- // final FC Layer
- void* gemm1out = tensorGemmGPU(pool6out, fc1_weights);
- void* gemm1biasout = tensorAdd(gemm1out, fc1_bias);
- void* result = tensorSoftmax(gemm1biasout);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
-
- float accuracy = computeAccuracy2(labels, batch_size, result);
- final_accuracy += accuracy;
-
- freeBatchMemory();
- }
-
- stopProfiling();
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-}
-
-
-int main(int argc, char* argv[]){
-
- llvm_hpvm_initTensorRt(0);
-
- testCifarNet();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet2_cifar10_tuner.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet2_cifar10_tuner.cc
deleted file mode 100644
index 08e5817fc4aa037bc59cceafc1baba382696e329..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet2_cifar10_tuner.cc
+++ /dev/null
@@ -1,162 +0,0 @@
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-
-
-int total_runs = 1;
-
-/* NOTE: Reference Architecture to use for profiling */
-void testCifarNet(){
-
- printf("********* Alexnet2 CIFAR-10 DNN ********** \n");
-
- std::string dir_prefix = std::string("../model_params/alexnet2_cifar10/");
- std::string input_path = dir_prefix + std::string("norm_cifar_input.bin");
- std::string labels_path = dir_prefix + std::string("test_labels.bin");
-
- void* conv1_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv1.bin",
- float_type, 32, 3, 3, 3);
- void* conv1_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv1_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv2.bin",
- float_type, 32, 32, 3, 3);
- void* conv2_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv2_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv3_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv3.bin",
- float_type, 64, 32, 3, 3);
- void* conv3_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv3_bias.bin",
- float_type, 1, 64, 1, 1);
- void* conv4_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv4.bin",
- float_type, 64, 64, 3, 3);
- void* conv4_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv4_bias.bin",
- float_type, 1, 64, 1, 1);
- void* conv5_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv5.bin",
- float_type, 128, 64, 3, 3);
- void* conv5_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv5_bias.bin",
- float_type, 1, 128, 1, 1);
- void* conv6_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv6.bin",
- float_type, 128, 128, 3, 3);
- void* conv6_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv6_bias.bin",
- float_type, 1, 128, 1, 1);
-
- void* fc1_weights = readTrainedWeights("../model_params/alexnet2_cifar10/fc1.bin",
- float_type, 1, 1, 2048, 10);
- void* fc1_bias = readTrainedWeights("../model_params/alexnet2_cifar10/fc1_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
-
- startMemTracking();
-
- int test_input_size = 500;
- int batch_size = 500;
- int offset = 5000;
-
- int batch_count = test_input_size / batch_size;
-
- // NOTE: Starting time profiling
- startProfiling();
-
- for(int j = 0; j < total_runs; j++){
-
- float final_accuracy = 0.0;
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size + offset;
- int end = (i + 1) * batch_size + offset;
- void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
-
- void* conv1out = tensorConvolution(input, conv1_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv1out, conv1_bias);
- void* conv1_tanh = tensorTanh(conv1out);
-
- // 2nd Layer
- void* conv2out = tensorConvolution(conv1_tanh, conv2_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv2out, conv2_bias);
- void* conv2_tanh = tensorTanh(conv2out);
- void* pool2out = tensorPooling(conv2_tanh, 0, 2, 2, 0, 0, 2, 2);
-
- // 3rd Layer
- void* conv3out = tensorConvolution(pool2out, conv3_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv3out, conv3_bias);
- void* conv3_tanh = tensorTanh(conv3out);
-
- // 4th Layer
- void* conv4out = tensorConvolution(conv3_tanh, conv4_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv4out, conv4_bias);
- void* conv4_tanh = tensorTanh(conv4out);
- void* pool4out = tensorPooling(conv4_tanh, 0, 2, 2, 0, 0, 2, 2);
-
- // 5th Layer
- void* conv5out = tensorConvolution(pool4out, conv5_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv5out, conv5_bias);
- void* conv5_tanh = tensorTanh(conv5out);
-
- // 6th Layer
- void* conv6out = tensorConvolution(conv5_tanh, conv6_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv6out, conv6_bias);
-
- void* conv6_tanh = tensorTanh(conv6out);
- void* pool6out = tensorPooling(conv6_tanh, 0, 2, 2, 0, 0, 2, 2);
-
- // final FC Layer
- void* gemm1out = tensorGemmGPU(pool6out, fc1_weights);
- void* gemm1biasout = tensorAdd(gemm1out, fc1_bias);
- void* result = tensorSoftmax(gemm1biasout);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
-
- float accuracy = computeAccuracy2(labels, batch_size, result);
- final_accuracy += accuracy;
-
- freeBatchMemory();
- }
-
- stopProfiling();
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
- }
-
-
- dumpExecutionAccuracies();
-
-
-}
-
-
-
-int main(int argc, char* argv[]){
-
- if (argc > 1){
- total_runs = atoi(argv[1]);
- }
-
- llvm_hpvm_initTensorRt(0);
-
- testCifarNet();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet_cifar10.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet_cifar10.cc
deleted file mode 100644
index 3e5cec7d0760252ebff1b31293a51bdf570415f4..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet_cifar10.cc
+++ /dev/null
@@ -1,196 +0,0 @@
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testCifarNet(){
-
- int total_runs = 100;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
-
- printf("********* CIFAR-10 DNN ********** \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 5000;
-
- //uint8_t* labels = readLabels("../model_params/cifar_keras/labels.bin", test_batch_size);
- uint8_t* labels = readLabels("../model_params/alexnet_cifar10/test_labels.bin", test_batch_size);
-
- void* input = readTrainedWeights("../model_params/alexnet_cifar10/norm_cifar_input.bin",
- float_type,
- test_batch_size, 3, 32, 32);
-
- void* conv1_filter = readTrainedWeights("../model_params/alexnet_cifar10/conv1.bin",
- float_type, 64, 3, 11, 11);
- void* conv1_bias = readTrainedWeights("../model_params/alexnet_cifar10/conv1_bias.bin",
- float_type, 1, 64, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/alexnet_cifar10/conv2.bin",
- float_type, 192, 64, 5, 5);
- void* conv2_bias = readTrainedWeights("../model_params/alexnet_cifar10/conv2_bias.bin",
- float_type, 1, 192, 1, 1);
-
- void* conv3_filter = readTrainedWeights("../model_params/alexnet_cifar10/conv3.bin",
- float_type, 384, 192, 3, 3);
- void* conv3_bias = readTrainedWeights("../model_params/alexnet_cifar10/conv3_bias.bin",
- float_type, 1, 384, 1, 1);
- void* conv4_filter = readTrainedWeights("../model_params/alexnet_cifar10/conv4.bin",
- float_type, 256, 384, 3, 3);
- void* conv4_bias = readTrainedWeights("../model_params/alexnet_cifar10/conv4_bias.bin",
- float_type, 1, 256, 1, 1);
- void* conv5_filter = readTrainedWeights("../model_params/alexnet_cifar10/conv5.bin",
- float_type, 256, 256, 3, 3);
- void* conv5_bias = readTrainedWeights("../model_params/alexnet_cifar10/conv5_bias.bin",
- float_type, 1, 256, 1, 1);
-
- void* fc1_weights = readTrainedWeights("../model_params/alexnet_cifar10/fc1.bin",
- float_type, 1, 1, 4096, 10);
- void* fc1_bias = readTrainedWeights("../model_params/alexnet_cifar10/fc1_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- clearTensorMap();
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
- // Start power and performnce profiling
- startProfiling();
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* conv1out = tensorConvolution(input, conv1_filter, 5, 5, 1, 1,
- conv_mode, conv_precision);
-
- tensorAdd(conv1out, conv1_bias); // NOTE: In place operation
-
- void* conv1_tanh = tensorTanh(conv1out);
-
- void* pool1out = tensorPooling(conv1_tanh, 0, 2, 2, 0, 0, 2, 2);
-
- // 2nd Layer
- void* conv2out = tensorConvolution(pool1out, conv2_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv2out, conv2_bias); // NOTE: In place operation
-
- void* conv2_tanh = tensorTanh(conv2out);
-
- void* pool2out = tensorPooling(conv2_tanh, 0, 2, 2, 0, 0, 2, 2);
-
-
- // 3rd Layer
- void* conv3out = tensorConvolution(pool2out, conv3_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv3out, conv3_bias); // NOTE: In place operation
-
- void* conv3_tanh = tensorTanh(conv3out);
-
- // 4th Layer
- void* conv4out = tensorConvolution(conv3_tanh, conv4_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv4out, conv4_bias); // NOTE: In place operation
-
- void* conv4_tanh = tensorTanh(conv4out);
-
- // 5th Layer
- void* conv5out = tensorConvolution(conv4_tanh, conv5_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv5out, conv5_bias); // NOTE: In place operation
-
- void* conv5_tanh = tensorTanh(conv5out);
-
- void* pool5out = tensorPooling(conv5_tanh, 0, 2, 2, 0, 0, 2, 2);
-
- // final FC Layer
- void* gemm1out = tensorGemmGPU(pool5out, fc1_weights);
-
- void* gemm1biasout = tensorAdd(gemm1out, fc1_bias);
-
- void* result = tensorSoftmax(gemm1biasout);
-
- printTensorDims(result);
-
- // End profiling and dump output to profile.txt
- stopProfiling();
-
- computeAccuracy2(labels, test_batch_size, result);
-
- dumpAccuracyNorms();
- freeOutputTensors();
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
-
-
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(1);
-
- testCifarNet();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet_cifar10_approx.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet_cifar10_approx.cc
deleted file mode 100644
index 7713b8105ac0f9bc6f1dae6899548599e5ede0ce..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet_cifar10_approx.cc
+++ /dev/null
@@ -1,196 +0,0 @@
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testCifarNet(){
-
- int total_runs = 100;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
-
- printf("********* CIFAR-10 DNN ********** \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 1000; //5000
-
- //uint8_t* labels = readLabels("../model_params/cifar_keras/labels.bin", test_batch_size);
- uint8_t* labels = readLabels("../model_params/alexnet_cifar10/test_labels.bin", test_batch_size);
-
- void* input = readTrainedWeights("../model_params/alexnet_cifar10/norm_cifar_input.bin",
- float_type,
- test_batch_size, 3, 32, 32);
-
- void* conv1_filter = readTrainedWeights("../model_params/alexnet_cifar10/conv1.bin",
- float_type, 64, 3, 11, 11);
- void* conv1_bias = readTrainedWeights("../model_params/alexnet_cifar10/conv1_bias.bin",
- float_type, 1, 64, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/alexnet_cifar10/conv2.bin",
- float_type, 192, 64, 5, 5);
- void* conv2_bias = readTrainedWeights("../model_params/alexnet_cifar10/conv2_bias.bin",
- float_type, 1, 192, 1, 1);
-
- void* conv3_filter = readTrainedWeights("../model_params/alexnet_cifar10/conv3.bin",
- float_type, 384, 192, 3, 3);
- void* conv3_bias = readTrainedWeights("../model_params/alexnet_cifar10/conv3_bias.bin",
- float_type, 1, 384, 1, 1);
- void* conv4_filter = readTrainedWeights("../model_params/alexnet_cifar10/conv4.bin",
- float_type, 256, 384, 3, 3);
- void* conv4_bias = readTrainedWeights("../model_params/alexnet_cifar10/conv4_bias.bin",
- float_type, 1, 256, 1, 1);
- void* conv5_filter = readTrainedWeights("../model_params/alexnet_cifar10/conv5.bin",
- float_type, 256, 256, 3, 3);
- void* conv5_bias = readTrainedWeights("../model_params/alexnet_cifar10/conv5_bias.bin",
- float_type, 1, 256, 1, 1);
-
- void* fc1_weights = readTrainedWeights("../model_params/alexnet_cifar10/fc1.bin",
- float_type, 1, 1, 4096, 10);
- void* fc1_bias = readTrainedWeights("../model_params/alexnet_cifar10/fc1_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- clearTensorMap();
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
- // Start power and performnce profiling
- startProfiling();
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* conv1out = tensorConvPerf(input, conv1_filter, 5, 5, 1, 1,
- conv_mode, conv_precision, 0, 0);
-
- tensorAdd(conv1out, conv1_bias); // NOTE: In place operation
-
- void* conv1_tanh = tensorTanh(conv1out);
-
- void* pool1out = tensorPooling(conv1_tanh, 0, 2, 2, 0, 0, 2, 2);
-
- // 2nd Layer
- void* conv2out = tensorConvPerf(pool1out, conv2_filter, 2, 2, 1, 1,
- conv_mode, conv_precision, 0, 0);
- tensorAdd(conv2out, conv2_bias); // NOTE: In place operation
-
- void* conv2_tanh = tensorTanh(conv2out);
-
- void* pool2out = tensorPooling(conv2_tanh, 0, 2, 2, 0, 0, 2, 2);
-
-
- // 3rd Layer
- void* conv3out = tensorConvPerf(pool2out, conv3_filter, 1, 1, 1, 1,
- conv_mode, conv_precision, 0, 0);
- tensorAdd(conv3out, conv3_bias); // NOTE: In place operation
-
- void* conv3_tanh = tensorTanh(conv3out);
-
- // 4th Layer
- void* conv4out = tensorConvPerf(conv3_tanh, conv4_filter, 1, 1, 1, 1,
- conv_mode, conv_precision, 2, 0);
- tensorAdd(conv4out, conv4_bias); // NOTE: In place operation
-
- void* conv4_tanh = tensorTanh(conv4out);
-
- // 5th Layer
- void* conv5out = tensorConvPerf(conv4_tanh, conv5_filter, 1, 1, 1, 1,
- conv_mode, conv_precision, 0, 0);
- tensorAdd(conv5out, conv5_bias); // NOTE: In place operation
-
- void* conv5_tanh = tensorTanh(conv5out);
-
- void* pool5out = tensorPooling(conv5_tanh, 0, 2, 2, 0, 0, 2, 2);
-
- // final FC Layer
- void* gemm1out = tensorGemmGPU(pool5out, fc1_weights);
-
- void* gemm1biasout = tensorAdd(gemm1out, fc1_bias);
-
- void* result = tensorSoftmax(gemm1biasout);
-
- printTensorDims(result);
-
- // End profiling and dump output to profile.txt
- stopProfiling();
-
- computeAccuracy2(labels, test_batch_size, result);
-
- dumpAccuracyNorms();
- freeOutputTensors();
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
-
-
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(1);
-
- testCifarNet();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet_cifar10_cpu.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet_cifar10_cpu.cc
deleted file mode 100644
index b64d52678238825fe6e6368d1d15f7958c3759aa..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet_cifar10_cpu.cc
+++ /dev/null
@@ -1,181 +0,0 @@
-
-
-#include <fcntl.h>
-#include <stdio.h>
-#include <stdlib.h>
-#include <string.h>
-#include <sys/stat.h>
-#include <sys/types.h>
-#include <unistd.h>
-
-#include "../../tensor_runtime/include/tensor_cpu_runtime.h"
-#include "../include/utils_cpu.h"
-#include "../include/types.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testCifarNet(){
-
- int total_runs = 100;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
-
- printf("********* CIFAR-10 DNN ********** \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 100;
-
- //uint8_t* labels = readLabels("../model_params/cifar_keras/labels.bin", test_batch_size);
- uint8_t* labels = readLabels("./model_params/alexnet_cifar10/test_labels.bin", test_batch_size);
-
- void* input = readTrainedWeightsCPU("./model_params/alexnet_cifar10/norm_cifar_input.bin",
- float_type,
- test_batch_size, 3, 32, 32);
-
- void* conv1_filter = readTrainedWeightsCPU("./model_params/alexnet_cifar10/conv1.bin",
- float_type, 64, 3, 11, 11);
- void* conv1_bias = readTrainedWeightsCPU("./model_params/alexnet_cifar10/conv1_bias.bin",
- float_type, 1, 64, 1, 1);
- void* conv2_filter = readTrainedWeightsCPU("./model_params/alexnet_cifar10/conv2.bin",
- float_type, 192, 64, 5, 5);
- void* conv2_bias = readTrainedWeightsCPU("./model_params/alexnet_cifar10/conv2_bias.bin",
- float_type, 1, 192, 1, 1);
-
- void* conv3_filter = readTrainedWeightsCPU("./model_params/alexnet_cifar10/conv3.bin",
- float_type, 384, 192, 3, 3);
- void* conv3_bias = readTrainedWeightsCPU("./model_params/alexnet_cifar10/conv3_bias.bin",
- float_type, 1, 384, 1, 1);
- void* conv4_filter = readTrainedWeightsCPU("./model_params/alexnet_cifar10/conv4.bin",
- float_type, 256, 384, 3, 3);
- void* conv4_bias = readTrainedWeightsCPU("./model_params/alexnet_cifar10/conv4_bias.bin",
- float_type, 1, 256, 1, 1);
- void* conv5_filter = readTrainedWeightsCPU("./model_params/alexnet_cifar10/conv5.bin",
- float_type, 256, 256, 3, 3);
- void* conv5_bias = readTrainedWeightsCPU("./model_params/alexnet_cifar10/conv5_bias.bin",
- float_type, 1, 256, 1, 1);
-
- void* fc1_weights = readTrainedWeightsCPU("./model_params/alexnet_cifar10/fc1.bin",
- float_type, 1, 1, 4096, 10);
- void* fc1_bias = readTrainedWeightsCPU("./model_params/alexnet_cifar10/fc1_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* conv1out = tensorConvolutionCPU(input, conv1_filter, 5, 5, 1, 1,
- conv_mode, conv_precision);
-
- tensorAddCPU(conv1out, conv1_bias); // NOTE: In place operation
-
- void* conv1_tanh = tensorTanhCPU(conv1out);
-
- void* pool1out = tensorPoolingCPU(conv1_tanh, 0, 2, 2, 0, 0, 2, 2);
-
- // 2nd Layer
- void* conv2out = tensorConvolutionCPU(pool1out, conv2_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
- tensorAddCPU(conv2out, conv2_bias); // NOTE: In place operation
-
- void* conv2_tanh = tensorTanhCPU(conv2out);
-
- void* pool2out = tensorPoolingCPU(conv2_tanh, 0, 2, 2, 0, 0, 2, 2);
-
-
- // 3rd Layer
- void* conv3out = tensorConvolutionCPU(pool2out, conv3_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAddCPU(conv3out, conv3_bias); // NOTE: In place operation
-
- void* conv3_tanh = tensorTanhCPU(conv3out);
-
- // 4th Layer
- void* conv4out = tensorConvolutionCPU(conv3_tanh, conv4_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAddCPU(conv4out, conv4_bias); // NOTE: In place operation
-
- void* conv4_tanh = tensorTanhCPU(conv4out);
-
- // 5th Layer
- void* conv5out = tensorConvolutionCPU(conv4_tanh, conv5_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAddCPU(conv5out, conv5_bias); // NOTE: In place operation
-
- void* conv5_tanh = tensorTanhCPU(conv5out);
-
- void* pool5out = tensorPoolingCPU(conv5_tanh, 0, 2, 2, 0, 0, 2, 2);
-
- // final FC Layer
- void* gemm1out = tensorGemmCPU(pool5out, fc1_weights);
-
- void* gemm1biasout = tensorAddCPU(gemm1out, fc1_bias);
-
- void* result = tensorSoftmaxCPU(gemm1biasout);
-
- computeAccuracy2(labels, test_batch_size, result);
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
-
-
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(1);
-
- testCifarNet();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet_cifar10_front.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet_cifar10_front.cc
deleted file mode 100644
index 84510c5342811eb20c8c7e834f4fcf34d5561ccb..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet_cifar10_front.cc
+++ /dev/null
@@ -1,104 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
-
- std::string dir_prefix = std::string("../model_params/alexnet_cifar10_front/");
- std::string input_path = dir_prefix + std::string("input.bin");
- //void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
- std::string labels_path = dir_prefix + std::string("labels.bin");
- //uint8_t* labels = readLabels(labels_path.c_str(),10000);
- std::string conv2d_1_w_path = dir_prefix + std::string("conv0.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,11,11);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv_bias0.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv3.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,192,64,5,5);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv_bias3.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,192,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv6.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,384,192,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv_bias6.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,384,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv7.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,256,384,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv_bias7.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv8.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv_bias8.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("fc12.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,4096,10);
- std::string dense_1_b_path = dir_prefix + std::string("fc_bias12.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 10000;
- int batch_size = 2500;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- // NOTE: Starting time profiling
- startProfiling();
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
- void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 5, 5, 1, 1, 1, 0);
- void* var_1 = tensorAdd(var_0, conv2d_1_b);
- void* var_2 = tensorTanh(var_1);
- void* var_3 = tensorPooling(var_2,0,2,2,0,0,2,2);
- void* var_5 = tensorConvolution(var_3, conv2d_2_w, 2, 2, 1, 1, 1, 0);
- void* var_6 = tensorAdd(var_5, conv2d_2_b);
- void* var_7 = tensorTanh(var_6);
- void* var_8 = tensorPooling(var_7,0,2,2,0,0,2,2);
- void* var_10 = tensorConvolution(var_8, conv2d_3_w, 1, 1, 1, 1, 1, 0);
- void* var_11 = tensorAdd(var_10, conv2d_3_b);
- void* var_12 = tensorTanh(var_11);
- void* var_13 = tensorConvolution(var_12, conv2d_4_w, 1, 1, 1, 1, 1, 0);
- void* var_14 = tensorAdd(var_13, conv2d_4_b);
- void* var_15 = tensorTanh(var_14);
- void* var_16 = tensorConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
- void* var_17 = tensorAdd(var_16, conv2d_5_b);
- void* var_18 = tensorTanh(var_17);
- void* var_19 = tensorPooling(var_18,0,2,2,0,0,2,2);
- void* var_22 = tensorGemmGPU(var_19, dense_1_w);
- void* var_23 = tensorAdd(var_22, dense_1_b);
- void* var_24 = tensorSoftmax(var_23);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
-
- float accuracy = computeAccuracy2(labels,batch_size,var_24);
- final_accuracy += accuracy;
-
- freeBatchMemory();
- }
-
- stopProfiling();
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet_cifar10_tuner.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet_cifar10_tuner.cc
deleted file mode 100644
index d45cfa9ef3294c4c588b3abb98100dd8391529b7..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet_cifar10_tuner.cc
+++ /dev/null
@@ -1,123 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-
-int main(int argc, char* argv[]){
-
- int total_runs = 1;
-
- if (argc > 1){
- printf("argv[1] = %s \n", argv[1]);
- total_runs = atoi(argv[1]);
- printf("total_runs %d \n", total_runs);
- }
-
-
- llvm_hpvm_initTensorRt(0);
-
-
- std::string dir_prefix = std::string("../model_params/alexnet_cifar10_front/");
- //std::string input_path = dir_prefix + std::string("alexnet_calib.bin");
- //std::string labels_path = dir_prefix + std::string("alexnet_train_labels.bin");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
-
- std::string conv2d_1_w_path = dir_prefix + std::string("conv0.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,11,11);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv_bias0.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv3.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,192,64,5,5);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv_bias3.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,192,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv6.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,384,192,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv_bias6.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,384,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv7.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,256,384,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv_bias7.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv8.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv_bias8.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("fc12.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,4096,10);
- std::string dense_1_b_path = dir_prefix + std::string("fc_bias12.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 500;
- int batch_size = 500;
- int offset = 5000;
-
- int batch_count = test_input_size / batch_size;
-
-
- // NOTE: Starting time profiling
- startProfiling();
-
- for(int j = 0; j < total_runs; j++){
-
- float final_accuracy = 0.0;
- for(int i = 0; i < batch_count; i++){
-
- int start = (i * batch_size) + offset;
- int end = (i + 1) * batch_size + offset;
- void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 5, 5, 1, 1, 1, 0);
- void* var_1 = tensorAdd(var_0, conv2d_1_b);
- void* var_2 = tensorTanh(var_1);
- void* var_3 = tensorPooling(var_2,0,2,2,0,0,2,2);
- void* var_5 = tensorConvolution(var_3, conv2d_2_w, 2, 2, 1, 1, 1, 0);
- void* var_6 = tensorAdd(var_5, conv2d_2_b);
- void* var_7 = tensorTanh(var_6);
- void* var_8 = tensorPooling(var_7,0,2,2,0,0,2,2);
- void* var_10 = tensorConvolution(var_8, conv2d_3_w, 1, 1, 1, 1, 1, 0);
- void* var_11 = tensorAdd(var_10, conv2d_3_b);
- void* var_12 = tensorTanh(var_11);
- void* var_13 = tensorConvolution(var_12, conv2d_4_w, 1, 1, 1, 1, 1, 0);
- void* var_14 = tensorAdd(var_13, conv2d_4_b);
- void* var_15 = tensorTanh(var_14);
- void* var_16 = tensorConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
- void* var_17 = tensorAdd(var_16, conv2d_5_b);
- void* var_18 = tensorTanh(var_17);
- void* var_19 = tensorPooling(var_18,0,2,2,0,0,2,2);
- void* var_22 = tensorGemmGPU(var_19, dense_1_w);
- void* var_23 = tensorAdd(var_22, dense_1_b);
- void* var_24 = tensorSoftmax(var_23);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
-
- float accuracy = computeAccuracy2(labels,batch_size,var_24);
- final_accuracy += accuracy;
-
- freeBatchMemory();
- }
-
- stopProfiling();
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
- }
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/cifar_keras.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/cifar_keras.cc
deleted file mode 100644
index c746e5de6116f701df7370f93969d40486e04e90..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/cifar_keras.cc
+++ /dev/null
@@ -1,203 +0,0 @@
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testCifarNet(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
-
- printf("********* CIFAR-10 DNN ********** \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 1000;
-
- uint8_t* labels = readLabels("../model_params/cifar_keras/labels.bin", test_batch_size);
-
- void* input = readTrainedWeights("../model_params/cifar_keras/input.bin",
- float_type,
- test_batch_size, 3, 32, 32);
-
- void* conv1_filter = readTrainedWeights("../model_params/cifar_keras/conv1.bin",
- float_type, 32, 3, 3, 3);
- void* conv1_bias = readTrainedWeights("../model_params/cifar_keras/conv1_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/cifar_keras/conv2.bin",
- float_type, 64, 32, 3, 3);
- void* conv2_bias = readTrainedWeights("../model_params/cifar_keras/conv2_bias.bin",
- float_type, 1, 64, 1, 1);
-
- void* conv3_filter = readTrainedWeights("../model_params/cifar_keras/conv3.bin",
- float_type, 128, 64, 3, 3);
- void* conv3_bias = readTrainedWeights("../model_params/cifar_keras/conv3_bias.bin",
- float_type, 1, 128, 1, 1);
- void* conv4_filter = readTrainedWeights("../model_params/cifar_keras/conv4.bin",
- float_type, 128, 128, 3, 3);
- void* conv4_bias = readTrainedWeights("../model_params/cifar_keras/conv4_bias.bin",
- float_type, 1, 128, 1, 1);
-
-
- void* fc1_weights = readTrainedWeights("../model_params/cifar_keras/fc1.bin",
- float_type, 1, 1, 2048, 1024);
- void* fc1_bias = readTrainedWeights("../model_params/cifar_keras/fc1_bias.bin",
- float_type, 1, 1024, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/cifar_keras/fc2.bin",
- float_type, 1, 1, 1024, 10);
- void* fc2_bias = readTrainedWeights("../model_params/cifar_keras/fc2_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
-
- clearTensorMap();
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
- // Start power and performnce profiling
- startProfiling();
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* conv1out = tensorConvolution(input, conv1_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
-
- tensorAdd(conv1out, conv1_bias); // NOTE: In place operation
-
- void* conv1_tanh = tensorTanh(conv1out);
-
-
- // 2nd Layer
- void* conv2out = tensorConvolution(conv1_tanh, conv2_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv2out, conv2_bias); // NOTE: In place operation
-
- void* conv2_tanh = tensorTanh(conv2out);
-
- void* pool2out = tensorPooling(conv2_tanh, 0, 2, 2, 0, 0, 2, 2);
-
-
- // 3rd Layer
- void* conv3out = tensorConvolution(pool2out, conv3_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv3out, conv3_bias); // NOTE: In place operation
-
- void* conv3_tanh = tensorTanh(conv3out);
-
- void* pool3out = tensorPooling(conv3_tanh, 0, 2, 2, 0, 0, 2, 2);
-
-
- // 4th Layer
- void* conv4out = tensorConvolution(pool3out, conv4_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv4out, conv4_bias); // NOTE: In place operation
-
- void* conv4_tanh = tensorTanh(conv4out);
-
- void* pool4out = tensorPooling(conv4_tanh, 0, 2, 2, 0, 0, 2, 2);
-
-
- printTensorDims(pool4out);
-
-
- void* gemm1out = tensorGemmGPU(pool4out, fc1_weights);
-
- void* gemm1biasout = tensorAdd(gemm1out, fc1_bias);
-
- void* tanh1out = tensorTanh(gemm1biasout);
-
- void* gemm2out = tensorGemmGPU(tanh1out, fc2_weights);
-
- void* gemm2_biasout = tensorAdd(gemm2out, fc2_bias);
-
- void* tanh2out = tensorTanh(gemm2_biasout);
-
- void* result = tensorSoftmax(tanh2out);
-
- printTensorDims(result);
-
- // End profiling and dump output to profile.txt
- stopProfiling();
-
- computeAccuracy2(labels, test_batch_size, result);
-
- dumpAccuracyNorms();
- freeOutputTensors();
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
-
-
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(0);
-
- testCifarNet();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/depthwise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/depthwise.cc
deleted file mode 100644
index 00e259079058f1be5163bd43d9982e07b82f1001..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/depthwise.cc
+++ /dev/null
@@ -1,84 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
-
- std::string dir_prefix = std::string("../model_params/depthwise_test_8/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,1,5,5);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string depthwise_conv2d_1_b_path = dir_prefix + std::string("depthwise_conv2d_1_b.bin");
- void* depthwise_conv2d_1_b = readTrainedWeights(depthwise_conv2d_1_b_path.c_str(), 0,1,32,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,6272,1024);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,1024,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,1024,10);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,10,1,1);
-
-
-
- startMemTracking();
-
- int test_input_size = 10000;
- int batch_size = 10000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,1,28,28);
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 2, 2, 1, 1, 1, 1);
- void* var_1 = tensorAdd(var_0, conv2d_1_b);
- void* var_2 = tensorRelu(var_1);
- void* var_3 = tensorPooling(var_2,0,2,2,0,0,2,2);
- void* var_4 = tensorConvolution(var_3, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_5 = tensorAdd(var_4, depthwise_conv2d_1_b);
- void* var_6 = tensorRelu(var_5);
- void* var_8 = tensorGemmGPU(var_6, dense_1_w);
- void* var_9 = tensorAdd(var_8, dense_1_b);
- void* var_10 = tensorRelu(var_9);
- void* var_11 = tensorGemmGPU(var_10, dense_2_w);
- void* var_12 = tensorAdd(var_11, dense_2_b);
- void* var_13 = tensorRelu(var_12);
- void* var_14 = tensorSoftmax(var_13);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_14);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/depthwise_batchnorm.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/depthwise_batchnorm.cc
deleted file mode 100644
index 84710565de3b2fdde6eca5d84c9e3f324eba1d50..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/depthwise_batchnorm.cc
+++ /dev/null
@@ -1,102 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
-
- std::string dir_prefix = std::string("../model_params/depthwise_batchnorm2/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,1,5,5);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string depthwise_conv2d_1_b_path = dir_prefix + std::string("depthwise_conv2d_1_b.bin");
- void* depthwise_conv2d_1_b = readTrainedWeights(depthwise_conv2d_1_b_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,6272,1024);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,1024,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,1024,10);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,10,1,1);
-
-
-
- startMemTracking();
-
- int test_input_size = 10000;
- int batch_size = 10000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,1,28,28);
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 2, 2, 1, 1, 1, 1);
- void* var_1 = tensorAdd(var_0, conv2d_1_b);
- void* var_2 = tensorRelu(var_1);
- void* var_3 = tensorBatchNorm(var_2,batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_4 = tensorPooling(var_3,0,2,2,0,0,2,2);
- void* var_5 = tensorConvolution(var_4, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_6 = tensorAdd(var_5, depthwise_conv2d_1_b);
- void* var_7 = tensorRelu(var_6);
- void* var_8 = tensorBatchNorm(var_7,batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_10 = tensorGemmGPU(var_8, dense_1_w);
- void* var_11 = tensorAdd(var_10, dense_1_b);
- void* var_12 = tensorRelu(var_11);
- void* var_13 = tensorGemmGPU(var_12, dense_2_w);
- void* var_14 = tensorAdd(var_13, dense_2_b);
- void* var_15 = tensorRelu(var_14);
- void* var_16 = tensorSoftmax(var_15);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_16);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/fc2_clipped.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/fc2_clipped.cc
deleted file mode 100644
index 575f9b164f865afe268a4692ee6c4fd88b6a45c6..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/fc2_clipped.cc
+++ /dev/null
@@ -1,132 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-#include "../include/types.h"
-
-
-bool Opentuner_run = false;
-
-void test_2_Layer_clipped_FC(){
-
- int total_runs = 10;
-
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
-
- printf("********* 2-Layer FC with clipped activations and weights ********* \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 5000;
-
- uint8_t* labels = readLabels("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size);
-
- void* input = readTrainedWeights("../model_params/FC_network2/mnist_float_input.bin",
- float_type, test_batch_size, 1, 28, 28);
- void* fc1_weights = readTrainedWeights("../model_params/fc2_clipped/fc1.bin",
- float_type, 1, 1, 784, 128);
- void* fc1_bias = readTrainedWeights("../model_params/fc2_clipped/fc1_bias.bin",
- float_type, 1, 128, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/fc2_clipped/fc2.bin",
- float_type, 1, 1, 128, 10);
- void* fc2_bias = readTrainedWeights("../model_params/fc2_clipped/fc2_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- clearTensorMap();
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
- // Start execution profiling Tensor ops
- startProfiling();
-
- // Layer-1
- void* fc1out = tensorGemmGPU(input, fc1_weights);
-
- void* fc1_bias_out = tensorAdd(fc1out, fc1_bias);
-
- void* fc1_relu = tensorRelu2(fc1_bias_out, 0, 2);
-
- // Layer-2
- void* fc2out = tensorGemmGPU(fc1_relu, fc2_weights);
-
- void* fc2_bias_out = tensorAdd(fc2out, fc2_bias);
-
- void* fc2_relu = tensorRelu2(fc2_bias_out, 0, 2);
-
- void* result = tensorSoftmax(fc2_relu);
-
- stopProfiling();
-
- computeAccuracy2(labels, test_batch_size, result);
-
- dumpAccuracyNorms();
- freeOutputTensors();
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
- }
-
-}
-
-
-// If an argument is passed - the run goes into OpenTuner mode - waiting on a pipe
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- // This initializes the runtime - must be called before anything
- llvm_hpvm_initTensorRt(0);
-
- test_2_Layer_clipped_FC();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/fc2_cpu.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/fc2_cpu.cc
deleted file mode 100644
index 41343afa0484d022758ee690e69f38221c9ece10..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/fc2_cpu.cc
+++ /dev/null
@@ -1,66 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-#include "../../tensor_runtime/include/tensor_cpu_runtime.h"
-#include "../include/utils_cpu.h"
-#include "../include/types.h"
-
-
-void FC2(){
-
- printf("********* 2-Layer FC with clipped activations and weights ********* \n");
-
- int test_batch_size = 100;
-
- uint8_t* labels = readLabels("./model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size);
-
- void* input = readTrainedWeightsCPU("./model_params/FC_network2/mnist_float_input.bin",
- float_type, test_batch_size, 1, 28, 28);
-
- void* fc1_weights = readTrainedWeightsCPU("./model_params/fc2_clipped/fc1.bin",
- float_type, 1, 1, 784, 128);
- void* fc1_bias = readTrainedWeightsCPU("./model_params/fc2_clipped/fc1_bias.bin",
- float_type, 1, 128, 1, 1);
- void* fc2_weights = readTrainedWeightsCPU("./model_params/fc2_clipped/fc2.bin",
- float_type, 1, 1, 128, 10);
- void* fc2_bias = readTrainedWeightsCPU("./model_params/fc2_clipped/fc2_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- // Layer-1
- void* fc1out = tensorGemmCPU(input, fc1_weights);
-
- void* fc1_bias_out = tensorAddCPU(fc1out, fc1_bias);
-
- void* fc1_relu = tensorRelu2CPU(fc1_bias_out, 0, 2);
-
- // Layer-2
- void* fc2out = tensorGemmCPU(fc1_relu, fc2_weights);
-
- void* fc2_bias_out = tensorAddCPU(fc2out, fc2_bias);
-
- void* fc2_relu = tensorRelu2CPU(fc2_bias_out, 0, 2);
-
- void* result = tensorSoftmaxCPU(fc2_relu);
-
- computeAccuracy2(labels, test_batch_size, result);
-
-}
-
-
-// If an argument is passed - the run goes into OpenTuner mode - waiting on a pipe
-int main(int argc, char* argv[]){
-
- llvm_hpvm_initTensorRt(0);
-
- FC2();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/fc3_clipped.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/fc3_clipped.cc
deleted file mode 100644
index f566fd98a717698966c258377c32eda9ee30739d..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/fc3_clipped.cc
+++ /dev/null
@@ -1,151 +0,0 @@
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-#include "../include/types.h"
-
-
-
-
-bool Opentuner_run = false;
-
-
-void test_3_Layer_clipped_FC(){
-
-
- int total_runs = 10000;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
-
- printf("********* 3-Layer FC with clipped activations and weights ********* \n");
-
- int test_batch_size = 5000;
-
-
- uint8_t* labels = readLabels("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size);
-
- void* input = readTrainedWeights("../model_params/FC_network2/mnist_float_input.bin",
- float_type, test_batch_size, 1, 28, 28);
- void* fc1_weights = readTrainedWeights("../model_params/fc3_clipped/fc1.bin",
- float_type, 1, 1, 784, 256);
- void* fc1_bias = readTrainedWeights("../model_params/fc3_clipped/fc1_bias.bin",
- float_type, 1, 256, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/fc3_clipped/fc2.bin",
- float_type, 1, 1, 256, 128);
- void* fc2_bias = readTrainedWeights("../model_params/fc3_clipped/fc2_bias.bin",
- float_type, 1, 128, 1, 1);
- void* fc3_weights = readTrainedWeights("../model_params/fc3_clipped/fc3.bin",
- float_type, 1, 1, 128, 10);
- void* fc3_bias = readTrainedWeights("../model_params/fc3_clipped/fc3_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- clearTensorMap();
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
- // Start execution profiling Tensor ops
- startProfiling();
-
-
- // Layer-1
- void* fc1out = tensorGemmGPU(input, fc1_weights);
-
- void* fc1_bias_out = tensorAdd(fc1out, fc1_bias);
-
- void* fc1_relu = tensorRelu2(fc1_bias_out, 0, 2);
-
- // Layer-2
- void* fc2out = tensorGemmGPU(fc1_relu, fc2_weights);
-
- void* fc2_bias_out = tensorAdd(fc2out, fc2_bias);
-
- void* fc2_relu = tensorRelu2(fc2_bias_out, 0, 2);
-
- // Layer-3
- void* fc3out = tensorGemmGPU(fc2_relu, fc3_weights);
-
- void* fc3_bias_out = tensorAdd(fc3out, fc3_bias);
-
- void* fc3_relu = tensorRelu2(fc3_bias_out, 0, 2);
-
- void* result = tensorSoftmax(fc3_relu);
-
-
- stopProfiling();
-
- computeAccuracy2(labels, test_batch_size, result);
-
- dumpAccuracyNorms();
- freeOutputTensors();
-
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
-
-}
-
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- // This initializes the runtime - must be called before anything
- llvm_hpvm_initTensorRt(0);
-
- test_3_Layer_clipped_FC();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/fc4_clipped.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/fc4_clipped.cc
deleted file mode 100644
index 24a4d888124c43e98c7b78a33c3b5eb29250808d..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/fc4_clipped.cc
+++ /dev/null
@@ -1,156 +0,0 @@
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-#include "../include/types.h"
-
-
-bool Opentuner_run = false;
-
-
-void test_4_Layer_clipped_FC(){
-
- int total_runs = 200;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
- printf("********* 3-Layer FC with clipped activations and weights ********* \n");
-
- int test_batch_size = 5000;
-
- uint8_t* labels = readLabels("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size);
-
- void* input = readTrainedWeights("../model_params/FC_network2/mnist_float_input.bin",
- float_type, test_batch_size, 1, 28, 28);
- void* fc1_weights = readTrainedWeights("../model_params/fc4_clipped/fc1.bin",
- float_type, 1, 1, 784, 512);
- void* fc1_bias = readTrainedWeights("../model_params/fc4_clipped/fc1_bias.bin",
- float_type, 1, 512, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/fc4_clipped/fc2.bin",
- float_type, 1, 1, 512, 256);
- void* fc2_bias = readTrainedWeights("../model_params/fc4_clipped/fc2_bias.bin",
- float_type, 1, 256, 1, 1);
- void* fc3_weights = readTrainedWeights("../model_params/fc4_clipped/fc3.bin",
- float_type, 1, 1, 256, 128);
- void* fc3_bias = readTrainedWeights("../model_params/fc4_clipped/fc3_bias.bin",
- float_type, 1, 128, 1, 1);
- void* fc4_weights = readTrainedWeights("../model_params/fc4_clipped/fc4.bin",
- float_type, 1, 1, 128, 10);
- void* fc4_bias = readTrainedWeights("../model_params/fc4_clipped/fc4_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
-
- clearTensorMap();
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
- // Start execution profiling Tensor ops
- startProfiling();
-
- // Layer-1
- void* fc1out = tensorGemmGPU(input, fc1_weights);
-
- void* fc1_bias_out = tensorAdd(fc1out, fc1_bias);
-
- void* fc1_relu = tensorRelu2(fc1_bias_out, 0, 2);
-
- // Layer-2
- void* fc2out = tensorGemmGPU(fc1_relu, fc2_weights);
-
- void* fc2_bias_out = tensorAdd(fc2out, fc2_bias);
-
- void* fc2_relu = tensorRelu2(fc2_bias_out, 0, 2);
-
- // Layer-3
- void* fc3out = tensorGemmGPU(fc2_relu, fc3_weights);
-
- void* fc3_bias_out = tensorAdd(fc3out, fc3_bias);
-
- void* fc3_relu = tensorRelu2(fc3_bias_out, 0, 2);
-
- // Layer-4
- void* fc4out = tensorGemmGPU(fc3_relu, fc4_weights);
-
- void* fc4_bias_out = tensorAdd(fc4out, fc4_bias);
-
- void* fc4_relu = tensorRelu2(fc4_bias_out, 0, 2);
-
- void* result = tensorSoftmax(fc4_relu);
-
- stopProfiling();
-
- computeAccuracy2(labels, test_batch_size, result);
-
- dumpAccuracyNorms();
- freeOutputTensors();
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
- }
-
-
-}
-
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- // This initializes the runtime - must be called before anything
- llvm_hpvm_initTensorRt(0);
-
- test_4_Layer_clipped_FC();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/fc4_cpu.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/fc4_cpu.cc
deleted file mode 100644
index b7f6e1eb5256ebd0dbcf718d3e8e30f0d93ecbc5..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/fc4_cpu.cc
+++ /dev/null
@@ -1,143 +0,0 @@
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-#include "../../tensor_runtime/include/tensor_cpu_runtime.h"
-#include "../include/utils_cpu.h"
-#include "../include/types.h"
-
-
-bool Opentuner_run = false;
-
-
-void test_4_Layer_clipped_FC(){
-
- int total_runs = 200;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
- printf("********* 3-Layer FC with clipped activations and weights ********* \n");
-
- int test_batch_size = 500;
-
- uint8_t* labels = readLabels("./model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size);
-
- void* input = readTrainedWeightsCPU("./model_params/FC_network2/mnist_float_input.bin",
- float_type, test_batch_size, 1, 28, 28);
- void* fc1_weights = readTrainedWeightsCPU("./model_params/fc4_clipped/fc1.bin",
- float_type, 1, 1, 784, 512);
- void* fc1_bias = readTrainedWeightsCPU("./model_params/fc4_clipped/fc1_bias.bin",
- float_type, 1, 512, 1, 1);
- void* fc2_weights = readTrainedWeightsCPU("./model_params/fc4_clipped/fc2.bin",
- float_type, 1, 1, 512, 256);
- void* fc2_bias = readTrainedWeightsCPU("./model_params/fc4_clipped/fc2_bias.bin",
- float_type, 1, 256, 1, 1);
- void* fc3_weights = readTrainedWeightsCPU("./model_params/fc4_clipped/fc3.bin",
- float_type, 1, 1, 256, 128);
- void* fc3_bias = readTrainedWeightsCPU("./model_params/fc4_clipped/fc3_bias.bin",
- float_type, 1, 128, 1, 1);
- void* fc4_weights = readTrainedWeightsCPU("./model_params/fc4_clipped/fc4.bin",
- float_type, 1, 1, 128, 10);
- void* fc4_bias = readTrainedWeightsCPU("./model_params/fc4_clipped/fc4_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
-
- // Layer-1
- void* fc1out = tensorGemmCPU(input, fc1_weights);
-
- void* fc1_bias_out = tensorAddCPU(fc1out, fc1_bias);
-
- void* fc1_relu = tensorRelu2CPU(fc1_bias_out, 0, 2);
-
- // Layer-2
- void* fc2out = tensorGemmCPU(fc1_relu, fc2_weights);
-
- void* fc2_bias_out = tensorAddCPU(fc2out, fc2_bias);
-
- void* fc2_relu = tensorRelu2CPU(fc2_bias_out, 0, 2);
-
- // Layer-3
- void* fc3out = tensorGemmCPU(fc2_relu, fc3_weights);
-
- void* fc3_bias_out = tensorAddCPU(fc3out, fc3_bias);
-
- void* fc3_relu = tensorRelu2CPU(fc3_bias_out, 0, 2);
-
- // Layer-4
- void* fc4out = tensorGemmCPU(fc3_relu, fc4_weights);
-
- void* fc4_bias_out = tensorAddCPU(fc4out, fc4_bias);
-
- void* fc4_relu = tensorRelu2CPU(fc4_bias_out, 0, 2);
-
- void* result = tensorSoftmaxCPU(fc4_relu);
-
- computeAccuracy2(labels, test_batch_size, result);
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
- }
-
-
-}
-
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- // This initializes the runtime - must be called before anything
- llvm_hpvm_initTensorRt(0);
-
- test_4_Layer_clipped_FC();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/alexnet2_cifar10_half.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/alexnet2_cifar10_half.cc
deleted file mode 100644
index 0c0c6aaa6291bcd67f3e3bff1eb7b2481bf72f1e..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/alexnet2_cifar10_half.cc
+++ /dev/null
@@ -1,146 +0,0 @@
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testCifarNet(){
-
- printf("********* Alexnet2 CIFAR-10 DNN ********** \n");
-
- std::string dir_prefix = std::string("../model_params/alexnet2_cifar10/");
- std::string input_path = dir_prefix + std::string("norm_cifar_input.bin");
- std::string labels_path = dir_prefix + std::string("test_labels.bin");
-
- void* conv1_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv1.bin",
- float_type, 32, 3, 3, 3);
- void* conv1_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv1_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv2.bin",
- float_type, 32, 32, 3, 3);
- void* conv2_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv2_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv3_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv3.bin",
- float_type, 64, 32, 3, 3);
- void* conv3_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv3_bias.bin",
- float_type, 1, 64, 1, 1);
- void* conv4_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv4.bin",
- float_type, 64, 64, 3, 3);
- void* conv4_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv4_bias.bin",
- float_type, 1, 64, 1, 1);
- void* conv5_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv5.bin",
- float_type, 128, 64, 3, 3);
- void* conv5_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv5_bias.bin",
- float_type, 1, 128, 1, 1);
- void* conv6_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv6.bin",
- float_type, 128, 128, 3, 3);
- void* conv6_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv6_bias.bin",
- float_type, 1, 128, 1, 1);
-
- void* fc1_weights = readTrainedWeights("../model_params/alexnet2_cifar10/fc1.bin",
- float_type, 1, 1, 2048, 10);
- void* fc1_bias = readTrainedWeights("../model_params/alexnet2_cifar10/fc1_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
-
- startMemTracking();
-
- int test_input_size = 2000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- // NOTE: Starting time profiling
- startProfiling();
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
- void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
-
- void* conv1out = tensorHalfConvolution(input, conv1_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorHalfAdd(conv1out, conv1_bias);
- void* conv1_tanh = tensorHalfTanh(conv1out);
-
- // 2nd Layer
- void* conv2out = tensorHalfConvolution(conv1_tanh, conv2_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorHalfAdd(conv2out, conv2_bias);
- void* conv2_tanh = tensorHalfTanh(conv2out);
- void* pool2out = tensorHalfPooling(conv2_tanh, 0, 2, 2, 0, 0, 2, 2);
-
- // 3rd Layer
- void* conv3out = tensorHalfConvolution(pool2out, conv3_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorHalfAdd(conv3out, conv3_bias);
- void* conv3_tanh = tensorHalfTanh(conv3out);
-
- // 4th Layer
- void* conv4out = tensorHalfConvolution(conv3_tanh, conv4_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorHalfAdd(conv4out, conv4_bias);
- void* conv4_tanh = tensorHalfTanh(conv4out);
- void* pool4out = tensorHalfPooling(conv4_tanh, 0, 2, 2, 0, 0, 2, 2);
-
- // 5th Layer
- void* conv5out = tensorHalfConvolution(pool4out, conv5_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorHalfAdd(conv5out, conv5_bias);
- void* conv5_tanh = tensorHalfTanh(conv5out);
-
- // 6th Layer
- void* conv6out = tensorHalfConvolution(conv5_tanh, conv6_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorHalfAdd(conv6out, conv6_bias);
-
- void* conv6_tanh = tensorHalfTanh(conv6out);
- void* pool6out = tensorHalfPooling(conv6_tanh, 0, 2, 2, 0, 0, 2, 2);
-
- // final FC Layer
- void* gemm1out = tensorHalfGemmGPU(pool6out, fc1_weights);
- void* gemm1biasout = tensorHalfAdd(gemm1out, fc1_bias);
- void* result = tensorSoftmax(gemm1biasout);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
-
- float accuracy = computeAccuracy2(labels, batch_size, result);
- final_accuracy += accuracy;
-
- freeBatchMemory();
- }
-
- stopProfiling();
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-}
-
-
-int main(int argc, char* argv[]){
-
- llvm_hpvm_initTensorRt(0);
-
- testCifarNet();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/alexnet_cifar10_half.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/alexnet_cifar10_half.cc
deleted file mode 100644
index 6cd19407f4780de516cb777d40644ca3a7e1abc3..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/alexnet_cifar10_half.cc
+++ /dev/null
@@ -1,104 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
-
- std::string dir_prefix = std::string("../model_params/alexnet_cifar10_front/");
- std::string input_path = dir_prefix + std::string("input.bin");
- //void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
- std::string labels_path = dir_prefix + std::string("labels.bin");
- //uint8_t* labels = readLabels(labels_path.c_str(),10000);
- std::string conv2d_1_w_path = dir_prefix + std::string("conv0.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,11,11);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv_bias0.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv3.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,192,64,5,5);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv_bias3.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,192,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv6.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,384,192,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv_bias6.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,384,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv7.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,256,384,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv_bias7.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv8.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv_bias8.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("fc12.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,4096,10);
- std::string dense_1_b_path = dir_prefix + std::string("fc_bias12.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 2000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- // NOTE: Starting time profiling
- startProfiling();
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
- void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
-
- void* var_0 = tensorHalfConvolution(input, conv2d_1_w, 5, 5, 1, 1, 1, 0);
- void* var_1 = tensorHalfAdd(var_0, conv2d_1_b);
- void* var_2 = tensorHalfTanh(var_1);
- void* var_3 = tensorHalfPooling(var_2,0,2,2,0,0,2,2);
- void* var_5 = tensorHalfConvolution(var_3, conv2d_2_w, 2, 2, 1, 1, 1, 0);
- void* var_6 = tensorHalfAdd(var_5, conv2d_2_b);
- void* var_7 = tensorHalfTanh(var_6);
- void* var_8 = tensorHalfPooling(var_7,0,2,2,0,0,2,2);
- void* var_10 = tensorHalfConvolution(var_8, conv2d_3_w, 1, 1, 1, 1, 1, 0);
- void* var_11 = tensorHalfAdd(var_10, conv2d_3_b);
- void* var_12 = tensorHalfTanh(var_11);
- void* var_13 = tensorHalfConvolution(var_12, conv2d_4_w, 1, 1, 1, 1, 1, 0);
- void* var_14 = tensorHalfAdd(var_13, conv2d_4_b);
- void* var_15 = tensorHalfTanh(var_14);
- void* var_16 = tensorHalfConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
- void* var_17 = tensorHalfAdd(var_16, conv2d_5_b);
- void* var_18 = tensorHalfTanh(var_17);
- void* var_19 = tensorHalfPooling(var_18,0,2,2,0,0,2,2);
- void* var_22 = tensorHalfGemmGPU(var_19, dense_1_w);
- void* var_23 = tensorHalfAdd(var_22, dense_1_b);
- void* var_24 = tensorSoftmax(var_23);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
-
- float accuracy = computeAccuracy2(labels,batch_size,var_24);
- final_accuracy += accuracy;
-
- freeBatchMemory();
- }
-
- stopProfiling();
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/fc2_half.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/fc2_half.cc
deleted file mode 100644
index 44c03aab875a6de4af6c87776241295cd1fd673b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/fc2_half.cc
+++ /dev/null
@@ -1,137 +0,0 @@
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-#include "../../include/types.h"
-
-
-bool Opentuner_run = false;
-
-
-void test_2_Layer_clipped_FC(){
-
- int total_runs = 1;
-
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
-
- printf("********* 2-Layer FC with clipped activations and weights ********* \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 5000;
-
-
- uint8_t* labels = readLabels("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size);
-
- void* input = readTrainedWeights("../model_params/FC_network2/mnist_float_input.bin",
- float_type, test_batch_size, 1, 28, 28);
- void* fc1_weights = readTrainedWeights("../model_params/fc2_clipped/fc1.bin",
- float_type, 1, 1, 784, 128);
- void* fc1_bias = readTrainedWeights("../model_params/fc2_clipped/fc1_bias.bin",
- float_type, 1, 128, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/fc2_clipped/fc2.bin",
- float_type, 1, 1, 128, 10);
- void* fc2_bias = readTrainedWeights("../model_params/fc2_clipped/fc2_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- clearTensorMap();
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
- // Start execution profiling Tensor ops
- startProfiling();
-
- // Layer-1
- void* fc1out = tensorHalfGemm(input, fc1_weights);
-
- void* fc1_bias_out = tensorHalfAdd(fc1out, fc1_bias);
-
- void* fc1_relu = tensorHalfRelu2(fc1_bias_out, 0, 2);
-
- // Layer-2
- void* fc2out = tensorHalfGemm(fc1_relu, fc2_weights);
-
- void* fc2_bias_out = tensorHalfAdd(fc2out, fc2_bias);
-
- void* fc2_relu = tensorHalfRelu2(fc2_bias_out, 0, 2);
-
- void* result = tensorSoftmax(fc2_relu);
-
- stopProfiling();
-
- computeAccuracy2(labels, test_batch_size, result);
-
- dumpAccuracyNorms();
- freeOutputTensors();
-
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
-}
-
-
-// If an argument is passed - the run goes into OpenTuner mode - waiting on a pipe
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- // This initializes the runtime - must be called before anything
- llvm_hpvm_initTensorRt(0);
-
- test_2_Layer_clipped_FC();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/fc3_half.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/fc3_half.cc
deleted file mode 100644
index 697fea9b8aa61a8c3cf5ec3e8d0d66466df9b1e8..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/fc3_half.cc
+++ /dev/null
@@ -1,151 +0,0 @@
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-#include "../../include/types.h"
-
-
-
-
-bool Opentuner_run = false;
-
-
-void test_3_Layer_clipped_FC(){
-
-
- int total_runs = 1000;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
-
- printf("********* 3-Layer FC with clipped activations and weights ********* \n");
-
- int test_batch_size = 5000;
-
-
- uint8_t* labels = readLabels("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size);
-
- void* input = readTrainedWeights("../model_params/FC_network2/mnist_float_input.bin",
- float_type, test_batch_size, 1, 28, 28);
- void* fc1_weights = readTrainedWeights("../model_params/fc3_clipped/fc1.bin",
- float_type, 1, 1, 784, 256);
- void* fc1_bias = readTrainedWeights("../model_params/fc3_clipped/fc1_bias.bin",
- float_type, 1, 256, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/fc3_clipped/fc2.bin",
- float_type, 1, 1, 256, 128);
- void* fc2_bias = readTrainedWeights("../model_params/fc3_clipped/fc2_bias.bin",
- float_type, 1, 128, 1, 1);
- void* fc3_weights = readTrainedWeights("../model_params/fc3_clipped/fc3.bin",
- float_type, 1, 1, 128, 10);
- void* fc3_bias = readTrainedWeights("../model_params/fc3_clipped/fc3_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- clearTensorMap();
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
- // Start execution profiling Tensor ops
- startProfiling();
-
-
- // Layer-1
- void* fc1out = tensorHalfGemm(input, fc1_weights);
-
- void* fc1_bias_out = tensorHalfAdd(fc1out, fc1_bias);
-
- void* fc1_relu = tensorHalfRelu2(fc1_bias_out, 0, 2);
-
- // Layer-2
- void* fc2out = tensorHalfGemm(fc1_relu, fc2_weights);
-
- void* fc2_bias_out = tensorHalfAdd(fc2out, fc2_bias);
-
- void* fc2_relu = tensorHalfRelu2(fc2_bias_out, 0, 2);
-
- // Layer-3
- void* fc3out = tensorHalfGemm(fc2_relu, fc3_weights);
-
- void* fc3_bias_out = tensorHalfAdd(fc3out, fc3_bias);
-
- void* fc3_relu = tensorHalfRelu2(fc3_bias_out, 0, 2);
-
- void* result = tensorSoftmax(fc3_relu);
-
-
- stopProfiling();
-
- computeAccuracy2(labels, test_batch_size, result);
-
- dumpAccuracyNorms();
- freeOutputTensors();
-
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
-
-}
-
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- // This initializes the runtime - must be called before anything
- llvm_hpvm_initTensorRt(0);
-
- test_3_Layer_clipped_FC();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/fc4_half.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/fc4_half.cc
deleted file mode 100644
index ad999165cfd4148479de58e24fed8291161da491..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/fc4_half.cc
+++ /dev/null
@@ -1,156 +0,0 @@
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-#include "../../include/types.h"
-
-
-bool Opentuner_run = false;
-
-
-void test_4_Layer_clipped_FC(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
- printf("********* 3-Layer FC with clipped activations and weights ********* \n");
-
- int test_batch_size = 5000;
-
- uint8_t* labels = readLabels("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size);
-
- void* input = readTrainedWeights("../model_params/FC_network2/mnist_float_input.bin",
- float_type, test_batch_size, 1, 28, 28);
- void* fc1_weights = readTrainedWeights("../model_params/fc4_clipped/fc1.bin",
- float_type, 1, 1, 784, 512);
- void* fc1_bias = readTrainedWeights("../model_params/fc4_clipped/fc1_bias.bin",
- float_type, 1, 512, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/fc4_clipped/fc2.bin",
- float_type, 1, 1, 512, 256);
- void* fc2_bias = readTrainedWeights("../model_params/fc4_clipped/fc2_bias.bin",
- float_type, 1, 256, 1, 1);
- void* fc3_weights = readTrainedWeights("../model_params/fc4_clipped/fc3.bin",
- float_type, 1, 1, 256, 128);
- void* fc3_bias = readTrainedWeights("../model_params/fc4_clipped/fc3_bias.bin",
- float_type, 1, 128, 1, 1);
- void* fc4_weights = readTrainedWeights("../model_params/fc4_clipped/fc4.bin",
- float_type, 1, 1, 128, 10);
- void* fc4_bias = readTrainedWeights("../model_params/fc4_clipped/fc4_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
-
- clearTensorMap();
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
- // Start execution profiling Tensor ops
- startProfiling();
-
- // Layer-1
- void* fc1out = tensorHalfGemm(input, fc1_weights);
-
- void* fc1_bias_out = tensorHalfAdd(fc1out, fc1_bias);
-
- void* fc1_relu = tensorHalfRelu2(fc1_bias_out, 0, 2);
-
- // Layer-2
- void* fc2out = tensorHalfGemm(fc1_relu, fc2_weights);
-
- void* fc2_bias_out = tensorHalfAdd(fc2out, fc2_bias);
-
- void* fc2_relu = tensorHalfRelu2(fc2_bias_out, 0, 2);
-
- // Layer-3
- void* fc3out = tensorHalfGemm(fc2_relu, fc3_weights);
-
- void* fc3_bias_out = tensorHalfAdd(fc3out, fc3_bias);
-
- void* fc3_relu = tensorHalfRelu2(fc3_bias_out, 0, 2);
-
- // Layer-4
- void* fc4out = tensorHalfGemm(fc3_relu, fc4_weights);
-
- void* fc4_bias_out = tensorHalfAdd(fc4out, fc4_bias);
-
- void* fc4_relu = tensorHalfRelu2(fc4_bias_out, 0, 2);
-
- void* result = tensorSoftmax(fc4_relu);
-
- stopProfiling();
-
- computeAccuracy2(labels, test_batch_size, result);
-
- dumpAccuracyNorms();
- freeOutputTensors();
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
- }
-
-
-}
-
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- // This initializes the runtime - must be called before anything
- llvm_hpvm_initTensorRt(0);
-
- test_4_Layer_clipped_FC();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/lenet_keras_half.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/lenet_keras_half.cc
deleted file mode 100644
index dd68f2b48eb66456061bb93decc1cbd985887be0..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/lenet_keras_half.cc
+++ /dev/null
@@ -1,171 +0,0 @@
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testLenetTanh(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
-
- printf("********* Lenet-2 Architecture ********** \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 5000;
-
- uint8_t* labels = readLabels("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size);
-
- void* input = readInputTensor("../model_params/lenet_params/datasets/t10k-images-idx3-ubyte",
- CUDNN_DATA_FLOAT,
- test_batch_size, 1, 28, 28);
-
- // NOTE: Filter descriptors do NOT have batch size
- // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
- // IMP: The output channels matches the trained model - not the Lenet arch proposed in Andrew Ng's class
- void* conv1_filter = readTrainedWeights("../model_params/lenet_keras/conv1.bin",
- float_type, 32, 1, 5, 5);
- void* conv1_bias = readTrainedWeights("../model_params/lenet_keras/conv1_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/lenet_keras/conv2.bin",
- float_type, 64, 32, 5, 5);
- void* conv2_bias = readTrainedWeights("../model_params/lenet_keras/conv2_bias.bin",
- float_type, 1, 64, 1, 1);
- void* fc1_weights = readTrainedWeights("../model_params/lenet_keras/fc1.bin",
- float_type, 1, 1, 7*7*64, 1024);
- void* fc1_bias = readTrainedWeights("../model_params/lenet_keras/fc1_bias.bin",
- float_type, 1, 1024, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/lenet_keras/fc2.bin",
- float_type, 1, 1, 1024, 10);
- void* fc2_bias = readTrainedWeights("../model_params/lenet_keras/fc2_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
-
- clearTensorMap();
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
- // Start power and performnce profiling
- startProfiling();
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* conv1out = tensorHalfConvolution(input, conv1_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
-
- // NOTE: For tensorAdd, the only dimension that MUST match is channels
- tensorHalfAdd(conv1out, conv1_bias); // NOTE: In place operation
-
- void* pool1out = tensorHalfPooling(conv1out, 0, 2, 2, 0, 0, 2, 2);
-
- void* conv1_tanh = tensorHalfTanh(pool1out);
-
- // NOTE: input channels have to match between tensor op inputs and outputs
- void* conv2out = tensorHalfConvolution(conv1_tanh, conv2_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
- tensorHalfAdd(conv2out, conv2_bias); // NOTE: In place operation
-
- void* pool2out = tensorHalfPooling(conv2out, 0, 2, 2, 0, 0, 2, 2);
-
- void* conv2_tanh = tensorHalfTanh(pool2out);
-
- void* gemm1out = tensorHalfGemm(conv2_tanh, fc1_weights);
-
- void* gemm1biasout = tensorHalfAdd(gemm1out, fc1_bias);
-
- void* tanh1out = tensorHalfTanh(gemm1biasout);
-
- void* gemm2out = tensorHalfGemm(tanh1out, fc2_weights);
-
- void* gemm2_biasout = tensorHalfAdd(gemm2out, fc2_bias);
-
- void* tanh2out = tensorHalfTanh(gemm2_biasout);
-
- void* result = tensorSoftmax(tanh2out);
-
- // End profiling and dump output to profile.txt
- stopProfiling();
-
- computeAccuracy2(labels, test_batch_size, result);
-
- dumpAccuracyNorms();
- freeOutputTensors();
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
-
-
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(0);
-
- testLenetTanh();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/lenet_tanh_half.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/lenet_tanh_half.cc
deleted file mode 100644
index bb45b14d62e061e704b252aa44e602e0c1d08ba7..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/lenet_tanh_half.cc
+++ /dev/null
@@ -1,173 +0,0 @@
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-#include "../../include/types.h"
-
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testLenetTanh(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
-
- printf("********* Lenet-2 Architecture ********** \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 5000;
-
- uint8_t* labels = readLabels("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size);
-
- void* input = readInputTensor("../model_params/lenet_params/datasets/t10k-images-idx3-ubyte",
- CUDNN_DATA_FLOAT,
- test_batch_size, 1, 28, 28);
-
- // NOTE: Filter descriptors do NOT have batch size
- // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
- // IMP: The output channels matches the trained model - not the Lenet arch proposed in Andrew Ng's class
- void* conv1_filter = readTrainedWeights("../model_params/lenet_tanh2/conv1.bin",
- float_type, 32, 1, 5, 5);
- void* conv1_bias = readTrainedWeights("../model_params/lenet_tanh2/conv1_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/lenet_tanh2/conv2.bin",
- float_type, 64, 32, 5, 5);
- void* conv2_bias = readTrainedWeights("../model_params/lenet_tanh2/conv2_bias.bin",
- float_type, 1, 64, 1, 1);
- void* fc1_weights = readTrainedWeights("../model_params/lenet_tanh2/fc1.bin",
- float_type, 1, 1, 7*7*64, 1024);
- void* fc1_bias = readTrainedWeights("../model_params/lenet_tanh2/fc1_bias.bin",
- float_type, 1, 1024, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/lenet_tanh2/fc2.bin",
- float_type, 1, 1, 1024, 10);
- void* fc2_bias = readTrainedWeights("../model_params/lenet_tanh2/fc2_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
-
- clearTensorMap();
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
- // Start power and performnce profiling
- startProfiling();
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* conv1out = tensorHalfConvolution(input, conv1_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
-
- // NOTE: For tensorAdd, the only dimension that MUST match is channels
- tensorHalfAdd(conv1out, conv1_bias); // NOTE: In place operation
-
- void* pool1out = tensorHalfPooling(conv1out, 0, 2, 2, 0, 0, 2, 2);
-
- void* conv1_tanh = tensorHalfTanh(pool1out);
-
- // NOTE: input channels have to match between tensor op inputs and outputs
- void* conv2out = tensorHalfConvolution(conv1_tanh, conv2_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
- tensorHalfAdd(conv2out, conv2_bias); // NOTE: In place operation
-
- void* pool2out = tensorHalfPooling(conv2out, 0, 2, 2, 0, 0, 2, 2);
-
- void* conv2_tanh = tensorHalfTanh(pool2out);
-
- void* gemm1out = tensorHalfGemm(conv2_tanh, fc1_weights);
-
- void* gemm1biasout = tensorHalfAdd(gemm1out, fc1_bias);
-
- void* tanh1out = tensorHalfTanh(gemm1biasout);
-
- void* gemm2out = tensorHalfGemm(tanh1out, fc2_weights);
-
- void* gemm2_biasout = tensorHalfAdd(gemm2out, fc2_bias);
-
- void* tanh2out = tensorHalfTanh(gemm2_biasout);
-
- void* result = tensorSoftmax(tanh2out);
-
- // End profiling and dump output to profile.txt
- stopProfiling();
-
- computeAccuracy2(labels, test_batch_size, result);
-
- dumpAccuracyNorms();
- freeOutputTensors();
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
-
-
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(0);
-
- testLenetTanh();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/mobilenet_depthwise_half.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/mobilenet_depthwise_half.cc
deleted file mode 100644
index dabafd4345f29d00c7271c796a8497aba8b7772d..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/mobilenet_depthwise_half.cc
+++ /dev/null
@@ -1,411 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
-
- std::string dir_prefix = std::string("../model_params/mobilenet/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
- void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
- void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
- void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
- void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
- void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
- std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
- void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
- void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
- void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
- void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
- void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
- void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
- void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
- void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
- void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
- void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
- void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
- void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
- void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
- void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
- void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
- void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
- void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
- void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
- void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
- void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
- void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
- void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
- void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
- void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
- void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
- void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
- void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
- void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
- void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
- void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
- void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
- void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
- void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
- void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
- void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
- void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
- void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
- void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
- void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
- void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
- void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
- void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
- void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
- void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
- void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
- void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
- void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
- void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
- void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
- void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
- void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
- void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
- void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
- void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1);
- std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
- void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
- void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
- void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
- void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1);
- std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
- void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3);
- std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
- void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
- void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
- void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
- void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1);
- std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
- void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
- void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
- void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
- void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-
- startMemTracking();
-
- int test_input_size = 2000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
-
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = tensorHalfConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
- void* var_1 = tensorHalfBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorHalfRelu(var_1);
- void* var_4 = tensorHalfConvCutlass(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_5 = tensorHalfBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_6 = tensorHalfRelu(var_5);
- void* var_7 = tensorHalfConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
- void* var_8 = tensorHalfBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_9 = tensorHalfRelu(var_8);
- void* var_11 = tensorHalfConvCutlass(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
- void* var_12 = tensorHalfBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_13 = tensorHalfRelu(var_12);
- void* var_14 = tensorHalfConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1);
- void* var_15 = tensorHalfBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_16 = tensorHalfRelu(var_15);
- void* var_18 = tensorHalfConvCutlass(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
- void* var_19 = tensorHalfBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_20 = tensorHalfRelu(var_19);
- void* var_21 = tensorHalfConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1);
- void* var_22 = tensorHalfBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_23 = tensorHalfRelu(var_22);
- void* var_26 = tensorHalfConvCutlass(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
- void* var_27 = tensorHalfBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_28 = tensorHalfRelu(var_27);
- void* var_29 = tensorHalfConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1);
- void* var_30 = tensorHalfBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_31 = tensorHalfRelu(var_30);
- void* var_33 = tensorHalfConvCutlass(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
- void* var_34 = tensorHalfBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_35 = tensorHalfRelu(var_34);
- void* var_36 = tensorHalfConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1);
- void* var_37 = tensorHalfBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_38 = tensorHalfRelu(var_37);
- void* var_41 = tensorHalfConvCutlass(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
- void* var_42 = tensorHalfBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
- void* var_43 = tensorHalfRelu(var_42);
- void* var_44 = tensorHalfConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1);
- void* var_45 = tensorHalfBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
- void* var_46 = tensorHalfRelu(var_45);
- void* var_48 = tensorHalfConvCutlass(var_46, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512);
- void* var_49 = tensorHalfBatchNorm(var_48, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001);
- void* var_50 = tensorHalfRelu(var_49);
- void* var_51 = tensorHalfConvolution(var_50, conv2d_8_w, 0, 0, 1, 1, 1, 1);
- void* var_52 = tensorHalfBatchNorm(var_51, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001);
- void* var_53 = tensorHalfRelu(var_52);
- void* var_55 = tensorHalfConvCutlass(var_53, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512);
- void* var_56 = tensorHalfBatchNorm(var_55, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001);
- void* var_57 = tensorHalfRelu(var_56);
- void* var_58 = tensorHalfConvolution(var_57, conv2d_9_w, 0, 0, 1, 1, 1, 1);
- void* var_59 = tensorHalfBatchNorm(var_58, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001);
- void* var_60 = tensorHalfRelu(var_59);
- void* var_63 = tensorHalfConvCutlass(var_60, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512);
- void* var_64 = tensorHalfBatchNorm(var_63, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001);
- void* var_65 = tensorHalfRelu(var_64);
- void* var_66 = tensorHalfConvolution(var_65, conv2d_10_w, 0, 0, 1, 1, 1, 1);
- void* var_67 = tensorHalfBatchNorm(var_66, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001);
- void* var_68 = tensorHalfRelu(var_67);
- void* var_70 = tensorHalfConvCutlass(var_68, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512);
- void* var_71 = tensorHalfBatchNorm(var_70, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001);
- void* var_72 = tensorHalfRelu(var_71);
- void* var_73 = tensorHalfConvolution(var_72, conv2d_11_w, 0, 0, 1, 1, 1, 1);
- void* var_74 = tensorHalfBatchNorm(var_73, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001);
- void* var_75 = tensorHalfRelu(var_74);
- void* var_77 = tensorHalfConvCutlass(var_75, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512);
- void* var_78 = tensorHalfBatchNorm(var_77, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001);
- void* var_79 = tensorHalfRelu(var_78);
- void* var_80 = tensorHalfConvolution(var_79, conv2d_12_w, 0, 0, 1, 1, 1, 1);
- void* var_81 = tensorHalfBatchNorm(var_80, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001);
- void* var_82 = tensorHalfRelu(var_81);
- void* var_85 = tensorHalfConvCutlass(var_82, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512);
- void* var_86 = tensorHalfBatchNorm(var_85, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001);
- void* var_87 = tensorHalfRelu(var_86);
- void* var_88 = tensorHalfConvolution(var_87, conv2d_13_w, 0, 0, 1, 1, 1, 1);
- void* var_89 = tensorHalfBatchNorm(var_88, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001);
- void* var_90 = tensorHalfRelu(var_89);
- void* var_92 = tensorHalfConvCutlass(var_90, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024);
- void* var_93 = tensorHalfBatchNorm(var_92, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001);
- void* var_94 = tensorHalfRelu(var_93);
- void* var_95 = tensorHalfConvolution(var_94, conv2d_14_w, 0, 0, 1, 1, 1, 1);
- void* var_96 = tensorHalfBatchNorm(var_95, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001);
- void* var_97 = tensorHalfRelu(var_96);
- void* var_99 = tensorHalfPooling(var_97,1,2,2,0,0,2,2);
- void* var_101 = tensorHalfGemmGPU(var_99, dense_1_w);
- void* var_102 = tensorHalfAdd(var_101, dense_1_b);
- void* var_103 = tensorSoftmax(var_102);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_103);
- final_accuracy += accuracy;
- freeBatchMemory();
- }
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/mobilenet_shallow_depthwise_half.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/mobilenet_shallow_depthwise_half.cc
deleted file mode 100644
index 6b0d02f01e8e3516d4b5d03899317760d3629388..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/mobilenet_shallow_depthwise_half.cc
+++ /dev/null
@@ -1,236 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-int main(int argc, char* argv[]){
-
- llvm_hpvm_initTensorRt(0);
-
- //std::string dir_prefix = std::string("../../keras/data/mobilenet_shallow_nathan/");
-
- std::string dir_prefix = std::string("../model_params/mobilenet_shallow/");
-
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
- void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
- void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
- void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
- void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
- void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
- std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
- void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
- void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
- void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
- void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,2048,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 2000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
-
-
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = tensorHalfConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
- void* var_1 = tensorHalfBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorHalfRelu(var_1);
- void* var_4 = tensorHalfConvCutlass(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_5 = tensorHalfBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_6 = tensorHalfRelu(var_5);
- void* var_7 = tensorHalfConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
- void* var_8 = tensorHalfBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_9 = tensorHalfRelu(var_8);
- void* var_11 = tensorHalfConvCutlass(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
- void* var_12 = tensorHalfBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_13 = tensorHalfRelu(var_12);
- void* var_14 = tensorHalfConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1);
- void* var_15 = tensorHalfBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_16 = tensorHalfRelu(var_15);
- void* var_18 = tensorHalfConvCutlass(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
- void* var_19 = tensorHalfBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_20 = tensorHalfRelu(var_19);
- void* var_21 = tensorHalfConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1);
- void* var_22 = tensorHalfBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_23 = tensorHalfRelu(var_22);
- void* var_26 = tensorHalfConvCutlass(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
- void* var_27 = tensorHalfBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_28 = tensorHalfRelu(var_27);
- void* var_29 = tensorHalfConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1);
- void* var_30 = tensorHalfBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_31 = tensorHalfRelu(var_30);
- void* var_33 = tensorHalfConvCutlass(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
- void* var_34 = tensorHalfBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_35 = tensorHalfRelu(var_34);
- void* var_36 = tensorHalfConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1);
- void* var_37 = tensorHalfBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_38 = tensorHalfRelu(var_37);
- void* var_41 = tensorHalfConvCutlass(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
- void* var_42 = tensorHalfBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
- void* var_43 = tensorHalfRelu(var_42);
- void* var_44 = tensorHalfConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1);
- void* var_45 = tensorHalfBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
- void* var_46 = tensorHalfRelu(var_45);
- void* var_47 = tensorHalfPooling(var_46,1,2,2,0,0,2,2);
- void* var_49 = tensorHalfGemmGPU(var_47, dense_1_w);
- void* var_50 = tensorHalfAdd(var_49, dense_1_b);
- void* var_51 = tensorSoftmax(var_50);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_51);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/pipeline_GEMO_half.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/pipeline_GEMO_half.cc
deleted file mode 100644
index 23fd15576ace419976a2b4d7f8191079a59c8c31..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/pipeline_GEMO_half.cc
+++ /dev/null
@@ -1,164 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testPipeline(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
- startProfiling();
-
- printf("********* Pipeline: Gaussian - Emboss - Motion Blur - Outline ********** \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- //long int test_batch_size = 9145;
- //long int test_batch_size = 4572;
- long int test_batch_size = 2000;
-
- long int H = 240;
- long int W = 300;
-
- printf("Reading input\n");
- void* input = readTrainedWeights("../model_params/pipeline/dataset/calibration_4572.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- printf("Reading golden output\n");
- void* golden_output = readTrainedWeights("../model_params/pipeline/golden_output/GEMO_calib.bin",
- float_type,
- test_batch_size, 1, H, W);
-
-
-
- // NOTE: Filter descriptors do NOT have batch size
- // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
- // IMP: The output channels matches the trained model - not the Lenet arch proposed in Andrew Ng's class
- void* gaussian_filter = readTrainedWeights("../model_params/pipeline/filters/GaussianFilter.bin",
- float_type, 1, 1, 9, 9);
- void* outline_filter = readTrainedWeights("../model_params/pipeline/filters/OutlineFilter.bin",
- float_type, 1, 1, 3, 3);
- void* sharpen_filter = readTrainedWeights("../model_params/pipeline/filters/SharpenFilter.bin",
- float_type, 1, 1, 3, 3);
- void* motionblur_filter = readTrainedWeights("../model_params/pipeline/filters/MotionblurFilter.bin",
- float_type, 1, 1, 9, 9);
- void* emboss_filter = readTrainedWeights("../model_params/pipeline/filters/EmbossFilter.bin",
- float_type, 1, 1, 5, 5);
- void* emboss_bias = readTrainedWeights("../model_params/pipeline/filters/EmbossBias.bin",
- float_type, 1, 1, 1, 1);
-
- clearTensorMap();
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* gaussian_out = tensorHalfConvolution(input, gaussian_filter, 4, 4, 1, 1,
- conv_mode, conv_precision);
-
- void * gaussian_out_clip = tensorHalfRelu2(gaussian_out, 0, 255);
-
- void* emboss_out = tensorHalfConvolution(gaussian_out_clip, emboss_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
- void* emboss_bias_out = tensorHalfAdd(emboss_out, emboss_bias);
- void* emboss_bias_out_clip = tensorHalfRelu2(emboss_bias_out, 0, 255);
-
- void* motionblur_out = tensorHalfConvolution(emboss_bias_out_clip, motionblur_filter, 4, 4, 1, 1,
- conv_mode, conv_precision);
- void * motionblur_out_clip = tensorHalfRelu2(motionblur_out, 0, 255);
-
- void* outline_out = tensorHalfConvolution(motionblur_out_clip, outline_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- void * result = tensorHalfRelu2(outline_out, 0, 255);
-
-
- // NOTE-IMP: Important to include this call always before doing dumpOutput and computePSNViolation
- hpvm_request_tensor(result, 0);
-
- //dumpOutput(result, "GEMO_calib.bin");
-
-
- computePSNRViolation(result, golden_output, 30);
-
-
- dumpAccuracyNorms();
- freeOutputTensors();
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
-
- stopProfiling();
-
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(0);
-
- testPipeline();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/pipeline_GEOM_half.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/pipeline_GEOM_half.cc
deleted file mode 100644
index 07875553d59a3635c21db8975db9e8986d1bc6c9..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/pipeline_GEOM_half.cc
+++ /dev/null
@@ -1,101 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testPipeline(){
-
- int total_runs = 1;
-
- startProfiling();
-
- printf("********* Pipeline: Gaussian - Emboss - Outline - Motion Blur ********** \n");
-
- long int test_batch_size = 2000;
- long int H = 240;
- long int W = 300;
-
- printf("Reading input\n");
- void* input = readTrainedWeights("../model_params/pipeline/dataset/calibration_4572.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- printf("Reading golden output\n");
- void* golden_output = readTrainedWeights("../model_params/pipeline/golden_output/GEOM_calib.bin",
- float_type,
- test_batch_size, 1, H, W);
-
-
- void* gaussian_filter = readTrainedWeights("../model_params/pipeline/filters/GaussianFilter.bin",
- float_type, 1, 1, 9, 9);
- void* outline_filter = readTrainedWeights("../model_params/pipeline/filters/OutlineFilter.bin",
- float_type, 1, 1, 3, 3);
- void* sharpen_filter = readTrainedWeights("../model_params/pipeline/filters/SharpenFilter.bin",
- float_type, 1, 1, 3, 3);
- void* motionblur_filter = readTrainedWeights("../model_params/pipeline/filters/MotionblurFilter.bin",
- float_type, 1, 1, 9, 9);
- void* emboss_filter = readTrainedWeights("../model_params/pipeline/filters/EmbossFilter.bin",
- float_type, 1, 1, 5, 5);
- void* emboss_bias = readTrainedWeights("../model_params/pipeline/filters/EmbossBias.bin",
- float_type, 1, 1, 1, 1);
-
-
- for(int i = 0; i < total_runs; i++){
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* gaussian_out = tensorHalfConvolution(input, gaussian_filter, 4, 4, 1, 1,
- conv_mode, conv_precision);
-
- void * gaussian_out_clip = tensorHalfRelu2(gaussian_out, 0, 255);
-
- void* emboss_out = tensorHalfConvolution(gaussian_out_clip, emboss_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
- void* emboss_bias_out = tensorHalfAdd(emboss_out, emboss_bias);
- void* emboss_bias_out_clip = tensorHalfRelu2(emboss_bias_out, 0, 255);
-
- void* outline_out = tensorHalfConvolution(emboss_bias_out_clip, outline_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- void * outline_out_clip = tensorHalfRelu2(outline_out, 0, 255);
-
- void* motionblur_out = tensorHalfConvolution(outline_out_clip, motionblur_filter, 4, 4, 1, 1,
- conv_mode, conv_precision);
- void * result = tensorHalfRelu2(motionblur_out, 0, 255);
-
-
- // NOTE-IMP: Important to include this call always before doing dumpOutput and computePSNViolation
- hpvm_request_tensor(result, 0);
-
- //dumpOutput(result, "GEOM_calib.bin");
-
- computePSNRViolation(result, golden_output, 30);
-
- }
-
- stopProfiling();
-}
-
-
-int main(int argc, char* argv[]){
-
- llvm_hpvm_initTensorRt(0);
-
- testPipeline();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/pipeline_GEO_half.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/pipeline_GEO_half.cc
deleted file mode 100644
index 369d8ff4f76aaff4663532ee634a79d54b94b2aa..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/pipeline_GEO_half.cc
+++ /dev/null
@@ -1,93 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testPipeline(){
-
- int total_runs = 1;
-
- printf("********* Pipeline: Gaussian - Emboss - Outline ********** \n");
-
- startProfiling();
-
- long int test_batch_size = 2000;
- long int H = 240;
- long int W = 300;
-
- printf("Reading input\n");
- void* input = readTrainedWeights("../model_params/pipeline/dataset/calibration_4572.bin",
- float_type,
- test_batch_size, 1, H, W);
- printf("Reading golden output\n");
- void* golden_output = readTrainedWeights("../model_params/pipeline/golden_output/GEO_calib.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- void* gaussian_filter = readTrainedWeights("../model_params/pipeline/filters/GaussianFilter.bin",
- float_type, 1, 1, 9, 9);
- void* outline_filter = readTrainedWeights("../model_params/pipeline/filters/OutlineFilter.bin",
- float_type, 1, 1, 3, 3);
- void* sharpen_filter = readTrainedWeights("../model_params/pipeline/filters/SharpenFilter.bin",
- float_type, 1, 1, 3, 3);
- void* motionblur_filter = readTrainedWeights("../model_params/pipeline/filters/MotionblurFilter.bin",
- float_type, 1, 1, 9, 9);
- void* emboss_filter = readTrainedWeights("../model_params/pipeline/filters/EmbossFilter.bin",
- float_type, 1, 1, 5, 5);
- void* emboss_bias = readTrainedWeights("../model_params/pipeline/filters/EmbossBias.bin",
- float_type, 1, 1, 1, 1);
-
-
- for(int i = 0; i < total_runs; i++){
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* gaussian_out = tensorHalfConvolution(input, gaussian_filter, 4, 4, 1, 1,
- conv_mode, conv_precision);
-
- void * gaussian_out_clip = tensorHalfRelu2(gaussian_out, 0, 255);
-
- void* emboss_out = tensorHalfConvolution(gaussian_out_clip, emboss_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
- void* emboss_bias_out = tensorHalfAdd(emboss_out, emboss_bias);
- void* emboss_bias_out_clip = tensorHalfRelu2(emboss_bias_out, 0, 255);
-
- void* outline_out = tensorHalfConvolution(emboss_bias_out_clip, outline_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- void * result = tensorHalfRelu2(outline_out, 0, 255);
-
-
- // NOTE-IMP: Important to include this call always before doing dumpOutput and computePSNViolation
- hpvm_request_tensor(result, 0);
-
- //dumpOutput(result, "GEO_calib.bin");
-
- computePSNRViolation(result, golden_output, 30);
-
- }
-
- stopProfiling();
-}
-
-
-int main(int argc, char* argv[]){
-
- llvm_hpvm_initTensorRt(0);
-
- testPipeline();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/pipeline_GSME_half.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/pipeline_GSME_half.cc
deleted file mode 100644
index e2d2ff18090c085405ec94902696e1a6631d94a7..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/pipeline_GSME_half.cc
+++ /dev/null
@@ -1,96 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testPipeline(){
-
- int total_runs = 1;
-
- printf("********* Pipeline: Gaussian - Sharpen - Motion Blur - Emboss ********** \n");
-
- startProfiling();
-
- //long int test_batch_size = 4572;
- long int test_batch_size = 2000;
- long int H = 240;
- long int W = 300;
-
- printf("Reading input\n");
- void* input = readTrainedWeights("../model_params/pipeline/dataset/calibration_4572.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- void* gaussian_filter = readTrainedWeights("../model_params/pipeline/filters/GaussianFilter.bin",
- float_type, 1, 1, 9, 9);
- void* outline_filter = readTrainedWeights("../model_params/pipeline/filters/OutlineFilter.bin",
- float_type, 1, 1, 3, 3);
- void* sharpen_filter = readTrainedWeights("../model_params/pipeline/filters/SharpenFilter.bin",
- float_type, 1, 1, 3, 3);
- void* motionblur_filter = readTrainedWeights("../model_params/pipeline/filters/MotionblurFilter.bin",
- float_type, 1, 1, 9, 9);
- void* emboss_filter = readTrainedWeights("../model_params/pipeline/filters/EmbossFilter.bin",
- float_type, 1, 1, 5, 5);
- void* emboss_bias = readTrainedWeights("../model_params/pipeline/filters/EmbossBias.bin",
- float_type, 1, 1, 1, 1);
-
-
- for(int i = 0; i < total_runs; i++){
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* gaussian_out = tensorHalfConvolution(input, gaussian_filter, 4, 4, 1, 1,
- conv_mode, conv_precision);
- void * gaussian_out_clip = tensorHalfRelu2(gaussian_out, 0, 255);
-
- void* sharpen_out = tensorHalfConvolution(gaussian_out_clip, sharpen_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- void * sharpen_out_clip = tensorHalfRelu2(sharpen_out, 0, 255);
-
- void* motionblur_out = tensorHalfConvolution(sharpen_out_clip, motionblur_filter, 4, 4, 1, 1,
- conv_mode, conv_precision);
- void * motionblur_out_clip = tensorHalfRelu2(motionblur_out, 0, 255);
-
- void* emboss_out = tensorHalfConvolution(motionblur_out_clip, emboss_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
- void* emboss_bias_out = tensorHalfAdd(emboss_out, emboss_bias);
- void* result = tensorHalfRelu2(emboss_bias_out, 0, 255);
-
-
- //void* result = gaussian_out;
-
- // NOTE-IMP: Important to include this call always before doing dumpOutput and computePSNViolation
- //hpvm_request_tensor(result, 0);
-
- //dumpOutput(result, "GSME_calib.bin");
-
- //computePSNRViolation(result, golden_output, 30);
- }
-
- stopProfiling();
-}
-
-
-int main(int argc, char* argv[]){
-
-
- llvm_hpvm_initTensorRt(0);
-
- testPipeline();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/pipeline_GSM_half.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/pipeline_GSM_half.cc
deleted file mode 100644
index 915dc596252576fb39dca073793d618d21634509..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/pipeline_GSM_half.cc
+++ /dev/null
@@ -1,91 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testPipeline(){
-
- int total_runs = 1;
-
- printf("********* Pipeline: Gaussian - Sharpen - Motion Blur ********** \n");
-
- // Start Profiling
- startProfiling();
-
- //long int test_batch_size = 9145;
- long int test_batch_size = 2000;
- long int H = 240;
- long int W = 300;
-
- printf("Reading input\n");
- void* input = readTrainedWeights("../model_params/pipeline/dataset/calibration_4572.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- void* gaussian_filter = readTrainedWeights("../model_params/pipeline/filters/GaussianFilter.bin",
- float_type, 1, 1, 9, 9);
- void* outline_filter = readTrainedWeights("../model_params/pipeline/filters/OutlineFilter.bin",
- float_type, 1, 1, 3, 3);
- void* sharpen_filter = readTrainedWeights("../model_params/pipeline/filters/SharpenFilter.bin",
- float_type, 1, 1, 3, 3);
- void* motionblur_filter = readTrainedWeights("../model_params/pipeline/filters/MotionblurFilter.bin",
- float_type, 1, 1, 9, 9);
- void* emboss_filter = readTrainedWeights("../model_params/pipeline/filters/EmbossFilter.bin",
- float_type, 1, 1, 5, 5);
- void* emboss_bias = readTrainedWeights("../model_params/pipeline/filters/EmbossBias.bin",
- float_type, 1, 1, 1, 1);
-
-
- for(int i = 0; i < total_runs; i++){
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* gaussian_out = tensorHalfConvolution(input, gaussian_filter, 4, 4, 1, 1,
- conv_mode, conv_precision);
- void * gaussian_out_clip = tensorHalfRelu2(gaussian_out, 0, 255);
-
- void* sharpen_out = tensorHalfConvolution(gaussian_out_clip, sharpen_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- void * sharpen_out_clip = tensorHalfRelu2(sharpen_out, 0, 255);
-
- void* motionblur_out = tensorHalfConvolution(sharpen_out_clip, motionblur_filter, 4, 4, 1, 1,
- conv_mode, conv_precision);
- void * result = tensorHalfRelu2(motionblur_out, 0, 255);
-
-
- // NOTE-IMP: Important to include this call always before doing dumpOutput and computePSNViolation
- //hpvm_request_tensor(result, 0);
-
- //dumpOutput(result, "GSM_calib.bin");
-
- //computePSNRViolation(result, golden_output, 30);
-
- }
-
- stopProfiling();
-}
-
-
-int main(int argc, char* argv[]){
-
- llvm_hpvm_initTensorRt(0);
-
- testPipeline();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/alexnet2_cifar10_half_profiling.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/alexnet2_cifar10_half_profiling.cc
deleted file mode 100644
index 82fe03247f36dbe6de31205a60344b7f44f85bad..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/alexnet2_cifar10_half_profiling.cc
+++ /dev/null
@@ -1,169 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-#include "../../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../../include/utils.h"
-
-/* NOTE: Reference Architecture to use for profiling */
-void testCifarNet(){
-
- printf("********* Alexnet2 CIFAR-10 DNN ********** \n");
-
- std::string dir_prefix = std::string("../model_params/alexnet2_cifar10/");
- std::string input_path = dir_prefix + std::string("norm_cifar_input.bin");
- std::string labels_path = dir_prefix + std::string("test_labels.bin");
-
- void* conv1_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv1.bin",
- float_type, 32, 3, 3, 3);
- void* conv1_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv1_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv2.bin",
- float_type, 32, 32, 3, 3);
- void* conv2_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv2_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv3_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv3.bin",
- float_type, 64, 32, 3, 3);
- void* conv3_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv3_bias.bin",
- float_type, 1, 64, 1, 1);
- void* conv4_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv4.bin",
- float_type, 64, 64, 3, 3);
- void* conv4_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv4_bias.bin",
- float_type, 1, 64, 1, 1);
- void* conv5_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv5.bin",
- float_type, 128, 64, 3, 3);
- void* conv5_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv5_bias.bin",
- float_type, 1, 128, 1, 1);
- void* conv6_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv6.bin",
- float_type, 128, 128, 3, 3);
- void* conv6_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv6_bias.bin",
- float_type, 1, 128, 1, 1);
-
- void* fc1_weights = readTrainedWeights("../model_params/alexnet2_cifar10/fc1.bin",
- float_type, 1, 1, 2048, 10);
- void* fc1_bias = readTrainedWeights("../model_params/alexnet2_cifar10/fc1_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- int total_runs = 10;
-
- // NOTE: Starting time profiling
- startProfiling();
-
- Profiler profiler;
- profiler.start_profiler();
-
- double total_time = 0.0;
- double total_energy = 0.0;
-
- for (int i = 0; i < total_runs; i++){
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
- void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
-
- profiler.resume_profiler();
-
- void* conv1out = tensorHalfConvolution(input, conv1_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorHalfAdd(conv1out, conv1_bias);
- void* conv1_tanh = tensorHalfTanh(conv1out);
-
- // 2nd Layer
- void* conv2out = tensorHalfConvolution(conv1_tanh, conv2_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorHalfAdd(conv2out, conv2_bias);
- void* conv2_tanh = tensorHalfTanh(conv2out);
- void* pool2out = tensorHalfPooling(conv2_tanh, 0, 2, 2, 0, 0, 2, 2);
-
- // 3rd Layer
- void* conv3out = tensorHalfConvolution(pool2out, conv3_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorHalfAdd(conv3out, conv3_bias);
- void* conv3_tanh = tensorHalfTanh(conv3out);
-
- // 4th Layer
- void* conv4out = tensorHalfConvolution(conv3_tanh, conv4_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorHalfAdd(conv4out, conv4_bias);
- void* conv4_tanh = tensorHalfTanh(conv4out);
- void* pool4out = tensorHalfPooling(conv4_tanh, 0, 2, 2, 0, 0, 2, 2);
-
- // 5th Layer
- void* conv5out = tensorHalfConvolution(pool4out, conv5_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorHalfAdd(conv5out, conv5_bias);
- void* conv5_tanh = tensorHalfTanh(conv5out);
-
- // 6th Layer
- void* conv6out = tensorHalfConvolution(conv5_tanh, conv6_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorHalfAdd(conv6out, conv6_bias);
-
- void* conv6_tanh = tensorHalfTanh(conv6out);
- void* pool6out = tensorHalfPooling(conv6_tanh, 0, 2, 2, 0, 0, 2, 2);
-
- // final FC Layer
- void* gemm1out = tensorHalfGemmGPU(pool6out, fc1_weights);
- void* gemm1biasout = tensorHalfAdd(gemm1out, fc1_bias);
- void* result = tensorSoftmax(gemm1biasout);
-
- profiler.pause_profiler();
- auto time_energy = profiler.get_time_energy();
- total_time += time_energy.first;
- total_energy += time_energy.second;
-
- profiler.reset();
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
-
- float accuracy = computeAccuracy2(labels, batch_size, result);
- final_accuracy += accuracy;
-
- freeBatchMemory();
- }
- }
- profiler.stop_profiler();
-
- std::cout<<"---------------------------------------\n";
- std::cout<<"Average time: " << total_time / total_runs << '\n';
- std::cout<<"Average energy: " << total_energy / total_runs << '\n';
- std::cout<<"---------------------------------------\n";
-
- stopProfiling();
-
- final_accuracy = final_accuracy / batch_count / total_runs;
- dumpFinalAccuracy(final_accuracy);
-
-}
-
-
-int main(int argc, char* argv[]){
-
- llvm_hpvm_initTensorRt(0);
-
- testCifarNet();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/alexnet_cifar10_half_profiling.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/alexnet_cifar10_half_profiling.cc
deleted file mode 100644
index 965e3170ea5c9df7dec1abe13d06581fe56f3b21..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/alexnet_cifar10_half_profiling.cc
+++ /dev/null
@@ -1,126 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
-
- std::string dir_prefix = std::string("../model_params/alexnet_cifar10_front/");
- std::string input_path = dir_prefix + std::string("input.bin");
- //void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
- std::string labels_path = dir_prefix + std::string("labels.bin");
- //uint8_t* labels = readLabels(labels_path.c_str(),10000);
- std::string conv2d_1_w_path = dir_prefix + std::string("conv0.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,11,11);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv_bias0.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv3.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,192,64,5,5);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv_bias3.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,192,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv6.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,384,192,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv_bias6.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,384,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv7.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,256,384,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv_bias7.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv8.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv_bias8.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("fc12.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,4096,10);
- std::string dense_1_b_path = dir_prefix + std::string("fc_bias12.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- int total_runs = 10;
- Profiler profiler;
- profiler.start_profiler();
-
- double total_time = 0.0;
- double total_energy = 0.0;
-
- // NOTE: Starting time profiling
- startProfiling();
- for (int i = 0; i < total_runs; i++){
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
- void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
-
- profiler.resume_profiler();
- void* var_0 = tensorHalfConvolution(input, conv2d_1_w, 5, 5, 1, 1, 1, 0);
- void* var_1 = tensorHalfAdd(var_0, conv2d_1_b);
- void* var_2 = tensorHalfTanh(var_1);
- void* var_3 = tensorHalfPooling(var_2,0,2,2,0,0,2,2);
- void* var_5 = tensorHalfConvolution(var_3, conv2d_2_w, 2, 2, 1, 1, 1, 0);
- void* var_6 = tensorHalfAdd(var_5, conv2d_2_b);
- void* var_7 = tensorHalfTanh(var_6);
- void* var_8 = tensorHalfPooling(var_7,0,2,2,0,0,2,2);
- void* var_10 = tensorHalfConvolution(var_8, conv2d_3_w, 1, 1, 1, 1, 1, 0);
- void* var_11 = tensorHalfAdd(var_10, conv2d_3_b);
- void* var_12 = tensorHalfTanh(var_11);
- void* var_13 = tensorHalfConvolution(var_12, conv2d_4_w, 1, 1, 1, 1, 1, 0);
- void* var_14 = tensorHalfAdd(var_13, conv2d_4_b);
- void* var_15 = tensorHalfTanh(var_14);
- void* var_16 = tensorHalfConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
- void* var_17 = tensorHalfAdd(var_16, conv2d_5_b);
- void* var_18 = tensorHalfTanh(var_17);
- void* var_19 = tensorHalfPooling(var_18,0,2,2,0,0,2,2);
- void* var_22 = tensorHalfGemmGPU(var_19, dense_1_w);
- void* var_23 = tensorHalfAdd(var_22, dense_1_b);
- void* var_24 = tensorSoftmax(var_23);
-
- profiler.pause_profiler();
- auto time_energy = profiler.get_time_energy();
- total_time += time_energy.first;
- total_energy += time_energy.second;
- profiler.reset();
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
-
- float accuracy = computeAccuracy2(labels,batch_size,var_24);
- final_accuracy += accuracy;
-
- freeBatchMemory();
- }
- }
- profiler.stop_profiler();
-
- std::cout<<"---------------------------------------\n";
- std::cout<<"Average time: " << total_time / total_runs << '\n';
- std::cout<<"Average energy: " << total_energy / total_runs << '\n';
- std::cout<<"---------------------------------------\n";
-
- stopProfiling();
-
- final_accuracy = final_accuracy / batch_count / total_runs;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/lenet_keras_half_profiling.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/lenet_keras_half_profiling.cc
deleted file mode 100644
index e6ffd6b03de4901780511e56afdb5faac85bb807..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/lenet_keras_half_profiling.cc
+++ /dev/null
@@ -1,186 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testLenetTanh(){
-
- int total_runs = 10;
-
-
- printf("********* Lenet-2 Architecture ********** \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 5000;
-
- uint8_t* labels = readLabels("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size);
-
- void* input = readInputTensor("../model_params/lenet_params/datasets/t10k-images-idx3-ubyte",
- CUDNN_DATA_FLOAT,
- test_batch_size, 1, 28, 28);
-
- // NOTE: Filter descriptors do NOT have batch size
- // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
- // IMP: The output channels matches the trained model - not the Lenet arch proposed in Andrew Ng's class
- void* conv1_filter = readTrainedWeights("../model_params/lenet_keras/conv1.bin",
- float_type, 32, 1, 5, 5);
- void* conv1_bias = readTrainedWeights("../model_params/lenet_keras/conv1_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/lenet_keras/conv2.bin",
- float_type, 64, 32, 5, 5);
- void* conv2_bias = readTrainedWeights("../model_params/lenet_keras/conv2_bias.bin",
- float_type, 1, 64, 1, 1);
- void* fc1_weights = readTrainedWeights("../model_params/lenet_keras/fc1.bin",
- float_type, 1, 1, 7*7*64, 1024);
- void* fc1_bias = readTrainedWeights("../model_params/lenet_keras/fc1_bias.bin",
- float_type, 1, 1024, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/lenet_keras/fc2.bin",
- float_type, 1, 1, 1024, 10);
- void* fc2_bias = readTrainedWeights("../model_params/lenet_keras/fc2_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
-
- clearTensorMap();
-
- Profiler profiler;
- profiler.start_profiler();
-
- double total_time = 0.0;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
- // Start power and performnce profiling
- profiler.resume_profiler();
- startProfiling();
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* conv1out = tensorHalfConvolution(input, conv1_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
-
- // NOTE: For tensorAdd, the only dimension that MUST match is channels
- tensorHalfAdd(conv1out, conv1_bias); // NOTE: In place operation
-
- void* pool1out = tensorHalfPooling(conv1out, 0, 2, 2, 0, 0, 2, 2);
-
- void* conv1_tanh = tensorHalfTanh(pool1out);
-
- // NOTE: input channels have to match between tensor op inputs and outputs
- void* conv2out = tensorHalfConvolution(conv1_tanh, conv2_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
- tensorHalfAdd(conv2out, conv2_bias); // NOTE: In place operation
-
- void* pool2out = tensorHalfPooling(conv2out, 0, 2, 2, 0, 0, 2, 2);
-
- void* conv2_tanh = tensorHalfTanh(pool2out);
-
- void* gemm1out = tensorHalfGemm(conv2_tanh, fc1_weights);
-
- void* gemm1biasout = tensorHalfAdd(gemm1out, fc1_bias);
-
- void* tanh1out = tensorHalfTanh(gemm1biasout);
-
- void* gemm2out = tensorHalfGemm(tanh1out, fc2_weights);
-
- void* gemm2_biasout = tensorHalfAdd(gemm2out, fc2_bias);
-
- void* tanh2out = tensorHalfTanh(gemm2_biasout);
-
- void* result = tensorSoftmax(tanh2out);
-
- profiler.pause_profiler();
- auto time_energy = profiler.get_time_energy();
- total_time += time_energy.first;
- profiler.reset();
-
- // End profiling and dump output to profile.txt
- stopProfiling();
-
- float accuracy = computeAccuracy2(labels, test_batch_size, result);
- final_accuracy += accuracy;
- dumpAccuracyNorms();
- freeOutputTensors();
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
- profiler.stop_profiler();
-
- std::cout<<"---------------------------------------\n";
- std::cout<<"Average time: " << total_time / total_runs << '\n';
- std::cout<<"---------------------------------------\n";
-
- final_accuracy = final_accuracy / total_runs;
- dumpFinalAccuracy(final_accuracy);
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(0);
-
- testLenetTanh();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/mobilenet_depthwise_half_profiling.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/mobilenet_depthwise_half_profiling.cc
deleted file mode 100644
index 641047b50dc1219f1d02bbfb75e2014840c90d96..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/mobilenet_depthwise_half_profiling.cc
+++ /dev/null
@@ -1,416 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-#include "../../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
-
- std::string dir_prefix = std::string("../model_params/mobilenet/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
- void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
- void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
- void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
- void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
- void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
- std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
- void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
- void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
- void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
- void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
- void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
- void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
- void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
- void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
- void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
- void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
- void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
- void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
- void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
- void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
- void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
- void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
- void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
- void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
- void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
- void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
- void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
- void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
- void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
- void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
- void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
- void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
- void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
- void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
- void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
- void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
- void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
- void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
- void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
- void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
- void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
- void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
- void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
- void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
- void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
- void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
- void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
- void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
- void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
- void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
- void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
- void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
- void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
- void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
- void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
- void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
- void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
- void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
- void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
- void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1);
- std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
- void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
- void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
- void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
- void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1);
- std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
- void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3);
- std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
- void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
- void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
- void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
- void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1);
- std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
- void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
- void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
- void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
- void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
-
- int total_runs = 10;
- float final_accuracy = 0.0;
-
- for (int run_num = 0; run_num < total_runs; run_num++){
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = tensorHalfConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
- void* var_1 = tensorHalfBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorHalfRelu(var_1);
- void* var_4 = tensorHalfConvCutlass(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_5 = tensorHalfBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_6 = tensorHalfRelu(var_5);
- void* var_7 = tensorHalfConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
- void* var_8 = tensorHalfBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_9 = tensorHalfRelu(var_8);
- void* var_11 = tensorHalfConvCutlass(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
- void* var_12 = tensorHalfBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_13 = tensorHalfRelu(var_12);
- void* var_14 = tensorHalfConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1);
- void* var_15 = tensorHalfBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_16 = tensorHalfRelu(var_15);
- void* var_18 = tensorHalfConvCutlass(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
- void* var_19 = tensorHalfBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_20 = tensorHalfRelu(var_19);
- void* var_21 = tensorHalfConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1);
- void* var_22 = tensorHalfBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_23 = tensorHalfRelu(var_22);
- void* var_26 = tensorHalfConvCutlass(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
- void* var_27 = tensorHalfBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_28 = tensorHalfRelu(var_27);
- void* var_29 = tensorHalfConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1);
- void* var_30 = tensorHalfBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_31 = tensorHalfRelu(var_30);
- void* var_33 = tensorHalfConvCutlass(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
- void* var_34 = tensorHalfBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_35 = tensorHalfRelu(var_34);
- void* var_36 = tensorHalfConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1);
- void* var_37 = tensorHalfBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_38 = tensorHalfRelu(var_37);
- void* var_41 = tensorHalfConvCutlass(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
- void* var_42 = tensorHalfBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
- void* var_43 = tensorHalfRelu(var_42);
- void* var_44 = tensorHalfConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1);
- void* var_45 = tensorHalfBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
- void* var_46 = tensorHalfRelu(var_45);
- void* var_48 = tensorHalfConvCutlass(var_46, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512);
- void* var_49 = tensorHalfBatchNorm(var_48, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001);
- void* var_50 = tensorHalfRelu(var_49);
- void* var_51 = tensorHalfConvolution(var_50, conv2d_8_w, 0, 0, 1, 1, 1, 1);
- void* var_52 = tensorHalfBatchNorm(var_51, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001);
- void* var_53 = tensorHalfRelu(var_52);
- void* var_55 = tensorHalfConvCutlass(var_53, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512);
- void* var_56 = tensorHalfBatchNorm(var_55, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001);
- void* var_57 = tensorHalfRelu(var_56);
- void* var_58 = tensorHalfConvolution(var_57, conv2d_9_w, 0, 0, 1, 1, 1, 1);
- void* var_59 = tensorHalfBatchNorm(var_58, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001);
- void* var_60 = tensorHalfRelu(var_59);
- void* var_63 = tensorHalfConvCutlass(var_60, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512);
- void* var_64 = tensorHalfBatchNorm(var_63, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001);
- void* var_65 = tensorHalfRelu(var_64);
- void* var_66 = tensorHalfConvolution(var_65, conv2d_10_w, 0, 0, 1, 1, 1, 1);
- void* var_67 = tensorHalfBatchNorm(var_66, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001);
- void* var_68 = tensorHalfRelu(var_67);
- void* var_70 = tensorHalfConvCutlass(var_68, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512);
- void* var_71 = tensorHalfBatchNorm(var_70, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001);
- void* var_72 = tensorHalfRelu(var_71);
- void* var_73 = tensorHalfConvolution(var_72, conv2d_11_w, 0, 0, 1, 1, 1, 1);
- void* var_74 = tensorHalfBatchNorm(var_73, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001);
- void* var_75 = tensorHalfRelu(var_74);
- void* var_77 = tensorHalfConvCutlass(var_75, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512);
- void* var_78 = tensorHalfBatchNorm(var_77, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001);
- void* var_79 = tensorHalfRelu(var_78);
- void* var_80 = tensorHalfConvolution(var_79, conv2d_12_w, 0, 0, 1, 1, 1, 1);
- void* var_81 = tensorHalfBatchNorm(var_80, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001);
- void* var_82 = tensorHalfRelu(var_81);
- void* var_85 = tensorHalfConvCutlass(var_82, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512);
- void* var_86 = tensorHalfBatchNorm(var_85, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001);
- void* var_87 = tensorHalfRelu(var_86);
- void* var_88 = tensorHalfConvolution(var_87, conv2d_13_w, 0, 0, 1, 1, 1, 1);
- void* var_89 = tensorHalfBatchNorm(var_88, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001);
- void* var_90 = tensorHalfRelu(var_89);
- void* var_92 = tensorHalfConvCutlass(var_90, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024);
- void* var_93 = tensorHalfBatchNorm(var_92, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001);
- void* var_94 = tensorHalfRelu(var_93);
- void* var_95 = tensorHalfConvolution(var_94, conv2d_14_w, 0, 0, 1, 1, 1, 1);
- void* var_96 = tensorHalfBatchNorm(var_95, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001);
- void* var_97 = tensorHalfRelu(var_96);
- void* var_99 = tensorHalfPooling(var_97,1,2,2,0,0,2,2);
- void* var_101 = tensorHalfGemmGPU(var_99, dense_1_w);
- void* var_102 = tensorHalfAdd(var_101, dense_1_b);
- void* var_103 = tensorSoftmax(var_102);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_103);
- final_accuracy += accuracy;
- freeBatchMemory();
- }
- }
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/mobilenet_half_cifar10_profiling.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/mobilenet_half_cifar10_profiling.cc
deleted file mode 100644
index 1c6a3955b1ad644363947106bb0f77d6b9a77050..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/mobilenet_half_cifar10_profiling.cc
+++ /dev/null
@@ -1,438 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-#include "../../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
-
- std::string dir_prefix = std::string("../model_params/mobilenet_quant/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
- void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
- void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
- void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
- void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
- void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
- std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
- void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
- void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
- void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
- void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
- void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
- void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
- void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
- void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
- void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
- void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
- void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
- void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
- void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
- void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
- void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
- void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
- void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
- void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
- void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
- void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
- void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
- void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
- void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
- void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
- void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
- void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
- void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
- void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
- void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
- void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
- void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
- void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
- void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
- void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
- void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
- void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
- void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
- void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
- void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
- void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
- void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
- void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
- void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
- void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
- void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
- void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
- void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
- void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
- void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
- void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
- void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
- void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
- void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
- void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1);
- std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
- void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
- void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
- void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
- void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1);
- std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
- void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3);
- std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
- void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
- void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
- void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
- void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1);
- std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
- void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
- void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
- void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
- void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- startProfiling();
-
- int test_input_size = 5000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- int total_runs = 10;
-
- Profiler profiler;
- profiler.start_profiler();
-
- double total_time = 0.0;
-
- for(int i = 0; i < total_runs; i++){
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- profiler.resume_profiler();
- void* var_0 = tensorHalfConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
- void* var_1 = tensorHalfBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorHalfRelu(var_1);
- void* var_4 = tensorHalfConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_5 = tensorHalfBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_6 = tensorHalfRelu(var_5);
- void* var_7 = tensorHalfConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
- void* var_8 = tensorHalfBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_9 = tensorHalfRelu(var_8);
- void* var_11 = tensorHalfConvolution(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
- void* var_12 = tensorHalfBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_13 = tensorHalfRelu(var_12);
- void* var_14 = tensorHalfConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1);
- void* var_15 = tensorHalfBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_16 = tensorHalfRelu(var_15);
- void* var_18 = tensorHalfConvolution(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
- void* var_19 = tensorHalfBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_20 = tensorHalfRelu(var_19);
- void* var_21 = tensorHalfConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1);
- void* var_22 = tensorHalfBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_23 = tensorHalfRelu(var_22);
- void* var_26 = tensorHalfConvolution(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
- void* var_27 = tensorHalfBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_28 = tensorHalfRelu(var_27);
- void* var_29 = tensorHalfConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1);
- void* var_30 = tensorHalfBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_31 = tensorHalfRelu(var_30);
- void* var_33 = tensorHalfConvolution(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
- void* var_34 = tensorHalfBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_35 = tensorHalfRelu(var_34);
- void* var_36 = tensorHalfConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1);
- void* var_37 = tensorHalfBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_38 = tensorHalfRelu(var_37);
- void* var_41 = tensorHalfConvolution(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
- void* var_42 = tensorHalfBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
- void* var_43 = tensorHalfRelu(var_42);
- void* var_44 = tensorHalfConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1);
- void* var_45 = tensorHalfBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
- void* var_46 = tensorHalfRelu(var_45);
- void* var_48 = tensorHalfConvolution(var_46, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512);
- void* var_49 = tensorHalfBatchNorm(var_48, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001);
- void* var_50 = tensorHalfRelu(var_49);
- void* var_51 = tensorHalfConvolution(var_50, conv2d_8_w, 0, 0, 1, 1, 1, 1);
- void* var_52 = tensorHalfBatchNorm(var_51, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001);
- void* var_53 = tensorHalfRelu(var_52);
- void* var_55 = tensorHalfConvolution(var_53, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512);
- void* var_56 = tensorHalfBatchNorm(var_55, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001);
- void* var_57 = tensorHalfRelu(var_56);
- void* var_58 = tensorHalfConvolution(var_57, conv2d_9_w, 0, 0, 1, 1, 1, 1);
- void* var_59 = tensorHalfBatchNorm(var_58, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001);
- void* var_60 = tensorHalfRelu(var_59);
- void* var_63 = tensorHalfConvolution(var_60, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512);
- void* var_64 = tensorHalfBatchNorm(var_63, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001);
- void* var_65 = tensorHalfRelu(var_64);
- void* var_66 = tensorHalfConvolution(var_65, conv2d_10_w, 0, 0, 1, 1, 1, 1);
- void* var_67 = tensorHalfBatchNorm(var_66, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001);
- void* var_68 = tensorHalfRelu(var_67);
- void* var_70 = tensorHalfConvolution(var_68, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512);
- void* var_71 = tensorHalfBatchNorm(var_70, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001);
- void* var_72 = tensorHalfRelu(var_71);
- void* var_73 = tensorHalfConvolution(var_72, conv2d_11_w, 0, 0, 1, 1, 1, 1);
- void* var_74 = tensorHalfBatchNorm(var_73, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001);
- void* var_75 = tensorHalfRelu(var_74);
- void* var_77 = tensorHalfConvolution(var_75, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512);
- void* var_78 = tensorHalfBatchNorm(var_77, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001);
- void* var_79 = tensorHalfRelu(var_78);
- void* var_80 = tensorHalfConvolution(var_79, conv2d_12_w, 0, 0, 1, 1, 1, 1);
- void* var_81 = tensorHalfBatchNorm(var_80, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001);
- void* var_82 = tensorHalfRelu(var_81);
- void* var_85 = tensorHalfConvolution(var_82, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512);
- void* var_86 = tensorHalfBatchNorm(var_85, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001);
- void* var_87 = tensorHalfRelu(var_86);
- void* var_88 = tensorHalfConvolution(var_87, conv2d_13_w, 0, 0, 1, 1, 1, 1);
- void* var_89 = tensorHalfBatchNorm(var_88, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001);
- void* var_90 = tensorHalfRelu(var_89);
- void* var_92 = tensorHalfConvolution(var_90, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024);
- void* var_93 = tensorHalfBatchNorm(var_92, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001);
- void* var_94 = tensorHalfRelu(var_93);
- void* var_95 = tensorHalfConvolution(var_94, conv2d_14_w, 0, 0, 1, 1, 1, 1);
- void* var_96 = tensorHalfBatchNorm(var_95, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001);
- void* var_97 = tensorHalfRelu(var_96);
- void* var_99 = tensorHalfPooling(var_97,1,2,2,0,0,2,2);
- void* var_101 = tensorHalfGemmGPU(var_99, dense_1_w);
- void* var_102 = tensorHalfAdd(var_101, dense_1_b);
- void* var_103 = tensorSoftmax(var_102);
-
- profiler.pause_profiler();
- auto time_energy = profiler.get_time_energy();
- total_time += time_energy.first;
- profiler.reset();
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_103);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
- }
- profiler.stop_profiler();
-
- std::cout<<"---------------------------------------\n";
- std::cout<<"Average time: " << total_time / total_runs << '\n';
- std::cout<<"---------------------------------------\n";
-
- stopProfiling();
-
- final_accuracy = final_accuracy / batch_count / total_runs;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/mobilenet_shallow_depthwise_half_profiling.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/mobilenet_shallow_depthwise_half_profiling.cc
deleted file mode 100644
index f68eb1793b66b0579f2ed6dbff26a56677f2aa95..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/mobilenet_shallow_depthwise_half_profiling.cc
+++ /dev/null
@@ -1,249 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-#include "../../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../../include/utils.h"
-
-
-int main(int argc, char* argv[]){
-
- int total_runs = 10;
- if (argc > 1){
- total_runs = atoi(argv[1]);
- }
-
-
- llvm_hpvm_initTensorRt(0);
-
- //std::string dir_prefix = std::string("../../keras/data/mobilenet_shallow_nathan/");
-
- std::string dir_prefix = std::string("../model_params/mobilenet_shallow/");
-
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
- void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
- void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
- void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
- void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
- void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
- std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
- void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
- void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
- void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
- void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,2048,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
-
-
- float final_accuracy = 0.0;
-
- for(int j = 0; j < total_runs; j++){
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = tensorHalfConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
- void* var_1 = tensorHalfBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorHalfRelu(var_1);
- void* var_4 = tensorHalfConvCutlass(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_5 = tensorHalfBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_6 = tensorHalfRelu(var_5);
- void* var_7 = tensorHalfConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
- void* var_8 = tensorHalfBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_9 = tensorHalfRelu(var_8);
- void* var_11 = tensorHalfConvCutlass(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
- void* var_12 = tensorHalfBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_13 = tensorHalfRelu(var_12);
- void* var_14 = tensorHalfConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1);
- void* var_15 = tensorHalfBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_16 = tensorHalfRelu(var_15);
- void* var_18 = tensorHalfConvCutlass(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
- void* var_19 = tensorHalfBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_20 = tensorHalfRelu(var_19);
- void* var_21 = tensorHalfConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1);
- void* var_22 = tensorHalfBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_23 = tensorHalfRelu(var_22);
- void* var_26 = tensorHalfConvCutlass(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
- void* var_27 = tensorHalfBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_28 = tensorHalfRelu(var_27);
- void* var_29 = tensorHalfConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1);
- void* var_30 = tensorHalfBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_31 = tensorHalfRelu(var_30);
- void* var_33 = tensorHalfConvCutlass(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
- void* var_34 = tensorHalfBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_35 = tensorHalfRelu(var_34);
- void* var_36 = tensorHalfConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1);
- void* var_37 = tensorHalfBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_38 = tensorHalfRelu(var_37);
- void* var_41 = tensorHalfConvCutlass(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
- void* var_42 = tensorHalfBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
- void* var_43 = tensorHalfRelu(var_42);
- void* var_44 = tensorHalfConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1);
- void* var_45 = tensorHalfBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
- void* var_46 = tensorHalfRelu(var_45);
- void* var_47 = tensorHalfPooling(var_46,1,2,2,0,0,2,2);
- void* var_49 = tensorHalfGemmGPU(var_47, dense_1_w);
- void* var_50 = tensorHalfAdd(var_49, dense_1_b);
- void* var_51 = tensorSoftmax(var_50);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_51);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- //final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy / batch_count);
- }
-
- final_accuracy = final_accuracy / batch_count / total_runs;
- dumpFinalAccuracy(final_accuracy);
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/mobilenet_shallow_half_profiling.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/mobilenet_shallow_half_profiling.cc
deleted file mode 100644
index c641db1a05efe44d4801da1ebdcaf2ae8945e7f2..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/mobilenet_shallow_half_profiling.cc
+++ /dev/null
@@ -1,225 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-#include "../../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- std::string dir_prefix = std::string("../model_params/mobilenet_shallow/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,64,64,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,64,1,1);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- int total_runs = 10;
- Profiler profiler;
- profiler.start_profiler();
-
- double total_time = 0.0;
-
- for(int i = 0; i < total_runs; i++){
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- profiler.resume_profiler();
-
- void* var_0 = tensorHalfConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
- void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorHalfRelu(var_1);
- void* var_4 = tensorHalfConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_5 = tensorBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_6 = tensorHalfRelu(var_5);
- void* var_7 = tensorHalfConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
- void* var_8 = tensorBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_9 = tensorHalfRelu(var_8);
- void* var_11 = tensorHalfConvolution(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
- void* var_12 = tensorBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_13 = tensorHalfRelu(var_12);
- void* var_14 = tensorHalfConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1);
- void* var_15 = tensorBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_16 = tensorHalfRelu(var_15);
- void* var_18 = tensorHalfConvolution(var_16, depthwise_conv2d_3_w, 1, 1, 2, 2, 1, 64);
- void* var_19 = tensorBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_20 = tensorHalfRelu(var_19);
- void* var_21 = tensorHalfConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1);
- void* var_22 = tensorBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_23 = tensorHalfRelu(var_22);
- void* var_26 = tensorHalfConvolution(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
- void* var_27 = tensorBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_28 = tensorHalfRelu(var_27);
- void* var_29 = tensorHalfConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1);
- void* var_30 = tensorBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_31 = tensorHalfRelu(var_30);
- void* var_33 = tensorHalfConvolution(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
- void* var_34 = tensorBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_35 = tensorHalfRelu(var_34);
- void* var_36 = tensorHalfConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1);
- void* var_37 = tensorBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_38 = tensorHalfRelu(var_37);
- void* var_40 = tensorHalfPooling(var_38,1,2,2,0,0,2,2);
- void* var_42 = tensorHalfGemmGPU(var_40, dense_1_w);
- void* var_43 = tensorHalfAdd(var_42, dense_1_b);
- void* var_44 = tensorSoftmax(var_43);
-
- profiler.pause_profiler();
- auto time_energy = profiler.get_time_energy();
- total_time += time_energy.first;
- profiler.reset();
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_44);
- final_accuracy += accuracy;
- freeBatchMemory();
- }
- }
-
- profiler.stop_profiler();
-
- std::cout<<"---------------------------------------\n";
- std::cout<<"Average time: " << total_time / total_runs << '\n';
- std::cout<<"---------------------------------------\n";
-
- final_accuracy = final_accuracy / batch_count / total_runs;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/resnet18_cifar10_half_profiling.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/resnet18_cifar10_half_profiling.cc
deleted file mode 100644
index f91814e8390a400159467298a3702147cbf2f4b3..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/resnet18_cifar10_half_profiling.cc
+++ /dev/null
@@ -1,242 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-#include "../../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../../include/utils.h"
-
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- std::string dir_prefix = std::string("../model_params/resnet18_cifar10_3/");
- std::string input_path = dir_prefix + std::string("input.bin");
- //void* input = readTrainedWeights(input_path.c_str(), 0, batch_size,3,32,32);
- std::string labels_path = dir_prefix + std::string("labels.bin");
- //uint8_t* labels = readLabels(labels_path.c_str(), batch_size);
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,16,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,16,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,16,1,1);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_14_b_path = dir_prefix + std::string("conv2d_14_b.bin");
- void* conv2d_14_b = readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_15_w_path = dir_prefix + std::string("conv2d_15_w.bin");
- void* conv2d_15_w = readTrainedWeights(conv2d_15_w_path.c_str(), 0,64,32,3,3);
- std::string conv2d_15_b_path = dir_prefix + std::string("conv2d_15_b.bin");
- void* conv2d_15_b = readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_17_w_path = dir_prefix + std::string("conv2d_17_w.bin");
- void* conv2d_17_w = readTrainedWeights(conv2d_17_w_path.c_str(), 0,64,32,1,1);
- std::string conv2d_17_b_path = dir_prefix + std::string("conv2d_17_b.bin");
- void* conv2d_17_b = readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_16_w_path = dir_prefix + std::string("conv2d_16_w.bin");
- void* conv2d_16_w = readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_16_b_path = dir_prefix + std::string("conv2d_16_b.bin");
- void* conv2d_16_b = readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_18_w_path = dir_prefix + std::string("conv2d_18_w.bin");
- void* conv2d_18_w = readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_18_b_path = dir_prefix + std::string("conv2d_18_b.bin");
- void* conv2d_18_b = readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_19_w_path = dir_prefix + std::string("conv2d_19_w.bin");
- void* conv2d_19_w = readTrainedWeights(conv2d_19_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_19_b_path = dir_prefix + std::string("conv2d_19_b.bin");
- void* conv2d_19_b = readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_20_w_path = dir_prefix + std::string("conv2d_20_w.bin");
- void* conv2d_20_w = readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_20_b_path = dir_prefix + std::string("conv2d_20_b.bin");
- void* conv2d_20_b = readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_21_w_path = dir_prefix + std::string("conv2d_21_w.bin");
- void* conv2d_21_w = readTrainedWeights(conv2d_21_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_21_b_path = dir_prefix + std::string("conv2d_21_b.bin");
- void* conv2d_21_b = readTrainedWeights(conv2d_21_b_path.c_str(), 0,1,64,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,64,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- int total_runs = 10; //100;
-
- // NOTE: Starting time profiling
- startProfiling();
-
- Profiler profiler;
- profiler.start_profiler();
- double total_time = 0.0;
-
- for (int itrs = 0; itrs < total_runs; itrs++){
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
-
- profiler.resume_profiler();
-
- void* var_2 = tensorHalfConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
- void* var_3 = tensorHalfAdd(var_2, conv2d_1_b);
- void* var_4 = tensorHalfRelu(var_3);
- void* var_6 = tensorHalfConvolution(var_4, conv2d_2_w, 1, 1, 1, 1, 1, 0);
- void* var_7 = tensorHalfAdd(var_6, conv2d_2_b);
- void* var_8 = tensorHalfRelu(var_7);
- void* var_10 = tensorHalfConvolution(var_8, conv2d_3_w, 1, 1, 1, 1, 1, 0);
- void* var_11 = tensorHalfAdd(var_10, conv2d_3_b);
- void* var_12 = tensorHalfAdd(var_4, var_11);
- void* var_13 = tensorHalfRelu(var_12);
- void* var_15 = tensorHalfConvolution(var_13, conv2d_4_w, 1, 1, 1, 1, 1, 0);
- void* var_16 = tensorHalfAdd(var_15, conv2d_4_b);
- void* var_17 = tensorHalfRelu(var_16);
- void* var_19 = tensorHalfConvolution(var_17, conv2d_5_w, 1, 1, 1, 1, 1, 0);
- void* var_20 = tensorHalfAdd(var_19, conv2d_5_b);
- void* var_21 = tensorHalfAdd(var_13, var_20);
- void* var_22 = tensorHalfRelu(var_21);
- void* var_24 = tensorHalfConvolution(var_22, conv2d_6_w, 1, 1, 1, 1, 1, 0);
- void* var_25 = tensorHalfAdd(var_24, conv2d_6_b);
- void* var_26 = tensorHalfRelu(var_25);
- void* var_28 = tensorHalfConvolution(var_26, conv2d_7_w, 1, 1, 1, 1, 1, 0);
- void* var_29 = tensorHalfAdd(var_28, conv2d_7_b);
- void* var_30 = tensorHalfAdd(var_22, var_29);
- void* var_31 = tensorHalfRelu(var_30);
- void* var_33 = tensorHalfConvolution(var_31, conv2d_8_w, 1, 1, 2, 2, 1, 0);
- void* var_34 = tensorHalfAdd(var_33, conv2d_8_b);
- void* var_35 = tensorHalfRelu(var_34);
- void* var_37 = tensorHalfConvolution(var_35, conv2d_9_w, 1, 1, 1, 1, 1, 0);
- void* var_38 = tensorHalfAdd(var_37, conv2d_9_b);
- void* var_40 = tensorHalfConvolution(var_31, conv2d_10_w, 0, 0, 2, 2, 1, 0);
- void* var_41 = tensorHalfAdd(var_40, conv2d_10_b);
- void* var_42 = tensorHalfAdd(var_41, var_38);
- void* var_43 = tensorHalfRelu(var_42);
- void* var_45 = tensorHalfConvolution(var_43, conv2d_11_w, 1, 1, 1, 1, 1, 0);
- void* var_46 = tensorHalfAdd(var_45, conv2d_11_b);
- void* var_47 = tensorHalfRelu(var_46);
- void* var_49 = tensorHalfConvolution(var_47, conv2d_12_w, 1, 1, 1, 1, 1, 0);
- void* var_50 = tensorHalfAdd(var_49, conv2d_12_b);
- void* var_51 = tensorHalfAdd(var_43, var_50);
- void* var_52 = tensorHalfRelu(var_51);
- void* var_54 = tensorHalfConvolution(var_52, conv2d_13_w, 1, 1, 1, 1, 1, 0);
- void* var_55 = tensorHalfAdd(var_54, conv2d_13_b);
- void* var_56 = tensorHalfRelu(var_55);
- void* var_58 = tensorHalfConvolution(var_56, conv2d_14_w, 1, 1, 1, 1, 1, 0);
- void* var_59 = tensorHalfAdd(var_58, conv2d_14_b);
- void* var_60 = tensorHalfAdd(var_52, var_59);
- void* var_61 = tensorHalfRelu(var_60);
- void* var_63 = tensorHalfConvolution(var_61, conv2d_15_w, 1, 1, 2, 2, 1, 0);
- void* var_64 = tensorHalfAdd(var_63, conv2d_15_b);
- void* var_65 = tensorHalfRelu(var_64);
- void* var_67 = tensorHalfConvolution(var_65, conv2d_16_w, 1, 1, 1, 1, 1, 0);
- void* var_68 = tensorHalfAdd(var_67, conv2d_16_b);
- void* var_70 = tensorHalfConvolution(var_61, conv2d_17_w, 0, 0, 2, 2, 1, 0);
- void* var_71 = tensorHalfAdd(var_70, conv2d_17_b);
- void* var_72 = tensorHalfAdd(var_71, var_68);
- void* var_73 = tensorHalfRelu(var_72);
- void* var_75 = tensorHalfConvolution(var_73, conv2d_18_w, 1, 1, 1, 1, 1, 0);
- void* var_76 = tensorHalfAdd(var_75, conv2d_18_b);
- void* var_77 = tensorHalfRelu(var_76);
- void* var_79 = tensorHalfConvolution(var_77, conv2d_19_w, 1, 1, 1, 1, 1, 0);
- void* var_80 = tensorHalfAdd(var_79, conv2d_19_b);
- void* var_81 = tensorHalfAdd(var_73, var_80);
- void* var_82 = tensorHalfRelu(var_81);
- void* var_84 = tensorHalfConvolution(var_82, conv2d_20_w, 1, 1, 1, 1, 1, 0);
- void* var_85 = tensorHalfAdd(var_84, conv2d_20_b);
- void* var_86 = tensorHalfRelu(var_85);
- void* var_88 = tensorHalfConvolution(var_86, conv2d_21_w, 1, 1, 1, 1, 1, 0);
- void* var_89 = tensorHalfAdd(var_88, conv2d_21_b);
- void* var_90 = tensorHalfAdd(var_82, var_89);
- void* var_91 = tensorHalfRelu(var_90);
- void* var_92 = tensorHalfPooling(var_91,1,8,8,0,0,8,8);
- void* var_94 = tensorHalfGemmGPU(var_92, dense_1_w);
- void* var_95 = tensorHalfAdd(var_94, dense_1_b);
- void* var_96 = tensorSoftmax(var_95);
-
- profiler.pause_profiler();
- auto time_energy = profiler.get_time_energy();
- total_time += time_energy.first;
- profiler.reset();
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
-
- float accuracy = computeAccuracy2(labels,batch_size,var_96);
- final_accuracy += accuracy;
-
- freeBatchMemory();
- }
- }
- stopProfiling();
-
- profiler.stop_profiler();
-
- final_accuracy = final_accuracy / batch_count / total_runs;
- dumpFinalAccuracy(final_accuracy);
-
- std::cout<<"---------------------------------------\n";
- std::cout<<"Average time: " << total_time / total_runs << '\n';
- std::cout<<"---------------------------------------\n";
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/vgg16_cifar100_half_profiling.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/vgg16_cifar100_half_profiling.cc
deleted file mode 100644
index b778b1720c8a2db2f90230c3e57d0e0928f8665b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/vgg16_cifar100_half_profiling.cc
+++ /dev/null
@@ -1,182 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-#include "../../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- std::string dir_prefix = std::string("../model_params/vgg16_cifar100_front/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,100);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,100,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- int total_runs = 10;
- Profiler profiler;
- profiler.start_profiler();
- double total_time = 0.0;
-
- for (int i = 0; i < total_runs; i++){
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- profiler.resume_profiler();
-
- void* var_0 = tensorHalfConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
- void* var_1 = tensorHalfAdd(var_0, conv2d_1_b);
- void* var_2 = tensorHalfRelu(var_1);
- void* var_4 = tensorHalfConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0);
- void* var_5 = tensorHalfAdd(var_4, conv2d_2_b);
- void* var_6 = tensorHalfRelu(var_5);
- void* var_7 = tensorHalfPooling(var_6,0,2,2,0,0,2,2);
- void* var_8 = tensorHalfConvolution(var_7, conv2d_3_w, 1, 1, 1, 1, 1, 0);
- void* var_9 = tensorHalfAdd(var_8, conv2d_3_b);
- void* var_10 = tensorHalfRelu(var_9);
- void* var_12 = tensorHalfConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0);
- void* var_13 = tensorHalfAdd(var_12, conv2d_4_b);
- void* var_14 = tensorHalfRelu(var_13);
- void* var_15 = tensorHalfPooling(var_14,0,2,2,0,0,2,2);
- void* var_16 = tensorHalfConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
- void* var_17 = tensorHalfAdd(var_16, conv2d_5_b);
- void* var_18 = tensorHalfRelu(var_17);
- void* var_20 = tensorHalfConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0);
- void* var_21 = tensorHalfAdd(var_20, conv2d_6_b);
- void* var_22 = tensorHalfRelu(var_21);
- void* var_24 = tensorHalfConvolution(var_22, conv2d_7_w, 1, 1, 1, 1, 1, 0);
- void* var_25 = tensorHalfAdd(var_24, conv2d_7_b);
- void* var_26 = tensorHalfRelu(var_25);
- void* var_27 = tensorHalfPooling(var_26,0,2,2,0,0,2,2);
- void* var_28 = tensorHalfConvolution(var_27, conv2d_8_w, 1, 1, 1, 1, 1, 0);
- void* var_29 = tensorHalfAdd(var_28, conv2d_8_b);
- void* var_30 = tensorHalfRelu(var_29);
- void* var_32 = tensorHalfConvolution(var_30, conv2d_9_w, 1, 1, 1, 1, 1, 0);
- void* var_33 = tensorHalfAdd(var_32, conv2d_9_b);
- void* var_34 = tensorHalfRelu(var_33);
- void* var_36 = tensorHalfConvolution(var_34, conv2d_10_w, 1, 1, 1, 1, 1, 0);
- void* var_37 = tensorHalfAdd(var_36, conv2d_10_b);
- void* var_38 = tensorHalfRelu(var_37);
- void* var_39 = tensorHalfPooling(var_38,0,2,2,0,0,2,2);
- void* var_40 = tensorHalfConvolution(var_39, conv2d_11_w, 1, 1, 1, 1, 1, 0);
- void* var_41 = tensorHalfAdd(var_40, conv2d_11_b);
- void* var_42 = tensorHalfRelu(var_41);
- void* var_44 = tensorHalfConvolution(var_42, conv2d_12_w, 1, 1, 1, 1, 1, 0);
- void* var_45 = tensorHalfAdd(var_44, conv2d_12_b);
- void* var_46 = tensorHalfRelu(var_45);
- void* var_48 = tensorHalfConvolution(var_46, conv2d_13_w, 1, 1, 1, 1, 1, 0);
- void* var_49 = tensorHalfAdd(var_48, conv2d_13_b);
- void* var_50 = tensorHalfRelu(var_49);
- void* var_51 = tensorHalfPooling(var_50,0,2,2,0,0,2,2);
- void* var_54 = tensorHalfGemmGPU(var_51, dense_1_w);
- void* var_55 = tensorHalfAdd(var_54, dense_1_b);
- void* var_56 = tensorHalfRelu(var_55);
- void* var_58 = tensorHalfGemmGPU(var_56, dense_2_w);
- void* var_59 = tensorHalfAdd(var_58, dense_2_b);
- void* var_60 = tensorSoftmax(var_59);
-
- profiler.pause_profiler();
- auto time_energy = profiler.get_time_energy();
- total_time += time_energy.first;
- profiler.reset();
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_60, 100);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
- }
-
- profiler.stop_profiler();
-
- std::cout<<"---------------------------------------\n";
- std::cout<<"Average time: " << total_time / total_runs << '\n';
- std::cout<<"---------------------------------------\n";
-
- final_accuracy = final_accuracy / batch_count / total_runs;
- dumpFinalAccuracy(final_accuracy);
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/vgg16_cifar10_half_profiling.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/vgg16_cifar10_half_profiling.cc
deleted file mode 100644
index 3f97e5dbde3b6d124888a8c74d435880097a394c..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/profiling/vgg16_cifar10_half_profiling.cc
+++ /dev/null
@@ -1,189 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-#include "../../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../../include/utils.h"
-
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- std::string dir_prefix = std::string("../model_params/vgg16_cifar10_2/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,10);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- int total_runs = 10;
-
- // NOTE: Starting time profiling
- startProfiling();
-
- Profiler profiler;
- profiler.start_profiler();
-
- double total_time = 0.0;
- for (int itrs = 0; itrs < total_runs; itrs++){
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
-
- profiler.resume_profiler();
-
- void* var_0 = tensorHalfConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
- void* var_1 = tensorHalfAdd(var_0, conv2d_1_b);
- void* var_2 = tensorHalfRelu(var_1);
- void* var_4 = tensorHalfConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0);
- void* var_5 = tensorHalfAdd(var_4, conv2d_2_b);
- void* var_6 = tensorHalfRelu(var_5);
- void* var_7 = tensorHalfPooling(var_6,0,2,2,0,0,2,2);
- void* var_8 = tensorHalfConvolution(var_7, conv2d_3_w, 1, 1, 1, 1, 1, 0);
- void* var_9 = tensorHalfAdd(var_8, conv2d_3_b);
- void* var_10 = tensorHalfRelu(var_9);
- void* var_12 = tensorHalfConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0);
- void* var_13 = tensorHalfAdd(var_12, conv2d_4_b);
- void* var_14 = tensorHalfRelu(var_13);
- void* var_15 = tensorHalfPooling(var_14,0,2,2,0,0,2,2);
- void* var_16 = tensorHalfConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
- void* var_17 = tensorHalfAdd(var_16, conv2d_5_b);
- void* var_18 = tensorHalfRelu(var_17);
- void* var_20 = tensorHalfConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0);
- void* var_21 = tensorHalfAdd(var_20, conv2d_6_b);
- void* var_22 = tensorHalfRelu(var_21);
- void* var_24 = tensorHalfConvolution(var_22, conv2d_7_w, 1, 1, 1, 1, 1, 0);
- void* var_25 = tensorHalfAdd(var_24, conv2d_7_b);
- void* var_26 = tensorHalfRelu(var_25);
- void* var_27 = tensorHalfPooling(var_26,0,2,2,0,0,2,2);
- void* var_28 = tensorHalfConvolution(var_27, conv2d_8_w, 1, 1, 1, 1, 1, 0);
- void* var_29 = tensorHalfAdd(var_28, conv2d_8_b);
- void* var_30 = tensorHalfRelu(var_29);
- void* var_32 = tensorHalfConvolution(var_30, conv2d_9_w, 1, 1, 1, 1, 1, 0);
- void* var_33 = tensorHalfAdd(var_32, conv2d_9_b);
- void* var_34 = tensorHalfRelu(var_33);
- void* var_36 = tensorHalfConvolution(var_34, conv2d_10_w, 1, 1, 1, 1, 1, 0);
- void* var_37 = tensorHalfAdd(var_36, conv2d_10_b);
- void* var_38 = tensorHalfRelu(var_37);
- void* var_39 = tensorHalfPooling(var_38,0,2,2,0,0,2,2);
- void* var_40 = tensorHalfConvolution(var_39, conv2d_11_w, 1, 1, 1, 1, 1, 0);
- void* var_41 = tensorHalfAdd(var_40, conv2d_11_b);
- void* var_42 = tensorHalfRelu(var_41);
- void* var_44 = tensorHalfConvolution(var_42, conv2d_12_w, 1, 1, 1, 1, 1, 0);
- void* var_45 = tensorHalfAdd(var_44, conv2d_12_b);
- void* var_46 = tensorHalfRelu(var_45);
- void* var_48 = tensorHalfConvolution(var_46, conv2d_13_w, 1, 1, 1, 1, 1, 0);
- void* var_49 = tensorHalfAdd(var_48, conv2d_13_b);
- void* var_50 = tensorHalfRelu(var_49);
- void* var_51 = tensorHalfPooling(var_50,0,2,2,0,0,2,2);
- void* var_54 = tensorHalfGemmGPU(var_51, dense_1_w);
- void* var_55 = tensorHalfAdd(var_54, dense_1_b);
- void* var_56 = tensorHalfRelu(var_55);
- void* var_58 = tensorHalfGemmGPU(var_56, dense_2_w);
- void* var_59 = tensorHalfAdd(var_58, dense_2_b);
- void* var_60 = tensorSoftmax(var_59);
-
- profiler.pause_profiler();
- auto time_energy = profiler.get_time_energy();
- total_time += time_energy.first;
- profiler.reset();
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
-
- float accuracy = computeAccuracy2(labels,batch_size,var_60);
- final_accuracy += accuracy;
-
- freeBatchMemory();
- }
- }
- std::cout<<"---------------------------------------\n";
- std::cout<<"Average time: " << total_time / total_runs << '\n';
- std::cout<<"---------------------------------------\n";
-
- profiler.stop_profiler();
- // Start power and performance profiling
- stopProfiling();
-
- final_accuracy = final_accuracy / batch_count / total_runs;
- dumpFinalAccuracy(final_accuracy);
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/resnet18_cifar10_half.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/resnet18_cifar10_half.cc
deleted file mode 100644
index fd1ef968591b61be66373e5b959605ce3dafb681..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/resnet18_cifar10_half.cc
+++ /dev/null
@@ -1,221 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- std::string dir_prefix = std::string("../model_params/resnet18_cifar10_3/");
- std::string input_path = dir_prefix + std::string("input.bin");
- //void* input = readTrainedWeights(input_path.c_str(), 0, batch_size,3,32,32);
- std::string labels_path = dir_prefix + std::string("labels.bin");
- //uint8_t* labels = readLabels(labels_path.c_str(), batch_size);
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,16,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,16,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,16,1,1);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_14_b_path = dir_prefix + std::string("conv2d_14_b.bin");
- void* conv2d_14_b = readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_15_w_path = dir_prefix + std::string("conv2d_15_w.bin");
- void* conv2d_15_w = readTrainedWeights(conv2d_15_w_path.c_str(), 0,64,32,3,3);
- std::string conv2d_15_b_path = dir_prefix + std::string("conv2d_15_b.bin");
- void* conv2d_15_b = readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_17_w_path = dir_prefix + std::string("conv2d_17_w.bin");
- void* conv2d_17_w = readTrainedWeights(conv2d_17_w_path.c_str(), 0,64,32,1,1);
- std::string conv2d_17_b_path = dir_prefix + std::string("conv2d_17_b.bin");
- void* conv2d_17_b = readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_16_w_path = dir_prefix + std::string("conv2d_16_w.bin");
- void* conv2d_16_w = readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_16_b_path = dir_prefix + std::string("conv2d_16_b.bin");
- void* conv2d_16_b = readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_18_w_path = dir_prefix + std::string("conv2d_18_w.bin");
- void* conv2d_18_w = readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_18_b_path = dir_prefix + std::string("conv2d_18_b.bin");
- void* conv2d_18_b = readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_19_w_path = dir_prefix + std::string("conv2d_19_w.bin");
- void* conv2d_19_w = readTrainedWeights(conv2d_19_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_19_b_path = dir_prefix + std::string("conv2d_19_b.bin");
- void* conv2d_19_b = readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_20_w_path = dir_prefix + std::string("conv2d_20_w.bin");
- void* conv2d_20_w = readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_20_b_path = dir_prefix + std::string("conv2d_20_b.bin");
- void* conv2d_20_b = readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_21_w_path = dir_prefix + std::string("conv2d_21_w.bin");
- void* conv2d_21_w = readTrainedWeights(conv2d_21_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_21_b_path = dir_prefix + std::string("conv2d_21_b.bin");
- void* conv2d_21_b = readTrainedWeights(conv2d_21_b_path.c_str(), 0,1,64,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,64,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 2000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- // NOTE: Starting time profiling
- startProfiling();
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
-
- void* var_2 = tensorHalfConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
- void* var_3 = tensorHalfAdd(var_2, conv2d_1_b);
- void* var_4 = tensorHalfRelu(var_3);
- void* var_6 = tensorHalfConvolution(var_4, conv2d_2_w, 1, 1, 1, 1, 1, 0);
- void* var_7 = tensorHalfAdd(var_6, conv2d_2_b);
- void* var_8 = tensorHalfRelu(var_7);
- void* var_10 = tensorHalfConvolution(var_8, conv2d_3_w, 1, 1, 1, 1, 1, 0);
- void* var_11 = tensorHalfAdd(var_10, conv2d_3_b);
- void* var_12 = tensorHalfAdd(var_4, var_11);
- void* var_13 = tensorHalfRelu(var_12);
- void* var_15 = tensorHalfConvolution(var_13, conv2d_4_w, 1, 1, 1, 1, 1, 0);
- void* var_16 = tensorHalfAdd(var_15, conv2d_4_b);
- void* var_17 = tensorHalfRelu(var_16);
- void* var_19 = tensorHalfConvolution(var_17, conv2d_5_w, 1, 1, 1, 1, 1, 0);
- void* var_20 = tensorHalfAdd(var_19, conv2d_5_b);
- void* var_21 = tensorHalfAdd(var_13, var_20);
- void* var_22 = tensorHalfRelu(var_21);
- void* var_24 = tensorHalfConvolution(var_22, conv2d_6_w, 1, 1, 1, 1, 1, 0);
- void* var_25 = tensorHalfAdd(var_24, conv2d_6_b);
- void* var_26 = tensorHalfRelu(var_25);
- void* var_28 = tensorHalfConvolution(var_26, conv2d_7_w, 1, 1, 1, 1, 1, 0);
- void* var_29 = tensorHalfAdd(var_28, conv2d_7_b);
- void* var_30 = tensorHalfAdd(var_22, var_29);
- void* var_31 = tensorHalfRelu(var_30);
- void* var_33 = tensorHalfConvolution(var_31, conv2d_8_w, 1, 1, 2, 2, 1, 0);
- void* var_34 = tensorHalfAdd(var_33, conv2d_8_b);
- void* var_35 = tensorHalfRelu(var_34);
- void* var_37 = tensorHalfConvolution(var_35, conv2d_9_w, 1, 1, 1, 1, 1, 0);
- void* var_38 = tensorHalfAdd(var_37, conv2d_9_b);
- void* var_40 = tensorHalfConvolution(var_31, conv2d_10_w, 0, 0, 2, 2, 1, 0);
- void* var_41 = tensorHalfAdd(var_40, conv2d_10_b);
- void* var_42 = tensorHalfAdd(var_41, var_38);
- void* var_43 = tensorHalfRelu(var_42);
- void* var_45 = tensorHalfConvolution(var_43, conv2d_11_w, 1, 1, 1, 1, 1, 0);
- void* var_46 = tensorHalfAdd(var_45, conv2d_11_b);
- void* var_47 = tensorHalfRelu(var_46);
- void* var_49 = tensorHalfConvolution(var_47, conv2d_12_w, 1, 1, 1, 1, 1, 0);
- void* var_50 = tensorHalfAdd(var_49, conv2d_12_b);
- void* var_51 = tensorHalfAdd(var_43, var_50);
- void* var_52 = tensorHalfRelu(var_51);
- void* var_54 = tensorHalfConvolution(var_52, conv2d_13_w, 1, 1, 1, 1, 1, 0);
- void* var_55 = tensorHalfAdd(var_54, conv2d_13_b);
- void* var_56 = tensorHalfRelu(var_55);
- void* var_58 = tensorHalfConvolution(var_56, conv2d_14_w, 1, 1, 1, 1, 1, 0);
- void* var_59 = tensorHalfAdd(var_58, conv2d_14_b);
- void* var_60 = tensorHalfAdd(var_52, var_59);
- void* var_61 = tensorHalfRelu(var_60);
- void* var_63 = tensorHalfConvolution(var_61, conv2d_15_w, 1, 1, 2, 2, 1, 0);
- void* var_64 = tensorHalfAdd(var_63, conv2d_15_b);
- void* var_65 = tensorHalfRelu(var_64);
- void* var_67 = tensorHalfConvolution(var_65, conv2d_16_w, 1, 1, 1, 1, 1, 0);
- void* var_68 = tensorHalfAdd(var_67, conv2d_16_b);
- void* var_70 = tensorHalfConvolution(var_61, conv2d_17_w, 0, 0, 2, 2, 1, 0);
- void* var_71 = tensorHalfAdd(var_70, conv2d_17_b);
- void* var_72 = tensorHalfAdd(var_71, var_68);
- void* var_73 = tensorHalfRelu(var_72);
- void* var_75 = tensorHalfConvolution(var_73, conv2d_18_w, 1, 1, 1, 1, 1, 0);
- void* var_76 = tensorHalfAdd(var_75, conv2d_18_b);
- void* var_77 = tensorHalfRelu(var_76);
- void* var_79 = tensorHalfConvolution(var_77, conv2d_19_w, 1, 1, 1, 1, 1, 0);
- void* var_80 = tensorHalfAdd(var_79, conv2d_19_b);
- void* var_81 = tensorHalfAdd(var_73, var_80);
- void* var_82 = tensorHalfRelu(var_81);
- void* var_84 = tensorHalfConvolution(var_82, conv2d_20_w, 1, 1, 1, 1, 1, 0);
- void* var_85 = tensorHalfAdd(var_84, conv2d_20_b);
- void* var_86 = tensorHalfRelu(var_85);
- void* var_88 = tensorHalfConvolution(var_86, conv2d_21_w, 1, 1, 1, 1, 1, 0);
- void* var_89 = tensorHalfAdd(var_88, conv2d_21_b);
- void* var_90 = tensorHalfAdd(var_82, var_89);
- void* var_91 = tensorHalfRelu(var_90);
- void* var_92 = tensorHalfPooling(var_91,1,8,8,0,0,8,8);
- void* var_94 = tensorHalfGemmGPU(var_92, dense_1_w);
- void* var_95 = tensorHalfAdd(var_94, dense_1_b);
- void* var_96 = tensorSoftmax(var_95);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
-
- float accuracy = computeAccuracy2(labels,batch_size,var_96);
- final_accuracy += accuracy;
-
- freeBatchMemory();
- }
-
- stopProfiling();
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/vgg16_cifar100_half.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/vgg16_cifar100_half.cc
deleted file mode 100644
index f6a6d0ecbf6b158d48f0cbdd39f6c417476dcc3e..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/vgg16_cifar100_half.cc
+++ /dev/null
@@ -1,160 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- std::string dir_prefix = std::string("../model_params/vgg16_cifar100_front/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,100);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,100,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 2000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = tensorHalfConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
- void* var_1 = tensorHalfAdd(var_0, conv2d_1_b);
- void* var_2 = tensorHalfRelu(var_1);
- void* var_4 = tensorHalfConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0);
- void* var_5 = tensorHalfAdd(var_4, conv2d_2_b);
- void* var_6 = tensorHalfRelu(var_5);
- void* var_7 = tensorHalfPooling(var_6,0,2,2,0,0,2,2);
- void* var_8 = tensorHalfConvolution(var_7, conv2d_3_w, 1, 1, 1, 1, 1, 0);
- void* var_9 = tensorHalfAdd(var_8, conv2d_3_b);
- void* var_10 = tensorHalfRelu(var_9);
- void* var_12 = tensorHalfConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0);
- void* var_13 = tensorHalfAdd(var_12, conv2d_4_b);
- void* var_14 = tensorHalfRelu(var_13);
- void* var_15 = tensorHalfPooling(var_14,0,2,2,0,0,2,2);
- void* var_16 = tensorHalfConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
- void* var_17 = tensorHalfAdd(var_16, conv2d_5_b);
- void* var_18 = tensorHalfRelu(var_17);
- void* var_20 = tensorHalfConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0);
- void* var_21 = tensorHalfAdd(var_20, conv2d_6_b);
- void* var_22 = tensorHalfRelu(var_21);
- void* var_24 = tensorHalfConvolution(var_22, conv2d_7_w, 1, 1, 1, 1, 1, 0);
- void* var_25 = tensorHalfAdd(var_24, conv2d_7_b);
- void* var_26 = tensorHalfRelu(var_25);
- void* var_27 = tensorHalfPooling(var_26,0,2,2,0,0,2,2);
- void* var_28 = tensorHalfConvolution(var_27, conv2d_8_w, 1, 1, 1, 1, 1, 0);
- void* var_29 = tensorHalfAdd(var_28, conv2d_8_b);
- void* var_30 = tensorHalfRelu(var_29);
- void* var_32 = tensorHalfConvolution(var_30, conv2d_9_w, 1, 1, 1, 1, 1, 0);
- void* var_33 = tensorHalfAdd(var_32, conv2d_9_b);
- void* var_34 = tensorHalfRelu(var_33);
- void* var_36 = tensorHalfConvolution(var_34, conv2d_10_w, 1, 1, 1, 1, 1, 0);
- void* var_37 = tensorHalfAdd(var_36, conv2d_10_b);
- void* var_38 = tensorHalfRelu(var_37);
- void* var_39 = tensorHalfPooling(var_38,0,2,2,0,0,2,2);
- void* var_40 = tensorHalfConvolution(var_39, conv2d_11_w, 1, 1, 1, 1, 1, 0);
- void* var_41 = tensorHalfAdd(var_40, conv2d_11_b);
- void* var_42 = tensorHalfRelu(var_41);
- void* var_44 = tensorHalfConvolution(var_42, conv2d_12_w, 1, 1, 1, 1, 1, 0);
- void* var_45 = tensorHalfAdd(var_44, conv2d_12_b);
- void* var_46 = tensorHalfRelu(var_45);
- void* var_48 = tensorHalfConvolution(var_46, conv2d_13_w, 1, 1, 1, 1, 1, 0);
- void* var_49 = tensorHalfAdd(var_48, conv2d_13_b);
- void* var_50 = tensorHalfRelu(var_49);
- void* var_51 = tensorHalfPooling(var_50,0,2,2,0,0,2,2);
- void* var_54 = tensorHalfGemmGPU(var_51, dense_1_w);
- void* var_55 = tensorHalfAdd(var_54, dense_1_b);
- void* var_56 = tensorHalfRelu(var_55);
- void* var_58 = tensorHalfGemmGPU(var_56, dense_2_w);
- void* var_59 = tensorHalfAdd(var_58, dense_2_b);
- void* var_60 = tensorSoftmax(var_59);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_60, 100);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/vgg16_cifar10_half.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/vgg16_cifar10_half.cc
deleted file mode 100644
index b296504a74c51de236da60e217f73925c805616f..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/vgg16_cifar10_half.cc
+++ /dev/null
@@ -1,167 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- std::string dir_prefix = std::string("../model_params/vgg16_cifar10_2/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,10);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 2000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- // Start power and performance profiling
- startProfiling();
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
-
- void* var_0 = tensorHalfConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
- void* var_1 = tensorHalfAdd(var_0, conv2d_1_b);
- void* var_2 = tensorHalfRelu(var_1);
- void* var_4 = tensorHalfConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0);
- void* var_5 = tensorHalfAdd(var_4, conv2d_2_b);
- void* var_6 = tensorHalfRelu(var_5);
- void* var_7 = tensorHalfPooling(var_6,0,2,2,0,0,2,2);
- void* var_8 = tensorHalfConvolution(var_7, conv2d_3_w, 1, 1, 1, 1, 1, 0);
- void* var_9 = tensorHalfAdd(var_8, conv2d_3_b);
- void* var_10 = tensorHalfRelu(var_9);
- void* var_12 = tensorHalfConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0);
- void* var_13 = tensorHalfAdd(var_12, conv2d_4_b);
- void* var_14 = tensorHalfRelu(var_13);
- void* var_15 = tensorHalfPooling(var_14,0,2,2,0,0,2,2);
- void* var_16 = tensorHalfConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
- void* var_17 = tensorHalfAdd(var_16, conv2d_5_b);
- void* var_18 = tensorHalfRelu(var_17);
- void* var_20 = tensorHalfConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0);
- void* var_21 = tensorHalfAdd(var_20, conv2d_6_b);
- void* var_22 = tensorHalfRelu(var_21);
- void* var_24 = tensorHalfConvolution(var_22, conv2d_7_w, 1, 1, 1, 1, 1, 0);
- void* var_25 = tensorHalfAdd(var_24, conv2d_7_b);
- void* var_26 = tensorHalfRelu(var_25);
- void* var_27 = tensorHalfPooling(var_26,0,2,2,0,0,2,2);
- void* var_28 = tensorHalfConvolution(var_27, conv2d_8_w, 1, 1, 1, 1, 1, 0);
- void* var_29 = tensorHalfAdd(var_28, conv2d_8_b);
- void* var_30 = tensorHalfRelu(var_29);
- void* var_32 = tensorHalfConvolution(var_30, conv2d_9_w, 1, 1, 1, 1, 1, 0);
- void* var_33 = tensorHalfAdd(var_32, conv2d_9_b);
- void* var_34 = tensorHalfRelu(var_33);
- void* var_36 = tensorHalfConvolution(var_34, conv2d_10_w, 1, 1, 1, 1, 1, 0);
- void* var_37 = tensorHalfAdd(var_36, conv2d_10_b);
- void* var_38 = tensorHalfRelu(var_37);
- void* var_39 = tensorHalfPooling(var_38,0,2,2,0,0,2,2);
- void* var_40 = tensorHalfConvolution(var_39, conv2d_11_w, 1, 1, 1, 1, 1, 0);
- void* var_41 = tensorHalfAdd(var_40, conv2d_11_b);
- void* var_42 = tensorHalfRelu(var_41);
- void* var_44 = tensorHalfConvolution(var_42, conv2d_12_w, 1, 1, 1, 1, 1, 0);
- void* var_45 = tensorHalfAdd(var_44, conv2d_12_b);
- void* var_46 = tensorHalfRelu(var_45);
- void* var_48 = tensorHalfConvolution(var_46, conv2d_13_w, 1, 1, 1, 1, 1, 0);
- void* var_49 = tensorHalfAdd(var_48, conv2d_13_b);
- void* var_50 = tensorHalfRelu(var_49);
- void* var_51 = tensorHalfPooling(var_50,0,2,2,0,0,2,2);
- void* var_54 = tensorHalfGemmGPU(var_51, dense_1_w);
- void* var_55 = tensorHalfAdd(var_54, dense_1_b);
- void* var_56 = tensorHalfRelu(var_55);
- void* var_58 = tensorHalfGemmGPU(var_56, dense_2_w);
- void* var_59 = tensorHalfAdd(var_58, dense_2_b);
- void* var_60 = tensorSoftmax(var_59);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
-
- float accuracy = computeAccuracy2(labels,batch_size,var_60);
- final_accuracy += accuracy;
-
- freeBatchMemory();
- }
-
- // Start power and performance profiling
- stopProfiling();
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/layers/cifar10_layers.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/layers/cifar10_layers.cc
deleted file mode 100644
index 4d083f58ba0d9db4ac2f1794be97b5409c7e1508..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/layers/cifar10_layers.cc
+++ /dev/null
@@ -1,157 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-/* NOTE: Reference Architecture to use for profiling */
-void testLenetTanh(){
-
- int total_runs = 2;
- if(Opentuner_run){
- total_runs = 100000;
- }
-
- printf("********* Lenet-2 Architecture ********** \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 5000;
-
- uint8_t* labels = readLabels("../model_params/cifar10/labels.bin", test_batch_size);
-
- for(int i = 0; i < total_runs; i++){
-
- void* input = readTrainedWeights("../model_params/cifar10/input.bin",
- float_type,
- test_batch_size, 3, 32, 32);
-
- void* conv1_filter = readTrainedWeights("../model_params/cifar10/conv1.bin",
- float_type, 32, 3, 3, 3);
- void* conv1_bias = readTrainedWeights("../model_params/cifar10/conv1_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/cifar10/conv2.bin",
- float_type, 64, 32, 3, 3);
- void* conv2_bias = readTrainedWeights("../model_params/cifar10/conv2_bias.bin",
- float_type, 1, 64, 1, 1);
- void* conv3_filter = readTrainedWeights("../model_params/cifar10/conv3.bin",
- float_type, 128, 64, 3, 3);
- void* conv3_bias = readTrainedWeights("../model_params/cifar10/conv3_bias.bin",
- float_type, 1, 128, 1, 1);
- void* conv4_filter = readTrainedWeights("../model_params/cifar10/conv4.bin",
- float_type, 128, 128, 3, 3);
- void* conv4_bias = readTrainedWeights("../model_params/cifar10/conv4_bias.bin",
- float_type, 1, 128, 1, 1);
-
-
- void* fc1_weights = readTrainedWeights("../model_params/cifar10/fc1.bin",
- float_type, 1, 1, 2048, 1024);
- void* fc1_bias = readTrainedWeights("../model_params/cifar10/fc1_bias.bin",
- float_type, 1, 1024, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/cifar10/fc2.bin",
- float_type, 1, 1, 1024, 10);
- void* fc2_bias = readTrainedWeights("../model_params/cifar10/fc2_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- clearTensorMap();
-
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
- // Start power and performance profiling
- startProfiling();
-
-
-
- void* conv1_out = ConvLayer_GPU(input, conv1_filter, conv1_bias,
- 1, 1, 1, 1, 0, 0, 0, -1,1);
-
- void* conv2_out = ConvLayer_GPU(conv1_out, conv2_filter, conv2_bias,
- 1, 1, 1, 1, 0, 2, 0, -1,1);
-
- void* conv3_out = ConvLayer_GPU(conv2_out, conv3_filter, conv3_bias,
- 1, 1, 1, 1, 0, 2, 0, -1,1);
-
- void* conv4_out = ConvLayer_GPU(conv3_out, conv4_filter, conv4_bias,
- 1, 1, 1, 1, 0, 2, 0, -1,1);
-
- void* fc1_out = FCLayer_GPU(conv4_out, fc1_weights, fc1_bias, 0, -1,1);
-
- void* fc2_out = FCLayer_GPU(fc1_out, fc2_weights, fc2_bias, 0, -1,1);
-
- void* result = tensorSoftmax(fc2_out);
-
- // End profiling and dump output to profile.txt
- stopProfiling();
-
- computeAccuracy2(labels, test_batch_size, result);
-
- dumpAccuracyNorms();
- freeOutputTensors();
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
-
-
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(0);
-
- testLenetTanh();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/lenet2_tanh.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/lenet2_tanh.cc
deleted file mode 100644
index d2d663552fdab6366f28655ca835ba63cb4fcee4..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/lenet2_tanh.cc
+++ /dev/null
@@ -1,171 +0,0 @@
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testLenetTanh(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
-
- printf("********* Lenet-2 Architecture ********** \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 5000;
-
- uint8_t* labels = readLabels("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size);
-
- void* input = readInputTensor("../model_params/lenet_params/datasets/t10k-images-idx3-ubyte",
- CUDNN_DATA_FLOAT,
- test_batch_size, 1, 28, 28);
-
- // NOTE: Filter descriptors do NOT have batch size
- // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
- // IMP: The output channels matches the trained model - not the Lenet arch proposed in Andrew Ng's class
- void* conv1_filter = readTrainedWeights("../model_params/lenet_tanh2/conv1.bin",
- float_type, 32, 1, 5, 5);
- void* conv1_bias = readTrainedWeights("../model_params/lenet_tanh2/conv1_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/lenet_tanh2/conv2.bin",
- float_type, 64, 32, 5, 5);
- void* conv2_bias = readTrainedWeights("../model_params/lenet_tanh2/conv2_bias.bin",
- float_type, 1, 64, 1, 1);
- void* fc1_weights = readTrainedWeights("../model_params/lenet_tanh2/fc1.bin",
- float_type, 1, 1, 7*7*64, 1024);
- void* fc1_bias = readTrainedWeights("../model_params/lenet_tanh2/fc1_bias.bin",
- float_type, 1, 1024, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/lenet_tanh2/fc2.bin",
- float_type, 1, 1, 1024, 10);
- void* fc2_bias = readTrainedWeights("../model_params/lenet_tanh2/fc2_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
-
- clearTensorMap();
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
- // Start power and performnce profiling
- startProfiling();
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* conv1out = tensorConvolution(input, conv1_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
-
- // NOTE: For tensorAdd, the only dimension that MUST match is channels
- tensorAdd(conv1out, conv1_bias); // NOTE: In place operation
-
- void* pool1out = tensorPooling(conv1out, 0, 2, 2, 0, 0, 2, 2);
-
- void* conv1_tanh = tensorTanh(pool1out);
-
- // NOTE: input channels have to match between tensor op inputs and outputs
- void* conv2out = tensorConvolution(conv1_tanh, conv2_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv2out, conv2_bias); // NOTE: In place operation
-
- void* pool2out = tensorPooling(conv2out, 0, 2, 2, 0, 0, 2, 2);
-
- void* conv2_tanh = tensorTanh(pool2out);
-
- void* gemm1out = tensorGemmGPU(conv2_tanh, fc1_weights);
-
- void* gemm1biasout = tensorAdd(gemm1out, fc1_bias);
-
- void* tanh1out = tensorTanh(gemm1biasout);
-
- void* gemm2out = tensorGemmGPU(tanh1out, fc2_weights);
-
- void* gemm2_biasout = tensorAdd(gemm2out, fc2_bias);
-
- void* tanh2out = tensorTanh(gemm2_biasout);
-
- void* result = tensorSoftmax(tanh2out);
-
- // End profiling and dump output to profile.txt
- stopProfiling();
-
- computeAccuracy2(labels, test_batch_size, result);
-
- dumpAccuracyNorms();
- freeOutputTensors();
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
-
-
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(0);
-
- testLenetTanh();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/lenet_front.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/lenet_front.cc
deleted file mode 100644
index effb293a8b63119015ed8dbf5f8938eb62f2f89c..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/lenet_front.cc
+++ /dev/null
@@ -1,61 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- std::string dir_prefix = std::string("../model_params/lenet_front/");
- std::string input_path = dir_prefix + std::string("input.bin");
- void* input = readTrainedWeights(input_path.c_str(), 0,10000,1,28,28);
- std::string labels_path = dir_prefix + std::string("labels.bin");
- uint8_t* labels = readLabels(labels_path.c_str(),10000);
- std::string conv2d_1_w_path = dir_prefix + std::string("conv0.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,1,5,5);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv_bias0.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,5,5);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv_bias2.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("fc5.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,3136,1024);
- std::string dense_1_b_path = dir_prefix + std::string("fc_bias5.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,1024,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("fc6.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,1024,10);
- std::string dense_2_b_path = dir_prefix + std::string("fc_bias6.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,10,1,1);
-
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 2, 2, 1, 1, 1, 0);
- void* var_1 = tensorAdd(var_0, conv2d_1_b);
- void* var_2 = tensorTanh(var_1);
- void* var_3 = tensorPooling(var_2,0,2,2,0,0,2,2);
- void* var_4 = tensorConvolution(var_3, conv2d_2_w, 2, 2, 1, 1, 1, 0);
- void* var_5 = tensorAdd(var_4, conv2d_2_b);
- void* var_6 = tensorTanh(var_5);
- void* var_7 = tensorPooling(var_6,0,2,2,0,0,2,2);
- void* var_9 = tensorGemmGPU(var_7, dense_1_w);
- void* var_10 = tensorAdd(var_9, dense_1_b);
- void* var_11 = tensorTanh(var_10);
- void* var_12 = tensorGemmGPU(var_11, dense_2_w);
- void* var_13 = tensorAdd(var_12, dense_2_b);
- void* var_14 = tensorTanh(var_13);
- void* var_15 = tensorSoftmax(var_14);
-
- computeAccuracy2(labels, 10000, var_15);
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/lenet_int32.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/lenet_int32.cc
deleted file mode 100644
index 03a4137004fe063a4536efec8fa7ecf2d8d2b374..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/lenet_int32.cc
+++ /dev/null
@@ -1,91 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
-
- std::string dir_prefix = std::string("../../keras/data/lenet_test_8/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,1,5,5);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,5,5);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,64,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,3136,1024);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,1024,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,1024,10);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,10,1,1);
-
-
-
- startMemTracking();
-
- int test_input_size = 10000;
- int batch_size = 10000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,1,28,28);
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 2, 2, 1, 1, 1, 1);
- void* var_1 = tensorAdd(var_0, conv2d_1_b);
- void* var_2 = tensorRelu(var_1);
- void* var_3 = tensorPooling(var_2,0,2,2,0,0,2,2);
- void* var_4 = tensorConvolution(var_3, conv2d_2_w, 2, 2, 1, 1, 1, 1);
- void* var_5 = tensorAdd(var_4, conv2d_2_b);
- void* var_6 = tensorRelu(var_5);
- void* var_8 = tensorConvolution(var_6, conv2d_3_w, 1, 1, 2, 2, 1, 1);
- void* var_9 = tensorAdd(var_8, conv2d_3_b);
- void* var_10 = tensorRelu(var_9);
- void* var_12 = tensorGemmGPU(var_10, dense_1_w);
- void* var_13 = tensorAdd(var_12, dense_1_b);
- void* var_14 = tensorRelu(var_13);
- void* var_15 = tensorGemmGPU(var_14, dense_2_w);
- void* var_16 = tensorAdd(var_15, dense_2_b);
- void* var_17 = tensorRelu(var_16);
- void* var_18 = tensorSoftmax(var_17);
-
- uint32_t* labels = readLabelsBatch3(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy3(labels, var_18);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/lenet_keras.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/lenet_keras.cc
deleted file mode 100644
index edf36410761c7ae19da50067296d4c47b203504a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/lenet_keras.cc
+++ /dev/null
@@ -1,171 +0,0 @@
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testLenetTanh(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
-
- printf("********* Lenet-2 Architecture ********** \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 10000;
-
- uint8_t* labels = readLabels("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size);
-
- void* input = readInputTensor("../model_params/lenet_params/datasets/t10k-images-idx3-ubyte",
- CUDNN_DATA_FLOAT,
- test_batch_size, 1, 28, 28);
-
- // NOTE: Filter descriptors do NOT have batch size
- // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
- // IMP: The output channels matches the trained model - not the Lenet arch proposed in Andrew Ng's class
- void* conv1_filter = readTrainedWeights("../model_params/lenet_keras/conv1.bin",
- float_type, 32, 1, 5, 5);
- void* conv1_bias = readTrainedWeights("../model_params/lenet_keras/conv1_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/lenet_keras/conv2.bin",
- float_type, 64, 32, 5, 5);
- void* conv2_bias = readTrainedWeights("../model_params/lenet_keras/conv2_bias.bin",
- float_type, 1, 64, 1, 1);
- void* fc1_weights = readTrainedWeights("../model_params/lenet_keras/fc1.bin",
- float_type, 1, 1, 7*7*64, 1024);
- void* fc1_bias = readTrainedWeights("../model_params/lenet_keras/fc1_bias.bin",
- float_type, 1, 1024, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/lenet_keras/fc2.bin",
- float_type, 1, 1, 1024, 10);
- void* fc2_bias = readTrainedWeights("../model_params/lenet_keras/fc2_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
-
- clearTensorMap();
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
- // Start power and performnce profiling
- startProfiling();
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* conv1out = tensorConvolution(input, conv1_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
-
- // NOTE: For tensorAdd, the only dimension that MUST match is channels
- tensorAdd(conv1out, conv1_bias); // NOTE: In place operation
-
- void* pool1out = tensorPooling(conv1out, 0, 2, 2, 0, 0, 2, 2);
-
- void* conv1_tanh = tensorTanh(pool1out);
-
- // NOTE: input channels have to match between tensor op inputs and outputs
- void* conv2out = tensorConvolution(conv1_tanh, conv2_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv2out, conv2_bias); // NOTE: In place operation
-
- void* pool2out = tensorPooling(conv2out, 0, 2, 2, 0, 0, 2, 2);
-
- void* conv2_tanh = tensorTanh(pool2out);
-
- void* gemm1out = tensorGemmGPU(conv2_tanh, fc1_weights);
-
- void* gemm1biasout = tensorAdd(gemm1out, fc1_bias);
-
- void* tanh1out = tensorTanh(gemm1biasout);
-
- void* gemm2out = tensorGemmGPU(tanh1out, fc2_weights);
-
- void* gemm2_biasout = tensorAdd(gemm2out, fc2_bias);
-
- void* tanh2out = tensorTanh(gemm2_biasout);
-
- void* result = tensorSoftmax(tanh2out);
-
- // End profiling and dump output to profile.txt
- stopProfiling();
-
- computeAccuracy2(labels, test_batch_size, result);
-
- dumpAccuracyNorms();
- freeOutputTensors();
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
-
-
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(0);
-
- testLenetTanh();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/lenet_layers.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/lenet_layers.cc
deleted file mode 100644
index a6b777e36c1b31440a3ad7d227df4915b1cc27df..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/lenet_layers.cc
+++ /dev/null
@@ -1,61 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-
-int main(){
-
- llvm_hpvm_initializeRuntimeController("tuner_confs.txt", "quant_ranges_rt.txt");
- llvm_hpvm_initApproxhpvmRt(0);
-
-
- std::string dir_prefix = std::string("../model_params/lenet_relu/");
- std::string input_path = dir_prefix + std::string("input.bin");
- void* input = readTrainedWeights(input_path.c_str(), 0,10000,1,28,28);
- std::string labels_path = dir_prefix + std::string("labels.bin");
- uint8_t* labels = readLabels(labels_path.c_str(),10000);
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,1,5,5);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,5,5);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,64,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,3136,1024);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,1024,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,1024,10);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,10,1,1);
-
-
- void* var_0 = wrapper_ConvLayer("1", input, conv2d_1_w, conv2d_1_b, 2, 2, 1, 1, 0, 2, 1, 0, 0);
- void* var_1 = wrapper_ConvLayer("2", var_0, conv2d_2_w, conv2d_2_b, 2, 2, 1, 1, -1, 0, 1, 0, 0);
- void* var_2 = wrapper_ConvLayer("3", var_1, conv2d_3_w, conv2d_3_b, 1, 1, 2, 2, -1, 0, 1, 0, 0);
- void* var_3 = wrapper_FCLayer("4", var_2, dense_1_w, dense_1_b, 1, 0, 0);
- void* var_4 = wrapper_FCLayer("5", var_3, dense_2_w, dense_2_b, 1, 0, 0);
- void* var_5 = tensorSoftmax(var_4);
-
- computeAccuracy2(labels,10000,var_5);
-
-
- llvm_hpvm_cleanupApproxhpvmRt();
- llvm_hpvm_clearRuntimeController();
-
-
- return 0;
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/lenet_perf.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/lenet_perf.cc
deleted file mode 100644
index 7c9583f291ea908c4c89a8b56045e06585a4f83a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/lenet_perf.cc
+++ /dev/null
@@ -1,185 +0,0 @@
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-int total_runs = 1;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testLenetTanh(){
-
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
-
- printf("********* Lenet-2 Architecture ********** \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 1000;
-
- uint8_t* labels = readLabels("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size);
-
- void* input = readInputTensor("../model_params/lenet_params/datasets/t10k-images-idx3-ubyte",
- CUDNN_DATA_FLOAT,
- test_batch_size, 1, 28, 28);
-
- // NOTE: Filter descriptors do NOT have batch size
- // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
- // IMP: The output channels matches the trained model - not the Lenet arch proposed in Andrew Ng's class
- void* conv1_filter = readTrainedWeights("../model_params/lenet_keras/conv1.bin",
- float_type, 32, 1, 5, 5);
- void* conv1_bias = readTrainedWeights("../model_params/lenet_keras/conv1_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/lenet_keras/conv2.bin",
- float_type, 64, 32, 5, 5);
- void* conv2_bias = readTrainedWeights("../model_params/lenet_keras/conv2_bias.bin",
- float_type, 1, 64, 1, 1);
- void* fc1_weights = readTrainedWeights("../model_params/lenet_keras/fc1.bin",
- float_type, 1, 1, 7*7*64, 1024);
- void* fc1_bias = readTrainedWeights("../model_params/lenet_keras/fc1_bias.bin",
- float_type, 1, 1024, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/lenet_keras/fc2.bin",
- float_type, 1, 1, 1024, 10);
- void* fc2_bias = readTrainedWeights("../model_params/lenet_keras/fc2_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
-
- clearTensorMap();
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
- // Start power and performnce profiling
- startProfiling();
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- //void* conv1out = tensorConvPerfCuda(input, conv1_filter, 2, 2, 1, 1,
- // conv_mode, conv_precision, 2, 2, 1);
-
- void* conv1out = tensorConvSampSim(input, conv1_filter, 2, 2, 1, 1,
- conv_mode, conv_precision, 4, 0);
-
- // NOTE: For tensorAdd, the only dimension that MUST match is channels
- tensorAdd(conv1out, conv1_bias); // NOTE: In place operation
-
- void* pool1out = tensorPooling(conv1out, 0, 2, 2, 0, 0, 2, 2);
-
- void* conv1_tanh = tensorTanh(pool1out);
-
- // NOTE: input channels have to match between tensor op inputs and outputs
- //void* conv2out = tensorConvPerfCuda(conv1_tanh, conv2_filter, 2, 2, 1, 1,
- // conv_mode, conv_precision, 1, 2, 1);
-
- void* conv2out = tensorConvSampSim(conv1_tanh, conv2_filter, 2, 2, 1, 1,
- conv_mode, conv_precision, 2, 0);
-
- tensorAdd(conv2out, conv2_bias); // NOTE: In place operation
-
- void* pool2out = tensorPooling(conv2out, 0, 2, 2, 0, 0, 2, 2);
-
- void* conv2_tanh = tensorTanh(pool2out);
-
- void* gemm1out = tensorGemmGPU(conv2_tanh, fc1_weights);
-
- void* gemm1biasout = tensorAdd(gemm1out, fc1_bias);
-
- void* tanh1out = tensorTanh(gemm1biasout);
-
- void* gemm2out = tensorGemmGPU(tanh1out, fc2_weights);
-
- void* gemm2_biasout = tensorAdd(gemm2out, fc2_bias);
-
- void* tanh2out = tensorTanh(gemm2_biasout);
-
- void* result = tensorSoftmax(tanh2out);
-
- // End profiling and dump output to profile.txt
- stopProfiling();
-
- float accuracy = computeAccuracy2(labels, test_batch_size, result);
- dumpFinalAccuracy(accuracy);
-
-
- //FIXME: remove the comment below to use piped autotuner
- //dumpAccuracyNorms();
- freeOutputTensors();
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
- dumpExecutionAccuracies();
-
-
-}
-
-
-
-int main(int argc, char* argv[]){
-
- if (argc > 1){
- total_runs = atoi(argv[1]);
- }
-
- llvm_hpvm_initTensorRt(0);
-
- testLenetTanh();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet.cc
deleted file mode 100644
index ba7af9846916057fedc05757bdad77fefb01590e..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet.cc
+++ /dev/null
@@ -1,413 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(1);
-
-
- std::string dir_prefix = std::string("../model_params/mobilenet_hpvm_3/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
- void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
- void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
- void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
- void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
- void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
- std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
- void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
- void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
- void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
- void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
- void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
- void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
- void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
- void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
- void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
- void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
- void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
- void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
- void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
- void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
- void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
- void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
- void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
- void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
- void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
- void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
- void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
- void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
- void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
- void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
- void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
- void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
- void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
- void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
- void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
- void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
- void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
- void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
- void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
- void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
- void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
- void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
- void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
- void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
- void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
- void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
- void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
- void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
- void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
- void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
- void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
- void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
- void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
- void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
- void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
- void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
- void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
- void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
- void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
- void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1);
- std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
- void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
- void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
- void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
- void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1);
- std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
- void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3);
- std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
- void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
- void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
- void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
- void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1);
- std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
- void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
- void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
- void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
- void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-
- startMemTracking();
-
- int test_input_size = 3000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
- void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorRelu(var_1);
- void* var_3 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_4 = tensorBatchNorm(var_3, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_5 = tensorRelu(var_4);
- void* var_6 = tensorConvolution(var_5, conv2d_2_w, 0, 0, 1, 1, 1, 1);
- void* var_7 = tensorBatchNorm(var_6, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_8 = tensorRelu(var_7);
- void* var_9 = tensorConvolution(var_8, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
- void* var_10 = tensorBatchNorm(var_9, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_11 = tensorRelu(var_10);
- void* var_12 = tensorConvolution(var_11, conv2d_3_w, 0, 0, 1, 1, 1, 1);
- void* var_13 = tensorBatchNorm(var_12, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_14 = tensorRelu(var_13);
- void* var_15 = tensorConvolution(var_14, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
- void* var_16 = tensorBatchNorm(var_15, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_17 = tensorRelu(var_16);
- void* var_18 = tensorConvolution(var_17, conv2d_4_w, 0, 0, 1, 1, 1, 1);
- void* var_19 = tensorBatchNorm(var_18, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_20 = tensorRelu(var_19);
- void* var_22 = tensorConvolution(var_20, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
- void* var_23 = tensorBatchNorm(var_22, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_24 = tensorRelu(var_23);
- void* var_25 = tensorConvolution(var_24, conv2d_5_w, 0, 0, 1, 1, 1, 1);
- void* var_26 = tensorBatchNorm(var_25, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_27 = tensorRelu(var_26);
- void* var_28 = tensorConvolution(var_27, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
- void* var_29 = tensorBatchNorm(var_28, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_30 = tensorRelu(var_29);
- void* var_31 = tensorConvolution(var_30, conv2d_6_w, 0, 0, 1, 1, 1, 1);
- void* var_32 = tensorBatchNorm(var_31, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_33 = tensorRelu(var_32);
- void* var_35 = tensorConvolution(var_33, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
- void* var_36 = tensorBatchNorm(var_35, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
- void* var_37 = tensorRelu(var_36);
- void* var_38 = tensorConvolution(var_37, conv2d_7_w, 0, 0, 1, 1, 1, 1);
- void* var_39 = tensorBatchNorm(var_38, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
- void* var_40 = tensorRelu(var_39);
- void* var_41 = tensorConvolution(var_40, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512);
- void* var_42 = tensorBatchNorm(var_41, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001);
- void* var_43 = tensorRelu(var_42);
- void* var_44 = tensorConvolution(var_43, conv2d_8_w, 0, 0, 1, 1, 1, 1);
- void* var_45 = tensorBatchNorm(var_44, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001);
- void* var_46 = tensorRelu(var_45);
- void* var_47 = tensorConvolution(var_46, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512);
- void* var_48 = tensorBatchNorm(var_47, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001);
- void* var_49 = tensorRelu(var_48);
- void* var_50 = tensorConvolution(var_49, conv2d_9_w, 0, 0, 1, 1, 1, 1);
- void* var_51 = tensorBatchNorm(var_50, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001);
- void* var_52 = tensorRelu(var_51);
- void* var_54 = tensorConvolution(var_52, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512);
- void* var_55 = tensorBatchNorm(var_54, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001);
- void* var_56 = tensorRelu(var_55);
- void* var_57 = tensorConvolution(var_56, conv2d_10_w, 0, 0, 1, 1, 1, 1);
- void* var_58 = tensorBatchNorm(var_57, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001);
- void* var_59 = tensorRelu(var_58);
- void* var_60 = tensorConvolution(var_59, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512);
- void* var_61 = tensorBatchNorm(var_60, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001);
- void* var_62 = tensorRelu(var_61);
- void* var_63 = tensorConvolution(var_62, conv2d_11_w, 0, 0, 1, 1, 1, 1);
- void* var_64 = tensorBatchNorm(var_63, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001);
- void* var_65 = tensorRelu(var_64);
- void* var_66 = tensorConvolution(var_65, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512);
- void* var_67 = tensorBatchNorm(var_66, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001);
- void* var_68 = tensorRelu(var_67);
- void* var_69 = tensorConvolution(var_68, conv2d_12_w, 0, 0, 1, 1, 1, 1);
- void* var_70 = tensorBatchNorm(var_69, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001);
- void* var_71 = tensorRelu(var_70);
- void* var_73 = tensorConvolution(var_71, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512);
- void* var_74 = tensorBatchNorm(var_73, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001);
- void* var_75 = tensorRelu(var_74);
- void* var_76 = tensorConvolution(var_75, conv2d_13_w, 0, 0, 1, 1, 1, 1);
- void* var_77 = tensorBatchNorm(var_76, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001);
- void* var_78 = tensorRelu(var_77);
- void* var_79 = tensorConvolution(var_78, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024);
- void* var_80 = tensorBatchNorm(var_79, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001);
- void* var_81 = tensorRelu(var_80);
- void* var_82 = tensorConvolution(var_81, conv2d_14_w, 0, 0, 1, 1, 1, 1);
- void* var_83 = tensorBatchNorm(var_82, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001);
- void* var_84 = tensorRelu(var_83);
- void* var_86 = tensorPooling(var_84,1,2,2,0,0,2,2);
- void* var_88 = tensorGemmGPU(var_86, dense_1_w);
- void* var_89 = tensorAdd(var_88, dense_1_b);
- void* var_90 = tensorSoftmax(var_89);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_90);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet_cifar10.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet_cifar10.cc
deleted file mode 100644
index 80a55dfd6b138ffa23b1ac9a7148025a491948ed..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet_cifar10.cc
+++ /dev/null
@@ -1,413 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
-
- std::string dir_prefix = std::string("../model_params/mobilenet_quant/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
- void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
- void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
- void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
- void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
- void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
- std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
- void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
- void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
- void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
- void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
- void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
- void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
- void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
- void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
- void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
- void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
- void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
- void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
- void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
- void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
- void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
- void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
- void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
- void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
- void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
- void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
- void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
- void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
- void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
- void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
- void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
- void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
- void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
- void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
- void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
- void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
- void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
- void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
- void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
- void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
- void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
- void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
- void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
- void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
- void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
- void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
- void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
- void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
- void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
- void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
- void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
- void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
- void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
- void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
- void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
- void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
- void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
- void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
- void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
- void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1);
- std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
- void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
- void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
- void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
- void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1);
- std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
- void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3);
- std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
- void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
- void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
- void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
- void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1);
- std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
- void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
- void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
- void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
- void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-
- startMemTracking();
-
- int test_input_size = 10000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
- void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorRelu(var_1);
- void* var_4 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_5 = tensorBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_6 = tensorRelu(var_5);
- void* var_7 = tensorConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
- void* var_8 = tensorBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_9 = tensorRelu(var_8);
- void* var_11 = tensorConvolution(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
- void* var_12 = tensorBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_13 = tensorRelu(var_12);
- void* var_14 = tensorConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1);
- void* var_15 = tensorBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_16 = tensorRelu(var_15);
- void* var_18 = tensorConvolution(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
- void* var_19 = tensorBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_20 = tensorRelu(var_19);
- void* var_21 = tensorConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1);
- void* var_22 = tensorBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_23 = tensorRelu(var_22);
- void* var_26 = tensorConvolution(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
- void* var_27 = tensorBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_28 = tensorRelu(var_27);
- void* var_29 = tensorConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1);
- void* var_30 = tensorBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_31 = tensorRelu(var_30);
- void* var_33 = tensorConvolution(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
- void* var_34 = tensorBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_35 = tensorRelu(var_34);
- void* var_36 = tensorConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1);
- void* var_37 = tensorBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_38 = tensorRelu(var_37);
- void* var_41 = tensorConvolution(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
- void* var_42 = tensorBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
- void* var_43 = tensorRelu(var_42);
- void* var_44 = tensorConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1);
- void* var_45 = tensorBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
- void* var_46 = tensorRelu(var_45);
- void* var_48 = tensorConvolution(var_46, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512);
- void* var_49 = tensorBatchNorm(var_48, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001);
- void* var_50 = tensorRelu(var_49);
- void* var_51 = tensorConvolution(var_50, conv2d_8_w, 0, 0, 1, 1, 1, 1);
- void* var_52 = tensorBatchNorm(var_51, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001);
- void* var_53 = tensorRelu(var_52);
- void* var_55 = tensorConvolution(var_53, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512);
- void* var_56 = tensorBatchNorm(var_55, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001);
- void* var_57 = tensorRelu(var_56);
- void* var_58 = tensorConvolution(var_57, conv2d_9_w, 0, 0, 1, 1, 1, 1);
- void* var_59 = tensorBatchNorm(var_58, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001);
- void* var_60 = tensorRelu(var_59);
- void* var_63 = tensorConvolution(var_60, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512);
- void* var_64 = tensorBatchNorm(var_63, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001);
- void* var_65 = tensorRelu(var_64);
- void* var_66 = tensorConvolution(var_65, conv2d_10_w, 0, 0, 1, 1, 1, 1);
- void* var_67 = tensorBatchNorm(var_66, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001);
- void* var_68 = tensorRelu(var_67);
- void* var_70 = tensorConvolution(var_68, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512);
- void* var_71 = tensorBatchNorm(var_70, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001);
- void* var_72 = tensorRelu(var_71);
- void* var_73 = tensorConvolution(var_72, conv2d_11_w, 0, 0, 1, 1, 1, 1);
- void* var_74 = tensorBatchNorm(var_73, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001);
- void* var_75 = tensorRelu(var_74);
- void* var_77 = tensorConvolution(var_75, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512);
- void* var_78 = tensorBatchNorm(var_77, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001);
- void* var_79 = tensorRelu(var_78);
- void* var_80 = tensorConvolution(var_79, conv2d_12_w, 0, 0, 1, 1, 1, 1);
- void* var_81 = tensorBatchNorm(var_80, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001);
- void* var_82 = tensorRelu(var_81);
- void* var_85 = tensorConvolution(var_82, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512);
- void* var_86 = tensorBatchNorm(var_85, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001);
- void* var_87 = tensorRelu(var_86);
- void* var_88 = tensorConvolution(var_87, conv2d_13_w, 0, 0, 1, 1, 1, 1);
- void* var_89 = tensorBatchNorm(var_88, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001);
- void* var_90 = tensorRelu(var_89);
- void* var_92 = tensorConvolution(var_90, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024);
- void* var_93 = tensorBatchNorm(var_92, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001);
- void* var_94 = tensorRelu(var_93);
- void* var_95 = tensorConvolution(var_94, conv2d_14_w, 0, 0, 1, 1, 1, 1);
- void* var_96 = tensorBatchNorm(var_95, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001);
- void* var_97 = tensorRelu(var_96);
- void* var_99 = tensorPooling(var_97,1,2,2,0,0,2,2);
- void* var_101 = tensorGemmGPU(var_99, dense_1_w);
- void* var_102 = tensorAdd(var_101, dense_1_b);
- void* var_103 = tensorSoftmax(var_102);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_103);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet_cifar10_shallow.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet_cifar10_shallow.cc
deleted file mode 100644
index 89aa451dc1a5ee7b532bd5375e2e71e520c1372e..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet_cifar10_shallow.cc
+++ /dev/null
@@ -1,242 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(int argc, char* argv[]){
-
- int total_runs = 1;
- if (argc > 1){
- total_runs = atoi(argv[1]);
- }
-
-
- llvm_hpvm_initTensorRt(0);
-
- //std::string dir_prefix = std::string("../../keras/data/mobilenet_shallow_nathan/");
-
- std::string dir_prefix = std::string("../model_params/mobilenet_cifar10_shallow/");
-
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
- void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
- void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
- void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
- void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
- void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
- std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
- void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
- void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
- void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
- void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,2048,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 1000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
-
-
- for(int j = 0; j < total_runs; j++){
- float final_accuracy = 0.0;
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
- void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorRelu(var_1);
- void* var_4 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_5 = tensorBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_6 = tensorRelu(var_5);
- void* var_7 = tensorConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
- void* var_8 = tensorBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_9 = tensorRelu(var_8);
- void* var_11 = tensorConvolution(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
- void* var_12 = tensorBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_13 = tensorRelu(var_12);
- void* var_14 = tensorConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1);
- void* var_15 = tensorBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_16 = tensorRelu(var_15);
- void* var_18 = tensorConvolution(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
- void* var_19 = tensorBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_20 = tensorRelu(var_19);
- void* var_21 = tensorConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1);
- void* var_22 = tensorBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_23 = tensorRelu(var_22);
- void* var_26 = tensorConvolution(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
- void* var_27 = tensorBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_28 = tensorRelu(var_27);
- void* var_29 = tensorConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1);
- void* var_30 = tensorBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_31 = tensorRelu(var_30);
- void* var_33 = tensorConvolution(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
- void* var_34 = tensorBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_35 = tensorRelu(var_34);
- void* var_36 = tensorConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1);
- void* var_37 = tensorBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_38 = tensorRelu(var_37);
- void* var_41 = tensorConvolution(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
- void* var_42 = tensorBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
- void* var_43 = tensorRelu(var_42);
- void* var_44 = tensorConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1);
- void* var_45 = tensorBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
- void* var_46 = tensorRelu(var_45);
- void* var_47 = tensorPooling(var_46,1,2,2,0,0,2,2);
- void* var_49 = tensorGemmGPU(var_47, dense_1_w);
- void* var_50 = tensorAdd(var_49, dense_1_b);
- void* var_51 = tensorSoftmax(var_50);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_51);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
- }
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet_custom_depthwise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet_custom_depthwise.cc
deleted file mode 100644
index e30844480947b89e5572c1687f66c445652d5536..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet_custom_depthwise.cc
+++ /dev/null
@@ -1,414 +0,0 @@
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
-
- std::string dir_prefix = std::string("../hpvm-tensor-rt/model_params/mobilenet_quant/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
- void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
- void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
- void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
- void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
- void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
- std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
- void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
- void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
- void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
- void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
- void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
- void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
- void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
- void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
- void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
- void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
- void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
- void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
- void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
- void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
- void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
- void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
- void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
- void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
- void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
- void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
- void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
- void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
- void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
- void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
- void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
- void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
- void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
- void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
- void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
- void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
- void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
- void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
- void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
- void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
- void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
- void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
- void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
- void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
- void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
- void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
- void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
- void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
- void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
- void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
- void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
- void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
- void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
- void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
- void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
- void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
- void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
- void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
- void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
- void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1);
- std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
- void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
- void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
- void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
- void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1);
- std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
- void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3);
- std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
- void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
- void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
- void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
- void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1);
- std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
- void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
- void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
- void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
- void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-
- startMemTracking();
-
- int test_input_size = 10000;
- int batch_size = 100;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = tensorConvCutlass(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
- void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorRelu(var_1);
- void* var_4 = tensorConvCutlass(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_5 = tensorBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_6 = tensorRelu(var_5);
- void* var_7 = tensorConvCutlass(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
- void* var_8 = tensorBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_9 = tensorRelu(var_8);
- void* var_11 = tensorConvCutlass(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
- void* var_12 = tensorBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_13 = tensorRelu(var_12);
- void* var_14 = tensorConvCutlass(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1);
- void* var_15 = tensorBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_16 = tensorRelu(var_15);
- void* var_18 = tensorConvCutlass(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
- void* var_19 = tensorBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_20 = tensorRelu(var_19);
- void* var_21 = tensorConvCutlass(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1);
- void* var_22 = tensorBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_23 = tensorRelu(var_22);
- void* var_26 = tensorConvCutlass(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
- void* var_27 = tensorBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_28 = tensorRelu(var_27);
- void* var_29 = tensorConvCutlass(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1);
- void* var_30 = tensorBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_31 = tensorRelu(var_30);
- void* var_33 = tensorConvCutlass(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
- void* var_34 = tensorBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_35 = tensorRelu(var_34);
- void* var_36 = tensorConvCutlass(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1);
- void* var_37 = tensorBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_38 = tensorRelu(var_37);
- void* var_41 = tensorConvCutlass(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
- void* var_42 = tensorBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
- void* var_43 = tensorRelu(var_42);
- void* var_44 = tensorConvCutlass(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1);
- void* var_45 = tensorBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
- void* var_46 = tensorRelu(var_45);
- void* var_48 = tensorConvCutlass(var_46, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512);
- void* var_49 = tensorBatchNorm(var_48, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001);
- void* var_50 = tensorRelu(var_49);
- void* var_51 = tensorConvCutlass(var_50, conv2d_8_w, 0, 0, 1, 1, 1, 1);
- void* var_52 = tensorBatchNorm(var_51, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001);
- void* var_53 = tensorRelu(var_52);
- void* var_55 = tensorConvCutlass(var_53, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512);
- void* var_56 = tensorBatchNorm(var_55, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001);
- void* var_57 = tensorRelu(var_56);
- void* var_58 = tensorConvCutlass(var_57, conv2d_9_w, 0, 0, 1, 1, 1, 1);
- void* var_59 = tensorBatchNorm(var_58, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001);
- void* var_60 = tensorRelu(var_59);
- void* var_63 = tensorConvCutlass(var_60, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512);
- void* var_64 = tensorBatchNorm(var_63, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001);
- void* var_65 = tensorRelu(var_64);
- void* var_66 = tensorConvCutlass(var_65, conv2d_10_w, 0, 0, 1, 1, 1, 1);
- void* var_67 = tensorBatchNorm(var_66, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001);
- void* var_68 = tensorRelu(var_67);
- void* var_70 = tensorConvCutlass(var_68, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512);
- void* var_71 = tensorBatchNorm(var_70, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001);
- void* var_72 = tensorRelu(var_71);
- void* var_73 = tensorConvCutlass(var_72, conv2d_11_w, 0, 0, 1, 1, 1, 1);
- void* var_74 = tensorBatchNorm(var_73, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001);
- void* var_75 = tensorRelu(var_74);
- void* var_77 = tensorConvCutlass(var_75, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512);
- void* var_78 = tensorBatchNorm(var_77, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001);
- void* var_79 = tensorRelu(var_78);
- void* var_80 = tensorConvCutlass(var_79, conv2d_12_w, 0, 0, 1, 1, 1, 1);
- void* var_81 = tensorBatchNorm(var_80, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001);
- void* var_82 = tensorRelu(var_81);
- void* var_85 = tensorConvCutlass(var_82, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512);
- void* var_86 = tensorBatchNorm(var_85, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001);
- void* var_87 = tensorRelu(var_86);
- void* var_88 = tensorConvCutlass(var_87, conv2d_13_w, 0, 0, 1, 1, 1, 1);
- void* var_89 = tensorBatchNorm(var_88, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001);
- void* var_90 = tensorRelu(var_89);
- void* var_92 = tensorConvCutlass(var_90, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024);
- void* var_93 = tensorBatchNorm(var_92, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001);
- void* var_94 = tensorRelu(var_93);
- void* var_95 = tensorConvCutlass(var_94, conv2d_14_w, 0, 0, 1, 1, 1, 1);
- void* var_96 = tensorBatchNorm(var_95, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001);
- void* var_97 = tensorRelu(var_96);
- void* var_99 = tensorPooling(var_97,1,2,2,0,0,2,2);
- void* var_101 = tensorGemmGPU(var_99, dense_1_w);
- void* var_102 = tensorAdd(var_101, dense_1_b);
- void* var_103 = tensorSoftmax(var_102);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_103);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet_depthwise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet_depthwise.cc
deleted file mode 100644
index 107024c81a7d8124a46528f7a59fac5af340bcac..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet_depthwise.cc
+++ /dev/null
@@ -1,414 +0,0 @@
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
-
- std::string dir_prefix = std::string("../model_params/mobilenet/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
- void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
- void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
- void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
- void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
- void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
- std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
- void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
- void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
- void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
- void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
- void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
- void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
- void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
- void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
- void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
- void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
- void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
- void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
- void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
- void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
- void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
- void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
- void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
- void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
- void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
- void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
- void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
- void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
- void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
- void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
- void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
- void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
- void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
- void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
- void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
- void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
- void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
- void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
- void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
- void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
- void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
- void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
- void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
- void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
- void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
- void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
- void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
- void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
- void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
- void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
- void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
- void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
- void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
- void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
- void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
- void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
- void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
- void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
- void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
- void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1);
- std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
- void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
- void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
- void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
- void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1);
- std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
- void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3);
- std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
- void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
- void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
- void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
- void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1);
- std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
- void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
- void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
- void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
- void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 2500;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
- void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorRelu(var_1);
- void* var_4 = tensorConvCutlass(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_5 = tensorBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_6 = tensorRelu(var_5);
- void* var_7 = tensorConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
- void* var_8 = tensorBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_9 = tensorRelu(var_8);
- void* var_11 = tensorConvCutlass(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
- void* var_12 = tensorBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_13 = tensorRelu(var_12);
- void* var_14 = tensorConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1);
- void* var_15 = tensorBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_16 = tensorRelu(var_15);
- void* var_18 = tensorConvCutlass(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
- void* var_19 = tensorBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_20 = tensorRelu(var_19);
- void* var_21 = tensorConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1);
- void* var_22 = tensorBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_23 = tensorRelu(var_22);
- void* var_26 = tensorConvCutlass(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
- void* var_27 = tensorBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_28 = tensorRelu(var_27);
- void* var_29 = tensorConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1);
- void* var_30 = tensorBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_31 = tensorRelu(var_30);
- void* var_33 = tensorConvCutlass(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
- void* var_34 = tensorBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_35 = tensorRelu(var_34);
- void* var_36 = tensorConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1);
- void* var_37 = tensorBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_38 = tensorRelu(var_37);
- void* var_41 = tensorConvCutlass(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
- void* var_42 = tensorBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
- void* var_43 = tensorRelu(var_42);
- void* var_44 = tensorConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1);
- void* var_45 = tensorBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
- void* var_46 = tensorRelu(var_45);
- void* var_48 = tensorConvCutlass(var_46, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512);
- void* var_49 = tensorBatchNorm(var_48, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001);
- void* var_50 = tensorRelu(var_49);
- void* var_51 = tensorConvolution(var_50, conv2d_8_w, 0, 0, 1, 1, 1, 1);
- void* var_52 = tensorBatchNorm(var_51, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001);
- void* var_53 = tensorRelu(var_52);
- void* var_55 = tensorConvCutlass(var_53, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512);
- void* var_56 = tensorBatchNorm(var_55, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001);
- void* var_57 = tensorRelu(var_56);
- void* var_58 = tensorConvolution(var_57, conv2d_9_w, 0, 0, 1, 1, 1, 1);
- void* var_59 = tensorBatchNorm(var_58, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001);
- void* var_60 = tensorRelu(var_59);
- void* var_63 = tensorConvCutlass(var_60, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512);
- void* var_64 = tensorBatchNorm(var_63, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001);
- void* var_65 = tensorRelu(var_64);
- void* var_66 = tensorConvolution(var_65, conv2d_10_w, 0, 0, 1, 1, 1, 1);
- void* var_67 = tensorBatchNorm(var_66, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001);
- void* var_68 = tensorRelu(var_67);
- void* var_70 = tensorConvCutlass(var_68, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512);
- void* var_71 = tensorBatchNorm(var_70, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001);
- void* var_72 = tensorRelu(var_71);
- void* var_73 = tensorConvolution(var_72, conv2d_11_w, 0, 0, 1, 1, 1, 1);
- void* var_74 = tensorBatchNorm(var_73, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001);
- void* var_75 = tensorRelu(var_74);
- void* var_77 = tensorConvCutlass(var_75, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512);
- void* var_78 = tensorBatchNorm(var_77, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001);
- void* var_79 = tensorRelu(var_78);
- void* var_80 = tensorConvolution(var_79, conv2d_12_w, 0, 0, 1, 1, 1, 1);
- void* var_81 = tensorBatchNorm(var_80, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001);
- void* var_82 = tensorRelu(var_81);
- void* var_85 = tensorConvCutlass(var_82, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512);
- void* var_86 = tensorBatchNorm(var_85, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001);
- void* var_87 = tensorRelu(var_86);
- void* var_88 = tensorConvolution(var_87, conv2d_13_w, 0, 0, 1, 1, 1, 1);
- void* var_89 = tensorBatchNorm(var_88, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001);
- void* var_90 = tensorRelu(var_89);
- void* var_92 = tensorConvCutlass(var_90, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024);
- void* var_93 = tensorBatchNorm(var_92, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001);
- void* var_94 = tensorRelu(var_93);
- void* var_95 = tensorConvolution(var_94, conv2d_14_w, 0, 0, 1, 1, 1, 1);
- void* var_96 = tensorBatchNorm(var_95, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001);
- void* var_97 = tensorRelu(var_96);
- void* var_99 = tensorPooling(var_97,1,2,2,0,0,2,2);
- void* var_101 = tensorGemmGPU(var_99, dense_1_w);
- void* var_102 = tensorAdd(var_101, dense_1_b);
- void* var_103 = tensorSoftmax(var_102);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_103);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet_shallow.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet_shallow.cc
deleted file mode 100644
index 8905a93edb825b36c9e301ad1e450428740b4cb1..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet_shallow.cc
+++ /dev/null
@@ -1,203 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(1);
-
- std::string dir_prefix = std::string("../model_params/mobilenet_shallow/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,64,64,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,64,1,1);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 10000;
- int batch_size = 2000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
- void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorRelu(var_1);
- void* var_4 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_5 = tensorBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_6 = tensorRelu(var_5);
- void* var_7 = tensorConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
- void* var_8 = tensorBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_9 = tensorRelu(var_8);
- void* var_11 = tensorConvolution(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
- void* var_12 = tensorBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_13 = tensorRelu(var_12);
- void* var_14 = tensorConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1);
- void* var_15 = tensorBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_16 = tensorRelu(var_15);
- void* var_18 = tensorConvolution(var_16, depthwise_conv2d_3_w, 1, 1, 2, 2, 1, 64);
- void* var_19 = tensorBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_20 = tensorRelu(var_19);
- void* var_21 = tensorConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1);
- void* var_22 = tensorBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_23 = tensorRelu(var_22);
- void* var_26 = tensorConvolution(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
- void* var_27 = tensorBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_28 = tensorRelu(var_27);
- void* var_29 = tensorConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1);
- void* var_30 = tensorBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_31 = tensorRelu(var_30);
- void* var_33 = tensorConvolution(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
- void* var_34 = tensorBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_35 = tensorRelu(var_34);
- void* var_36 = tensorConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1);
- void* var_37 = tensorBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_38 = tensorRelu(var_37);
- void* var_40 = tensorPooling(var_38,1,2,2,0,0,2,2);
- void* var_42 = tensorGemmGPU(var_40, dense_1_w);
- void* var_43 = tensorAdd(var_42, dense_1_b);
- void* var_44 = tensorSoftmax(var_43);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_44);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet_shallow2.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet_shallow2.cc
deleted file mode 100644
index ee2c51b2399505d3a98b54920d9700dbd0548b86..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet_shallow2.cc
+++ /dev/null
@@ -1,231 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
-
- std::string dir_prefix = std::string("../model_params/mobilenet_shallow2/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
- void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
- void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
- void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
- void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
- void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
- std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
- void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
- void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
- void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
- void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,2048,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 2500;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
- void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorRelu(var_1);
- void* var_4 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_5 = tensorBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_6 = tensorRelu(var_5);
- void* var_7 = tensorConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
- void* var_8 = tensorBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_9 = tensorRelu(var_8);
- void* var_11 = tensorConvolution(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
- void* var_12 = tensorBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_13 = tensorRelu(var_12);
- void* var_14 = tensorConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1);
- void* var_15 = tensorBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_16 = tensorRelu(var_15);
- void* var_18 = tensorConvolution(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
- void* var_19 = tensorBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_20 = tensorRelu(var_19);
- void* var_21 = tensorConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1);
- void* var_22 = tensorBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_23 = tensorRelu(var_22);
- void* var_26 = tensorConvolution(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
- void* var_27 = tensorBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_28 = tensorRelu(var_27);
- void* var_29 = tensorConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1);
- void* var_30 = tensorBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_31 = tensorRelu(var_30);
- void* var_33 = tensorConvolution(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
- void* var_34 = tensorBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_35 = tensorRelu(var_34);
- void* var_36 = tensorConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1);
- void* var_37 = tensorBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_38 = tensorRelu(var_37);
- void* var_41 = tensorConvolution(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
- void* var_42 = tensorBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
- void* var_43 = tensorRelu(var_42);
- void* var_44 = tensorConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1);
- void* var_45 = tensorBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
- void* var_46 = tensorRelu(var_45);
- void* var_47 = tensorPooling(var_46,1,2,2,0,0,2,2);
- void* var_49 = tensorGemmGPU(var_47, dense_1_w);
- void* var_50 = tensorAdd(var_49, dense_1_b);
- void* var_51 = tensorSoftmax(var_50);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_51);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet_shallow_depthwise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet_shallow_depthwise.cc
deleted file mode 100644
index 4e8e6008927c1a5e85ff74884842c0ae310a7d75..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenet_shallow_depthwise.cc
+++ /dev/null
@@ -1,244 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(int argc, char* argv[]){
-
- int total_runs = 1;
- if (argc > 1){
- total_runs = atoi(argv[1]);
- }
-
-
- llvm_hpvm_initTensorRt(0);
-
- //std::string dir_prefix = std::string("../../keras/data/mobilenet_shallow_nathan/");
-
- //std::string dir_prefix = std::string("../model_params/mobilenet_cifar10_shallow/");
-
- std::string dir_prefix = std::string("../model_params/mobilenet_shallow/");
-
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
- void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
- void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
- void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
- void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
- void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
- std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
- void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
- void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
- void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
- void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,2048,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 2500;
- int batch_count = test_input_size / batch_size;
-
-
- for(int j = 0; j < total_runs; j++){
- float final_accuracy = 0.0;
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
- void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorRelu(var_1);
- void* var_4 = tensorConvCutlass(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_5 = tensorBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_6 = tensorRelu(var_5);
- void* var_7 = tensorConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
- void* var_8 = tensorBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_9 = tensorRelu(var_8);
- void* var_11 = tensorConvCutlass(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
- void* var_12 = tensorBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_13 = tensorRelu(var_12);
- void* var_14 = tensorConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1);
- void* var_15 = tensorBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_16 = tensorRelu(var_15);
- void* var_18 = tensorConvCutlass(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
- void* var_19 = tensorBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_20 = tensorRelu(var_19);
- void* var_21 = tensorConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1);
- void* var_22 = tensorBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_23 = tensorRelu(var_22);
- void* var_26 = tensorConvCutlass(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
- void* var_27 = tensorBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_28 = tensorRelu(var_27);
- void* var_29 = tensorConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1);
- void* var_30 = tensorBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_31 = tensorRelu(var_30);
- void* var_33 = tensorConvCutlass(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
- void* var_34 = tensorBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_35 = tensorRelu(var_34);
- void* var_36 = tensorConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1);
- void* var_37 = tensorBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_38 = tensorRelu(var_37);
- void* var_41 = tensorConvCutlass(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
- void* var_42 = tensorBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
- void* var_43 = tensorRelu(var_42);
- void* var_44 = tensorConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1);
- void* var_45 = tensorBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
- void* var_46 = tensorRelu(var_45);
- void* var_47 = tensorPooling(var_46,1,2,2,0,0,2,2);
- void* var_49 = tensorGemmGPU(var_47, dense_1_w);
- void* var_50 = tensorAdd(var_49, dense_1_b);
- void* var_51 = tensorSoftmax(var_50);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_51);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
- }
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenetv2_cifar10.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenetv2_cifar10.cc
deleted file mode 100644
index fcbb17f411adaf5e46162a0524efc97c90174506..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/mobilenetv2_cifar10.cc
+++ /dev/null
@@ -1,721 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
-
- std::string dir_prefix = std::string("../model_params/mobilenetv2_quant/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,16,32,1,1);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,16,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,16,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,16,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,16,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,96,16,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,96,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,96,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,96,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,96,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,96,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,96,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,96,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,96,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,96,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,24,96,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,24,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,24,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,24,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,24,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,144,24,1,1);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,144,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,144,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,144,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,144,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,144,1,3,3);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,144,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,144,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,144,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,144,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,24,144,1,1);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,24,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,24,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,24,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,24,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,144,24,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,144,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,144,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,144,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,144,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,144,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,144,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,144,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,144,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,144,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,144,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,192,32,1,1);
- std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
- void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,192,1,1);
- std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
- void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,192,1,1);
- std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
- void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,192,1,1);
- std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
- void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,192,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,192,1,3,3);
- std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
- void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,192,1,1);
- std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
- void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,192,1,1);
- std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
- void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,192,1,1);
- std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
- void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,192,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,192,1,1);
- std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
- void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
- void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
- void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
- void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,192,32,1,1);
- std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
- void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,192,1,1);
- std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
- void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,192,1,1);
- std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
- void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,192,1,1);
- std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
- void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,192,1,1);
- std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
- void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,192,1,3,3);
- std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
- void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,192,1,1);
- std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
- void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,192,1,1);
- std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
- void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,192,1,1);
- std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
- void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,192,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,192,1,1);
- std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
- void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
- void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
- void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
- void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,192,32,1,1);
- std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
- void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,192,1,1);
- std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
- void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,192,1,1);
- std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
- void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,192,1,1);
- std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
- void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,192,1,1);
- std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
- void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,192,1,3,3);
- std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
- void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,192,1,1);
- std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
- void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,192,1,1);
- std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
- void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,192,1,1);
- std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
- void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,192,1,1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,64,192,1,1);
- std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
- void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
- void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
- void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
- void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_15_w_path = dir_prefix + std::string("conv2d_15_w.bin");
- void* conv2d_15_w = readTrainedWeights(conv2d_15_w_path.c_str(), 0,384,64,1,1);
- std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
- void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,384,1,1);
- std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
- void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,384,1,1);
- std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
- void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,384,1,1);
- std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
- void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,384,1,1);
- std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
- void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,384,1,3,3);
- std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
- void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,384,1,1);
- std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
- void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,384,1,1);
- std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
- void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,384,1,1);
- std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
- void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,384,1,1);
- std::string conv2d_16_w_path = dir_prefix + std::string("conv2d_16_w.bin");
- void* conv2d_16_w = readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,384,1,1);
- std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
- void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
- void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
- void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
- void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_17_w_path = dir_prefix + std::string("conv2d_17_w.bin");
- void* conv2d_17_w = readTrainedWeights(conv2d_17_w_path.c_str(), 0,384,64,1,1);
- std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
- void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,384,1,1);
- std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
- void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,384,1,1);
- std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
- void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,384,1,1);
- std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
- void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,384,1,1);
- std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
- void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,384,1,3,3);
- std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
- void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,384,1,1);
- std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
- void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,384,1,1);
- std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
- void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,384,1,1);
- std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
- void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,384,1,1);
- std::string conv2d_18_w_path = dir_prefix + std::string("conv2d_18_w.bin");
- void* conv2d_18_w = readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,384,1,1);
- std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
- void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
- void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
- void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
- void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_19_w_path = dir_prefix + std::string("conv2d_19_w.bin");
- void* conv2d_19_w = readTrainedWeights(conv2d_19_w_path.c_str(), 0,384,64,1,1);
- std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
- void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,384,1,1);
- std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
- void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,384,1,1);
- std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
- void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,384,1,1);
- std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
- void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,384,1,1);
- std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
- void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,384,1,3,3);
- std::string batch_normalization_28_gamma_path = dir_prefix + std::string("batch_normalization_28_gamma.bin");
- void* batch_normalization_28_gamma = readTrainedWeights(batch_normalization_28_gamma_path.c_str(), 0,1,384,1,1);
- std::string batch_normalization_28_beta_path = dir_prefix + std::string("batch_normalization_28_beta.bin");
- void* batch_normalization_28_beta = readTrainedWeights(batch_normalization_28_beta_path.c_str(), 0,1,384,1,1);
- std::string batch_normalization_28_mean_path = dir_prefix + std::string("batch_normalization_28_mean.bin");
- void* batch_normalization_28_mean = readTrainedWeights(batch_normalization_28_mean_path.c_str(), 0,1,384,1,1);
- std::string batch_normalization_28_variance_path = dir_prefix + std::string("batch_normalization_28_variance.bin");
- void* batch_normalization_28_variance = readTrainedWeights(batch_normalization_28_variance_path.c_str(), 0,1,384,1,1);
- std::string conv2d_20_w_path = dir_prefix + std::string("conv2d_20_w.bin");
- void* conv2d_20_w = readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,384,1,1);
- std::string batch_normalization_29_gamma_path = dir_prefix + std::string("batch_normalization_29_gamma.bin");
- void* batch_normalization_29_gamma = readTrainedWeights(batch_normalization_29_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_29_beta_path = dir_prefix + std::string("batch_normalization_29_beta.bin");
- void* batch_normalization_29_beta = readTrainedWeights(batch_normalization_29_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_29_mean_path = dir_prefix + std::string("batch_normalization_29_mean.bin");
- void* batch_normalization_29_mean = readTrainedWeights(batch_normalization_29_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_29_variance_path = dir_prefix + std::string("batch_normalization_29_variance.bin");
- void* batch_normalization_29_variance = readTrainedWeights(batch_normalization_29_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_21_w_path = dir_prefix + std::string("conv2d_21_w.bin");
- void* conv2d_21_w = readTrainedWeights(conv2d_21_w_path.c_str(), 0,384,64,1,1);
- std::string batch_normalization_30_gamma_path = dir_prefix + std::string("batch_normalization_30_gamma.bin");
- void* batch_normalization_30_gamma = readTrainedWeights(batch_normalization_30_gamma_path.c_str(), 0,1,384,1,1);
- std::string batch_normalization_30_beta_path = dir_prefix + std::string("batch_normalization_30_beta.bin");
- void* batch_normalization_30_beta = readTrainedWeights(batch_normalization_30_beta_path.c_str(), 0,1,384,1,1);
- std::string batch_normalization_30_mean_path = dir_prefix + std::string("batch_normalization_30_mean.bin");
- void* batch_normalization_30_mean = readTrainedWeights(batch_normalization_30_mean_path.c_str(), 0,1,384,1,1);
- std::string batch_normalization_30_variance_path = dir_prefix + std::string("batch_normalization_30_variance.bin");
- void* batch_normalization_30_variance = readTrainedWeights(batch_normalization_30_variance_path.c_str(), 0,1,384,1,1);
- std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
- void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,384,1,3,3);
- std::string batch_normalization_31_gamma_path = dir_prefix + std::string("batch_normalization_31_gamma.bin");
- void* batch_normalization_31_gamma = readTrainedWeights(batch_normalization_31_gamma_path.c_str(), 0,1,384,1,1);
- std::string batch_normalization_31_beta_path = dir_prefix + std::string("batch_normalization_31_beta.bin");
- void* batch_normalization_31_beta = readTrainedWeights(batch_normalization_31_beta_path.c_str(), 0,1,384,1,1);
- std::string batch_normalization_31_mean_path = dir_prefix + std::string("batch_normalization_31_mean.bin");
- void* batch_normalization_31_mean = readTrainedWeights(batch_normalization_31_mean_path.c_str(), 0,1,384,1,1);
- std::string batch_normalization_31_variance_path = dir_prefix + std::string("batch_normalization_31_variance.bin");
- void* batch_normalization_31_variance = readTrainedWeights(batch_normalization_31_variance_path.c_str(), 0,1,384,1,1);
- std::string conv2d_22_w_path = dir_prefix + std::string("conv2d_22_w.bin");
- void* conv2d_22_w = readTrainedWeights(conv2d_22_w_path.c_str(), 0,96,384,1,1);
- std::string batch_normalization_32_gamma_path = dir_prefix + std::string("batch_normalization_32_gamma.bin");
- void* batch_normalization_32_gamma = readTrainedWeights(batch_normalization_32_gamma_path.c_str(), 0,1,96,1,1);
- std::string batch_normalization_32_beta_path = dir_prefix + std::string("batch_normalization_32_beta.bin");
- void* batch_normalization_32_beta = readTrainedWeights(batch_normalization_32_beta_path.c_str(), 0,1,96,1,1);
- std::string batch_normalization_32_mean_path = dir_prefix + std::string("batch_normalization_32_mean.bin");
- void* batch_normalization_32_mean = readTrainedWeights(batch_normalization_32_mean_path.c_str(), 0,1,96,1,1);
- std::string batch_normalization_32_variance_path = dir_prefix + std::string("batch_normalization_32_variance.bin");
- void* batch_normalization_32_variance = readTrainedWeights(batch_normalization_32_variance_path.c_str(), 0,1,96,1,1);
- std::string conv2d_23_w_path = dir_prefix + std::string("conv2d_23_w.bin");
- void* conv2d_23_w = readTrainedWeights(conv2d_23_w_path.c_str(), 0,576,96,1,1);
- std::string batch_normalization_33_gamma_path = dir_prefix + std::string("batch_normalization_33_gamma.bin");
- void* batch_normalization_33_gamma = readTrainedWeights(batch_normalization_33_gamma_path.c_str(), 0,1,576,1,1);
- std::string batch_normalization_33_beta_path = dir_prefix + std::string("batch_normalization_33_beta.bin");
- void* batch_normalization_33_beta = readTrainedWeights(batch_normalization_33_beta_path.c_str(), 0,1,576,1,1);
- std::string batch_normalization_33_mean_path = dir_prefix + std::string("batch_normalization_33_mean.bin");
- void* batch_normalization_33_mean = readTrainedWeights(batch_normalization_33_mean_path.c_str(), 0,1,576,1,1);
- std::string batch_normalization_33_variance_path = dir_prefix + std::string("batch_normalization_33_variance.bin");
- void* batch_normalization_33_variance = readTrainedWeights(batch_normalization_33_variance_path.c_str(), 0,1,576,1,1);
- std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
- void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,576,1,3,3);
- std::string batch_normalization_34_gamma_path = dir_prefix + std::string("batch_normalization_34_gamma.bin");
- void* batch_normalization_34_gamma = readTrainedWeights(batch_normalization_34_gamma_path.c_str(), 0,1,576,1,1);
- std::string batch_normalization_34_beta_path = dir_prefix + std::string("batch_normalization_34_beta.bin");
- void* batch_normalization_34_beta = readTrainedWeights(batch_normalization_34_beta_path.c_str(), 0,1,576,1,1);
- std::string batch_normalization_34_mean_path = dir_prefix + std::string("batch_normalization_34_mean.bin");
- void* batch_normalization_34_mean = readTrainedWeights(batch_normalization_34_mean_path.c_str(), 0,1,576,1,1);
- std::string batch_normalization_34_variance_path = dir_prefix + std::string("batch_normalization_34_variance.bin");
- void* batch_normalization_34_variance = readTrainedWeights(batch_normalization_34_variance_path.c_str(), 0,1,576,1,1);
- std::string conv2d_24_w_path = dir_prefix + std::string("conv2d_24_w.bin");
- void* conv2d_24_w = readTrainedWeights(conv2d_24_w_path.c_str(), 0,96,576,1,1);
- std::string batch_normalization_35_gamma_path = dir_prefix + std::string("batch_normalization_35_gamma.bin");
- void* batch_normalization_35_gamma = readTrainedWeights(batch_normalization_35_gamma_path.c_str(), 0,1,96,1,1);
- std::string batch_normalization_35_beta_path = dir_prefix + std::string("batch_normalization_35_beta.bin");
- void* batch_normalization_35_beta = readTrainedWeights(batch_normalization_35_beta_path.c_str(), 0,1,96,1,1);
- std::string batch_normalization_35_mean_path = dir_prefix + std::string("batch_normalization_35_mean.bin");
- void* batch_normalization_35_mean = readTrainedWeights(batch_normalization_35_mean_path.c_str(), 0,1,96,1,1);
- std::string batch_normalization_35_variance_path = dir_prefix + std::string("batch_normalization_35_variance.bin");
- void* batch_normalization_35_variance = readTrainedWeights(batch_normalization_35_variance_path.c_str(), 0,1,96,1,1);
- std::string conv2d_25_w_path = dir_prefix + std::string("conv2d_25_w.bin");
- void* conv2d_25_w = readTrainedWeights(conv2d_25_w_path.c_str(), 0,576,96,1,1);
- std::string batch_normalization_36_gamma_path = dir_prefix + std::string("batch_normalization_36_gamma.bin");
- void* batch_normalization_36_gamma = readTrainedWeights(batch_normalization_36_gamma_path.c_str(), 0,1,576,1,1);
- std::string batch_normalization_36_beta_path = dir_prefix + std::string("batch_normalization_36_beta.bin");
- void* batch_normalization_36_beta = readTrainedWeights(batch_normalization_36_beta_path.c_str(), 0,1,576,1,1);
- std::string batch_normalization_36_mean_path = dir_prefix + std::string("batch_normalization_36_mean.bin");
- void* batch_normalization_36_mean = readTrainedWeights(batch_normalization_36_mean_path.c_str(), 0,1,576,1,1);
- std::string batch_normalization_36_variance_path = dir_prefix + std::string("batch_normalization_36_variance.bin");
- void* batch_normalization_36_variance = readTrainedWeights(batch_normalization_36_variance_path.c_str(), 0,1,576,1,1);
- std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
- void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,576,1,3,3);
- std::string batch_normalization_37_gamma_path = dir_prefix + std::string("batch_normalization_37_gamma.bin");
- void* batch_normalization_37_gamma = readTrainedWeights(batch_normalization_37_gamma_path.c_str(), 0,1,576,1,1);
- std::string batch_normalization_37_beta_path = dir_prefix + std::string("batch_normalization_37_beta.bin");
- void* batch_normalization_37_beta = readTrainedWeights(batch_normalization_37_beta_path.c_str(), 0,1,576,1,1);
- std::string batch_normalization_37_mean_path = dir_prefix + std::string("batch_normalization_37_mean.bin");
- void* batch_normalization_37_mean = readTrainedWeights(batch_normalization_37_mean_path.c_str(), 0,1,576,1,1);
- std::string batch_normalization_37_variance_path = dir_prefix + std::string("batch_normalization_37_variance.bin");
- void* batch_normalization_37_variance = readTrainedWeights(batch_normalization_37_variance_path.c_str(), 0,1,576,1,1);
- std::string conv2d_26_w_path = dir_prefix + std::string("conv2d_26_w.bin");
- void* conv2d_26_w = readTrainedWeights(conv2d_26_w_path.c_str(), 0,96,576,1,1);
- std::string batch_normalization_38_gamma_path = dir_prefix + std::string("batch_normalization_38_gamma.bin");
- void* batch_normalization_38_gamma = readTrainedWeights(batch_normalization_38_gamma_path.c_str(), 0,1,96,1,1);
- std::string batch_normalization_38_beta_path = dir_prefix + std::string("batch_normalization_38_beta.bin");
- void* batch_normalization_38_beta = readTrainedWeights(batch_normalization_38_beta_path.c_str(), 0,1,96,1,1);
- std::string batch_normalization_38_mean_path = dir_prefix + std::string("batch_normalization_38_mean.bin");
- void* batch_normalization_38_mean = readTrainedWeights(batch_normalization_38_mean_path.c_str(), 0,1,96,1,1);
- std::string batch_normalization_38_variance_path = dir_prefix + std::string("batch_normalization_38_variance.bin");
- void* batch_normalization_38_variance = readTrainedWeights(batch_normalization_38_variance_path.c_str(), 0,1,96,1,1);
- std::string conv2d_27_w_path = dir_prefix + std::string("conv2d_27_w.bin");
- void* conv2d_27_w = readTrainedWeights(conv2d_27_w_path.c_str(), 0,576,96,1,1);
- std::string batch_normalization_39_gamma_path = dir_prefix + std::string("batch_normalization_39_gamma.bin");
- void* batch_normalization_39_gamma = readTrainedWeights(batch_normalization_39_gamma_path.c_str(), 0,1,576,1,1);
- std::string batch_normalization_39_beta_path = dir_prefix + std::string("batch_normalization_39_beta.bin");
- void* batch_normalization_39_beta = readTrainedWeights(batch_normalization_39_beta_path.c_str(), 0,1,576,1,1);
- std::string batch_normalization_39_mean_path = dir_prefix + std::string("batch_normalization_39_mean.bin");
- void* batch_normalization_39_mean = readTrainedWeights(batch_normalization_39_mean_path.c_str(), 0,1,576,1,1);
- std::string batch_normalization_39_variance_path = dir_prefix + std::string("batch_normalization_39_variance.bin");
- void* batch_normalization_39_variance = readTrainedWeights(batch_normalization_39_variance_path.c_str(), 0,1,576,1,1);
- std::string depthwise_conv2d_14_w_path = dir_prefix + std::string("depthwise_conv2d_14_w.bin");
- void* depthwise_conv2d_14_w = readTrainedWeights(depthwise_conv2d_14_w_path.c_str(), 0,576,1,3,3);
- std::string batch_normalization_40_gamma_path = dir_prefix + std::string("batch_normalization_40_gamma.bin");
- void* batch_normalization_40_gamma = readTrainedWeights(batch_normalization_40_gamma_path.c_str(), 0,1,576,1,1);
- std::string batch_normalization_40_beta_path = dir_prefix + std::string("batch_normalization_40_beta.bin");
- void* batch_normalization_40_beta = readTrainedWeights(batch_normalization_40_beta_path.c_str(), 0,1,576,1,1);
- std::string batch_normalization_40_mean_path = dir_prefix + std::string("batch_normalization_40_mean.bin");
- void* batch_normalization_40_mean = readTrainedWeights(batch_normalization_40_mean_path.c_str(), 0,1,576,1,1);
- std::string batch_normalization_40_variance_path = dir_prefix + std::string("batch_normalization_40_variance.bin");
- void* batch_normalization_40_variance = readTrainedWeights(batch_normalization_40_variance_path.c_str(), 0,1,576,1,1);
- std::string conv2d_28_w_path = dir_prefix + std::string("conv2d_28_w.bin");
- void* conv2d_28_w = readTrainedWeights(conv2d_28_w_path.c_str(), 0,160,576,1,1);
- std::string batch_normalization_41_gamma_path = dir_prefix + std::string("batch_normalization_41_gamma.bin");
- void* batch_normalization_41_gamma = readTrainedWeights(batch_normalization_41_gamma_path.c_str(), 0,1,160,1,1);
- std::string batch_normalization_41_beta_path = dir_prefix + std::string("batch_normalization_41_beta.bin");
- void* batch_normalization_41_beta = readTrainedWeights(batch_normalization_41_beta_path.c_str(), 0,1,160,1,1);
- std::string batch_normalization_41_mean_path = dir_prefix + std::string("batch_normalization_41_mean.bin");
- void* batch_normalization_41_mean = readTrainedWeights(batch_normalization_41_mean_path.c_str(), 0,1,160,1,1);
- std::string batch_normalization_41_variance_path = dir_prefix + std::string("batch_normalization_41_variance.bin");
- void* batch_normalization_41_variance = readTrainedWeights(batch_normalization_41_variance_path.c_str(), 0,1,160,1,1);
- std::string conv2d_29_w_path = dir_prefix + std::string("conv2d_29_w.bin");
- void* conv2d_29_w = readTrainedWeights(conv2d_29_w_path.c_str(), 0,960,160,1,1);
- std::string batch_normalization_42_gamma_path = dir_prefix + std::string("batch_normalization_42_gamma.bin");
- void* batch_normalization_42_gamma = readTrainedWeights(batch_normalization_42_gamma_path.c_str(), 0,1,960,1,1);
- std::string batch_normalization_42_beta_path = dir_prefix + std::string("batch_normalization_42_beta.bin");
- void* batch_normalization_42_beta = readTrainedWeights(batch_normalization_42_beta_path.c_str(), 0,1,960,1,1);
- std::string batch_normalization_42_mean_path = dir_prefix + std::string("batch_normalization_42_mean.bin");
- void* batch_normalization_42_mean = readTrainedWeights(batch_normalization_42_mean_path.c_str(), 0,1,960,1,1);
- std::string batch_normalization_42_variance_path = dir_prefix + std::string("batch_normalization_42_variance.bin");
- void* batch_normalization_42_variance = readTrainedWeights(batch_normalization_42_variance_path.c_str(), 0,1,960,1,1);
- std::string depthwise_conv2d_15_w_path = dir_prefix + std::string("depthwise_conv2d_15_w.bin");
- void* depthwise_conv2d_15_w = readTrainedWeights(depthwise_conv2d_15_w_path.c_str(), 0,960,1,3,3);
- std::string batch_normalization_43_gamma_path = dir_prefix + std::string("batch_normalization_43_gamma.bin");
- void* batch_normalization_43_gamma = readTrainedWeights(batch_normalization_43_gamma_path.c_str(), 0,1,960,1,1);
- std::string batch_normalization_43_beta_path = dir_prefix + std::string("batch_normalization_43_beta.bin");
- void* batch_normalization_43_beta = readTrainedWeights(batch_normalization_43_beta_path.c_str(), 0,1,960,1,1);
- std::string batch_normalization_43_mean_path = dir_prefix + std::string("batch_normalization_43_mean.bin");
- void* batch_normalization_43_mean = readTrainedWeights(batch_normalization_43_mean_path.c_str(), 0,1,960,1,1);
- std::string batch_normalization_43_variance_path = dir_prefix + std::string("batch_normalization_43_variance.bin");
- void* batch_normalization_43_variance = readTrainedWeights(batch_normalization_43_variance_path.c_str(), 0,1,960,1,1);
- std::string conv2d_30_w_path = dir_prefix + std::string("conv2d_30_w.bin");
- void* conv2d_30_w = readTrainedWeights(conv2d_30_w_path.c_str(), 0,160,960,1,1);
- std::string batch_normalization_44_gamma_path = dir_prefix + std::string("batch_normalization_44_gamma.bin");
- void* batch_normalization_44_gamma = readTrainedWeights(batch_normalization_44_gamma_path.c_str(), 0,1,160,1,1);
- std::string batch_normalization_44_beta_path = dir_prefix + std::string("batch_normalization_44_beta.bin");
- void* batch_normalization_44_beta = readTrainedWeights(batch_normalization_44_beta_path.c_str(), 0,1,160,1,1);
- std::string batch_normalization_44_mean_path = dir_prefix + std::string("batch_normalization_44_mean.bin");
- void* batch_normalization_44_mean = readTrainedWeights(batch_normalization_44_mean_path.c_str(), 0,1,160,1,1);
- std::string batch_normalization_44_variance_path = dir_prefix + std::string("batch_normalization_44_variance.bin");
- void* batch_normalization_44_variance = readTrainedWeights(batch_normalization_44_variance_path.c_str(), 0,1,160,1,1);
- std::string conv2d_31_w_path = dir_prefix + std::string("conv2d_31_w.bin");
- void* conv2d_31_w = readTrainedWeights(conv2d_31_w_path.c_str(), 0,960,160,1,1);
- std::string batch_normalization_45_gamma_path = dir_prefix + std::string("batch_normalization_45_gamma.bin");
- void* batch_normalization_45_gamma = readTrainedWeights(batch_normalization_45_gamma_path.c_str(), 0,1,960,1,1);
- std::string batch_normalization_45_beta_path = dir_prefix + std::string("batch_normalization_45_beta.bin");
- void* batch_normalization_45_beta = readTrainedWeights(batch_normalization_45_beta_path.c_str(), 0,1,960,1,1);
- std::string batch_normalization_45_mean_path = dir_prefix + std::string("batch_normalization_45_mean.bin");
- void* batch_normalization_45_mean = readTrainedWeights(batch_normalization_45_mean_path.c_str(), 0,1,960,1,1);
- std::string batch_normalization_45_variance_path = dir_prefix + std::string("batch_normalization_45_variance.bin");
- void* batch_normalization_45_variance = readTrainedWeights(batch_normalization_45_variance_path.c_str(), 0,1,960,1,1);
- std::string depthwise_conv2d_16_w_path = dir_prefix + std::string("depthwise_conv2d_16_w.bin");
- void* depthwise_conv2d_16_w = readTrainedWeights(depthwise_conv2d_16_w_path.c_str(), 0,960,1,3,3);
- std::string batch_normalization_46_gamma_path = dir_prefix + std::string("batch_normalization_46_gamma.bin");
- void* batch_normalization_46_gamma = readTrainedWeights(batch_normalization_46_gamma_path.c_str(), 0,1,960,1,1);
- std::string batch_normalization_46_beta_path = dir_prefix + std::string("batch_normalization_46_beta.bin");
- void* batch_normalization_46_beta = readTrainedWeights(batch_normalization_46_beta_path.c_str(), 0,1,960,1,1);
- std::string batch_normalization_46_mean_path = dir_prefix + std::string("batch_normalization_46_mean.bin");
- void* batch_normalization_46_mean = readTrainedWeights(batch_normalization_46_mean_path.c_str(), 0,1,960,1,1);
- std::string batch_normalization_46_variance_path = dir_prefix + std::string("batch_normalization_46_variance.bin");
- void* batch_normalization_46_variance = readTrainedWeights(batch_normalization_46_variance_path.c_str(), 0,1,960,1,1);
- std::string conv2d_32_w_path = dir_prefix + std::string("conv2d_32_w.bin");
- void* conv2d_32_w = readTrainedWeights(conv2d_32_w_path.c_str(), 0,160,960,1,1);
- std::string batch_normalization_47_gamma_path = dir_prefix + std::string("batch_normalization_47_gamma.bin");
- void* batch_normalization_47_gamma = readTrainedWeights(batch_normalization_47_gamma_path.c_str(), 0,1,160,1,1);
- std::string batch_normalization_47_beta_path = dir_prefix + std::string("batch_normalization_47_beta.bin");
- void* batch_normalization_47_beta = readTrainedWeights(batch_normalization_47_beta_path.c_str(), 0,1,160,1,1);
- std::string batch_normalization_47_mean_path = dir_prefix + std::string("batch_normalization_47_mean.bin");
- void* batch_normalization_47_mean = readTrainedWeights(batch_normalization_47_mean_path.c_str(), 0,1,160,1,1);
- std::string batch_normalization_47_variance_path = dir_prefix + std::string("batch_normalization_47_variance.bin");
- void* batch_normalization_47_variance = readTrainedWeights(batch_normalization_47_variance_path.c_str(), 0,1,160,1,1);
- std::string conv2d_33_w_path = dir_prefix + std::string("conv2d_33_w.bin");
- void* conv2d_33_w = readTrainedWeights(conv2d_33_w_path.c_str(), 0,960,160,1,1);
- std::string batch_normalization_48_gamma_path = dir_prefix + std::string("batch_normalization_48_gamma.bin");
- void* batch_normalization_48_gamma = readTrainedWeights(batch_normalization_48_gamma_path.c_str(), 0,1,960,1,1);
- std::string batch_normalization_48_beta_path = dir_prefix + std::string("batch_normalization_48_beta.bin");
- void* batch_normalization_48_beta = readTrainedWeights(batch_normalization_48_beta_path.c_str(), 0,1,960,1,1);
- std::string batch_normalization_48_mean_path = dir_prefix + std::string("batch_normalization_48_mean.bin");
- void* batch_normalization_48_mean = readTrainedWeights(batch_normalization_48_mean_path.c_str(), 0,1,960,1,1);
- std::string batch_normalization_48_variance_path = dir_prefix + std::string("batch_normalization_48_variance.bin");
- void* batch_normalization_48_variance = readTrainedWeights(batch_normalization_48_variance_path.c_str(), 0,1,960,1,1);
- std::string depthwise_conv2d_17_w_path = dir_prefix + std::string("depthwise_conv2d_17_w.bin");
- void* depthwise_conv2d_17_w = readTrainedWeights(depthwise_conv2d_17_w_path.c_str(), 0,960,1,3,3);
- std::string batch_normalization_49_gamma_path = dir_prefix + std::string("batch_normalization_49_gamma.bin");
- void* batch_normalization_49_gamma = readTrainedWeights(batch_normalization_49_gamma_path.c_str(), 0,1,960,1,1);
- std::string batch_normalization_49_beta_path = dir_prefix + std::string("batch_normalization_49_beta.bin");
- void* batch_normalization_49_beta = readTrainedWeights(batch_normalization_49_beta_path.c_str(), 0,1,960,1,1);
- std::string batch_normalization_49_mean_path = dir_prefix + std::string("batch_normalization_49_mean.bin");
- void* batch_normalization_49_mean = readTrainedWeights(batch_normalization_49_mean_path.c_str(), 0,1,960,1,1);
- std::string batch_normalization_49_variance_path = dir_prefix + std::string("batch_normalization_49_variance.bin");
- void* batch_normalization_49_variance = readTrainedWeights(batch_normalization_49_variance_path.c_str(), 0,1,960,1,1);
- std::string conv2d_34_w_path = dir_prefix + std::string("conv2d_34_w.bin");
- void* conv2d_34_w = readTrainedWeights(conv2d_34_w_path.c_str(), 0,320,960,1,1);
- std::string batch_normalization_50_gamma_path = dir_prefix + std::string("batch_normalization_50_gamma.bin");
- void* batch_normalization_50_gamma = readTrainedWeights(batch_normalization_50_gamma_path.c_str(), 0,1,320,1,1);
- std::string batch_normalization_50_beta_path = dir_prefix + std::string("batch_normalization_50_beta.bin");
- void* batch_normalization_50_beta = readTrainedWeights(batch_normalization_50_beta_path.c_str(), 0,1,320,1,1);
- std::string batch_normalization_50_mean_path = dir_prefix + std::string("batch_normalization_50_mean.bin");
- void* batch_normalization_50_mean = readTrainedWeights(batch_normalization_50_mean_path.c_str(), 0,1,320,1,1);
- std::string batch_normalization_50_variance_path = dir_prefix + std::string("batch_normalization_50_variance.bin");
- void* batch_normalization_50_variance = readTrainedWeights(batch_normalization_50_variance_path.c_str(), 0,1,320,1,1);
- std::string conv2d_35_w_path = dir_prefix + std::string("conv2d_35_w.bin");
- void* conv2d_35_w = readTrainedWeights(conv2d_35_w_path.c_str(), 0,1280,320,1,1);
- std::string batch_normalization_51_gamma_path = dir_prefix + std::string("batch_normalization_51_gamma.bin");
- void* batch_normalization_51_gamma = readTrainedWeights(batch_normalization_51_gamma_path.c_str(), 0,1,1280,1,1);
- std::string batch_normalization_51_beta_path = dir_prefix + std::string("batch_normalization_51_beta.bin");
- void* batch_normalization_51_beta = readTrainedWeights(batch_normalization_51_beta_path.c_str(), 0,1,1280,1,1);
- std::string batch_normalization_51_mean_path = dir_prefix + std::string("batch_normalization_51_mean.bin");
- void* batch_normalization_51_mean = readTrainedWeights(batch_normalization_51_mean_path.c_str(), 0,1,1280,1,1);
- std::string batch_normalization_51_variance_path = dir_prefix + std::string("batch_normalization_51_variance.bin");
- void* batch_normalization_51_variance = readTrainedWeights(batch_normalization_51_variance_path.c_str(), 0,1,1280,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,5120,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-
- startMemTracking();
-
- int test_input_size = 10000;
- int batch_size = 500;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_2 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
- void* var_4 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_5 = tensorBatchNorm(var_4, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_6 = tensorRelu(var_5);
- void* var_7 = tensorConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
- void* var_8 = tensorBatchNorm(var_7, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_9 = tensorConvolution(var_8, conv2d_3_w, 0, 0, 1, 1, 1, 1);
- void* var_10 = tensorBatchNorm(var_9, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_11 = tensorRelu(var_10);
- void* var_13 = tensorConvolution(var_11, depthwise_conv2d_2_w, 1, 1, 1, 1, 1, 96);
- void* var_14 = tensorBatchNorm(var_13, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_15 = tensorRelu(var_14);
- void* var_16 = tensorConvolution(var_15, conv2d_4_w, 0, 0, 1, 1, 1, 1);
- void* var_17 = tensorBatchNorm(var_16, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_18 = tensorConvolution(var_17, conv2d_5_w, 0, 0, 1, 1, 1, 1);
- void* var_19 = tensorBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_20 = tensorRelu(var_19);
- void* var_22 = tensorConvolution(var_20, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 144);
- void* var_23 = tensorBatchNorm(var_22, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_24 = tensorRelu(var_23);
- void* var_25 = tensorConvolution(var_24, conv2d_6_w, 0, 0, 1, 1, 1, 1);
- void* var_26 = tensorBatchNorm(var_25, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_27 = tensorAdd(var_17, var_26);
- void* var_28 = tensorConvolution(var_27, conv2d_7_w, 0, 0, 1, 1, 1, 1);
- void* var_29 = tensorBatchNorm(var_28, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_30 = tensorRelu(var_29);
- void* var_32 = tensorConvolution(var_30, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 144);
- void* var_33 = tensorBatchNorm(var_32, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_34 = tensorRelu(var_33);
- void* var_35 = tensorConvolution(var_34, conv2d_8_w, 0, 0, 1, 1, 1, 1);
- void* var_36 = tensorBatchNorm(var_35, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_37 = tensorConvolution(var_36, conv2d_9_w, 0, 0, 1, 1, 1, 1);
- void* var_38 = tensorBatchNorm(var_37, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
- void* var_39 = tensorRelu(var_38);
- void* var_41 = tensorConvolution(var_39, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 192);
- void* var_42 = tensorBatchNorm(var_41, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
- void* var_43 = tensorRelu(var_42);
- void* var_44 = tensorConvolution(var_43, conv2d_10_w, 0, 0, 1, 1, 1, 1);
- void* var_45 = tensorBatchNorm(var_44, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001);
- void* var_46 = tensorAdd(var_36, var_45);
- void* var_47 = tensorConvolution(var_46, conv2d_11_w, 0, 0, 1, 1, 1, 1);
- void* var_48 = tensorBatchNorm(var_47, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001);
- void* var_49 = tensorRelu(var_48);
- void* var_51 = tensorConvolution(var_49, depthwise_conv2d_6_w, 1, 1, 1, 1, 1, 192);
- void* var_52 = tensorBatchNorm(var_51, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001);
- void* var_53 = tensorRelu(var_52);
- void* var_54 = tensorConvolution(var_53, conv2d_12_w, 0, 0, 1, 1, 1, 1);
- void* var_55 = tensorBatchNorm(var_54, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001);
- void* var_56 = tensorAdd(var_46, var_55);
- void* var_57 = tensorConvolution(var_56, conv2d_13_w, 0, 0, 1, 1, 1, 1);
- void* var_58 = tensorBatchNorm(var_57, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001);
- void* var_59 = tensorRelu(var_58);
- void* var_61 = tensorConvolution(var_59, depthwise_conv2d_7_w, 1, 1, 2, 2, 1, 192);
- void* var_62 = tensorBatchNorm(var_61, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001);
- void* var_63 = tensorRelu(var_62);
- void* var_64 = tensorConvolution(var_63, conv2d_14_w, 0, 0, 1, 1, 1, 1);
- void* var_65 = tensorBatchNorm(var_64, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001);
- void* var_66 = tensorConvolution(var_65, conv2d_15_w, 0, 0, 1, 1, 1, 1);
- void* var_67 = tensorBatchNorm(var_66, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001);
- void* var_68 = tensorRelu(var_67);
- void* var_70 = tensorConvolution(var_68, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 384);
- void* var_71 = tensorBatchNorm(var_70, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001);
- void* var_72 = tensorRelu(var_71);
- void* var_73 = tensorConvolution(var_72, conv2d_16_w, 0, 0, 1, 1, 1, 1);
- void* var_74 = tensorBatchNorm(var_73, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001);
- void* var_75 = tensorAdd(var_65, var_74);
- void* var_76 = tensorConvolution(var_75, conv2d_17_w, 0, 0, 1, 1, 1, 1);
- void* var_77 = tensorBatchNorm(var_76, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001);
- void* var_78 = tensorRelu(var_77);
- void* var_80 = tensorConvolution(var_78, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 384);
- void* var_81 = tensorBatchNorm(var_80, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001);
- void* var_82 = tensorRelu(var_81);
- void* var_83 = tensorConvolution(var_82, conv2d_18_w, 0, 0, 1, 1, 1, 1);
- void* var_84 = tensorBatchNorm(var_83, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001);
- void* var_85 = tensorAdd(var_75, var_84);
- void* var_86 = tensorConvolution(var_85, conv2d_19_w, 0, 0, 1, 1, 1, 1);
- void* var_87 = tensorBatchNorm(var_86, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001);
- void* var_88 = tensorRelu(var_87);
- void* var_90 = tensorConvolution(var_88, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 384);
- void* var_91 = tensorBatchNorm(var_90, batch_normalization_28_gamma, batch_normalization_28_beta, batch_normalization_28_mean, batch_normalization_28_variance, 0.001);
- void* var_92 = tensorRelu(var_91);
- void* var_93 = tensorConvolution(var_92, conv2d_20_w, 0, 0, 1, 1, 1, 1);
- void* var_94 = tensorBatchNorm(var_93, batch_normalization_29_gamma, batch_normalization_29_beta, batch_normalization_29_mean, batch_normalization_29_variance, 0.001);
- void* var_95 = tensorAdd(var_85, var_94);
- void* var_97 = tensorConvolution(var_95, conv2d_21_w, 0, 0, 1, 1, 1, 1);
- void* var_98 = tensorBatchNorm(var_97, batch_normalization_30_gamma, batch_normalization_30_beta, batch_normalization_30_mean, batch_normalization_30_variance, 0.001);
- void* var_99 = tensorRelu(var_98);
- void* var_101 = tensorConvolution(var_99, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 384);
- void* var_102 = tensorBatchNorm(var_101, batch_normalization_31_gamma, batch_normalization_31_beta, batch_normalization_31_mean, batch_normalization_31_variance, 0.001);
- void* var_103 = tensorRelu(var_102);
- void* var_104 = tensorConvolution(var_103, conv2d_22_w, 0, 0, 1, 1, 1, 1);
- void* var_105 = tensorBatchNorm(var_104, batch_normalization_32_gamma, batch_normalization_32_beta, batch_normalization_32_mean, batch_normalization_32_variance, 0.001);
- void* var_106 = tensorConvolution(var_105, conv2d_23_w, 0, 0, 1, 1, 1, 1);
- void* var_107 = tensorBatchNorm(var_106, batch_normalization_33_gamma, batch_normalization_33_beta, batch_normalization_33_mean, batch_normalization_33_variance, 0.001);
- void* var_108 = tensorRelu(var_107);
- void* var_110 = tensorConvolution(var_108, depthwise_conv2d_12_w, 1, 1, 1, 1, 1, 576);
- void* var_111 = tensorBatchNorm(var_110, batch_normalization_34_gamma, batch_normalization_34_beta, batch_normalization_34_mean, batch_normalization_34_variance, 0.001);
- void* var_112 = tensorRelu(var_111);
- void* var_113 = tensorConvolution(var_112, conv2d_24_w, 0, 0, 1, 1, 1, 1);
- void* var_114 = tensorBatchNorm(var_113, batch_normalization_35_gamma, batch_normalization_35_beta, batch_normalization_35_mean, batch_normalization_35_variance, 0.001);
- void* var_115 = tensorAdd(var_105, var_114);
- void* var_116 = tensorConvolution(var_115, conv2d_25_w, 0, 0, 1, 1, 1, 1);
- void* var_117 = tensorBatchNorm(var_116, batch_normalization_36_gamma, batch_normalization_36_beta, batch_normalization_36_mean, batch_normalization_36_variance, 0.001);
- void* var_118 = tensorRelu(var_117);
- void* var_120 = tensorConvolution(var_118, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 576);
- void* var_121 = tensorBatchNorm(var_120, batch_normalization_37_gamma, batch_normalization_37_beta, batch_normalization_37_mean, batch_normalization_37_variance, 0.001);
- void* var_122 = tensorRelu(var_121);
- void* var_123 = tensorConvolution(var_122, conv2d_26_w, 0, 0, 1, 1, 1, 1);
- void* var_124 = tensorBatchNorm(var_123, batch_normalization_38_gamma, batch_normalization_38_beta, batch_normalization_38_mean, batch_normalization_38_variance, 0.001);
- void* var_125 = tensorAdd(var_115, var_124);
- void* var_127 = tensorConvolution(var_125, conv2d_27_w, 0, 0, 1, 1, 1, 1);
- void* var_128 = tensorBatchNorm(var_127, batch_normalization_39_gamma, batch_normalization_39_beta, batch_normalization_39_mean, batch_normalization_39_variance, 0.001);
- void* var_129 = tensorRelu(var_128);
- void* var_131 = tensorConvolution(var_129, depthwise_conv2d_14_w, 1, 1, 2, 2, 1, 576);
- void* var_132 = tensorBatchNorm(var_131, batch_normalization_40_gamma, batch_normalization_40_beta, batch_normalization_40_mean, batch_normalization_40_variance, 0.001);
- void* var_133 = tensorRelu(var_132);
- void* var_134 = tensorConvolution(var_133, conv2d_28_w, 0, 0, 1, 1, 1, 1);
- void* var_135 = tensorBatchNorm(var_134, batch_normalization_41_gamma, batch_normalization_41_beta, batch_normalization_41_mean, batch_normalization_41_variance, 0.001);
- void* var_136 = tensorConvolution(var_135, conv2d_29_w, 0, 0, 1, 1, 1, 1);
- void* var_137 = tensorBatchNorm(var_136, batch_normalization_42_gamma, batch_normalization_42_beta, batch_normalization_42_mean, batch_normalization_42_variance, 0.001);
- void* var_138 = tensorRelu(var_137);
- void* var_140 = tensorConvolution(var_138, depthwise_conv2d_15_w, 1, 1, 1, 1, 1, 960);
- void* var_141 = tensorBatchNorm(var_140, batch_normalization_43_gamma, batch_normalization_43_beta, batch_normalization_43_mean, batch_normalization_43_variance, 0.001);
- void* var_142 = tensorRelu(var_141);
- void* var_143 = tensorConvolution(var_142, conv2d_30_w, 0, 0, 1, 1, 1, 1);
- void* var_144 = tensorBatchNorm(var_143, batch_normalization_44_gamma, batch_normalization_44_beta, batch_normalization_44_mean, batch_normalization_44_variance, 0.001);
- void* var_145 = tensorAdd(var_135, var_144);
- void* var_146 = tensorConvolution(var_145, conv2d_31_w, 0, 0, 1, 1, 1, 1);
- void* var_147 = tensorBatchNorm(var_146, batch_normalization_45_gamma, batch_normalization_45_beta, batch_normalization_45_mean, batch_normalization_45_variance, 0.001);
- void* var_148 = tensorRelu(var_147);
- void* var_150 = tensorConvolution(var_148, depthwise_conv2d_16_w, 1, 1, 1, 1, 1, 960);
- void* var_151 = tensorBatchNorm(var_150, batch_normalization_46_gamma, batch_normalization_46_beta, batch_normalization_46_mean, batch_normalization_46_variance, 0.001);
- void* var_152 = tensorRelu(var_151);
- void* var_153 = tensorConvolution(var_152, conv2d_32_w, 0, 0, 1, 1, 1, 1);
- void* var_154 = tensorBatchNorm(var_153, batch_normalization_47_gamma, batch_normalization_47_beta, batch_normalization_47_mean, batch_normalization_47_variance, 0.001);
- void* var_155 = tensorAdd(var_145, var_154);
- void* var_157 = tensorConvolution(var_155, conv2d_33_w, 0, 0, 1, 1, 1, 1);
- void* var_158 = tensorBatchNorm(var_157, batch_normalization_48_gamma, batch_normalization_48_beta, batch_normalization_48_mean, batch_normalization_48_variance, 0.001);
- void* var_159 = tensorRelu(var_158);
- void* var_161 = tensorConvolution(var_159, depthwise_conv2d_17_w, 1, 1, 1, 1, 1, 960);
- void* var_162 = tensorBatchNorm(var_161, batch_normalization_49_gamma, batch_normalization_49_beta, batch_normalization_49_mean, batch_normalization_49_variance, 0.001);
- void* var_163 = tensorRelu(var_162);
- void* var_164 = tensorConvolution(var_163, conv2d_34_w, 0, 0, 1, 1, 1, 1);
- void* var_165 = tensorBatchNorm(var_164, batch_normalization_50_gamma, batch_normalization_50_beta, batch_normalization_50_mean, batch_normalization_50_variance, 0.001);
- void* var_167 = tensorConvolution(var_165, conv2d_35_w, 0, 0, 1, 1, 1, 1);
- void* var_168 = tensorBatchNorm(var_167, batch_normalization_51_gamma, batch_normalization_51_beta, batch_normalization_51_mean, batch_normalization_51_variance, 0.001);
- void* var_169 = tensorRelu(var_168);
- void* var_170 = tensorPooling(var_169,1,2,2,0,0,2,2);
- void* var_172 = tensorGemmGPU(var_170, dense_1_w);
- void* var_173 = tensorAdd(var_172, dense_1_b);
- void* var_174 = tensorSoftmax(var_173);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_174);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/pipeline.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/pipeline.cc
deleted file mode 100644
index b694f007b2e1c4cbe71bbe53c1065888542b23f1..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/pipeline.cc
+++ /dev/null
@@ -1,161 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testPipeline(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
-
- printf("********* Pipeline: Gaussian - Outline - Motion Blur - Emboss ********** \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- //long int test_batch_size = 9145;
- long int test_batch_size = 2000;
- long int H = 240;
- long int W = 300;
-
- printf("Reading input\n");
- void* input = readTrainedWeights("../model_params/pipeline/dataset/caltech101_255_float32.bin",
- float_type,
- test_batch_size, 1, H, W);
- printf("Reading golden output\n");
- void* golden_output = readTrainedWeights("../model_params/pipeline/golden_output/caltech-G-O-M-E-FP32-clipped-2000.bin",
- //void* golden_output = readTrainedWeights("../model_params/pipeline/golden_output/caltech-gaussian.bin",
- float_type,
- test_batch_size, 1, H, W);
-
-
- // NOTE: Filter descriptors do NOT have batch size
- // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
- // IMP: The output channels matches the trained model - not the Lenet arch proposed in Andrew Ng's class
- void* gaussian_filter = readTrainedWeights("../model_params/pipeline/filters/GaussianFilter.bin",
- float_type, 1, 1, 9, 9);
- void* outline_filter = readTrainedWeights("../model_params/pipeline/filters/OutlineFilter.bin",
- float_type, 1, 1, 3, 3);
- void* sharpen_filter = readTrainedWeights("../model_params/pipeline/filters/SharpenFilter.bin",
- float_type, 1, 1, 3, 3);
- void* motionblur_filter = readTrainedWeights("../model_params/pipeline/filters/MotionblurFilter.bin",
- float_type, 1, 1, 9, 9);
- void* emboss_filter = readTrainedWeights("../model_params/pipeline/filters/EmbossFilter.bin",
- float_type, 1, 1, 5, 5);
- void* emboss_bias = readTrainedWeights("../model_params/pipeline/filters/EmbossBias.bin",
- float_type, 1, 1, 1, 1);
-
- clearTensorMap();
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* gaussian_out = tensorConvolution(input, gaussian_filter, 4, 4, 1, 1,
- conv_mode, conv_precision);
-
- void * gaussian_out_clip = tensorRelu2(gaussian_out, 0, 255);
-
- void* outline_out = tensorConvolution(gaussian_out_clip, outline_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- void * outline_out_clip = tensorRelu2(outline_out, 0, 255);
-
- void* motionblur_out = tensorConvolution(outline_out_clip, motionblur_filter, 4, 4, 1, 1,
- conv_mode, conv_precision);
- void * motionblur_out_clip = tensorRelu2(motionblur_out, 0, 255);
-
- void* emboss_out = tensorConvolution(motionblur_out_clip, emboss_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
- void* emboss_bias_out = tensorAdd(emboss_out, emboss_bias);
- void* result = tensorRelu2(emboss_bias_out, 0, 255);
- //void* result = gaussian_out;
-
- // NOTE-IMP: Important to include this call always before doing dumpOutput and computePSNViolation
- hpvm_request_tensor(result, 0);
-
- dumpOutput(result);
-
- //void* psnr_output = readTrainedWeights("../model_params/pipeline/golden_output/caltech-gaussian.bin",
- //float_type,
- //test_batch_size, 1, H, W);
- computePSNRViolation(result, golden_output, 30);
-
-
- dumpAccuracyNorms();
- freeOutputTensors();
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
-
-
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(0);
-
- testPipeline();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/pipeline_GEMO.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/pipeline_GEMO.cc
deleted file mode 100644
index 199a29a73af1a98fac31ae55f93c8bc8e7e2d6d4..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/pipeline_GEMO.cc
+++ /dev/null
@@ -1,153 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testPipeline(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
-
- printf("********* Pipeline: Gaussian - Emboss - Motion Blur - Outline ********** \n");
-
- //long int test_batch_size = 9145;
- long int test_batch_size = 1000;
- long int H = 240;
- long int W = 300;
-
- printf("Reading input\n");
- void* input = readTrainedWeights("../model_params/pipeline/dataset/calibration_4572.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- printf("Reading golden output\n");
- void* golden_output = readTrainedWeights("../model_params/pipeline/golden_output/GEMO_calib.bin",
- float_type,
- test_batch_size, 1, H, W);
-
-
- void* gaussian_filter = readTrainedWeights("../model_params/pipeline/filters/GaussianFilter.bin",
- float_type, 1, 1, 9, 9);
- void* outline_filter = readTrainedWeights("../model_params/pipeline/filters/OutlineFilter.bin",
- float_type, 1, 1, 3, 3);
- void* sharpen_filter = readTrainedWeights("../model_params/pipeline/filters/SharpenFilter.bin",
- float_type, 1, 1, 3, 3);
- void* motionblur_filter = readTrainedWeights("../model_params/pipeline/filters/MotionblurFilter.bin",
- float_type, 1, 1, 9, 9);
- void* emboss_filter = readTrainedWeights("../model_params/pipeline/filters/EmbossFilter.bin",
- float_type, 1, 1, 5, 5);
- void* emboss_bias = readTrainedWeights("../model_params/pipeline/filters/EmbossBias.bin",
- float_type, 1, 1, 1, 1);
-
-
-
- //printTensorValues(input);
- //printTensorValues(motionblur_filter);
-
- clearTensorMap();
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* gaussian_out = tensorConvolution(input, gaussian_filter, 4, 4, 1, 1,
- conv_mode, conv_precision);
-
- void * gaussian_out_clip = tensorRelu2(gaussian_out, 0, 255);
-
- void* emboss_out = tensorConvolution(gaussian_out_clip, emboss_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
- void* emboss_bias_out = tensorAdd(emboss_out, emboss_bias);
- void* emboss_bias_out_clip = tensorRelu2(emboss_bias_out, 0, 255);
-
- void* motionblur_out = tensorConvolution(emboss_bias_out_clip, motionblur_filter, 4, 4, 1, 1,
- conv_mode, conv_precision);
- void * motionblur_out_clip = tensorRelu2(motionblur_out, 0, 255);
-
- void* outline_out = tensorConvolution(motionblur_out_clip, outline_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- void* result = tensorRelu2(outline_out, 0, 255);
-
-
- // NOTE-IMP: Important to include this call always before doing dumpOutput and computePSNViolation
- hpvm_request_tensor(result, 0);
-
- //dumpOutput(result, "GEMO_calib.bin");
-
- computePSNRViolation(result, golden_output, 30);
-
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
-
-
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(0);
-
- testPipeline();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/pipeline_GEO.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/pipeline_GEO.cc
deleted file mode 100644
index c2d6d1fb4dd3f8e0fe25db4f2628700a60aa44da..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/pipeline_GEO.cc
+++ /dev/null
@@ -1,147 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testPipeline(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
-
- printf("********* Pipeline: Gaussian - Emboss - Outline ********** \n");
-
- //long int test_batch_size = 9145;
- long int test_batch_size = 1000;
- long int H = 240;
- long int W = 300;
-
- printf("Reading input\n");
- void* input = readTrainedWeights("../model_params/pipeline/dataset/calibration_4572.bin",
- float_type,
- test_batch_size, 1, H, W);
- printf("Reading golden output\n");
- void* golden_output = readTrainedWeights("../model_params/pipeline/golden_output/GEO_calib.bin",
- float_type,
- test_batch_size, 1, H, W);
-
-
- void* gaussian_filter = readTrainedWeights("../model_params/pipeline/filters/GaussianFilter.bin",
- float_type, 1, 1, 9, 9);
- void* outline_filter = readTrainedWeights("../model_params/pipeline/filters/OutlineFilter.bin",
- float_type, 1, 1, 3, 3);
- void* sharpen_filter = readTrainedWeights("../model_params/pipeline/filters/SharpenFilter.bin",
- float_type, 1, 1, 3, 3);
- void* motionblur_filter = readTrainedWeights("../model_params/pipeline/filters/MotionblurFilter.bin",
- float_type, 1, 1, 9, 9);
- void* emboss_filter = readTrainedWeights("../model_params/pipeline/filters/EmbossFilter.bin",
- float_type, 1, 1, 5, 5);
- void* emboss_bias = readTrainedWeights("../model_params/pipeline/filters/EmbossBias.bin",
- float_type, 1, 1, 1, 1);
-
- clearTensorMap();
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* gaussian_out = tensorConvolution(input, gaussian_filter, 4, 4, 1, 1,
- conv_mode, conv_precision);
-
- void * gaussian_out_clip = tensorRelu2(gaussian_out, 0, 255);
-
- void* emboss_out = tensorConvolution(gaussian_out_clip, emboss_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
- void* emboss_bias_out = tensorAdd(emboss_out, emboss_bias);
- void* emboss_bias_out_clip = tensorRelu2(emboss_bias_out, 0, 255);
-
- void* outline_out = tensorConvolution(emboss_bias_out_clip, outline_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- void * result = tensorRelu2(outline_out, 0, 255);
-
- //void* result = gaussian_out;
-
- // NOTE-IMP: Important to include this call always before doing dumpOutput and computePSNViolation
- hpvm_request_tensor(result, 0);
-
- //dumpOutput(result, "GEO_calib.bin");
-
- computePSNRViolation(result, golden_output, 30);
-
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
-
-
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(0);
-
- testPipeline();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/pipeline_GEOM.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/pipeline_GEOM.cc
deleted file mode 100644
index 9c219f4daac2af708f02a64d97bf84ae36047316..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/pipeline_GEOM.cc
+++ /dev/null
@@ -1,151 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testPipeline(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
-
- printf("********* Pipeline: Gaussian - Emboss - Outline - Motion Blur ********** \n");
- //long int test_batch_size = 9145;
- long int test_batch_size = 1000;
- long int H = 240;
- long int W = 300;
-
- printf("Reading input\n");
-
- void* input = readTrainedWeights("../model_params/pipeline/dataset/calibration_4572.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- printf("Reading golden output\n");
- void* golden_output = readTrainedWeights("../model_params/pipeline/golden_output/GEOM_calib.bin",
- float_type,
- test_batch_size, 1, H, W);
-
-
- void* gaussian_filter = readTrainedWeights("../model_params/pipeline/filters/GaussianFilter.bin",
- float_type, 1, 1, 9, 9);
- void* outline_filter = readTrainedWeights("../model_params/pipeline/filters/OutlineFilter.bin",
- float_type, 1, 1, 3, 3);
- void* sharpen_filter = readTrainedWeights("../model_params/pipeline/filters/SharpenFilter.bin",
- float_type, 1, 1, 3, 3);
- void* motionblur_filter = readTrainedWeights("../model_params/pipeline/filters/MotionblurFilter.bin",
- float_type, 1, 1, 9, 9);
- void* emboss_filter = readTrainedWeights("../model_params/pipeline/filters/EmbossFilter.bin",
- float_type, 1, 1, 5, 5);
- void* emboss_bias = readTrainedWeights("../model_params/pipeline/filters/EmbossBias.bin",
- float_type, 1, 1, 1, 1);
-
- clearTensorMap();
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* gaussian_out = tensorConvolution(input, gaussian_filter, 4, 4, 1, 1,
- conv_mode, conv_precision);
-
- void * gaussian_out_clip = tensorRelu2(gaussian_out, 0, 255);
-
- void* emboss_out = tensorConvolution(gaussian_out_clip, emboss_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
- void* emboss_bias_out = tensorAdd(emboss_out, emboss_bias);
- void* emboss_bias_out_clip = tensorRelu2(emboss_bias_out, 0, 255);
-
- void* outline_out = tensorConvolution(emboss_bias_out_clip, outline_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- void * outline_out_clip = tensorRelu2(outline_out, 0, 255);
-
- void* motionblur_out = tensorConvolution(outline_out_clip, motionblur_filter, 4, 4, 1, 1,
- conv_mode, conv_precision);
- void * result = tensorRelu2(motionblur_out, 0, 255);
-
-
- // NOTE-IMP: Important to include this call always before doing dumpOutput and computePSNViolation
- hpvm_request_tensor(result, 0);
-
- //dumpOutput(result, "GEOM_calib.bin");
-
- computePSNRViolation(result, golden_output, 30);
-
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
-
-
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(0);
-
- testPipeline();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/pipeline_GSM.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/pipeline_GSM.cc
deleted file mode 100644
index 721eb887ca0a6cd9e6cb5c992f7c0559716a1259..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/pipeline_GSM.cc
+++ /dev/null
@@ -1,146 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testPipeline(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
-
- printf("********* Pipeline: Gaussian - Sharpen - Motion Blur ********** \n");
-
- //long int test_batch_size = 9145;
- long int test_batch_size = 1000;
- long int H = 240;
- long int W = 300;
-
- printf("Reading input\n");
- void* input = readTrainedWeights("../model_params/pipeline/dataset/calibration_4572.bin",
- float_type,
- test_batch_size, 1, H, W);
- printf("Reading golden output\n");
- void* golden_output = readTrainedWeights("../model_params/pipeline/golden_output/GSM_calib.bin",
- float_type,
- test_batch_size, 1, H, W);
-
-
- // NOTE: Filter descriptors do NOT have batch size
- // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
- // IMP: The output channels matches the trained model - not the Lenet arch proposed in Andrew Ng's class
- void* gaussian_filter = readTrainedWeights("../model_params/pipeline/filters/GaussianFilter.bin",
- float_type, 1, 1, 9, 9);
- void* outline_filter = readTrainedWeights("../model_params/pipeline/filters/OutlineFilter.bin",
- float_type, 1, 1, 3, 3);
- void* sharpen_filter = readTrainedWeights("../model_params/pipeline/filters/SharpenFilter.bin",
- float_type, 1, 1, 3, 3);
- void* motionblur_filter = readTrainedWeights("../model_params/pipeline/filters/MotionblurFilter.bin",
- float_type, 1, 1, 9, 9);
- void* emboss_filter = readTrainedWeights("../model_params/pipeline/filters/EmbossFilter.bin",
- float_type, 1, 1, 5, 5);
- void* emboss_bias = readTrainedWeights("../model_params/pipeline/filters/EmbossBias.bin",
- float_type, 1, 1, 1, 1);
-
- clearTensorMap();
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* gaussian_out = tensorConvolution(input, gaussian_filter, 4, 4, 1, 1,
- conv_mode, conv_precision);
- void * gaussian_out_clip = tensorRelu2(gaussian_out, 0, 255);
-
- void* sharpen_out = tensorConvolution(gaussian_out_clip, sharpen_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- void * sharpen_out_clip = tensorRelu2(sharpen_out, 0, 255);
-
- void* motionblur_out = tensorConvolution(sharpen_out_clip, motionblur_filter, 4, 4, 1, 1,
- conv_mode, conv_precision);
- void * result = tensorRelu2(motionblur_out, 0, 255);
-
- // NOTE-IMP: Important to include this call always before doing dumpOutput and computePSNViolation
- hpvm_request_tensor(result, 0);
-
- //dumpOutput(result, "GSM_calib.bin");
-
- computePSNRViolation(result, golden_output, 30);
-
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
-
-
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(0);
-
- testPipeline();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/pipeline_GSME.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/pipeline_GSME.cc
deleted file mode 100644
index 19f0210e83939568ae2ca9b198ccfa95be70113e..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/pipeline_GSME.cc
+++ /dev/null
@@ -1,153 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testPipeline(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
-
- printf("********* Pipeline: Gaussian - Sharpen - Motion Blur - Emboss ********** \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- //long int test_batch_size = 9145;
- long int test_batch_size = 1000;
- long int H = 240;
- long int W = 300;
-
- printf("Reading input\n");
- void* input = readTrainedWeights("../model_params/pipeline/dataset/calibration_4572.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- printf("Reading golden output\n");
- void* golden_output = readTrainedWeights("../model_params/pipeline/golden_output/GSME_calib.bin",
- float_type,
- test_batch_size, 1, H, W);
-
-
- void* gaussian_filter = readTrainedWeights("../model_params/pipeline/filters/GaussianFilter.bin",
- float_type, 1, 1, 9, 9);
- void* outline_filter = readTrainedWeights("../model_params/pipeline/filters/OutlineFilter.bin",
- float_type, 1, 1, 3, 3);
- void* sharpen_filter = readTrainedWeights("../model_params/pipeline/filters/SharpenFilter.bin",
- float_type, 1, 1, 3, 3);
- void* motionblur_filter = readTrainedWeights("../model_params/pipeline/filters/MotionblurFilter.bin",
- float_type, 1, 1, 9, 9);
- void* emboss_filter = readTrainedWeights("../model_params/pipeline/filters/EmbossFilter.bin",
- float_type, 1, 1, 5, 5);
- void* emboss_bias = readTrainedWeights("../model_params/pipeline/filters/EmbossBias.bin",
- float_type, 1, 1, 1, 1);
-
- clearTensorMap();
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* gaussian_out = tensorConvolution(input, gaussian_filter, 4, 4, 1, 1,
- conv_mode, conv_precision);
- void * gaussian_out_clip = tensorRelu2(gaussian_out, 0, 255);
-
- void* sharpen_out = tensorConvolution(gaussian_out_clip, sharpen_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- void * sharpen_out_clip = tensorRelu2(sharpen_out, 0, 255);
-
- void* motionblur_out = tensorConvolution(sharpen_out_clip, motionblur_filter, 4, 4, 1, 1,
- conv_mode, conv_precision);
- void * motionblur_out_clip = tensorRelu2(motionblur_out, 0, 255);
-
- void* emboss_out = tensorConvolution(motionblur_out_clip, emboss_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
- void* emboss_bias_out = tensorAdd(emboss_out, emboss_bias);
- void* result = tensorRelu2(emboss_bias_out, 0, 255);
-
-
- //void* result = gaussian_out;
-
- // NOTE-IMP: Important to include this call always before doing dumpOutput and computePSNViolation
- hpvm_request_tensor(result, 0);
-
- //dumpOutput(result, "GSME_calib.bin");
-
- computePSNRViolation(result, golden_output, 30);
-
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
-
-
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(0);
-
- testPipeline();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/alexnet2_profiling.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/alexnet2_profiling.cc
deleted file mode 100644
index 50df874874592a94238e596189b6a477fb66f05f..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/alexnet2_profiling.cc
+++ /dev/null
@@ -1,166 +0,0 @@
-// Per tensor operation
-
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-/* NOTE: Reference Architecture to use for profiling */
-void testCifarNet(){
-
- printf("********* Alexnet2 CIFAR-10 DNN ********** \n");
-
- std::string dir_prefix = std::string("../model_params/alexnet2_cifar10/");
- std::string input_path = dir_prefix + std::string("norm_cifar_input.bin");
- std::string labels_path = dir_prefix + std::string("test_labels.bin");
-
- void* conv1_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv1.bin",
- float_type, 32, 3, 3, 3);
- void* conv1_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv1_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv2.bin",
- float_type, 32, 32, 3, 3);
- void* conv2_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv2_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv3_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv3.bin",
- float_type, 64, 32, 3, 3);
- void* conv3_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv3_bias.bin",
- float_type, 1, 64, 1, 1);
- void* conv4_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv4.bin",
- float_type, 64, 64, 3, 3);
- void* conv4_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv4_bias.bin",
- float_type, 1, 64, 1, 1);
- void* conv5_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv5.bin",
- float_type, 128, 64, 3, 3);
- void* conv5_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv5_bias.bin",
- float_type, 1, 128, 1, 1);
- void* conv6_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv6.bin",
- float_type, 128, 128, 3, 3);
- void* conv6_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv6_bias.bin",
- float_type, 1, 128, 1, 1);
-
- void* fc1_weights = readTrainedWeights("../model_params/alexnet2_cifar10/fc1.bin",
- float_type, 1, 1, 2048, 10);
- void* fc1_bias = readTrainedWeights("../model_params/alexnet2_cifar10/fc1_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- startMemTracking();
-
- int total_runs = 10;
-
- int test_input_size = 5000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- // NOTE: Starting time profiling
- startProfiling();
-
- Profiler profiler;
- profiler.start_profiler();
-
- double total_time = 0.0;
- double total_energy = 0.0;
-
- for(int i = 0; i < total_runs; i++){
- for(int i = 0; i < batch_count; i++){
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
- void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
-
- // FIRST Tensor Runtime CALL
- profiler.resume_profiler();
- void* conv1out = tensorConvolution(input, conv1_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv1out, conv1_bias);
- void* conv1_tanh = tensorTanh(conv1out);
-
- // 2nd Layer
- void* conv2out = tensorConvolution(conv1_tanh, conv2_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv2out, conv2_bias);
- void* conv2_tanh = tensorTanh(conv2out);
- void* pool2out = tensorPooling(conv2_tanh, 0, 2, 2, 0, 0, 2, 2);
-
- // 3rd Layer
- void* conv3out = tensorConvolution(pool2out, conv3_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv3out, conv3_bias);
- void* conv3_tanh = tensorTanh(conv3out);
-
- // 4th Layer
- void* conv4out = tensorConvolution(conv3_tanh, conv4_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv4out, conv4_bias);
- void* conv4_tanh = tensorTanh(conv4out);
- void* pool4out = tensorPooling(conv4_tanh, 0, 2, 2, 0, 0, 2, 2);
-
- // 5th Layer
- void* conv5out = tensorConvolution(pool4out, conv5_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv5out, conv5_bias);
- void* conv5_tanh = tensorTanh(conv5out);
-
- // 6th Layer
- void* conv6out = tensorConvolution(conv5_tanh, conv6_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv6out, conv6_bias);
- void* conv6_tanh = tensorTanh(conv6out);
- void* pool6out = tensorPooling(conv6_tanh, 0, 2, 2, 0, 0, 2, 2);
-
- // final FC Layer
- void* gemm1out = tensorGemmGPU(pool6out, fc1_weights);
- void* gemm1biasout = tensorAdd(gemm1out, fc1_bias);
- void* result = tensorSoftmax(gemm1biasout);
-
- profiler.pause_profiler();
- auto time_energy = profiler.get_time_energy();
- total_time += time_energy.first;
- total_energy += time_energy.second;
-
- profiler.reset();
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
-
- float accuracy = computeAccuracy2(labels, batch_size, result);
- final_accuracy += accuracy;
-
- freeBatchMemory();
- }
- }
- profiler.stop_profiler();
-
- std::cout<<"---------------------------------------\n";
- std::cout<<"Average time: " << total_time / total_runs << '\n';
- std::cout<<"Average energy: " << total_energy / total_runs << '\n';
- std::cout<<"---------------------------------------\n";
-
- stopProfiling();
- final_accuracy = (final_accuracy / batch_count) / total_runs;
- dumpFinalAccuracy(final_accuracy);
-}
-
-
-int main(int argc, char* argv[]){
-
- llvm_hpvm_initTensorRt(0);
-
- testCifarNet();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/alexnet2_profiling_tensors.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/alexnet2_profiling_tensors.cc
deleted file mode 100644
index f95a7bda4fc581e4c40d4882304156f2420f22a5..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/alexnet2_profiling_tensors.cc
+++ /dev/null
@@ -1,262 +0,0 @@
-// Per tensor operation
-
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-void add_data(std::unordered_map<std::string, std::pair<double, double> >& total_time_energies, Profiler& profiler, const std::string& op_name){
- profiler.pause_profiler();
- auto time_energy = profiler.get_time_energy();
-
- auto itr = total_time_energies.find(op_name);
- if (itr == total_time_energies.end()){
- total_time_energies.insert(std::make_pair(op_name, time_energy));
- } else {
- itr->second.first += time_energy.first;
- itr->second.second += time_energy.second;
- }
- profiler.reset();
-}
-
-/* NOTE: Reference Architecture to use for profiling */
-void testCifarNet(){
-
- printf("********* Alexnet2 CIFAR-10 DNN ********** \n");
-
- std::string dir_prefix = std::string("../model_params/alexnet2_cifar10/");
- std::string input_path = dir_prefix + std::string("norm_cifar_input.bin");
- std::string labels_path = dir_prefix + std::string("test_labels.bin");
-
- void* conv1_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv1.bin",
- float_type, 32, 3, 3, 3);
- void* conv1_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv1_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv2.bin",
- float_type, 32, 32, 3, 3);
- void* conv2_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv2_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv3_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv3.bin",
- float_type, 64, 32, 3, 3);
- void* conv3_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv3_bias.bin",
- float_type, 1, 64, 1, 1);
- void* conv4_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv4.bin",
- float_type, 64, 64, 3, 3);
- void* conv4_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv4_bias.bin",
- float_type, 1, 64, 1, 1);
- void* conv5_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv5.bin",
- float_type, 128, 64, 3, 3);
- void* conv5_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv5_bias.bin",
- float_type, 1, 128, 1, 1);
- void* conv6_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv6.bin",
- float_type, 128, 128, 3, 3);
- void* conv6_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv6_bias.bin",
- float_type, 1, 128, 1, 1);
-
- void* fc1_weights = readTrainedWeights("../model_params/alexnet2_cifar10/fc1.bin",
- float_type, 1, 1, 2048, 10);
- void* fc1_bias = readTrainedWeights("../model_params/alexnet2_cifar10/fc1_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- std::ofstream online_profiler_output;
- online_profiler_output.open("online_output.txt");
-
- startMemTracking();
-
- // NOTE: CHANGED INPUT TO STANDARDIZE
- int total_runs = 50; // FOR NOW 100;
-
- int test_input_size = 5000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- // NOTE: Starting time profiling
- startProfiling();
-
- Profiler profiler;
- profiler.start_profiler();
-
- // Get the total time and energy per tensor per run
- std::unordered_map<std::string, std::pair<double, double> > total_time_energies;
-
- for(int i = 0; i < total_runs; i++){
- for(int i = 0; i < batch_count; i++){
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
- void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
-
- // FIRST Tensor Runtime CALL
- profiler.resume_profiler();
- void* conv1out = tensorConvolution(input, conv1_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- add_data(total_time_energies, profiler, "Conv1");
-
- profiler.resume_profiler();
- tensorAdd(conv1out, conv1_bias);
- add_data(total_time_energies, profiler, "Add1");
-
- profiler.resume_profiler();
- void* conv1_tanh = tensorTanh(conv1out);
- add_data(total_time_energies, profiler, "Tanh1");
-
- // 2nd Layer
- profiler.resume_profiler();
- void* conv2out = tensorConvolution(conv1_tanh, conv2_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- add_data(total_time_energies, profiler, "Conv2");
-
- profiler.resume_profiler();
- tensorAdd(conv2out, conv2_bias);
- add_data(total_time_energies, profiler, "Add2");
-
- profiler.resume_profiler();
- void* conv2_tanh = tensorTanh(conv2out);
- add_data(total_time_energies, profiler, "Tanh2");
-
- profiler.resume_profiler();
- void* pool2out = tensorPooling(conv2_tanh, 0, 2, 2, 0, 0, 2, 2);
- add_data(total_time_energies, profiler, "Pool1");
-
- // 3rd Layer
- profiler.resume_profiler();
- void* conv3out = tensorConvolution(pool2out, conv3_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- add_data(total_time_energies, profiler, "Conv3");
-
- profiler.resume_profiler();
- tensorAdd(conv3out, conv3_bias);
- add_data(total_time_energies, profiler, "Add3");
-
- profiler.resume_profiler();
- void* conv3_tanh = tensorTanh(conv3out);
- add_data(total_time_energies, profiler, "Tanh3");
-
- // 4th Layer
- profiler.resume_profiler();
- void* conv4out = tensorConvolution(conv3_tanh, conv4_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- add_data(total_time_energies, profiler, "Conv4");
-
- profiler.resume_profiler();
- tensorAdd(conv4out, conv4_bias);
- add_data(total_time_energies, profiler, "Add4");
-
- profiler.resume_profiler();
- void* conv4_tanh = tensorTanh(conv4out);
- add_data(total_time_energies, profiler, "Tanh4");
-
- profiler.resume_profiler();
- void* pool4out = tensorPooling(conv4_tanh, 0, 2, 2, 0, 0, 2, 2);
- add_data(total_time_energies, profiler, "Pool2");
-
- // 5th Layer
- profiler.resume_profiler();
- void* conv5out = tensorConvolution(pool4out, conv5_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- add_data(total_time_energies, profiler, "Conv5");
-
- profiler.resume_profiler();
- tensorAdd(conv5out, conv5_bias);
- add_data(total_time_energies, profiler, "Add5");
-
- profiler.resume_profiler();
- void* conv5_tanh = tensorTanh(conv5out);
- add_data(total_time_energies, profiler, "Tanh5");
-
- // 6th Layer
- profiler.resume_profiler();
- void* conv6out = tensorConvolution(conv5_tanh, conv6_filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
- add_data(total_time_energies, profiler, "Conv6");
-
- profiler.resume_profiler();
- tensorAdd(conv6out, conv6_bias);
- add_data(total_time_energies, profiler, "Add6");
-
- profiler.resume_profiler();
- void* conv6_tanh = tensorTanh(conv6out);
- add_data(total_time_energies, profiler, "Tanh6");
-
- profiler.resume_profiler();
- void* pool6out = tensorPooling(conv6_tanh, 0, 2, 2, 0, 0, 2, 2);
- add_data(total_time_energies, profiler, "Pool3");
-
- // final FC Layer
- profiler.resume_profiler();
- void* gemm1out = tensorGemmGPU(pool6out, fc1_weights);
- add_data(total_time_energies, profiler, "Mul1"); // ASSUMING that this is mul1
-
- std::cout<<"-----------------------------------ADD 7--------------------------------\n";
- profiler.resume_profiler();
- void* gemm1biasout = tensorAdd(gemm1out, fc1_bias);
- add_data(total_time_energies, profiler, "Add7");
- std::cout<<"-----------------------------------ADD 7 ENDS --------------------------------\n";
-
- profiler.resume_profiler();
- void* result = tensorSoftmax(gemm1biasout);
- add_data(total_time_energies, profiler, "Softmax1");
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
-
- float accuracy = computeAccuracy2(labels, batch_size, result);
- final_accuracy += accuracy;
-
- freeBatchMemory();
- }
- }
- profiler.stop_profiler();
-
- stopProfiling();
- //online_profiler_output << "Total time: " << total_time << ", " << total_energy << "\n";
- // Now compute the averages across batches
- std::ofstream ofs;
- std::string arr[] = {"Add1", "Add2", "Add3", "Add4", "Add5", "Add6", "Add7",
- "Conv1", "Conv2", "Conv3", "Conv4", "Conv5", "Conv6",
- "Mul1",
- "Pool1", "Pool2", "Pool3",
- "Softmax1",
- "Tanh1", "Tanh2", "Tanh3", "Tanh4", "Tanh5", "Tanh6"};
- ofs.open("online_profiler_tensor_data.txt");
- std::vector<std::string> ordered_keys(std::begin(arr), std::end(arr));
- for (const std::string& key : ordered_keys){
- const auto& data_pair = total_time_energies[key];
- ofs << key << ": " << data_pair.first / total_runs << "\t" << data_pair.second / total_runs << '\n';
- std::cout<< key << ": " << data_pair.first / total_runs << "\t" << data_pair.second / total_runs << '\n';
- }
-
- /*
- ofs.open("online_profiler_tensor_data.txt");
- for (const auto& tensor_data : total_time_energies){
- ofs << tensor_data.first << ": " << tensor_data.second.first / total_runs << "\t" << tensor_data.second.second / total_runs << '\n';
- }*/
- ofs.close();
- final_accuracy = (final_accuracy / batch_count) / total_runs;
- dumpFinalAccuracy(final_accuracy);
- online_profiler_output.close();
-}
-
-
-int main(int argc, char* argv[]){
-
- llvm_hpvm_initTensorRt(0);
-
- testCifarNet();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/alexnet_cifar10_profiling.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/alexnet_cifar10_profiling.cc
deleted file mode 100644
index 94cef7fba14e0b9d1d9ec72b508ccd11cb560a87..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/alexnet_cifar10_profiling.cc
+++ /dev/null
@@ -1,127 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
-
- std::string dir_prefix = std::string("../model_params/alexnet_cifar10_front/");
- std::string input_path = dir_prefix + std::string("input.bin");
- //void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
- std::string labels_path = dir_prefix + std::string("labels.bin");
- //uint8_t* labels = readLabels(labels_path.c_str(),10000);
- std::string conv2d_1_w_path = dir_prefix + std::string("conv0.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,11,11);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv_bias0.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv3.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,192,64,5,5);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv_bias3.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,192,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv6.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,384,192,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv_bias6.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,384,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv7.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,256,384,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv_bias7.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv8.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv_bias8.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("fc12.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,4096,10);
- std::string dense_1_b_path = dir_prefix + std::string("fc_bias12.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- int total_runs = 10;
- Profiler profiler;
- profiler.start_profiler();
-
- double total_time = 0.0;
- double total_energy = 0.0;
-
- // NOTE: Starting time profiling
- startProfiling();
-
- for(int i = 0; i < total_runs; i++){
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
- void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
-
- profiler.resume_profiler();
- void* var_0 = tensorConvolution(input, conv2d_1_w, 5, 5, 1, 1, 1, 0);
- void* var_1 = tensorAdd(var_0, conv2d_1_b);
- void* var_2 = tensorTanh(var_1);
- void* var_3 = tensorPooling(var_2,0,2,2,0,0,2,2);
- void* var_5 = tensorConvolution(var_3, conv2d_2_w, 2, 2, 1, 1, 1, 0);
- void* var_6 = tensorAdd(var_5, conv2d_2_b);
- void* var_7 = tensorTanh(var_6);
- void* var_8 = tensorPooling(var_7,0,2,2,0,0,2,2);
- void* var_10 = tensorConvolution(var_8, conv2d_3_w, 1, 1, 1, 1, 1, 0);
- void* var_11 = tensorAdd(var_10, conv2d_3_b);
- void* var_12 = tensorTanh(var_11);
- void* var_13 = tensorConvolution(var_12, conv2d_4_w, 1, 1, 1, 1, 1, 0);
- void* var_14 = tensorAdd(var_13, conv2d_4_b);
- void* var_15 = tensorTanh(var_14);
- void* var_16 = tensorConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
- void* var_17 = tensorAdd(var_16, conv2d_5_b);
- void* var_18 = tensorTanh(var_17);
- void* var_19 = tensorPooling(var_18,0,2,2,0,0,2,2);
- void* var_22 = tensorGemmGPU(var_19, dense_1_w);
- void* var_23 = tensorAdd(var_22, dense_1_b);
- void* var_24 = tensorSoftmax(var_23);
-
- profiler.pause_profiler();
- auto time_energy = profiler.get_time_energy();
- total_time += time_energy.first;
- total_energy += time_energy.second;
- profiler.reset();
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
-
- float accuracy = computeAccuracy2(labels,batch_size,var_24);
- final_accuracy += accuracy;
-
- freeBatchMemory();
- }
- }
- profiler.stop_profiler();
-
- std::cout<<"---------------------------------------\n";
- std::cout<<"Average time: " << total_time / total_runs << '\n';
- std::cout<<"Average energy: " << total_energy / total_runs << '\n';
- std::cout<<"---------------------------------------\n";
-
- stopProfiling();
-
- final_accuracy = final_accuracy / batch_count / total_runs;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/lenet_keras_profiling.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/lenet_keras_profiling.cc
deleted file mode 100644
index f55d883c2c11f1f0721b64d4ab00de373f685a3e..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/lenet_keras_profiling.cc
+++ /dev/null
@@ -1,186 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testLenetTanh(){
-
- int total_runs = 10;
-
- printf("********* Lenet-2 Architecture ********** \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 5000;
-
- uint8_t* labels = readLabels("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size);
-
- void* input = readInputTensor("../model_params/lenet_params/datasets/t10k-images-idx3-ubyte",
- CUDNN_DATA_FLOAT,
- test_batch_size, 1, 28, 28);
-
- // NOTE: Filter descriptors do NOT have batch size
- // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
- // IMP: The output channels matches the trained model - not the Lenet arch proposed in Andrew Ng's class
- void* conv1_filter = readTrainedWeights("../model_params/lenet_keras/conv1.bin",
- float_type, 32, 1, 5, 5);
- void* conv1_bias = readTrainedWeights("../model_params/lenet_keras/conv1_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/lenet_keras/conv2.bin",
- float_type, 64, 32, 5, 5);
- void* conv2_bias = readTrainedWeights("../model_params/lenet_keras/conv2_bias.bin",
- float_type, 1, 64, 1, 1);
- void* fc1_weights = readTrainedWeights("../model_params/lenet_keras/fc1.bin",
- float_type, 1, 1, 7*7*64, 1024);
- void* fc1_bias = readTrainedWeights("../model_params/lenet_keras/fc1_bias.bin",
- float_type, 1, 1024, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/lenet_keras/fc2.bin",
- float_type, 1, 1, 1024, 10);
- void* fc2_bias = readTrainedWeights("../model_params/lenet_keras/fc2_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
-
- clearTensorMap();
-
- Profiler profiler;
- profiler.start_profiler();
-
- double total_time = 0.0;
- double total_energy = 0.0;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < total_runs; i++){
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
- // Start power and performnce profiling
- startProfiling();
- profiler.resume_profiler();
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* conv1out = tensorConvolution(input, conv1_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
-
- // NOTE: For tensorAdd, the only dimension that MUST match is channels
- tensorAdd(conv1out, conv1_bias); // NOTE: In place operation
-
- void* pool1out = tensorPooling(conv1out, 0, 2, 2, 0, 0, 2, 2);
-
- void* conv1_tanh = tensorTanh(pool1out);
-
- // NOTE: input channels have to match between tensor op inputs and outputs
- void* conv2out = tensorConvolution(conv1_tanh, conv2_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv2out, conv2_bias); // NOTE: In place operation
-
- void* pool2out = tensorPooling(conv2out, 0, 2, 2, 0, 0, 2, 2);
-
- void* conv2_tanh = tensorTanh(pool2out);
-
- void* gemm1out = tensorGemmGPU(conv2_tanh, fc1_weights);
-
- void* gemm1biasout = tensorAdd(gemm1out, fc1_bias);
-
- void* tanh1out = tensorTanh(gemm1biasout);
-
- void* gemm2out = tensorGemmGPU(tanh1out, fc2_weights);
-
- void* gemm2_biasout = tensorAdd(gemm2out, fc2_bias);
-
- void* tanh2out = tensorTanh(gemm2_biasout);
-
- void* result = tensorSoftmax(tanh2out);
-
- profiler.pause_profiler();
- auto time_energy = profiler.get_time_energy();
- total_time += time_energy.first;
- total_energy += time_energy.second;
-
- profiler.reset();
-
- // End profiling and dump output to profile.txt
- stopProfiling();
-
- final_accuracy += computeAccuracy2(labels, test_batch_size, result);
-
- dumpAccuracyNorms();
- freeOutputTensors();
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
- profiler.stop_profiler();
-
- std::cout<<"---------------------------------------\n";
- std::cout<<"Average time: " << total_time / total_runs << '\n';
- std::cout<<"Average energy: " << total_energy / total_runs << '\n';
- std::cout<<"---------------------------------------\n";
-
- final_accuracy = final_accuracy / total_runs;
- dumpFinalAccuracy(final_accuracy);
-}
-
-
-int main(int argc, char* argv[]){
-
- llvm_hpvm_initTensorRt(0);
-
- testLenetTanh();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/mobilenet_cifar10_profiling.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/mobilenet_cifar10_profiling.cc
deleted file mode 100644
index e84ca25f6fc3b44b02ce5b45f4517ba6ad6bc3be..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/mobilenet_cifar10_profiling.cc
+++ /dev/null
@@ -1,438 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
-
- std::string dir_prefix = std::string("../model_params/mobilenet_quant/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
- void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
- void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
- void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
- void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
- void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
- std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
- void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
- void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
- void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
- void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
- void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
- void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
- void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
- void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
- void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
- void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
- void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
- void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
- void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
- void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
- void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
- void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
- void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
- void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
- void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
- void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
- void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
- void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
- void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
- void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
- void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
- void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
- void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
- void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
- void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
- void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
- void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
- void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
- void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
- void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
- void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
- void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
- void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
- void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
- void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
- void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
- void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
- void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
- void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
- void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
- void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
- void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
- void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
- void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
- void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
- void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
- void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
- void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
- void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
- void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1);
- std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
- void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
- void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
- void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
- void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1);
- std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
- void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3);
- std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
- void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
- void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
- void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
- void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1);
- std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
- void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
- void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
- void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
- void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-
- startMemTracking();
- startProfiling();
-
- int test_input_size = 5000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- int total_runs = 10;
- Profiler profiler;
- profiler.start_profiler();
-
- double total_time = 0.0;
- double total_energy = 0.0;
-
- for(int i = 0; i < total_runs; i++){
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- profiler.resume_profiler();
- void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
- void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorRelu(var_1);
- void* var_4 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_5 = tensorBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_6 = tensorRelu(var_5);
- void* var_7 = tensorConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
- void* var_8 = tensorBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_9 = tensorRelu(var_8);
- void* var_11 = tensorConvolution(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
- void* var_12 = tensorBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_13 = tensorRelu(var_12);
- void* var_14 = tensorConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1);
- void* var_15 = tensorBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_16 = tensorRelu(var_15);
- void* var_18 = tensorConvolution(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
- void* var_19 = tensorBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_20 = tensorRelu(var_19);
- void* var_21 = tensorConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1);
- void* var_22 = tensorBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_23 = tensorRelu(var_22);
- void* var_26 = tensorConvolution(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
- void* var_27 = tensorBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_28 = tensorRelu(var_27);
- void* var_29 = tensorConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1);
- void* var_30 = tensorBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_31 = tensorRelu(var_30);
- void* var_33 = tensorConvolution(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
- void* var_34 = tensorBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_35 = tensorRelu(var_34);
- void* var_36 = tensorConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1);
- void* var_37 = tensorBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_38 = tensorRelu(var_37);
- void* var_41 = tensorConvolution(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
- void* var_42 = tensorBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
- void* var_43 = tensorRelu(var_42);
- void* var_44 = tensorConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1);
- void* var_45 = tensorBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
- void* var_46 = tensorRelu(var_45);
- void* var_48 = tensorConvolution(var_46, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512);
- void* var_49 = tensorBatchNorm(var_48, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001);
- void* var_50 = tensorRelu(var_49);
- void* var_51 = tensorConvolution(var_50, conv2d_8_w, 0, 0, 1, 1, 1, 1);
- void* var_52 = tensorBatchNorm(var_51, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001);
- void* var_53 = tensorRelu(var_52);
- void* var_55 = tensorConvolution(var_53, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512);
- void* var_56 = tensorBatchNorm(var_55, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001);
- void* var_57 = tensorRelu(var_56);
- void* var_58 = tensorConvolution(var_57, conv2d_9_w, 0, 0, 1, 1, 1, 1);
- void* var_59 = tensorBatchNorm(var_58, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001);
- void* var_60 = tensorRelu(var_59);
- void* var_63 = tensorConvolution(var_60, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512);
- void* var_64 = tensorBatchNorm(var_63, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001);
- void* var_65 = tensorRelu(var_64);
- void* var_66 = tensorConvolution(var_65, conv2d_10_w, 0, 0, 1, 1, 1, 1);
- void* var_67 = tensorBatchNorm(var_66, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001);
- void* var_68 = tensorRelu(var_67);
- void* var_70 = tensorConvolution(var_68, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512);
- void* var_71 = tensorBatchNorm(var_70, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001);
- void* var_72 = tensorRelu(var_71);
- void* var_73 = tensorConvolution(var_72, conv2d_11_w, 0, 0, 1, 1, 1, 1);
- void* var_74 = tensorBatchNorm(var_73, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001);
- void* var_75 = tensorRelu(var_74);
- void* var_77 = tensorConvolution(var_75, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512);
- void* var_78 = tensorBatchNorm(var_77, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001);
- void* var_79 = tensorRelu(var_78);
- void* var_80 = tensorConvolution(var_79, conv2d_12_w, 0, 0, 1, 1, 1, 1);
- void* var_81 = tensorBatchNorm(var_80, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001);
- void* var_82 = tensorRelu(var_81);
- void* var_85 = tensorConvolution(var_82, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512);
- void* var_86 = tensorBatchNorm(var_85, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001);
- void* var_87 = tensorRelu(var_86);
- void* var_88 = tensorConvolution(var_87, conv2d_13_w, 0, 0, 1, 1, 1, 1);
- void* var_89 = tensorBatchNorm(var_88, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001);
- void* var_90 = tensorRelu(var_89);
- void* var_92 = tensorConvolution(var_90, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024);
- void* var_93 = tensorBatchNorm(var_92, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001);
- void* var_94 = tensorRelu(var_93);
- void* var_95 = tensorConvolution(var_94, conv2d_14_w, 0, 0, 1, 1, 1, 1);
- void* var_96 = tensorBatchNorm(var_95, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001);
- void* var_97 = tensorRelu(var_96);
- void* var_99 = tensorPooling(var_97,1,2,2,0,0,2,2);
- void* var_101 = tensorGemmGPU(var_99, dense_1_w);
- void* var_102 = tensorAdd(var_101, dense_1_b);
- void* var_103 = tensorSoftmax(var_102);
-
- profiler.pause_profiler();
- auto time_energy = profiler.get_time_energy();
- total_time += time_energy.first;
- total_energy += time_energy.second;
- profiler.reset();
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_103);
- final_accuracy += accuracy;
- freeBatchMemory();
- }
- }
- profiler.stop_profiler();
-
- std::cout<<"---------------------------------------\n";
- std::cout<<"Average time: " << total_time / total_runs << '\n';
- std::cout<<"Average energy: " << total_energy / total_runs << '\n';
- std::cout<<"---------------------------------------\n";
-
- stopProfiling();
-
- final_accuracy = final_accuracy / batch_count / total_runs;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/mobilenet_depthwise_profiling.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/mobilenet_depthwise_profiling.cc
deleted file mode 100644
index 3dcce8ada9c74a439440594eb7df8a33c169cf6c..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/mobilenet_depthwise_profiling.cc
+++ /dev/null
@@ -1,415 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
-
- std::string dir_prefix = std::string("../model_params/mobilenet/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
- void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
- void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
- void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
- void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
- void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
- std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
- void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
- void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
- void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
- void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
- void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
- void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
- void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
- void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
- void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
- void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
- void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
- void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
- void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
- void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
- void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
- void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
- void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
- void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
- void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
- void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
- void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
- void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
- void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
- void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
- void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
- void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
- void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
- void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
- void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
- void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
- void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
- void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
- void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
- void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
- void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
- void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
- void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
- void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
- void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
- void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
- void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
- void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
- void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
- void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
- void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
- void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
- void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
- void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
- void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
- void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
- void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
- void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
- void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
- void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1);
- std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
- void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
- void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
- void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
- void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1);
- std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
- void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3);
- std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
- void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
- void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
- void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
- void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1);
- std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
- void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
- void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
- void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
- void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
-
- int total_runs = 10;
- float final_accuracy = 0.0;
-
- for (int run_num = 0; run_num < total_runs; run_num++){
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
- void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorRelu(var_1);
- void* var_4 = tensorConvCutlass(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_5 = tensorBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_6 = tensorRelu(var_5);
- void* var_7 = tensorConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
- void* var_8 = tensorBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_9 = tensorRelu(var_8);
- void* var_11 = tensorConvCutlass(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
- void* var_12 = tensorBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_13 = tensorRelu(var_12);
- void* var_14 = tensorConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1);
- void* var_15 = tensorBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_16 = tensorRelu(var_15);
- void* var_18 = tensorConvCutlass(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
- void* var_19 = tensorBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_20 = tensorRelu(var_19);
- void* var_21 = tensorConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1);
- void* var_22 = tensorBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_23 = tensorRelu(var_22);
- void* var_26 = tensorConvCutlass(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
- void* var_27 = tensorBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_28 = tensorRelu(var_27);
- void* var_29 = tensorConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1);
- void* var_30 = tensorBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_31 = tensorRelu(var_30);
- void* var_33 = tensorConvCutlass(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
- void* var_34 = tensorBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_35 = tensorRelu(var_34);
- void* var_36 = tensorConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1);
- void* var_37 = tensorBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_38 = tensorRelu(var_37);
- void* var_41 = tensorConvCutlass(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
- void* var_42 = tensorBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
- void* var_43 = tensorRelu(var_42);
- void* var_44 = tensorConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1);
- void* var_45 = tensorBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
- void* var_46 = tensorRelu(var_45);
- void* var_48 = tensorConvCutlass(var_46, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512);
- void* var_49 = tensorBatchNorm(var_48, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001);
- void* var_50 = tensorRelu(var_49);
- void* var_51 = tensorConvolution(var_50, conv2d_8_w, 0, 0, 1, 1, 1, 1);
- void* var_52 = tensorBatchNorm(var_51, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001);
- void* var_53 = tensorRelu(var_52);
- void* var_55 = tensorConvCutlass(var_53, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512);
- void* var_56 = tensorBatchNorm(var_55, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001);
- void* var_57 = tensorRelu(var_56);
- void* var_58 = tensorConvolution(var_57, conv2d_9_w, 0, 0, 1, 1, 1, 1);
- void* var_59 = tensorBatchNorm(var_58, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001);
- void* var_60 = tensorRelu(var_59);
- void* var_63 = tensorConvCutlass(var_60, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512);
- void* var_64 = tensorBatchNorm(var_63, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001);
- void* var_65 = tensorRelu(var_64);
- void* var_66 = tensorConvolution(var_65, conv2d_10_w, 0, 0, 1, 1, 1, 1);
- void* var_67 = tensorBatchNorm(var_66, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001);
- void* var_68 = tensorRelu(var_67);
- void* var_70 = tensorConvCutlass(var_68, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512);
- void* var_71 = tensorBatchNorm(var_70, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001);
- void* var_72 = tensorRelu(var_71);
- void* var_73 = tensorConvolution(var_72, conv2d_11_w, 0, 0, 1, 1, 1, 1);
- void* var_74 = tensorBatchNorm(var_73, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001);
- void* var_75 = tensorRelu(var_74);
- void* var_77 = tensorConvCutlass(var_75, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512);
- void* var_78 = tensorBatchNorm(var_77, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001);
- void* var_79 = tensorRelu(var_78);
- void* var_80 = tensorConvolution(var_79, conv2d_12_w, 0, 0, 1, 1, 1, 1);
- void* var_81 = tensorBatchNorm(var_80, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001);
- void* var_82 = tensorRelu(var_81);
- void* var_85 = tensorConvCutlass(var_82, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512);
- void* var_86 = tensorBatchNorm(var_85, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001);
- void* var_87 = tensorRelu(var_86);
- void* var_88 = tensorConvolution(var_87, conv2d_13_w, 0, 0, 1, 1, 1, 1);
- void* var_89 = tensorBatchNorm(var_88, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001);
- void* var_90 = tensorRelu(var_89);
- void* var_92 = tensorConvCutlass(var_90, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024);
- void* var_93 = tensorBatchNorm(var_92, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001);
- void* var_94 = tensorRelu(var_93);
- void* var_95 = tensorConvolution(var_94, conv2d_14_w, 0, 0, 1, 1, 1, 1);
- void* var_96 = tensorBatchNorm(var_95, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001);
- void* var_97 = tensorRelu(var_96);
- void* var_99 = tensorPooling(var_97,1,2,2,0,0,2,2);
- void* var_101 = tensorGemmGPU(var_99, dense_1_w);
- void* var_102 = tensorAdd(var_101, dense_1_b);
- void* var_103 = tensorSoftmax(var_102);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_103);
- final_accuracy += accuracy;
- freeBatchMemory();
- }
- }
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/mobilenet_shallow_depthwise_profiling.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/mobilenet_shallow_depthwise_profiling.cc
deleted file mode 100644
index 5af17774b41d4d265e110dd988e3458442312226..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/mobilenet_shallow_depthwise_profiling.cc
+++ /dev/null
@@ -1,247 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(int argc, char* argv[]){
-
- int total_runs = 10;
- if (argc > 1){
- total_runs = atoi(argv[1]);
- }
-
-
- llvm_hpvm_initTensorRt(0);
-
- //std::string dir_prefix = std::string("../../keras/data/mobilenet_shallow_nathan/");
-
- std::string dir_prefix = std::string("../model_params/mobilenet_shallow/");
-
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
- void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
- void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
- void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
- void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
- void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
- std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
- void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
- void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
- void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
- void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,2048,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 500;
- int batch_count = test_input_size / batch_size;
-
-
- float final_accuracy = 0.0;
-
- for(int j = 0; j < total_runs; j++){
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
- void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorRelu(var_1);
- void* var_4 = tensorConvCutlass(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_5 = tensorBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_6 = tensorRelu(var_5);
- void* var_7 = tensorConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
- void* var_8 = tensorBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_9 = tensorRelu(var_8);
- void* var_11 = tensorConvCutlass(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
- void* var_12 = tensorBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_13 = tensorRelu(var_12);
- void* var_14 = tensorConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1);
- void* var_15 = tensorBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_16 = tensorRelu(var_15);
- void* var_18 = tensorConvCutlass(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
- void* var_19 = tensorBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_20 = tensorRelu(var_19);
- void* var_21 = tensorConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1);
- void* var_22 = tensorBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_23 = tensorRelu(var_22);
- void* var_26 = tensorConvCutlass(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
- void* var_27 = tensorBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_28 = tensorRelu(var_27);
- void* var_29 = tensorConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1);
- void* var_30 = tensorBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_31 = tensorRelu(var_30);
- void* var_33 = tensorConvCutlass(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
- void* var_34 = tensorBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_35 = tensorRelu(var_34);
- void* var_36 = tensorConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1);
- void* var_37 = tensorBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_38 = tensorRelu(var_37);
- void* var_41 = tensorConvCutlass(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
- void* var_42 = tensorBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
- void* var_43 = tensorRelu(var_42);
- void* var_44 = tensorConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1);
- void* var_45 = tensorBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
- void* var_46 = tensorRelu(var_45);
- void* var_47 = tensorPooling(var_46,1,2,2,0,0,2,2);
- void* var_49 = tensorGemmGPU(var_47, dense_1_w);
- void* var_50 = tensorAdd(var_49, dense_1_b);
- void* var_51 = tensorSoftmax(var_50);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_51);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- //final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy / batch_count);
- }
-
- final_accuracy = final_accuracy / batch_count / total_runs;
- dumpFinalAccuracy(final_accuracy);
-
- //dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/mobilenet_shallow_profiling.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/mobilenet_shallow_profiling.cc
deleted file mode 100644
index c60f15e3cb71d9fa81b444be8348803e1d7891d6..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/mobilenet_shallow_profiling.cc
+++ /dev/null
@@ -1,227 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- std::string dir_prefix = std::string("../model_params/mobilenet_shallow/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,64,64,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,64,1,1);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- int total_runs = 10;
- Profiler profiler;
- profiler.start_profiler();
-
- double total_time = 0.0;
- double total_energy = 0.0;
-
- for(int i = 0; i < total_runs; i++){
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- profiler.resume_profiler();
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
- void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorRelu(var_1);
- void* var_4 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_5 = tensorBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_6 = tensorRelu(var_5);
- void* var_7 = tensorConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
- void* var_8 = tensorBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_9 = tensorRelu(var_8);
- void* var_11 = tensorConvolution(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
- void* var_12 = tensorBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_13 = tensorRelu(var_12);
- void* var_14 = tensorConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1);
- void* var_15 = tensorBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_16 = tensorRelu(var_15);
- void* var_18 = tensorConvolution(var_16, depthwise_conv2d_3_w, 1, 1, 2, 2, 1, 64);
- void* var_19 = tensorBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_20 = tensorRelu(var_19);
- void* var_21 = tensorConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1);
- void* var_22 = tensorBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_23 = tensorRelu(var_22);
- void* var_26 = tensorConvolution(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
- void* var_27 = tensorBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_28 = tensorRelu(var_27);
- void* var_29 = tensorConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1);
- void* var_30 = tensorBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_31 = tensorRelu(var_30);
- void* var_33 = tensorConvolution(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
- void* var_34 = tensorBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_35 = tensorRelu(var_34);
- void* var_36 = tensorConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1);
- void* var_37 = tensorBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_38 = tensorRelu(var_37);
- void* var_40 = tensorPooling(var_38,1,2,2,0,0,2,2);
- void* var_42 = tensorGemmGPU(var_40, dense_1_w);
- void* var_43 = tensorAdd(var_42, dense_1_b);
- void* var_44 = tensorSoftmax(var_43);
-
- profiler.pause_profiler();
- auto time_energy = profiler.get_time_energy();
- total_time += time_energy.first;
- total_energy += time_energy.second;
- profiler.reset();
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_44);
- final_accuracy += accuracy;
- freeBatchMemory();
- }
- }
-
- profiler.stop_profiler();
-
- std::cout<<"---------------------------------------\n";
- std::cout<<"Average time: " << total_time / total_runs << '\n';
- std::cout<<"Average energy: " << total_energy / total_runs << '\n';
- std::cout<<"---------------------------------------\n";
-
- final_accuracy = final_accuracy / batch_count / total_runs;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/resnet18_cifar10_profiling.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/resnet18_cifar10_profiling.cc
deleted file mode 100644
index 1b0664200170235e2d0dac5682108de97b094776..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/resnet18_cifar10_profiling.cc
+++ /dev/null
@@ -1,245 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- std::string dir_prefix = std::string("../model_params/resnet18_cifar10_3/");
- std::string input_path = dir_prefix + std::string("input.bin");
- //void* input = readTrainedWeights(input_path.c_str(), 0, batch_size,3,32,32);
- std::string labels_path = dir_prefix + std::string("labels.bin");
- //uint8_t* labels = readLabels(labels_path.c_str(), batch_size);
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,16,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,16,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,16,1,1);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_14_b_path = dir_prefix + std::string("conv2d_14_b.bin");
- void* conv2d_14_b = readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_15_w_path = dir_prefix + std::string("conv2d_15_w.bin");
- void* conv2d_15_w = readTrainedWeights(conv2d_15_w_path.c_str(), 0,64,32,3,3);
- std::string conv2d_15_b_path = dir_prefix + std::string("conv2d_15_b.bin");
- void* conv2d_15_b = readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_17_w_path = dir_prefix + std::string("conv2d_17_w.bin");
- void* conv2d_17_w = readTrainedWeights(conv2d_17_w_path.c_str(), 0,64,32,1,1);
- std::string conv2d_17_b_path = dir_prefix + std::string("conv2d_17_b.bin");
- void* conv2d_17_b = readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_16_w_path = dir_prefix + std::string("conv2d_16_w.bin");
- void* conv2d_16_w = readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_16_b_path = dir_prefix + std::string("conv2d_16_b.bin");
- void* conv2d_16_b = readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_18_w_path = dir_prefix + std::string("conv2d_18_w.bin");
- void* conv2d_18_w = readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_18_b_path = dir_prefix + std::string("conv2d_18_b.bin");
- void* conv2d_18_b = readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_19_w_path = dir_prefix + std::string("conv2d_19_w.bin");
- void* conv2d_19_w = readTrainedWeights(conv2d_19_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_19_b_path = dir_prefix + std::string("conv2d_19_b.bin");
- void* conv2d_19_b = readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_20_w_path = dir_prefix + std::string("conv2d_20_w.bin");
- void* conv2d_20_w = readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_20_b_path = dir_prefix + std::string("conv2d_20_b.bin");
- void* conv2d_20_b = readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_21_w_path = dir_prefix + std::string("conv2d_21_w.bin");
- void* conv2d_21_w = readTrainedWeights(conv2d_21_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_21_b_path = dir_prefix + std::string("conv2d_21_b.bin");
- void* conv2d_21_b = readTrainedWeights(conv2d_21_b_path.c_str(), 0,1,64,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,64,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- int total_runs = 10;
-
- Profiler profiler;
- profiler.start_profiler();
-
- double total_time = 0.0;
- double total_energy = 0.0;
-
- // NOTE: Starting time profiling
- startProfiling();
-
- for(int i = 0; i < total_runs; i++){
- for(int i = 0; i < batch_count; i++){
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
-
- profiler.resume_profiler();
-
- void* var_2 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
- void* var_3 = tensorAdd(var_2, conv2d_1_b);
- void* var_4 = tensorRelu(var_3);
- void* var_6 = tensorConvolution(var_4, conv2d_2_w, 1, 1, 1, 1, 1, 0);
- void* var_7 = tensorAdd(var_6, conv2d_2_b);
- void* var_8 = tensorRelu(var_7);
- void* var_10 = tensorConvolution(var_8, conv2d_3_w, 1, 1, 1, 1, 1, 0);
- void* var_11 = tensorAdd(var_10, conv2d_3_b);
- void* var_12 = tensorAdd(var_4, var_11);
- void* var_13 = tensorRelu(var_12);
- void* var_15 = tensorConvolution(var_13, conv2d_4_w, 1, 1, 1, 1, 1, 0);
- void* var_16 = tensorAdd(var_15, conv2d_4_b);
- void* var_17 = tensorRelu(var_16);
- void* var_19 = tensorConvolution(var_17, conv2d_5_w, 1, 1, 1, 1, 1, 0);
- void* var_20 = tensorAdd(var_19, conv2d_5_b);
- void* var_21 = tensorAdd(var_13, var_20);
- void* var_22 = tensorRelu(var_21);
- void* var_24 = tensorConvolution(var_22, conv2d_6_w, 1, 1, 1, 1, 1, 0);
- void* var_25 = tensorAdd(var_24, conv2d_6_b);
- void* var_26 = tensorRelu(var_25);
- void* var_28 = tensorConvolution(var_26, conv2d_7_w, 1, 1, 1, 1, 1, 0);
- void* var_29 = tensorAdd(var_28, conv2d_7_b);
- void* var_30 = tensorAdd(var_22, var_29);
- void* var_31 = tensorRelu(var_30);
- void* var_33 = tensorConvolution(var_31, conv2d_8_w, 1, 1, 2, 2, 1, 0);
- void* var_34 = tensorAdd(var_33, conv2d_8_b);
- void* var_35 = tensorRelu(var_34);
- void* var_37 = tensorConvolution(var_35, conv2d_9_w, 1, 1, 1, 1, 1, 0);
- void* var_38 = tensorAdd(var_37, conv2d_9_b);
- void* var_40 = tensorConvolution(var_31, conv2d_10_w, 0, 0, 2, 2, 1, 0);
- void* var_41 = tensorAdd(var_40, conv2d_10_b);
- void* var_42 = tensorAdd(var_41, var_38);
- void* var_43 = tensorRelu(var_42);
- void* var_45 = tensorConvolution(var_43, conv2d_11_w, 1, 1, 1, 1, 1, 0);
- void* var_46 = tensorAdd(var_45, conv2d_11_b);
- void* var_47 = tensorRelu(var_46);
- void* var_49 = tensorConvolution(var_47, conv2d_12_w, 1, 1, 1, 1, 1, 0);
- void* var_50 = tensorAdd(var_49, conv2d_12_b);
- void* var_51 = tensorAdd(var_43, var_50);
- void* var_52 = tensorRelu(var_51);
- void* var_54 = tensorConvolution(var_52, conv2d_13_w, 1, 1, 1, 1, 1, 0);
- void* var_55 = tensorAdd(var_54, conv2d_13_b);
- void* var_56 = tensorRelu(var_55);
- void* var_58 = tensorConvolution(var_56, conv2d_14_w, 1, 1, 1, 1, 1, 0);
- void* var_59 = tensorAdd(var_58, conv2d_14_b);
- void* var_60 = tensorAdd(var_52, var_59);
- void* var_61 = tensorRelu(var_60);
- void* var_63 = tensorConvolution(var_61, conv2d_15_w, 1, 1, 2, 2, 1, 0);
- void* var_64 = tensorAdd(var_63, conv2d_15_b);
- void* var_65 = tensorRelu(var_64);
- void* var_67 = tensorConvolution(var_65, conv2d_16_w, 1, 1, 1, 1, 1, 0);
- void* var_68 = tensorAdd(var_67, conv2d_16_b);
- void* var_70 = tensorConvolution(var_61, conv2d_17_w, 0, 0, 2, 2, 1, 0);
- void* var_71 = tensorAdd(var_70, conv2d_17_b);
- void* var_72 = tensorAdd(var_71, var_68);
- void* var_73 = tensorRelu(var_72);
- void* var_75 = tensorConvolution(var_73, conv2d_18_w, 1, 1, 1, 1, 1, 0);
- void* var_76 = tensorAdd(var_75, conv2d_18_b);
- void* var_77 = tensorRelu(var_76);
- void* var_79 = tensorConvolution(var_77, conv2d_19_w, 1, 1, 1, 1, 1, 0);
- void* var_80 = tensorAdd(var_79, conv2d_19_b);
- void* var_81 = tensorAdd(var_73, var_80);
- void* var_82 = tensorRelu(var_81);
- void* var_84 = tensorConvolution(var_82, conv2d_20_w, 1, 1, 1, 1, 1, 0);
- void* var_85 = tensorAdd(var_84, conv2d_20_b);
- void* var_86 = tensorRelu(var_85);
- void* var_88 = tensorConvolution(var_86, conv2d_21_w, 1, 1, 1, 1, 1, 0);
- void* var_89 = tensorAdd(var_88, conv2d_21_b);
- void* var_90 = tensorAdd(var_82, var_89);
- void* var_91 = tensorRelu(var_90);
- void* var_92 = tensorPooling(var_91,1,8,8,0,0,8,8);
- void* var_94 = tensorGemmGPU(var_92, dense_1_w);
- void* var_95 = tensorAdd(var_94, dense_1_b);
- void* var_96 = tensorSoftmax(var_95);
-
- profiler.pause_profiler();
- auto time_energy = profiler.get_time_energy();
- total_time += time_energy.first;
- total_energy += time_energy.second;
- profiler.reset();
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
-
- float accuracy = computeAccuracy2(labels,batch_size,var_96);
- final_accuracy += accuracy;
-
- freeBatchMemory();
- }
- }
- profiler.stop_profiler();
-
- std::cout<<"---------------------------------------\n";
- std::cout<<"Average time: " << total_time / total_runs << '\n';
- std::cout<<"Average energy: " << total_energy / total_runs << '\n';
- std::cout<<"---------------------------------------\n";
-
- stopProfiling();
-
- final_accuracy = final_accuracy / batch_count / total_runs;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/vgg16_cifar100_profiling.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/vgg16_cifar100_profiling.cc
deleted file mode 100644
index 70246dac4b3d43550f49a0a653d1c13396f3a84a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/vgg16_cifar100_profiling.cc
+++ /dev/null
@@ -1,184 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- std::string dir_prefix = std::string("../model_params/vgg16_cifar100_front/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,100);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,100,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 500;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- int total_runs = 10;
- Profiler profiler;
- profiler.start_profiler();
- double total_time = 0.0;
- double total_energy = 0.0;
-
- for (int i = 0; i < total_runs; i++){
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- profiler.resume_profiler();
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
- void* var_1 = tensorAdd(var_0, conv2d_1_b);
- void* var_2 = tensorRelu(var_1);
- void* var_4 = tensorConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0);
- void* var_5 = tensorAdd(var_4, conv2d_2_b);
- void* var_6 = tensorRelu(var_5);
- void* var_7 = tensorPooling(var_6,0,2,2,0,0,2,2);
- void* var_8 = tensorConvolution(var_7, conv2d_3_w, 1, 1, 1, 1, 1, 0);
- void* var_9 = tensorAdd(var_8, conv2d_3_b);
- void* var_10 = tensorRelu(var_9);
- void* var_12 = tensorConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0);
- void* var_13 = tensorAdd(var_12, conv2d_4_b);
- void* var_14 = tensorRelu(var_13);
- void* var_15 = tensorPooling(var_14,0,2,2,0,0,2,2);
- void* var_16 = tensorConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
- void* var_17 = tensorAdd(var_16, conv2d_5_b);
- void* var_18 = tensorRelu(var_17);
- void* var_20 = tensorConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0);
- void* var_21 = tensorAdd(var_20, conv2d_6_b);
- void* var_22 = tensorRelu(var_21);
- void* var_24 = tensorConvolution(var_22, conv2d_7_w, 1, 1, 1, 1, 1, 0);
- void* var_25 = tensorAdd(var_24, conv2d_7_b);
- void* var_26 = tensorRelu(var_25);
- void* var_27 = tensorPooling(var_26,0,2,2,0,0,2,2);
- void* var_28 = tensorConvolution(var_27, conv2d_8_w, 1, 1, 1, 1, 1, 0);
- void* var_29 = tensorAdd(var_28, conv2d_8_b);
- void* var_30 = tensorRelu(var_29);
- void* var_32 = tensorConvolution(var_30, conv2d_9_w, 1, 1, 1, 1, 1, 0);
- void* var_33 = tensorAdd(var_32, conv2d_9_b);
- void* var_34 = tensorRelu(var_33);
- void* var_36 = tensorConvolution(var_34, conv2d_10_w, 1, 1, 1, 1, 1, 0);
- void* var_37 = tensorAdd(var_36, conv2d_10_b);
- void* var_38 = tensorRelu(var_37);
- void* var_39 = tensorPooling(var_38,0,2,2,0,0,2,2);
- void* var_40 = tensorConvolution(var_39, conv2d_11_w, 1, 1, 1, 1, 1, 0);
- void* var_41 = tensorAdd(var_40, conv2d_11_b);
- void* var_42 = tensorRelu(var_41);
- void* var_44 = tensorConvolution(var_42, conv2d_12_w, 1, 1, 1, 1, 1, 0);
- void* var_45 = tensorAdd(var_44, conv2d_12_b);
- void* var_46 = tensorRelu(var_45);
- void* var_48 = tensorConvolution(var_46, conv2d_13_w, 1, 1, 1, 1, 1, 0);
- void* var_49 = tensorAdd(var_48, conv2d_13_b);
- void* var_50 = tensorRelu(var_49);
- void* var_51 = tensorPooling(var_50,0,2,2,0,0,2,2);
- void* var_54 = tensorGemmGPU(var_51, dense_1_w);
- void* var_55 = tensorAdd(var_54, dense_1_b);
- void* var_56 = tensorRelu(var_55);
- void* var_58 = tensorGemmGPU(var_56, dense_2_w);
- void* var_59 = tensorAdd(var_58, dense_2_b);
- void* var_60 = tensorSoftmax(var_59);
-
- profiler.pause_profiler();
- auto time_energy = profiler.get_time_energy();
- total_time += time_energy.first;
- total_energy += time_energy.second;
- profiler.reset();
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_60, 100);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
- }
-
- profiler.stop_profiler();
-
- std::cout<<"---------------------------------------\n";
- std::cout<<"Average time: " << total_time / total_runs << '\n';
- std::cout<<"Average energy: " << total_energy / total_runs << '\n';
- std::cout<<"---------------------------------------\n";
-
- final_accuracy = final_accuracy / batch_count / total_runs;
- dumpFinalAccuracy(final_accuracy);
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/vgg16_cifar10_profiling.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/vgg16_cifar10_profiling.cc
deleted file mode 100644
index 7ed583884a3fa2fca745bde4d27f8ca92cfcda02..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/profiling/vgg16_cifar10_profiling.cc
+++ /dev/null
@@ -1,185 +0,0 @@
-#include "/home/nvidia/Gitlab/hpvm/llvm/projects/gpu_profiler/include/profiler.h"
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- std::string dir_prefix = std::string("../model_params/vgg16_cifar10_2/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,10);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 500;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- Profiler profiler;
- profiler.start_profiler();
-
- double total_time = 0.0;
- double total_energy = 0.0;
-
- int total_runs = 10;
- for(int i = 0; i < total_runs; i++){
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
-
- profiler.resume_profiler();
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
- void* var_1 = tensorAdd(var_0, conv2d_1_b);
- void* var_2 = tensorRelu(var_1);
- void* var_4 = tensorConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0);
- void* var_5 = tensorAdd(var_4, conv2d_2_b);
- void* var_6 = tensorRelu(var_5);
- void* var_7 = tensorPooling(var_6,0,2,2,0,0,2,2);
- void* var_8 = tensorConvolution(var_7, conv2d_3_w, 1, 1, 1, 1, 1, 0);
- void* var_9 = tensorAdd(var_8, conv2d_3_b);
- void* var_10 = tensorRelu(var_9);
- void* var_12 = tensorConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0);
- void* var_13 = tensorAdd(var_12, conv2d_4_b);
- void* var_14 = tensorRelu(var_13);
- void* var_15 = tensorPooling(var_14,0,2,2,0,0,2,2);
- void* var_16 = tensorConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
- void* var_17 = tensorAdd(var_16, conv2d_5_b);
- void* var_18 = tensorRelu(var_17);
- void* var_20 = tensorConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0);
- void* var_21 = tensorAdd(var_20, conv2d_6_b);
- void* var_22 = tensorRelu(var_21);
- void* var_24 = tensorConvolution(var_22, conv2d_7_w, 1, 1, 1, 1, 1, 0);
- void* var_25 = tensorAdd(var_24, conv2d_7_b);
- void* var_26 = tensorRelu(var_25);
- void* var_27 = tensorPooling(var_26,0,2,2,0,0,2,2);
- void* var_28 = tensorConvolution(var_27, conv2d_8_w, 1, 1, 1, 1, 1, 0);
- void* var_29 = tensorAdd(var_28, conv2d_8_b);
- void* var_30 = tensorRelu(var_29);
- void* var_32 = tensorConvolution(var_30, conv2d_9_w, 1, 1, 1, 1, 1, 0);
- void* var_33 = tensorAdd(var_32, conv2d_9_b);
- void* var_34 = tensorRelu(var_33);
- void* var_36 = tensorConvolution(var_34, conv2d_10_w, 1, 1, 1, 1, 1, 0);
- void* var_37 = tensorAdd(var_36, conv2d_10_b);
- void* var_38 = tensorRelu(var_37);
- void* var_39 = tensorPooling(var_38,0,2,2,0,0,2,2);
- void* var_40 = tensorConvolution(var_39, conv2d_11_w, 1, 1, 1, 1, 1, 0);
- void* var_41 = tensorAdd(var_40, conv2d_11_b);
- void* var_42 = tensorRelu(var_41);
- void* var_44 = tensorConvolution(var_42, conv2d_12_w, 1, 1, 1, 1, 1, 0);
- void* var_45 = tensorAdd(var_44, conv2d_12_b);
- void* var_46 = tensorRelu(var_45);
- void* var_48 = tensorConvolution(var_46, conv2d_13_w, 1, 1, 1, 1, 1, 0);
- void* var_49 = tensorAdd(var_48, conv2d_13_b);
- void* var_50 = tensorRelu(var_49);
- void* var_51 = tensorPooling(var_50,0,2,2,0,0,2,2);
- void* var_54 = tensorGemmGPU(var_51, dense_1_w);
- void* var_55 = tensorAdd(var_54, dense_1_b);
- void* var_56 = tensorRelu(var_55);
- void* var_58 = tensorGemmGPU(var_56, dense_2_w);
- void* var_59 = tensorAdd(var_58, dense_2_b);
- void* var_60 = tensorSoftmax(var_59);
-
- profiler.pause_profiler();
- auto time_energy = profiler.get_time_energy();
- total_time += time_energy.first;
- total_energy += time_energy.second;
- profiler.reset();
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
-
- float accuracy = computeAccuracy2(labels,batch_size,var_60);
- final_accuracy += accuracy;
-
- freeBatchMemory();
- }
- }
- profiler.stop_profiler();
-
- std::cout<<"---------------------------------------\n";
- std::cout<<"Average time: " << total_time / total_runs << '\n';
- std::cout<<"Average energy: " << total_energy / total_runs << '\n';
- std::cout<<"---------------------------------------\n";
-
- final_accuracy = final_accuracy / batch_count / total_runs;
- dumpFinalAccuracy(final_accuracy);
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet2_cifar10_promise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet2_cifar10_promise.cc
deleted file mode 100644
index fbc9d038505313adefdf9100a1e55e3a98d823f8..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet2_cifar10_promise.cc
+++ /dev/null
@@ -1,163 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-/* NOTE: Reference Architecture to use for profiling */
-void testLenetTanh(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 100000;
- }
-
- printf("********* Lenet-2 Architecture ********** \n");
-
- int test_batch_size = 5000;
-
- uint8_t* labels = readLabels("../model_params/alexnet2_cifar10/test_labels.bin", test_batch_size);
-
- for(int i = 0; i < total_runs; i++){
-
- void* input = readTrainedWeights("../model_params/alexnet2_cifar10/norm_cifar_input.bin",
- float_type,
- test_batch_size, 3, 32, 32);
-
- void* conv1_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv1.bin",
- float_type, 32, 3, 3, 3);
- void* conv1_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv1_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv2.bin",
- float_type, 32, 32, 3, 3);
- void* conv2_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv2_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv3_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv3.bin",
- float_type, 64, 32, 3, 3);
- void* conv3_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv3_bias.bin",
- float_type, 1, 64, 1, 1);
- void* conv4_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv4.bin",
- float_type, 64, 64, 3, 3);
- void* conv4_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv4_bias.bin",
- float_type, 1, 64, 1, 1);
- void* conv5_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv5.bin",
- float_type, 128, 64, 3, 3);
- void* conv5_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv5_bias.bin",
- float_type, 1, 128, 1, 1);
- void* conv6_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv6.bin",
- float_type, 128, 128, 3, 3);
- void* conv6_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv6_bias.bin",
- float_type, 1, 128, 1, 1);
-
- void* fc1_weights = readTrainedWeights("../model_params/alexnet2_cifar10/fc1.bin",
- float_type, 1, 1, 2048, 10);
- void* fc1_bias = readTrainedWeights("../model_params/alexnet2_cifar10/fc1_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- clearTensorMap();
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
- // Start power and performance profiling
- startProfiling();
-
- //-1.881, 2.09
- //-0.18,0.174
- void* conv1_out = ConvLayer_PROMISE(input, -1.881, 2.09, conv1_filter, -0.542,0.371, conv1_bias, -0.066,0.04,
- 1, 1, 1, 1, 0, 0, 0, -1,1, 9);
-
- void* conv2_out = ConvLayer_PROMISE(conv1_out, -1,1, conv2_filter, -0.424,0.314, conv2_bias, -0.355,-0.172,
- 1, 1, 1, 1, 0, 2, 0, -1,1, 9);
-
- void* conv3_out = ConvLayer_PROMISE(conv2_out, -1,1, conv3_filter, -0.441,0.795, conv3_bias, -0.804,0.753,
- 1, 1, 1, 1, 0, 0, 0, -1,1, 9);
-
- void* conv4_out = ConvLayer_PROMISE(conv3_out, -1,1, conv4_filter, -0.288,0.31, conv4_bias, -0.635,0.29,
- 1, 1, 1, 1, 0, 2, 0, -1,1, 9);
-
- void* conv5_out = ConvLayer_PROMISE(conv4_out, -1,1, conv5_filter, -0.279,0.376, conv5_bias, -1.13, 1.239,
- 1, 1, 1, 1, 0, 0, 0, -1,1, 9);
-
- void* conv6_out = ConvLayer_PROMISE(conv5_out, -1,1, conv6_filter, -0.27,0.279, conv6_bias, -0.503,0.127,
- 1, 1, 1, 1, 0, 2, 0, -1,1, 9);
-
- // No Activation
- void* fc1_out = FCLayer_PROMISE(conv6_out, -1,1, fc1_weights, -0.242,0.584, fc1_bias, -0.537,0.558, -1, -1,1, 9);
-
- void* result = tensorSoftmax(fc1_out);
-
- // End profiling and dump output to profile.txt
- stopProfiling();
-
- computeAccuracy2(labels, test_batch_size, result);
-
- dumpAccuracyNorms();
- freeOutputTensors();
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
-
-
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(1);
-
- testLenetTanh();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet2_promise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet2_promise.cc
deleted file mode 100644
index 0b6e819b8ccd5f6eae7b0ac4c4a593e3c1998c0d..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet2_promise.cc
+++ /dev/null
@@ -1,132 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-int main(int argc, char* argv[]){
-
- int test_input_size = 3000;
- int batch_size = 1000;
- int offset = 5000;
-
- if (argc > 1){
- total_runs = atoi(argv[1]);
- }
-
- if (argc > 2){
- bench_acc = atof(argv[2]);
- }
-
- if(argc > 3){
- to_skip = atoi(argv[3]);
- }
-
- if(argc > 4){
- test_input_size = atoi(argv[4]);
- }
-
- if(argc > 5){
- offset = atoi(argv[5]);
- }
-
-
- llvm_hpvm_initTensorRt(1);
-
- int missed = 0;
- for (int i = 0 ; i < total_runs; i++){
-
- if (missed >= to_skip){
- break;
- }
-
- startMemTracking();
-
-
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- std::string dir_prefix = std::string("../model_params/alexnet2_cifar10_test/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,64,32,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,128,64,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,128,128,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,128,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,2048,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- int start = i * batch_size + offset;
- int end = (i + 1) * batch_size + offset;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = ConvLayer_PROMISE(input, -1.8816435, 2.0934134, conv2d_1_w, -0.5421946, 0.3710851, conv2d_1_b, -0.06697306, 0.040868897, 1, 1, 1, 1, -1, 0, 0, -0.9998477, 0.99987465, 9);
- // void* var_1 = ConvLayer_PROMISE(var_0, -0.9998477, 0.99987465, conv2d_2_w, -0.42474225, 0.31460348, conv2d_2_b, -0.3557253, -0.17281663, 1, 1, 1, 1, 0, 2, 0, -0.99997115, 1.0, 9);
- void* var_1 = ConvLayer_PROMISE(var_0, -0.8, 0.8, conv2d_2_w, -0.42474225, 0.31460348, conv2d_2_b, -0.3557253, -0.17281663, 1, 1, 1, 1, 0, 2, 0, -0.99997115, 1.0, 9);
-
- void* var_2 = ConvLayer_PROMISE(var_1, -0.99997115, 1.0, conv2d_3_w, -0.44134507, 0.79587924, conv2d_3_b, -0.80424446, 0.75330096, 1, 1, 1, 1, -1, 0, 0, -0.9999999, 1.0, 9);
- void* var_3 = ConvLayer_PROMISE(var_2, -0.9999999, 1.0, conv2d_4_w, -0.2883836, 0.31025785, conv2d_4_b, -0.6353164, 0.29015934, 1, 1, 1, 1, 0, 2, 0, -0.9999999, 0.99999934, 9);
- void* var_4 = ConvLayer_PROMISE(var_3, -0.9999999, 0.99999934, conv2d_5_w, -0.2792431, 0.37689754, conv2d_5_b, -1.1379756, 1.2391574, 1, 1, 1, 1, -1, 0, 0, -1.0, 1.0, 9);
- void* var_5 = ConvLayer_PROMISE(var_4, -1.0, 1.0, conv2d_6_w, -0.27078503, 0.27942517, conv2d_6_b, -0.503003, 0.12762362, 1, 1, 1, 1, 0, 2, 0, -0.9999941, 0.9999964, 9);
- void* var_6 = FCLayer_PROMISE(var_5, -0.9999941, 0.9999964, dense_1_w, -0.24273404, 0.5845544, dense_1_b, -0.53745, 0.558251, -1, -140.6419, 16.402884, 9);
- void* var_7 = tensorSoftmax(var_6);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_7);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
- if (final_accuracy < bench_acc)
- missed += 1;
- }
-
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet2_promise_quant.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet2_promise_quant.cc
deleted file mode 100644
index 3c3bc018518cf6ab3ff7dd7a608900308efa1e49..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet2_promise_quant.cc
+++ /dev/null
@@ -1,93 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(1);
-
- int total_runs = 1;
- for (int i = 0 ; i < total_runs; i++){
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 5000;
- int offset = 5000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- std::string dir_prefix = std::string("../model_params/alexnet2_cifar10_test/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,64,32,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,128,64,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,128,128,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,128,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,2048,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
- int start = i * batch_size + offset;
- int end = (i + 1) * batch_size + offset;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = ConvLayer_PROMISE(input, -1.8816435, 2.0934134, conv2d_1_w, -0.5421946, 0.3710851, conv2d_1_b, -0.06697306, 0.040868897, 1, 1, 1, 1, -1, 0, 0, -0.7750273948907852, 0.7799443006515503, 9);
- void* var_1 = ConvLayer_PROMISE(var_0, -0.7750273948907852, 0.7799443006515503, conv2d_2_w, -0.42474225, 0.31460348, conv2d_2_b, -0.3557253, -0.17281663, 1, 1, 1, 1, 0, 2, 0, -0.8086670643091202, 0.98395329773426, 9);
- void* var_2 = ConvLayer_PROMISE(var_1, -0.8086670643091202, 0.98395329773426, conv2d_3_w, -0.44134507, 0.79587924, conv2d_3_b, -0.80424446, 0.75330096, 1, 1, 1, 1, -1, 0, 0, -0.9956784248352051, 0.9985664486885071, 9);
- void* var_3 = ConvLayer_PROMISE(var_2, -0.9956784248352051, 0.9985664486885071, conv2d_4_w, -0.2883836, 0.31025785, conv2d_4_b, -0.6353164, 0.29015934, 1, 1, 1, 1, 0, 2, 0, -0.9932191967964172, 0.9923790097236633, 9);
- void* var_4 = ConvLayer_PROMISE(var_3, -0.9932191967964172, 0.9923790097236633, conv2d_5_w, -0.2792431, 0.37689754, conv2d_5_b, -1.1379756, 1.2391574, 1, 1, 1, 1, -1, 0, 0, -0.9999013543128967, 0.9999108910560608, 9);
- void* var_5 = ConvLayer_PROMISE(var_4, -0.9999013543128967, 0.9999108910560608, conv2d_6_w, -0.27078503, 0.27942517, conv2d_6_b, -0.503003, 0.12762362, 1, 1, 1, 1, 0, 2, 0, -0.991036117374897, 0.9714049702882765, 9);
- void* var_6 = FCLayer_PROMISE(var_5, -0.991036117374897, 0.9714049702882765, dense_1_w, -0.24273404, 0.5845544, dense_1_b, -0.53745, 0.558251, -1, -119.27973731994629, -25.226281957626327, 9);
- void* var_7 = tensorSoftmax(var_6);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_7);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
- }
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet2_valid.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet2_valid.cc
deleted file mode 100644
index 1799480796fb988d0e9624fb482339d2345d2728..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet2_valid.cc
+++ /dev/null
@@ -1,92 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- int total_runs = 1;
- for (int i = 0 ; i < total_runs; i++){
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 5000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- std::string dir_prefix = std::string("../model_params/alexnet2_cifar10_test/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,64,32,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,128,64,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,128,128,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,128,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,2048,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = ConvLayer_PROMISE(input, -1.8816435, 2.0934134, conv2d_1_w, -0.5421946, 0.3710851, conv2d_1_b, -0.06697306, 0.040868897, 1, 1, 1, 1, -1, 0, 0, -0.7750273948907852, 0.7799443006515503, 9);
- void* var_1 = ConvLayer_PROMISE(var_0, -0.7750273948907852, 0.7799443006515503, conv2d_2_w, -0.42474225, 0.31460348, conv2d_2_b, -0.3557253, -0.17281663, 1, 1, 1, 1, 0, 2, 0, -0.8086670643091202, 0.98395329773426, 9);
- void* var_2 = ConvLayer_PROMISE(var_1, -0.8086670643091202, 0.98395329773426, conv2d_3_w, -0.44134507, 0.79587924, conv2d_3_b, -0.80424446, 0.75330096, 1, 1, 1, 1, -1, 0, 0, -0.9956784248352051, 0.9985664486885071, 9);
- void* var_3 = ConvLayer_PROMISE(var_2, -0.9956784248352051, 0.9985664486885071, conv2d_4_w, -0.2883836, 0.31025785, conv2d_4_b, -0.6353164, 0.29015934, 1, 1, 1, 1, 0, 2, 0, -0.9932191967964172, 0.9923790097236633, 9);
- void* var_4 = ConvLayer_PROMISE(var_3, -0.9932191967964172, 0.9923790097236633, conv2d_5_w, -0.2792431, 0.37689754, conv2d_5_b, -1.1379756, 1.2391574, 1, 1, 1, 1, -1, 0, 0, -0.9999013543128967, 0.9999108910560608, 9);
- void* var_5 = ConvLayer_PROMISE(var_4, -0.9999013543128967, 0.9999108910560608, conv2d_6_w, -0.27078503, 0.27942517, conv2d_6_b, -0.503003, 0.12762362, 1, 1, 1, 1, 0, 2, 0, -0.991036117374897, 0.9714049702882765, 9);
- void* var_6 = FCLayer_PROMISE(var_5, -0.991036117374897, 0.9714049702882765, dense_1_w, -0.24273404, 0.5845544, dense_1_b, -0.53745, 0.558251, -1, -119.27973731994629, -25.226281957626327, 9);
- void* var_7 = tensorSoftmax(var_6);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_7);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
- }
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_cifar10_promise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_cifar10_promise.cc
deleted file mode 100644
index 3e39f5cf03ce25511429d84ada9812fef0998194..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_cifar10_promise.cc
+++ /dev/null
@@ -1,158 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-/* NOTE: Reference Architecture to use for profiling */
-void testLenetTanh(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 100000;
- }
-
- printf("********* Lenet-2 Architecture ********** \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 5000;
-
- uint8_t* labels = readLabels("../model_params/alexnet_cifar10/test_labels.bin", test_batch_size);
-
- for(int i = 0; i < total_runs; i++){
-
- void* input = readTrainedWeights("../model_params/alexnet_cifar10/norm_cifar_input.bin",
- float_type,
- test_batch_size, 3, 32, 32);
-
- void* conv1_filter = readTrainedWeights("../model_params/alexnet_cifar10/conv1.bin",
- float_type, 64, 3, 11, 11);
- void* conv1_bias = readTrainedWeights("../model_params/alexnet_cifar10/conv1_bias.bin",
- float_type, 1, 64, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/alexnet_cifar10/conv2.bin",
- float_type, 192, 64, 5, 5);
- void* conv2_bias = readTrainedWeights("../model_params/alexnet_cifar10/conv2_bias.bin",
- float_type, 1, 192, 1, 1);
-
- void* conv3_filter = readTrainedWeights("../model_params/alexnet_cifar10/conv3.bin",
- float_type, 384, 192, 3, 3);
- void* conv3_bias = readTrainedWeights("../model_params/alexnet_cifar10/conv3_bias.bin",
- float_type, 1, 384, 1, 1);
- void* conv4_filter = readTrainedWeights("../model_params/alexnet_cifar10/conv4.bin",
- float_type, 256, 384, 3, 3);
- void* conv4_bias = readTrainedWeights("../model_params/alexnet_cifar10/conv4_bias.bin",
- float_type, 1, 256, 1, 1);
- void* conv5_filter = readTrainedWeights("../model_params/alexnet_cifar10/conv5.bin",
- float_type, 256, 256, 3, 3);
- void* conv5_bias = readTrainedWeights("../model_params/alexnet_cifar10/conv5_bias.bin",
- float_type, 1, 256, 1, 1);
-
- void* fc1_weights = readTrainedWeights("../model_params/alexnet_cifar10/fc1.bin",
- float_type, 1, 1, 4096, 10);
- void* fc1_bias = readTrainedWeights("../model_params/alexnet_cifar10/fc1_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- clearTensorMap();
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
- // Start power and performance profiling
- startProfiling();
-
- //-1.881, 2.09
- //-0.18,0.174
- void* conv1_out = ConvLayer_PROMISE(input, -1.881, 2.09, conv1_filter, -0.345,0.331, conv1_bias, -0.76,0.59,
- 5, 5, 1, 1, 0, 2, 0, -1,1, 9);
-
- void* conv2_out = ConvLayer_PROMISE(conv1_out, -1,1, conv2_filter, -0.22,0.264, conv2_bias, -0.448,0.343,
- 2, 2, 1, 1, 0, 2, 0, -1,1, 9);
-
- void* conv3_out = ConvLayer_PROMISE(conv2_out, -1,1, conv3_filter, -0.192,0.187, conv3_bias, -0.91,0.67,
- 1, 1, 1, 1, 0, 0, 0, -1,1, 9);
-
- void* conv4_out = ConvLayer_PROMISE(conv3_out, -1,1, conv4_filter, -0.131,0.141, conv4_bias, -0.416,0.342,
- 1, 1, 1, 1, 0, 0, 0, -1,1, 9);
-
- void* conv5_out = ConvLayer_PROMISE(conv4_out, -1,1, conv5_filter, -0.165,0.188, conv5_bias, -0.283,0.062,
- 1, 1, 1, 1, 0, 2, 0, -1,1, 9);
-
- // No Activation
- void* fc1_out = FCLayer_PROMISE(conv5_out, -1,1, fc1_weights, -0.181,0.233, fc1_bias, -0.063,0.137, -1, -1,1, 9);
-
- void* result = tensorSoftmax(fc1_out);
-
- // End profiling and dump output to profile.txt
- stopProfiling();
-
- computeAccuracy2(labels, test_batch_size, result);
-
- dumpAccuracyNorms();
- freeOutputTensors();
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
-
-
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(1);
-
- testLenetTanh();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_promise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_promise.cc
deleted file mode 100644
index 2529e8eabfb061ae26798612ce9f30fb57e096f5..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_promise.cc
+++ /dev/null
@@ -1,130 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-
-int main(int argc, char* argv[]){
-
- int test_input_size = 3000;
- int batch_size = 1000;
- int offset = 5000;
-
- if (argc > 1){
- total_runs = atoi(argv[1]);
- }
-
- if (argc > 2){
- bench_acc = atof(argv[2]);
- }
-
- if(argc > 3){
- to_skip = atoi(argv[3]);
- }
-
- if(argc > 4){
- test_input_size = atoi(argv[4]);
- }
-
- if(argc > 5){
- offset = atoi(argv[5]);
- }
-
-
- llvm_hpvm_initTensorRt(0);
-
-
-
- int missed = 0;
- for (int i = 0 ; i < total_runs; i++){
-
- if (missed >= to_skip){
- break;
- }
-
- startMemTracking();
-
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- std::string dir_prefix = std::string("../model_params/alexnet_cifar10_test/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,11,11);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,192,64,5,5);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,192,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,384,192,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,384,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,256,384,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,4096,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-
- int start = i * batch_size + offset;
- int end = (i + 1) * batch_size + offset;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = ConvLayer_PROMISE(input, -1.8816426241908337, 2.0934095498544254, conv2d_1_w, -0.33087718, 0.3323643, conv2d_1_b, -0.7782218, 0.6020472, 5, 5, 1, 1, 0, 2, 0, -0.978641152381897, 0.9989452958106995, 9);
- void* var_1 = ConvLayer_PROMISE(var_0, -0.978641152381897, 0.9989452958106995, conv2d_2_w, -0.2095158, 0.33543423, conv2d_2_b, -0.45020863, 0.30596754, 2, 2, 1, 1, 0, 2, 0, -0.9997039437294006, 0.999930202960968, 9);
- void* var_2 = ConvLayer_PROMISE(var_1, -0.9997039437294006, 0.999930202960968, conv2d_3_w, -0.1715614, 0.17037082, conv2d_3_b, -0.6519161, 0.5939945, 1, 1, 1, 1, -1, 0, 0, -0.9999336004257202, 0.999940037727356, 9);
- void* var_3 = ConvLayer_PROMISE(var_2, -0.9999336004257202, 0.999940037727356, conv2d_4_w, -0.15575546, 0.14456555, conv2d_4_b, -0.55873865, 0.4704539, 1, 1, 1, 1, -1, 0, 0, -0.9999991059303284, 0.9999993443489075, 9);
- void* var_4 = ConvLayer_PROMISE(var_3, -0.9999991059303284, 0.9999993443489075, conv2d_5_w, -0.16108225, 0.16864482, conv2d_5_b, -0.22135437, 0.10401678, 1, 1, 1, 1, 0, 2, 0, -0.9994344115257263, 0.9996342062950134, 9);
- void* var_5 = FCLayer_PROMISE(var_4, -0.9994344115257263, 0.9996342062950134, dense_1_w, -0.18183032, 0.19018902, dense_1_b, -0.07189204, 0.106005594, -1, -15.076565380096437, 19.422585220336913, 9);
- void* var_6 = tensorSoftmax(var_5);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_6);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- if (final_accuracy < bench_acc)
- missed += 1;
- }
-
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_promise_quant.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_promise_quant.cc
deleted file mode 100644
index 6b2b0e80ba92fa449cdd06036946101df76317e7..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_promise_quant.cc
+++ /dev/null
@@ -1,90 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-int main(){
-
- llvm_hpvm_initTensorRt(1);
-
- int total_runs = 1;
- for (int i = 0 ; i < total_runs; i++){
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 5000;
- int offset = 5000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- std::string dir_prefix = std::string("../model_params/alexnet_cifar10_test/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,11,11);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,192,64,5,5);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,192,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,384,192,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,384,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,256,384,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,4096,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- int start = i * batch_size + offset;
- int end = (i + 1) * batch_size + offset;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = ConvLayer_PROMISE(input, -1.8816426241908337, 2.0934095498544254, conv2d_1_w, -0.33087718, 0.3323643, conv2d_1_b, -0.7782218, 0.6020472, 5, 5, 1, 1, 0, 2, 0, -0.978641152381897, 0.9989452958106995, 9);
- void* var_1 = ConvLayer_PROMISE(var_0, -0.978641152381897, 0.9989452958106995, conv2d_2_w, -0.2095158, 0.33543423, conv2d_2_b, -0.45020863, 0.30596754, 2, 2, 1, 1, 0, 2, 0, -0.9997039437294006, 0.999930202960968, 9);
- void* var_2 = ConvLayer_PROMISE(var_1, -0.9997039437294006, 0.999930202960968, conv2d_3_w, -0.1715614, 0.17037082, conv2d_3_b, -0.6519161, 0.5939945, 1, 1, 1, 1, -1, 0, 0, -0.9999336004257202, 0.999940037727356, 9);
- void* var_3 = ConvLayer_PROMISE(var_2, -0.9999336004257202, 0.999940037727356, conv2d_4_w, -0.15575546, 0.14456555, conv2d_4_b, -0.55873865, 0.4704539, 1, 1, 1, 1, -1, 0, 0, -0.9999991059303284, 0.9999993443489075, 9);
- void* var_4 = ConvLayer_PROMISE(var_3, -0.9999991059303284, 0.9999993443489075, conv2d_5_w, -0.16108225, 0.16864482, conv2d_5_b, -0.22135437, 0.10401678, 1, 1, 1, 1, 0, 2, 0, -0.9994344115257263, 0.9996342062950134, 9);
- void* var_5 = FCLayer_PROMISE(var_4, -0.9994344115257263, 0.9996342062950134, dense_1_w, -0.18183032, 0.19018902, dense_1_b, -0.07189204, 0.106005594, -1, -15.076565380096437, 19.422585220336913, 9);
- void* var_6 = tensorSoftmax(var_5);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_6);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
- }
-
- //dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_valid.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_valid.cc
deleted file mode 100644
index 7a0a40adb30367866635993de3de94ca1413938e..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_valid.cc
+++ /dev/null
@@ -1,90 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- int total_runs = 20;
- for (int i = 0 ; i < total_runs; i++){
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 5000;
- int offset = 0;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- std::string dir_prefix = std::string("../model_params/alexnet_cifar10_test/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,11,11);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,192,64,5,5);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,192,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,384,192,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,384,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,256,384,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,4096,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- int start = i * batch_size + offset;
- int end = (i + 1) * batch_size + offset;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = ConvLayer_PROMISE(input, -1.8816426241908337, 2.0934095498544254, conv2d_1_w, -0.33087718, 0.3323643, conv2d_1_b, -0.7782218, 0.6020472, 5, 5, 1, 1, 0, 2, 0, -0.978641152381897, 0.9989452958106995, 9);
- void* var_1 = ConvLayer_PROMISE(var_0, -0.978641152381897, 0.9989452958106995, conv2d_2_w, -0.2095158, 0.33543423, conv2d_2_b, -0.45020863, 0.30596754, 2, 2, 1, 1, 0, 2, 0, -0.9997039437294006, 0.999930202960968, 9);
- void* var_2 = ConvLayer_PROMISE(var_1, -0.9997039437294006, 0.999930202960968, conv2d_3_w, -0.1715614, 0.17037082, conv2d_3_b, -0.6519161, 0.5939945, 1, 1, 1, 1, -1, 0, 0, -0.9999336004257202, 0.999940037727356, 9);
- void* var_3 = ConvLayer_PROMISE(var_2, -0.9999336004257202, 0.999940037727356, conv2d_4_w, -0.15575546, 0.14456555, conv2d_4_b, -0.55873865, 0.4704539, 1, 1, 1, 1, -1, 0, 0, -0.9999991059303284, 0.9999993443489075, 9);
- void* var_4 = ConvLayer_PROMISE(var_3, -0.9999991059303284, 0.9999993443489075, conv2d_5_w, -0.16108225, 0.16864482, conv2d_5_b, -0.22135437, 0.10401678, 1, 1, 1, 1, 0, 2, 0, -0.9994344115257263, 0.9996342062950134, 9);
- void* var_5 = FCLayer_PROMISE(var_4, -0.9994344115257263, 0.9996342062950134, dense_1_w, -0.18183032, 0.19018902, dense_1_b, -0.07189204, 0.106005594, -1, -15.076565380096437, 19.422585220336913, 9);
- void* var_6 = tensorSoftmax(var_5);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_6);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
- }
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/depthwise_batchnorm_promise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/depthwise_batchnorm_promise.cc
deleted file mode 100644
index 9ca67a6d6d6b11281ab36531d58e56c3e5fa4347..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/depthwise_batchnorm_promise.cc
+++ /dev/null
@@ -1,101 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- int total_runs = 1;
- for (int i = 0 ; i < total_runs; i++){
-
- startMemTracking();
-
- int test_input_size = 10000;
- int batch_size = 10000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- std::string dir_prefix = std::string("../model_params/depthwise_batchnorm2/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,1,5,5);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string depthwise_conv2d_1_b_path = dir_prefix + std::string("depthwise_conv2d_1_b.bin");
- void* depthwise_conv2d_1_b = readTrainedWeights(depthwise_conv2d_1_b_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,6272,1024);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,1024,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,1024,10);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,10,1,1);
-
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,1,28,28);
-
- void* var_0 = ConvLayer_PROMISE(input, 0.0, 1.0, conv2d_1_w, -0.21894497, 0.20240873, conv2d_1_b, -0.2508162, 0.031047817, 2, 2, 1, 1, -1, 0, 1, 0.0, 0.3616602423787114, 9);
- void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorPooling(var_1,0,2,2,0,0,2,2);
- void* var_3 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_4 = tensorAdd(var_3, depthwise_conv2d_1_b);
- void* var_5 = tensorRelu(var_4);
- void* var_6 = tensorBatchNorm(var_5, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_7 = FCLayer_PROMISE(var_6, -1.0247770547866821, 5.120966439247134, dense_1_w, -0.04442959, 0.04483322, dense_1_b, -0.001500695, 0.002055318, 1, 0.0, 4.769639563560498, 9);
- void* var_8 = FCLayer_PROMISE(var_7, 0.0, 4.769639563560498, dense_2_w, -0.123055816, 0.12699054, dense_2_b, -0.00430397, 0.004860983, 1, 0.0, 21.660391826629642, 9);
- void* var_9 = tensorSoftmax(var_8);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_9);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- }
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/depthwise_promise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/depthwise_promise.cc
deleted file mode 100644
index 176f1253f7d0120cc4c5600234e543edd1ddf4d1..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/depthwise_promise.cc
+++ /dev/null
@@ -1,83 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(1);
-
- int total_runs = 1;
- for (int i = 0 ; i < total_runs; i++){
-
- startMemTracking();
-
- int test_input_size = 10000;
- int batch_size = 10000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- std::string dir_prefix = std::string("../model_params/depthwise_test_8/");
-
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,1,5,5);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string depthwise_conv2d_1_b_path = dir_prefix + std::string("depthwise_conv2d_1_b.bin");
- void* depthwise_conv2d_1_b = readTrainedWeights(depthwise_conv2d_1_b_path.c_str(), 0,1,32,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,6272,1024);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,1024,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,1024,10);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,10,1,1);
-
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,1,28,28);
-
- void* var_0 = ConvLayer_PROMISE(input, 0.0, 1.0, conv2d_1_w, -0.45243406, 0.4331673, conv2d_1_b, -0.2225991, 0.05682303, 2, 2, 1, 1, 0, 2, 1, 0.0, 2.593297730684286, 9);
- void* var_1 = tensorConvolution(var_0, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_2 = tensorAdd(var_1, depthwise_conv2d_1_b);
- void* var_3 = tensorRelu(var_2);
- void* var_4 = FCLayer_PROMISE(var_3, 0.0, 1.4831079334020663, dense_1_w, -0.1172131, 0.105426796, dense_1_b, -0.027105594, 0.04015947, 1, 0.0, 2.723612790107728, 9);
- void* var_5 = FCLayer_PROMISE(var_4, 0.0, 2.723612790107728, dense_2_w, -0.23769215, 0.20409682, dense_2_b, -0.004073992, 0.049675815, 1, 0.0, 24.92628944396973, 9);
- void* var_6 = tensorSoftmax(var_5);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_6);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- }
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/fc2_clipped_promise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/fc2_clipped_promise.cc
deleted file mode 100644
index 9bf088b1d5497d3f96d117d9c3d2cc79479d2f1e..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/fc2_clipped_promise.cc
+++ /dev/null
@@ -1,122 +0,0 @@
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-#include "../../include/types.h"
-
-
-bool Opentuner_run = false;
-
-
-void test_4_Layer_clipped_FC(){
-
- int total_runs = 100000;
- if(Opentuner_run){
- total_runs = 100000;
- }
-
- printf("********* 3-Layer FC with clipped activations and weights ********* \n");
-
- int test_batch_size = 5000;
-
- uint8_t* labels = readLabels("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size);
-
-
- clearTensorMap();
-
- for(int i = 0; i < total_runs; i++){
-
- void* input = readTrainedWeights("../model_params/FC_network2/mnist_float_input.bin",
- float_type, test_batch_size, 1, 28, 28);
- void* fc1_weights = readTrainedWeights("../model_params/fc2_clipped/fc1.bin",
- float_type, 1, 1, 784, 128);
- void* fc1_bias = readTrainedWeights("../model_params/fc2_clipped/fc1_bias.bin",
- float_type, 1, 128, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/fc2_clipped/fc2.bin",
- float_type, 1, 1, 128, 10);
- void* fc2_bias = readTrainedWeights("../model_params/fc2_clipped/fc2_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
- close(fd);
- }
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
-
- void* fc1_out = FCLayer_PROMISE(input, 0,1, fc1_weights, -1,1, fc1_bias, -1,1,
- 2, 0,2, 8);
-
- void* fc2_out = FCLayer_PROMISE(fc1_out, 0,2, fc2_weights, -1,1, fc2_bias, -1,1,
- 2, 0,2, 8);
-
- void* result = tensorSoftmax(fc2_out);
-
-
- computeAccuracy2(labels, test_batch_size, result);
-
- freeOutputTensors();
-
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
- printf("**** ALL RUNS COMPLETTED **** \n\n");
-
-}
-
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- // This initializes the runtime - must be called before anything
- llvm_hpvm_initTensorRt(0);
-
- test_4_Layer_clipped_FC();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/fc3_clipped_promise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/fc3_clipped_promise.cc
deleted file mode 100644
index 43eca42ee884e7f65a3ed5958c733cec64b820ed..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/fc3_clipped_promise.cc
+++ /dev/null
@@ -1,129 +0,0 @@
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-#include "../../include/types.h"
-
-
-bool Opentuner_run = false;
-
-
-void test_4_Layer_clipped_FC(){
-
- int total_runs = 100000;
- if(Opentuner_run){
- total_runs = 100000;
- }
-
- printf("********* 3-Layer FC with clipped activations and weights ********* \n");
-
- int test_batch_size = 5000;
-
- uint8_t* labels = readLabels("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size);
-
-
- clearTensorMap();
-
- for(int i = 0; i < total_runs; i++){
-
- void* input = readTrainedWeights("../model_params/FC_network2/mnist_float_input.bin",
- float_type, test_batch_size, 1, 28, 28);
- void* fc1_weights = readTrainedWeights("../model_params/fc3_clipped/fc1.bin",
- float_type, 1, 1, 784, 256);
- void* fc1_bias = readTrainedWeights("../model_params/fc3_clipped/fc1_bias.bin",
- float_type, 1, 256, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/fc3_clipped/fc2.bin",
- float_type, 1, 1, 256, 128);
- void* fc2_bias = readTrainedWeights("../model_params/fc3_clipped/fc2_bias.bin",
- float_type, 1, 128, 1, 1);
- void* fc3_weights = readTrainedWeights("../model_params/fc3_clipped/fc3.bin",
- float_type, 1, 1, 128, 10);
- void* fc3_bias = readTrainedWeights("../model_params/fc3_clipped/fc3_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
- close(fd);
- }
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
-
- void* fc1_out = FCLayer_PROMISE(input, 0,1, fc1_weights, -1,1, fc1_bias, -1,1,
- 2, 0,2, 8);
-
- void* fc2_out = FCLayer_PROMISE(fc1_out, 0,2, fc2_weights, -1,1, fc2_bias, -1,1,
- 2, 0,2, 8);
-
- void* fc3_out = FCLayer_PROMISE(fc2_out, 0,2, fc3_weights, -1,1, fc3_bias, -1,1,
- 2, 0,2, 8);
-
- void* result = tensorSoftmax(fc3_out);
-
-
- computeAccuracy2(labels, test_batch_size, result);
-
- freeOutputTensors();
-
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
-
- }
-
- printf("**** ALL RUNS COMPLETTED **** \n\n");
-
-}
-
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- // This initializes the runtime - must be called before anything
- llvm_hpvm_initTensorRt(0);
-
- test_4_Layer_clipped_FC();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/fc4_clipped_promise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/fc4_clipped_promise.cc
deleted file mode 100644
index e31002debd9793557e195f598a14a840b7014c28..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/fc4_clipped_promise.cc
+++ /dev/null
@@ -1,135 +0,0 @@
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-#include "../../include/types.h"
-
-
-bool Opentuner_run = false;
-
-
-void test_4_Layer_clipped_FC(){
-
- int total_runs = 1000;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
- printf("********* 3-Layer FC with clipped activations and weights ********* \n");
-
- int test_batch_size = 5000;
-
- uint8_t* labels = readLabels("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size);
-
-
- clearTensorMap();
-
- for(int i = 0; i < total_runs; i++){
-
- void* input = readTrainedWeights("../model_params/FC_network2/mnist_float_input.bin",
- float_type, test_batch_size, 1, 28, 28);
- void* fc1_weights = readTrainedWeights("../model_params/fc4_clipped/fc1.bin",
- float_type, 1, 1, 784, 512);
- void* fc1_bias = readTrainedWeights("../model_params/fc4_clipped/fc1_bias.bin",
- float_type, 1, 512, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/fc4_clipped/fc2.bin",
- float_type, 1, 1, 512, 256);
- void* fc2_bias = readTrainedWeights("../model_params/fc4_clipped/fc2_bias.bin",
- float_type, 1, 256, 1, 1);
- void* fc3_weights = readTrainedWeights("../model_params/fc4_clipped/fc3.bin",
- float_type, 1, 1, 256, 128);
- void* fc3_bias = readTrainedWeights("../model_params/fc4_clipped/fc3_bias.bin",
- float_type, 1, 128, 1, 1);
- void* fc4_weights = readTrainedWeights("../model_params/fc4_clipped/fc4.bin",
- float_type, 1, 1, 128, 10);
- void* fc4_bias = readTrainedWeights("../model_params/fc4_clipped/fc4_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
-
- void* fc1_out = FCLayer_PROMISE(input, 0,1, fc1_weights, -1,1, fc1_bias, -1,1,
- 2, 0,2, 9);
-
- void* fc2_out = FCLayer_PROMISE(fc1_out, 0,2, fc2_weights, -1,1, fc2_bias, -1,1,
- 2, 0,2, 9);
-
- void* fc3_out = FCLayer_PROMISE(fc2_out, 0,2, fc3_weights, -1,1, fc3_bias, -1,1,
- 2, 0,2, 9);
-
- void* fc4_out = FCLayer_PROMISE(fc3_out, 0,2, fc4_weights, -1,1, fc4_bias, -1,1,
- 2, 0,2, 9);
-
- void* result = tensorSoftmax(fc4_out);
-
- computeAccuracy2(labels, test_batch_size, result);
-
- freeOutputTensors();
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
- }
-
-
-}
-
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- // This initializes the runtime - must be called before anything
- llvm_hpvm_initTensorRt(0);
-
- test_4_Layer_clipped_FC();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/lenet_promise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/lenet_promise.cc
deleted file mode 100644
index 91026260a94c851234a865e83eda82f24eb2918c..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/lenet_promise.cc
+++ /dev/null
@@ -1,137 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-int test_input_size = 3000;
-int batch_size = 3000;
-int offset = 5000;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testLenetTanh(){
-
- printf("********* Lenet-5 Architecture ********** \n");
-
- std::string dir_prefix = std::string("../model_params/lenet_params/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
-
- clearTensorMap();
-
- int missed = 0;
- for(int i = 0; i < total_runs; i++){
-
- if (missed >= to_skip){
- break;
- }
-
- int start = offset;
- int end = batch_size + offset;
-
- // Loading Input Batch
- void* input = readInputBatch(input_path.c_str(),0,start,end,1,28,28);
-
- // Loading Weights
- void* conv1_filter = readTrainedWeights("../model_params/lenet_keras/conv1.bin",
- float_type, 32, 1, 5, 5);
- void* conv1_bias = readTrainedWeights("../model_params/lenet_keras/conv1_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/lenet_keras/conv2.bin",
- float_type, 64, 32, 5, 5);
- void* conv2_bias = readTrainedWeights("../model_params/lenet_keras/conv2_bias.bin",
- float_type, 1, 64, 1, 1);
- void* fc1_weights = readTrainedWeights("../model_params/lenet_keras/fc1.bin",
- float_type, 1, 1, 7*7*64, 1024);
- void* fc1_bias = readTrainedWeights("../model_params/lenet_keras/fc1_bias.bin",
- float_type, 1, 1024, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/lenet_keras/fc2.bin",
- float_type, 1, 1, 1024, 10);
- void* fc2_bias = readTrainedWeights("../model_params/lenet_keras/fc2_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- // DNN Operations
- void* conv1_out = ConvLayer_PROMISE(input, 0,1, conv1_filter, -1,1, conv1_bias, -1,1,
- 2, 2, 1, 1, 0, 2, 0, -1,1, 9);
- void* conv2_out = ConvLayer_PROMISE(conv1_out, -1,1, conv2_filter, -1,1,
- conv2_bias, -1,1,
- 2, 2, 1, 1, 0, 2, 0, -1,1, 9);
-
- void* fc1_out = FCLayer_PROMISE(conv2_out, -1,1, fc1_weights, -1,1, fc1_bias, -1,1,
- 0, -1,1, 9);
- void* fc2_out = FCLayer_PROMISE(fc1_out, -1,1, fc2_weights, -1,1, fc2_bias, -1,1,
- 0, -1,1, 9);
-
- void* result = tensorSoftmax(fc2_out);
-
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, result);
-
-
- freeOutputTensors();
-
- dumpFinalAccuracy(accuracy);
-
-
- if (accuracy < bench_acc)
- missed += 1;
-
- }
-
- dumpExecutionAccuracies();
-}
-
-
-int main(int argc, char* argv[]){
-
-
- if (argc > 1){
- total_runs = atoi(argv[1]);
- }
-
- if (argc > 2){
- bench_acc = atof(argv[2]);
- }
-
- if(argc > 3){
- to_skip = atoi(argv[3]);
- }
-
- if(argc > 4){
- test_input_size = atoi(argv[4]);
- batch_size = atoi(argv[4]);
- }
-
- if(argc > 5){
- offset = atoi(argv[5]);
- }
-
-
-
- llvm_hpvm_initTensorRt(1);
-
- testLenetTanh();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/lenet_promise_relu.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/lenet_promise_relu.cc
deleted file mode 100644
index 5c7699026fe6e0860718e5986f4fec990ab08c6c..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/lenet_promise_relu.cc
+++ /dev/null
@@ -1,65 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- std::string dir_prefix = std::string("../model_params/lenet_relu/");
- std::string input_path = dir_prefix + std::string("input.bin");
- void* input = readTrainedWeights(input_path.c_str(), 0,10000,1,28,28);
- std::string labels_path = dir_prefix + std::string("labels.bin");
- uint8_t* labels = readLabels(labels_path.c_str(),10000);
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,1,5,5);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,5,5);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,64,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,3136,1024);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,1024,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,1024,10);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,10,1,1);
-
-
- void* var_0 = ConvLayer_PROMISE(input, 0.0, 1.0, conv2d_1_w, -0.2722561, 0.25817025,
- conv2d_1_b, -0.041063767, 0.031912163,
- 2, 2, 1, 1, 0, 2, 1, 0.0, 1.5512946, 9);
- void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 1.5512946, conv2d_2_w, -0.17580177, 0.16332611,
- conv2d_2_b, -0.041385915, 0.05869476,
- 2, 2, 1, 1, -1, 0, 1, 0.0, 4.916329, 9);
- void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 4.916329, conv2d_3_w, -0.20324017, 0.18275258,
- conv2d_3_b, -0.039915435, 0.04589232,
- 1, 1, 2, 2, -1, 0, 1, 0.0, 9.447418, 9);
- void* var_3 = FCLayer_PROMISE(var_2, 0.0, 9.447418, dense_1_w, -0.10757191, 0.123126,
- dense_1_b, -0.025070198, 0.027000334, 1, 0.0, 9.926857, 9);
- void* var_4 = FCLayer_PROMISE(var_3, 0.0, 9.926857, dense_2_w, -0.18867673, 0.16425411,
- dense_2_b, -0.012622595, 0.04586973, 1, 0.0, 42.018578, 9);
- void* var_5 = tensorSoftmax(var_4);
-
- computeAccuracy2(labels,10000,var_5);
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_promise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_promise.cc
deleted file mode 100644
index cbfc534681efdea9967bd6d0096572e2bad87c16..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_promise.cc
+++ /dev/null
@@ -1,454 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-
-int main(int argc, char* argv[]){
-
- int test_input_size = 3000;
- int batch_size = 1000;
- int offset = 5000;
-
- if (argc > 1){
- total_runs = atoi(argv[1]);
- }
-
- if (argc > 2){
- bench_acc = atof(argv[2]);
- }
-
- if(argc > 3){
- to_skip = atoi(argv[3]);
- }
-
- if(argc > 4){
- test_input_size = atoi(argv[4]);
- }
-
- if(argc > 5){
- offset = atoi(argv[5]);
- }
-
-
- llvm_hpvm_initTensorRt(1);
-
-
- int missed = 0;
- for (int i = 0 ; i < total_runs; i++){
-
- if (missed >= to_skip){
- break;
- }
-
- startMemTracking();
-
-
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
-
- std::string dir_prefix = std::string("../model_params/mobilenet/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
- void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
- void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
- void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
- void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
- void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
- std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
- void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
- void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
- void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
- void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
- void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
- void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
- void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
- void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
- void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
- void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
- void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
- void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
- void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
- void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
- void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
- void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
- void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
- void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
- void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
- void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
- void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
- void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
- void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
- void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
- void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
- void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
- void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
- void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
- void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
- void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
- void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
- void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
- void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
- void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
- void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
- void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
- void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
- void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
- void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
- void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
- void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
- void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
- void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
- void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
- void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
- void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
- void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
- void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
- void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
- void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
- void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
- void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
- void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
- void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1);
- std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
- void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
- void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
- void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
- void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1);
- std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
- void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3);
- std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
- void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
- void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
- void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
- void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1);
- std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
- void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
- void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
- void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
- void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- int start = i * batch_size + offset;
- int end = (i + 1) * batch_size + offset;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = ConvLayer_PROMISE(input, -1.9892114, 2.126797, conv2d_1_w, -2.196306920051575, 1.347581704139706, NULL, 0, 0, 1, 1, 1, 1, -1, 0, -1, -60.89275047302246, 51.99256916046146, 9);
- void* var_1 = tensorHalfBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorRelu(var_1);
- void* var_3 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_4 = tensorHalfBatchNorm(var_3, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_5 = tensorRelu(var_4);
- void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 5.713541553974245, conv2d_2_w, -0.9317721160650253, 1.0774258937835774, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.518589503288269, 6.810842518806449, 9);
- void* var_7 = tensorHalfBatchNorm(var_6, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_8 = tensorRelu(var_7);
- void* var_9 = tensorConvolution(var_8, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
- void* var_10 = tensorHalfBatchNorm(var_9, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_11 = tensorRelu(var_10);
- void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.932139402866376, conv2d_3_w, -0.5316544661521911, 0.5753790403604531, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.482631235122681, 3.96730119752885, 9);
- void* var_13 = tensorHalfBatchNorm(var_12, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_14 = tensorRelu(var_13);
- void* var_15 = tensorConvolution(var_14, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
- void* var_16 = tensorHalfBatchNorm(var_15, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_17 = tensorRelu(var_16);
- void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 4.103263397693674, conv2d_4_w, -0.36234098821878435, 0.4076913900375366, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.04261828327179, 3.88677932929993, 9);
- void* var_19 = tensorHalfBatchNorm(var_18, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_20 = tensorRelu(var_19);
- void* var_21 = tensorConvolution(var_20, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
- void* var_22 = tensorHalfBatchNorm(var_21, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_23 = tensorRelu(var_22);
- void* var_24 = ConvLayer_PROMISE(var_23, 0.0, 5.383221302509475, conv2d_5_w, -0.3131200549006462, 0.29357679939270065, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.921469215393066, 4.338679324150087, 9);
- void* var_25 = tensorHalfBatchNorm(var_24, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_26 = tensorRelu(var_25);
- void* var_27 = tensorConvolution(var_26, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
- void* var_28 = tensorHalfBatchNorm(var_27, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_29 = tensorRelu(var_28);
- void* var_30 = ConvLayer_PROMISE(var_29, 0.0, 4.316738154411368, conv2d_6_w, -0.23299247801303866, 0.2580290257930756, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.207789947509766, 3.932436970710759, 9);
- void* var_31 = tensorHalfBatchNorm(var_30, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_32 = tensorRelu(var_31);
- void* var_33 = tensorConvolution(var_32, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
- void* var_34 = tensorHalfBatchNorm(var_33, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
- void* var_35 = tensorRelu(var_34);
- void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 5.830408106803901, conv2d_7_w, -0.20233777219057084, 0.18998308175802117, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.298286915779113, 4.848135117530843, 9);
- void* var_37 = tensorHalfBatchNorm(var_36, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
- void* var_38 = tensorRelu(var_37);
- void* var_39 = tensorConvolution(var_38, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512);
- void* var_40 = tensorHalfBatchNorm(var_39, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001);
- void* var_41 = tensorRelu(var_40);
- void* var_42 = ConvLayer_PROMISE(var_41, 0.0, 4.446417809963227, conv2d_8_w, -0.17442735651135444, 0.17695830866694454, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.347910885810852, 3.6144364695549145, 9);
- void* var_43 = tensorHalfBatchNorm(var_42, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001);
- void* var_44 = tensorRelu(var_43);
- void* var_45 = tensorConvolution(var_44, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512);
- void* var_46 = tensorHalfBatchNorm(var_45, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001);
- void* var_47 = tensorRelu(var_46);
- void* var_48 = ConvLayer_PROMISE(var_47, 0.0, 4.518095604896667, conv2d_9_w, -0.14546796187758446, 0.15256431668996823, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -3.0287702755928043, 2.9487365779876953, 9);
- void* var_49 = tensorHalfBatchNorm(var_48, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001);
- void* var_50 = tensorRelu(var_49);
- void* var_51 = tensorConvolution(var_50, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512);
- void* var_52 = tensorHalfBatchNorm(var_51, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001);
- void* var_53 = tensorRelu(var_52);
- void* var_54 = ConvLayer_PROMISE(var_53, 0.0, 6.348575634956407, conv2d_10_w, -0.13025874522328376, 0.13558243343234128, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.2293100805282595, 3.5315046372413645, 9);
- void* var_55 = tensorHalfBatchNorm(var_54, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001);
- void* var_56 = tensorRelu(var_55);
- void* var_57 = tensorConvolution(var_56, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512);
- void* var_58 = tensorHalfBatchNorm(var_57, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001);
- void* var_59 = tensorRelu(var_58);
- void* var_60 = ConvLayer_PROMISE(var_59, 0.0, 5.221003110408843, conv2d_11_w, -0.11900172759592534, 0.12536374783515936, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.038203780174255, 4.004009407043483, 9);
- void* var_61 = tensorHalfBatchNorm(var_60, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001);
- void* var_62 = tensorRelu(var_61);
- void* var_63 = tensorConvolution(var_62, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512);
- void* var_64 = tensorHalfBatchNorm(var_63, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001);
- void* var_65 = tensorRelu(var_64);
- void* var_66 = ConvLayer_PROMISE(var_65, 0.0, 5.732498347759442, conv2d_12_w, -0.10839721685647964, 0.11625668607652187, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -3.3111015114784244, 4.462933233261136, 9);
- void* var_67 = tensorHalfBatchNorm(var_66, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001);
- void* var_68 = tensorRelu(var_67);
- void* var_69 = tensorConvolution(var_68, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512);
- void* var_70 = tensorHalfBatchNorm(var_69, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001);
- void* var_71 = tensorHalfRelu(var_70);
- void* var_72 = ConvLayer_PROMISE(var_71, 0.0, 7.240498211860681, conv2d_13_w, -0.08623744961619377, 0.08859449951350662, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.175431394577027, 6.2043294754027345, 9);
- void* var_73 = tensorHalfBatchNorm(var_72, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001);
- void* var_74 = tensorHalfRelu(var_73);
- void* var_75 = tensorConvolution(var_74, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024);
- void* var_76 = tensorHalfBatchNorm(var_75, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001);
- void* var_77 = tensorRelu(var_76);
- void* var_78 = ConvLayer_PROMISE(var_77, 0.0, 7.813958834648251, conv2d_14_w, -0.06813025139272214, 0.07002027779817581, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -10.920566423416137, 2.6442912578582534, 9);
- void* var_79 = tensorHalfBatchNorm(var_78, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001);
- void* var_80 = tensorHalfRelu(var_79);
- void* var_81 = tensorHalfPooling(var_80,1,2,2,0,0,2,2);
- void* var_82 = FCLayer_PROMISE(var_81, 0.0, 2.8692066650391013, dense_1_w, -0.22301019695401192, 0.1442659378200768, dense_1_b, -0.1654396, 0.23336112, -1, -12.245949958801269, 23.80532513427739, 9);
- void* var_83 = tensorSoftmax(var_82);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_83);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
- if (final_accuracy < bench_acc)
- missed += 1;
- }
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_quant.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_quant.cc
deleted file mode 100644
index 3cb28def9b48bf29f3cffd5611991b0fbaeb4c55..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_quant.cc
+++ /dev/null
@@ -1,419 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(1);
-
-int total_runs = 1;
-for (int i = 0 ; i < total_runs; i++){
-
-
-startMemTracking();
-
-int test_input_size = 10000;
-int batch_size = 2000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-
-std::string dir_prefix = std::string("../../keras/data/mobilenet_quant/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
-void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
-void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
-void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
-void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
-std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
-void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
-std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
-void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
-void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
-void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
-void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
-std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
-void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
-void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
-void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
-void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
-std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
-void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
-std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
-void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
-void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
-void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
-void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
-std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
-void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
-void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
-void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
-void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
-void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
-void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
-void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
-void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
-void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
-std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
-void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
-void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
-void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
-void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
-void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
-void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
-void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
-void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
-void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
-std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
-void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
-void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
-void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
-void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
-void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
-void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
-void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
-void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
-void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
-std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
-void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
-void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
-void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
-void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
-void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
-void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
-void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
-void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
-void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
-std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
-void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
-void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
-void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
-void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
-void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
-void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
-void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
-void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
-void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
-void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
-void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
-void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
-void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
-void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
-void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
-void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
-void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
-void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
-void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
-void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
-void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
-void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
-void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
-void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
-void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
-void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
-void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
-void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
-void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
-void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
-void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
-void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
-void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
-void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
-void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
-void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
-void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
-void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
-void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
-void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
-void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
-void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
-void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
-void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
-void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
-void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
-void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
-void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
-void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
-void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
-void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
-void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
-void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
-void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1);
-std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
-void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
-void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
-void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
-void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1);
-std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
-void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3);
-std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
-void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
-void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
-void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
-void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1);
-std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
-void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1);
-std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
-void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
-void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
-void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
-void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = ConvLayer_PROMISE(input, -1.9892114, 2.126797, conv2d_1_w, -2.196306920051575, 1.347581704139706, NULL, 0, 0, 1, 1, 1, 1, -1, 0, -1, -60.89275047302246, 51.99256916046146, 9);
-void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
-void* var_2 = tensorRelu(var_1);
-void* var_3 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
-void* var_4 = tensorBatchNorm(var_3, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
-void* var_5 = tensorRelu(var_4);
-void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 5.713541553974245, conv2d_2_w, -0.9317721160650253, 1.0774258937835774, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.518589503288269, 6.810842518806449, 9);
-void* var_7 = tensorBatchNorm(var_6, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
-void* var_8 = tensorRelu(var_7);
-void* var_9 = tensorConvolution(var_8, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
-void* var_10 = tensorBatchNorm(var_9, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
-void* var_11 = tensorRelu(var_10);
-void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.932139402866376, conv2d_3_w, -0.5316544661521911, 0.5753790403604531, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.482631235122681, 3.96730119752885, 9);
-void* var_13 = tensorBatchNorm(var_12, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
-void* var_14 = tensorRelu(var_13);
-void* var_15 = tensorConvolution(var_14, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
-void* var_16 = tensorBatchNorm(var_15, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
-void* var_17 = tensorRelu(var_16);
-void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 4.103263397693674, conv2d_4_w, -0.36234098821878435, 0.4076913900375366, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.04261828327179, 3.88677932929993, 9);
-void* var_19 = tensorBatchNorm(var_18, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
-void* var_20 = tensorRelu(var_19);
-void* var_21 = tensorConvolution(var_20, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
-void* var_22 = tensorBatchNorm(var_21, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
-void* var_23 = tensorRelu(var_22);
-void* var_24 = ConvLayer_PROMISE(var_23, 0.0, 5.383221302509475, conv2d_5_w, -0.3131200549006462, 0.29357679939270065, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.921469215393066, 4.338679324150087, 9);
-void* var_25 = tensorBatchNorm(var_24, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
-void* var_26 = tensorRelu(var_25);
-void* var_27 = tensorConvolution(var_26, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
-void* var_28 = tensorBatchNorm(var_27, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
-void* var_29 = tensorRelu(var_28);
-void* var_30 = ConvLayer_PROMISE(var_29, 0.0, 4.316738154411368, conv2d_6_w, -0.23299247801303866, 0.2580290257930756, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.207789947509766, 3.932436970710759, 9);
-void* var_31 = tensorBatchNorm(var_30, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
-void* var_32 = tensorRelu(var_31);
-void* var_33 = tensorConvolution(var_32, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
-void* var_34 = tensorBatchNorm(var_33, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
-void* var_35 = tensorRelu(var_34);
-void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 5.830408106803901, conv2d_7_w, -0.20233777219057084, 0.18998308175802117, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.298286915779113, 4.848135117530843, 9);
-void* var_37 = tensorBatchNorm(var_36, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
-void* var_38 = tensorRelu(var_37);
-void* var_39 = tensorConvolution(var_38, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512);
-void* var_40 = tensorBatchNorm(var_39, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001);
-void* var_41 = tensorRelu(var_40);
-void* var_42 = ConvLayer_PROMISE(var_41, 0.0, 4.446417809963227, conv2d_8_w, -0.17442735651135444, 0.17695830866694454, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.347910885810852, 3.6144364695549145, 9);
-void* var_43 = tensorBatchNorm(var_42, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001);
-void* var_44 = tensorRelu(var_43);
-void* var_45 = tensorConvolution(var_44, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512);
-void* var_46 = tensorBatchNorm(var_45, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001);
-void* var_47 = tensorRelu(var_46);
-void* var_48 = ConvLayer_PROMISE(var_47, 0.0, 4.518095604896667, conv2d_9_w, -0.14546796187758446, 0.15256431668996823, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -3.0287702755928043, 2.9487365779876953, 9);
-void* var_49 = tensorBatchNorm(var_48, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001);
-void* var_50 = tensorRelu(var_49);
-void* var_51 = tensorConvolution(var_50, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512);
-void* var_52 = tensorBatchNorm(var_51, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001);
-void* var_53 = tensorRelu(var_52);
-void* var_54 = ConvLayer_PROMISE(var_53, 0.0, 6.348575634956407, conv2d_10_w, -0.13025874522328376, 0.13558243343234128, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.2293100805282595, 3.5315046372413645, 9);
-void* var_55 = tensorBatchNorm(var_54, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001);
-void* var_56 = tensorRelu(var_55);
-void* var_57 = tensorConvolution(var_56, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512);
-void* var_58 = tensorBatchNorm(var_57, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001);
-void* var_59 = tensorRelu(var_58);
-void* var_60 = ConvLayer_PROMISE(var_59, 0.0, 5.221003110408843, conv2d_11_w, -0.11900172759592534, 0.12536374783515936, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.038203780174255, 4.004009407043483, 9);
-void* var_61 = tensorBatchNorm(var_60, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001);
-void* var_62 = tensorRelu(var_61);
-void* var_63 = tensorConvolution(var_62, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512);
-void* var_64 = tensorBatchNorm(var_63, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001);
-void* var_65 = tensorRelu(var_64);
-void* var_66 = ConvLayer_PROMISE(var_65, 0.0, 5.732498347759442, conv2d_12_w, -0.10839721685647964, 0.11625668607652187, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -3.3111015114784244, 4.462933233261136, 9);
-void* var_67 = tensorBatchNorm(var_66, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001);
-void* var_68 = tensorRelu(var_67);
-void* var_69 = tensorConvolution(var_68, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512);
-void* var_70 = tensorBatchNorm(var_69, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001);
-void* var_71 = tensorRelu(var_70);
-void* var_72 = ConvLayer_PROMISE(var_71, 0.0, 7.240498211860681, conv2d_13_w, -0.08623744961619377, 0.08859449951350662, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.175431394577027, 6.2043294754027345, 9);
-void* var_73 = tensorBatchNorm(var_72, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001);
-void* var_74 = tensorRelu(var_73);
-void* var_75 = tensorConvolution(var_74, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024);
-void* var_76 = tensorBatchNorm(var_75, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001);
-void* var_77 = tensorRelu(var_76);
-void* var_78 = ConvLayer_PROMISE(var_77, 0.0, 7.813958834648251, conv2d_14_w, -0.06813025139272214, 0.07002027779817581, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -10.920566423416137, 2.6442912578582534, 9);
-void* var_79 = tensorBatchNorm(var_78, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001);
-void* var_80 = tensorRelu(var_79);
-void* var_81 = tensorPooling(var_80,1,2,2,0,0,2,2);
-void* var_82 = FCLayer_PROMISE(var_81, 0.0, 2.8692066650391013, dense_1_w, -0.22301019695401192, 0.1442659378200768, dense_1_b, -0.1654396, 0.23336112, -1, -12.245949958801269, 23.80532513427739, 9);
-void* var_83 = tensorSoftmax(var_82);
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy2(labels, batch_size, var_83);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-}
-
-dumpExecutionAccuracies();
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_shallow_promise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_shallow_promise.cc
deleted file mode 100644
index 7a8136d7d3f66e971f010f17bda1b78dde8ee181..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_shallow_promise.cc
+++ /dev/null
@@ -1,270 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-
-int main(int argc, char* argv[]){
-
- int test_input_size = 3000;
- int batch_size = 1000;
- int offset = 5000;
-
- if (argc > 1){
- total_runs = atoi(argv[1]);
- }
-
- if (argc > 2){
- bench_acc = atof(argv[2]);
- }
-
- if(argc > 3){
- to_skip = atoi(argv[3]);
- }
-
- if(argc > 4){
- test_input_size = atoi(argv[4]);
- }
-
- if(argc > 5){
- offset = atoi(argv[5]);
- }
-
-
- llvm_hpvm_initTensorRt(1);
-
- int missed = 0;
- for (int i = 0 ; i < total_runs; i++){
-
- if (missed >= to_skip){
- break;
- }
-
- startMemTracking();
-
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- std::string dir_prefix = std::string("../model_params/mobilenet_shallow/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
- void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
- void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
- void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
- void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
- void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
- std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
- void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
- void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
- void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
- void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,2048,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- int start = i * batch_size + offset;
- int end = (i + 1) * batch_size + offset;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = ConvLayer_PROMISE(input, -1.9892114, 2.126797, conv2d_1_w, -1.5164621164798737, 1.6472081774473288, NULL, 0, 0, 1, 1, 1, 1, -1, 0, -1, -9.868980642318725, 10.560956018447879, 9);
- void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorRelu(var_1);
- void* var_3 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_4 = tensorBatchNorm(var_3, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_5 = tensorRelu(var_4);
- void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 6.821381127357554, conv2d_2_w, -1.1834390873908995, 1.2731596627235617, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -9.875998497009277, 7.51305247974393, 9);
- void* var_7 = tensorBatchNorm(var_6, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_8 = tensorRelu(var_7);
- void* var_9 = tensorConvolution(var_8, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
- void* var_10 = tensorBatchNorm(var_9, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_11 = tensorRelu(var_10);
- void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.826067455768602, conv2d_3_w, -0.599876856982708, 0.6812073457241064, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.633289833068848, 5.177892235755925, 9);
- void* var_13 = tensorBatchNorm(var_12, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_14 = tensorRelu(var_13);
- void* var_15 = tensorConvolution(var_14, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
- void* var_16 = tensorBatchNorm(var_15, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_17 = tensorRelu(var_16);
- void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 4.02646304416659, conv2d_4_w, -0.4555967862010002, 0.4942613914608956, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.316803941726685, 4.605850250244146, 9);
- void* var_19 = tensorBatchNorm(var_18, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_20 = tensorRelu(var_19);
- void* var_21 = tensorConvolution(var_20, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
- void* var_22 = tensorBatchNorm(var_21, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_23 = tensorRelu(var_22);
- void* var_24 = ConvLayer_PROMISE(var_23, 0.0, 4.532649063110355, conv2d_5_w, -0.35657615590095515, 0.3382165088057521, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.1012511816024775, 4.3630500688553, 9);
- void* var_25 = tensorBatchNorm(var_24, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_26 = tensorRelu(var_25);
- void* var_27 = tensorConvolution(var_26, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
- void* var_28 = tensorBatchNorm(var_27, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_29 = tensorRelu(var_28);
- void* var_30 = ConvLayer_PROMISE(var_29, 0.0, 3.9874704387188977, conv2d_6_w, -0.28502783328294756, 0.28604640334844594, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.243851703643799, 3.486250406742097, 9);
- void* var_31 = tensorBatchNorm(var_30, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_32 = tensorRelu(var_31);
- void* var_33 = tensorConvolution(var_32, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
- void* var_34 = tensorBatchNorm(var_33, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
- void* var_35 = tensorRelu(var_34);
- void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 6.563065901756522, conv2d_7_w, -0.18946402323246003, 0.19012390717864017, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.938115713119507, 3.538363476753238, 9);
- void* var_37 = tensorBatchNorm(var_36, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
- void* var_38 = tensorRelu(var_37);
- void* var_39 = tensorPooling(var_38,1,2,2,0,0,2,2);
- void* var_40 = FCLayer_PROMISE(var_39, 0.0, 1.8908388000727185, dense_1_w, -0.35140394401550296, 0.422872786462307, dense_1_b, -0.23878151, 0.26507422, -1, -14.630816223144532, 27.27252123260504, 9);
- void* var_41 = tensorSoftmax(var_40);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_41);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- if (final_accuracy < bench_acc)
- missed += 1;
- }
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_shallow_quant.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_shallow_quant.cc
deleted file mode 100644
index c3f11e1f2ff7f0a255d40ecd5916fbdada2b0be3..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_shallow_quant.cc
+++ /dev/null
@@ -1,210 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(1);
-
- int total_runs = 1;
-
- for (int i = 0 ; i < total_runs; i++){
-
- startMemTracking();
-
- int test_input_size = 10000;
- int batch_size = 2000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- std::string dir_prefix = std::string("../../keras/data/mobilenet_shallow/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,64,64,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,64,1,1);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = ConvLayer_PROMISE(input, -1.9892114, 2.126797, conv2d_1_w, -1.340709443449974, 1.3555025291442875, NULL, 0, 0, 1, 1, 1, 1, -1, 0, -1, -7.2273098745346065, 8.197232282638556, 9);
- void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorRelu(var_1);
- void* var_3 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_4 = tensorBatchNorm(var_3, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_5 = tensorRelu(var_4);
- void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 6.053754347324407, conv2d_2_w, -1.1412922372817993, 0.9433415410518639, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.87497807598114, 5.3558874282836655, 9);
- void* var_7 = tensorBatchNorm(var_6, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_8 = tensorRelu(var_7);
- void* var_9 = tensorConvolution(var_8, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
- void* var_10 = tensorBatchNorm(var_9, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_11 = tensorRelu(var_10);
- void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.144686742782682, conv2d_3_w, -0.6453772538900375, 0.6694499132037164, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.349411018371582, 4.33332164001466, 9);
- void* var_13 = tensorBatchNorm(var_12, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_14 = tensorRelu(var_13);
- void* var_15 = tensorConvolution(var_14, depthwise_conv2d_3_w, 1, 1, 2, 2, 1, 64);
- void* var_16 = tensorBatchNorm(var_15, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_17 = tensorRelu(var_16);
- void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 4.248231422424324, conv2d_4_w, -0.44596208560466766, 0.49276923143864204, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -3.3628717079162596, 3.058014160633088, 9);
- void* var_19 = tensorBatchNorm(var_18, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_20 = tensorRelu(var_19);
- void* var_21 = tensorConvolution(var_20, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
- void* var_22 = tensorBatchNorm(var_21, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_23 = tensorRelu(var_22);
- void* var_24 = ConvLayer_PROMISE(var_23, 0.0, 5.569213481903169, conv2d_5_w, -0.3239764194488525, 0.2983359285593033, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.473401127815246, 4.425663429260224, 9);
- void* var_25 = tensorBatchNorm(var_24, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_26 = tensorRelu(var_25);
- void* var_27 = tensorConvolution(var_26, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
- void* var_28 = tensorBatchNorm(var_27, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_29 = tensorRelu(var_28);
- void* var_30 = ConvLayer_PROMISE(var_29, 0.0, 3.728998669862753, conv2d_6_w, -0.2290773878991604, 0.22830345794558554, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -3.1163714165687564, 2.065946404457088, 9);
- void* var_31 = tensorBatchNorm(var_30, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_32 = tensorRelu(var_31);
- void* var_33 = tensorPooling(var_32,1,2,2,0,0,2,2);
- void* var_34 = FCLayer_PROMISE(var_33, 0.0, 2.191649281263443, dense_1_w, -0.3627079802751541, 0.3849146918058397, dense_1_b, -0.37858343, 0.377391, -1, -11.424064125061035, 18.695249080657973, 9);
- void* var_35 = tensorSoftmax(var_34);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_35);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- }
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_shallow_valid.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_shallow_valid.cc
deleted file mode 100644
index ba2a14d990a7b7d3d1cc6ad4bc818b2c199a0c6b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_shallow_valid.cc
+++ /dev/null
@@ -1,235 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- int total_runs = 40;
- for (int i = 0 ; i < total_runs; i++){
-
- startMemTracking();
-
- int test_input_size = 4000;
- int batch_size = 2000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- std::string dir_prefix = std::string("../../keras/data/mobilenet_shallow_nathan/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
- void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
- void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
- void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
- void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
- void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
- std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
- void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
- void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
- void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
- void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,2048,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- int start = (i * batch_size) + 4000;
- int end = ((i + 1) * batch_size) + 4000;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = ConvLayer_PROMISE(input, -1.9892114, 2.126797, conv2d_1_w, -1.5164621164798737, 1.6472081774473288, NULL, 0, 0, 1, 1, 1, 1, -1, 0, -1, -9.868980642318725, 10.560956018447879, 9);
- void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorRelu(var_1);
- void* var_3 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_4 = tensorBatchNorm(var_3, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_5 = tensorRelu(var_4);
- void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 6.821381127357554, conv2d_2_w, -1.1834390873908995, 1.2731596627235617, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -9.875998497009277, 7.51305247974393, 9);
- void* var_7 = tensorBatchNorm(var_6, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_8 = tensorRelu(var_7);
- void* var_9 = tensorConvolution(var_8, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
- void* var_10 = tensorBatchNorm(var_9, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_11 = tensorRelu(var_10);
- void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.826067455768602, conv2d_3_w, -0.599876856982708, 0.6812073457241064, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.633289833068848, 5.177892235755925, 9);
- void* var_13 = tensorBatchNorm(var_12, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_14 = tensorRelu(var_13);
- void* var_15 = tensorConvolution(var_14, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
- void* var_16 = tensorBatchNorm(var_15, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_17 = tensorRelu(var_16);
- void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 4.02646304416659, conv2d_4_w, -0.4555967862010002, 0.4942613914608956, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.316803941726685, 4.605850250244146, 9);
- void* var_19 = tensorBatchNorm(var_18, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_20 = tensorRelu(var_19);
- void* var_21 = tensorConvolution(var_20, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
- void* var_22 = tensorBatchNorm(var_21, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_23 = tensorRelu(var_22);
- void* var_24 = ConvLayer_PROMISE(var_23, 0.0, 4.532649063110355, conv2d_5_w, -0.35657615590095515, 0.3382165088057521, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.1012511816024775, 4.3630500688553, 9);
- void* var_25 = tensorBatchNorm(var_24, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_26 = tensorRelu(var_25);
- void* var_27 = tensorConvolution(var_26, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
- void* var_28 = tensorBatchNorm(var_27, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_29 = tensorRelu(var_28);
- void* var_30 = ConvLayer_PROMISE(var_29, 0.0, 3.9874704387188977, conv2d_6_w, -0.28502783328294756, 0.28604640334844594, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.243851703643799, 3.486250406742097, 9);
- void* var_31 = tensorBatchNorm(var_30, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_32 = tensorRelu(var_31);
- void* var_33 = tensorConvolution(var_32, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
- void* var_34 = tensorBatchNorm(var_33, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
- void* var_35 = tensorRelu(var_34);
- void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 6.563065901756522, conv2d_7_w, -0.18946402323246003, 0.19012390717864017, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.938115713119507, 3.538363476753238, 9);
- void* var_37 = tensorBatchNorm(var_36, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
- void* var_38 = tensorRelu(var_37);
- void* var_39 = tensorPooling(var_38,1,2,2,0,0,2,2);
- void* var_40 = FCLayer_PROMISE(var_39, 0.0, 1.8908388000727185, dense_1_w, -0.35140394401550296, 0.422872786462307, dense_1_b, -0.23878151, 0.26507422, -1, -14.630816223144532, 27.27252123260504, 9);
- void* var_41 = tensorSoftmax(var_40);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_41);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- }
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_valid.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_valid.cc
deleted file mode 100644
index 8cf22349346230889f9a4f1385b94ceabe04612e..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/mobilenet_valid.cc
+++ /dev/null
@@ -1,418 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(1);
-
- int total_runs = 40;
- for (int i = 0 ; i < total_runs; i++){
-
- startMemTracking();
-
- int test_input_size = 4000;
- int batch_size = 2000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
-
- std::string dir_prefix = std::string("../../keras/data/mobilenet_quant/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
- void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
- void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
- void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
- void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
- void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
- std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
- void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
- void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
- void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
- void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
- void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
- void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
- void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
- void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
- void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
- void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
- void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
- void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
- void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
- void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
- void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
- void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
- void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
- void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
- void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
- void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
- void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
- void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
- void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
- void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
- void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
- void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
- void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
- void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
- void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
- void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
- void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
- void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
- void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
- void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
- void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
- void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
- void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
- void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
- void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
- void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
- void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
- void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
- void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
- void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
- void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
- void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
- void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
- void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
- void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
- void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
- void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
- void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
- void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
- void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1);
- std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
- void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
- void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
- void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
- void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1);
- std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
- void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3);
- std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
- void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
- void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
- void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
- void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1);
- std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
- void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
- void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
- void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
- void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- int start = (i * batch_size) + 4000;
- int end = ((i + 1) * batch_size) + 4000;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = ConvLayer_PROMISE(input, -1.9892114, 2.126797, conv2d_1_w, -2.196306920051575, 1.347581704139706, NULL, 0, 0, 1, 1, 1, 1, -1, 0, -1, -60.89275047302246, 51.99256916046146, 9);
- void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorRelu(var_1);
- void* var_3 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_4 = tensorBatchNorm(var_3, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_5 = tensorRelu(var_4);
- void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 5.713541553974245, conv2d_2_w, -0.9317721160650253, 1.0774258937835774, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.518589503288269, 6.810842518806449, 9);
- void* var_7 = tensorBatchNorm(var_6, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_8 = tensorRelu(var_7);
- void* var_9 = tensorConvolution(var_8, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
- void* var_10 = tensorBatchNorm(var_9, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_11 = tensorRelu(var_10);
- void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.932139402866376, conv2d_3_w, -0.5316544661521911, 0.5753790403604531, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.482631235122681, 3.96730119752885, 9);
- void* var_13 = tensorBatchNorm(var_12, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_14 = tensorRelu(var_13);
- void* var_15 = tensorConvolution(var_14, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
- void* var_16 = tensorBatchNorm(var_15, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_17 = tensorRelu(var_16);
- void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 4.103263397693674, conv2d_4_w, -0.36234098821878435, 0.4076913900375366, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.04261828327179, 3.88677932929993, 9);
- void* var_19 = tensorBatchNorm(var_18, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_20 = tensorRelu(var_19);
- void* var_21 = tensorConvolution(var_20, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
- void* var_22 = tensorBatchNorm(var_21, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_23 = tensorRelu(var_22);
- void* var_24 = ConvLayer_PROMISE(var_23, 0.0, 5.383221302509475, conv2d_5_w, -0.3131200549006462, 0.29357679939270065, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.921469215393066, 4.338679324150087, 9);
- void* var_25 = tensorBatchNorm(var_24, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_26 = tensorRelu(var_25);
- void* var_27 = tensorConvolution(var_26, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
- void* var_28 = tensorBatchNorm(var_27, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_29 = tensorRelu(var_28);
- void* var_30 = ConvLayer_PROMISE(var_29, 0.0, 4.316738154411368, conv2d_6_w, -0.23299247801303866, 0.2580290257930756, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.207789947509766, 3.932436970710759, 9);
- void* var_31 = tensorBatchNorm(var_30, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_32 = tensorRelu(var_31);
- void* var_33 = tensorConvolution(var_32, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
- void* var_34 = tensorBatchNorm(var_33, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
- void* var_35 = tensorRelu(var_34);
- void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 5.830408106803901, conv2d_7_w, -0.20233777219057084, 0.18998308175802117, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.298286915779113, 4.848135117530843, 9);
- void* var_37 = tensorBatchNorm(var_36, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
- void* var_38 = tensorRelu(var_37);
- void* var_39 = tensorConvolution(var_38, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512);
- void* var_40 = tensorBatchNorm(var_39, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001);
- void* var_41 = tensorRelu(var_40);
- void* var_42 = ConvLayer_PROMISE(var_41, 0.0, 4.446417809963227, conv2d_8_w, -0.17442735651135444, 0.17695830866694454, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.347910885810852, 3.6144364695549145, 9);
- void* var_43 = tensorBatchNorm(var_42, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001);
- void* var_44 = tensorRelu(var_43);
- void* var_45 = tensorConvolution(var_44, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512);
- void* var_46 = tensorBatchNorm(var_45, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001);
- void* var_47 = tensorRelu(var_46);
- void* var_48 = ConvLayer_PROMISE(var_47, 0.0, 4.518095604896667, conv2d_9_w, -0.14546796187758446, 0.15256431668996823, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -3.0287702755928043, 2.9487365779876953, 9);
- void* var_49 = tensorBatchNorm(var_48, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001);
- void* var_50 = tensorRelu(var_49);
- void* var_51 = tensorConvolution(var_50, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512);
- void* var_52 = tensorBatchNorm(var_51, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001);
- void* var_53 = tensorRelu(var_52);
- void* var_54 = ConvLayer_PROMISE(var_53, 0.0, 6.348575634956407, conv2d_10_w, -0.13025874522328376, 0.13558243343234128, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.2293100805282595, 3.5315046372413645, 9);
- void* var_55 = tensorBatchNorm(var_54, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001);
- void* var_56 = tensorRelu(var_55);
- void* var_57 = tensorConvolution(var_56, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512);
- void* var_58 = tensorBatchNorm(var_57, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001);
- void* var_59 = tensorRelu(var_58);
- void* var_60 = ConvLayer_PROMISE(var_59, 0.0, 5.221003110408843, conv2d_11_w, -0.11900172759592534, 0.12536374783515936, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.038203780174255, 4.004009407043483, 9);
- void* var_61 = tensorBatchNorm(var_60, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001);
- void* var_62 = tensorRelu(var_61);
- void* var_63 = tensorConvolution(var_62, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512);
- void* var_64 = tensorBatchNorm(var_63, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001);
- void* var_65 = tensorRelu(var_64);
- void* var_66 = ConvLayer_PROMISE(var_65, 0.0, 5.732498347759442, conv2d_12_w, -0.10839721685647964, 0.11625668607652187, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -3.3111015114784244, 4.462933233261136, 9);
- void* var_67 = tensorBatchNorm(var_66, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001);
- void* var_68 = tensorRelu(var_67);
- void* var_69 = tensorConvolution(var_68, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512);
- void* var_70 = tensorBatchNorm(var_69, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001);
- void* var_71 = tensorRelu(var_70);
- void* var_72 = ConvLayer_PROMISE(var_71, 0.0, 7.240498211860681, conv2d_13_w, -0.08623744961619377, 0.08859449951350662, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.175431394577027, 6.2043294754027345, 9);
- void* var_73 = tensorBatchNorm(var_72, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001);
- void* var_74 = tensorRelu(var_73);
- void* var_75 = tensorConvolution(var_74, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024);
- void* var_76 = tensorBatchNorm(var_75, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001);
- void* var_77 = tensorRelu(var_76);
- void* var_78 = ConvLayer_PROMISE(var_77, 0.0, 7.813958834648251, conv2d_14_w, -0.06813025139272214, 0.07002027779817581, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -10.920566423416137, 2.6442912578582534, 9);
- void* var_79 = tensorBatchNorm(var_78, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001);
- void* var_80 = tensorRelu(var_79);
- void* var_81 = tensorPooling(var_80,1,2,2,0,0,2,2);
- void* var_82 = FCLayer_PROMISE(var_81, 0.0, 2.8692066650391013, dense_1_w, -0.22301019695401192, 0.1442659378200768, dense_1_b, -0.1654396, 0.23336112, -1, -12.245949958801269, 23.80532513427739, 9);
- void* var_83 = tensorSoftmax(var_82);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_83);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- }
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GEMO_promise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GEMO_promise.cc
deleted file mode 100644
index f696bbf259b26eb4e45b73aa05658f3208c6fae6..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GEMO_promise.cc
+++ /dev/null
@@ -1,146 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testPipeline(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
- printf("********* Pipeline: Gaussian - Emboss - Motion Blur - Outline ********** \n");
-
- int test_batch_size = 1000;
- int H = 240;
- int W = 300;
- float PSNR = 30;
-
- void* golden_output = readTrainedWeights("../model_params/pipeline/golden_output/GEMO_calib.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- clearTensorMap();
- for(int i = 0; i < total_runs; i++){
- void* input = readTrainedWeights("../model_params/pipeline/dataset/calibration_4572.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- void* gaussian_filter = readTrainedWeights("../model_params/pipeline/filters/GaussianFilter.bin",
- float_type, 1, 1, 9, 9);
- void* outline_filter = readTrainedWeights("../model_params/pipeline/filters/OutlineFilter.bin",
- float_type, 1, 1, 3, 3);
- void* sharpen_filter = readTrainedWeights("../model_params/pipeline/filters/SharpenFilter.bin",
- float_type, 1, 1, 3, 3);
- void* motionblur_filter = readTrainedWeights("../model_params/pipeline/filters/MotionblurFilter.bin",
- float_type, 1, 1, 9, 9);
- void* emboss_filter = readTrainedWeights("../model_params/pipeline/filters/EmbossFilter.bin",
- float_type, 1, 1, 5, 5);
- void* emboss_bias = readTrainedWeights("../model_params/pipeline/filters/EmbossBias.bin",
- float_type, 1, 1, 1, 1);
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
- readOpenTunerFlags("promise_flags"); // Resets the OpenTuner counters
-
-
- void* gaussian_out = ConvLayer_PROMISE(input, 0, 255, gaussian_filter, 0, 1, NULL, 0, 0,
- 4, 4, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* emboss_out = ConvLayer_PROMISE(gaussian_out, 0, 255, emboss_filter, -1, 1, emboss_bias, 128, 128,
- 2, 2, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* motionblur_out = ConvLayer_PROMISE(emboss_out, 0, 255, motionblur_filter, 0, 1, NULL, 0, 0,
- 4, 4, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* result = ConvLayer_PROMISE(motionblur_out, 0, 255, outline_filter, -1, 8, NULL, 0, 0,
- 1, 1, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
-
- hpvm_request_tensor(result, 0);
- //dumpOutput(result);
-
- computePSNRViolation(golden_output, result, PSNR);
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
- }
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(1);
-
- testPipeline();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GEMO_valid.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GEMO_valid.cc
deleted file mode 100644
index a8d049f7aca85fbc00e7bcd2b47c083d4f6ea377..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GEMO_valid.cc
+++ /dev/null
@@ -1,146 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testPipeline(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
- printf("********* Pipeline: Gaussian - Emboss - Motion Blur - Outline ********** \n");
-
- int test_batch_size = 1000;
- int H = 240;
- int W = 300;
- float PSNR = 30;
-
- void* golden_output = readTrainedWeights("../model_params/pipeline/golden_output/GEMO_valid.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- clearTensorMap();
- for(int i = 0; i < total_runs; i++){
- void* input = readTrainedWeights("../model_params/pipeline/dataset/test_4573.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- void* gaussian_filter = readTrainedWeights("../model_params/pipeline/filters/GaussianFilter.bin",
- float_type, 1, 1, 9, 9);
- void* outline_filter = readTrainedWeights("../model_params/pipeline/filters/OutlineFilter.bin",
- float_type, 1, 1, 3, 3);
- void* sharpen_filter = readTrainedWeights("../model_params/pipeline/filters/SharpenFilter.bin",
- float_type, 1, 1, 3, 3);
- void* motionblur_filter = readTrainedWeights("../model_params/pipeline/filters/MotionblurFilter.bin",
- float_type, 1, 1, 9, 9);
- void* emboss_filter = readTrainedWeights("../model_params/pipeline/filters/EmbossFilter.bin",
- float_type, 1, 1, 5, 5);
- void* emboss_bias = readTrainedWeights("../model_params/pipeline/filters/EmbossBias.bin",
- float_type, 1, 1, 1, 1);
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
- readOpenTunerFlags("promise_flags"); // Resets the OpenTuner counters
-
-
- void* gaussian_out = ConvLayer_PROMISE(input, 0, 255, gaussian_filter, 0, 1, NULL, 0, 0,
- 4, 4, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* emboss_out = ConvLayer_PROMISE(gaussian_out, 0, 255, emboss_filter, -1, 1, emboss_bias, 128, 128,
- 2, 2, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* motionblur_out = ConvLayer_PROMISE(emboss_out, 0, 255, motionblur_filter, 0, 1, NULL, 0, 0,
- 4, 4, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* result = ConvLayer_PROMISE(motionblur_out, 0, 255, outline_filter, -1, 8, NULL, 0, 0,
- 1, 1, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
-
- hpvm_request_tensor(result, 0);
- //dumpOutput(result, "GEMO_valid.bin");
-
- computePSNRViolation(golden_output, result, PSNR);
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
- }
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(0);
-
- testPipeline();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GEOM_promise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GEOM_promise.cc
deleted file mode 100644
index 26ab88f81a70e032a723537864fb5eb4fead6a5b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GEOM_promise.cc
+++ /dev/null
@@ -1,146 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testPipeline(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
- printf("********* Pipeline: Gaussian - Emboss - Outline - Motion Blur ********** \n");
-
- int test_batch_size = 1000;
- int H = 240;
- int W = 300;
- float PSNR = 30;
-
- void* golden_output = readTrainedWeights("../model_params/pipeline/golden_output/GEOM_calib.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- clearTensorMap();
- for(int i = 0; i < total_runs; i++){
-
- void* input = readTrainedWeights("../model_params/pipeline/dataset/calibration_4572.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- void* gaussian_filter = readTrainedWeights("../model_params/pipeline/filters/GaussianFilter.bin",
- float_type, 1, 1, 9, 9);
- void* outline_filter = readTrainedWeights("../model_params/pipeline/filters/OutlineFilter.bin",
- float_type, 1, 1, 3, 3);
- void* sharpen_filter = readTrainedWeights("../model_params/pipeline/filters/SharpenFilter.bin",
- float_type, 1, 1, 3, 3);
- void* motionblur_filter = readTrainedWeights("../model_params/pipeline/filters/MotionblurFilter.bin",
- float_type, 1, 1, 9, 9);
- void* emboss_filter = readTrainedWeights("../model_params/pipeline/filters/EmbossFilter.bin",
- float_type, 1, 1, 5, 5);
- void* emboss_bias = readTrainedWeights("../model_params/pipeline/filters/EmbossBias.bin",
- float_type, 1, 1, 1, 1);
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
- readOpenTunerFlags("promise_flags"); // Resets the OpenTuner counters
-
-
- void* gaussian_out = ConvLayer_PROMISE(input, 0, 255, gaussian_filter, 0, 1, NULL, 0, 0,
- 4, 4, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* emboss_out = ConvLayer_PROMISE(gaussian_out, 0, 255, emboss_filter, -1, 1, emboss_bias, 128, 128,
- 2, 2, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* outline_out = ConvLayer_PROMISE(emboss_out, 0, 255, outline_filter, -1, 8, NULL, 0, 0,
- 1, 1, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* result = ConvLayer_PROMISE(outline_out, 0, 255, motionblur_filter, 0, 1, NULL, 0, 0,
- 4, 4, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
-
- hpvm_request_tensor(result, 0);
-
- computePSNRViolation(golden_output, result, PSNR);
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
- }
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(1);
-
- testPipeline();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GEOM_valid.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GEOM_valid.cc
deleted file mode 100644
index 1fd996f701664358d408f3b7e7a095a66c78f9ef..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GEOM_valid.cc
+++ /dev/null
@@ -1,148 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testPipeline(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
- printf("********* Pipeline: Gaussian - Emboss - Outline - Motion Blur ********** \n");
-
- int test_batch_size = 1000;
- int H = 240;
- int W = 300;
- float PSNR = 30;
-
- void* golden_output = readTrainedWeights("../model_params/pipeline/golden_output/GEOM_valid.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- clearTensorMap();
- for(int i = 0; i < total_runs; i++){
- void* input = readTrainedWeights("../model_params/pipeline/dataset/test_4573.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- void* gaussian_filter = readTrainedWeights("../model_params/pipeline/filters/GaussianFilter.bin",
- float_type, 1, 1, 9, 9);
- void* outline_filter = readTrainedWeights("../model_params/pipeline/filters/OutlineFilter.bin",
- float_type, 1, 1, 3, 3);
- void* sharpen_filter = readTrainedWeights("../model_params/pipeline/filters/SharpenFilter.bin",
- float_type, 1, 1, 3, 3);
- void* motionblur_filter = readTrainedWeights("../model_params/pipeline/filters/MotionblurFilter.bin",
- float_type, 1, 1, 9, 9);
- void* emboss_filter = readTrainedWeights("../model_params/pipeline/filters/EmbossFilter.bin",
- float_type, 1, 1, 5, 5);
- void* emboss_bias = readTrainedWeights("../model_params/pipeline/filters/EmbossBias.bin",
- float_type, 1, 1, 1, 1);
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
- readOpenTunerFlags("promise_flags"); // Resets the OpenTuner counters
-
-
- void* gaussian_out = ConvLayer_PROMISE(input, 0, 255, gaussian_filter, 0, 1, NULL, 0, 0,
- 4, 4, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* emboss_out = ConvLayer_PROMISE(gaussian_out, 0, 255, emboss_filter, -1, 1, emboss_bias, 128, 128,
- 2, 2, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* outline_out = ConvLayer_PROMISE(emboss_out, 0, 255, outline_filter, -1, 8, NULL, 0, 0,
- 1, 1, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* result = ConvLayer_PROMISE(outline_out, 0, 255, motionblur_filter, 0, 1, NULL, 0, 0,
- 4, 4, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
-
- hpvm_request_tensor(result, 0);
- //dumpOutput(result, "GEOM_valid.bin");
-
- computePSNRViolation(golden_output, result, PSNR);
-
- freeOutputTensors();
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
- }
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(0);
-
- testPipeline();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GEO_promise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GEO_promise.cc
deleted file mode 100644
index 577145a01eb3e5e941588fbfddf153c78156dc0c..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GEO_promise.cc
+++ /dev/null
@@ -1,139 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testPipeline(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
- printf("********* Pipeline: Gaussian - Emboss - Outline ********** \n");
-
- int test_batch_size = 1000;
- int H = 240;
- int W = 300;
- float PSNR = 30;
-
- void* golden_output = readTrainedWeights("../model_params/pipeline/golden_output/GEO_calib.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- clearTensorMap();
- for(int i = 0; i < total_runs; i++){
- void* input = readTrainedWeights("../model_params/pipeline/dataset/calibration_4572.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- void* gaussian_filter = readTrainedWeights("../model_params/pipeline/filters/GaussianFilter.bin",
- float_type, 1, 1, 9, 9);
- void* outline_filter = readTrainedWeights("../model_params/pipeline/filters/OutlineFilter.bin",
- float_type, 1, 1, 3, 3);
- void* sharpen_filter = readTrainedWeights("../model_params/pipeline/filters/SharpenFilter.bin",
- float_type, 1, 1, 3, 3);
- void* motionblur_filter = readTrainedWeights("../model_params/pipeline/filters/MotionblurFilter.bin",
- float_type, 1, 1, 9, 9);
- void* emboss_filter = readTrainedWeights("../model_params/pipeline/filters/EmbossFilter.bin",
- float_type, 1, 1, 5, 5);
- void* emboss_bias = readTrainedWeights("../model_params/pipeline/filters/EmbossBias.bin",
- float_type, 1, 1, 1, 1);
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
- readOpenTunerFlags("promise_flags"); // Resets the OpenTuner counters
-
-
- void* gaussian_out = ConvLayer_PROMISE(input, 0, 255, gaussian_filter, 0, 1, NULL, 0, 0,
- 4, 4, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* emboss_out = ConvLayer_PROMISE(gaussian_out, 0, 255, emboss_filter, -1, 1, emboss_bias, 128, 128,
- 2, 2, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* result = ConvLayer_PROMISE(emboss_out, 0, 255, outline_filter, -1, 8, NULL, 0, 0,
- 1, 1, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
-
- hpvm_request_tensor(result, 0);
- dumpOutput(result, "GEO_approx.bin");
-
- computePSNRViolation(golden_output, result, PSNR);
- freeOutputTensors();
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
- }
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(1);
-
- testPipeline();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GEO_valid.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GEO_valid.cc
deleted file mode 100644
index 6f269a8ad51734e372ce14dd5c5b94342417e2b2..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GEO_valid.cc
+++ /dev/null
@@ -1,143 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testPipeline(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
- printf("********* Pipeline: Gaussian - Emboss - Outline ********** \n");
-
- int test_batch_size = 1000;
- int H = 240;
- int W = 300;
- float PSNR = 30;
-
- void* golden_output = readTrainedWeights("../model_params/pipeline/golden_output/GEO_valid.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- clearTensorMap();
- for(int i = 0; i < total_runs; i++){
- void* input = readTrainedWeights("../model_params/pipeline/dataset/test_4573.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- // NOTE: Filter descriptors do NOT have batch size
- // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
- // IMP: The output channels matches the trained model - not the Lenet arch proposed in Andrew Ng's class
- void* gaussian_filter = readTrainedWeights("../model_params/pipeline/filters/GaussianFilter.bin",
- float_type, 1, 1, 9, 9);
- void* outline_filter = readTrainedWeights("../model_params/pipeline/filters/OutlineFilter.bin",
- float_type, 1, 1, 3, 3);
- void* sharpen_filter = readTrainedWeights("../model_params/pipeline/filters/SharpenFilter.bin",
- float_type, 1, 1, 3, 3);
- void* motionblur_filter = readTrainedWeights("../model_params/pipeline/filters/MotionblurFilter.bin",
- float_type, 1, 1, 9, 9);
- void* emboss_filter = readTrainedWeights("../model_params/pipeline/filters/EmbossFilter.bin",
- float_type, 1, 1, 5, 5);
- void* emboss_bias = readTrainedWeights("../model_params/pipeline/filters/EmbossBias.bin",
- float_type, 1, 1, 1, 1);
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
- readOpenTunerFlags("promise_flags"); // Resets the OpenTuner counters
-
-
- void* gaussian_out = ConvLayer_PROMISE(input, 0, 255, gaussian_filter, 0, 1, NULL, 0, 0,
- 4, 4, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* emboss_out = ConvLayer_PROMISE(gaussian_out, 0, 255, emboss_filter, -1, 1, emboss_bias, 128, 128,
- 2, 2, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* result = ConvLayer_PROMISE(emboss_out, 0, 255, outline_filter, -1, 8, NULL, 0, 0,
- 1, 1, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
-
-
- hpvm_request_tensor(result, 0);
- dumpOutput(result, "GEO_40_psnr.bin");
-
- computePSNRViolation(golden_output, result, PSNR);
- freeOutputTensors();
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
- }
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(0);
-
- testPipeline();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GSME_promise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GSME_promise.cc
deleted file mode 100644
index d6150a9ee4b5d99eaa736a911e0fc9da2d593150..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GSME_promise.cc
+++ /dev/null
@@ -1,146 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testPipeline(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
- printf("********* Pipeline: Gaussian - Sharpen - Motion Blur - Emboss ********** \n");
-
- int test_batch_size = 1000;
- int H = 240;
- int W = 300;
- float PSNR = 30;
-
- void* golden_output = readTrainedWeights("../model_params/pipeline/golden_output/GSME_calib.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- clearTensorMap();
- for(int i = 0; i < total_runs; i++){
- void* input = readTrainedWeights("../model_params/pipeline/dataset/calibration_4572.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- void* gaussian_filter = readTrainedWeights("../model_params/pipeline/filters/GaussianFilter.bin",
- float_type, 1, 1, 9, 9);
- void* outline_filter = readTrainedWeights("../model_params/pipeline/filters/OutlineFilter.bin",
- float_type, 1, 1, 3, 3);
- void* sharpen_filter = readTrainedWeights("../model_params/pipeline/filters/SharpenFilter.bin",
- float_type, 1, 1, 3, 3);
- void* motionblur_filter = readTrainedWeights("../model_params/pipeline/filters/MotionblurFilter.bin",
- float_type, 1, 1, 9, 9);
- void* emboss_filter = readTrainedWeights("../model_params/pipeline/filters/EmbossFilter.bin",
- float_type, 1, 1, 5, 5);
- void* emboss_bias = readTrainedWeights("../model_params/pipeline/filters/EmbossBias.bin",
- float_type, 1, 1, 1, 1);
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
- readOpenTunerFlags("promise_flags"); // Resets the OpenTuner counters
-
-
- void* gaussian_out = ConvLayer_PROMISE(input, 0, 255, gaussian_filter, 0, 1, NULL, 0, 0,
- 4, 4, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* sharpen_out = ConvLayer_PROMISE(gaussian_out, 0, 255, sharpen_filter, -1, 8, NULL, 0, 0,
- 1, 1, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* motionblur_out = ConvLayer_PROMISE(sharpen_out, 0, 255, motionblur_filter, 0, 1, NULL, 0, 0,
- 4, 4, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* result = ConvLayer_PROMISE(motionblur_out, 0, 255, emboss_filter, -1, 1, emboss_bias, 128, 128,
- 2, 2, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- hpvm_request_tensor(result, 0);
- //dumpOutput(result);
-
- computePSNRViolation(golden_output, result, PSNR);
- freeOutputTensors();
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
- }
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(1);
-
- testPipeline();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GSME_valid.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GSME_valid.cc
deleted file mode 100644
index 8c2a1b9f63d41654bf6425a5670cb41fd64b64cf..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GSME_valid.cc
+++ /dev/null
@@ -1,146 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testPipeline(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
- printf("********* Pipeline: Gaussian - Sharpen - Motion Blur - Emboss ********** \n");
-
- int test_batch_size = 1000;
- int H = 240;
- int W = 300;
- float PSNR = 30;
-
- void* golden_output = readTrainedWeights("../model_params/pipeline/golden_output/GSME_valid.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- clearTensorMap();
- for(int i = 0; i < total_runs; i++){
- void* input = readTrainedWeights("../model_params/pipeline/dataset/test_4573.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- void* gaussian_filter = readTrainedWeights("../model_params/pipeline/filters/GaussianFilter.bin",
- float_type, 1, 1, 9, 9);
- void* outline_filter = readTrainedWeights("../model_params/pipeline/filters/OutlineFilter.bin",
- float_type, 1, 1, 3, 3);
- void* sharpen_filter = readTrainedWeights("../model_params/pipeline/filters/SharpenFilter.bin",
- float_type, 1, 1, 3, 3);
- void* motionblur_filter = readTrainedWeights("../model_params/pipeline/filters/MotionblurFilter.bin",
- float_type, 1, 1, 9, 9);
- void* emboss_filter = readTrainedWeights("../model_params/pipeline/filters/EmbossFilter.bin",
- float_type, 1, 1, 5, 5);
- void* emboss_bias = readTrainedWeights("../model_params/pipeline/filters/EmbossBias.bin",
- float_type, 1, 1, 1, 1);
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
- readOpenTunerFlags("promise_flags"); // Resets the OpenTuner counters
-
-
- void* gaussian_out = ConvLayer_PROMISE(input, 0, 255, gaussian_filter, 0, 1, NULL, 0, 0,
- 4, 4, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* sharpen_out = ConvLayer_PROMISE(gaussian_out, 0, 255, sharpen_filter, -1, 8, NULL, 0, 0,
- 1, 1, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* motionblur_out = ConvLayer_PROMISE(sharpen_out, 0, 255, motionblur_filter, 0, 1, NULL, 0, 0,
- 4, 4, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* result = ConvLayer_PROMISE(motionblur_out, 0, 255, emboss_filter, -1, 1, emboss_bias, 128, 128,
- 2, 2, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- hpvm_request_tensor(result, 0);
- dumpOutput(result, "GSME_valid_20db.bin");
-
- computePSNRViolation(golden_output, result, PSNR);
- freeOutputTensors();
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
- }
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(0);
-
- testPipeline();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GSM_promise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GSM_promise.cc
deleted file mode 100644
index 59077e94a918a8d5540b713c08af5eb6e73cb86f..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GSM_promise.cc
+++ /dev/null
@@ -1,138 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testPipeline(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
- printf("********* Pipeline: Gaussian - Sharpen - Motion Blur ********** \n");
-
- int test_batch_size = 1000;
- int H = 240;
- int W = 300;
- float PSNR = 30;
-
- void* golden_output = readTrainedWeights("../model_params/pipeline/golden_output/GSM_calib.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- clearTensorMap();
- for(int i = 0; i < total_runs; i++){
- void* input = readTrainedWeights("../model_params/pipeline/dataset/calibration_4572.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- void* gaussian_filter = readTrainedWeights("../model_params/pipeline/filters/GaussianFilter.bin",
- float_type, 1, 1, 9, 9);
- void* outline_filter = readTrainedWeights("../model_params/pipeline/filters/OutlineFilter.bin",
- float_type, 1, 1, 3, 3);
- void* sharpen_filter = readTrainedWeights("../model_params/pipeline/filters/SharpenFilter.bin",
- float_type, 1, 1, 3, 3);
- void* motionblur_filter = readTrainedWeights("../model_params/pipeline/filters/MotionblurFilter.bin",
- float_type, 1, 1, 9, 9);
- void* emboss_filter = readTrainedWeights("../model_params/pipeline/filters/EmbossFilter.bin",
- float_type, 1, 1, 5, 5);
- void* emboss_bias = readTrainedWeights("../model_params/pipeline/filters/EmbossBias.bin",
- float_type, 1, 1, 1, 1);
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
- readOpenTunerFlags("promise_flags"); // Resets the OpenTuner counters
-
-
- void* gaussian_out = ConvLayer_PROMISE(input, 0, 255, gaussian_filter, 0, 1, NULL, 0, 0,
- 4, 4, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* sharpen_out = ConvLayer_PROMISE(gaussian_out, 0, 255, sharpen_filter, -1, 8, NULL, 0, 0,
- 1, 1, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* result = ConvLayer_PROMISE(sharpen_out, 0, 255, motionblur_filter, 0, 1, NULL, 0, 0,
- 4, 4, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
-
- hpvm_request_tensor(result, 0);
- dumpOutput(result, "GSM_approx.bin");
-
- computePSNRViolation(golden_output, result, PSNR);
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
- }
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(1);
-
- testPipeline();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GSM_valid.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GSM_valid.cc
deleted file mode 100644
index 45b38e82864b97be220eecbe91ce3d6bfdce6318..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_GSM_valid.cc
+++ /dev/null
@@ -1,141 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testPipeline(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
- printf("********* Pipeline: Gaussian - Sharpen - Motion Blur ********** \n");
-
- int test_batch_size = 1000;
- int H = 240;
- int W = 300;
- float PSNR = 30;
-
- void* golden_output = readTrainedWeights("../model_params/pipeline/golden_output/GSM_valid.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- clearTensorMap();
- for(int i = 0; i < total_runs; i++){
- void* input = readTrainedWeights("../model_params/pipeline/dataset/test_4573.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- // NOTE: Filter descriptors do NOT have batch size
- // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
- // IMP: The output channels matches the trained model - not the Lenet arch proposed in Andrew Ng's class
- void* gaussian_filter = readTrainedWeights("../model_params/pipeline/filters/GaussianFilter.bin",
- float_type, 1, 1, 9, 9);
- void* outline_filter = readTrainedWeights("../model_params/pipeline/filters/OutlineFilter.bin",
- float_type, 1, 1, 3, 3);
- void* sharpen_filter = readTrainedWeights("../model_params/pipeline/filters/SharpenFilter.bin",
- float_type, 1, 1, 3, 3);
- void* motionblur_filter = readTrainedWeights("../model_params/pipeline/filters/MotionblurFilter.bin",
- float_type, 1, 1, 9, 9);
- void* emboss_filter = readTrainedWeights("../model_params/pipeline/filters/EmbossFilter.bin",
- float_type, 1, 1, 5, 5);
- void* emboss_bias = readTrainedWeights("../model_params/pipeline/filters/EmbossBias.bin",
- float_type, 1, 1, 1, 1);
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
- readOpenTunerFlags("promise_flags"); // Resets the OpenTuner counters
-
-
- void* gaussian_out = ConvLayer_PROMISE(input, 0, 255, gaussian_filter, 0, 1, NULL, 0, 0,
- 4, 4, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* sharpen_out = ConvLayer_PROMISE(gaussian_out, 0, 255, sharpen_filter, -1, 8, NULL, 0, 0,
- 1, 1, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* result = ConvLayer_PROMISE(sharpen_out, 0, 255, motionblur_filter, 0, 1, NULL, 0, 0,
- 4, 4, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
-
- hpvm_request_tensor(result, 0);
- dumpOutput(result, "GSM_valid_30db.bin");
-
- computePSNRViolation(golden_output, result, PSNR);
-
- if(Opentuner_run){
-
- const char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
- }
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(0);
-
- testPipeline();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_promise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_promise.cc
deleted file mode 100644
index 7dd70134731c4fc13d6b7ca239f0566942c02885..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/pipeline_promise.cc
+++ /dev/null
@@ -1,149 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-bool Opentuner_run = false;
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testPipeline(){
-
- int total_runs = 1;
- if(Opentuner_run){
- total_runs = 1000000;
- }
-
- printf("********* Pipeline: Gaussian - Outline - Motion Blur - Emboss ********** \n");
-
- int test_batch_size = 2000;
- int H = 240;
- int W = 300;
- float PSNR = 30;
-
- void* golden_output = readTrainedWeights("../model_params/pipeline/golden_output/caltech-G-O-M-E-FP32-clipped-2000.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- clearTensorMap();
- for(int i = 0; i < total_runs; i++){
- void* input = readTrainedWeights("../model_params/pipeline/dataset/caltech101_255_float32.bin",
- float_type,
- test_batch_size, 1, H, W);
-
- // NOTE: Filter descriptors do NOT have batch size
- // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
- // IMP: The output channels matches the trained model - not the Lenet arch proposed in Andrew Ng's class
- void* gaussian_filter = readTrainedWeights("../model_params/pipeline/filters/GaussianFilter.bin",
- float_type, 1, 1, 9, 9);
- void* outline_filter = readTrainedWeights("../model_params/pipeline/filters/OutlineFilter.bin",
- float_type, 1, 1, 3, 3);
- void* sharpen_filter = readTrainedWeights("../model_params/pipeline/filters/SharpenFilter.bin",
- float_type, 1, 1, 3, 3);
- void* motionblur_filter = readTrainedWeights("../model_params/pipeline/filters/MotionblurFilter.bin",
- float_type, 1, 1, 9, 9);
- void* emboss_filter = readTrainedWeights("../model_params/pipeline/filters/EmbossFilter.bin",
- float_type, 1, 1, 5, 5);
- void* emboss_bias = readTrainedWeights("../model_params/pipeline/filters/EmbossBias.bin",
- float_type, 1, 1, 1, 1);
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd = open(myfifo, O_RDONLY);
-
- int ret_val = fcntl(fd, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
-
- char str[100];
- read(fd, str, 80);
- if(strcmp(str, "stop_run") == 0){
- abort();
- }
-
- close(fd);
- }
-
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
-
- void* gaussian_out = ConvLayer_PROMISE(input, 0, 255, gaussian_filter, 0, 1, NULL, 0, 0,
- 4, 4, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
- printf("Gaussian done\n");
- void* outline_out = ConvLayer_PROMISE(gaussian_out, 0, 255, outline_filter, -1, 8, NULL, 0, 0,
- 1, 1, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* motionblur_out = ConvLayer_PROMISE(outline_out, 0, 255, motionblur_filter, 0, 1, NULL, 0, 0,
- 4, 4, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- void* result = ConvLayer_PROMISE(motionblur_out, 0, 255, emboss_filter, -1, 1, emboss_bias, 128, 128,
- 2, 2, 1, 1,
- 0, 0, // pool? no pooling needed
- 2,
- 0, 255, // out min max? should we assume 0 - 255 for all filters.
- // Will have to rerun to generate golden output
- 9);
-
- hpvm_request_tensor(result, 0);
- dumpOutput(result);
-
- computePSNRViolation(golden_output, result, PSNR);
- freeOutputTensors();
-
- if(Opentuner_run){
-
- char* myfifo = "/tmp/myfifo";
- int fd_out = open(myfifo, O_WRONLY);
- int ret_val = fcntl(fd_out, F_GETFD);
- if(ret_val == -1){
- printf("Invalid descriptor \n");
- abort();
- }
- const char* str = "completed***!\n\0";
- write(fd_out, str, 80);
- close(fd_out);
- }
- }
-}
-
-
-int main(int argc, char* argv[]){
-
- if(argc > 1)
- Opentuner_run = true;
-
- llvm_hpvm_initTensorRt(0);
-
- testPipeline();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/resnet18_promise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/resnet18_promise.cc
deleted file mode 100644
index b229fc9c2b81703c2d29039480297192a0a3c746..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/resnet18_promise.cc
+++ /dev/null
@@ -1,224 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-
-int main(int argc, char* argv[]){
-
- int test_input_size = 3000;
- int batch_size = 1000;
- int offset = 5000;
-
- if (argc > 1){
- total_runs = atoi(argv[1]);
- }
-
- if (argc > 2){
- bench_acc = atof(argv[2]);
- }
-
- if(argc > 3){
- to_skip = atoi(argv[3]);
- }
-
- if(argc > 4){
- test_input_size = atoi(argv[4]);
- }
-
- if(argc > 5){
- offset = atoi(argv[5]);
- }
-
-
-
- llvm_hpvm_initTensorRt(1);
-
- int missed = 0;
- for (int i = 0 ; i < total_runs; i++){
-
- if (missed >= to_skip){
- break;
- }
-
- startMemTracking();
-
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- std::string dir_prefix = std::string("../model_params/resnet18_cifar10_promise/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,16,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,16,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,16,1,1);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_14_b_path = dir_prefix + std::string("conv2d_14_b.bin");
- void* conv2d_14_b = readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_15_w_path = dir_prefix + std::string("conv2d_15_w.bin");
- void* conv2d_15_w = readTrainedWeights(conv2d_15_w_path.c_str(), 0,64,32,3,3);
- std::string conv2d_15_b_path = dir_prefix + std::string("conv2d_15_b.bin");
- void* conv2d_15_b = readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_17_w_path = dir_prefix + std::string("conv2d_17_w.bin");
- void* conv2d_17_w = readTrainedWeights(conv2d_17_w_path.c_str(), 0,64,32,1,1);
- std::string conv2d_17_b_path = dir_prefix + std::string("conv2d_17_b.bin");
- void* conv2d_17_b = readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_16_w_path = dir_prefix + std::string("conv2d_16_w.bin");
- void* conv2d_16_w = readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_16_b_path = dir_prefix + std::string("conv2d_16_b.bin");
- void* conv2d_16_b = readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_18_w_path = dir_prefix + std::string("conv2d_18_w.bin");
- void* conv2d_18_w = readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_18_b_path = dir_prefix + std::string("conv2d_18_b.bin");
- void* conv2d_18_b = readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_19_w_path = dir_prefix + std::string("conv2d_19_w.bin");
- void* conv2d_19_w = readTrainedWeights(conv2d_19_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_19_b_path = dir_prefix + std::string("conv2d_19_b.bin");
- void* conv2d_19_b = readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_20_w_path = dir_prefix + std::string("conv2d_20_w.bin");
- void* conv2d_20_w = readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_20_b_path = dir_prefix + std::string("conv2d_20_b.bin");
- void* conv2d_20_b = readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_21_w_path = dir_prefix + std::string("conv2d_21_w.bin");
- void* conv2d_21_w = readTrainedWeights(conv2d_21_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_21_b_path = dir_prefix + std::string("conv2d_21_b.bin");
- void* conv2d_21_b = readTrainedWeights(conv2d_21_b_path.c_str(), 0,1,64,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,64,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
- int start = i * batch_size + offset;
- int end = (i + 1) * batch_size + offset;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = ConvLayer_PROMISE(input, -0.5500815, 0.60786617, conv2d_1_w, -1.0248864, 1.2929907, conv2d_1_b, -0.36291853, 0.2533059, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.8791630274057383, 9);
- void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 0.8791630274057383, conv2d_2_w, -0.69884616, 0.71849966, conv2d_2_b, -0.2781147, 0.45571187, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.1859495645761484, 9);
- void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 1.1859495645761484, conv2d_3_w, -0.59568167, 0.7714691, conv2d_3_b, -0.8602873, 0.19743633, 1, 1, 1, 1, -1, 0, -1, -2.2316832554340365, 2.266301159858699, 9);
- void* var_3 = tensorAdd(var_0, var_2);
- void* var_4 = tensorRelu(var_3);
- void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 2.789569139480591, conv2d_4_w, -0.41976976, 0.43748936, conv2d_4_b, -0.7021962, 0.3033103, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.3341254055499974, 9);
- void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 1.3341254055499974, conv2d_5_w, -0.46757826, 0.4635873, conv2d_5_b, -0.20662616, 0.1778044, 1, 1, 1, 1, -1, 0, -1, -0.9912706619501114, 1.0245310074090952, 9);
- void* var_7 = tensorAdd(var_4, var_6);
- void* var_8 = tensorRelu(var_7);
- void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 2.998989346027372, conv2d_6_w, -0.64404047, 0.45383143, conv2d_6_b, -0.819547, 0.38550296, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.2850778144597967, 9);
- void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 1.2850778144597967, conv2d_7_w, -0.41986948, 0.33654243, conv2d_7_b, -0.3563013, 0.22371122, 1, 1, 1, 1, -1, 0, -1, -1.2940701305866242, 0.7332147359848022, 9);
- void* var_11 = tensorAdd(var_8, var_10);
- void* var_12 = tensorRelu(var_11);
- void* var_13 = ConvLayer_PROMISE(var_12, 0.0, 2.8626382386684384, conv2d_8_w, -0.4805263, 0.50655717, conv2d_8_b, -0.296758, 0.7742441, 1, 1, 2, 2, -1, 0, 1, 0.0, 3.6232483506202584, 9);
- void* var_14 = ConvLayer_PROMISE(var_13, 0.0, 3.6232483506202584, conv2d_9_w, -0.52083415, 0.45517674, conv2d_9_b, -0.20242067, 0.8236838, 1, 1, 1, 1, -1, 0, -1, -6.319877154827118, 6.882811555862418, 9);
- void* var_15 = ConvLayer_PROMISE(var_12, 0.0, 2.8626382386684384, conv2d_10_w, -0.5338656, 1.3395424, conv2d_10_b, -0.20242067, 0.8236838, 0, 0, 2, 2, -1, 0, -1, -0.9930689406394959, 2.8721754658222096, 9);
- void* var_16 = tensorAdd(var_15, var_14);
- void* var_17 = tensorRelu(var_16);
- void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 8.315858840942383, conv2d_11_w, -0.34429058, 0.43629733, conv2d_11_b, -1.0744808, 0.056708273, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.6893706333637226, 9);
- void* var_19 = ConvLayer_PROMISE(var_18, 0.0, 2.6893706333637226, conv2d_12_w, -0.30342352, 0.39493486, conv2d_12_b, -0.44630566, 0.6492069, 1, 1, 1, 1, -1, 0, -1, -1.8801953810453416, 1.714934362173068, 9);
- void* var_20 = tensorAdd(var_17, var_19);
- void* var_21 = tensorRelu(var_20);
- void* var_22 = ConvLayer_PROMISE(var_21, 0.0, 8.381670951843262, conv2d_13_w, -0.38351893, 0.45775774, conv2d_13_b, -1.4733055, -0.014426912, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.569231034517287, 9);
- void* var_23 = ConvLayer_PROMISE(var_22, 0.0, 2.569231034517287, conv2d_14_w, -0.25695276, 0.45372736, conv2d_14_b, -0.5259744, 0.26591402, 1, 1, 1, 1, -1, 0, -1, -1.9701244848966597, 1.4661400413513093, 9);
- void* var_24 = tensorAdd(var_21, var_23);
- void* var_25 = tensorRelu(var_24);
- void* var_26 = ConvLayer_PROMISE(var_25, 0.0, 8.188224797248836, conv2d_15_w, -0.55299705, 0.5443531, conv2d_15_b, -0.71790683, 1.2730768, 1, 1, 2, 2, -1, 0, 1, 0.0, 12.411911067962677, 9);
- void* var_27 = ConvLayer_PROMISE(var_26, 0.0, 12.411911067962677, conv2d_16_w, -0.4203967, 0.48641303, conv2d_16_b, -0.90653443, 1.3546854, 1, 1, 1, 1, -1, 0, -1, -25.407194147109987, 20.519153985977383, 9);
- void* var_28 = ConvLayer_PROMISE(var_25, 0.0, 8.188224797248836, conv2d_17_w, -0.4365755, 0.84913826, conv2d_17_b, -0.90653443, 1.3546851, 0, 0, 2, 2, -1, 0, -1, -4.256520752906799, 5.730506427288059, 9);
- void* var_29 = tensorAdd(var_28, var_27);
- void* var_30 = tensorRelu(var_29);
- void* var_31 = ConvLayer_PROMISE(var_30, 0.0, 22.350475664138983, conv2d_18_w, -0.38657624, 0.5228989, conv2d_18_b, -1.2083547, 0.76361173, 1, 1, 1, 1, -1, 0, 1, 0.0, 23.93387042045599, 9);
- void* var_32 = ConvLayer_PROMISE(var_31, 0.0, 23.93387042045599, conv2d_19_w, -0.40857902, 0.575035, conv2d_19_b, -1.8731614, 1.0960501, 1, 1, 1, 1, -1, 0, -1, -35.37134181976318, 19.209569931030273, 9);
- void* var_33 = tensorAdd(var_30, var_32);
- void* var_34 = tensorRelu(var_33);
- void* var_35 = ConvLayer_PROMISE(var_34, 0.0, 29.434949998855657, conv2d_20_w, -0.33079496, 0.5893278, conv2d_20_b, -1.0234511, 1.0016295, 1, 1, 1, 1, -1, 0, 1, 0.0, 27.216757345199866, 9);
- void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 27.216757345199866, conv2d_21_w, -0.27897888, 0.38280907, conv2d_21_b, -2.2086356, 1.0066502, 1, 1, 1, 1, -1, 0, -1, -42.31447326660156, 29.365212144852038, 9);
- void* var_37 = tensorAdd(var_34, var_36);
- void* var_38 = tensorRelu(var_37);
- void* var_39 = tensorPooling(var_38,1,8,8,0,0,8,8);
- void* var_40 = FCLayer_PROMISE(var_39, 0.0, 13.736315393447876, dense_1_w, -1.5092047, 1.0279838, dense_1_b, -0.49379802, 0.61032647, -1, -45.52749088287353, 31.64324799537669, 9);
- void* var_41 = tensorSoftmax(var_40);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_41);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- if (final_accuracy < bench_acc)
- missed += 1;
- }
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/resnet18_promise_relu.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/resnet18_promise_relu.cc
deleted file mode 100644
index 7b7f989c16d9203778a602bc03b79a5d41c7a3ba..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/resnet18_promise_relu.cc
+++ /dev/null
@@ -1,162 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-
-std::string dir_prefix = std::string("../model_params/resnet18_cifar10_promise/");
-std::string input_path = dir_prefix + std::string("input.bin");
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
-std::string labels_path = dir_prefix + std::string("labels.bin");
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,16,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
-void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
-void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,16,3,3);
-std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
-void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,16,1,1);
-std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
-void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
-void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
-void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
-void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
-void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
-void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_14_b_path = dir_prefix + std::string("conv2d_14_b.bin");
-void* conv2d_14_b = readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_15_w_path = dir_prefix + std::string("conv2d_15_w.bin");
-void* conv2d_15_w = readTrainedWeights(conv2d_15_w_path.c_str(), 0,64,32,3,3);
-std::string conv2d_15_b_path = dir_prefix + std::string("conv2d_15_b.bin");
-void* conv2d_15_b = readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_17_w_path = dir_prefix + std::string("conv2d_17_w.bin");
-void* conv2d_17_w = readTrainedWeights(conv2d_17_w_path.c_str(), 0,64,32,1,1);
-std::string conv2d_17_b_path = dir_prefix + std::string("conv2d_17_b.bin");
-void* conv2d_17_b = readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_16_w_path = dir_prefix + std::string("conv2d_16_w.bin");
-void* conv2d_16_w = readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_16_b_path = dir_prefix + std::string("conv2d_16_b.bin");
-void* conv2d_16_b = readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_18_w_path = dir_prefix + std::string("conv2d_18_w.bin");
-void* conv2d_18_w = readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_18_b_path = dir_prefix + std::string("conv2d_18_b.bin");
-void* conv2d_18_b = readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_19_w_path = dir_prefix + std::string("conv2d_19_w.bin");
-void* conv2d_19_w = readTrainedWeights(conv2d_19_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_19_b_path = dir_prefix + std::string("conv2d_19_b.bin");
-void* conv2d_19_b = readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_20_w_path = dir_prefix + std::string("conv2d_20_w.bin");
-void* conv2d_20_w = readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_20_b_path = dir_prefix + std::string("conv2d_20_b.bin");
-void* conv2d_20_b = readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_21_w_path = dir_prefix + std::string("conv2d_21_w.bin");
-void* conv2d_21_w = readTrainedWeights(conv2d_21_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_21_b_path = dir_prefix + std::string("conv2d_21_b.bin");
-void* conv2d_21_b = readTrainedWeights(conv2d_21_b_path.c_str(), 0,1,64,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,64,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-void* var_0 = ConvLayer_PROMISE(input, -0.5500815, 0.60786617, conv2d_1_w, -0.71850556, 0.79279953, conv2d_1_b, -0.2551266, 0.14472985, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.2546353, 9);
-void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 2.2546353, conv2d_2_w, -0.5433847, 0.5556715, conv2d_2_b, -0.19323121, 0.20603828, 1, 1, 1, 1, -1, 0, 1, 0.0, 3.6603086, 9);
-void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 3.6603086, conv2d_3_w, -0.541787, 0.51889443, conv2d_3_b, -0.2030649, 0.21818772, 1, 1, 1, 1, -1, 0, -1, -5.471612, 5.295037, 9);
-void* var_3 = tensorAdd(var_0, var_2);
-void* var_4 = tensorRelu(var_3);
-void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 6.738059, conv2d_4_w, -0.691922, 0.3410589, conv2d_4_b, -0.5095374, 0.18683507, 1, 1, 1, 1, -1, 0, 1, 0.0, 5.2085133, 9);
-void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 5.2085133, conv2d_5_w, -0.40904462, 0.39255425, conv2d_5_b, -0.2069035, 0.117769495, 1, 1, 1, 1, -1, 0, -1, -5.6378636, 6.844163, 9);
-void* var_7 = tensorAdd(var_4, var_6);
-void* var_8 = tensorRelu(var_7);
-void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 8.4156885, conv2d_6_w, -0.38497055, 0.3736088, conv2d_6_b, -0.14458452, 0.18792383, 1, 1, 1, 1, -1, 0, 1, 0.0, 6.5020022, 9);
-void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 6.5020022, conv2d_7_w, -0.30858195, 0.4282964, conv2d_7_b, -0.1807645, 0.07482771, 1, 1, 1, 1, -1, 0, -1, -3.3083274, 5.364109, 9);
-void* var_11 = tensorAdd(var_8, var_10);
-void* var_12 = tensorRelu(var_11);
-void* var_13 = ConvLayer_PROMISE(var_12, 0.0, 9.382513, conv2d_8_w, -0.5838584, 0.44527876, conv2d_8_b, -0.2637087, 0.22768898, 1, 1, 2, 2, -1, 0, 1, 0.0, 12.158108, 9);
-void* var_14 = ConvLayer_PROMISE(var_13, 0.0, 12.158108, conv2d_9_w, -0.46162197, 0.42936426, conv2d_9_b, -0.1289545, 0.51804763, 1, 1, 1, 1, -1, 0, -1, -17.15394, 12.169734, 9);
-void* var_15 = ConvLayer_PROMISE(var_12, 0.0, 9.382513, conv2d_10_w, -0.69971406, 0.99415976, conv2d_10_b, -0.1289545, 0.51804763, 0, 0, 2, 2, -1, 0, -1, -5.418469, 11.448848, 9);
-void* var_16 = tensorAdd(var_15, var_14);
-void* var_17 = tensorRelu(var_16);
-void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 18.46502, conv2d_11_w, -0.43470153, 0.36867705, conv2d_11_b, -0.51738244, 0.15350178, 1, 1, 1, 1, -1, 0, 1, 0.0, 13.211603, 9);
-void* var_19 = ConvLayer_PROMISE(var_18, 0.0, 13.211603, conv2d_12_w, -0.29948497, 0.38820583, conv2d_12_b, -0.37389848, 0.26664862, 1, 1, 1, 1, -1, 0, -1, -10.085186, 13.555471, 9);
-void* var_20 = tensorAdd(var_17, var_19);
-void* var_21 = tensorRelu(var_20);
-void* var_22 = ConvLayer_PROMISE(var_21, 0.0, 22.695429, conv2d_13_w, -0.44317818, 0.30531815, conv2d_13_b, -0.36851564, 0.06573071, 1, 1, 1, 1, -1, 0, 1, 0.0, 19.886229, 9);
-void* var_23 = ConvLayer_PROMISE(var_22, 0.0, 19.886229, conv2d_14_w, -0.3271309, 0.33153397, conv2d_14_b, -0.38927156, 0.066472165, 1, 1, 1, 1, -1, 0, -1, -8.295334, 15.001421, 9);
-void* var_24 = tensorAdd(var_21, var_23);
-void* var_25 = tensorRelu(var_24);
-void* var_26 = ConvLayer_PROMISE(var_25, 0.0, 28.637527, conv2d_15_w, -0.44983515, 0.43999374, conv2d_15_b, -0.21998975, 0.36213604, 1, 1, 2, 2, -1, 0, 1, 0.0, 44.106163, 9);
-void* var_27 = ConvLayer_PROMISE(var_26, 0.0, 44.106163, conv2d_16_w, -0.4508994, 0.41697323, conv2d_16_b, -0.27649263, 0.42242092, 1, 1, 1, 1, -1, 0, -1, -47.52727, 75.15572, 9);
-void* var_28 = ConvLayer_PROMISE(var_25, 0.0, 28.637527, conv2d_17_w, -0.57827795, 0.7829617, conv2d_17_b, -0.27649248, 0.42242065, 0, 0, 2, 2, -1, 0, -1, -8.998529, 10.628808, 9);
-void* var_29 = tensorAdd(var_28, var_27);
-void* var_30 = tensorRelu(var_29);
-void* var_31 = ConvLayer_PROMISE(var_30, 0.0, 77.00688, conv2d_18_w, -0.37020415, 0.4076619, conv2d_18_b, -0.572569, 0.288411, 1, 1, 1, 1, -1, 0, 1, 0.0, 58.209835, 9);
-void* var_32 = ConvLayer_PROMISE(var_31, 0.0, 58.209835, conv2d_19_w, -0.40217596, 0.4803875, conv2d_19_b, -0.84837836, 0.41470897, 1, 1, 1, 1, -1, 0, -1, -61.702118, 45.982677, 9);
-void* var_33 = tensorAdd(var_30, var_32);
-void* var_34 = tensorRelu(var_33);
-void* var_35 = ConvLayer_PROMISE(var_34, 0.0, 98.688995, conv2d_20_w, -0.41761914, 0.4584275, conv2d_20_b, -0.335136, 0.42988807, 1, 1, 1, 1, -1, 0, 1, 0.0, 95.06278, 9);
-void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 95.06278, conv2d_21_w, -0.32336038, 0.35931262, conv2d_21_b, -0.645176, 0.45402992, 1, 1, 1, 1, -1, 0, -1, -116.62798, 127.2517, 9);
-void* var_37 = tensorAdd(var_34, var_36);
-void* var_38 = tensorRelu(var_37);
-void* var_39 = tensorPooling(var_38,1,8,8,0,0,8,8);
-void* var_40 = FCLayer_PROMISE(var_39, 0.0, 22.762705, dense_1_w, -0.876813, 0.6065728, dense_1_b, -0.36824417, 0.25160706, -1, -30.914663, 49.802082, 9);
-void* var_41 = tensorSoftmax(var_40);
-
-computeAccuracy2(labels,10000,var_41);
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/resnet18_valid.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/resnet18_valid.cc
deleted file mode 100644
index 63aef3744fabc598ccc6653534074283edecef03..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/resnet18_valid.cc
+++ /dev/null
@@ -1,189 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- int total_runs = 20;
- for (int i = 0 ; i < total_runs; i++){
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 2500;
- int offset = 0;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- std::string dir_prefix = std::string("../model_params/resnet18_cifar10_promise/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,16,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,16,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,16,1,1);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_14_b_path = dir_prefix + std::string("conv2d_14_b.bin");
- void* conv2d_14_b = readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_15_w_path = dir_prefix + std::string("conv2d_15_w.bin");
- void* conv2d_15_w = readTrainedWeights(conv2d_15_w_path.c_str(), 0,64,32,3,3);
- std::string conv2d_15_b_path = dir_prefix + std::string("conv2d_15_b.bin");
- void* conv2d_15_b = readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_17_w_path = dir_prefix + std::string("conv2d_17_w.bin");
- void* conv2d_17_w = readTrainedWeights(conv2d_17_w_path.c_str(), 0,64,32,1,1);
- std::string conv2d_17_b_path = dir_prefix + std::string("conv2d_17_b.bin");
- void* conv2d_17_b = readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_16_w_path = dir_prefix + std::string("conv2d_16_w.bin");
- void* conv2d_16_w = readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_16_b_path = dir_prefix + std::string("conv2d_16_b.bin");
- void* conv2d_16_b = readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_18_w_path = dir_prefix + std::string("conv2d_18_w.bin");
- void* conv2d_18_w = readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_18_b_path = dir_prefix + std::string("conv2d_18_b.bin");
- void* conv2d_18_b = readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_19_w_path = dir_prefix + std::string("conv2d_19_w.bin");
- void* conv2d_19_w = readTrainedWeights(conv2d_19_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_19_b_path = dir_prefix + std::string("conv2d_19_b.bin");
- void* conv2d_19_b = readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_20_w_path = dir_prefix + std::string("conv2d_20_w.bin");
- void* conv2d_20_w = readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_20_b_path = dir_prefix + std::string("conv2d_20_b.bin");
- void* conv2d_20_b = readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_21_w_path = dir_prefix + std::string("conv2d_21_w.bin");
- void* conv2d_21_w = readTrainedWeights(conv2d_21_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_21_b_path = dir_prefix + std::string("conv2d_21_b.bin");
- void* conv2d_21_b = readTrainedWeights(conv2d_21_b_path.c_str(), 0,1,64,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,64,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
- int start = i * batch_size + offset;
- int end = (i + 1) * batch_size + offset;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = ConvLayer_PROMISE(input, -0.5500815, 0.60786617, conv2d_1_w, -1.0248864, 1.2929907, conv2d_1_b, -0.36291853, 0.2533059, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.8791630274057383, 9);
- void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 0.8791630274057383, conv2d_2_w, -0.69884616, 0.71849966, conv2d_2_b, -0.2781147, 0.45571187, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.1859495645761484, 9);
- void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 1.1859495645761484, conv2d_3_w, -0.59568167, 0.7714691, conv2d_3_b, -0.8602873, 0.19743633, 1, 1, 1, 1, -1, 0, -1, -2.2316832554340365, 2.266301159858699, 9);
- void* var_3 = tensorAdd(var_0, var_2);
- void* var_4 = tensorRelu(var_3);
- void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 2.789569139480591, conv2d_4_w, -0.41976976, 0.43748936, conv2d_4_b, -0.7021962, 0.3033103, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.3341254055499974, 9);
- void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 1.3341254055499974, conv2d_5_w, -0.46757826, 0.4635873, conv2d_5_b, -0.20662616, 0.1778044, 1, 1, 1, 1, -1, 0, -1, -0.9912706619501114, 1.0245310074090952, 9);
- void* var_7 = tensorAdd(var_4, var_6);
- void* var_8 = tensorRelu(var_7);
- void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 2.998989346027372, conv2d_6_w, -0.64404047, 0.45383143, conv2d_6_b, -0.819547, 0.38550296, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.2850778144597967, 9);
- void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 1.2850778144597967, conv2d_7_w, -0.41986948, 0.33654243, conv2d_7_b, -0.3563013, 0.22371122, 1, 1, 1, 1, -1, 0, -1, -1.2940701305866242, 0.7332147359848022, 9);
- void* var_11 = tensorAdd(var_8, var_10);
- void* var_12 = tensorRelu(var_11);
- void* var_13 = ConvLayer_PROMISE(var_12, 0.0, 2.8626382386684384, conv2d_8_w, -0.4805263, 0.50655717, conv2d_8_b, -0.296758, 0.7742441, 1, 1, 2, 2, -1, 0, 1, 0.0, 3.6232483506202584, 9);
- void* var_14 = ConvLayer_PROMISE(var_13, 0.0, 3.6232483506202584, conv2d_9_w, -0.52083415, 0.45517674, conv2d_9_b, -0.20242067, 0.8236838, 1, 1, 1, 1, -1, 0, -1, -6.319877154827118, 6.882811555862418, 9);
- void* var_15 = ConvLayer_PROMISE(var_12, 0.0, 2.8626382386684384, conv2d_10_w, -0.5338656, 1.3395424, conv2d_10_b, -0.20242067, 0.8236838, 0, 0, 2, 2, -1, 0, -1, -0.9930689406394959, 2.8721754658222096, 9);
- void* var_16 = tensorAdd(var_15, var_14);
- void* var_17 = tensorRelu(var_16);
- void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 8.315858840942383, conv2d_11_w, -0.34429058, 0.43629733, conv2d_11_b, -1.0744808, 0.056708273, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.6893706333637226, 9);
- void* var_19 = ConvLayer_PROMISE(var_18, 0.0, 2.6893706333637226, conv2d_12_w, -0.30342352, 0.39493486, conv2d_12_b, -0.44630566, 0.6492069, 1, 1, 1, 1, -1, 0, -1, -1.8801953810453416, 1.714934362173068, 9);
- void* var_20 = tensorAdd(var_17, var_19);
- void* var_21 = tensorRelu(var_20);
- void* var_22 = ConvLayer_PROMISE(var_21, 0.0, 8.381670951843262, conv2d_13_w, -0.38351893, 0.45775774, conv2d_13_b, -1.4733055, -0.014426912, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.569231034517287, 9);
- void* var_23 = ConvLayer_PROMISE(var_22, 0.0, 2.569231034517287, conv2d_14_w, -0.25695276, 0.45372736, conv2d_14_b, -0.5259744, 0.26591402, 1, 1, 1, 1, -1, 0, -1, -1.9701244848966597, 1.4661400413513093, 9);
- void* var_24 = tensorAdd(var_21, var_23);
- void* var_25 = tensorRelu(var_24);
- void* var_26 = ConvLayer_PROMISE(var_25, 0.0, 8.188224797248836, conv2d_15_w, -0.55299705, 0.5443531, conv2d_15_b, -0.71790683, 1.2730768, 1, 1, 2, 2, -1, 0, 1, 0.0, 12.411911067962677, 9);
- void* var_27 = ConvLayer_PROMISE(var_26, 0.0, 12.411911067962677, conv2d_16_w, -0.4203967, 0.48641303, conv2d_16_b, -0.90653443, 1.3546854, 1, 1, 1, 1, -1, 0, -1, -25.407194147109987, 20.519153985977383, 9);
- void* var_28 = ConvLayer_PROMISE(var_25, 0.0, 8.188224797248836, conv2d_17_w, -0.4365755, 0.84913826, conv2d_17_b, -0.90653443, 1.3546851, 0, 0, 2, 2, -1, 0, -1, -4.256520752906799, 5.730506427288059, 9);
- void* var_29 = tensorAdd(var_28, var_27);
- void* var_30 = tensorRelu(var_29);
- void* var_31 = ConvLayer_PROMISE(var_30, 0.0, 22.350475664138983, conv2d_18_w, -0.38657624, 0.5228989, conv2d_18_b, -1.2083547, 0.76361173, 1, 1, 1, 1, -1, 0, 1, 0.0, 23.93387042045599, 9);
- void* var_32 = ConvLayer_PROMISE(var_31, 0.0, 23.93387042045599, conv2d_19_w, -0.40857902, 0.575035, conv2d_19_b, -1.8731614, 1.0960501, 1, 1, 1, 1, -1, 0, -1, -35.37134181976318, 19.209569931030273, 9);
- void* var_33 = tensorAdd(var_30, var_32);
- void* var_34 = tensorRelu(var_33);
- void* var_35 = ConvLayer_PROMISE(var_34, 0.0, 29.434949998855657, conv2d_20_w, -0.33079496, 0.5893278, conv2d_20_b, -1.0234511, 1.0016295, 1, 1, 1, 1, -1, 0, 1, 0.0, 27.216757345199866, 9);
- void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 27.216757345199866, conv2d_21_w, -0.27897888, 0.38280907, conv2d_21_b, -2.2086356, 1.0066502, 1, 1, 1, 1, -1, 0, -1, -42.31447326660156, 29.365212144852038, 9);
- void* var_37 = tensorAdd(var_34, var_36);
- void* var_38 = tensorRelu(var_37);
- void* var_39 = tensorPooling(var_38,1,8,8,0,0,8,8);
- void* var_40 = FCLayer_PROMISE(var_39, 0.0, 13.736315393447876, dense_1_w, -1.5092047, 1.0279838, dense_1_b, -0.49379802, 0.61032647, -1, -45.52749088287353, 31.64324799537669, 9);
- void* var_41 = tensorSoftmax(var_40);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_41);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- }
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_promise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_promise.cc
deleted file mode 100644
index ed11c5158d63c9e07188200e28cbfe32f08a87b2..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_promise.cc
+++ /dev/null
@@ -1,175 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-
-int main(int argc, char* argv[]){
-
- int test_input_size = 3000;
- int batch_size = 1000;
- int offset = 5000;
-
-
- if (argc > 1){
- total_runs = atoi(argv[1]);
- }
-
- if (argc > 2){
- bench_acc = atof(argv[2]);
- }
-
- if(argc > 3){
- to_skip = atoi(argv[3]);
- }
-
- if(argc > 4){
- test_input_size = atoi(argv[4]);
- }
-
- if(argc > 5){
- offset = atoi(argv[5]);
- }
-
-
- llvm_hpvm_initTensorRt(1);
-
-
- int missed = 0;
- for (int i = 0 ; i < total_runs; i++){
-
- if (missed >= to_skip){
- break;
- }
-
- startMemTracking();
-
-
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- std::string dir_prefix = std::string("../model_params/vgg16_cifar100_front/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,100);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,100,1,1);
-
-
- int start = i * batch_size + offset;
- int end = (i + 1) * batch_size + offset;
-
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = ConvLayer_PROMISE(input, -1.7829767, 1.9456929, conv2d_1_w, -0.7450515, 0.71249133, conv2d_1_b, -1.5885142, 0.275554, 1, 1, 1, 1, -1, 0, 1, 0.0, 8.190712, 9);
- void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 8.190712, conv2d_2_w, -0.30790088, 0.43504623, conv2d_2_b, -1.4242363, 1.2602744, 1, 1, 1, 1, 0, 2, 1, 0.0, 19.023172, 9);
- void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 19.023172, conv2d_3_w, -0.29189092, 0.26958522, conv2d_3_b, -1.0527138, 0.9075671, 1, 1, 1, 1, -1, 0, 1, 0.0, 14.428051, 9);
- void* var_3 = ConvLayer_PROMISE(var_2, 0.0, 14.428051, conv2d_4_w, -0.15521508, 0.1829038, conv2d_4_b, -0.845419, 1.9358484, 1, 1, 1, 1, 0, 2, 1, 0.0, 23.065294, 9);
- void* var_4 = ConvLayer_PROMISE(var_3, 0.0, 23.065294, conv2d_5_w, -0.13149762, 0.14811686, conv2d_5_b, -0.7162557, 1.0370971, 1, 1, 1, 1, -1, 0, 1, 0.0, 15.165984, 9);
- void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 15.165984, conv2d_6_w, -0.06236292, 0.08321518, conv2d_6_b, -0.9067523, 0.9922458, 1, 1, 1, 1, -1, 0, 1, 0.0, 13.664733, 9);
- void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 13.664733, conv2d_7_w, -0.06471479, 0.1024472, conv2d_7_b, -0.15943134, 0.7988499, 1, 1, 1, 1, 0, 2, 1, 0.0, 19.025272, 9);
- void* var_7 = ConvLayer_PROMISE(var_6, 0.0, 19.025272, conv2d_8_w, -0.06320205, 0.08291938, conv2d_8_b, -0.32540628, 0.5203079, 1, 1, 1, 1, -1, 0, 1, 0.0, 6.727217, 9);
- void* var_8 = ConvLayer_PROMISE(var_7, 0.0, 6.727217, conv2d_9_w, -0.037707984, 0.051601283, conv2d_9_b, -0.25622904, 0.11251946, 1, 1, 1, 1, -1, 0, 1, 0.0, 3.2003012, 9);
- void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 3.2003012, conv2d_10_w, -0.056007143, 0.09549151, conv2d_10_b, -0.11591503, 0.06267536, 1, 1, 1, 1, 0, 2, 1, 0.0, 4.321189, 9);
- void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 4.321189, conv2d_11_w, -0.060094673, 0.10868926, conv2d_11_b, -0.105962686, 0.09584572, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.936297, 9);
- void* var_11 = ConvLayer_PROMISE(var_10, 0.0, 2.936297, conv2d_12_w, -0.034618977, 0.05792674, conv2d_12_b, -0.4237576, 0.11035452, 1, 1, 1, 1, -1, 0, 1, 0.0, 4.87262, 9);
- void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.87262, conv2d_13_w, -0.035480656, 0.058295887, conv2d_13_b, -0.21477045, 0.14263579, 1, 1, 1, 1, 0, 2, 1, 0.0, 10.32133, 9);
- void* var_13 = FCLayer_PROMISE(var_12, 0.0, 10.32133, dense_1_w, -0.08929961, 0.11301676, dense_1_b, -0.20798548, 0.47405547, 1, 0.0, 13.91, 9);
- void* var_14 = FCLayer_PROMISE(var_13, 0.0, 13.91, dense_2_w, -0.6627122, 0.35539475, dense_2_b, -1.0631907, 0.9830786, -1, -70.45701, 87.34367, 9);
- void* var_15 = tensorSoftmax(var_14);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_15, 100);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- if (final_accuracy < bench_acc)
- missed += 1;
- }
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_promise_quant.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_promise_quant.cc
deleted file mode 100644
index bbc247fc46fa553a2d8fb479c77023c9960375d6..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_promise_quant.cc
+++ /dev/null
@@ -1,133 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
- int total_runs = 1;
- for (int i = 0 ; i < total_runs; i++){
-
- startMemTracking();
-
- int test_input_size = 10000;
- int batch_size = 2500;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- std::string dir_prefix = std::string("../model_params/vgg16_cifar100_front/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,100);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,100,1,1);
-
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = ConvLayer_PROMISE(input, -1.7829767, 1.9456929, conv2d_1_w, -0.7450515, 0.71249133, conv2d_1_b, -1.5885142, 0.275554, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.7384350299835205, 9);
- void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 1.7384350299835205, conv2d_2_w, -0.30790088, 0.43504623, conv2d_2_b, -1.4242363, 1.2602744, 1, 1, 1, 1, 0, 2, 1, 0.0, 4.417154796123498, 9);
- void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 4.417154796123498, conv2d_3_w, -0.29189092, 0.26958522, conv2d_3_b, -1.0527138, 0.9075671, 1, 1, 1, 1, -1, 0, 1, 0.0, 3.1919608163833573, 9);
- void* var_3 = ConvLayer_PROMISE(var_2, 0.0, 3.1919608163833573, conv2d_4_w, -0.15521508, 0.1829038, conv2d_4_b, -0.845419, 1.9358484, 1, 1, 1, 1, 0, 2, 1, 0.0, 5.108994026184064, 9);
- void* var_4 = ConvLayer_PROMISE(var_3, 0.0, 5.108994026184064, conv2d_5_w, -0.13149762, 0.14811686, conv2d_5_b, -0.7162557, 1.0370971, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.8264513099193493, 9);
- void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 2.8264513099193493, conv2d_6_w, -0.06236292, 0.08321518, conv2d_6_b, -0.9067523, 0.9922458, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.507186658382409, 9);
- void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 2.507186658382409, conv2d_7_w, -0.06471479, 0.1024472, conv2d_7_b, -0.15943134, 0.7988499, 1, 1, 1, 1, 0, 2, 1, 0.0, 2.550416946411133, 9);
- void* var_7 = ConvLayer_PROMISE(var_6, 0.0, 2.550416946411133, conv2d_8_w, -0.06320205, 0.08291938, conv2d_8_b, -0.32540628, 0.5203079, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.7303829237818675, 9);
- void* var_8 = ConvLayer_PROMISE(var_7, 0.0, 0.7303829237818675, conv2d_9_w, -0.037707984, 0.051601283, conv2d_9_b, -0.25622904, 0.11251946, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.32286912292241965, 9);
- void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 0.32286912292241965, conv2d_10_w, -0.056007143, 0.09549151, conv2d_10_b, -0.11591503, 0.06267536, 1, 1, 1, 1, 0, 2, 1, 0.0, 0.47936276525258825, 9);
- void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 0.47936276525258825, conv2d_11_w, -0.060094673, 0.10868926, conv2d_11_b, -0.105962686, 0.09584572, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.6409912902116734, 9);
- void* var_11 = ConvLayer_PROMISE(var_10, 0.0, 0.6409912902116734, conv2d_12_w, -0.034618977, 0.05792674, conv2d_12_b, -0.4237576, 0.11035452, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.1027569955587349, 9);
- void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 1.1027569955587349, conv2d_13_w, -0.035480656, 0.058295887, conv2d_13_b, -0.21477045, 0.14263579, 1, 1, 1, 1, 0, 2, 1, 0.0, 2.4708798038959503, 9);
- void* var_13 = FCLayer_PROMISE(var_12, 0.0, 2.4708798038959503, dense_1_w, -0.08929961, 0.11301676, dense_1_b, -0.20798548, 0.47405547, 1, 0.0, 2.8148007798194876, 9);
- void* var_14 = FCLayer_PROMISE(var_13, 0.0, 2.8148007798194876, dense_2_w, -0.6627122, 0.35539475, dense_2_b, -1.0631907, 0.9830786, -1, -21.189617557525633, 22.645009384155276, 9);
- void* var_15 = tensorSoftmax(var_14);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_15, 100);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
- }
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_top5_promise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_top5_promise.cc
deleted file mode 100644
index 7911c645679f31171e1c1f87facc1c1f82640adc..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_top5_promise.cc
+++ /dev/null
@@ -1,137 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(3);
-
- int total_runs = 1;
- for (int i = 0 ; i < total_runs; i++){
-
- startMemTracking();
-
- int test_input_size = 4000;
- //int batch_size = 2500;
- int batch_size = 4000;
- int offset = 5000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- std::string dir_prefix = std::string("../model_params/vgg16_cifar100_front/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,100);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,100,1,1);
-
-
- int start = i * batch_size + offset;
- int end = (i + 1) * batch_size + offset;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = ConvLayer_PROMISE(input, -1.7829767, 1.9456929, conv2d_1_w, -0.7450515, 0.71249133, conv2d_1_b, -1.5885142, 0.275554, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.7384350299835205, 9);
- void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 1.7384350299835205, conv2d_2_w, -0.30790088, 0.43504623, conv2d_2_b, -1.4242363, 1.2602744, 1, 1, 1, 1, 0, 2, 1, 0.0, 4.417154796123498, 9);
- void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 4.417154796123498, conv2d_3_w, -0.29189092, 0.26958522, conv2d_3_b, -1.0527138, 0.9075671, 1, 1, 1, 1, -1, 0, 1, 0.0, 3.1919608163833573, 9);
- void* var_3 = ConvLayer_PROMISE(var_2, 0.0, 3.1919608163833573, conv2d_4_w, -0.15521508, 0.1829038, conv2d_4_b, -0.845419, 1.9358484, 1, 1, 1, 1, 0, 2, 1, 0.0, 5.108994026184064, 9);
- void* var_4 = ConvLayer_PROMISE(var_3, 0.0, 5.108994026184064, conv2d_5_w, -0.13149762, 0.14811686, conv2d_5_b, -0.7162557, 1.0370971, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.8264513099193493, 9);
- void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 2.8264513099193493, conv2d_6_w, -0.06236292, 0.08321518, conv2d_6_b, -0.9067523, 0.9922458, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.507186658382409, 9);
- void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 2.507186658382409, conv2d_7_w, -0.06471479, 0.1024472, conv2d_7_b, -0.15943134, 0.7988499, 1, 1, 1, 1, 0, 2, 1, 0.0, 2.550416946411133, 9);
- void* var_7 = ConvLayer_PROMISE(var_6, 0.0, 2.550416946411133, conv2d_8_w, -0.06320205, 0.08291938, conv2d_8_b, -0.32540628, 0.5203079, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.7303829237818675, 9);
- void* var_8 = ConvLayer_PROMISE(var_7, 0.0, 0.7303829237818675, conv2d_9_w, -0.037707984, 0.051601283, conv2d_9_b, -0.25622904, 0.11251946, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.32286912292241965, 9);
- void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 0.32286912292241965, conv2d_10_w, -0.056007143, 0.09549151, conv2d_10_b, -0.11591503, 0.06267536, 1, 1, 1, 1, 0, 2, 1, 0.0, 0.47936276525258825, 9);
- void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 0.47936276525258825, conv2d_11_w, -0.060094673, 0.10868926, conv2d_11_b, -0.105962686, 0.09584572, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.6409912902116734, 9);
- void* var_11 = ConvLayer_PROMISE(var_10, 0.0, 0.6409912902116734, conv2d_12_w, -0.034618977, 0.05792674, conv2d_12_b, -0.4237576, 0.11035452, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.1027569955587349, 9);
- void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 1.1027569955587349, conv2d_13_w, -0.035480656, 0.058295887, conv2d_13_b, -0.21477045, 0.14263579, 1, 1, 1, 1, 0, 2, 1, 0.0, 2.4708798038959503, 9);
- void* var_13 = FCLayer_PROMISE(var_12, 0.0, 2.4708798038959503, dense_1_w, -0.08929961, 0.11301676, dense_1_b, -0.20798548, 0.47405547, 1, 0.0, 2.8148007798194876, 9);
- void* var_14 = FCLayer_PROMISE(var_13, 0.0, 2.8148007798194876, dense_2_w, -0.6627122, 0.35539475, dense_2_b, -1.0631907, 0.9830786, -1, -21.189617557525633, 22.645009384155276, 9);
- void* var_15 = tensorSoftmax(var_14);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- // float accuracy = computeAccuracy2(labels, batch_size, var_15, 100);
- float accuracy = computeTop5Accuracy(labels, batch_size, var_15, 100);
-
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
- }
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_top5_valid.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_top5_valid.cc
deleted file mode 100644
index 21eb3ba3e09a7a8ef5ae8940d4c60501ac01abe1..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_top5_valid.cc
+++ /dev/null
@@ -1,136 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(2);
-
- int total_runs = 20;
- for (int i = 0 ; i < total_runs; i++){
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 5000;
- int offset = 0;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- std::string dir_prefix = std::string("../model_params/vgg16_cifar100_front/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,100);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,100,1,1);
-
-
- int start = i * batch_size + offset;
- int end = (i + 1) * batch_size + offset;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = ConvLayer_PROMISE(input, -1.7829767, 1.9456929, conv2d_1_w, -0.7450515, 0.71249133, conv2d_1_b, -1.5885142, 0.275554, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.7384350299835205, 9);
- void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 1.7384350299835205, conv2d_2_w, -0.30790088, 0.43504623, conv2d_2_b, -1.4242363, 1.2602744, 1, 1, 1, 1, 0, 2, 1, 0.0, 4.417154796123498, 9);
- void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 4.417154796123498, conv2d_3_w, -0.29189092, 0.26958522, conv2d_3_b, -1.0527138, 0.9075671, 1, 1, 1, 1, -1, 0, 1, 0.0, 3.1919608163833573, 9);
- void* var_3 = ConvLayer_PROMISE(var_2, 0.0, 3.1919608163833573, conv2d_4_w, -0.15521508, 0.1829038, conv2d_4_b, -0.845419, 1.9358484, 1, 1, 1, 1, 0, 2, 1, 0.0, 5.108994026184064, 9);
- void* var_4 = ConvLayer_PROMISE(var_3, 0.0, 5.108994026184064, conv2d_5_w, -0.13149762, 0.14811686, conv2d_5_b, -0.7162557, 1.0370971, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.8264513099193493, 9);
- void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 2.8264513099193493, conv2d_6_w, -0.06236292, 0.08321518, conv2d_6_b, -0.9067523, 0.9922458, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.507186658382409, 9);
- void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 2.507186658382409, conv2d_7_w, -0.06471479, 0.1024472, conv2d_7_b, -0.15943134, 0.7988499, 1, 1, 1, 1, 0, 2, 1, 0.0, 2.550416946411133, 9);
- void* var_7 = ConvLayer_PROMISE(var_6, 0.0, 2.550416946411133, conv2d_8_w, -0.06320205, 0.08291938, conv2d_8_b, -0.32540628, 0.5203079, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.7303829237818675, 9);
- void* var_8 = ConvLayer_PROMISE(var_7, 0.0, 0.7303829237818675, conv2d_9_w, -0.037707984, 0.051601283, conv2d_9_b, -0.25622904, 0.11251946, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.32286912292241965, 9);
- void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 0.32286912292241965, conv2d_10_w, -0.056007143, 0.09549151, conv2d_10_b, -0.11591503, 0.06267536, 1, 1, 1, 1, 0, 2, 1, 0.0, 0.47936276525258825, 9);
- void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 0.47936276525258825, conv2d_11_w, -0.060094673, 0.10868926, conv2d_11_b, -0.105962686, 0.09584572, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.6409912902116734, 9);
- void* var_11 = ConvLayer_PROMISE(var_10, 0.0, 0.6409912902116734, conv2d_12_w, -0.034618977, 0.05792674, conv2d_12_b, -0.4237576, 0.11035452, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.1027569955587349, 9);
- void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 1.1027569955587349, conv2d_13_w, -0.035480656, 0.058295887, conv2d_13_b, -0.21477045, 0.14263579, 1, 1, 1, 1, 0, 2, 1, 0.0, 2.4708798038959503, 9);
- void* var_13 = FCLayer_PROMISE(var_12, 0.0, 2.4708798038959503, dense_1_w, -0.08929961, 0.11301676, dense_1_b, -0.20798548, 0.47405547, 1, 0.0, 2.8148007798194876, 9);
- void* var_14 = FCLayer_PROMISE(var_13, 0.0, 2.8148007798194876, dense_2_w, -0.6627122, 0.35539475, dense_2_b, -1.0631907, 0.9830786, -1, -21.189617557525633, 22.645009384155276, 9);
- void* var_15 = tensorSoftmax(var_14);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- //-- float accuracy = computeAccuracy2(labels, batch_size, var_15, 100);
- float accuracy = computeTop5Accuracy(labels, batch_size, var_15, 100);
-
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
- }
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_valid.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_valid.cc
deleted file mode 100644
index b78c506e618535be50e92d9e77a59ecdd793d720..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_valid.cc
+++ /dev/null
@@ -1,134 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- int total_runs = 20;
- for (int i = 0 ; i < total_runs; i++){
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 2500;
- int offset = 0;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- std::string dir_prefix = std::string("../model_params/vgg16_cifar100_front/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,100);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,100,1,1);
-
-
- int start = i * batch_size + offset;
- int end = (i + 1) * batch_size + offset;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = ConvLayer_PROMISE(input, -1.7829767, 1.9456929, conv2d_1_w, -0.7450515, 0.71249133, conv2d_1_b, -1.5885142, 0.275554, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.7384350299835205, 9);
- void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 1.7384350299835205, conv2d_2_w, -0.30790088, 0.43504623, conv2d_2_b, -1.4242363, 1.2602744, 1, 1, 1, 1, 0, 2, 1, 0.0, 4.417154796123498, 9);
- void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 4.417154796123498, conv2d_3_w, -0.29189092, 0.26958522, conv2d_3_b, -1.0527138, 0.9075671, 1, 1, 1, 1, -1, 0, 1, 0.0, 3.1919608163833573, 9);
- void* var_3 = ConvLayer_PROMISE(var_2, 0.0, 3.1919608163833573, conv2d_4_w, -0.15521508, 0.1829038, conv2d_4_b, -0.845419, 1.9358484, 1, 1, 1, 1, 0, 2, 1, 0.0, 5.108994026184064, 9);
- void* var_4 = ConvLayer_PROMISE(var_3, 0.0, 5.108994026184064, conv2d_5_w, -0.13149762, 0.14811686, conv2d_5_b, -0.7162557, 1.0370971, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.8264513099193493, 9);
- void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 2.8264513099193493, conv2d_6_w, -0.06236292, 0.08321518, conv2d_6_b, -0.9067523, 0.9922458, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.507186658382409, 9);
- void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 2.507186658382409, conv2d_7_w, -0.06471479, 0.1024472, conv2d_7_b, -0.15943134, 0.7988499, 1, 1, 1, 1, 0, 2, 1, 0.0, 2.550416946411133, 9);
- void* var_7 = ConvLayer_PROMISE(var_6, 0.0, 2.550416946411133, conv2d_8_w, -0.06320205, 0.08291938, conv2d_8_b, -0.32540628, 0.5203079, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.7303829237818675, 9);
- void* var_8 = ConvLayer_PROMISE(var_7, 0.0, 0.7303829237818675, conv2d_9_w, -0.037707984, 0.051601283, conv2d_9_b, -0.25622904, 0.11251946, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.32286912292241965, 9);
- void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 0.32286912292241965, conv2d_10_w, -0.056007143, 0.09549151, conv2d_10_b, -0.11591503, 0.06267536, 1, 1, 1, 1, 0, 2, 1, 0.0, 0.47936276525258825, 9);
- void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 0.47936276525258825, conv2d_11_w, -0.060094673, 0.10868926, conv2d_11_b, -0.105962686, 0.09584572, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.6409912902116734, 9);
- void* var_11 = ConvLayer_PROMISE(var_10, 0.0, 0.6409912902116734, conv2d_12_w, -0.034618977, 0.05792674, conv2d_12_b, -0.4237576, 0.11035452, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.1027569955587349, 9);
- void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 1.1027569955587349, conv2d_13_w, -0.035480656, 0.058295887, conv2d_13_b, -0.21477045, 0.14263579, 1, 1, 1, 1, 0, 2, 1, 0.0, 2.4708798038959503, 9);
- void* var_13 = FCLayer_PROMISE(var_12, 0.0, 2.4708798038959503, dense_1_w, -0.08929961, 0.11301676, dense_1_b, -0.20798548, 0.47405547, 1, 0.0, 2.8148007798194876, 9);
- void* var_14 = FCLayer_PROMISE(var_13, 0.0, 2.8148007798194876, dense_2_w, -0.6627122, 0.35539475, dense_2_b, -1.0631907, 0.9830786, -1, -21.189617557525633, 22.645009384155276, 9);
- void* var_15 = tensorSoftmax(var_14);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_15, 100);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
- }
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar10_promise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar10_promise.cc
deleted file mode 100644
index 4398e721f9f7bebd28e54d1d5e682b712a159f8e..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar10_promise.cc
+++ /dev/null
@@ -1,174 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-int total_runs = 1;
-float bench_acc = 0;
-int to_skip = 5;
-
-
-int main(int argc, char* argv[]){
-
- int test_input_size = 3000;
- int batch_size = 1000;
- int offset = 5000;
-
-
- if (argc > 1){
- total_runs = atoi(argv[1]);
- }
-
- if (argc > 2){
- bench_acc = atof(argv[2]);
- }
-
- if(argc > 3){
- to_skip = atoi(argv[3]);
- }
-
- if(argc > 4){
- test_input_size = atoi(argv[4]);
- }
-
- if(argc > 5){
- offset = atoi(argv[5]);
- }
-
-
- llvm_hpvm_initTensorRt(1);
-
- int missed = 0;
- for (int i = 0 ; i < total_runs; i++){
-
- if (missed >= to_skip){
- break;
- }
-
- startMemTracking();
-
-
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- std::string dir_prefix = std::string("../model_params/vgg16_cifar10_2/");
-
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,10);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,10,1,1);
-
-
- int start = i * batch_size + offset;
- int end = (i + 1) * batch_size + offset;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = ConvLayer_PROMISE(input, -1.8816367, 2.0934217, conv2d_1_w, -0.53275156, 0.49437004, conv2d_1_b, -0.6403629, 0.2490165, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.3590874671936035, 9);
- void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 1.3590874671936035, conv2d_2_w, -0.2688396, 0.20639156, conv2d_2_b, -0.7745511, 0.82006615, 1, 1, 1, 1, 0, 2, 1, 0.0, 2.521231179237361, 9);
- void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 2.521231179237361, conv2d_3_w, -0.16776876, 0.14878987, conv2d_3_b, -0.35283303, 0.5154362, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.2011985784769053, 9);
- void* var_3 = ConvLayer_PROMISE(var_2, 0.0, 1.2011985784769053, conv2d_4_w, -0.088948585, 0.114222586, conv2d_4_b, -0.30250227, 0.36856708, 1, 1, 1, 1, 0, 2, 1, 0.0, 1.0359880930185312, 9);
- void* var_4 = ConvLayer_PROMISE(var_3, 0.0, 1.0359880930185312, conv2d_5_w, -0.07739562, 0.10973293, conv2d_5_b, -0.15568458, 0.17634983, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.3004955950379369, 9);
- void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 0.3004955950379369, conv2d_6_w, -0.051649556, 0.05435231, conv2d_6_b, -0.07395447, 0.07996062, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.11490475405007583, 9);
- void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 0.11490475405007583, conv2d_7_w, -0.043513633, 0.07577866, conv2d_7_b, -0.06921874, 0.02660573, 1, 1, 1, 1, 0, 2, 1, 0.0, 0.16232508487999475, 9);
- void* var_7 = ConvLayer_PROMISE(var_6, 0.0, 0.16232508487999475, conv2d_8_w, -0.033842053, 0.045218028, conv2d_8_b, -0.022827804, 0.023845317, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.12424996573477909, 9);
- void* var_8 = ConvLayer_PROMISE(var_7, 0.0, 0.12424996573477909, conv2d_9_w, -0.02211613, 0.032084666, conv2d_9_b, -0.02699063, 0.03773564, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.1746344865113496, 9);
- void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 0.1746344865113496, conv2d_10_w, -0.01979376, 0.034854397, conv2d_10_b, -0.036107242, 0.07056531, 1, 1, 1, 1, 0, 2, 1, 0.0, 0.5751757621765137, 9);
- void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 0.5751757621765137, conv2d_11_w, -0.03452098, 0.046055835, conv2d_11_b, -0.051925894, 0.07039055, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.7718751144409115, 9);
- void* var_11 = ConvLayer_PROMISE(var_10, 0.0, 0.7718751144409115, conv2d_12_w, -0.025946895, 0.040090334, conv2d_12_b, -0.06049362, 0.12658806, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.1728516906499844, 9);
- void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 1.1728516906499844, conv2d_13_w, -0.021766115, 0.03315237, conv2d_13_b, -0.20705001, 0.117947325, 1, 1, 1, 1, 0, 2, 1, 0.0, 2.0015769386291495, 9);
- void* var_13 = FCLayer_PROMISE(var_12, 0.0, 2.0015769386291495, dense_1_w, -0.042597745, 0.046707444, dense_1_b, -0.21937433, 0.2545502, 1, 0.0, 2.002361118793486, 9);
- void* var_14 = FCLayer_PROMISE(var_13, 0.0, 2.002361118793486, dense_2_w, -0.32550547, 0.30829763, dense_2_b, -1.1787822, 1.2378151, -1, -18.251470546722413, 24.17363445281988, 9);
- void* var_15 = tensorSoftmax(var_14);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_15);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- if (final_accuracy < bench_acc)
- missed += 1;
- }
-
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar10_valid.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar10_valid.cc
deleted file mode 100644
index fbaea86634e2b561f78fd3971a731b1a734dcbaf..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar10_valid.cc
+++ /dev/null
@@ -1,137 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- int total_runs = 20;
- for (int i = 0 ; i < total_runs; i++){
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 2500;
- int offset = 0;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- std::string dir_prefix = std::string("../model_params/vgg16_cifar10_2/");
-
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,10);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,10,1,1);
-
-
- int start = i * batch_size + offset;
- int end = (i + 1) * batch_size + offset;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = ConvLayer_PROMISE(input, -1.8816367, 2.0934217, conv2d_1_w, -0.53275156, 0.49437004, conv2d_1_b, -0.6403629, 0.2490165, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.3590874671936035, 9);
- void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 1.3590874671936035, conv2d_2_w, -0.2688396, 0.20639156, conv2d_2_b, -0.7745511, 0.82006615, 1, 1, 1, 1, 0, 2, 1, 0.0, 2.521231179237361, 9);
- void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 2.521231179237361, conv2d_3_w, -0.16776876, 0.14878987, conv2d_3_b, -0.35283303, 0.5154362, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.2011985784769053, 9);
- void* var_3 = ConvLayer_PROMISE(var_2, 0.0, 1.2011985784769053, conv2d_4_w, -0.088948585, 0.114222586, conv2d_4_b, -0.30250227, 0.36856708, 1, 1, 1, 1, 0, 2, 1, 0.0, 1.0359880930185312, 9);
- void* var_4 = ConvLayer_PROMISE(var_3, 0.0, 1.0359880930185312, conv2d_5_w, -0.07739562, 0.10973293, conv2d_5_b, -0.15568458, 0.17634983, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.3004955950379369, 9);
- void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 0.3004955950379369, conv2d_6_w, -0.051649556, 0.05435231, conv2d_6_b, -0.07395447, 0.07996062, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.11490475405007583, 9);
- void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 0.11490475405007583, conv2d_7_w, -0.043513633, 0.07577866, conv2d_7_b, -0.06921874, 0.02660573, 1, 1, 1, 1, 0, 2, 1, 0.0, 0.16232508487999475, 9);
- void* var_7 = ConvLayer_PROMISE(var_6, 0.0, 0.16232508487999475, conv2d_8_w, -0.033842053, 0.045218028, conv2d_8_b, -0.022827804, 0.023845317, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.12424996573477909, 9);
- void* var_8 = ConvLayer_PROMISE(var_7, 0.0, 0.12424996573477909, conv2d_9_w, -0.02211613, 0.032084666, conv2d_9_b, -0.02699063, 0.03773564, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.1746344865113496, 9);
- void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 0.1746344865113496, conv2d_10_w, -0.01979376, 0.034854397, conv2d_10_b, -0.036107242, 0.07056531, 1, 1, 1, 1, 0, 2, 1, 0.0, 0.5751757621765137, 9);
- void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 0.5751757621765137, conv2d_11_w, -0.03452098, 0.046055835, conv2d_11_b, -0.051925894, 0.07039055, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.7718751144409115, 9);
- void* var_11 = ConvLayer_PROMISE(var_10, 0.0, 0.7718751144409115, conv2d_12_w, -0.025946895, 0.040090334, conv2d_12_b, -0.06049362, 0.12658806, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.1728516906499844, 9);
- void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 1.1728516906499844, conv2d_13_w, -0.021766115, 0.03315237, conv2d_13_b, -0.20705001, 0.117947325, 1, 1, 1, 1, 0, 2, 1, 0.0, 2.0015769386291495, 9);
- void* var_13 = FCLayer_PROMISE(var_12, 0.0, 2.0015769386291495, dense_1_w, -0.042597745, 0.046707444, dense_1_b, -0.21937433, 0.2545502, 1, 0.0, 2.002361118793486, 9);
- void* var_14 = FCLayer_PROMISE(var_13, 0.0, 2.002361118793486, dense_2_w, -0.32550547, 0.30829763, dense_2_b, -1.1787822, 1.2378151, -1, -18.251470546722413, 24.17363445281988, 9);
- void* var_15 = tensorSoftmax(var_14);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_15);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- }
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet18_cifar10.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet18_cifar10.cc
deleted file mode 100644
index 97f9ef79a58b0cbac3c38352b1ca3354ec803cf0..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet18_cifar10.cc
+++ /dev/null
@@ -1,221 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- std::string dir_prefix = std::string("../model_params/resnet18_cifar10_3/");
- std::string input_path = dir_prefix + std::string("input.bin");
- //void* input = readTrainedWeights(input_path.c_str(), 0, batch_size,3,32,32);
- std::string labels_path = dir_prefix + std::string("labels.bin");
- //uint8_t* labels = readLabels(labels_path.c_str(), batch_size);
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,16,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,16,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,16,1,1);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_14_b_path = dir_prefix + std::string("conv2d_14_b.bin");
- void* conv2d_14_b = readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_15_w_path = dir_prefix + std::string("conv2d_15_w.bin");
- void* conv2d_15_w = readTrainedWeights(conv2d_15_w_path.c_str(), 0,64,32,3,3);
- std::string conv2d_15_b_path = dir_prefix + std::string("conv2d_15_b.bin");
- void* conv2d_15_b = readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_17_w_path = dir_prefix + std::string("conv2d_17_w.bin");
- void* conv2d_17_w = readTrainedWeights(conv2d_17_w_path.c_str(), 0,64,32,1,1);
- std::string conv2d_17_b_path = dir_prefix + std::string("conv2d_17_b.bin");
- void* conv2d_17_b = readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_16_w_path = dir_prefix + std::string("conv2d_16_w.bin");
- void* conv2d_16_w = readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_16_b_path = dir_prefix + std::string("conv2d_16_b.bin");
- void* conv2d_16_b = readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_18_w_path = dir_prefix + std::string("conv2d_18_w.bin");
- void* conv2d_18_w = readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_18_b_path = dir_prefix + std::string("conv2d_18_b.bin");
- void* conv2d_18_b = readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_19_w_path = dir_prefix + std::string("conv2d_19_w.bin");
- void* conv2d_19_w = readTrainedWeights(conv2d_19_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_19_b_path = dir_prefix + std::string("conv2d_19_b.bin");
- void* conv2d_19_b = readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_20_w_path = dir_prefix + std::string("conv2d_20_w.bin");
- void* conv2d_20_w = readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_20_b_path = dir_prefix + std::string("conv2d_20_b.bin");
- void* conv2d_20_b = readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_21_w_path = dir_prefix + std::string("conv2d_21_w.bin");
- void* conv2d_21_w = readTrainedWeights(conv2d_21_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_21_b_path = dir_prefix + std::string("conv2d_21_b.bin");
- void* conv2d_21_b = readTrainedWeights(conv2d_21_b_path.c_str(), 0,1,64,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,64,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 10000;
- int batch_size = 2000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- // NOTE: Starting time profiling
- startProfiling();
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
-
- void* var_2 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
- void* var_3 = tensorAdd(var_2, conv2d_1_b);
- void* var_4 = tensorRelu(var_3);
- void* var_6 = tensorConvolution(var_4, conv2d_2_w, 1, 1, 1, 1, 1, 0);
- void* var_7 = tensorAdd(var_6, conv2d_2_b);
- void* var_8 = tensorRelu(var_7);
- void* var_10 = tensorConvolution(var_8, conv2d_3_w, 1, 1, 1, 1, 1, 0);
- void* var_11 = tensorAdd(var_10, conv2d_3_b);
- void* var_12 = tensorAdd(var_4, var_11);
- void* var_13 = tensorRelu(var_12);
- void* var_15 = tensorConvolution(var_13, conv2d_4_w, 1, 1, 1, 1, 1, 0);
- void* var_16 = tensorAdd(var_15, conv2d_4_b);
- void* var_17 = tensorRelu(var_16);
- void* var_19 = tensorConvolution(var_17, conv2d_5_w, 1, 1, 1, 1, 1, 0);
- void* var_20 = tensorAdd(var_19, conv2d_5_b);
- void* var_21 = tensorAdd(var_13, var_20);
- void* var_22 = tensorRelu(var_21);
- void* var_24 = tensorConvolution(var_22, conv2d_6_w, 1, 1, 1, 1, 1, 0);
- void* var_25 = tensorAdd(var_24, conv2d_6_b);
- void* var_26 = tensorRelu(var_25);
- void* var_28 = tensorConvolution(var_26, conv2d_7_w, 1, 1, 1, 1, 1, 0);
- void* var_29 = tensorAdd(var_28, conv2d_7_b);
- void* var_30 = tensorAdd(var_22, var_29);
- void* var_31 = tensorRelu(var_30);
- void* var_33 = tensorConvolution(var_31, conv2d_8_w, 1, 1, 2, 2, 1, 0);
- void* var_34 = tensorAdd(var_33, conv2d_8_b);
- void* var_35 = tensorRelu(var_34);
- void* var_37 = tensorConvolution(var_35, conv2d_9_w, 1, 1, 1, 1, 1, 0);
- void* var_38 = tensorAdd(var_37, conv2d_9_b);
- void* var_40 = tensorConvolution(var_31, conv2d_10_w, 0, 0, 2, 2, 1, 0);
- void* var_41 = tensorAdd(var_40, conv2d_10_b);
- void* var_42 = tensorAdd(var_41, var_38);
- void* var_43 = tensorRelu(var_42);
- void* var_45 = tensorConvolution(var_43, conv2d_11_w, 1, 1, 1, 1, 1, 0);
- void* var_46 = tensorAdd(var_45, conv2d_11_b);
- void* var_47 = tensorRelu(var_46);
- void* var_49 = tensorConvolution(var_47, conv2d_12_w, 1, 1, 1, 1, 1, 0);
- void* var_50 = tensorAdd(var_49, conv2d_12_b);
- void* var_51 = tensorAdd(var_43, var_50);
- void* var_52 = tensorRelu(var_51);
- void* var_54 = tensorConvolution(var_52, conv2d_13_w, 1, 1, 1, 1, 1, 0);
- void* var_55 = tensorAdd(var_54, conv2d_13_b);
- void* var_56 = tensorRelu(var_55);
- void* var_58 = tensorConvolution(var_56, conv2d_14_w, 1, 1, 1, 1, 1, 0);
- void* var_59 = tensorAdd(var_58, conv2d_14_b);
- void* var_60 = tensorAdd(var_52, var_59);
- void* var_61 = tensorRelu(var_60);
- void* var_63 = tensorConvolution(var_61, conv2d_15_w, 1, 1, 2, 2, 1, 0);
- void* var_64 = tensorAdd(var_63, conv2d_15_b);
- void* var_65 = tensorRelu(var_64);
- void* var_67 = tensorConvolution(var_65, conv2d_16_w, 1, 1, 1, 1, 1, 0);
- void* var_68 = tensorAdd(var_67, conv2d_16_b);
- void* var_70 = tensorConvolution(var_61, conv2d_17_w, 0, 0, 2, 2, 1, 0);
- void* var_71 = tensorAdd(var_70, conv2d_17_b);
- void* var_72 = tensorAdd(var_71, var_68);
- void* var_73 = tensorRelu(var_72);
- void* var_75 = tensorConvolution(var_73, conv2d_18_w, 1, 1, 1, 1, 1, 0);
- void* var_76 = tensorAdd(var_75, conv2d_18_b);
- void* var_77 = tensorRelu(var_76);
- void* var_79 = tensorConvolution(var_77, conv2d_19_w, 1, 1, 1, 1, 1, 0);
- void* var_80 = tensorAdd(var_79, conv2d_19_b);
- void* var_81 = tensorAdd(var_73, var_80);
- void* var_82 = tensorRelu(var_81);
- void* var_84 = tensorConvolution(var_82, conv2d_20_w, 1, 1, 1, 1, 1, 0);
- void* var_85 = tensorAdd(var_84, conv2d_20_b);
- void* var_86 = tensorRelu(var_85);
- void* var_88 = tensorConvolution(var_86, conv2d_21_w, 1, 1, 1, 1, 1, 0);
- void* var_89 = tensorAdd(var_88, conv2d_21_b);
- void* var_90 = tensorAdd(var_82, var_89);
- void* var_91 = tensorRelu(var_90);
- void* var_92 = tensorPooling(var_91,1,8,8,0,0,8,8);
- void* var_94 = tensorGemmGPU(var_92, dense_1_w);
- void* var_95 = tensorAdd(var_94, dense_1_b);
- void* var_96 = tensorSoftmax(var_95);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
-
- float accuracy = computeAccuracy2(labels,batch_size,var_96);
- final_accuracy += accuracy;
-
- freeBatchMemory();
- }
-
- stopProfiling();
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet18_cifar10_approx.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet18_cifar10_approx.cc
deleted file mode 100644
index 11cc3a38dd5da9dfcee7dd1181ab7e9a099fef88..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet18_cifar10_approx.cc
+++ /dev/null
@@ -1,221 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- std::string dir_prefix = std::string("../model_params/resnet18_cifar10_3/");
- std::string input_path = dir_prefix + std::string("input.bin");
- //void* input = readTrainedWeights(input_path.c_str(), 0, batch_size,3,32,32);
- std::string labels_path = dir_prefix + std::string("labels.bin");
- //uint8_t* labels = readLabels(labels_path.c_str(), batch_size);
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,16,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,16,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,16,1,1);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_14_b_path = dir_prefix + std::string("conv2d_14_b.bin");
- void* conv2d_14_b = readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_15_w_path = dir_prefix + std::string("conv2d_15_w.bin");
- void* conv2d_15_w = readTrainedWeights(conv2d_15_w_path.c_str(), 0,64,32,3,3);
- std::string conv2d_15_b_path = dir_prefix + std::string("conv2d_15_b.bin");
- void* conv2d_15_b = readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_17_w_path = dir_prefix + std::string("conv2d_17_w.bin");
- void* conv2d_17_w = readTrainedWeights(conv2d_17_w_path.c_str(), 0,64,32,1,1);
- std::string conv2d_17_b_path = dir_prefix + std::string("conv2d_17_b.bin");
- void* conv2d_17_b = readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_16_w_path = dir_prefix + std::string("conv2d_16_w.bin");
- void* conv2d_16_w = readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_16_b_path = dir_prefix + std::string("conv2d_16_b.bin");
- void* conv2d_16_b = readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_18_w_path = dir_prefix + std::string("conv2d_18_w.bin");
- void* conv2d_18_w = readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_18_b_path = dir_prefix + std::string("conv2d_18_b.bin");
- void* conv2d_18_b = readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_19_w_path = dir_prefix + std::string("conv2d_19_w.bin");
- void* conv2d_19_w = readTrainedWeights(conv2d_19_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_19_b_path = dir_prefix + std::string("conv2d_19_b.bin");
- void* conv2d_19_b = readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_20_w_path = dir_prefix + std::string("conv2d_20_w.bin");
- void* conv2d_20_w = readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_20_b_path = dir_prefix + std::string("conv2d_20_b.bin");
- void* conv2d_20_b = readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_21_w_path = dir_prefix + std::string("conv2d_21_w.bin");
- void* conv2d_21_w = readTrainedWeights(conv2d_21_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_21_b_path = dir_prefix + std::string("conv2d_21_b.bin");
- void* conv2d_21_b = readTrainedWeights(conv2d_21_b_path.c_str(), 0,1,64,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,64,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 10000;
- int batch_size = 2000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- // NOTE: Starting time profiling
- startProfiling();
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
-
- void* var_2 = tensorConvPerf(input, conv2d_1_w, 1, 1, 1, 1, 1, 0, 0, 0);
- void* var_3 = tensorAdd(var_2, conv2d_1_b);
- void* var_4 = tensorRelu(var_3);
- void* var_6 = tensorConvPerf(var_4, conv2d_2_w, 1, 1, 1, 1, 1, 0, 0, 0);
- void* var_7 = tensorAdd(var_6, conv2d_2_b);
- void* var_8 = tensorRelu(var_7);
- void* var_10 = tensorConvPerf(var_8, conv2d_3_w, 1, 1, 1, 1, 1, 0, 0, 0);
- void* var_11 = tensorAdd(var_10, conv2d_3_b);
- void* var_12 = tensorAdd(var_4, var_11);
- void* var_13 = tensorRelu(var_12);
- void* var_15 = tensorConvPerf(var_13, conv2d_4_w, 1, 1, 1, 1, 1, 0, 0, 0);
- void* var_16 = tensorAdd(var_15, conv2d_4_b);
- void* var_17 = tensorRelu(var_16);
- void* var_19 = tensorConvPerf(var_17, conv2d_5_w, 1, 1, 1, 1, 1, 0, 0, 0);
- void* var_20 = tensorAdd(var_19, conv2d_5_b);
- void* var_21 = tensorAdd(var_13, var_20);
- void* var_22 = tensorRelu(var_21);
- void* var_24 = tensorConvPerf(var_22, conv2d_6_w, 1, 1, 1, 1, 1, 0, 0, 0);
- void* var_25 = tensorAdd(var_24, conv2d_6_b);
- void* var_26 = tensorRelu(var_25);
- void* var_28 = tensorConvPerf(var_26, conv2d_7_w, 1, 1, 1, 1, 1, 0, 0, 0);
- void* var_29 = tensorAdd(var_28, conv2d_7_b);
- void* var_30 = tensorAdd(var_22, var_29);
- void* var_31 = tensorRelu(var_30);
- void* var_33 = tensorConvPerf(var_31, conv2d_8_w, 1, 1, 2, 2, 1, 0, 0, 0);
- void* var_34 = tensorAdd(var_33, conv2d_8_b);
- void* var_35 = tensorRelu(var_34);
- void* var_37 = tensorConvPerf(var_35, conv2d_9_w, 1, 1, 1, 1, 1, 0, 0, 0);
- void* var_38 = tensorAdd(var_37, conv2d_9_b);
- void* var_40 = tensorConvPerf(var_31, conv2d_10_w, 0, 0, 2, 2, 1, 0, 0, 0);
- void* var_41 = tensorAdd(var_40, conv2d_10_b);
- void* var_42 = tensorAdd(var_41, var_38);
- void* var_43 = tensorRelu(var_42);
- void* var_45 = tensorConvPerf(var_43, conv2d_11_w, 1, 1, 1, 1, 1, 0, 0, 0);
- void* var_46 = tensorAdd(var_45, conv2d_11_b);
- void* var_47 = tensorRelu(var_46);
- void* var_49 = tensorConvPerf(var_47, conv2d_12_w, 1, 1, 1, 1, 1, 0, 0, 0);
- void* var_50 = tensorAdd(var_49, conv2d_12_b);
- void* var_51 = tensorAdd(var_43, var_50);
- void* var_52 = tensorRelu(var_51);
- void* var_54 = tensorConvPerf(var_52, conv2d_13_w, 1, 1, 1, 1, 1, 0, 1, 0);
- void* var_55 = tensorAdd(var_54, conv2d_13_b);
- void* var_56 = tensorRelu(var_55);
- void* var_58 = tensorConvPerf(var_56, conv2d_14_w, 1, 1, 1, 1, 1, 0, 0, 1);
- void* var_59 = tensorAdd(var_58, conv2d_14_b);
- void* var_60 = tensorAdd(var_52, var_59);
- void* var_61 = tensorRelu(var_60);
- void* var_63 = tensorConvPerf(var_61, conv2d_15_w, 1, 1, 2, 2, 1, 0, 0, 0);
- void* var_64 = tensorAdd(var_63, conv2d_15_b);
- void* var_65 = tensorRelu(var_64);
- void* var_67 = tensorConvPerf(var_65, conv2d_16_w, 1, 1, 1, 1, 1, 0, 0, 0);
- void* var_68 = tensorAdd(var_67, conv2d_16_b);
- void* var_70 = tensorConvPerf(var_61, conv2d_17_w, 0, 0, 2, 2, 1, 0, 0, 0);
- void* var_71 = tensorAdd(var_70, conv2d_17_b);
- void* var_72 = tensorAdd(var_71, var_68);
- void* var_73 = tensorRelu(var_72);
- void* var_75 = tensorConvPerf(var_73, conv2d_18_w, 1, 1, 1, 1, 1, 0, 0, 0);
- void* var_76 = tensorAdd(var_75, conv2d_18_b);
- void* var_77 = tensorRelu(var_76);
- void* var_79 = tensorConvPerf(var_77, conv2d_19_w, 1, 1, 1, 1, 1, 0, 0, 0);
- void* var_80 = tensorAdd(var_79, conv2d_19_b);
- void* var_81 = tensorAdd(var_73, var_80);
- void* var_82 = tensorRelu(var_81);
- void* var_84 = tensorConvPerf(var_82, conv2d_20_w, 1, 1, 1, 1, 1, 0, 0, 0);
- void* var_85 = tensorAdd(var_84, conv2d_20_b);
- void* var_86 = tensorRelu(var_85);
- void* var_88 = tensorConvPerf(var_86, conv2d_21_w, 1, 1, 1, 1, 1, 0, 0, 0);
- void* var_89 = tensorAdd(var_88, conv2d_21_b);
- void* var_90 = tensorAdd(var_82, var_89);
- void* var_91 = tensorRelu(var_90);
- void* var_92 = tensorPooling(var_91,1,8,8,0,0,8,8);
- void* var_94 = tensorGemmGPU(var_92, dense_1_w);
- void* var_95 = tensorAdd(var_94, dense_1_b);
- void* var_96 = tensorSoftmax(var_95);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
-
- float accuracy = computeAccuracy2(labels,batch_size,var_96);
- final_accuracy += accuracy;
-
- freeBatchMemory();
- }
-
- stopProfiling();
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet18_cifar10_cpu.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet18_cifar10_cpu.cc
deleted file mode 100644
index df540551d71814bf4bf18d349bf08cb03151e1dc..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet18_cifar10_cpu.cc
+++ /dev/null
@@ -1,253 +0,0 @@
-
-#include <fcntl.h>
-#include <stdio.h>
-#include <stdlib.h>
-#include <string.h>
-#include <sys/stat.h>
-#include <sys/types.h>
-#include <unistd.h>
-
-#include "../../tensor_runtime/include/tensor_cpu_runtime.h"
-#include "../include/types.h"
-#include "../include/utils_cpu.h"
-int main() {
-
- llvm_hpvm_initTensorRt(0);
-
- std::string dir_prefix = std::string("./model_params/resnet18_cifar10_3/");
- std::string input_path = dir_prefix + std::string("input.bin");
- // void* input = readTrainedWeightsCPU(input_path.c_str(), 0, batch_size,3,32,32);
- std::string labels_path = dir_prefix + std::string("labels.bin");
-
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void *conv2d_1_w =
- readTrainedWeightsCPU(conv2d_1_w_path.c_str(), 0, 16, 3, 3, 3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void *conv2d_1_b =
- readTrainedWeightsCPU(conv2d_1_b_path.c_str(), 0, 1, 16, 1, 1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void *conv2d_2_w =
- readTrainedWeightsCPU(conv2d_2_w_path.c_str(), 0, 16, 16, 3, 3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void *conv2d_2_b =
- readTrainedWeightsCPU(conv2d_2_b_path.c_str(), 0, 1, 16, 1, 1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void *conv2d_3_w =
- readTrainedWeightsCPU(conv2d_3_w_path.c_str(), 0, 16, 16, 3, 3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void *conv2d_3_b =
- readTrainedWeightsCPU(conv2d_3_b_path.c_str(), 0, 1, 16, 1, 1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void *conv2d_4_w =
- readTrainedWeightsCPU(conv2d_4_w_path.c_str(), 0, 16, 16, 3, 3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void *conv2d_4_b =
- readTrainedWeightsCPU(conv2d_4_b_path.c_str(), 0, 1, 16, 1, 1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void *conv2d_5_w =
- readTrainedWeightsCPU(conv2d_5_w_path.c_str(), 0, 16, 16, 3, 3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void *conv2d_5_b =
- readTrainedWeightsCPU(conv2d_5_b_path.c_str(), 0, 1, 16, 1, 1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void *conv2d_6_w =
- readTrainedWeightsCPU(conv2d_6_w_path.c_str(), 0, 16, 16, 3, 3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void *conv2d_6_b =
- readTrainedWeightsCPU(conv2d_6_b_path.c_str(), 0, 1, 16, 1, 1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void *conv2d_7_w =
- readTrainedWeightsCPU(conv2d_7_w_path.c_str(), 0, 16, 16, 3, 3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void *conv2d_7_b =
- readTrainedWeightsCPU(conv2d_7_b_path.c_str(), 0, 1, 16, 1, 1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void *conv2d_8_w =
- readTrainedWeightsCPU(conv2d_8_w_path.c_str(), 0, 32, 16, 3, 3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void *conv2d_8_b =
- readTrainedWeightsCPU(conv2d_8_b_path.c_str(), 0, 1, 32, 1, 1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void *conv2d_10_w =
- readTrainedWeightsCPU(conv2d_10_w_path.c_str(), 0, 32, 16, 1, 1);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void *conv2d_10_b =
- readTrainedWeightsCPU(conv2d_10_b_path.c_str(), 0, 1, 32, 1, 1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void *conv2d_9_w =
- readTrainedWeightsCPU(conv2d_9_w_path.c_str(), 0, 32, 32, 3, 3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void *conv2d_9_b =
- readTrainedWeightsCPU(conv2d_9_b_path.c_str(), 0, 1, 32, 1, 1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void *conv2d_11_w =
- readTrainedWeightsCPU(conv2d_11_w_path.c_str(), 0, 32, 32, 3, 3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void *conv2d_11_b =
- readTrainedWeightsCPU(conv2d_11_b_path.c_str(), 0, 1, 32, 1, 1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void *conv2d_12_w =
- readTrainedWeightsCPU(conv2d_12_w_path.c_str(), 0, 32, 32, 3, 3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void *conv2d_12_b =
- readTrainedWeightsCPU(conv2d_12_b_path.c_str(), 0, 1, 32, 1, 1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void *conv2d_13_w =
- readTrainedWeightsCPU(conv2d_13_w_path.c_str(), 0, 32, 32, 3, 3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void *conv2d_13_b =
- readTrainedWeightsCPU(conv2d_13_b_path.c_str(), 0, 1, 32, 1, 1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void *conv2d_14_w =
- readTrainedWeightsCPU(conv2d_14_w_path.c_str(), 0, 32, 32, 3, 3);
- std::string conv2d_14_b_path = dir_prefix + std::string("conv2d_14_b.bin");
- void *conv2d_14_b =
- readTrainedWeightsCPU(conv2d_14_b_path.c_str(), 0, 1, 32, 1, 1);
- std::string conv2d_15_w_path = dir_prefix + std::string("conv2d_15_w.bin");
- void *conv2d_15_w =
- readTrainedWeightsCPU(conv2d_15_w_path.c_str(), 0, 64, 32, 3, 3);
- std::string conv2d_15_b_path = dir_prefix + std::string("conv2d_15_b.bin");
- void *conv2d_15_b =
- readTrainedWeightsCPU(conv2d_15_b_path.c_str(), 0, 1, 64, 1, 1);
- std::string conv2d_17_w_path = dir_prefix + std::string("conv2d_17_w.bin");
- void *conv2d_17_w =
- readTrainedWeightsCPU(conv2d_17_w_path.c_str(), 0, 64, 32, 1, 1);
- std::string conv2d_17_b_path = dir_prefix + std::string("conv2d_17_b.bin");
- void *conv2d_17_b =
- readTrainedWeightsCPU(conv2d_17_b_path.c_str(), 0, 1, 64, 1, 1);
- std::string conv2d_16_w_path = dir_prefix + std::string("conv2d_16_w.bin");
- void *conv2d_16_w =
- readTrainedWeightsCPU(conv2d_16_w_path.c_str(), 0, 64, 64, 3, 3);
- std::string conv2d_16_b_path = dir_prefix + std::string("conv2d_16_b.bin");
- void *conv2d_16_b =
- readTrainedWeightsCPU(conv2d_16_b_path.c_str(), 0, 1, 64, 1, 1);
- std::string conv2d_18_w_path = dir_prefix + std::string("conv2d_18_w.bin");
- void *conv2d_18_w =
- readTrainedWeightsCPU(conv2d_18_w_path.c_str(), 0, 64, 64, 3, 3);
- std::string conv2d_18_b_path = dir_prefix + std::string("conv2d_18_b.bin");
- void *conv2d_18_b =
- readTrainedWeightsCPU(conv2d_18_b_path.c_str(), 0, 1, 64, 1, 1);
- std::string conv2d_19_w_path = dir_prefix + std::string("conv2d_19_w.bin");
- void *conv2d_19_w =
- readTrainedWeightsCPU(conv2d_19_w_path.c_str(), 0, 64, 64, 3, 3);
- std::string conv2d_19_b_path = dir_prefix + std::string("conv2d_19_b.bin");
- void *conv2d_19_b =
- readTrainedWeightsCPU(conv2d_19_b_path.c_str(), 0, 1, 64, 1, 1);
- std::string conv2d_20_w_path = dir_prefix + std::string("conv2d_20_w.bin");
- void *conv2d_20_w =
- readTrainedWeightsCPU(conv2d_20_w_path.c_str(), 0, 64, 64, 3, 3);
- std::string conv2d_20_b_path = dir_prefix + std::string("conv2d_20_b.bin");
- void *conv2d_20_b =
- readTrainedWeightsCPU(conv2d_20_b_path.c_str(), 0, 1, 64, 1, 1);
- std::string conv2d_21_w_path = dir_prefix + std::string("conv2d_21_w.bin");
- void *conv2d_21_w =
- readTrainedWeightsCPU(conv2d_21_w_path.c_str(), 0, 64, 64, 3, 3);
- std::string conv2d_21_b_path = dir_prefix + std::string("conv2d_21_b.bin");
- void *conv2d_21_b =
- readTrainedWeightsCPU(conv2d_21_b_path.c_str(), 0, 1, 64, 1, 1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void *dense_1_w =
- readTrainedWeightsCPU(dense_1_w_path.c_str(), 0, 1, 1, 64, 10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void *dense_1_b =
- readTrainedWeightsCPU(dense_1_b_path.c_str(), 0, 1, 10, 1, 1);
-
- int test_input_size = 10000;
- int batch_size = 100;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for (int i = 0; i < batch_count; i++) {
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readTrainedWeightsCPU(input_path.c_str(), 0,batch_size,3,32,32);
- uint8_t *labels = readLabels(labels_path.c_str(), batch_size);
-
- void* var_2 = tensorConvolutionCPU(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
- void* var_3 = tensorAddCPU(var_2, conv2d_1_b);
- void* var_4 = tensorReluCPU(var_3);
- void* var_6 = tensorConvolutionCPU(var_4, conv2d_2_w, 1, 1, 1, 1, 1, 0);
- void* var_7 = tensorAddCPU(var_6, conv2d_2_b);
- void* var_8 = tensorReluCPU(var_7);
- void* var_10 = tensorConvolutionCPU(var_8, conv2d_3_w, 1, 1, 1, 1, 1, 0);
- void* var_11 = tensorAddCPU(var_10, conv2d_3_b);
- void* var_12 = tensorAddCPU(var_4, var_11);
- void *var_13 = tensorReluCPU(var_12);
- void* var_15 = tensorConvolutionCPU(var_13, conv2d_4_w, 1, 1, 1, 1, 1, 0);
- void* var_16 = tensorAddCPU(var_15, conv2d_4_b);
- void* var_17 = tensorReluCPU(var_16);
- void* var_19 = tensorConvolutionCPU(var_17, conv2d_5_w, 1, 1, 1, 1, 1, 0);
- void* var_20 = tensorAddCPU(var_19, conv2d_5_b);
- void* var_21 = tensorAddCPU(var_13, var_20);
- void* var_22 = tensorReluCPU(var_21);
- void* var_24 = tensorConvolutionCPU(var_22, conv2d_6_w, 1, 1, 1, 1, 1, 0);
- void* var_25 = tensorAddCPU(var_24, conv2d_6_b);
- void *var_26 = tensorReluCPU(var_25);
- void* var_28 = tensorConvolutionCPU(var_26, conv2d_7_w, 1, 1, 1, 1, 1, 0);
- void* var_29 = tensorAddCPU(var_28, conv2d_7_b);
- void *var_30 = tensorAddCPU(var_22, var_29);
- void *var_31 = tensorReluCPU(var_30);
- void* var_33 = tensorConvolutionCPU(var_31, conv2d_8_w, 1, 1, 2, 2, 1, 0);
- void *var_34 = tensorAddCPU(var_33, conv2d_8_b);
- void *var_35 = tensorReluCPU(var_34);
- void *var_37 = tensorConvolutionCPU(var_35, conv2d_9_w, 1, 1, 1, 1, 1, 0);
- void* var_38 = tensorAddCPU(var_37, conv2d_9_b);
- void* var_40 = tensorConvolutionCPU(var_31, conv2d_10_w, 0, 0, 2, 2, 1, 0);
- void *var_41 = tensorAddCPU(var_40, conv2d_10_b);
- void* var_42 = tensorAddCPU(var_41, var_38);
- void* var_43 = tensorReluCPU(var_42);
- void* var_45 = tensorConvolutionCPU(var_43, conv2d_11_w, 1, 1, 1, 1, 1, 0);
- void* var_46 = tensorAddCPU(var_45, conv2d_11_b);
- void *var_47 = tensorReluCPU(var_46);
- void* var_49 = tensorConvolutionCPU(var_47, conv2d_12_w, 1, 1, 1, 1, 1, 0);
- void* var_50 = tensorAddCPU(var_49, conv2d_12_b);
- void* var_51 = tensorAddCPU(var_43, var_50);
- void* var_52 = tensorReluCPU(var_51);
- void* var_54 = tensorConvolutionCPU(var_52, conv2d_13_w, 1, 1, 1, 1, 1, 0);
- void* var_55 = tensorAddCPU(var_54, conv2d_13_b);
- void* var_56 = tensorReluCPU(var_55);
- void* var_58 = tensorConvolutionCPU(var_56, conv2d_14_w, 1, 1, 1, 1, 1, 0);
- void* var_59 = tensorAddCPU(var_58, conv2d_14_b);
- void* var_60 = tensorAddCPU(var_52, var_59);
- void* var_61 = tensorReluCPU(var_60);
- void* var_63 = tensorConvolutionCPU(var_61, conv2d_15_w, 1, 1, 2, 2, 1, 0);
- void* var_64 = tensorAddCPU(var_63, conv2d_15_b);
- void* var_65 = tensorReluCPU(var_64);
- void* var_67 = tensorConvolutionCPU(var_65, conv2d_16_w, 1, 1, 1, 1, 1, 0);
- void* var_68 = tensorAddCPU(var_67, conv2d_16_b);
- void* var_70 = tensorConvolutionCPU(var_61, conv2d_17_w, 0, 0, 2, 2, 1, 0);
- void* var_71 = tensorAddCPU(var_70, conv2d_17_b);
- void* var_72 = tensorAddCPU(var_71, var_68);
- void* var_73 = tensorReluCPU(var_72);
- void* var_75 = tensorConvolutionCPU(var_73, conv2d_18_w, 1, 1, 1, 1, 1, 0);
- void* var_76 = tensorAddCPU(var_75, conv2d_18_b);
- void* var_77 = tensorReluCPU(var_76);
- void* var_79 = tensorConvolutionCPU(var_77, conv2d_19_w, 1, 1, 1, 1, 1, 0);
- void* var_80 = tensorAddCPU(var_79, conv2d_19_b);
- void* var_81 = tensorAddCPU(var_73, var_80);
- void* var_82 = tensorReluCPU(var_81);
- void* var_84 = tensorConvolutionCPU(var_82, conv2d_20_w, 1, 1, 1, 1, 1, 0);
- void* var_85 = tensorAddCPU(var_84, conv2d_20_b);
- void* var_86 = tensorReluCPU(var_85);
- void* var_88 = tensorConvolutionCPU(var_86, conv2d_21_w, 1, 1, 1, 1, 1, 0);
- void* var_89 = tensorAddCPU(var_88, conv2d_21_b);
- void* var_90 = tensorAddCPU(var_82, var_89);
- void* var_91 = tensorReluCPU(var_90);
- void* var_92 = tensorPoolingCPU(var_91,1,8,8,0,0,8,8);
- void* var_94 = tensorGemmCPU(var_92, dense_1_w);
- void* var_95 = tensorAddCPU(var_94, dense_1_b);
- void* var_96 = tensorSoftmaxCPU(var_95);
-
- float accuracy = computeAccuracy2(labels,batch_size, var_96);
- final_accuracy += accuracy;
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet18_cifar10_cudaperf.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet18_cifar10_cudaperf.cc
deleted file mode 100644
index 2e33715e8c6972966e7359a1e7b8fc5069e1f16f..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet18_cifar10_cudaperf.cc
+++ /dev/null
@@ -1,221 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- std::string dir_prefix = std::string("../model_params/resnet18_cifar10_3/");
- std::string input_path = dir_prefix + std::string("input.bin");
- //void* input = readTrainedWeights(input_path.c_str(), 0, batch_size,3,32,32);
- std::string labels_path = dir_prefix + std::string("labels.bin");
- //uint8_t* labels = readLabels(labels_path.c_str(), batch_size);
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,16,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,16,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,16,1,1);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_14_b_path = dir_prefix + std::string("conv2d_14_b.bin");
- void* conv2d_14_b = readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_15_w_path = dir_prefix + std::string("conv2d_15_w.bin");
- void* conv2d_15_w = readTrainedWeights(conv2d_15_w_path.c_str(), 0,64,32,3,3);
- std::string conv2d_15_b_path = dir_prefix + std::string("conv2d_15_b.bin");
- void* conv2d_15_b = readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_17_w_path = dir_prefix + std::string("conv2d_17_w.bin");
- void* conv2d_17_w = readTrainedWeights(conv2d_17_w_path.c_str(), 0,64,32,1,1);
- std::string conv2d_17_b_path = dir_prefix + std::string("conv2d_17_b.bin");
- void* conv2d_17_b = readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_16_w_path = dir_prefix + std::string("conv2d_16_w.bin");
- void* conv2d_16_w = readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_16_b_path = dir_prefix + std::string("conv2d_16_b.bin");
- void* conv2d_16_b = readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_18_w_path = dir_prefix + std::string("conv2d_18_w.bin");
- void* conv2d_18_w = readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_18_b_path = dir_prefix + std::string("conv2d_18_b.bin");
- void* conv2d_18_b = readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_19_w_path = dir_prefix + std::string("conv2d_19_w.bin");
- void* conv2d_19_w = readTrainedWeights(conv2d_19_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_19_b_path = dir_prefix + std::string("conv2d_19_b.bin");
- void* conv2d_19_b = readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_20_w_path = dir_prefix + std::string("conv2d_20_w.bin");
- void* conv2d_20_w = readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_20_b_path = dir_prefix + std::string("conv2d_20_b.bin");
- void* conv2d_20_b = readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_21_w_path = dir_prefix + std::string("conv2d_21_w.bin");
- void* conv2d_21_w = readTrainedWeights(conv2d_21_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_21_b_path = dir_prefix + std::string("conv2d_21_b.bin");
- void* conv2d_21_b = readTrainedWeights(conv2d_21_b_path.c_str(), 0,1,64,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,64,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 10000;
- int batch_size = 2000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- // NOTE: Starting time profiling
- startProfiling();
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
-
- void* var_2 = tensorConvPerfCuda(input, conv2d_1_w, 1, 1, 1, 1, 1, 0, 1, 1, 0);
- void* var_3 = tensorAdd(var_2, conv2d_1_b);
- void* var_4 = tensorRelu(var_3);
- void* var_6 = tensorConvPerfCuda(var_4, conv2d_2_w, 1, 1, 1, 1, 1, 0, 1, 1, 0);
- void* var_7 = tensorAdd(var_6, conv2d_2_b);
- void* var_8 = tensorRelu(var_7);
- void* var_10 = tensorConvPerfCuda(var_8, conv2d_3_w, 1, 1, 1, 1, 1, 0, 1, 1, 0);
- void* var_11 = tensorAdd(var_10, conv2d_3_b);
- void* var_12 = tensorAdd(var_4, var_11);
- void* var_13 = tensorRelu(var_12);
- void* var_15 = tensorConvPerfCuda(var_13, conv2d_4_w, 1, 1, 1, 1, 1, 0, 1, 1, 0);
- void* var_16 = tensorAdd(var_15, conv2d_4_b);
- void* var_17 = tensorRelu(var_16);
- void* var_19 = tensorConvPerfCuda(var_17, conv2d_5_w, 1, 1, 1, 1, 1, 0, 1, 1, 0);
- void* var_20 = tensorAdd(var_19, conv2d_5_b);
- void* var_21 = tensorAdd(var_13, var_20);
- void* var_22 = tensorRelu(var_21);
- void* var_24 = tensorConvPerfCuda(var_22, conv2d_6_w, 1, 1, 1, 1, 1, 0, 3, 1, 2);
- void* var_25 = tensorAdd(var_24, conv2d_6_b);
- void* var_26 = tensorRelu(var_25);
- void* var_28 = tensorConvPerfCuda(var_26, conv2d_7_w, 1, 1, 1, 1, 1, 0, 1, 1, 0);
- void* var_29 = tensorAdd(var_28, conv2d_7_b);
- void* var_30 = tensorAdd(var_22, var_29);
- void* var_31 = tensorRelu(var_30);
- void* var_33 = tensorConvPerfCuda(var_31, conv2d_8_w, 1, 1, 2, 2, 1, 0, 1, 1, 0);
- void* var_34 = tensorAdd(var_33, conv2d_8_b);
- void* var_35 = tensorRelu(var_34);
- void* var_37 = tensorConvPerfCuda(var_35, conv2d_9_w, 1, 1, 1, 1, 1, 0, 1, 1, 0);
- void* var_38 = tensorAdd(var_37, conv2d_9_b);
- void* var_40 = tensorConvPerfCuda(var_31, conv2d_10_w, 0, 0, 2, 2, 1, 0, 1, 1, 0);
- void* var_41 = tensorAdd(var_40, conv2d_10_b);
- void* var_42 = tensorAdd(var_41, var_38);
- void* var_43 = tensorRelu(var_42);
- void* var_45 = tensorConvPerfCuda(var_43, conv2d_11_w, 1, 1, 1, 1, 1, 0, 3, 1, 0);
- void* var_46 = tensorAdd(var_45, conv2d_11_b);
- void* var_47 = tensorRelu(var_46);
- void* var_49 = tensorConvPerfCuda(var_47, conv2d_12_w, 1, 1, 1, 1, 1, 0, 1, 1, 0);
- void* var_50 = tensorAdd(var_49, conv2d_12_b);
- void* var_51 = tensorAdd(var_43, var_50);
- void* var_52 = tensorRelu(var_51);
- void* var_54 = tensorConvPerfCuda(var_52, conv2d_13_w, 1, 1, 1, 1, 1, 0, 1, 1, 0);
- void* var_55 = tensorAdd(var_54, conv2d_13_b);
- void* var_56 = tensorRelu(var_55);
- void* var_58 = tensorConvPerfCuda(var_56, conv2d_14_w, 1, 1, 1, 1, 1, 0, 1, 3, 1);
- void* var_59 = tensorAdd(var_58, conv2d_14_b);
- void* var_60 = tensorAdd(var_52, var_59);
- void* var_61 = tensorRelu(var_60);
- void* var_63 = tensorConvPerfCuda(var_61, conv2d_15_w, 1, 1, 2, 2, 1, 0, 1, 1, 0);
- void* var_64 = tensorAdd(var_63, conv2d_15_b);
- void* var_65 = tensorRelu(var_64);
- void* var_67 = tensorConvPerfCuda(var_65, conv2d_16_w, 1, 1, 1, 1, 1, 0, 1, 1, 0);
- void* var_68 = tensorAdd(var_67, conv2d_16_b);
- void* var_70 = tensorConvPerfCuda(var_61, conv2d_17_w, 0, 0, 2, 2, 1, 0, 3, 1, 2);
- void* var_71 = tensorAdd(var_70, conv2d_17_b);
- void* var_72 = tensorAdd(var_71, var_68);
- void* var_73 = tensorRelu(var_72);
- void* var_75 = tensorConvPerfCuda(var_73, conv2d_18_w, 1, 1, 1, 1, 1, 0, 1, 1, 0);
- void* var_76 = tensorAdd(var_75, conv2d_18_b);
- void* var_77 = tensorRelu(var_76);
- void* var_79 = tensorConvPerfCuda(var_77, conv2d_19_w, 1, 1, 1, 1, 1, 0, 1, 3, 0);
- void* var_80 = tensorAdd(var_79, conv2d_19_b);
- void* var_81 = tensorAdd(var_73, var_80);
- void* var_82 = tensorRelu(var_81);
- void* var_84 = tensorConvPerfCuda(var_82, conv2d_20_w, 1, 1, 1, 1, 1, 0, 1, 1, 0);
- void* var_85 = tensorAdd(var_84, conv2d_20_b);
- void* var_86 = tensorRelu(var_85);
- void* var_88 = tensorConvPerfCuda(var_86, conv2d_21_w, 1, 1, 1, 1, 1, 0, 1, 1, 0);
- void* var_89 = tensorAdd(var_88, conv2d_21_b);
- void* var_90 = tensorAdd(var_82, var_89);
- void* var_91 = tensorRelu(var_90);
- void* var_92 = tensorPooling(var_91,1,8,8,0,0,8,8);
- void* var_94 = tensorGemmGPU(var_92, dense_1_w);
- void* var_95 = tensorAdd(var_94, dense_1_b);
- void* var_96 = tensorSoftmax(var_95);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
-
- float accuracy = computeAccuracy2(labels,batch_size,var_96);
- final_accuracy += accuracy;
-
- freeBatchMemory();
- }
-
- stopProfiling();
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet18_cifar10_half.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet18_cifar10_half.cc
deleted file mode 100644
index f7c6593d525351085ee99606bc90fc1419980d8e..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet18_cifar10_half.cc
+++ /dev/null
@@ -1,194 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(1);
-
- int batch_size = 4000;
-
- std::string dir_prefix = std::string("../model_params/resnet18_cifar10_3/");
- std::string input_path = dir_prefix + std::string("input.bin");
- void* input = readTrainedWeights(input_path.c_str(), 0, batch_size,3,32,32);
- std::string labels_path = dir_prefix + std::string("labels.bin");
- uint8_t* labels = readLabels(labels_path.c_str(), batch_size);
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,16,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,16,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,16,1,1);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_14_b_path = dir_prefix + std::string("conv2d_14_b.bin");
- void* conv2d_14_b = readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_15_w_path = dir_prefix + std::string("conv2d_15_w.bin");
- void* conv2d_15_w = readTrainedWeights(conv2d_15_w_path.c_str(), 0,64,32,3,3);
- std::string conv2d_15_b_path = dir_prefix + std::string("conv2d_15_b.bin");
- void* conv2d_15_b = readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_17_w_path = dir_prefix + std::string("conv2d_17_w.bin");
- void* conv2d_17_w = readTrainedWeights(conv2d_17_w_path.c_str(), 0,64,32,1,1);
- std::string conv2d_17_b_path = dir_prefix + std::string("conv2d_17_b.bin");
- void* conv2d_17_b = readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_16_w_path = dir_prefix + std::string("conv2d_16_w.bin");
- void* conv2d_16_w = readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_16_b_path = dir_prefix + std::string("conv2d_16_b.bin");
- void* conv2d_16_b = readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_18_w_path = dir_prefix + std::string("conv2d_18_w.bin");
- void* conv2d_18_w = readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_18_b_path = dir_prefix + std::string("conv2d_18_b.bin");
- void* conv2d_18_b = readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_19_w_path = dir_prefix + std::string("conv2d_19_w.bin");
- void* conv2d_19_w = readTrainedWeights(conv2d_19_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_19_b_path = dir_prefix + std::string("conv2d_19_b.bin");
- void* conv2d_19_b = readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_20_w_path = dir_prefix + std::string("conv2d_20_w.bin");
- void* conv2d_20_w = readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_20_b_path = dir_prefix + std::string("conv2d_20_b.bin");
- void* conv2d_20_b = readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_21_w_path = dir_prefix + std::string("conv2d_21_w.bin");
- void* conv2d_21_w = readTrainedWeights(conv2d_21_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_21_b_path = dir_prefix + std::string("conv2d_21_b.bin");
- void* conv2d_21_b = readTrainedWeights(conv2d_21_b_path.c_str(), 0,1,64,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,64,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- void* var_2 = tensorHalfConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
- void* var_3 = tensorHalfAdd(var_2, conv2d_1_b);
- void* var_4 = tensorHalfRelu(var_3);
- void* var_6 = tensorHalfConvolution(var_4, conv2d_2_w, 1, 1, 1, 1, 1, 0);
- void* var_7 = tensorHalfAdd(var_6, conv2d_2_b);
- void* var_8 = tensorHalfRelu(var_7);
- void* var_10 = tensorHalfConvolution(var_8, conv2d_3_w, 1, 1, 1, 1, 1, 0);
- void* var_11 = tensorHalfAdd(var_10, conv2d_3_b);
- void* var_12 = tensorHalfAdd(var_4, var_11);
- void* var_13 = tensorHalfRelu(var_12);
- void* var_15 = tensorHalfConvolution(var_13, conv2d_4_w, 1, 1, 1, 1, 1, 0);
- void* var_16 = tensorHalfAdd(var_15, conv2d_4_b);
- void* var_17 = tensorHalfRelu(var_16);
- void* var_19 = tensorHalfConvolution(var_17, conv2d_5_w, 1, 1, 1, 1, 1, 0);
- void* var_20 = tensorHalfAdd(var_19, conv2d_5_b);
- void* var_21 = tensorHalfAdd(var_13, var_20);
- void* var_22 = tensorHalfRelu(var_21);
- void* var_24 = tensorHalfConvolution(var_22, conv2d_6_w, 1, 1, 1, 1, 1, 0);
- void* var_25 = tensorHalfAdd(var_24, conv2d_6_b);
- void* var_26 = tensorHalfRelu(var_25);
- void* var_28 = tensorHalfConvolution(var_26, conv2d_7_w, 1, 1, 1, 1, 1, 0);
- void* var_29 = tensorHalfAdd(var_28, conv2d_7_b);
- void* var_30 = tensorHalfAdd(var_22, var_29);
- void* var_31 = tensorHalfRelu(var_30);
- void* var_33 = tensorHalfConvolution(var_31, conv2d_8_w, 1, 1, 2, 2, 1, 0);
- void* var_34 = tensorHalfAdd(var_33, conv2d_8_b);
- void* var_35 = tensorHalfRelu(var_34);
- void* var_37 = tensorHalfConvolution(var_35, conv2d_9_w, 1, 1, 1, 1, 1, 0);
- void* var_38 = tensorHalfAdd(var_37, conv2d_9_b);
- void* var_40 = tensorHalfConvolution(var_31, conv2d_10_w, 0, 0, 2, 2, 1, 0);
- void* var_41 = tensorHalfAdd(var_40, conv2d_10_b);
- void* var_42 = tensorHalfAdd(var_41, var_38);
- void* var_43 = tensorHalfRelu(var_42);
- void* var_45 = tensorHalfConvolution(var_43, conv2d_11_w, 1, 1, 1, 1, 1, 0);
- void* var_46 = tensorHalfAdd(var_45, conv2d_11_b);
- void* var_47 = tensorHalfRelu(var_46);
- void* var_49 = tensorHalfConvolution(var_47, conv2d_12_w, 1, 1, 1, 1, 1, 0);
- void* var_50 = tensorHalfAdd(var_49, conv2d_12_b);
- void* var_51 = tensorHalfAdd(var_43, var_50);
- void* var_52 = tensorHalfRelu(var_51);
- void* var_54 = tensorHalfConvolution(var_52, conv2d_13_w, 1, 1, 1, 1, 1, 0);
- void* var_55 = tensorHalfAdd(var_54, conv2d_13_b);
- void* var_56 = tensorHalfRelu(var_55);
- void* var_58 = tensorHalfConvolution(var_56, conv2d_14_w, 1, 1, 1, 1, 1, 0);
- void* var_59 = tensorHalfAdd(var_58, conv2d_14_b);
- void* var_60 = tensorHalfAdd(var_52, var_59);
- void* var_61 = tensorHalfRelu(var_60);
- void* var_63 = tensorHalfConvolution(var_61, conv2d_15_w, 1, 1, 2, 2, 1, 0);
- void* var_64 = tensorHalfAdd(var_63, conv2d_15_b);
- void* var_65 = tensorHalfRelu(var_64);
- void* var_67 = tensorHalfConvolution(var_65, conv2d_16_w, 1, 1, 1, 1, 1, 0);
- void* var_68 = tensorHalfAdd(var_67, conv2d_16_b);
- void* var_70 = tensorHalfConvolution(var_61, conv2d_17_w, 0, 0, 2, 2, 1, 0);
- void* var_71 = tensorHalfAdd(var_70, conv2d_17_b);
- void* var_72 = tensorHalfAdd(var_71, var_68);
- void* var_73 = tensorHalfRelu(var_72);
- void* var_75 = tensorHalfConvolution(var_73, conv2d_18_w, 1, 1, 1, 1, 1, 0);
- void* var_76 = tensorHalfAdd(var_75, conv2d_18_b);
- void* var_77 = tensorHalfRelu(var_76);
- void* var_79 = tensorHalfConvolution(var_77, conv2d_19_w, 1, 1, 1, 1, 1, 0);
- void* var_80 = tensorHalfAdd(var_79, conv2d_19_b);
- void* var_81 = tensorHalfAdd(var_73, var_80);
- void* var_82 = tensorHalfRelu(var_81);
- void* var_84 = tensorHalfConvolution(var_82, conv2d_20_w, 1, 1, 1, 1, 1, 0);
- void* var_85 = tensorHalfAdd(var_84, conv2d_20_b);
- void* var_86 = tensorHalfRelu(var_85);
- void* var_88 = tensorHalfConvolution(var_86, conv2d_21_w, 1, 1, 1, 1, 1, 0);
- void* var_89 = tensorHalfAdd(var_88, conv2d_21_b);
- void* var_90 = tensorHalfAdd(var_82, var_89);
- void* var_91 = tensorHalfRelu(var_90);
- void* var_92 = tensorHalfPooling(var_91,1,8,8,0,0,8,8);
- void* var_94 = tensorHalfGemmGPU(var_92, dense_1_w);
- void* var_95 = tensorHalfAdd(var_94, dense_1_b);
- void* var_96 = tensorSoftmax(var_95);
-
- computeAccuracy2(labels, batch_size,var_96);
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet18_cifar10_inputapprox.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet18_cifar10_inputapprox.cc
deleted file mode 100644
index 6634ce92c9aed0fbcc32e68580fb3171145ee297..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet18_cifar10_inputapprox.cc
+++ /dev/null
@@ -1,221 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- std::string dir_prefix = std::string("../model_params/resnet18_cifar10_3/");
- std::string input_path = dir_prefix + std::string("input.bin");
- //void* input = readTrainedWeights(input_path.c_str(), 0, batch_size,3,32,32);
- std::string labels_path = dir_prefix + std::string("labels.bin");
- //uint8_t* labels = readLabels(labels_path.c_str(), batch_size);
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,16,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,16,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,16,1,1);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_14_b_path = dir_prefix + std::string("conv2d_14_b.bin");
- void* conv2d_14_b = readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_15_w_path = dir_prefix + std::string("conv2d_15_w.bin");
- void* conv2d_15_w = readTrainedWeights(conv2d_15_w_path.c_str(), 0,64,32,3,3);
- std::string conv2d_15_b_path = dir_prefix + std::string("conv2d_15_b.bin");
- void* conv2d_15_b = readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_17_w_path = dir_prefix + std::string("conv2d_17_w.bin");
- void* conv2d_17_w = readTrainedWeights(conv2d_17_w_path.c_str(), 0,64,32,1,1);
- std::string conv2d_17_b_path = dir_prefix + std::string("conv2d_17_b.bin");
- void* conv2d_17_b = readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_16_w_path = dir_prefix + std::string("conv2d_16_w.bin");
- void* conv2d_16_w = readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_16_b_path = dir_prefix + std::string("conv2d_16_b.bin");
- void* conv2d_16_b = readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_18_w_path = dir_prefix + std::string("conv2d_18_w.bin");
- void* conv2d_18_w = readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_18_b_path = dir_prefix + std::string("conv2d_18_b.bin");
- void* conv2d_18_b = readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_19_w_path = dir_prefix + std::string("conv2d_19_w.bin");
- void* conv2d_19_w = readTrainedWeights(conv2d_19_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_19_b_path = dir_prefix + std::string("conv2d_19_b.bin");
- void* conv2d_19_b = readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_20_w_path = dir_prefix + std::string("conv2d_20_w.bin");
- void* conv2d_20_w = readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_20_b_path = dir_prefix + std::string("conv2d_20_b.bin");
- void* conv2d_20_b = readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_21_w_path = dir_prefix + std::string("conv2d_21_w.bin");
- void* conv2d_21_w = readTrainedWeights(conv2d_21_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_21_b_path = dir_prefix + std::string("conv2d_21_b.bin");
- void* conv2d_21_b = readTrainedWeights(conv2d_21_b_path.c_str(), 0,1,64,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,64,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 10000;
- int batch_size = 2000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- // NOTE: Starting time profiling
- startProfiling();
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
-
- void* var_2 = tensorConvolutionKernelSamp(input, conv2d_1_w, 1, 1, 1, 1, 1, 0, 30);
- void* var_3 = tensorAdd(var_2, conv2d_1_b);
- void* var_4 = tensorRelu(var_3);
- void* var_6 = tensorConvolutionKernelSamp(var_4, conv2d_2_w, 1, 1, 1, 1, 1, 0, 30);
- void* var_7 = tensorAdd(var_6, conv2d_2_b);
- void* var_8 = tensorRelu(var_7);
- void* var_10 = tensorConvolutionKernelSamp(var_8, conv2d_3_w, 1, 1, 1, 1, 1, 0, 30);
- void* var_11 = tensorAdd(var_10, conv2d_3_b);
- void* var_12 = tensorAdd(var_4, var_11);
- void* var_13 = tensorRelu(var_12);
- void* var_15 = tensorConvolutionKernelSamp(var_13, conv2d_4_w, 1, 1, 1, 1, 1, 0, 30);
- void* var_16 = tensorAdd(var_15, conv2d_4_b);
- void* var_17 = tensorRelu(var_16);
- void* var_19 = tensorConvolutionKernelSamp(var_17, conv2d_5_w, 1, 1, 1, 1, 1, 0, 30);
- void* var_20 = tensorAdd(var_19, conv2d_5_b);
- void* var_21 = tensorAdd(var_13, var_20);
- void* var_22 = tensorRelu(var_21);
- void* var_24 = tensorConvolutionKernelSamp(var_22, conv2d_6_w, 1, 1, 1, 1, 1, 0, 4);
- void* var_25 = tensorAdd(var_24, conv2d_6_b);
- void* var_26 = tensorRelu(var_25);
- void* var_28 = tensorConvolutionKernelSamp(var_26, conv2d_7_w, 1, 1, 1, 1, 1, 0, 4);
- void* var_29 = tensorAdd(var_28, conv2d_7_b);
- void* var_30 = tensorAdd(var_22, var_29);
- void* var_31 = tensorRelu(var_30);
- void* var_33 = tensorConvolutionKernelSamp(var_31, conv2d_8_w, 1, 1, 2, 2, 1, 0, 30);
- void* var_34 = tensorAdd(var_33, conv2d_8_b);
- void* var_35 = tensorRelu(var_34);
- void* var_37 = tensorConvolutionKernelSamp(var_35, conv2d_9_w, 1, 1, 1, 1, 1, 0, 30);
- void* var_38 = tensorAdd(var_37, conv2d_9_b);
- void* var_40 = tensorConvolutionKernelSamp(var_31, conv2d_10_w, 0, 0, 2, 2, 1, 0, 30);
- void* var_41 = tensorAdd(var_40, conv2d_10_b);
- void* var_42 = tensorAdd(var_41, var_38);
- void* var_43 = tensorRelu(var_42);
- void* var_45 = tensorConvolutionKernelSamp(var_43, conv2d_11_w, 1, 1, 1, 1, 1, 0, 4);
- void* var_46 = tensorAdd(var_45, conv2d_11_b);
- void* var_47 = tensorRelu(var_46);
- void* var_49 = tensorConvolutionKernelSamp(var_47, conv2d_12_w, 1, 1, 1, 1, 1, 0, 30);
- void* var_50 = tensorAdd(var_49, conv2d_12_b);
- void* var_51 = tensorAdd(var_43, var_50);
- void* var_52 = tensorRelu(var_51);
- void* var_54 = tensorConvolutionKernelSamp(var_52, conv2d_13_w, 1, 1, 1, 1, 1, 0, 30);
- void* var_55 = tensorAdd(var_54, conv2d_13_b);
- void* var_56 = tensorRelu(var_55);
- void* var_58 = tensorConvolutionKernelSamp(var_56, conv2d_14_w, 1, 1, 1, 1, 1, 0, 30);
- void* var_59 = tensorAdd(var_58, conv2d_14_b);
- void* var_60 = tensorAdd(var_52, var_59);
- void* var_61 = tensorRelu(var_60);
- void* var_63 = tensorConvolutionKernelSamp(var_61, conv2d_15_w, 1, 1, 2, 2, 1, 0, 30);
- void* var_64 = tensorAdd(var_63, conv2d_15_b);
- void* var_65 = tensorRelu(var_64);
- void* var_67 = tensorConvolutionKernelSamp(var_65, conv2d_16_w, 1, 1, 1, 1, 1, 0, 30);
- void* var_68 = tensorAdd(var_67, conv2d_16_b);
- void* var_70 = tensorConvolutionKernelSamp(var_61, conv2d_17_w, 0, 0, 2, 2, 1, 0, 30);
- void* var_71 = tensorAdd(var_70, conv2d_17_b);
- void* var_72 = tensorAdd(var_71, var_68);
- void* var_73 = tensorRelu(var_72);
- void* var_75 = tensorConvolutionKernelSamp(var_73, conv2d_18_w, 1, 1, 1, 1, 1, 0, 30);
- void* var_76 = tensorAdd(var_75, conv2d_18_b);
- void* var_77 = tensorRelu(var_76);
- void* var_79 = tensorConvolutionKernelSamp(var_77, conv2d_19_w, 1, 1, 1, 1, 1, 0, 30);
- void* var_80 = tensorAdd(var_79, conv2d_19_b);
- void* var_81 = tensorAdd(var_73, var_80);
- void* var_82 = tensorRelu(var_81);
- void* var_84 = tensorConvolutionKernelSamp(var_82, conv2d_20_w, 1, 1, 1, 1, 1, 0, 30);
- void* var_85 = tensorAdd(var_84, conv2d_20_b);
- void* var_86 = tensorRelu(var_85);
- void* var_88 = tensorConvolutionKernelSamp(var_86, conv2d_21_w, 1, 1, 1, 1, 1, 0, 30);
- void* var_89 = tensorAdd(var_88, conv2d_21_b);
- void* var_90 = tensorAdd(var_82, var_89);
- void* var_91 = tensorRelu(var_90);
- void* var_92 = tensorPooling(var_91,1,8,8,0,0,8,8);
- void* var_94 = tensorGemmGPU(var_92, dense_1_w);
- void* var_95 = tensorAdd(var_94, dense_1_b);
- void* var_96 = tensorSoftmax(var_95);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
-
- float accuracy = computeAccuracy2(labels,batch_size,var_96);
- final_accuracy += accuracy;
-
- freeBatchMemory();
- }
-
- stopProfiling();
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet18_cifar10_tuner.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet18_cifar10_tuner.cc
deleted file mode 100644
index 689e241c5b4a0a5e1c5b98326998f37d5e803f75..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/resnet18_cifar10_tuner.cc
+++ /dev/null
@@ -1,265 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-
-
-int main(int argc, char* argv[]){
-
- int total_runs = 1;
- if (argc > 1){
- total_runs = atoi(argv[1]);
- }
-
-
- llvm_hpvm_initTensorRt(0);
-
-
- /*int skip_tensor_ids[22];
- skip_tensor_ids[0] = 0;
- skip_tensor_ids[1] = 1;
- skip_tensor_ids[2] = 1;
- skip_tensor_ids[3] = 3;
- skip_tensor_ids[4] = 4;
- skip_tensor_ids[5] = 4;
- skip_tensor_ids[6] = 10;
- skip_tensor_ids[7] = 11;
- skip_tensor_ids[8] = 17;
- skip_tensor_ids[9] = 18;
- skip_tensor_ids[10] = 24;
- skip_tensor_ids[11] = 25;
- skip_tensor_ids[12] = 25;
- skip_tensor_ids[13] = 33;
- skip_tensor_ids[14] = 34;
- skip_tensor_ids[15] = 35;
- skip_tensor_ids[16] = 40;
- skip_tensor_ids[17] = 41;
- skip_tensor_ids[18] = 47;
- skip_tensor_ids[19] = 48;
- //--- readSkipTensors(skip_tensor_ids, 22);
- //-- readSkipTensors(skip_tensor_ids, 10);
- readSkipTensors(skip_tensor_ids, 20);
- */
-
- std::string dir_prefix = std::string("../model_params/resnet18_cifar10_promise/");
- // std::string input_path = dir_prefix + std::string("resnet18_calib.bin");
- // std::string labels_path = dir_prefix + std::string("resnet18_train_labels.bin");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,16,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,16,16,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,16,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,16,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,16,1,1);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,32,32,3,3);
- std::string conv2d_14_b_path = dir_prefix + std::string("conv2d_14_b.bin");
- void* conv2d_14_b = readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,32,1,1);
- std::string conv2d_15_w_path = dir_prefix + std::string("conv2d_15_w.bin");
- void* conv2d_15_w = readTrainedWeights(conv2d_15_w_path.c_str(), 0,64,32,3,3);
- std::string conv2d_15_b_path = dir_prefix + std::string("conv2d_15_b.bin");
- void* conv2d_15_b = readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_17_w_path = dir_prefix + std::string("conv2d_17_w.bin");
- void* conv2d_17_w = readTrainedWeights(conv2d_17_w_path.c_str(), 0,64,32,1,1);
- std::string conv2d_17_b_path = dir_prefix + std::string("conv2d_17_b.bin");
- void* conv2d_17_b = readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_16_w_path = dir_prefix + std::string("conv2d_16_w.bin");
- void* conv2d_16_w = readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_16_b_path = dir_prefix + std::string("conv2d_16_b.bin");
- void* conv2d_16_b = readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_18_w_path = dir_prefix + std::string("conv2d_18_w.bin");
- void* conv2d_18_w = readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_18_b_path = dir_prefix + std::string("conv2d_18_b.bin");
- void* conv2d_18_b = readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_19_w_path = dir_prefix + std::string("conv2d_19_w.bin");
- void* conv2d_19_w = readTrainedWeights(conv2d_19_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_19_b_path = dir_prefix + std::string("conv2d_19_b.bin");
- void* conv2d_19_b = readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_20_w_path = dir_prefix + std::string("conv2d_20_w.bin");
- void* conv2d_20_w = readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_20_b_path = dir_prefix + std::string("conv2d_20_b.bin");
- void* conv2d_20_b = readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_21_w_path = dir_prefix + std::string("conv2d_21_w.bin");
- void* conv2d_21_w = readTrainedWeights(conv2d_21_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_21_b_path = dir_prefix + std::string("conv2d_21_b.bin");
- void* conv2d_21_b = readTrainedWeights(conv2d_21_b_path.c_str(), 0,1,64,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,64,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 500;
- int batch_size = 500;
- int offset = 5000;
-
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- // NOTE: Starting time profiling
- startProfiling();
-
-
- for(int j = 0; j < total_runs; j++){
-
- float final_accuracy = 0.0;
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size + offset;
- int end = (i + 1) * batch_size + offset;
-
- void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
-
- void* var_2 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
- void* var_3 = tensorAdd(var_2, conv2d_1_b);
- void* var_4 = tensorRelu(var_3);
- void* var_6 = tensorConvolution(var_4, conv2d_2_w, 1, 1, 1, 1, 1, 0);
- void* var_7 = tensorAdd(var_6, conv2d_2_b);
- void* var_8 = tensorRelu(var_7);
- void* var_10 = tensorConvolution(var_8, conv2d_3_w, 1, 1, 1, 1, 1, 0);
- void* var_11 = tensorAdd(var_10, conv2d_3_b);
- void* var_12 = tensorAdd(var_4, var_11);
- void* var_13 = tensorRelu(var_12);
- void* var_15 = tensorConvolution(var_13, conv2d_4_w, 1, 1, 1, 1, 1, 0);
- void* var_16 = tensorAdd(var_15, conv2d_4_b);
- void* var_17 = tensorRelu(var_16);
- void* var_19 = tensorConvolution(var_17, conv2d_5_w, 1, 1, 1, 1, 1, 0);
- void* var_20 = tensorAdd(var_19, conv2d_5_b);
- void* var_21 = tensorAdd(var_13, var_20);
- void* var_22 = tensorRelu(var_21);
- void* var_24 = tensorConvolution(var_22, conv2d_6_w, 1, 1, 1, 1, 1, 0);
- void* var_25 = tensorAdd(var_24, conv2d_6_b);
- void* var_26 = tensorRelu(var_25);
- void* var_28 = tensorConvolution(var_26, conv2d_7_w, 1, 1, 1, 1, 1, 0);
- void* var_29 = tensorAdd(var_28, conv2d_7_b);
- void* var_30 = tensorAdd(var_22, var_29);
- void* var_31 = tensorRelu(var_30);
- void* var_33 = tensorConvolution(var_31, conv2d_8_w, 1, 1, 2, 2, 1, 0);
- void* var_34 = tensorAdd(var_33, conv2d_8_b);
- void* var_35 = tensorRelu(var_34);
- void* var_37 = tensorConvolution(var_35, conv2d_9_w, 1, 1, 1, 1, 1, 0);
- void* var_38 = tensorAdd(var_37, conv2d_9_b);
- void* var_40 = tensorConvolution(var_31, conv2d_10_w, 0, 0, 2, 2, 1, 0);
- void* var_41 = tensorAdd(var_40, conv2d_10_b);
- void* var_42 = tensorAdd(var_41, var_38);
- void* var_43 = tensorRelu(var_42);
- void* var_45 = tensorConvolution(var_43, conv2d_11_w, 1, 1, 1, 1, 1, 0);
- void* var_46 = tensorAdd(var_45, conv2d_11_b);
- void* var_47 = tensorRelu(var_46);
- void* var_49 = tensorConvolution(var_47, conv2d_12_w, 1, 1, 1, 1, 1, 0);
- void* var_50 = tensorAdd(var_49, conv2d_12_b);
- void* var_51 = tensorAdd(var_43, var_50);
- void* var_52 = tensorRelu(var_51);
- void* var_54 = tensorConvolution(var_52, conv2d_13_w, 1, 1, 1, 1, 1, 0);
- void* var_55 = tensorAdd(var_54, conv2d_13_b);
- void* var_56 = tensorRelu(var_55);
- void* var_58 = tensorConvolution(var_56, conv2d_14_w, 1, 1, 1, 1, 1, 0);
- void* var_59 = tensorAdd(var_58, conv2d_14_b);
- void* var_60 = tensorAdd(var_52, var_59);
- void* var_61 = tensorRelu(var_60);
- void* var_63 = tensorConvolution(var_61, conv2d_15_w, 1, 1, 2, 2, 1, 0);
- void* var_64 = tensorAdd(var_63, conv2d_15_b);
- void* var_65 = tensorRelu(var_64);
- void* var_67 = tensorConvolution(var_65, conv2d_16_w, 1, 1, 1, 1, 1, 0);
- void* var_68 = tensorAdd(var_67, conv2d_16_b);
- void* var_70 = tensorConvolution(var_61, conv2d_17_w, 0, 0, 2, 2, 1, 0);
- void* var_71 = tensorAdd(var_70, conv2d_17_b);
- void* var_72 = tensorAdd(var_71, var_68);
- void* var_73 = tensorRelu(var_72);
- void* var_75 = tensorConvolution(var_73, conv2d_18_w, 1, 1, 1, 1, 1, 0);
- void* var_76 = tensorAdd(var_75, conv2d_18_b);
- void* var_77 = tensorRelu(var_76);
- void* var_79 = tensorConvolution(var_77, conv2d_19_w, 1, 1, 1, 1, 1, 0);
- void* var_80 = tensorAdd(var_79, conv2d_19_b);
- void* var_81 = tensorAdd(var_73, var_80);
- void* var_82 = tensorRelu(var_81);
- void* var_84 = tensorConvolution(var_82, conv2d_20_w, 1, 1, 1, 1, 1, 0);
- void* var_85 = tensorAdd(var_84, conv2d_20_b);
- void* var_86 = tensorRelu(var_85);
- void* var_88 = tensorConvolution(var_86, conv2d_21_w, 1, 1, 1, 1, 1, 0);
- void* var_89 = tensorAdd(var_88, conv2d_21_b);
- void* var_90 = tensorAdd(var_82, var_89);
- void* var_91 = tensorRelu(var_90);
- void* var_92 = tensorPooling(var_91,1,8,8,0,0,8,8);
- void* var_94 = tensorGemmGPU(var_92, dense_1_w);
- void* var_95 = tensorAdd(var_94, dense_1_b);
- void* var_96 = tensorSoftmax(var_95);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
-
- float accuracy = computeAccuracy2(labels,batch_size,var_96);
- final_accuracy += accuracy;
-
- freeBatchMemory();
- }
-
- stopProfiling();
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
- }
-
- dumpExecutionAccuracies();
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test/fc2_clipped_promise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test/fc2_clipped_promise.cc
deleted file mode 100644
index d7addd7283e24bedfc32d57d84c4ce17d9966f57..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test/fc2_clipped_promise.cc
+++ /dev/null
@@ -1,80 +0,0 @@
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-#include "../../include/types.h"
-
-
-
-void test_2_Layer_clipped_FC(){
-
- printf("********* 2-Layer FC with clipped activations and weights ********* \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 10000;
-
- void* input = readTrainedWeights("../model_params/FC_network2/mnist_float_input.bin",
- float_type, test_batch_size, 1, 28, 28);
- void* fc1_weights = readTrainedWeights("../model_params/fc2_clipped/fc1.bin",
- float_type, 1, 1, 784, 128);
- void* fc1_bias = readTrainedWeights("../model_params/fc2_clipped/fc1_bias.bin",
- float_type, 1, 128, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/fc2_clipped/fc2.bin",
- float_type, 1, 1, 128, 10);
- void* fc2_bias = readTrainedWeights("../model_params/fc2_clipped/fc2_bias.bin",
- float_type, 1, 10, 1, 1);
-
- // Start execution profiling Tensor ops
- startProfiling();
-
- // Layer-1
- void* fc1out = tensorGemmGPU(input, fc1_weights);
- printTensorDims(fc1out);
- dumpWeightsToFile("tensors_out2/fc1out.out", fc1out);
-
- void* fc1_bias_out = tensorAdd(fc1out, fc1_bias);
- printTensorDims(fc1_bias_out);
- dumpWeightsToFile("tensors_out2/fc1_bias.out", fc1_bias_out);
-
- void* fc1_relu = tensorRelu2(fc1_bias_out, 0, 2);
- printTensorDims(fc1_relu);
- dumpWeightsToFile("tensors_out2/fc1_clipped_relu.out", fc1_relu);
-
- // Layer-2
- void* fc2out = tensorGemmGPU(fc1_relu, fc2_weights);
- printTensorDims(fc2out);
- dumpWeightsToFile("tensors_out2/fc2out.out", fc2out);
-
-
- void* fc2_bias_out = tensorAdd(fc2out, fc2_bias);
- printTensorDims(fc2_bias_out);
-
- void* fc2_relu = tensorRelu2(fc2_bias_out, 0, 2);
- printTensorDims(fc2_relu);
-
- void* result = tensorSoftmax(fc2_relu);
- printTensorDims(result);
-
- stopProfiling();
-
- computeAccuracy("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size, result);
-}
-
-
-
-int main(){
-
- // This initializes the runtime - must be called before anything
- llvm_hpvm_initTensorRt(0);
-
- test_2_Layer_clipped_FC();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test/test_lenet2.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test/test_lenet2.cc
deleted file mode 100644
index d5211be3918adcd030fc40c13cba1ff0d7c53c18..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test/test_lenet2.cc
+++ /dev/null
@@ -1,112 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testLenet2Arch(){
-
- printf("********* Lenet-2 Architecture ********** \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 10000;
- void* input = readInputTensor("../model_params/lenet_params/datasets/t10k-images-idx3-ubyte",
- CUDNN_DATA_FLOAT,
- test_batch_size, 1, 28, 28);
-
- // NOTE: Filter descriptors do NOT have batch size
- // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
- // IMP: The output channels matches the trained model - not the Lenet arch proposed in Andrew Ng's class
- void* conv1_filter = readTrainedWeights("../model_params/lenet2_params/conv1.bin",
- float_type, 32, 1, 5, 5);
- void* conv1_bias = readTrainedWeights("../model_params/lenet2_params/conv1_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/lenet2_params/conv2.bin",
- float_type, 64, 32, 5, 5);
- void* conv2_bias = readTrainedWeights("../model_params/lenet2_params/conv2_bias.bin",
- float_type, 1, 64, 1, 1);
- void* fc1_weights = readTrainedWeights("../model_params/lenet2_params/fc1.bin",
- float_type, 1, 1, 7*7*64, 1024);
- void* fc1_bias = readTrainedWeights("../model_params/lenet2_params/fc1_bias.bin",
- float_type, 1, 1024, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/lenet2_params/fc2.bin",
- float_type, 1, 1, 1024, 10);
- void* fc2_bias = readTrainedWeights("../model_params/lenet2_params/fc2_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- // Start power and performnce profiling
- startProfiling();
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* conv1out = tensorConvolution(input, conv1_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
-
- // NOTE: For tensorAdd, the only dimension that MUST match is channels
- tensorAdd(conv1out, conv1_bias); // NOTE: In place operation
- printTensorDims(conv1out);
-
- void* conv1_reluout = tensorRelu(conv1out);
- //dumpWeightsToFile("tensors_out/conv1_relu.out", conv1_reluout);
-
- void* pool1out = tensorPooling(conv1_reluout, 0, 2, 2, 0, 0, 2, 2);
- printTensorDims(pool1out);
- //dumpWeightsToFile("tensors_out/pool1.out", pool1out);
- // NOTE: input channels have to match between tensor op inputs and outputs
- void* conv2out = tensorConvolution(pool1out, conv2_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv2out, conv2_bias); // NOTE: In place operation
- printTensorDims(conv2out);
-
- void* conv2_reluout = tensorRelu(conv2out);
- //dumpWeightsToFile("tensors_out/conv2.out", conv2_reluout);
-
- void* pool2out = tensorPooling(conv2_reluout, 0, 2, 2, 0, 0, 2, 2);
- printTensorDims(pool2out);
- //dumpWeightsToFile("tensors_out/maxpool2.out", pool2out);
-
- void* gemm1out = tensorGemmGPU(pool2out, fc1_weights);
- printTensorDims(gemm1out);
- //dumpWeightsToFile("tensors_out/gemm1.out", gemm1out);
-
- void* gemm1biasout = tensorAdd(gemm1out, fc1_bias);
- printTensorDims(gemm1biasout);
-
- void* relu1out = tensorRelu(gemm1biasout);
- printTensorDims(relu1out);
-
- void* gemm2out = tensorGemmGPU(relu1out, fc2_weights);
- printTensorDims(gemm2out);
-
- void* gemm2_biasout = tensorAdd(gemm2out, fc2_bias);
- printTensorDims(gemm2_biasout);
-
- void* result = tensorSoftmax(gemm2_biasout);
- printTensorDims(result);
-
- // End profiling and dump output to profile.txt
- stopProfiling();
-
- computeAccuracy("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte",
- test_batch_size, result);
- // THINK: I believe that comparing the results do not need to be part of the HPVM graph
-}
-
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- testLenet2Arch();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test/test_lenet2_promise.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test/test_lenet2_promise.cc
deleted file mode 100644
index 358cb6a75b8e63ca0a0bd964c9f73f2d16c39b4f..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test/test_lenet2_promise.cc
+++ /dev/null
@@ -1,113 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testLenet2Arch(){
-
- printf("********* Lenet-2 Architecture ********** \n");
-
- int test_batch_size = 10000;
- void* input = readInputTensor("../model_params/lenet_params/datasets/t10k-images-idx3-ubyte",
- CUDNN_DATA_FLOAT,
- test_batch_size, 1, 28, 28);
-
- // NOTE: Filter descriptors do NOT have batch size
- // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
- // IMP: The output channels matches the trained model - not the Lenet arch proposed in Andrew Ng's class
- void* conv1_filter = readTrainedWeights("../model_params/lenet2_params/conv1.bin",
- float_type, 32, 1, 5, 5);
- void* conv1_bias = readTrainedWeights("../model_params/lenet2_params/conv1_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/lenet2_params/conv2.bin",
- float_type, 64, 32, 5, 5);
- void* conv2_bias = readTrainedWeights("../model_params/lenet2_params/conv2_bias.bin",
- float_type, 1, 64, 1, 1);
- void* fc1_weights = readTrainedWeights("../model_params/lenet2_params/fc1.bin",
- float_type, 1, 1, 7*7*64, 1024);
- void* fc1_bias = readTrainedWeights("../model_params/lenet2_params/fc1_bias.bin",
- float_type, 1, 1024, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/lenet2_params/fc2.bin",
- float_type, 1, 1, 1024, 10);
- void* fc2_bias = readTrainedWeights("../model_params/lenet2_params/fc2_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- // Start power and performnce profiling
- startProfiling();
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* conv1out = tensorConvolution(input, conv1_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
- dumpWeightsToFile("tensors_out/conv1_out.out", conv1out);
-
- tensorAdd(conv1out, conv1_bias); // NOTE: In-place operation
- printTensorDims(conv1out);
-
- dumpWeightsToFile("tensors_out/conv1_bias_add.out", conv1out);
-
- void* conv1_reluout = tensorRelu(conv1out);
- dumpWeightsToFile("tensors_out/conv1_relu.out", conv1_reluout);
-
- void* pool1out = tensorPooling(conv1_reluout, 0, 2, 2, 0, 0, 2, 2);
- printTensorDims(pool1out);
- dumpWeightsToFile("tensors_out/conv1_pool.out", pool1out);
-
- // NOTE: input channels have to match between tensor op inputs and outputs
- void* conv2out = tensorConvolution(pool1out, conv2_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv2out, conv2_bias); // NOTE: In place operation
- printTensorDims(conv2out);
-
- void* conv2_reluout = tensorRelu(conv2out);
- //dumpWeightsToFile("tensors_out/conv2.out", conv2_reluout);
-
- void* pool2out = tensorPooling(conv2_reluout, 0, 2, 2, 0, 0, 2, 2);
- printTensorDims(pool2out);
- //dumpWeightsToFile("tensors_out/maxpool2.out", pool2out);
-
- void* gemm1out = tensorGemmGPU(pool2out, fc1_weights);
- printTensorDims(gemm1out);
- //dumpWeightsToFile("tensors_out/gemm1.out", gemm1out);
-
- void* gemm1biasout = tensorAdd(gemm1out, fc1_bias);
- printTensorDims(gemm1biasout);
-
- void* relu1out = tensorRelu(gemm1biasout);
- printTensorDims(relu1out);
-
- void* gemm2out = tensorGemmGPU(relu1out, fc2_weights);
- printTensorDims(gemm2out);
-
- void* gemm2_biasout = tensorAdd(gemm2out, fc2_bias);
- printTensorDims(gemm2_biasout);
-
- void* result = tensorSoftmax(gemm2_biasout);
- printTensorDims(result);
-
- // End profiling and dump output to profile.txt
- stopProfiling();
-
- computeAccuracy("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte",
- test_batch_size, result);
-}
-
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- testLenet2Arch();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test/test_lenet_acc.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test/test_lenet_acc.cc
deleted file mode 100644
index 42e364289e499d92591692a04e42988fd1a66dc5..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test/test_lenet_acc.cc
+++ /dev/null
@@ -1,109 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testLenet2Arch(){
-
- printf("********* Lenet-2 Architecture ********** \n");
-
- int test_batch_size = 1000;
- void* input = readInputTensor("../model_params/lenet_params/datasets/t10k-images-idx3-ubyte",
- CUDNN_DATA_FLOAT,
- test_batch_size, 1, 28, 28);
-
- // NOTE: Filter descriptors do NOT have batch size
- // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
- // IMP: The output channels matches the trained model - not the Lenet arch proposed in Andrew Ng's class
- void* conv1_filter = readTrainedWeights("../model_params/lenet_test_params/conv1.bin",
- float_type, 32, 1, 5, 5);
- void* conv1_bias = readTrainedWeights("../model_params/lenet_test_params/conv1_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/lenet_test_params/conv2.bin",
- float_type, 64, 32, 5, 5);
- void* conv2_bias = readTrainedWeights("../model_params/lenet_test_params/conv2_bias.bin",
- float_type, 1, 64, 1, 1);
- void* fc1_weights = readTrainedWeights("../model_params/lenet_test_params/fc1.bin",
- float_type, 1, 1, 7*7*64, 1024);
- void* fc1_bias = readTrainedWeights("../model_params/lenet_test_params/fc1_bias.bin",
- float_type, 1, 1024, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/lenet_test_params/fc2.bin",
- float_type, 1, 1, 1024, 10);
- void* fc2_bias = readTrainedWeights("../model_params/lenet_test_params/fc2_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- // Start power and performnce profiling
- startProfiling();
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* conv1out = tensorConvolution(input, conv1_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
-
- // NOTE: For tensorAdd, the only dimension that MUST match is channels
- tensorAdd(conv1out, conv1_bias); // NOTE: In place operation
- printTensorDims(conv1out);
-
- void* pool1out = tensorPooling(conv1out, 0, 2, 2, 0, 0, 2, 2);
- printTensorDims(pool1out);
- //dumpWeightsToFile("tensors_out/pool1.out", pool1out);
- // NOTE: input channels have to match between tensor op inputs and outputs
- void* conv2out = tensorConvolution(pool1out, conv2_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv2out, conv2_bias); // NOTE: In place operation
- printTensorDims(conv2out);
-
- //void* conv2_reluout = tensorRelu(conv2out);
-
- void* pool2out = tensorPooling(conv2out, 0, 2, 2, 0, 0, 2, 2);
- printTensorDims(pool2out);
- //dumpWeightsToFile("tensors_out/maxpool2.out", pool2out);
-
- void* gemm1out = tensorGemmGPU(pool2out, fc1_weights);
- printTensorDims(gemm1out);
- //dumpWeightsToFile("tensors_out/gemm1.out", gemm1out);
-
- void* gemm1biasout = tensorAdd(gemm1out, fc1_bias);
- printTensorDims(gemm1biasout);
-
- void* relu1out = tensorRelu(gemm1biasout);
- printTensorDims(relu1out);
-
- void* gemm2out = tensorGemmGPU(relu1out, fc2_weights);
- printTensorDims(gemm2out);
-
- void* gemm2_biasout = tensorAdd(gemm2out, fc2_bias);
- printTensorDims(gemm2_biasout);
-
- void* result = tensorSoftmax(gemm2_biasout);
- printTensorDims(result);
-
- // End profiling and dump output to profile.txt
- stopProfiling();
-
- computeAccuracy("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte",
- test_batch_size, result);
- // THINK: I believe that comparing the results do not need to be part of the HPVM graph
-}
-
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- testLenet2Arch();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_alexnet.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_alexnet.cc
deleted file mode 100644
index a8129a1e459a15e26f595972724451e01d81b0a1..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_alexnet.cc
+++ /dev/null
@@ -1,480 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-
-// FIXIT: Data allocations may need to organized - Alexnet may consume more than available mem
-
-/*void testAlexnet2(){
-
- struct Tensor* input = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 100, 3, 224, 224);
- initTensorValues(input);
-
- struct Tensor* conv1filter = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 96, 3, 11, 11);
- printTensorDims(conv1filter);
-
- /****** Start of Layer 1 ***************
-
- // NOTE: Padding for same conv is computed as P = (F - 1 /2)
- struct Tensor* conv1out = tensorConvolution(input, conv1filter, 5, 5, 4, 4);
- printTensorDims(conv1out);
-
- struct Tensor* conv1bias = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 1, 96, 1, 1);
- struct Tensor* conv1bias_out = tensorAdd(conv1out, conv1bias);
-
- struct Tensor* relu1out = tensorRelu(conv1bias_out);
-
- // NOTE: These parameters are a deviation from the original paper
- // The parameters match the alexnet TF model
- // TODO: Try removing LRN and measure - seems like impact of LRN may be minimal
- unsigned int LRN_window = 5;
- double LRN_alpha = 2e-05;
- double LRN_beta = 0.75;
- double LRN_k = 1.0;
- struct Tensor* lrn1out = tensorLRN(relu1out, LRN_window, LRN_alpha, LRN_beta, LRN_k);
- printTensorDims(lrn1out);
-
- struct Tensor* maxpool1out = tensorPooling(lrn1out, 0, 3, 3, 0, 0, 2, 2);
-
- /****** End of Conv Layer 1 **********
-
- struct Tensor** splits = tensorSplit(maxpool1out, 2, 1);
-
- struct Tensor* conv2W = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 256, 48, 5, 5);
- struct Tensor** conv2fils = tensorSplit(conv2W, 2, 0);
-
- struct Tensor* conv2a_out = tensorConvolution(splits[0], conv2fils[0], 2, 2, 1, 1);
- printTensorDims(conv2a_out);
-
- struct Tensor* conv2b_out = tensorConvolution(splits[1], conv2fils[1], 2, 2, 1, 1);
- printTensorDims(conv2b_out);
-
- struct Tensor* conv2_outs[2];
- conv2_outs[0] = conv2a_out;
- conv2_outs[1] = conv2b_out;
-
- struct Tensor* conv2_concat_out = tensorConcat(conv2_outs, 2, 1);
- printTensorDims(conv2_concat_out);
-
- struct Tensor* conv2bias = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 1, 256, 1, 1);
- struct Tensor* conv2bias_out = tensorAdd(conv2_concat_out, conv2bias);
- struct Tensor* relu2out = tensorRelu(conv2bias_out);
- struct Tensor* lrn2out = tensorLRN(relu2out, LRN_window, LRN_alpha, LRN_beta, LRN_k);
- printTensorDims(lrn2out);
-
- struct Tensor* maxpool2out = tensorPooling(lrn2out, 0, 3, 3, 0, 0, 2, 2);
- printTensorDims(maxpool2out);
-
- /******** End of Conv Layer 2 ************
-
- struct Tensor* conv3filter = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 384, 256, 3, 3);
- struct Tensor* conv3_out = tensorConvolution(maxpool2out, conv3filter, 1, 1, 1, 1);
-
- struct Tensor* conv3bias = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 1, 384, 1, 1);
- struct Tensor* conv3bias_out = tensorAdd(conv3_out, conv3bias);
- struct Tensor* relu3out = tensorRelu(conv3bias_out);
- printTensorDims(relu3out);
-
- /********* End of Conv layer 3 ******
-
- struct Tensor** splits2 = tensorSplit(relu3out, 2, 1);
-
- struct Tensor* conv4W = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 384, 192, 3, 3);
- struct Tensor** conv4fils = tensorSplit(conv4W, 2, 0);
-
- printTensorDims(splits2[0]);
- printTensorDims(conv4fils[0]);
-
- struct Tensor* conv4a_out = tensorConvolution(splits2[0], conv4fils[0], 1, 1, 1, 1);
- printTensorDims(conv4a_out);
-
- struct Tensor* conv4b_out = tensorConvolution(splits2[1], conv4fils[1], 1, 1, 1, 1);
- printTensorDims(conv4b_out);
-
- struct Tensor* conv4_outs[2];
- conv4_outs[0] = conv4a_out;
- conv4_outs[1] = conv4b_out;
-
- struct Tensor* conv4_concat_out = tensorConcat(conv4_outs, 2, 1);
- printTensorDims(conv4_concat_out);
-
- struct Tensor* conv4bias = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 1, 384, 1, 1);
- struct Tensor* conv4bias_out = tensorAdd(conv4_concat_out, conv4bias);
- struct Tensor* relu4out = tensorRelu(conv4bias_out);
- printTensorDims(relu4out);
-
- /********* End of Conv layer 4 ******
-
- struct Tensor** splits3 = tensorSplit(relu4out, 2, 1);
-
- struct Tensor* conv5W = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 256, 192, 3, 3);
- struct Tensor** conv5fils = tensorSplit(conv5W, 2, 0);
-
- printTensorDims(splits3[0]);
- printTensorDims(conv5fils[0]);
-
- struct Tensor* conv5a_out = tensorConvolution(splits3[0], conv5fils[0], 1, 1, 1, 1);
- printTensorDims(conv5a_out);
-
- struct Tensor* conv5b_out = tensorConvolution(splits3[1], conv5fils[1], 1, 1, 1, 1);
- printTensorDims(conv5b_out);
-
- struct Tensor* conv5_outs[2];
- conv5_outs[0] = conv5a_out;
- conv5_outs[1] = conv5b_out;
-
- struct Tensor* conv5_concat_out = tensorConcat(conv5_outs, 2, 1);
- printTensorDims(conv5_concat_out);
-
- struct Tensor* conv5bias = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 1, 256, 1, 1);
- struct Tensor* conv5bias_out = tensorAdd(conv5_concat_out, conv5bias);
- struct Tensor* relu5out = tensorRelu(conv5bias_out);
- printTensorDims(relu5out);
-
- struct Tensor* maxpool5out = tensorPooling(relu5out, 0, 3, 3, 0, 0, 2, 2);
- printTensorDims(maxpool5out);
-
- /********* End of Conv layer 5 ******
-
- struct Tensor* fc1_weights = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW,
- 1, 1, 256*6*6, 4096);
- struct Tensor* gemm1out = tensorGemm(maxpool5out, fc1_weights);
- printTensorDims(gemm1out);
-
- struct Tensor* bias = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW,
- 1, 1, 1, 4096);
- struct Tensor* gemm1biasout = tensorGemmBias(gemm1out, bias);
- printTensorDims(gemm1biasout);
-
- struct Tensor* relu6out = tensorRelu(gemm1biasout);
- printTensorDims(relu6out);
-
- /***** End of FC1 layer ********
-
- struct Tensor* fc2_weights = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW,
- 1, 1, 4096, 4096);
- struct Tensor* gemm2out = tensorGemm(relu6out, fc2_weights);
- printTensorDims(gemm2out);
-
- struct Tensor* bias2 = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW,
- 1, 1, 1, 4096);
- struct Tensor* gemm2biasout = tensorGemmBias(gemm2out, bias2);
- printTensorDims(gemm2biasout);
-
- struct Tensor* relu7out = tensorRelu(gemm2biasout);
- printTensorDims(relu7out);
-
- /***** End of FC2 layer ********
-
- struct Tensor* fc3_weights = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW,
- 1, 1, 4096, 1000);
- struct Tensor* gemm3out = tensorGemm(relu7out, fc3_weights);
- printTensorDims(gemm3out);
-
- struct Tensor* bias3 = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW,
- 1, 1, 1, 1000);
- struct Tensor* gemm3biasout = tensorGemmBias(gemm3out, bias3);
- printTensorDims(gemm3biasout);
-
- /******** End of FC3 Layer **********
- struct Tensor* result = tensorSoftmax(gemm3biasout);
- printTensorDims(result);
-
-} */
-
-
-
-void printLikelihood(char* labels_file, int num_labels, void* result_ptr){
-
- struct Tensor* result = (struct Tensor*) result_ptr;
-
- size_t batch_dim = result->dims.dim_sizes[0];
- size_t channels = result->dims.dim_sizes[1];
- float* data = (float*) result->host_data;
-
- for(int i = 0; i < batch_dim; i++){
- int chosen = 0;
- for (int id = 1; id < channels; ++id){
- if (data[i * channels + chosen] < data[i * channels + id]) chosen = id;
- }
-
- printf("** chosen = %d, label = %f, label+3 = %f \n",
- chosen, data[chosen], data[chosen+3]);
- }
-
- //float accuracy = ((batch_dim - num_errors) * 1.0 / batch_dim * 1.0) * 100.0;
- //printf("****** Accuracy = %f \n\n", accuracy);
-}
-
-
-//--- Results not matching
-// *** CHECK:
-// 1) cudnnCrossCorrelation vs cudnnConvolution
-// 2) Weights
-// 3) Tensor outputs
-// 4) Data layouts
-
-
-
-/*** NOTE: REFERECNCE ARCHITECTURE **/
-// FIXIT: Data allocations may need to organized - Alexnet may consume more than available mem
-void testAlexnet3(){
-
- int test_batch_size = 2;
- int conv_mode = 1; // CROSS_CORRELATION matches the TF conv2d implementation
- int conv_precision = 0; // floating point precision for convolution
-
- printf("****** AlexNet Architecture 3 ******** \n\n");
- void* input = readTrainedWeights("../alexnet/params/combined_imgs.bin",
- CUDNN_DATA_FLOAT, test_batch_size, 3, 227, 227);
- dumpWeightsToFile("tensors_out/input.out", input);
-
- /****** Start of Layer 1 ****************/
- void* conv1filter = readTrainedWeights("../alexnet/params/conv1.bin",
- CUDNN_DATA_FLOAT, 96, 3, 11, 11);
-
- printTensorDims(conv1filter);
- dumpWeightsToFile("tensors_out/conv1filter.out", conv1filter);
-
- // NOTE: the trained model does NOT have any padding in this conv
- void* conv1out = tensorConvolution(input, conv1filter, 4, 4, 4, 4,
- conv_mode, conv_precision);
- printTensorDims(conv1out);
-
- void* conv1bias = readTrainedWeights("../alexnet/params/conv1.bias.bin",
- CUDNN_DATA_FLOAT, 1, 96, 1, 1);
- void* conv1bias_out = tensorAdd(conv1out, conv1bias);
-
- dumpWeightsToFile("tensors_out/conv1_init.out", conv1out);
-
- void* relu1out = tensorRelu(conv1bias_out);
- printTensorDims(relu1out);
- dumpWeightsToFile("tensors_out/conv1.out", relu1out);
-
- // NOTE: These parameters are a deviation from the original paper
- // The parameters match the alexnet TF model
- // TODO: Try removing LRN and measure - seems like impact of LRN may be minimal
- unsigned int LRN_window = 5;
- double LRN_alpha = 2e-05 * LRN_window;
- double LRN_beta = 0.75;
- double LRN_k = 1.0;
-
- // TEST-point - Compare TF vs CUDNN
- void* lrn1out = tensorLRN(relu1out, LRN_window, LRN_alpha, LRN_beta, LRN_k);
- printTensorDims(lrn1out);
- dumpWeightsToFile("tensors_out/lrn1.out", lrn1out);
-
- void* maxpool1out = tensorPooling(lrn1out, 0, 3, 3, 0, 0, 2, 2);
- printTensorDims(maxpool1out);
- dumpWeightsToFile("tensors_out/maxpool1.out", maxpool1out);
-
- /****** End of Conv Layer 1 ***********/
-
- // TEST-point
- void** splits = tensorSplit(maxpool1out, 2, 1);
-
- void* concat_test1 = tensorConcat(splits, 2, 1);
- compareTensors(maxpool1out, concat_test1);
-
- void* conv2W = readTrainedWeights("../alexnet/params/conv2.bin",
- CUDNN_DATA_FLOAT, 256, 48, 5, 5);
-
- dumpWeightsToFile("tensors_out/conv2filter.out", conv2W);
-
- // TEST point - compare split convolution across TF vs cuDNN
- void** conv2fils = tensorSplit(conv2W, 2, 0);
-
- void* concat_test2 = tensorConcat(conv2fils, 2, 0);
- compareTensors(conv2W, concat_test2);
-
- // NOTE: Padding for same conv is computed as P = ((F - 1) / 2)
- void* conv2a_out = tensorConvolution(splits[0], conv2fils[0], 2, 2, 1, 1,
- conv_mode, conv_precision);
- printTensorDims(conv2a_out);
-
- void* conv2b_out = tensorConvolution(splits[1], conv2fils[1], 2, 2, 1, 1,
- conv_mode, conv_precision);
- printTensorDims(conv2b_out);
-
- void* conv2_outs[2];
- conv2_outs[0] = conv2a_out;
- conv2_outs[1] = conv2b_out;
-
- // Test point
- void* conv2_concat_out = tensorConcat(conv2_outs, 2, 1);
- printTensorDims(conv2_concat_out);
- dumpWeightsToFile("tensors_out/conv2_init.out", conv2_concat_out);
-
- void* conv2bias = readTrainedWeights("../alexnet/params/conv2.bias.bin",
- CUDNN_DATA_FLOAT, 1, 256, 1, 1);
- void* conv2bias_out = tensorAdd(conv2_concat_out, conv2bias);
- printTensorDims(conv2bias_out);
-
- dumpWeightsToFile("tensors_out/conv2_bias_init.out", conv2bias_out);
-
- void* relu2out = tensorRelu(conv2bias_out);
- dumpWeightsToFile("tensors_out/conv2.out", relu2out);
- printTensorDims(relu2out);
-
- void* lrn2out = tensorLRN(relu2out, LRN_window, LRN_alpha, LRN_beta, LRN_k);
- printTensorDims(lrn2out);
-
- void* maxpool2out = tensorPooling(lrn2out, 0, 3, 3, 0, 0, 2, 2);
- printTensorDims(maxpool2out);
-
- /******** End of Conv Layer 2 *************/
-
- void* conv3filter = readTrainedWeights("../alexnet/params/conv3.bin",
- CUDNN_DATA_FLOAT, 384, 256, 3, 3);
- void* conv3_out = tensorConvolution(maxpool2out, conv3filter, 1, 1, 1, 1,
- conv_mode, conv_precision);
-
- void* conv3bias = readTrainedWeights("../alexnet/params/conv3.bias.bin",
- CUDNN_DATA_FLOAT, 1, 384, 1, 1);
- void* conv3bias_out = tensorAdd(conv3_out, conv3bias);
- void* relu3out = tensorRelu(conv3bias_out);
- dumpWeightsToFile("tensors_out/conv3.out", relu3out);
- printTensorDims(relu3out);
-
- /********* End of Conv layer 3 *******/
-
- void** splits2 = tensorSplit(relu3out, 2, 1);
-
- void* conv4W = readTrainedWeights("../alexnet/params/conv4.bin",
- CUDNN_DATA_FLOAT, 384, 192, 3, 3);
- void** conv4fils = tensorSplit(conv4W, 2, 0);
-
- printTensorDims(splits2[0]);
- printTensorDims(conv4fils[0]);
-
- // Test-point DOES the pairing of splits and filters make sense?
- void* conv4a_out = tensorConvolution(splits2[0], conv4fils[0], 1, 1, 1, 1,
- conv_mode, conv_precision);
- printTensorDims(conv4a_out);
-
- void* conv4b_out = tensorConvolution(splits2[1], conv4fils[1], 1, 1, 1, 1,
- conv_mode, conv_precision);
- printTensorDims(conv4b_out);
-
- void* conv4_outs[2];
- conv4_outs[0] = conv4a_out;
- conv4_outs[1] = conv4b_out;
-
- void* conv4_concat_out = tensorConcat(conv4_outs, 2, 1);
- printTensorDims(conv4_concat_out);
-
- void* conv4bias = readTrainedWeights("../alexnet/params/conv4.bias.bin",
- CUDNN_DATA_FLOAT, 1, 384, 1, 1);
- void* conv4bias_out = tensorAdd(conv4_concat_out, conv4bias);
-
- void* relu4out = tensorRelu(conv4bias_out);
- printTensorDims(relu4out);
-
- /********* End of Conv layer 4 *******/
-
- void** splits3 = tensorSplit(relu4out, 2, 1);
-
- void* conv5W = readTrainedWeights("../alexnet/params/conv5.bin",
- CUDNN_DATA_FLOAT, 256, 192, 3, 3);
- void** conv5fils = tensorSplit(conv5W, 2, 0);
-
- printTensorDims(splits3[0]);
- printTensorDims(conv5fils[0]);
-
- void* conv5a_out = tensorConvolution(splits3[0], conv5fils[0], 1, 1, 1, 1,
- conv_mode, conv_precision);
- printTensorDims(conv5a_out);
-
- void* conv5b_out = tensorConvolution(splits3[1], conv5fils[1], 1, 1, 1, 1,
- conv_mode, conv_precision);
- printTensorDims(conv5b_out);
-
- void* conv5_outs[2];
- conv5_outs[0] = conv5a_out;
- conv5_outs[1] = conv5b_out;
-
- void* conv5_concat_out = tensorConcat(conv5_outs, 2, 1);
- printTensorDims(conv5_concat_out);
-
- void* conv5bias = readTrainedWeights("../alexnet/params/conv5.bias.bin",
- CUDNN_DATA_FLOAT, 1, 256, 1, 1);
- void* conv5bias_out = tensorAdd(conv5_concat_out, conv5bias);
- void* relu5out = tensorRelu(conv5bias_out);
- printTensorDims(relu5out);
-
- void* maxpool5out = tensorPooling(relu5out, 0, 3, 3, 0, 0, 2, 2);
- printTensorDims(maxpool5out);
-
- /********* End of Conv layer 5 *******/
-
- // Test-point: I suspect the data may not be layed out correct (either in file or after loading)
- void* fc1_weights = readTrainedWeights("../alexnet/params/fc1.bin",
- CUDNN_DATA_FLOAT, 1, 1, 256*6*6, 4096);
- void* gemm1out = tensorGemm(maxpool5out, fc1_weights);
- printTensorDims(gemm1out);
-
- void* bias = readTrainedWeights("../alexnet/params/fc1.bias.bin",
- CUDNN_DATA_FLOAT, 1, 1, 1, 4096);
-
- void* gemm1biasout = tensorGemmBias(gemm1out, bias);
- printTensorDims(gemm1biasout);
-
- void* relu6out = tensorRelu(gemm1biasout);
- printTensorDims(relu6out);
-
- /***** End of FC1 layer *********/
-
- void* fc2_weights = readTrainedWeights("../alexnet/params/fc2.bin",
- CUDNN_DATA_FLOAT, 1, 1, 4096, 4096);
- void* gemm2out = tensorGemm(relu6out, fc2_weights);
- printTensorDims(gemm2out);
-
- void* bias2 = readTrainedWeights("../alexnet/params/fc2.bias.bin",
- CUDNN_DATA_FLOAT, 1, 1, 1, 4096);
- void* gemm2biasout = tensorGemmBias(gemm2out, bias2);
- printTensorDims(gemm2biasout);
-
- void* relu7out = tensorRelu(gemm2biasout);
- printTensorDims(relu7out);
-
- /***** End of FC2 layer *********/
-
- void* fc3_weights = readTrainedWeights("../alexnet/params/fc3.bin",
- CUDNN_DATA_FLOAT, 1, 1, 4096, 1000);
- void* gemm3out = tensorGemm(relu7out, fc3_weights);
- printTensorDims(gemm3out);
-
- void* bias3 = readTrainedWeights("../alexnet/params/fc3.bias.bin",
- CUDNN_DATA_FLOAT, 1, 1, 1, 1000);
- void* gemm3biasout = tensorGemmBias(gemm3out, bias3);
- printTensorDims(gemm3biasout);
-
- /******** End of FC3 Layer ***********/
- void* result = tensorSoftmax(gemm3biasout);
- printTensorDims(result);
-
- // FIXIT: Pass file with the labels
- printLikelihood("", test_batch_size, result);
- // THINK: I believe that comparing the results do not need to be part of the HPVM graph
- printf("END of Alexnet3 -- \n");
-}
-
-
-
-
-
-int main(){
-
- // IMP-NOTE: Always initialize the runtime
- initializeRuntime(0);
-
- //testAlexnet1();
- //testAlexnet2();
- testAlexnet3();
-
- return 0;
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_fc_half.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_fc_half.cc
deleted file mode 100644
index c0fee9b659db9ff45f56b75b989fbbed68523d43..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_fc_half.cc
+++ /dev/null
@@ -1,74 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-#include "../include/types.h"
-
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testFC_half(){
-
- printf("********* Fully Connected DNN-1 ********* \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 10000;
- void* input = readTrainedWeights("../model_params/FC_network2/mnist_float_input.bin",
- float_type, test_batch_size, 1, 28, 28);
-
- void* fc1_weights = readTrainedWeights("../model_params/FC_network2/fc1.bin",
- float_type, 1, 1, 784, 128);
- void* fc1_bias = readTrainedWeights("../model_params/FC_network2/fc1_bias.bin",
- float_type, 1, 128, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/FC_network2/fc2.bin",
- float_type, 1, 1, 128, 10);
- void* fc2_bias = readTrainedWeights("../model_params/FC_network2/fc2_bias.bin",
- float_type, 1, 10, 1, 1);
-
- // Start execution profiling Tensor ops
- startProfiling();
-
- // Layer-1
- void* fc1out = tensorHgemm(input, fc1_weights);
- printTensorDims(fc1out);
-
- void* fc1_bias_out = tensorAdd(fc1out, fc1_bias);
- printTensorDims(fc1_bias_out);
-
- void* fc1_relu = tensorRelu(fc1_bias_out);
- printTensorDims(fc1_relu);
-
- // Layer-2
- void* fc2out = tensorHgemm(fc1_relu, fc2_weights);
- printTensorDims(fc2out);
-
- void* fc2_bias_out = tensorAdd(fc2out, fc2_bias);
- printTensorDims(fc2_bias_out);
-
- void* fc2_relu = tensorRelu(fc2_bias_out);
- printTensorDims(fc2_relu);
-
- void* result = tensorSoftmax(fc2_relu);
- printTensorDims(result);
-
- stopProfiling();
-
- computeAccuracy("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte",
- test_batch_size, result);
-}
-
-
-
-int main(){
-
- // This initializes the runtime - must be called before anything
- llvm_hpvm_initTensorRt(0);
-
- testFC_half();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_fc_network.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_fc_network.cc
deleted file mode 100644
index e8b70146a10359bf2df7420ae388325e6a658557..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_fc_network.cc
+++ /dev/null
@@ -1,152 +0,0 @@
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-#include "../include/types.h"
-
-
-
-void testFCNetworkArchCPU(){
-
- printf("********* Fully Connected DNN-1 ********* \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 10000;
-
- void* input = readTrainedWeights("../model_params/FC_network2/mnist_float_input.bin",
- float_type, test_batch_size, 1, 28, 28);
- void* fc1_weights = readTrainedWeights("../model_params/FC_network2/fc1.bin",
- float_type, 1, 1, 784, 128);
- void* fc1_bias = readTrainedWeights("../model_params/FC_network2/fc1_bias.bin",
- float_type, 1, 128, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/FC_network2/fc2.bin",
- float_type, 1, 1, 128, 10);
- void* fc2_bias = readTrainedWeights("../model_params/FC_network2/fc2_bias.bin",
- float_type, 1, 10, 1, 1);
-
- //dumpWeightsToFile("tensors_out/input_fc.out", input);
- //dumpWeightsToFile("tensors_out/fc1_w_fc.out", fc1_weights);
-
- printTensorDims(input);
- printTensorDims(fc1_weights);
-
- // Start profiling tensor ops
- startProfiling();
-
- // Layer-1
- void* fc1out = tensorGemmCPU(input, fc1_weights);
- printTensorDims(fc1out);
- //dumpWeightsToFile("tensors_out/fc1out_fc.out", fc1out);
-
- void* fc1_bias_out = tensorAdd(fc1out, fc1_bias);
- //dumpWeightsToFile("tensors_out/fc1_biasout_fc.out", fc1_bias_out);
- printTensorDims(fc1_bias_out);
-
- void* fc1_relu = tensorRelu(fc1_bias_out);
- //dumpWeightsToFile("tensors_out/fc1_relu_fc.out", fc1_relu);
- printTensorDims(fc1_relu);
-
- // Layer-2
- void* fc2out = tensorGemmCPU(fc1_relu, fc2_weights);
- //dumpWeightsToFile("tensors_out/fc2out_fc.out", fc2out);
- printTensorDims(fc2out);
-
- void* fc2_bias_out = tensorAdd(fc2out, fc2_bias);
- //dumpWeightsToFile("tensors_out/fc2_biasout_fc.out", fc2_bias_out);
- printTensorDims(fc2_bias_out);
-
- void* fc2_relu = tensorRelu(fc2_bias_out);
- //dumpWeightsToFile("tensors_out/fc2_relu_fc.out", fc2_relu);
- printTensorDims(fc2_relu);
-
- void* result = tensorSoftmax(fc2_relu);
- printTensorDims(result);
-
- // stopProfiling
- stopProfiling();
-
- computeAccuracy("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size, result);
- // THINK: I believe that comparing the results do not need to be part of the HPVM graph
-}
-
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testFCNetworkArchGPU(){
-
- printf("********* Fully Connected DNN-1 ********* \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 10000;
- void* input = readTrainedWeights("../model_params/FC_network2/mnist_float_input.bin",
- float_type, test_batch_size, 1, 28, 28);
-
- void* fc1_weights = readTrainedWeights("../model_params/FC_network2/fc1.bin",
- float_type, 1, 1, 784, 128);
- void* fc1_bias = readTrainedWeights("../model_params/FC_network2/fc1_bias.bin",
- float_type, 1, 128, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/FC_network2/fc2.bin",
- float_type, 1, 1, 128, 10);
- void* fc2_bias = readTrainedWeights("../model_params/FC_network2/fc2_bias.bin",
- float_type, 1, 10, 1, 1);
-
- // Start execution profiling Tensor ops
- startProfiling();
-
- // Layer-1
- void* fc1out = tensorGemmGPU(input, fc1_weights);
- printTensorDims(fc1out);
-
- void* fc1_bias_out = tensorAdd(fc1out, fc1_bias);
- //dumpWeightsToFile("tensors_out/fc1_biasout_fc.out", fc1_bias_out);
- printTensorDims(fc1_bias_out);
-
- void* fc1_relu = tensorRelu(fc1_bias_out);
- //dumpWeightsToFile("tensors_out/fc1_relu_fc.out", fc1_relu);
- printTensorDims(fc1_relu);
-
- // IMPORTANT: Adding errors to the FC1 layer output
- //tensorAddError(fc1_relu, 3);
-
- // Layer-2
- void* fc2out = tensorGemmGPU(fc1_relu, fc2_weights);
- //dumpWeightsToFile("tensors_out/fc2out_fc.out", fc2out);
- printTensorDims(fc2out);
-
- void* fc2_bias_out = tensorAdd(fc2out, fc2_bias);
- //dumpWeightsToFile("tensors_out/fc2_biasout_fc.out", fc2_bias_out);
- printTensorDims(fc2_bias_out);
-
- void* fc2_relu = tensorRelu(fc2_bias_out);
- //dumpWeightsToFile("tensors_out/fc2_relu_fc.out", fc2_relu);
- printTensorDims(fc2_relu);
-
- void* result = tensorSoftmax(fc2_relu);
- printTensorDims(result);
-
- stopProfiling();
-
- computeAccuracy("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size, result);
- // THINK: I believe that comparing the results do not need to be part of the HPVM graph
-}
-
-
-
-int main(){
-
- // This initializes the runtime - must be called before anything
- llvm_hpvm_initTensorRt(0);
-
- //testFCNetworkArchCPU();
-
- testFCNetworkArchGPU();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_fc_network2.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_fc_network2.cc
deleted file mode 100644
index fc00532a1b3712fab9d098a9a8e1a1586f1458a5..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_fc_network2.cc
+++ /dev/null
@@ -1,94 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-#include "../include/types.h"
-
-
-void test4LayerFC(){
-
- printf("********* 4-layer FC Network ********* \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 10000;
- void* input = readInputTensor("../model_params/lenet_params/datasets/t10k-images-idx3-ubyte",
- float_type,
- test_batch_size, 1, 28, 28);
- void* fc1_weights = readTrainedWeights("../model_params/FC_network1/fc1.bin",
- float_type, 1, 1, 784, 1000);
- void* fc1_bias = readTrainedWeights("../model_params/FC_network1/fc1_bias.bin",
- float_type, 1, 1000, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/FC_network1/fc2.bin",
- float_type, 1, 1, 1000, 500);
- void* fc2_bias = readTrainedWeights("../model_params/FC_network1/fc2_bias.bin",
- float_type, 1, 500, 1, 1);
- void* fc3_weights = readTrainedWeights("../model_params/FC_network1/fc3.bin",
- float_type, 1, 1, 500, 200);
- void* fc3_bias = readTrainedWeights("../model_params/FC_network1/fc3_bias.bin",
- float_type, 1, 200, 1, 1);
- void* fc4_weights = readTrainedWeights("../model_params/FC_network1/fc4.bin",
- float_type, 1, 1, 200, 10);
- void* fc4_bias = readTrainedWeights("../model_params/FC_network1/fc4_bias.bin",
- float_type, 1, 10, 1, 1);
-
- //dumpWeightsToFile("tensors_out/input_fc.out", input);
- //dumpWeightsToFile("tensors_out/fc1_w_fc.out", fc1_weights);
-
- // Start Profiling execution times of Tensor operations
- startProfiling();
-
- // Layer-1
- void* fc1out = tensorGemmGPU(input, fc1_weights);
- printTensorDims(fc1out);
- //dumpWeightsToFile("tensors_out/fc1out_fc.out", fc1out);
-
- void* fc1_bias_out = tensorAdd(fc1out, fc1_bias);
- printTensorDims(fc1_bias_out);
- //dumpWeightsToFile("tensors_out/fc_fc1.out", fc1_bias_out);
-
- // Layer-2
- void* fc2out = tensorGemmGPU(fc1_bias_out, fc2_weights);
- printTensorDims(fc2out);
-
- void* fc2_bias_out = tensorAdd(fc2out, fc2_bias);
- printTensorDims(fc2_bias_out);
-
- // Layer-3
- void* fc3out = tensorGemmGPU(fc2_bias_out, fc3_weights);
- printTensorDims(fc3out);
-
- void* fc3_bias_out = tensorAdd(fc3out, fc3_bias);
- printTensorDims(fc3_bias_out);
-
- // Layer-4
- void* fc4out = tensorGemmGPU(fc3_bias_out, fc4_weights);
- printTensorDims(fc4out);
-
- void* fc4_bias_out = tensorAdd(fc4out, fc4_bias);
- printTensorDims(fc4_bias_out);
-
- void* result = tensorSoftmax(fc4_bias_out);
- printTensorDims(result);
-
- stopProfiling();
-
- computeAccuracy("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size, result);
-}
-
-
-
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- test4LayerFC();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_fc_network3.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_fc_network3.cc
deleted file mode 100644
index 531bb01695cddb70de0f9bea90f6b229679e9bce..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_fc_network3.cc
+++ /dev/null
@@ -1,93 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-#include "../include/types.h"
-
-
-void test4LayerFC(){
-
- printf("********* 4-layer FC Network ********* \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 10000;
- void* input = readInputTensor("../model_params/lenet_params/datasets/t10k-images-idx3-ubyte",
- float_type,
- test_batch_size, 1, 28, 28);
- void* fc1_weights = readTrainedWeights("../model_params/FC_network3/fc1.bin",
- float_type, 1, 1, 784, 512);
- void* fc1_bias = readTrainedWeights("../model_params/FC_network3/fc1_bias.bin",
- float_type, 1, 512, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/FC_network3/fc2.bin",
- float_type, 1, 1, 512, 256);
- void* fc2_bias = readTrainedWeights("../model_params/FC_network3/fc2_bias.bin",
- float_type, 1, 256, 1, 1);
- void* fc3_weights = readTrainedWeights("../model_params/FC_network3/fc3.bin",
- float_type, 1, 1, 256, 128);
- void* fc3_bias = readTrainedWeights("../model_params/FC_network3/fc3_bias.bin",
- float_type, 1, 128, 1, 1);
- void* fc4_weights = readTrainedWeights("../model_params/FC_network3/fc4.bin",
- float_type, 1, 1, 128, 10);
- void* fc4_bias = readTrainedWeights("../model_params/FC_network3/fc4_bias.bin",
- float_type, 1, 10, 1, 1);
-
- // Start Profiling execution times of Tensor operations
- startProfiling();
-
- // Layer-1
- void* fc1out = tensorGemmGPU(input, fc1_weights);
- printTensorDims(fc1out);
-
- void* fc1_bias_out = tensorAdd(fc1out, fc1_bias);
- printTensorDims(fc1_bias_out);
- void* fc1_relu = tensorRelu(fc1_bias_out);
-
- // Layer-2
- void* fc2out = tensorGemmGPU(fc1_relu, fc2_weights);
- printTensorDims(fc2out);
-
- void* fc2_bias_out = tensorAdd(fc2out, fc2_bias);
- printTensorDims(fc2_bias_out);
- void* fc2_relu = tensorRelu(fc2_bias_out);
-
- // Layer-3
- void* fc3out = tensorGemmGPU(fc2_relu, fc3_weights);
- printTensorDims(fc3out);
-
- void* fc3_bias_out = tensorAdd(fc3out, fc3_bias);
- printTensorDims(fc3_bias_out);
- void* fc3_relu = tensorRelu(fc3_bias_out);
-
- // Layer-4
- void* fc4out = tensorGemmGPU(fc3_relu, fc4_weights);
- printTensorDims(fc4out);
-
- void* fc4_bias_out = tensorAdd(fc4out, fc4_bias);
- printTensorDims(fc4_bias_out);
- void* fc4_relu = tensorRelu(fc4_bias_out);
-
- void* result = tensorSoftmax(fc4_relu);
- printTensorDims(result);
-
- stopProfiling();
-
- computeAccuracy("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size, result);
-}
-
-
-
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- test4LayerFC();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_lenet.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_lenet.cc
deleted file mode 100644
index e21b09fbf59c6ceee2adcf6df798ef04351a03ef..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_lenet.cc
+++ /dev/null
@@ -1,178 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testLenetArch2(){
-
- printf("********* Lenet Architecture ********** \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 10000;
- void* input = readInputTensor("../model_params/lenet_params/datasets/t10k-images-idx3-ubyte",
- CUDNN_DATA_FLOAT,
- test_batch_size, 1, 28, 28);
-
- // NOTE: Filter descriptors do NOT have batch size
- // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
- // IMP: The output channels matches the trained model - not the Lenet arch proposed in Andrew Ng's class
- void* conv1_filter = readTrainedWeights("../model_params/lenet_params/conv1.bin",
- CUDNN_DATA_FLOAT, 20, 1, 5, 5);
- void* conv1_bias = readTrainedWeights("../model_params/lenet_params/conv1.bias.bin",
- CUDNN_DATA_FLOAT, 1, 20, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/lenet_params/conv2.bin",
- CUDNN_DATA_FLOAT, 50, 20, 5, 5);
- void* conv2_bias = readTrainedWeights("../model_params/lenet_params/conv2.bias.bin",
- CUDNN_DATA_FLOAT, 1, 50, 1, 1);
- void* fc1_weights = readTrainedWeights("../model_params/lenet_params/ip1.bin",
- CUDNN_DATA_FLOAT, 1, 1, 800, 500);
- void* fc1_bias = readTrainedWeights("../model_params/lenet_params/ip1.bias.bin",
- CUDNN_DATA_FLOAT, 1, 1, 1, 500);
- void* fc2_weights = readTrainedWeights("../model_params/lenet_params/ip2.bin",
- CUDNN_DATA_FLOAT, 1, 1, 500, 10);
- void* fc2_bias = readTrainedWeights("../model_params/lenet_params/ip2.bias.bin",
- CUDNN_DATA_FLOAT, 1, 1, 1, 10);
-
-
- // Start power and performnce profiling
- startProfiling();
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- void* conv1out = tensorConvolution(input, conv1_filter, 0, 0, 1, 1,
- conv_mode, conv_precision);
- // NOTE: For tensorAdd, the only dimension that MUST match is channels
- tensorAdd(conv1out, conv1_bias); // NOTE: In place operation
- printTensorDims(conv1out);
-
- void* pool1out = tensorPooling(conv1out, 0, 2, 2, 0, 0, 2, 2);
- printTensorDims(pool1out);
-
- // NOTE: input channels have to match between tensor op inputs and outputs
- void* conv2out = tensorConvolution(pool1out, conv2_filter, 0, 0, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv2out, conv2_bias); // NOTE: In place operation
-
- printTensorDims(conv2out);
-
- void* pool2out = tensorPooling(conv2out, 0, 2, 2, 0, 0, 2, 2);
- printTensorDims(pool2out);
-
- void* gemm1out = tensorGemm(pool2out, fc1_weights);
- printTensorDims(gemm1out);
-
- void* gemm1biasout = tensorGemmBias(gemm1out, fc1_bias);
- printTensorDims(gemm1biasout);
-
- void* relu1out = tensorRelu(gemm1biasout);
- printTensorDims(relu1out);
-
- void* gemm2out = tensorGemm(relu1out, fc2_weights);
- printTensorDims(gemm2out);
-
- void* gemm2_biasout = tensorGemmBias(gemm2out, fc2_bias);
- printTensorDims(gemm2_biasout);
-
- void* result = tensorSoftmax(gemm2_biasout);
- printTensorDims(result);
-
- // End profiling and dump output to profile.txt
- stopProfiling();
-
- computeAccuracy("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size, result);
- // THINK: I believe that comparing the results do not need to be part of the HPVM graph
-}
-
-
-/* This architecture REMOVES the bias adds */
-void testLenetArch3(){
-
- printf("********* Lenet Architecture ********** \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 10000;
-
- void* input = readInputTensor("../model_params/lenet_params/datasets/t10k-images-idx3-ubyte",
- CUDNN_DATA_FLOAT,
- test_batch_size, 1, 28, 28);
- // NOTE: Filter descriptors do NOT have batch size
- // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
- // IMP: The output channels matches the trained model - not the Lenet arch proposed in Andrew Ng's class
- void* conv1_filter = readTrainedWeights("../model_params/lenet_params/conv1.bin",
- CUDNN_DATA_FLOAT, 20, 1, 5, 5);
- void* conv1_bias = readTrainedWeights("../model_params/lenet_params/conv1.bias.bin",
- CUDNN_DATA_FLOAT, 1, 20, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/lenet_params/conv2.bin",
- CUDNN_DATA_FLOAT, 50, 20, 5, 5);
- void* conv2_bias = readTrainedWeights("../model_params/lenet_params/conv2.bias.bin",
- CUDNN_DATA_FLOAT, 1, 50, 1, 1);
- void* fc1_weights = readTrainedWeights("../model_params/lenet_params/ip1.bin",
- CUDNN_DATA_FLOAT, 1, 1, 800, 500);
- void* fc2_weights = readTrainedWeights("../model_params/lenet_params/ip2.bin",
- CUDNN_DATA_FLOAT, 1, 1, 500, 10);
-
- /* Convolution specific parameters */
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- void* conv1out = tensorConvolution(input, conv1_filter, 0, 0, 1, 1,
- conv_mode, conv_precision);
- // NOTE: For tensorAdd, the only dimension that MUST match is channels
- tensorAdd(conv1out, conv1_bias); // NOTE: In place operation
- printTensorDims(conv1out);
-
- void* pool1out = tensorPooling(conv1out, 0, 2, 2, 0, 0, 2, 2);
- printTensorDims(pool1out);
-
- // NOTE: input channels have to match between tensor op inputs and outputs
- void* conv2out = tensorConvolution(pool1out, conv2_filter, 0, 0, 1, 1,
- conv_mode, conv_precision);
- printTensorDims(conv2out);
-
- void* pool2out = tensorPooling(conv2out, 0, 2, 2, 0, 0, 2, 2);
- printTensorDims(pool2out);
-
- void* gemm1out = tensorGemm(pool2out, fc1_weights);
- printTensorDims(gemm1out);
-
- void* relu1out = tensorRelu(gemm1out);
- printTensorDims(relu1out);
-
- void* gemm2out = tensorGemm(relu1out, fc2_weights);
- printTensorDims(gemm2out);
-
- void* result = tensorSoftmax(gemm2out);
- printTensorDims(result);
-
- computeAccuracy("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size, result);
- // THINK: I believe that comparing the results do not need to be part of the HPVM graph
- printf("END of Lenet Arch3 -- \n");
-}
-
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- //testTensorAdd();
- //testTensorConv();
- //testTensorPool();
- //testTensorGemm();
- //testTensorGemmBias();
- //testTensorRelu();
- //testTensorSoftmax();
-
- //testLenetArch();
- testLenetArch2();
- //testLenetArch3();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_lenet2.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_lenet2.cc
deleted file mode 100644
index 77f256320e158fb13555e83d0fbe260ce9d3a83f..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_lenet2.cc
+++ /dev/null
@@ -1,111 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testLenet2Arch(){
-
- printf("********* Lenet-2 Architecture ********** \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 10000;
- void* input = readInputTensor("../model_params/lenet_params/datasets/t10k-images-idx3-ubyte",
- CUDNN_DATA_FLOAT,
- test_batch_size, 1, 28, 28);
-
- // NOTE: Filter descriptors do NOT have batch size
- // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
- // IMP: The output channels matches the trained model - not the Lenet arch proposed in Andrew Ng's class
- void* conv1_filter = readTrainedWeights("../model_params/lenet2_params/conv1.bin",
- float_type, 32, 1, 5, 5);
- void* conv1_bias = readTrainedWeights("../model_params/lenet2_params/conv1_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/lenet2_params/conv2.bin",
- float_type, 64, 32, 5, 5);
- void* conv2_bias = readTrainedWeights("../model_params/lenet2_params/conv2_bias.bin",
- float_type, 1, 64, 1, 1);
- void* fc1_weights = readTrainedWeights("../model_params/lenet2_params/fc1.bin",
- float_type, 1, 1, 7*7*64, 1024);
- void* fc1_bias = readTrainedWeights("../model_params/lenet2_params/fc1_bias.bin",
- float_type, 1, 1024, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/lenet2_params/fc2.bin",
- float_type, 1, 1, 1024, 10);
- void* fc2_bias = readTrainedWeights("../model_params/lenet2_params/fc2_bias.bin",
- float_type, 1, 10, 1, 1);
-
-
- // Start power and performnce profiling
- startProfiling();
-
- int conv_mode = 1; // NOTE: using CROSS_CORRELATION
- int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
- // NOTE: 'SAME' convolution
- void* conv1out = tensorConvolution(input, conv1_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
-
- // NOTE: For tensorAdd, the only dimension that MUST match is channels
- tensorAdd(conv1out, conv1_bias); // NOTE: In place operation
- printTensorDims(conv1out);
-
- void* conv1_reluout = tensorRelu(conv1out);
- //dumpWeightsToFile("tensors_out/conv1.out", conv1_reluout);
-
- void* pool1out = tensorPooling(conv1_reluout, 0, 2, 2, 0, 0, 2, 2);
- printTensorDims(pool1out);
- // NOTE: input channels have to match between tensor op inputs and outputs
- void* conv2out = tensorConvolution(pool1out, conv2_filter, 2, 2, 1, 1,
- conv_mode, conv_precision);
- tensorAdd(conv2out, conv2_bias); // NOTE: In place operation
- printTensorDims(conv2out);
-
- void* conv2_reluout = tensorRelu(conv2out);
- //dumpWeightsToFile("tensors_out/conv2.out", conv2_reluout);
-
- void* pool2out = tensorPooling(conv2_reluout, 0, 2, 2, 0, 0, 2, 2);
- printTensorDims(pool2out);
- //dumpWeightsToFile("tensors_out/maxpool2.out", pool2out);
-
- void* gemm1out = tensorGemmGPU(pool2out, fc1_weights);
- printTensorDims(gemm1out);
- //dumpWeightsToFile("tensors_out/gemm1.out", gemm1out);
-
- void* gemm1biasout = tensorAdd(gemm1out, fc1_bias);
- printTensorDims(gemm1biasout);
-
- void* relu1out = tensorRelu(gemm1biasout);
- printTensorDims(relu1out);
-
- void* gemm2out = tensorGemmGPU(relu1out, fc2_weights);
- printTensorDims(gemm2out);
-
- void* gemm2_biasout = tensorAdd(gemm2out, fc2_bias);
- printTensorDims(gemm2_biasout);
-
- void* result = tensorSoftmax(gemm2_biasout);
- printTensorDims(result);
-
- // End profiling and dump output to profile.txt
- stopProfiling();
-
- computeAccuracy("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte",
- test_batch_size, result);
- // THINK: I believe that comparing the results do not need to be part of the HPVM graph
-}
-
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- testLenet2Arch();
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_ops.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_ops.cc
deleted file mode 100644
index 43151dd965841861b1535c3dc3ad53c0335eeeb7..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_ops.cc
+++ /dev/null
@@ -1,557 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-
-void testTensorGemm(){
-
- printf("***** TensorSgemm ***** \n\n");
- void* lhs_ptr = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 5, 4, 1, 1);
- struct Tensor* lhs = (struct Tensor*) lhs_ptr;
- fillTensorWithOnes(lhs);
-
- float* data_arr = (float*) lhs->host_data;
- for(int i = 0; i < lhs->num_elems; i++){
- data_arr[i] = (i / 4) + 1;
- }
-
- void* rhs = create4DTensor(CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, 1, 4, 3);
- fillTensorWithOnes(rhs);
-
- void* output = tensorGemmCPU(lhs, rhs);
- printTensorValues(output);
-
- void* bias_ptr = create4DTensor(CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, 3, 1, 1);
- struct Tensor* bias = (struct Tensor*) bias_ptr;
- fillTensorWithOnes(bias);
-
- float* bias_arr = (float*) bias->host_data;
- for(int i = 0; i < bias->num_elems; i++){
- bias_arr[i] = i + 1;
- }
-
- void* output2 = tensorAdd(output, bias);
- printTensorValues(output2);
-}
-
-
-void testTensorHgemm(){
-
- printf("***** TensorHgemm ***** \n\n");
- void* lhs_ptr = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 5, 4, 1, 1);
- struct Tensor* lhs = (struct Tensor*) lhs_ptr;
- fillTensorWithOnes(lhs);
-
- float* data_arr = (float*) lhs->host_data;
- for(int i = 0; i < lhs->num_elems; i++){
- data_arr[i] = (i / 4) + 1;
- }
-
- void* rhs = create4DTensor(CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, 1, 4, 3);
- fillTensorWithOnes(rhs);
-
- void* output = tensorHalfGemm(lhs, rhs);
- printTensorValues(output);
-
- void* bias_ptr = create4DTensor(CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, 3, 1, 1);
- struct Tensor* bias = (struct Tensor*) bias_ptr;
- fillTensorWithOnes(bias);
-
- float* bias_arr = (float*) bias->host_data;
- for(int i = 0; i < bias->num_elems; i++){
- bias_arr[i] = i + 1;
- }
-
- void* output2 = tensorAdd(output, bias);
- printTensorValues(output2);
-}
-
-
-void testTensorHgemm2(){
-
- printf("***** TensorHgemm ***** \n\n");
- void* lhs_ptr = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW,
- 10000, 800, 1, 1);
- struct Tensor* lhs = (struct Tensor*) lhs_ptr;
-
- float* data_arr = (float*) lhs->host_data;
- for(int i = 0; i < lhs->num_elems; i++){
- data_arr[i] = (i / 4) + 1;
- }
-
- void* rhs = create4DTensor(CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT,
- 1, 1, 800, 800);
- fillTensorWithOnes(rhs);
-
- void* output = tensorHalfGemm(lhs, rhs);
- //printTensorValues(output);
-}
-
-
-void testTensorSgemm2(){
-
- printf("***** TensorSgemm ***** \n\n");
- void* lhs_ptr = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW,
- 10000, 800, 1, 1);
- struct Tensor* lhs = (struct Tensor*) lhs_ptr;
-
- float* data_arr = (float*) lhs->host_data;
- for(int i = 0; i < lhs->num_elems; i++){
- data_arr[i] = (i / 4) + 1;
- }
-
- void* rhs = create4DTensor(CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT,
- 1, 1, 800, 800);
- fillTensorWithOnes(rhs);
-
- void* output = tensorGemmGPU(lhs, rhs);
- //printTensorValues(output);
-}
-
-
-
-void testTensorGemmGPU(){
-
- printf("***** TensorSgemm ***** \n\n");
- void* lhs_ptr = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 5, 4, 1, 1);
- struct Tensor* lhs = (struct Tensor*) lhs_ptr;
- fillTensorWithOnes(lhs);
-
- float* data_arr = (float*) lhs->host_data;
- for(int i = 0; i < lhs->num_elems; i++){
- data_arr[i] = (i / 4) + 1;
- }
-
- void* rhs = create4DTensor(CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, 1, 4, 3);
- fillTensorWithOnes(rhs);
-
- void* output = tensorGemmGPU(lhs, rhs);
- printTensorValues(output);
-
- void* bias_ptr = create4DTensor(CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, 3, 1, 1);
- struct Tensor* bias = (struct Tensor*) bias_ptr;
- fillTensorWithOnes(bias);
-
- float* bias_arr = (float*) bias->host_data;
- for(int i = 0; i < bias->num_elems; i++){
- bias_arr[i] = i + 1;
- }
-
- void* output2 = tensorAdd(output, bias);
- printTensorValues(output2);
-
-}
-
-
-
-void testTensorGemmBias(){
-
- // NOTE: 2nd dim of bias and d2*d3*d4 for the input tensor MUST match
- printf("***** TensorGemmBias ***** \n\n");
- void* input = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 2, 1, 2, 2);
- fillTensorWithOnes(input);
- void* bias = create2DTensor(CUDNN_DATA_FLOAT, 1, 4);
- fillTensorWithOnes(bias);
-
- void* output = tensorGemmBias(input, bias);
- printTensorValues(output);
-}
-
-
-
-void testTensorConv2(){
-
- int conv_mode = 1; // CROSS_CORRELATION mode
- int compute_precision = 0; // floating point precision
-
- void* input = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 2, 2, 3, 3);
- fillWithOnesAndTwos(input);
- void** splits = tensorSplit(input, 2, 1);
-
- void* conv2W = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 2, 1, 2, 2);
- fillTensorWithOnes(conv2W);
-
- void** conv2fils = tensorSplit(conv2W, 2, 0);
-
- void* conv2a_out = tensorConvolution(splits[0], conv2fils[0], 0, 0,
- 1, 1, conv_mode, compute_precision);
- printTensorDims(conv2a_out);
-
- void* conv2b_out = tensorConvolution(splits[1], conv2fils[1], 0, 0,
- 1, 1, conv_mode, compute_precision);
- printTensorDims(conv2b_out);
-
- void* conv2_outs[2];
- conv2_outs[0] = conv2a_out;
- conv2_outs[1] = conv2b_out;
-
- void* conv2_concat_out = tensorConcat(conv2_outs, 2, 1);
- printTensorDims(conv2_concat_out);
- printTensorValues(conv2_concat_out);
-
-}
-
-
-
-void testTensorConv3(){
-
- int conv_mode = 1; // CROSS_CORRELATION mode
- int compute_precision = 0; // floating point precision
-
- void* input = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 2, 96, 28, 28);
- fillTensorWithOnes(input);
- void** splits = tensorSplit(input, 2, 1);
-
- void* conv2W = readTrainedWeights("../alexnet/params/conv2.bin",
- CUDNN_DATA_FLOAT, 256, 48, 5, 5);
-
- void** conv2fils = tensorSplit(conv2W, 2, 0);
-
- void* conv2a_out = tensorConvolution(splits[0], conv2fils[0], 2, 2,
- 1, 1, conv_mode, compute_precision);
- printTensorDims(conv2a_out);
-
- void* conv2b_out = tensorConvolution(splits[1], conv2fils[1], 2, 2,
- 1, 1, conv_mode, compute_precision);
- printTensorDims(conv2b_out);
-
- void* conv2_outs[2];
- conv2_outs[0] = conv2a_out;
- conv2_outs[1] = conv2b_out;
-
- void* conv2_concat_out = tensorConcat(conv2_outs, 2, 1);
- printTensorDims(conv2_concat_out);
- //printTensorValues(conv2_concat_out);
- dumpWeightsToFile("tensors_out/conv2_test.out", conv2_concat_out);
-
- void* conv2bias = readTrainedWeights("../alexnet/params/conv2.bias.bin",
- CUDNN_DATA_FLOAT, 1, 256, 1, 1);
- void* conv2bias_out = tensorAdd(conv2_concat_out, conv2bias);
- printTensorDims(conv2bias_out);
-
- dumpWeightsToFile("tensors_out/conv2_bias_test.out", conv2bias_out);
-
-}
-
-
-
-
-
-
-
-void testLRN(){
-
- void* input = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 20, 20, 20, 20);
- fillTensorWithOnes(input);
-
- unsigned LRN_window = 5;
- double LRN_alpha = 2e-05;
- printf("LRN_alpha = %f \n", LRN_alpha);
-
- double LRN_beta = 0.75;
- double LRN_k = 1.0;
-
- // TEST-point - Compare TF vs CUDNN
- void* lrn1out = tensorLRN(input, LRN_window, LRN_alpha, LRN_beta, LRN_k);
- printTensorDims(lrn1out);
- dumpWeightsToFile("tensors_out/lrn1_test.out", lrn1out);
-
- void* input2 = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 7, 7, 7, 7);
- fillTensorWithOnes(input2);
-
- LRN_window = 5;
- LRN_alpha = 0.5 * LRN_window;
-
- LRN_beta = 0.75;
- LRN_k = 1.0;
-
- void* lrn2out = tensorLRN(input2, LRN_window, LRN_alpha, LRN_beta, LRN_k);
- printTensorDims(lrn2out);
- dumpWeightsToFile("tensors_out/lrn2_test.out", lrn2out);
-}
-
-
-
-
-void testTensorAdd(){
-
- // Tensor add with equal dimensions
- void* x = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 2, 2, 2, 2);
- void* bias = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 2, 2, 2, 2);
- fillTensorWithOnes(x);
- fillTensorWithOnes(bias);
-
- printTensorValues(x);
- printTensorValues(bias);
-
- tensorAdd(x, bias);
- printTensorValues(x);
-
- // Tensor addd with matching channel dimension
- void* x2 = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 1, 2, 2, 2);
- void* bias2 = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 1, 2, 1, 1);
- fillTensorWithOnes(x2);
- fillTensorWithOnes(bias2);
-
- tensorAdd(x2, bias2);
- printTensorValues(x2);
-}
-
-
-void testTensorError(){
-
- // Tensor add with equal dimensions
- void* x = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 2, 2, 2, 128);
- fillTensorWithOnes(x);
-
- Tensor* x_tensor = (Tensor*) x;
- float* data_arr = (float*) x_tensor->host_data;
- for(int i = 0; i < x_tensor->num_elems; i++){
- data_arr[i] = 0.2;
- }
-
- tensorAddError(x, 3);
- printTensorValues(x);
-}
-
-
-void testTensorConv(){
-
- // NOTE: The input channel count value (param2 to Tensor and Filter) must be the same
- void* x3 = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 1, 2, 4, 4);
- // NOTE: Filter descriptors do NOT have batch size
- // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
- void* filter = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 2, 2, 2, 2);
- fillTensorWithOnes(x3);
- fillTensorWithOnes(filter);
-
- int conv_mode = 1; // NOTE: uses CROSS_CORRELATION
- int compute_precision = 0; // floating point precision for conv
-
- void* conv1 = tensorConvolution(x3, filter, 0, 0,
- 1, 1, conv_mode, compute_precision);
- printTensorValues(conv1);
-
-}
-
-
-void testTensorGroupedConv(){
-
- // NOTE: The input channel count value (param2 to Tensor and Filter) must be the same
- void* x3 = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 1, 2, 4, 4);
- // NOTE: Filter descriptors do NOT have batch size
- // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
- void* filter = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 2, 1, 3, 3);
- fillTensorWithOnes(x3);
- fillTensorWithOnes(filter);
-
- int conv_mode = 1; // NOTE: uses CROSS_CORRELATION
- int conv_groups = 2;
-
- void* conv1 = tensorConvolution(x3, filter, 2, 2,
- 2, 2, conv_mode, conv_groups);
- printTensorValues(conv1);
-
- // NOTE: For cudnnTensorAdd, the only dimension that MUST match is channels
- //void* bias3 = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 1, 3, 1, 1);
- // fillTensorWithOnes(bias3);
- //tensorAdd(conv1, bias3);
- //printTensorValues(conv1);
-}
-
-
-void testTensorPool(){
- void* x = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 2, 1, 4, 4);
- fillTensorWithOnes(x);
- void* output = tensorPooling(x, 0, 2, 2, 0, 0, 1, 1);
- printTensorValues(output);
-}
-
-
-void testTensorBatchNorm(){
-
- void* x = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 1, 3, 2, 2);
- fillTensorWithVal(x, 3);
-
- void* gamma = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 1, 3, 1, 1);
- fillTensorWithVal(gamma, 1);
-
- void* beta = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 1, 3, 1, 1);
- fillTensorWithVal(beta, 0);
-
- void* mean = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 1, 3, 1, 1);
- fillTensorWithVal(mean, 1);
-
- void* variance = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 1, 3, 1, 1);
- fillTensorWithVal(variance, 1);
-
-
- void* output = tensorBatchNorm(x, gamma, beta, mean, variance, 0.01);
- printTensorValues(output);
-}
-
-
-void testTensorRelu(){
-
- // NOTE: 2nd dim of bias and d2*d3*d4 for the input tensor MUST match
- printf("***** TensorRelu ***** \n\n");
- void* input = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 2, 1, 2, 2);
- fillTensorWithNegOnes(input);
-
- void* output = tensorRelu(input);
- printTensorValues(output);
-}
-
-
-void testTensorSoftmax(){
-
- printf("***** TensorSoftmax ***** \n\n");
- void* input = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 2, 4, 1, 1);
-
- float* host_ptr = (float*) ((struct Tensor*) input)->host_data;
- host_ptr[0] = 0.1;
- host_ptr[1] = 0.2;
- host_ptr[2] = 0.3;
- host_ptr[3] = 0.4;
- host_ptr[4] = 0.5;
- host_ptr[5] = 0.6;
- host_ptr[6] = 0.7;
- host_ptr[7] = 2.5;
-
- void* output = tensorSoftmax(input);
- printTensorValues(output);
-}
-
-
-void testSoftmaxOutput(void* output_ptr){
-
- struct Tensor* output = (struct Tensor*) output_ptr;
-
- size_t batch_dim = output->dims.dim_sizes[0];
- size_t channels = output->dims.dim_sizes[1];
-
- float* data = (float*) output->host_data;
- for(int i = 0; i < batch_dim; i++){
- float sum = 0.0;
- for(int j = 0; j < channels; j++){
- sum += data[i * channels + j];
- }
- printf("output_sum = %f \n", sum);
- }
-
-}
-
-
-void testQuantization(){
-
- printf("***** TensorQuantize ***** \n\n");
- void* input = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 2, 4, 1, 1);
-
- float* host_ptr = (float*) ((struct Tensor*) input)->host_data;
- host_ptr[0] = -0.1;
- host_ptr[1] = -25;
- host_ptr[2] = 0.2;
- host_ptr[3] = -0.4;
- host_ptr[4] = 1.7;
- host_ptr[5] = -2.9;
- host_ptr[6] = 0.7;
- host_ptr[7] = 0.99;
-
- void* quantize_result = quantizeTensorPromise(input, -4, 6);
-
- printTensorValues(quantize_result);
-
- void* error_out = addPromiseError(quantize_result, 7);
-
- printTensorValues(error_out);
-
-}
-
-
-
-
-void testSampleFilter(){
-
- printf("***** Tensor Sample Filter ***** \n\n");
- Tensor* input = (Tensor*) create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 2, 2, 3, 3);
- //fillTensorWithVal(input, 3);
- fillWithOnesAndTwos(input);
-
- Tensor* input2 = (Tensor*) create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 3, 2, 32, 32);
- fillTensorWithVal(input2, 1);
-
- /* float* host_ptr = (float*) ((struct Tensor*) input)->host_data;
- host_ptr[0] = -0.1;
- host_ptr[1] = -25;
- host_ptr[2] = 0.2;
- host_ptr[3] = -0.4;
- host_ptr[4] = 1.7;
- host_ptr[5] = -2.9;
- host_ptr[6] = 0.7;
- host_ptr[7] = 0.99;
- */
-
- printTensorValues(input);
-
- /* printf("\n\n");
-
- hpvm_request_tensor(input, DEVICE);
-
- sampleFilter(input, 2, 1);
-
- hpvm_request_tensor(input, HOST);
-
- printTensorValues(input);
- */
-
- void* exact_res = tensorConvolution(input2, input, 0, 0,
- 1, 1, 1, 1);
- printTensorValues(exact_res);
-
- void* res = tensorConvSampSim(input2, input, 0, 0, 1, 1, 1, 1, 4, 0);
-
- //void* res = tensorConvApprox(input2, input, 0, 0, 1, 1, 1, 1, 1, 1, 4, 3);
-
- printTensorValues(res);
-
-}
-
-
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- startProfiling();
-
- //testTensorHgemm2();
- //testTensorSgemm2();
- //testTensorConv();
- //testTensorError();
-
- //testQuantization();
-
- //testTensorConv();
- //testTensorGroupedConv();
-
- //testTensorBatchNorm();
-
- //testTensorGemm();
- //testTensorGemmGPU();
- //testTensorGemmBias();
- //testTensorConv2();
- //testTensorConv3();
- //testLRN();
-
-
- testSampleFilter();
-
-
- stopProfiling();
-
- return 0;
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar10.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar10.cc
deleted file mode 100644
index 60f09dd5d7b91eda7e23e23bc76dbae0f27beaf2..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar10.cc
+++ /dev/null
@@ -1,161 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- std::string dir_prefix = std::string("../model_params/vgg16_cifar10_2/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,10);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 10000;
- int batch_size = 1000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
- void* var_1 = tensorAdd(var_0, conv2d_1_b);
- void* var_2 = tensorRelu(var_1);
- void* var_4 = tensorConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0);
- void* var_5 = tensorAdd(var_4, conv2d_2_b);
- void* var_6 = tensorRelu(var_5);
- void* var_7 = tensorPooling(var_6,0,2,2,0,0,2,2);
- void* var_8 = tensorConvolution(var_7, conv2d_3_w, 1, 1, 1, 1, 1, 0);
- void* var_9 = tensorAdd(var_8, conv2d_3_b);
- void* var_10 = tensorRelu(var_9);
- void* var_12 = tensorConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0);
- void* var_13 = tensorAdd(var_12, conv2d_4_b);
- void* var_14 = tensorRelu(var_13);
- void* var_15 = tensorPooling(var_14,0,2,2,0,0,2,2);
- void* var_16 = tensorConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
- void* var_17 = tensorAdd(var_16, conv2d_5_b);
- void* var_18 = tensorRelu(var_17);
- void* var_20 = tensorConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0);
- void* var_21 = tensorAdd(var_20, conv2d_6_b);
- void* var_22 = tensorRelu(var_21);
- void* var_24 = tensorConvolution(var_22, conv2d_7_w, 1, 1, 1, 1, 1, 0);
- void* var_25 = tensorAdd(var_24, conv2d_7_b);
- void* var_26 = tensorRelu(var_25);
- void* var_27 = tensorPooling(var_26,0,2,2,0,0,2,2);
- void* var_28 = tensorConvolution(var_27, conv2d_8_w, 1, 1, 1, 1, 1, 0);
- void* var_29 = tensorAdd(var_28, conv2d_8_b);
- void* var_30 = tensorRelu(var_29);
- void* var_32 = tensorConvolution(var_30, conv2d_9_w, 1, 1, 1, 1, 1, 0);
- void* var_33 = tensorAdd(var_32, conv2d_9_b);
- void* var_34 = tensorRelu(var_33);
- void* var_36 = tensorConvolution(var_34, conv2d_10_w, 1, 1, 1, 1, 1, 0);
- void* var_37 = tensorAdd(var_36, conv2d_10_b);
- void* var_38 = tensorRelu(var_37);
- void* var_39 = tensorPooling(var_38,0,2,2,0,0,2,2);
- void* var_40 = tensorConvolution(var_39, conv2d_11_w, 1, 1, 1, 1, 1, 0);
- void* var_41 = tensorAdd(var_40, conv2d_11_b);
- void* var_42 = tensorRelu(var_41);
- void* var_44 = tensorConvolution(var_42, conv2d_12_w, 1, 1, 1, 1, 1, 0);
- void* var_45 = tensorAdd(var_44, conv2d_12_b);
- void* var_46 = tensorRelu(var_45);
- void* var_48 = tensorConvolution(var_46, conv2d_13_w, 1, 1, 1, 1, 1, 0);
- void* var_49 = tensorAdd(var_48, conv2d_13_b);
- void* var_50 = tensorRelu(var_49);
- void* var_51 = tensorPooling(var_50,0,2,2,0,0,2,2);
- void* var_54 = tensorGemmGPU(var_51, dense_1_w);
- void* var_55 = tensorAdd(var_54, dense_1_b);
- void* var_56 = tensorRelu(var_55);
- void* var_58 = tensorGemmGPU(var_56, dense_2_w);
- void* var_59 = tensorAdd(var_58, dense_2_b);
- void* var_60 = tensorSoftmax(var_59);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
-
- float accuracy = computeAccuracy2(labels,batch_size,var_60);
- final_accuracy += accuracy;
-
- freeBatchMemory();
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar100.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar100.cc
deleted file mode 100644
index 63ca073e15d817a2f2898756353b958d00988e82..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar100.cc
+++ /dev/null
@@ -1,160 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- std::string dir_prefix = std::string("../model_params/vgg16_cifar100_front/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,100);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,100,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 10000;
- int batch_size = 2500;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
- void* var_1 = tensorAdd(var_0, conv2d_1_b);
- void* var_2 = tensorRelu(var_1);
- void* var_4 = tensorConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0);
- void* var_5 = tensorAdd(var_4, conv2d_2_b);
- void* var_6 = tensorRelu(var_5);
- void* var_7 = tensorPooling(var_6,0,2,2,0,0,2,2);
- void* var_8 = tensorConvolution(var_7, conv2d_3_w, 1, 1, 1, 1, 1, 0);
- void* var_9 = tensorAdd(var_8, conv2d_3_b);
- void* var_10 = tensorRelu(var_9);
- void* var_12 = tensorConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0);
- void* var_13 = tensorAdd(var_12, conv2d_4_b);
- void* var_14 = tensorRelu(var_13);
- void* var_15 = tensorPooling(var_14,0,2,2,0,0,2,2);
- void* var_16 = tensorConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
- void* var_17 = tensorAdd(var_16, conv2d_5_b);
- void* var_18 = tensorRelu(var_17);
- void* var_20 = tensorConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0);
- void* var_21 = tensorAdd(var_20, conv2d_6_b);
- void* var_22 = tensorRelu(var_21);
- void* var_24 = tensorConvolution(var_22, conv2d_7_w, 1, 1, 1, 1, 1, 0);
- void* var_25 = tensorAdd(var_24, conv2d_7_b);
- void* var_26 = tensorRelu(var_25);
- void* var_27 = tensorPooling(var_26,0,2,2,0,0,2,2);
- void* var_28 = tensorConvolution(var_27, conv2d_8_w, 1, 1, 1, 1, 1, 0);
- void* var_29 = tensorAdd(var_28, conv2d_8_b);
- void* var_30 = tensorRelu(var_29);
- void* var_32 = tensorConvolution(var_30, conv2d_9_w, 1, 1, 1, 1, 1, 0);
- void* var_33 = tensorAdd(var_32, conv2d_9_b);
- void* var_34 = tensorRelu(var_33);
- void* var_36 = tensorConvolution(var_34, conv2d_10_w, 1, 1, 1, 1, 1, 0);
- void* var_37 = tensorAdd(var_36, conv2d_10_b);
- void* var_38 = tensorRelu(var_37);
- void* var_39 = tensorPooling(var_38,0,2,2,0,0,2,2);
- void* var_40 = tensorConvolution(var_39, conv2d_11_w, 1, 1, 1, 1, 1, 0);
- void* var_41 = tensorAdd(var_40, conv2d_11_b);
- void* var_42 = tensorRelu(var_41);
- void* var_44 = tensorConvolution(var_42, conv2d_12_w, 1, 1, 1, 1, 1, 0);
- void* var_45 = tensorAdd(var_44, conv2d_12_b);
- void* var_46 = tensorRelu(var_45);
- void* var_48 = tensorConvolution(var_46, conv2d_13_w, 1, 1, 1, 1, 1, 0);
- void* var_49 = tensorAdd(var_48, conv2d_13_b);
- void* var_50 = tensorRelu(var_49);
- void* var_51 = tensorPooling(var_50,0,2,2,0,0,2,2);
- void* var_54 = tensorGemmGPU(var_51, dense_1_w);
- void* var_55 = tensorAdd(var_54, dense_1_b);
- void* var_56 = tensorRelu(var_55);
- void* var_58 = tensorGemmGPU(var_56, dense_2_w);
- void* var_59 = tensorAdd(var_58, dense_2_b);
- void* var_60 = tensorSoftmax(var_59);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_60, 100);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar100_5.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar100_5.cc
deleted file mode 100644
index 3ee273d70aea6d74cfa55f250e999b05506f9b21..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar100_5.cc
+++ /dev/null
@@ -1,167 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- std::string dir_prefix = std::string("../model_params/vgg16_cifar100_front/");
- //std::string input_path = dir_prefix + std::string("vgg16_cifar100_calib.bin");
- //std::string labels_path = dir_prefix + std::string("vgg16_cifar100_train_labels.bin");
-
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
-
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,100);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,100,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 2500;
- int offset = 5000;
-
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size + offset;
- int end = (i + 1) * batch_size + offset;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
- void* var_1 = tensorAdd(var_0, conv2d_1_b);
- void* var_2 = tensorRelu(var_1);
- void* var_4 = tensorConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0);
- void* var_5 = tensorAdd(var_4, conv2d_2_b);
- void* var_6 = tensorRelu(var_5);
- void* var_7 = tensorPooling(var_6,0,2,2,0,0,2,2);
- void* var_8 = tensorConvolution(var_7, conv2d_3_w, 1, 1, 1, 1, 1, 0);
- void* var_9 = tensorAdd(var_8, conv2d_3_b);
- void* var_10 = tensorRelu(var_9);
- void* var_12 = tensorConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0);
- void* var_13 = tensorAdd(var_12, conv2d_4_b);
- void* var_14 = tensorRelu(var_13);
- void* var_15 = tensorPooling(var_14,0,2,2,0,0,2,2);
- void* var_16 = tensorConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
- void* var_17 = tensorAdd(var_16, conv2d_5_b);
- void* var_18 = tensorRelu(var_17);
- void* var_20 = tensorConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0);
- void* var_21 = tensorAdd(var_20, conv2d_6_b);
- void* var_22 = tensorRelu(var_21);
- void* var_24 = tensorConvolution(var_22, conv2d_7_w, 1, 1, 1, 1, 1, 0);
- void* var_25 = tensorAdd(var_24, conv2d_7_b);
- void* var_26 = tensorRelu(var_25);
- void* var_27 = tensorPooling(var_26,0,2,2,0,0,2,2);
- void* var_28 = tensorConvolution(var_27, conv2d_8_w, 1, 1, 1, 1, 1, 0);
- void* var_29 = tensorAdd(var_28, conv2d_8_b);
- void* var_30 = tensorRelu(var_29);
- void* var_32 = tensorConvolution(var_30, conv2d_9_w, 1, 1, 1, 1, 1, 0);
- void* var_33 = tensorAdd(var_32, conv2d_9_b);
- void* var_34 = tensorRelu(var_33);
- void* var_36 = tensorConvolution(var_34, conv2d_10_w, 1, 1, 1, 1, 1, 0);
- void* var_37 = tensorAdd(var_36, conv2d_10_b);
- void* var_38 = tensorRelu(var_37);
- void* var_39 = tensorPooling(var_38,0,2,2,0,0,2,2);
- void* var_40 = tensorConvolution(var_39, conv2d_11_w, 1, 1, 1, 1, 1, 0);
- void* var_41 = tensorAdd(var_40, conv2d_11_b);
- void* var_42 = tensorRelu(var_41);
- void* var_44 = tensorConvolution(var_42, conv2d_12_w, 1, 1, 1, 1, 1, 0);
- void* var_45 = tensorAdd(var_44, conv2d_12_b);
- void* var_46 = tensorRelu(var_45);
- void* var_48 = tensorConvolution(var_46, conv2d_13_w, 1, 1, 1, 1, 1, 0);
- void* var_49 = tensorAdd(var_48, conv2d_13_b);
- void* var_50 = tensorRelu(var_49);
- void* var_51 = tensorPooling(var_50,0,2,2,0,0,2,2);
- void* var_54 = tensorGemmGPU(var_51, dense_1_w);
- void* var_55 = tensorAdd(var_54, dense_1_b);
- void* var_56 = tensorRelu(var_55);
- void* var_58 = tensorGemmGPU(var_56, dense_2_w);
- void* var_59 = tensorAdd(var_58, dense_2_b);
- void* var_60 = tensorSoftmax(var_59);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- //float accuracy = computeAccuracy2(labels, batch_size, var_60, 100);
- float accuracy = computeTop5Accuracy(labels, batch_size, var_60, 100);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar100_top5_tuner.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar100_top5_tuner.cc
deleted file mode 100644
index 56e0e7016f16ce6548d9947e13fda96a931e436b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar100_top5_tuner.cc
+++ /dev/null
@@ -1,167 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- std::string dir_prefix = std::string("../model_params/vgg16_cifar100_front/");
- //std::string input_path = dir_prefix + std::string("vgg16_cifar100_calib.bin");
- //std::string labels_path = dir_prefix + std::string("vgg16_cifar100_train_labels.bin");
-
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
-
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,100);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,100,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 4000;
- int batch_size = 4000;
- int offset = 5000;
-
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size + offset;
- int end = (i + 1) * batch_size + offset;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
- void* var_1 = tensorAdd(var_0, conv2d_1_b);
- void* var_2 = tensorRelu(var_1);
- void* var_4 = tensorConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0);
- void* var_5 = tensorAdd(var_4, conv2d_2_b);
- void* var_6 = tensorRelu(var_5);
- void* var_7 = tensorPooling(var_6,0,2,2,0,0,2,2);
- void* var_8 = tensorConvolution(var_7, conv2d_3_w, 1, 1, 1, 1, 1, 0);
- void* var_9 = tensorAdd(var_8, conv2d_3_b);
- void* var_10 = tensorRelu(var_9);
- void* var_12 = tensorConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0);
- void* var_13 = tensorAdd(var_12, conv2d_4_b);
- void* var_14 = tensorRelu(var_13);
- void* var_15 = tensorPooling(var_14,0,2,2,0,0,2,2);
- void* var_16 = tensorConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
- void* var_17 = tensorAdd(var_16, conv2d_5_b);
- void* var_18 = tensorRelu(var_17);
- void* var_20 = tensorConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0);
- void* var_21 = tensorAdd(var_20, conv2d_6_b);
- void* var_22 = tensorRelu(var_21);
- void* var_24 = tensorConvolution(var_22, conv2d_7_w, 1, 1, 1, 1, 1, 0);
- void* var_25 = tensorAdd(var_24, conv2d_7_b);
- void* var_26 = tensorRelu(var_25);
- void* var_27 = tensorPooling(var_26,0,2,2,0,0,2,2);
- void* var_28 = tensorConvolution(var_27, conv2d_8_w, 1, 1, 1, 1, 1, 0);
- void* var_29 = tensorAdd(var_28, conv2d_8_b);
- void* var_30 = tensorRelu(var_29);
- void* var_32 = tensorConvolution(var_30, conv2d_9_w, 1, 1, 1, 1, 1, 0);
- void* var_33 = tensorAdd(var_32, conv2d_9_b);
- void* var_34 = tensorRelu(var_33);
- void* var_36 = tensorConvolution(var_34, conv2d_10_w, 1, 1, 1, 1, 1, 0);
- void* var_37 = tensorAdd(var_36, conv2d_10_b);
- void* var_38 = tensorRelu(var_37);
- void* var_39 = tensorPooling(var_38,0,2,2,0,0,2,2);
- void* var_40 = tensorConvolution(var_39, conv2d_11_w, 1, 1, 1, 1, 1, 0);
- void* var_41 = tensorAdd(var_40, conv2d_11_b);
- void* var_42 = tensorRelu(var_41);
- void* var_44 = tensorConvolution(var_42, conv2d_12_w, 1, 1, 1, 1, 1, 0);
- void* var_45 = tensorAdd(var_44, conv2d_12_b);
- void* var_46 = tensorRelu(var_45);
- void* var_48 = tensorConvolution(var_46, conv2d_13_w, 1, 1, 1, 1, 1, 0);
- void* var_49 = tensorAdd(var_48, conv2d_13_b);
- void* var_50 = tensorRelu(var_49);
- void* var_51 = tensorPooling(var_50,0,2,2,0,0,2,2);
- void* var_54 = tensorGemmGPU(var_51, dense_1_w);
- void* var_55 = tensorAdd(var_54, dense_1_b);
- void* var_56 = tensorRelu(var_55);
- void* var_58 = tensorGemmGPU(var_56, dense_2_w);
- void* var_59 = tensorAdd(var_58, dense_2_b);
- void* var_60 = tensorSoftmax(var_59);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- //float accuracy = computeAccuracy2(labels, batch_size, var_60, 100);
- float accuracy = computeTop5Accuracy(labels, batch_size, var_60, 100);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar100_tuner.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar100_tuner.cc
deleted file mode 100644
index 18e419553641160d59930a72695ec0a191c06d74..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar100_tuner.cc
+++ /dev/null
@@ -1,166 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(0);
-
- std::string dir_prefix = std::string("../model_params/vgg16_cifar100_front/");
- //std::string input_path = dir_prefix + std::string("vgg16_cifar100_calib.bin");
- //std::string labels_path = dir_prefix + std::string("vgg16_cifar100_train_labels.bin");
-
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
-
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,100);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,100,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 5000;
- int batch_size = 5000;
- int offset = 5000;
-
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size + offset;
- int end = (i + 1) * batch_size + offset;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
- void* var_1 = tensorAdd(var_0, conv2d_1_b);
- void* var_2 = tensorRelu(var_1);
- void* var_4 = tensorConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0);
- void* var_5 = tensorAdd(var_4, conv2d_2_b);
- void* var_6 = tensorRelu(var_5);
- void* var_7 = tensorPooling(var_6,0,2,2,0,0,2,2);
- void* var_8 = tensorConvolution(var_7, conv2d_3_w, 1, 1, 1, 1, 1, 0);
- void* var_9 = tensorAdd(var_8, conv2d_3_b);
- void* var_10 = tensorRelu(var_9);
- void* var_12 = tensorConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0);
- void* var_13 = tensorAdd(var_12, conv2d_4_b);
- void* var_14 = tensorRelu(var_13);
- void* var_15 = tensorPooling(var_14,0,2,2,0,0,2,2);
- void* var_16 = tensorConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
- void* var_17 = tensorAdd(var_16, conv2d_5_b);
- void* var_18 = tensorRelu(var_17);
- void* var_20 = tensorConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0);
- void* var_21 = tensorAdd(var_20, conv2d_6_b);
- void* var_22 = tensorRelu(var_21);
- void* var_24 = tensorConvolution(var_22, conv2d_7_w, 1, 1, 1, 1, 1, 0);
- void* var_25 = tensorAdd(var_24, conv2d_7_b);
- void* var_26 = tensorRelu(var_25);
- void* var_27 = tensorPooling(var_26,0,2,2,0,0,2,2);
- void* var_28 = tensorConvolution(var_27, conv2d_8_w, 1, 1, 1, 1, 1, 0);
- void* var_29 = tensorAdd(var_28, conv2d_8_b);
- void* var_30 = tensorRelu(var_29);
- void* var_32 = tensorConvolution(var_30, conv2d_9_w, 1, 1, 1, 1, 1, 0);
- void* var_33 = tensorAdd(var_32, conv2d_9_b);
- void* var_34 = tensorRelu(var_33);
- void* var_36 = tensorConvolution(var_34, conv2d_10_w, 1, 1, 1, 1, 1, 0);
- void* var_37 = tensorAdd(var_36, conv2d_10_b);
- void* var_38 = tensorRelu(var_37);
- void* var_39 = tensorPooling(var_38,0,2,2,0,0,2,2);
- void* var_40 = tensorConvolution(var_39, conv2d_11_w, 1, 1, 1, 1, 1, 0);
- void* var_41 = tensorAdd(var_40, conv2d_11_b);
- void* var_42 = tensorRelu(var_41);
- void* var_44 = tensorConvolution(var_42, conv2d_12_w, 1, 1, 1, 1, 1, 0);
- void* var_45 = tensorAdd(var_44, conv2d_12_b);
- void* var_46 = tensorRelu(var_45);
- void* var_48 = tensorConvolution(var_46, conv2d_13_w, 1, 1, 1, 1, 1, 0);
- void* var_49 = tensorAdd(var_48, conv2d_13_b);
- void* var_50 = tensorRelu(var_49);
- void* var_51 = tensorPooling(var_50,0,2,2,0,0,2,2);
- void* var_54 = tensorGemmGPU(var_51, dense_1_w);
- void* var_55 = tensorAdd(var_54, dense_1_b);
- void* var_56 = tensorRelu(var_55);
- void* var_58 = tensorGemmGPU(var_56, dense_2_w);
- void* var_59 = tensorAdd(var_58, dense_2_b);
- void* var_60 = tensorSoftmax(var_59);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_60, 100);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar10_tuner.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar10_tuner.cc
deleted file mode 100644
index 552001ba7af481845f75cd95e3249bc7ba7d0a97..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar10_tuner.cc
+++ /dev/null
@@ -1,180 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-
-int main(int argc, char* argv[]){
-
- int total_runs = 1;
- if (argc > 1){
- total_runs = atoi(argv[1]);
- }
-
-
- llvm_hpvm_initTensorRt(1);
-
- std::string dir_prefix = std::string("../model_params/vgg16_cifar10_2/");
- //std::string input_path = dir_prefix + std::string("vgg16_cifar_calib.bin");
- //std::string labels_path = dir_prefix + std::string("vgg16_train_labels.bin");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
-
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
- std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
- void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
- std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
- void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
- std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
- void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
- std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
- void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
- std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
- void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
- void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
- std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
- void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
- std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
- void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
- void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
- void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
- void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
- void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
- std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
- void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
- std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
- void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,10);
- std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
- void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,10,1,1);
-
-
- startMemTracking();
-
- int test_input_size = 500;
- int batch_size = 500;
- int offset = 5000;
-
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
-
- for(int j = 0; j < total_runs; j++){
-
- float final_accuracy = 0.0;
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size + offset;
- int end = (i + 1) * batch_size + offset;
-
- void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
- void* var_1 = tensorAdd(var_0, conv2d_1_b);
- void* var_2 = tensorRelu(var_1);
- void* var_4 = tensorConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0);
- void* var_5 = tensorAdd(var_4, conv2d_2_b);
- void* var_6 = tensorRelu(var_5);
- void* var_7 = tensorPooling(var_6,0,2,2,0,0,2,2);
- void* var_8 = tensorConvolution(var_7, conv2d_3_w, 1, 1, 1, 1, 1, 0);
- void* var_9 = tensorAdd(var_8, conv2d_3_b);
- void* var_10 = tensorRelu(var_9);
- void* var_12 = tensorConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0);
- void* var_13 = tensorAdd(var_12, conv2d_4_b);
- void* var_14 = tensorRelu(var_13);
- void* var_15 = tensorPooling(var_14,0,2,2,0,0,2,2);
- void* var_16 = tensorConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
- void* var_17 = tensorAdd(var_16, conv2d_5_b);
- void* var_18 = tensorRelu(var_17);
- void* var_20 = tensorConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0);
- void* var_21 = tensorAdd(var_20, conv2d_6_b);
- void* var_22 = tensorRelu(var_21);
- void* var_24 = tensorConvolution(var_22, conv2d_7_w, 1, 1, 1, 1, 1, 0);
- void* var_25 = tensorAdd(var_24, conv2d_7_b);
- void* var_26 = tensorRelu(var_25);
- void* var_27 = tensorPooling(var_26,0,2,2,0,0,2,2);
- void* var_28 = tensorConvolution(var_27, conv2d_8_w, 1, 1, 1, 1, 1, 0);
- void* var_29 = tensorAdd(var_28, conv2d_8_b);
- void* var_30 = tensorRelu(var_29);
- void* var_32 = tensorConvolution(var_30, conv2d_9_w, 1, 1, 1, 1, 1, 0);
- void* var_33 = tensorAdd(var_32, conv2d_9_b);
- void* var_34 = tensorRelu(var_33);
- void* var_36 = tensorConvolution(var_34, conv2d_10_w, 1, 1, 1, 1, 1, 0);
- void* var_37 = tensorAdd(var_36, conv2d_10_b);
- void* var_38 = tensorRelu(var_37);
- void* var_39 = tensorPooling(var_38,0,2,2,0,0,2,2);
- void* var_40 = tensorConvolution(var_39, conv2d_11_w, 1, 1, 1, 1, 1, 0);
- void* var_41 = tensorAdd(var_40, conv2d_11_b);
- void* var_42 = tensorRelu(var_41);
- void* var_44 = tensorConvolution(var_42, conv2d_12_w, 1, 1, 1, 1, 1, 0);
- void* var_45 = tensorAdd(var_44, conv2d_12_b);
- void* var_46 = tensorRelu(var_45);
- void* var_48 = tensorConvolution(var_46, conv2d_13_w, 1, 1, 1, 1, 1, 0);
- void* var_49 = tensorAdd(var_48, conv2d_13_b);
- void* var_50 = tensorRelu(var_49);
- void* var_51 = tensorPooling(var_50,0,2,2,0,0,2,2);
- void* var_54 = tensorGemmGPU(var_51, dense_1_w);
- void* var_55 = tensorAdd(var_54, dense_1_b);
- void* var_56 = tensorRelu(var_55);
- void* var_58 = tensorGemmGPU(var_56, dense_2_w);
- void* var_59 = tensorAdd(var_58, dense_2_b);
- void* var_60 = tensorSoftmax(var_59);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
-
- float accuracy = computeAccuracy2(labels,batch_size,var_60);
- final_accuracy += accuracy;
-
- freeBatchMemory();
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
- }
-
- dumpExecutionAccuracies();
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/tuning_src/fc_network_acc.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/tuning_src/fc_network_acc.cc
deleted file mode 100644
index 7ab357e4ac6e8b6550a71b14ce73c79e20879cf3..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/tuning_src/fc_network_acc.cc
+++ /dev/null
@@ -1,108 +0,0 @@
-
-
-#include <stdio.h>
-#include <cstdlib>
-#include <stdlib.h>
-#include <unistd.h>
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-#include "../include/types.h"
-#include "../include/op_overheads.h"
-
-
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testFCNetworkArch(int op1_acc, int op2_acc, int op3_acc,
- int op4_acc, int op5_acc, int op6_acc, int op7_acc){
-
- printf("********* Fully Connected DNN-1 - Accuracy Tuned ********* \n");
- // FIXIT: Extend this to batch of images - currently 5 images
-
- int test_batch_size = 10000;
- void* input = readTrainedWeights("../model_params/FC_network2/mnist_float_input.bin",
- float_type, test_batch_size, 1, 28, 28);
-
- void* fc1_weights = readTrainedWeights("../model_params/FC_network2/fc1.bin",
- float_type, 1, 1, 784, 128);
- void* fc1_bias = readTrainedWeights("../model_params/FC_network2/fc1_bias.bin",
- float_type, 1, 128, 1, 1);
- void* fc2_weights = readTrainedWeights("../model_params/FC_network2/fc2.bin",
- float_type, 1, 1, 128, 10);
- void* fc2_bias = readTrainedWeights("../model_params/FC_network2/fc2_bias.bin",
- float_type, 1, 10, 1, 1);
-
- // Start execution profiling Tensor ops
- startProfiling();
-
- // Layer-1
- void* fc1out = tensorGemmGPU(input, fc1_weights);
- void* error_norms = tensorAddError(fc1out, op1_acc);
- add_norms(error_norms);
- add_gemm_overheads(input, fc1_weights, op1_acc);
-
- void* fc1_bias_out = tensorAdd(fc1out, fc1_bias);
- error_norms = tensorAddError(fc1_bias_out, op2_acc);
- add_norms(error_norms);
- add_bias_overheads(fc1_bias_out, op2_acc);
-
- void* fc1_relu = tensorRelu(fc1_bias_out);
- error_norms = tensorAddError(fc1_relu, op3_acc);
- add_norms(error_norms);
- add_relu_overheads(fc1_relu, op3_acc);
-
- // Layer-2
- void* fc2out = tensorGemmGPU(fc1_relu, fc2_weights);
- error_norms = tensorAddError(fc2out, op4_acc);
- add_norms(error_norms);
- add_gemm_overheads(fc1_relu, fc2_weights, op4_acc);
-
- void* fc2_bias_out = tensorAdd(fc2out, fc2_bias);
- error_norms = tensorAddError(fc2_bias_out, op5_acc);
- add_norms(error_norms);
- add_bias_overheads(fc2_bias_out, op5_acc);
-
- void* fc2_relu = tensorRelu(fc2_bias_out);
- error_norms = tensorAddError(fc2_relu, op6_acc);
- add_norms(error_norms);
- add_relu_overheads(fc2_relu, op6_acc);
-
- void* result = tensorSoftmax(fc2_relu);
- error_norms = tensorAddError(result, op7_acc);
- add_norms(error_norms);
- add_bias_overheads(result, op7_acc);
-
- stopProfiling();
-
- computeAccuracy("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size, result);
-
- dump_result("accuracy_summary");
-}
-
-
-
-int main(int argc, char* argv[]){
-
- if(argc < 8){
- printf("Must provide 7 knobs for accuracy tuning \n");
- abort();
- }
-
- // This initializes the runtime - must be called before anything
- llvm_hpvm_initTensorRt(0);
-
- int op1_acc = atoi(argv[1]);
- int op2_acc = atoi(argv[2]);
- int op3_acc = atoi(argv[3]);
- int op4_acc = atoi(argv[4]);
- int op5_acc = atoi(argv[5]);
- int op6_acc = atoi(argv[6]);
- int op7_acc = atoi(argv[7]);
-
- testFCNetworkArch(op1_acc, op2_acc, op3_acc,
- op4_acc, op5_acc, op6_acc, op7_acc);
-
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/lib/tensor_runtime.ll b/llvm/projects/hpvm-tensor-rt/lib/tensor_runtime.ll
deleted file mode 100644
index 89c8da90f8ab740062bd84cdd365baa67311a7a4..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/lib/tensor_runtime.ll
+++ /dev/null
@@ -1,201 +0,0 @@
-; ModuleID = '/home/hsharif3/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt//lib/tensor_runtime.bc'
-source_filename = "/home/hsharif3/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt//tensor_runtime/include/tensor_signatures.cc"
-target datalayout = "e-m:e-i64:64-f80:128-n8:16:32:64-S128"
-target triple = "x86_64-unknown-linux-gnu"
-
-; Function Attrs: noinline nounwind uwtable
-define void @_Z13dummyFunctionv() #0 {
-entry:
- %initRT = alloca i8*, align 8
- %cleanRT = alloca i8*, align 8
- %initApproxRT = alloca i8*, align 8
- %cleanApproxRT = alloca i8*, align 8
- %initRTController = alloca i8*, align 8
- %cleanRTController = alloca i8*, align 8
- %request_tensorPtr = alloca i8*, align 8
- %startProf = alloca i8*, align 8
- %stopProf = alloca i8*, align 8
- %create2Dptr = alloca i8*, align 8
- %create3Dptr = alloca i8*, align 8
- %create4Dptr = alloca i8*, align 8
- %initTensorPtr = alloca i8*, align 8
- %tensorSplitPtr = alloca i8*, align 8
- %tensorConcatPtr = alloca i8*, align 8
- %tensorConvPtr = alloca i8*, align 8
- %tensorHConvPtr = alloca i8*, align 8
- %tensorPoolPtr = alloca i8*, align 8
- %tensorHalfPoolPtr = alloca i8*, align 8
- %tensorLRNPtr = alloca i8*, align 8
- %tensorGemmPr = alloca i8*, align 8
- %tensorGemmCPUPtr = alloca i8*, align 8
- %tensorGemmGPUPtr = alloca i8*, align 8
- %tensorHgemmPtr = alloca i8*, align 8
- %tensorGemmBiasPtr = alloca i8*, align 8
- %tensorAddPtr = alloca i8*, align 8
- %tensorHalfAddPtr = alloca i8*, align 8
- %tensorReluPtr = alloca i8*, align 8
- %tensorRelu2Ptr = alloca i8*, align 8
- %tensorHalfRelu2Ptr = alloca i8*, align 8
- %tensorTanhPtr = alloca i8*, align 8
- %tensorHalfTanhPtr = alloca i8*, align 8
- %tensorSoftmaxPtr = alloca i8*, align 8
- %tensorBatchNormPtr = alloca i8*, align 8
- %tensorAddErrorPtr = alloca i8*, align 8
- %ConvLayer = alloca i8*, align 8
- %FCLayer = alloca i8*, align 8
- %ConvLayer2 = alloca i8*, align 8
- %ConvLayer3 = alloca i8*, align 8
- %FCLayer2 = alloca i8*, align 8
- %AddWrapper = alloca i8*, align 8
- %ReluWrapper = alloca i8*, align 8
- %TanhWrapper = alloca i8*, align 8
- %BatchNormWrapper = alloca i8*, align 8
- %PoolingWrapper = alloca i8*, align 8
- %softmaxWrapper = alloca i8*, align 8
- store i8* bitcast (void (i32)* @llvm_hpvm_initTensorRt to i8*), i8** %initRT, align 8
- store i8* bitcast (void ()* @llvm_hpvm_cleanupTensorRt to i8*), i8** %cleanRT, align 8
- store i8* bitcast (void (i32)* @llvm_hpvm_initApproxhpvmRt to i8*), i8** %initApproxRT, align 8
- store i8* bitcast (void ()* @llvm_hpvm_cleanupApproxhpvmRt to i8*), i8** %cleanApproxRT, align 8
- store i8* bitcast (void (i8*, i8*)* @llvm_hpvm_initializeRuntimeController to i8*), i8** %initRTController, align 8
- store i8* bitcast (void ()* @llvm_hpvm_clearRuntimeController to i8*), i8** %cleanRTController, align 8
- store i8* bitcast (void (i8*, i32)* @hpvm_request_tensor to i8*), i8** %request_tensorPtr, align 8
- store i8* bitcast (void ()* @startProfiling to i8*), i8** %startProf, align 8
- store i8* bitcast (void ()* @stopProfiling to i8*), i8** %stopProf, align 8
- store i8* bitcast (i8* (i32, i64, i64)* @create2DTensor to i8*), i8** %create2Dptr, align 8
- store i8* bitcast (i8* (i32, i64, i64, i64)* @create3DTensor to i8*), i8** %create3Dptr, align 8
- store i8* bitcast (i8* (i32, i32, i64, i64, i64, i64)* @create4DTensor to i8*), i8** %create4Dptr, align 8
- store i8* bitcast (void (i8*, i8*, i64)* @initTensorData to i8*), i8** %initTensorPtr, align 8
- store i8* bitcast (i8** (i8*, i32, i32)* @tensorSplit to i8*), i8** %tensorSplitPtr, align 8
- store i8* bitcast (i8* (i8**, i32, i32)* @tensorConcat to i8*), i8** %tensorConcatPtr, align 8
- store i8* bitcast (i8* (i8*, i8*, i32, i32, i32, i32, i32, i32)* @tensorConvolution to i8*), i8** %tensorConvPtr, align 8
- store i8* bitcast (i8* (i8*, i8*, i32, i32, i32, i32, i32, i32)* @tensorHalfConvolution to i8*), i8** %tensorHConvPtr, align 8
- store i8* bitcast (i8* (i8*, i32, i32, i32, i32, i32, i32, i32)* @tensorPooling to i8*), i8** %tensorPoolPtr, align 8
- store i8* bitcast (i8* (i8*, i32, i32, i32, i32, i32, i32, i32)* @tensorHalfPooling to i8*), i8** %tensorHalfPoolPtr, align 8
- store i8* bitcast (i8* (i8*, i32, double, double, double)* @tensorLRN to i8*), i8** %tensorLRNPtr, align 8
- store i8* bitcast (i8* (i8*, i8*)* @tensorGemm to i8*), i8** %tensorGemmPr, align 8
- store i8* bitcast (i8* (i8*, i8*)* @tensorGemmCPU to i8*), i8** %tensorGemmCPUPtr, align 8
- store i8* bitcast (i8* (i8*, i8*)* @tensorGemmGPU to i8*), i8** %tensorGemmGPUPtr, align 8
- store i8* bitcast (i8* (i8*, i8*)* @tensorHalfGemm to i8*), i8** %tensorHgemmPtr, align 8
- store i8* bitcast (i8* (i8*, i8*)* @tensorGemmBias to i8*), i8** %tensorGemmBiasPtr, align 8
- store i8* bitcast (i8* (i8*, i8*)* @tensorAdd to i8*), i8** %tensorAddPtr, align 8
- store i8* bitcast (i8* (i8*, i8*)* @tensorHalfAdd to i8*), i8** %tensorHalfAddPtr, align 8
- store i8* bitcast (i8* (i8*)* @tensorRelu to i8*), i8** %tensorReluPtr, align 8
- store i8* bitcast (i8* (i8*, float, float)* @tensorRelu2 to i8*), i8** %tensorRelu2Ptr, align 8
- store i8* bitcast (i8* (i8*, float, float)* @tensorHalfRelu2 to i8*), i8** %tensorHalfRelu2Ptr, align 8
- store i8* bitcast (i8* (i8*)* @tensorTanh to i8*), i8** %tensorTanhPtr, align 8
- store i8* bitcast (i8* (i8*)* @tensorHalfTanh to i8*), i8** %tensorHalfTanhPtr, align 8
- store i8* bitcast (i8* (i8*)* @tensorSoftmax to i8*), i8** %tensorSoftmaxPtr, align 8
- store i8* bitcast (i8* (i8*, i8*, i8*, i8*, i8*, double)* @tensorBatchNorm to i8*), i8** %tensorBatchNormPtr, align 8
- store i8* bitcast (i8* (i8*, i32)* @tensorAddError to i8*), i8** %tensorAddErrorPtr, align 8
- store i8* bitcast (i8* (i8*, float, float, i8*, float, float, i8*, float, float, i32, i32, i32, i32, i32, i32, i32, float, float, i32)* @ConvLayer_PROMISE to i8*), i8** %ConvLayer, align 8
- store i8* bitcast (i8* (i8*, float, float, i8*, float, float, i8*, float, float, i32, float, float, i32)* @FCLayer_PROMISE to i8*), i8** %FCLayer, align 8
- store i8* bitcast (i8* (i8*, i8*, i8*, i8*, i32, i32, i32, i32, i32, i32, i32, float, float)* @wrapper_ConvLayer to i8*), i8** %ConvLayer2, align 8
- store i8* bitcast (i8* (i8*, i8*, i8*, i32, i32, i32, i32, i32, i32)* @wrapper_tensorGroupConvolution to i8*), i8** %ConvLayer3, align 8
- store i8* bitcast (i8* (i8*, i8*, i8*, i8*, i32, float, float)* @wrapper_FCLayer to i8*), i8** %FCLayer2, align 8
- store i8* bitcast (i8* (i8*, i8*, i8*)* @wrapper_tensorAdd to i8*), i8** %AddWrapper, align 8
- store i8* bitcast (i8* (i8*, i8*)* @wrapper_tensorRelu to i8*), i8** %ReluWrapper, align 8
- store i8* bitcast (i8* (i8*, i8*)* @wrapper_tensorTanh to i8*), i8** %TanhWrapper, align 8
- store i8* bitcast (i8* (i8*, i8*, i8*, i8*, i8*, i8*, double)* @wrapper_tensorBatchNorm to i8*), i8** %BatchNormWrapper, align 8
- store i8* bitcast (i8* (i8*, i8*, i32, i32, i32, i32, i32, i32, i32)* @wrapper_tensorPooling to i8*), i8** %PoolingWrapper, align 8
- store i8* bitcast (i8* (i8*, i8*)* @wrapper_tensorSoftmax to i8*), i8** %softmaxWrapper, align 8
- ret void
-}
-
-declare void @llvm_hpvm_initTensorRt(i32) #1
-
-declare void @llvm_hpvm_cleanupTensorRt() #1
-
-declare void @llvm_hpvm_initApproxhpvmRt(i32) #1
-
-declare void @llvm_hpvm_cleanupApproxhpvmRt() #1
-
-declare void @llvm_hpvm_initializeRuntimeController(i8*, i8*) #1
-
-declare void @llvm_hpvm_clearRuntimeController() #1
-
-declare void @hpvm_request_tensor(i8*, i32) #1
-
-declare void @startProfiling() #1
-
-declare void @stopProfiling() #1
-
-declare i8* @create2DTensor(i32, i64, i64) #1
-
-declare i8* @create3DTensor(i32, i64, i64, i64) #1
-
-declare i8* @create4DTensor(i32, i32, i64, i64, i64, i64) #1
-
-declare void @initTensorData(i8*, i8*, i64) #1
-
-declare i8** @tensorSplit(i8*, i32, i32) #1
-
-declare i8* @tensorConcat(i8**, i32, i32) #1
-
-declare i8* @tensorConvolution(i8*, i8*, i32, i32, i32, i32, i32, i32) #1
-
-declare i8* @tensorHalfConvolution(i8*, i8*, i32, i32, i32, i32, i32, i32) #1
-
-declare i8* @tensorPooling(i8*, i32, i32, i32, i32, i32, i32, i32) #1
-
-declare i8* @tensorHalfPooling(i8*, i32, i32, i32, i32, i32, i32, i32) #1
-
-declare i8* @tensorLRN(i8*, i32, double, double, double) #1
-
-declare i8* @tensorGemm(i8*, i8*) #1
-
-declare i8* @tensorGemmCPU(i8*, i8*) #1
-
-declare i8* @tensorGemmGPU(i8*, i8*) #1
-
-declare i8* @tensorHalfGemm(i8*, i8*) #1
-
-declare i8* @tensorGemmBias(i8*, i8*) #1
-
-declare i8* @tensorAdd(i8*, i8*) #1
-
-declare i8* @tensorHalfAdd(i8*, i8*) #1
-
-declare i8* @tensorRelu(i8*) #1
-
-declare i8* @tensorRelu2(i8*, float, float) #1
-
-declare i8* @tensorHalfRelu2(i8*, float, float) #1
-
-declare i8* @tensorTanh(i8*) #1
-
-declare i8* @tensorHalfTanh(i8*) #1
-
-declare i8* @tensorSoftmax(i8*) #1
-
-declare i8* @tensorBatchNorm(i8*, i8*, i8*, i8*, i8*, double) #1
-
-declare i8* @tensorAddError(i8*, i32) #1
-
-declare i8* @ConvLayer_PROMISE(i8*, float, float, i8*, float, float, i8*, float, float, i32, i32, i32, i32, i32, i32, i32, float, float, i32) #1
-
-declare i8* @FCLayer_PROMISE(i8*, float, float, i8*, float, float, i8*, float, float, i32, float, float, i32) #1
-
-declare i8* @wrapper_ConvLayer(i8*, i8*, i8*, i8*, i32, i32, i32, i32, i32, i32, i32, float, float) #1
-
-declare i8* @wrapper_tensorGroupConvolution(i8*, i8*, i8*, i32, i32, i32, i32, i32, i32) #1
-
-declare i8* @wrapper_FCLayer(i8*, i8*, i8*, i8*, i32, float, float) #1
-
-declare i8* @wrapper_tensorAdd(i8*, i8*, i8*) #1
-
-declare i8* @wrapper_tensorRelu(i8*, i8*) #1
-
-declare i8* @wrapper_tensorTanh(i8*, i8*) #1
-
-declare i8* @wrapper_tensorBatchNorm(i8*, i8*, i8*, i8*, i8*, i8*, double) #1
-
-declare i8* @wrapper_tensorPooling(i8*, i8*, i32, i32, i32, i32, i32, i32, i32) #1
-
-declare i8* @wrapper_tensorSoftmax(i8*, i8*) #1
-
-attributes #0 = { noinline nounwind uwtable "correctly-rounded-divide-sqrt-fp-math"="false" "disable-tail-calls"="false" "less-precise-fpmad"="false" "no-frame-pointer-elim"="true" "no-frame-pointer-elim-non-leaf" "no-infs-fp-math"="false" "no-jump-tables"="false" "no-nans-fp-math"="false" "no-signed-zeros-fp-math"="false" "no-trapping-math"="false" "stack-protector-buffer-size"="8" "target-cpu"="x86-64" "target-features"="+fxsr,+mmx,+sse,+sse2,+x87" "unsafe-fp-math"="false" "use-soft-float"="false" }
-attributes #1 = { "correctly-rounded-divide-sqrt-fp-math"="false" "disable-tail-calls"="false" "less-precise-fpmad"="false" "no-frame-pointer-elim"="true" "no-frame-pointer-elim-non-leaf" "no-infs-fp-math"="false" "no-nans-fp-math"="false" "no-signed-zeros-fp-math"="false" "no-trapping-math"="false" "stack-protector-buffer-size"="8" "target-cpu"="x86-64" "target-features"="+fxsr,+mmx,+sse,+sse2,+x87" "unsafe-fp-math"="false" "use-soft-float"="false" }
-
-!llvm.ident = !{!0}
-
-!0 = !{!"clang version 4.0.1 "}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/FC_network1/fc1.bin b/llvm/projects/hpvm-tensor-rt/model_params/FC_network1/fc1.bin
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index 493f78d98eec57da2bb3004079a64f0584ea60d9..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/FC_network1/fc4.bin b/llvm/projects/hpvm-tensor-rt/model_params/FC_network1/fc4.bin
deleted file mode 100644
index 4f940102eb8a17051c44e8fe12a6b7730a61c15d..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/FC_network1/fc4_bias.bin b/llvm/projects/hpvm-tensor-rt/model_params/FC_network1/fc4_bias.bin
deleted file mode 100644
index fd3305efb194f19475cb0a260f845efc8bd986e7..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/FC_network1/fc4_bias.bin
+++ /dev/null
@@ -1 +0,0 @@
-Ǜ±¿žÓ?Ü‚ ?Sˆ¾6;bZö>c÷”¿¡™Ê<`–i?
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/FC_network2/fc1.bin b/llvm/projects/hpvm-tensor-rt/model_params/FC_network2/fc1.bin
deleted file mode 100644
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index 9cca044999ee589bbfdfada84db040751559cd26..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/FC_network2/fc2.bin b/llvm/projects/hpvm-tensor-rt/model_params/FC_network2/fc2.bin
deleted file mode 100644
index 7fc42c720505d595c80dc426cc739dcdc5e5c7e2..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/FC_network2/fc2_bias.bin b/llvm/projects/hpvm-tensor-rt/model_params/FC_network2/fc2_bias.bin
deleted file mode 100644
index eb2e7856b366465f0574ad68d71c88432d021b27..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/FC_network2/fc2_bias.bin
+++ /dev/null
@@ -1 +0,0 @@
-°F¾Êgw>I‹q<?N½»ƒ¾Ò…¹> „m½É > ݆¾ÝH/½
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/FC_network2/mnist_float_input.bin b/llvm/projects/hpvm-tensor-rt/model_params/FC_network2/mnist_float_input.bin
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index 6b20a053997e30f768995fbf4f27ab6b04f8403f..0000000000000000000000000000000000000000
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index 2f19c0f9435e2ce6c8f1e9b502faf633d178da0c..0000000000000000000000000000000000000000
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deleted file mode 100644
index bf41661c9e6d173175f226fcabfe2233b579809b..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10/conv2.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10/conv2.bin
deleted file mode 100644
index 92a39675e8da83e41c612695ec60877f7f40d925..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10/conv2_bias.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10/conv2_bias.bin
deleted file mode 100644
index 8a83ce22c0ba6aa30837de0f86d7a18e0f679792..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10/conv2_bias.bin
+++ /dev/null
@@ -1 +0,0 @@
- !¶¾Â®€¾)`…¾àDŠ¾ d¾([¾A6…¾4€¾òû”¾^Ød¾å㤾ªf¾t¾)\¾ñdv¾6EH¾zíR¾!†¾a¾;¾4°{¾óÉU¾ïbH¾ÏJ¾»ÿ<¾˜2‘¾Ü`¾{Hw¾”Wd¾Øö0¾ÏÃ`¾ž‡l¾
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10/conv3.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10/conv3.bin
deleted file mode 100644
index bb59b47987bd796b22faa02b6bf9508c166ef2e0..0000000000000000000000000000000000000000
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deleted file mode 100644
index e1f7a16ed65bdd9e40a4c96b6310e2da605df9c8..0000000000000000000000000000000000000000
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index 3c5508709a01f293aed21a1ae94fdcf171e706f5..0000000000000000000000000000000000000000
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index 89c50f51bb3f9e9cbd3e9082d24be1bd460cdb9b..0000000000000000000000000000000000000000
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index 6d43fb53fe93405422aa09da68b22e41eaec615b..0000000000000000000000000000000000000000
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index 5220270e4aae3adf3c7b6c24a1480f595b036fc8..0000000000000000000000000000000000000000
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index 5425f434ba456b735482110d641067a645ee0843..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10/fc1.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10/fc1.bin
deleted file mode 100644
index 2dc81917a1267633a1c7ffa37b877126b1272b2c..0000000000000000000000000000000000000000
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deleted file mode 100644
index 22a3fb3a7a21e4966d7bdb701338f9ad241c4384..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10/fc1_bias.bin
+++ /dev/null
@@ -1 +0,0 @@
-g¡b½âdÖ>Šé?7Å>*8{¾`V9=S– ¿ŽC½u´ë½¾_¾
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10/norm_cifar_input.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10/norm_cifar_input.bin
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index 8b244c89d3503750d6fe23e82cda5493ef3a259e..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10/test_labels.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10/test_labels.bin
deleted file mode 100644
index 7172750913a297f331af9ba88bce0d3e49968d47..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/approxhpvm_src.cc b/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/approxhpvm_src.cc
deleted file mode 100644
index 0b241dbc4efd53909da1f0f5046c9794d23314b1..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/approxhpvm_src.cc
+++ /dev/null
@@ -1,500 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/stat.h>
-#include <cstring>
-#include <visc.h>
-#include <tensorTypes.h>
-#include <tensorUtils.h>
-
-void var_0_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_1_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_2_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_tanh(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_3_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_4_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_5_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_tanh(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_6_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_7_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_8_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_9_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_tanh(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_10_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_11_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_12_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_tanh(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_13_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_14_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_15_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_16_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_tanh(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_17_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_18_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_19_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_tanh(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_20_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_21_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_mul(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_22_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_23_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_softmax(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void root(void* input, size_t input_bytes,
- void* conv2d_1_w, size_t conv2d_1_w_bytes,
- void* conv2d_1_b, size_t conv2d_1_b_bytes,
- void* conv2d_2_w, size_t conv2d_2_w_bytes,
- void* conv2d_2_b, size_t conv2d_2_b_bytes,
- void* conv2d_3_w, size_t conv2d_3_w_bytes,
- void* conv2d_3_b, size_t conv2d_3_b_bytes,
- void* conv2d_4_w, size_t conv2d_4_w_bytes,
- void* conv2d_4_b, size_t conv2d_4_b_bytes,
- void* conv2d_5_w, size_t conv2d_5_w_bytes,
- void* conv2d_5_b, size_t conv2d_5_b_bytes,
- void* conv2d_6_w, size_t conv2d_6_w_bytes,
- void* conv2d_6_b, size_t conv2d_6_b_bytes,
- void* dense_1_w, size_t dense_1_w_bytes,
- void* dense_1_b, size_t dense_1_b_bytes){
-
-
- __visc__hint(visc::CPU_TARGET);
- __visc__attributes(15, input, conv2d_1_w, conv2d_1_b, conv2d_2_w, conv2d_2_b, conv2d_3_w, conv2d_3_b, conv2d_4_w, conv2d_4_b, conv2d_5_w, conv2d_5_b, conv2d_6_w, conv2d_6_b, dense_1_w, dense_1_b, 0);
-
-
- void* var_0 = __visc__createNodeND(0, var_0_node);
-
- __visc__bindIn(var_0, 0, 0, 0);
- __visc__bindIn(var_0, 1, 1, 0);
- __visc__bindIn(var_0, 2, 2, 0);
- __visc__bindIn(var_0, 3, 3, 0);
-
- void* var_1 = __visc__createNodeND(0, var_1_node);
-
- __visc__edge(var_0, var_1, 1, 0, 0, 0);
- __visc__edge(var_0, var_1, 1, 1, 1, 0);
- __visc__bindIn(var_1, 4, 2, 0);
- __visc__bindIn(var_1, 5, 3, 0);
-
- void* var_2 = __visc__createNodeND(0, var_2_node);
-
- __visc__edge(var_1, var_2, 1, 0, 0, 0);
- __visc__edge(var_1, var_2, 1, 1, 1, 0);
-
- void* var_3 = __visc__createNodeND(0, var_3_node);
-
- __visc__edge(var_2, var_3, 1, 0, 0, 0);
- __visc__edge(var_2, var_3, 1, 1, 1, 0);
- __visc__bindIn(var_3, 6, 2, 0);
- __visc__bindIn(var_3, 7, 3, 0);
-
- void* var_4 = __visc__createNodeND(0, var_4_node);
-
- __visc__edge(var_3, var_4, 1, 0, 0, 0);
- __visc__edge(var_3, var_4, 1, 1, 1, 0);
- __visc__bindIn(var_4, 8, 2, 0);
- __visc__bindIn(var_4, 9, 3, 0);
-
- void* var_5 = __visc__createNodeND(0, var_5_node);
-
- __visc__edge(var_4, var_5, 1, 0, 0, 0);
- __visc__edge(var_4, var_5, 1, 1, 1, 0);
-
- void* var_6 = __visc__createNodeND(0, var_6_node);
-
- __visc__edge(var_5, var_6, 1, 0, 0, 0);
- __visc__edge(var_5, var_6, 1, 1, 1, 0);
-
- void* var_7 = __visc__createNodeND(0, var_7_node);
-
- __visc__edge(var_6, var_7, 1, 0, 0, 0);
- __visc__edge(var_6, var_7, 1, 1, 1, 0);
- __visc__bindIn(var_7, 10, 2, 0);
- __visc__bindIn(var_7, 11, 3, 0);
-
- void* var_8 = __visc__createNodeND(0, var_8_node);
-
- __visc__edge(var_7, var_8, 1, 0, 0, 0);
- __visc__edge(var_7, var_8, 1, 1, 1, 0);
- __visc__bindIn(var_8, 12, 2, 0);
- __visc__bindIn(var_8, 13, 3, 0);
-
- void* var_9 = __visc__createNodeND(0, var_9_node);
-
- __visc__edge(var_8, var_9, 1, 0, 0, 0);
- __visc__edge(var_8, var_9, 1, 1, 1, 0);
-
- void* var_10 = __visc__createNodeND(0, var_10_node);
-
- __visc__edge(var_9, var_10, 1, 0, 0, 0);
- __visc__edge(var_9, var_10, 1, 1, 1, 0);
- __visc__bindIn(var_10, 14, 2, 0);
- __visc__bindIn(var_10, 15, 3, 0);
-
- void* var_11 = __visc__createNodeND(0, var_11_node);
-
- __visc__edge(var_10, var_11, 1, 0, 0, 0);
- __visc__edge(var_10, var_11, 1, 1, 1, 0);
- __visc__bindIn(var_11, 16, 2, 0);
- __visc__bindIn(var_11, 17, 3, 0);
-
- void* var_12 = __visc__createNodeND(0, var_12_node);
-
- __visc__edge(var_11, var_12, 1, 0, 0, 0);
- __visc__edge(var_11, var_12, 1, 1, 1, 0);
-
- void* var_13 = __visc__createNodeND(0, var_13_node);
-
- __visc__edge(var_12, var_13, 1, 0, 0, 0);
- __visc__edge(var_12, var_13, 1, 1, 1, 0);
-
- void* var_14 = __visc__createNodeND(0, var_14_node);
-
- __visc__edge(var_13, var_14, 1, 0, 0, 0);
- __visc__edge(var_13, var_14, 1, 1, 1, 0);
- __visc__bindIn(var_14, 18, 2, 0);
- __visc__bindIn(var_14, 19, 3, 0);
-
- void* var_15 = __visc__createNodeND(0, var_15_node);
-
- __visc__edge(var_14, var_15, 1, 0, 0, 0);
- __visc__edge(var_14, var_15, 1, 1, 1, 0);
- __visc__bindIn(var_15, 20, 2, 0);
- __visc__bindIn(var_15, 21, 3, 0);
-
- void* var_16 = __visc__createNodeND(0, var_16_node);
-
- __visc__edge(var_15, var_16, 1, 0, 0, 0);
- __visc__edge(var_15, var_16, 1, 1, 1, 0);
-
- void* var_17 = __visc__createNodeND(0, var_17_node);
-
- __visc__edge(var_16, var_17, 1, 0, 0, 0);
- __visc__edge(var_16, var_17, 1, 1, 1, 0);
- __visc__bindIn(var_17, 22, 2, 0);
- __visc__bindIn(var_17, 23, 3, 0);
-
- void* var_18 = __visc__createNodeND(0, var_18_node);
-
- __visc__edge(var_17, var_18, 1, 0, 0, 0);
- __visc__edge(var_17, var_18, 1, 1, 1, 0);
- __visc__bindIn(var_18, 24, 2, 0);
- __visc__bindIn(var_18, 25, 3, 0);
-
- void* var_19 = __visc__createNodeND(0, var_19_node);
-
- __visc__edge(var_18, var_19, 1, 0, 0, 0);
- __visc__edge(var_18, var_19, 1, 1, 1, 0);
-
- void* var_20 = __visc__createNodeND(0, var_20_node);
-
- __visc__edge(var_19, var_20, 1, 0, 0, 0);
- __visc__edge(var_19, var_20, 1, 1, 1, 0);
-
- void* var_21 = __visc__createNodeND(0, var_21_node);
-
- __visc__edge(var_20, var_21, 1, 0, 0, 0);
- __visc__edge(var_20, var_21, 1, 1, 1, 0);
- __visc__bindIn(var_21, 26, 2, 0);
- __visc__bindIn(var_21, 27, 3, 0);
-
- void* var_22 = __visc__createNodeND(0, var_22_node);
-
- __visc__edge(var_21, var_22, 1, 0, 0, 0);
- __visc__edge(var_21, var_22, 1, 1, 1, 0);
- __visc__bindIn(var_22, 28, 2, 0);
- __visc__bindIn(var_22, 29, 3, 0);
-
- void* var_23 = __visc__createNodeND(0, var_23_node);
-
- __visc__edge(var_22, var_23, 1, 0, 0, 0);
- __visc__edge(var_22, var_23, 1, 1, 1, 0);
-
- __visc__bindOut(var_23, 0, 0, 0);
- __visc__bindOut(var_23, 1, 1, 0);
-
-}
-
-struct ret_t {
- void* tensor;
- size_t bytes;
-};
-
-typedef struct __attribute__((__packed__)) {
- void* input;
- size_t input_bytes;
- void* conv2d_1_w;
- size_t conv2d_1_w_bytes;
- void* conv2d_1_b;
- size_t conv2d_1_b_bytes;
- void* conv2d_2_w;
- size_t conv2d_2_w_bytes;
- void* conv2d_2_b;
- size_t conv2d_2_b_bytes;
- void* conv2d_3_w;
- size_t conv2d_3_w_bytes;
- void* conv2d_3_b;
- size_t conv2d_3_b_bytes;
- void* conv2d_4_w;
- size_t conv2d_4_w_bytes;
- void* conv2d_4_b;
- size_t conv2d_4_b_bytes;
- void* conv2d_5_w;
- size_t conv2d_5_w_bytes;
- void* conv2d_5_b;
- size_t conv2d_5_b_bytes;
- void* conv2d_6_w;
- size_t conv2d_6_w_bytes;
- void* conv2d_6_b;
- size_t conv2d_6_b_bytes;
- void* dense_1_w;
- size_t dense_1_w_bytes;
- void* dense_1_b;
- size_t dense_1_b_bytes;
-
- struct ret_t r;
-}
-RootIn;
-
-int main(){
-
-std::string dir_prefix = std::string("alexnet2_cifar10_test/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,64,32,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,128,64,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,128,128,3,3);
-std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
-void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,128,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,2048,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-
-__visc__init();
-RootIn* args = static_cast<RootIn*>(malloc(sizeof(RootIn)));
-
-args->input = input;
-args->input_bytes = 0;
-args->conv2d_1_w = conv2d_1_w;
-args->conv2d_1_w_bytes = 0;
-args->conv2d_1_b = conv2d_1_b;
-args->conv2d_1_b_bytes = 0;
-args->conv2d_2_w = conv2d_2_w;
-args->conv2d_2_w_bytes = 0;
-args->conv2d_2_b = conv2d_2_b;
-args->conv2d_2_b_bytes = 0;
-args->conv2d_3_w = conv2d_3_w;
-args->conv2d_3_w_bytes = 0;
-args->conv2d_3_b = conv2d_3_b;
-args->conv2d_3_b_bytes = 0;
-args->conv2d_4_w = conv2d_4_w;
-args->conv2d_4_w_bytes = 0;
-args->conv2d_4_b = conv2d_4_b;
-args->conv2d_4_b_bytes = 0;
-args->conv2d_5_w = conv2d_5_w;
-args->conv2d_5_w_bytes = 0;
-args->conv2d_5_b = conv2d_5_b;
-args->conv2d_5_b_bytes = 0;
-args->conv2d_6_w = conv2d_6_w;
-args->conv2d_6_w_bytes = 0;
-args->conv2d_6_b = conv2d_6_b;
-args->conv2d_6_b_bytes = 0;
-args->dense_1_w = dense_1_w;
-args->dense_1_w_bytes = 0;
-args->dense_1_b = dense_1_b;
-args->dense_1_b_bytes = 0;
-
-void* dfg = __visc__launch(0, root, (void*) args);
-
-__visc__wait(dfg);
-
-void *result = static_cast<RootIn*>(args)->input;
-hpvm_request_tensor(result, 0);
-
-__visc__cleanup();
- computeAccuracy2(labels, 10000, result);
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/conv2d_1_b.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/conv2d_1_b.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/conv2d_1_w.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/conv2d_1_w.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/conv2d_2_b.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/conv2d_2_b.bin
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--- a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/conv2d_2_b.bin
+++ /dev/null
@@ -1 +0,0 @@
- !¶¾Â®€¾)`…¾àDŠ¾ d¾([¾A6…¾4€¾òû”¾^Ød¾å㤾ªf¾t¾)\¾ñdv¾6EH¾zíR¾!†¾a¾;¾4°{¾óÉU¾ïbH¾ÏJ¾»ÿ<¾˜2‘¾Ü`¾{Hw¾”Wd¾Øö0¾ÏÃ`¾ž‡l¾
\ No newline at end of file
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/conv2d_5_w.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/conv2d_5_w.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/conv2d_6_b.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/conv2d_6_b.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/conv2d_6_w.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/conv2d_6_w.bin
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index 5220270e4aae3adf3c7b6c24a1480f595b036fc8..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/dense_1_b.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/dense_1_b.bin
deleted file mode 100644
index 22a3fb3a7a21e4966d7bdb701338f9ad241c4384..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/dense_1_b.bin
+++ /dev/null
@@ -1 +0,0 @@
-g¡b½âdÖ>Šé?7Å>*8{¾`V9=S– ¿ŽC½u´ë½¾_¾
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/dense_1_w.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/dense_1_w.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/input.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/input.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/labels.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/labels.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/labels32.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/labels32.bin
deleted file mode 100644
index 0f344f699707b2e15c4364f369235ca997930dfa..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/layer_composition.txt b/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/layer_composition.txt
deleted file mode 100644
index 793ca7fcaf00561403ac65ecc0ef5f9a1efb8c43..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/layer_composition.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-conv add activation
-conv add activation pool
-conv add activation
-conv add activation pool
-conv add activation
-conv add activation pool
-dense add
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/layers.txt b/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/layers.txt
deleted file mode 100644
index c179b38f70acd66fedfabc434ce3a637b5c9bf7a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/layers.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-Conv1,10000,3,32,32,32,3,3,3
-Conv2,10000,32,32,32,32,32,3,3
-Conv3,10000,32,16,16,64,32,3,3
-Conv4,10000,64,16,16,64,64,3,3
-Conv5,10000,64,8,8,128,64,3,3
-Conv6,10000,128,8,8,128,128,3,3
-FC1,10000,2048,2048,10
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/promise_src.cc b/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/promise_src.cc
deleted file mode 100644
index b40e93d759c095c397c60ea4e4ee5bbf1ce512a5..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/promise_src.cc
+++ /dev/null
@@ -1,98 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-int total_runs = 100;
-for (int i = 0 ; i < total_runs; i++){
-
-
-startMemTracking();
-
-int test_input_size = 10000;
-int batch_size = 10000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-
-
-std::string dir_prefix = std::string("alexnet2_cifar10_test/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,64,32,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,128,64,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,128,128,3,3);
-std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
-void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,128,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,2048,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = ConvLayer_PROMISE(input, -1.8816435, 2.0934134, conv2d_1_w, -0.5421946, 0.3710851, conv2d_1_b, -0.06697306, 0.040868897, 1, 1, 1, 1, -1, 0, 0, -0.9998477, 0.99987465, 9);
-void* var_1 = ConvLayer_PROMISE(var_0, -0.9998477, 0.99987465, conv2d_2_w, -0.42474225, 0.31460348, conv2d_2_b, -0.3557253, -0.17281663, 1, 1, 1, 1, 0, 2, 0, -0.99997115, 1.0, 9);
-void* var_2 = ConvLayer_PROMISE(var_1, -0.99997115, 1.0, conv2d_3_w, -0.44134507, 0.79587924, conv2d_3_b, -0.80424446, 0.75330096, 1, 1, 1, 1, -1, 0, 0, -0.9999999, 1.0, 9);
-void* var_3 = ConvLayer_PROMISE(var_2, -0.9999999, 1.0, conv2d_4_w, -0.2883836, 0.31025785, conv2d_4_b, -0.6353164, 0.29015934, 1, 1, 1, 1, 0, 2, 0, -0.9999999, 0.99999934, 9);
-void* var_4 = ConvLayer_PROMISE(var_3, -0.9999999, 0.99999934, conv2d_5_w, -0.2792431, 0.37689754, conv2d_5_b, -1.1379756, 1.2391574, 1, 1, 1, 1, -1, 0, 0, -1.0, 1.0, 9);
-void* var_5 = ConvLayer_PROMISE(var_4, -1.0, 1.0, conv2d_6_w, -0.27078503, 0.27942517, conv2d_6_b, -0.503003, 0.12762362, 1, 1, 1, 1, 0, 2, 0, -0.9999941, 0.9999964, 9);
-void* var_6 = FCLayer_PROMISE(var_5, -0.9999941, 0.9999964, dense_1_w, -0.24273404, 0.5845544, dense_1_b, -0.53745, 0.558251, -1, -140.6419, 16.402884, 9);
-void* var_7 = tensorSoftmax(var_6);
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy2(labels, batch_size, var_7);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-}
-
-dumpExecutionAccuracies();
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/src.cc b/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/src.cc
deleted file mode 100644
index 097db4657f2cd5b8002f5965a12b178b55bfeb84..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/alexnet2_cifar10_test/src.cc
+++ /dev/null
@@ -1,106 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-std::string dir_prefix = std::string("alexnet2_cifar10_test/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,64,32,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,128,64,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,128,128,3,3);
-std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
-void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,128,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,2048,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-
-startMemTracking();
-
-int test_input_size = 10000;
-int batch_size = 10000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
-void* var_1 = tensorAdd(var_0, conv2d_1_b);
-void* var_2 = tensorTanh(var_1);
-void* var_3 = tensorConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0);
-void* var_4 = tensorAdd(var_3, conv2d_2_b);
-void* var_5 = tensorTanh(var_4);
-void* var_6 = tensorPooling(var_5,0,2,2,0,0,2,2);
-void* var_8 = tensorConvolution(var_6, conv2d_3_w, 1, 1, 1, 1, 1, 0);
-void* var_9 = tensorAdd(var_8, conv2d_3_b);
-void* var_10 = tensorTanh(var_9);
-void* var_11 = tensorConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0);
-void* var_12 = tensorAdd(var_11, conv2d_4_b);
-void* var_13 = tensorTanh(var_12);
-void* var_14 = tensorPooling(var_13,0,2,2,0,0,2,2);
-void* var_16 = tensorConvolution(var_14, conv2d_5_w, 1, 1, 1, 1, 1, 0);
-void* var_17 = tensorAdd(var_16, conv2d_5_b);
-void* var_18 = tensorTanh(var_17);
-void* var_19 = tensorConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0);
-void* var_20 = tensorAdd(var_19, conv2d_6_b);
-void* var_21 = tensorTanh(var_20);
-void* var_22 = tensorPooling(var_21,0,2,2,0,0,2,2);
-void* var_25 = tensorGemmGPU(var_22, dense_1_w);
-void* var_26 = tensorAdd(var_25, dense_1_b);
-void* var_27 = tensorSoftmax(var_26);
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy2(labels, batch_size, var_27);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/backup/conv1.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/backup/conv1.bin
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index 2fb2657fc96895b4d95b697c20a9df72b3c99787..0000000000000000000000000000000000000000
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deleted file mode 100644
index caff7117a5e002b2a4877b8fe70b4b01328f6e26..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/backup/fc1_bias.bin
+++ /dev/null
@@ -1 +0,0 @@
-”=[=»=æ9ËÍz= Ù£<Œ‚<Ž£¼¼S»j1%½CÅÓ»†ÍE½
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/backup/norm_cifar_input.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/backup/norm_cifar_input.bin
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deleted file mode 100644
index 7b633f3e1c9ba5063f43797a178046586b4b291c..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/conv1_bias.bin
+++ /dev/null
@@ -1,5 +0,0 @@
-§¿Uïò¾cÉ¿ØÙ=¹£Y=ù>a‹+¿}å¾…á˾‚?—¾ Ì>
Û7¿Á…#¿ÀO\=ˆš9¿È"¿¬Õ?Jþݾ Ïë¾¢Lѽð)¿æÅþ¾‡¿a9¿>î%¿’.?H¶¿Ð
-D¿Õ ¿¬‹ ? a4¾šî¾º¿zûâ½@Jz¾C¡À¾ >Ì»¾ë®=ë-¾aû>w\¬>f
-ý¾h"¿=¾Ÿ¾@*¿¸;¾Ó)±¾ìÇ ¿Ç¼‡>!
-¿‹ ¿Áó&¾ªî¾H 6>?î)¿aêö¾*¿±/ÿ¾Okž۾ó1¿‡l¥¾fz
-¿
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/conv2.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/conv2.bin
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index 3f841c276156bfc688514aeb58bb3544765ebfe2..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/fc1_bias.bin
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-öñ¹=—>ª<Á˜ >2JÉ< [½ZOÒ»’n€½`f ½²u2½ì‚€½
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index d4981bfaed00dc48e9ef5fe7d77dda9163e64ce1..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_front/fc_bias12.bin
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-ÿº="į<vÙ=ðJ=/
-P½xcµ»"<“½äKH½@kV¼ÃÊ/½
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_front/input.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_front/input.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_front/src.cc b/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_front/src.cc
deleted file mode 100644
index 5bf0f36e985ab8603847fa077c5b0d7571b693c8..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_front/src.cc
+++ /dev/null
@@ -1,76 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-std::string dir_prefix = std::string("alexnet_cifar10_front/");
-std::string input_path = dir_prefix + std::string("input.bin");
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
-std::string labels_path = dir_prefix + std::string("labels.bin");
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-std::string conv2d_1_w_path = dir_prefix + std::string("conv0.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,11,11);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv_bias0.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv3.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,192,64,5,5);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv_bias3.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,192,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv6.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,384,192,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv_bias6.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,384,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv7.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,256,384,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv_bias7.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv8.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv_bias8.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("fc12.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,4096,10);
-std::string dense_1_b_path = dir_prefix + std::string("fc_bias12.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-void* var_0 = tensorConvolution(input, conv2d_1_w, 5, 5, 1, 1, 1, 0);
-void* var_1 = tensorAdd(var_0, conv2d_1_b);
-void* var_2 = tensorTanh(var_1);
-void* var_3 = tensorPooling(var_2,0,2,2,0,0,2,2);
-void* var_5 = tensorConvolution(var_3, conv2d_2_w, 2, 2, 1, 1, 1, 0);
-void* var_6 = tensorAdd(var_5, conv2d_2_b);
-void* var_7 = tensorTanh(var_6);
-void* var_8 = tensorPooling(var_7,0,2,2,0,0,2,2);
-void* var_10 = tensorConvolution(var_8, conv2d_3_w, 1, 1, 1, 1, 1, 0);
-void* var_11 = tensorAdd(var_10, conv2d_3_b);
-void* var_12 = tensorTanh(var_11);
-void* var_13 = tensorConvolution(var_12, conv2d_4_w, 1, 1, 1, 1, 1, 0);
-void* var_14 = tensorAdd(var_13, conv2d_4_b);
-void* var_15 = tensorTanh(var_14);
-void* var_16 = tensorConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
-void* var_17 = tensorAdd(var_16, conv2d_5_b);
-void* var_18 = tensorTanh(var_17);
-void* var_19 = tensorPooling(var_18,0,2,2,0,0,2,2);
-void* var_22 = tensorGemmGPU(var_19, dense_1_w);
-void* var_23 = tensorAdd(var_22, dense_1_b);
-void* var_24 = tensorSoftmax(var_23);
-
-computeAccuracy2(labels,10000,var_24);
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_test/approxhpvm_src.cc b/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_test/approxhpvm_src.cc
deleted file mode 100644
index 6255f6af32362abd1b37fa4409046410eefcd030..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_test/approxhpvm_src.cc
+++ /dev/null
@@ -1,443 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/stat.h>
-#include <cstring>
-#include <visc.h>
-#include <tensorTypes.h>
-#include <tensorUtils.h>
-
-void var_0_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 5, 5, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_1_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_2_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_tanh(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_3_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_4_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 2, 2, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_5_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_6_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_tanh(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_7_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_8_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_9_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_10_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_tanh(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_11_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_12_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_13_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_tanh(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_14_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_15_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_16_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_tanh(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_17_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_18_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_mul(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_19_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_20_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_softmax(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void root(void* input, size_t input_bytes,
- void* conv2d_1_w, size_t conv2d_1_w_bytes,
- void* conv2d_1_b, size_t conv2d_1_b_bytes,
- void* conv2d_2_w, size_t conv2d_2_w_bytes,
- void* conv2d_2_b, size_t conv2d_2_b_bytes,
- void* conv2d_3_w, size_t conv2d_3_w_bytes,
- void* conv2d_3_b, size_t conv2d_3_b_bytes,
- void* conv2d_4_w, size_t conv2d_4_w_bytes,
- void* conv2d_4_b, size_t conv2d_4_b_bytes,
- void* conv2d_5_w, size_t conv2d_5_w_bytes,
- void* conv2d_5_b, size_t conv2d_5_b_bytes,
- void* dense_1_w, size_t dense_1_w_bytes,
- void* dense_1_b, size_t dense_1_b_bytes){
-
-
- __visc__hint(visc::CPU_TARGET);
- __visc__attributes(13, input, conv2d_1_w, conv2d_1_b, conv2d_2_w, conv2d_2_b, conv2d_3_w, conv2d_3_b, conv2d_4_w, conv2d_4_b, conv2d_5_w, conv2d_5_b, dense_1_w, dense_1_b, 0);
-
-
- void* var_0 = __visc__createNodeND(0, var_0_node);
-
- __visc__bindIn(var_0, 0, 0, 0);
- __visc__bindIn(var_0, 1, 1, 0);
- __visc__bindIn(var_0, 2, 2, 0);
- __visc__bindIn(var_0, 3, 3, 0);
-
- void* var_1 = __visc__createNodeND(0, var_1_node);
-
- __visc__edge(var_0, var_1, 1, 0, 0, 0);
- __visc__edge(var_0, var_1, 1, 1, 1, 0);
- __visc__bindIn(var_1, 4, 2, 0);
- __visc__bindIn(var_1, 5, 3, 0);
-
- void* var_2 = __visc__createNodeND(0, var_2_node);
-
- __visc__edge(var_1, var_2, 1, 0, 0, 0);
- __visc__edge(var_1, var_2, 1, 1, 1, 0);
-
- void* var_3 = __visc__createNodeND(0, var_3_node);
-
- __visc__edge(var_2, var_3, 1, 0, 0, 0);
- __visc__edge(var_2, var_3, 1, 1, 1, 0);
-
- void* var_4 = __visc__createNodeND(0, var_4_node);
-
- __visc__edge(var_3, var_4, 1, 0, 0, 0);
- __visc__edge(var_3, var_4, 1, 1, 1, 0);
- __visc__bindIn(var_4, 6, 2, 0);
- __visc__bindIn(var_4, 7, 3, 0);
-
- void* var_5 = __visc__createNodeND(0, var_5_node);
-
- __visc__edge(var_4, var_5, 1, 0, 0, 0);
- __visc__edge(var_4, var_5, 1, 1, 1, 0);
- __visc__bindIn(var_5, 8, 2, 0);
- __visc__bindIn(var_5, 9, 3, 0);
-
- void* var_6 = __visc__createNodeND(0, var_6_node);
-
- __visc__edge(var_5, var_6, 1, 0, 0, 0);
- __visc__edge(var_5, var_6, 1, 1, 1, 0);
-
- void* var_7 = __visc__createNodeND(0, var_7_node);
-
- __visc__edge(var_6, var_7, 1, 0, 0, 0);
- __visc__edge(var_6, var_7, 1, 1, 1, 0);
-
- void* var_8 = __visc__createNodeND(0, var_8_node);
-
- __visc__edge(var_7, var_8, 1, 0, 0, 0);
- __visc__edge(var_7, var_8, 1, 1, 1, 0);
- __visc__bindIn(var_8, 10, 2, 0);
- __visc__bindIn(var_8, 11, 3, 0);
-
- void* var_9 = __visc__createNodeND(0, var_9_node);
-
- __visc__edge(var_8, var_9, 1, 0, 0, 0);
- __visc__edge(var_8, var_9, 1, 1, 1, 0);
- __visc__bindIn(var_9, 12, 2, 0);
- __visc__bindIn(var_9, 13, 3, 0);
-
- void* var_10 = __visc__createNodeND(0, var_10_node);
-
- __visc__edge(var_9, var_10, 1, 0, 0, 0);
- __visc__edge(var_9, var_10, 1, 1, 1, 0);
-
- void* var_11 = __visc__createNodeND(0, var_11_node);
-
- __visc__edge(var_10, var_11, 1, 0, 0, 0);
- __visc__edge(var_10, var_11, 1, 1, 1, 0);
- __visc__bindIn(var_11, 14, 2, 0);
- __visc__bindIn(var_11, 15, 3, 0);
-
- void* var_12 = __visc__createNodeND(0, var_12_node);
-
- __visc__edge(var_11, var_12, 1, 0, 0, 0);
- __visc__edge(var_11, var_12, 1, 1, 1, 0);
- __visc__bindIn(var_12, 16, 2, 0);
- __visc__bindIn(var_12, 17, 3, 0);
-
- void* var_13 = __visc__createNodeND(0, var_13_node);
-
- __visc__edge(var_12, var_13, 1, 0, 0, 0);
- __visc__edge(var_12, var_13, 1, 1, 1, 0);
-
- void* var_14 = __visc__createNodeND(0, var_14_node);
-
- __visc__edge(var_13, var_14, 1, 0, 0, 0);
- __visc__edge(var_13, var_14, 1, 1, 1, 0);
- __visc__bindIn(var_14, 18, 2, 0);
- __visc__bindIn(var_14, 19, 3, 0);
-
- void* var_15 = __visc__createNodeND(0, var_15_node);
-
- __visc__edge(var_14, var_15, 1, 0, 0, 0);
- __visc__edge(var_14, var_15, 1, 1, 1, 0);
- __visc__bindIn(var_15, 20, 2, 0);
- __visc__bindIn(var_15, 21, 3, 0);
-
- void* var_16 = __visc__createNodeND(0, var_16_node);
-
- __visc__edge(var_15, var_16, 1, 0, 0, 0);
- __visc__edge(var_15, var_16, 1, 1, 1, 0);
-
- void* var_17 = __visc__createNodeND(0, var_17_node);
-
- __visc__edge(var_16, var_17, 1, 0, 0, 0);
- __visc__edge(var_16, var_17, 1, 1, 1, 0);
-
- void* var_18 = __visc__createNodeND(0, var_18_node);
-
- __visc__edge(var_17, var_18, 1, 0, 0, 0);
- __visc__edge(var_17, var_18, 1, 1, 1, 0);
- __visc__bindIn(var_18, 22, 2, 0);
- __visc__bindIn(var_18, 23, 3, 0);
-
- void* var_19 = __visc__createNodeND(0, var_19_node);
-
- __visc__edge(var_18, var_19, 1, 0, 0, 0);
- __visc__edge(var_18, var_19, 1, 1, 1, 0);
- __visc__bindIn(var_19, 24, 2, 0);
- __visc__bindIn(var_19, 25, 3, 0);
-
- void* var_20 = __visc__createNodeND(0, var_20_node);
-
- __visc__edge(var_19, var_20, 1, 0, 0, 0);
- __visc__edge(var_19, var_20, 1, 1, 1, 0);
-
- __visc__bindOut(var_20, 0, 0, 0);
- __visc__bindOut(var_20, 1, 1, 0);
-
-}
-
-struct ret_t {
- void* tensor;
- size_t bytes;
-};
-
-typedef struct __attribute__((__packed__)) {
- void* input;
- size_t input_bytes;
- void* conv2d_1_w;
- size_t conv2d_1_w_bytes;
- void* conv2d_1_b;
- size_t conv2d_1_b_bytes;
- void* conv2d_2_w;
- size_t conv2d_2_w_bytes;
- void* conv2d_2_b;
- size_t conv2d_2_b_bytes;
- void* conv2d_3_w;
- size_t conv2d_3_w_bytes;
- void* conv2d_3_b;
- size_t conv2d_3_b_bytes;
- void* conv2d_4_w;
- size_t conv2d_4_w_bytes;
- void* conv2d_4_b;
- size_t conv2d_4_b_bytes;
- void* conv2d_5_w;
- size_t conv2d_5_w_bytes;
- void* conv2d_5_b;
- size_t conv2d_5_b_bytes;
- void* dense_1_w;
- size_t dense_1_w_bytes;
- void* dense_1_b;
- size_t dense_1_b_bytes;
-
- struct ret_t r;
-}
-RootIn;
-
-int main(){
-
-std::string dir_prefix = std::string("alexnet_cifar10_test/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,11,11);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,192,64,5,5);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,192,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,384,192,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,384,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,256,384,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,4096,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-
-__visc__init();
-RootIn* args = static_cast<RootIn*>(malloc(sizeof(RootIn)));
-
-args->input = input;
-args->input_bytes = 0;
-args->conv2d_1_w = conv2d_1_w;
-args->conv2d_1_w_bytes = 0;
-args->conv2d_1_b = conv2d_1_b;
-args->conv2d_1_b_bytes = 0;
-args->conv2d_2_w = conv2d_2_w;
-args->conv2d_2_w_bytes = 0;
-args->conv2d_2_b = conv2d_2_b;
-args->conv2d_2_b_bytes = 0;
-args->conv2d_3_w = conv2d_3_w;
-args->conv2d_3_w_bytes = 0;
-args->conv2d_3_b = conv2d_3_b;
-args->conv2d_3_b_bytes = 0;
-args->conv2d_4_w = conv2d_4_w;
-args->conv2d_4_w_bytes = 0;
-args->conv2d_4_b = conv2d_4_b;
-args->conv2d_4_b_bytes = 0;
-args->conv2d_5_w = conv2d_5_w;
-args->conv2d_5_w_bytes = 0;
-args->conv2d_5_b = conv2d_5_b;
-args->conv2d_5_b_bytes = 0;
-args->dense_1_w = dense_1_w;
-args->dense_1_w_bytes = 0;
-args->dense_1_b = dense_1_b;
-args->dense_1_b_bytes = 0;
-
-void* dfg = __visc__launch(0, root, (void*) args);
-
-__visc__wait(dfg);
-
-void *result = static_cast<RootIn*>(args)->input;
-hpvm_request_tensor(result, 0);
-
-__visc__cleanup();
- computeAccuracy2(labels, 10000, result);
-return 0;
-
-}
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@@ -1,2 +0,0 @@
-ÿº="į<vÙ=ðJ=/
-P½xcµ»"<“½äKH½@kV¼ÃÊ/½
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_test/dense_1_w.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_test/dense_1_w.bin
deleted file mode 100644
index b6b31a7990f166f9e50b273b40e1750244966217..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_test/input.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_test/input.bin
deleted file mode 100644
index 17e72b23c956926964ad3c8a8becc69267cc854d..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_test/labels.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_test/labels.bin
deleted file mode 100644
index 6a9c75fdbe3754a721541269cc6b03d6a0ff3485..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_test/labels32.bin b/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_test/labels32.bin
deleted file mode 100644
index 4c875ae0ae30adbbe79df60ae5f8dc70e21a5ed8..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_test/layer_composition.txt b/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_test/layer_composition.txt
deleted file mode 100644
index f4a3785d2c039705b15d159fa2beb972de4fcb3f..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_test/layer_composition.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-conv add activation pool
-conv add activation pool
-conv add activation
-conv add activation
-conv add activation pool
-dense add
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_test/layers.txt b/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_test/layers.txt
deleted file mode 100644
index 640c28e7cb31bd8032860a5ef63e74495335d94c..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_test/layers.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-Conv1,10000,3,32,32,64,3,11,11
-Conv2,10000,64,16,16,192,64,5,5
-Conv3,10000,192,8,8,384,192,3,3
-Conv4,10000,384,8,8,256,384,3,3
-Conv5,10000,256,8,8,256,256,3,3
-FC1,10000,4096,4096,10
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_test/promise_src.cc b/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_test/promise_src.cc
deleted file mode 100644
index 60b7e1e7661078c0e7791421e0d64aacff60c178..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_test/promise_src.cc
+++ /dev/null
@@ -1,93 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-int total_runs = 100;
-for (int i = 0 ; i < total_runs; i++){
-
-
-startMemTracking();
-
-int test_input_size = 10000;
-int batch_size = 10000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-
-
-std::string dir_prefix = std::string("alexnet_cifar10_test/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,11,11);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,192,64,5,5);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,192,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,384,192,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,384,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,256,384,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,4096,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = ConvLayer_PROMISE(input, -1.8816426241908337, 2.0934095498544254, conv2d_1_w, -0.33087718, 0.3323643, conv2d_1_b, -0.7782218, 0.6020472, 5, 5, 1, 1, 0, 2, 0, -1.0, 1.0, 9);
-void* var_1 = ConvLayer_PROMISE(var_0, -1.0, 1.0, conv2d_2_w, -0.2095158, 0.33543423, conv2d_2_b, -0.45020863, 0.30596754, 2, 2, 1, 1, 0, 2, 0, -1.0, 1.0, 9);
-void* var_2 = ConvLayer_PROMISE(var_1, -1.0, 1.0, conv2d_3_w, -0.1715614, 0.17037082, conv2d_3_b, -0.6519161, 0.5939945, 1, 1, 1, 1, -1, 0, 0, -1.0, 1.0, 9);
-void* var_3 = ConvLayer_PROMISE(var_2, -1.0, 1.0, conv2d_4_w, -0.15575546, 0.14456555, conv2d_4_b, -0.55873865, 0.4704539, 1, 1, 1, 1, -1, 0, 0, -1.0, 1.0, 9);
-void* var_4 = ConvLayer_PROMISE(var_3, -1.0, 1.0, conv2d_5_w, -0.16108225, 0.16864482, conv2d_5_b, -0.22135437, 0.10401678, 1, 1, 1, 1, 0, 2, 0, -1.0, 1.0, 9);
-void* var_5 = FCLayer_PROMISE(var_4, -1.0, 1.0, dense_1_w, -0.18183032, 0.19018902, dense_1_b, -0.07189204, 0.106005594, -1, -30.96493, 44.04829, 9);
-void* var_6 = tensorSoftmax(var_5);
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy2(labels, batch_size, var_6);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-}
-
-dumpExecutionAccuracies();
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_test/src.cc b/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_test/src.cc
deleted file mode 100644
index d972cc0c30bfbf0fc185ac70499a7578e3946572..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10_test/src.cc
+++ /dev/null
@@ -1,99 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-std::string dir_prefix = std::string("alexnet_cifar10_test/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,11,11);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,192,64,5,5);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,192,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,384,192,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,384,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,256,384,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,4096,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-
-startMemTracking();
-
-int test_input_size = 10000;
-int batch_size = 10000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = tensorConvolution(input, conv2d_1_w, 5, 5, 1, 1, 1, 0);
-void* var_1 = tensorAdd(var_0, conv2d_1_b);
-void* var_2 = tensorTanh(var_1);
-void* var_3 = tensorPooling(var_2,0,2,2,0,0,2,2);
-void* var_5 = tensorConvolution(var_3, conv2d_2_w, 2, 2, 1, 1, 1, 0);
-void* var_6 = tensorAdd(var_5, conv2d_2_b);
-void* var_7 = tensorTanh(var_6);
-void* var_8 = tensorPooling(var_7,0,2,2,0,0,2,2);
-void* var_10 = tensorConvolution(var_8, conv2d_3_w, 1, 1, 1, 1, 1, 0);
-void* var_11 = tensorAdd(var_10, conv2d_3_b);
-void* var_12 = tensorTanh(var_11);
-void* var_13 = tensorConvolution(var_12, conv2d_4_w, 1, 1, 1, 1, 1, 0);
-void* var_14 = tensorAdd(var_13, conv2d_4_b);
-void* var_15 = tensorTanh(var_14);
-void* var_16 = tensorConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
-void* var_17 = tensorAdd(var_16, conv2d_5_b);
-void* var_18 = tensorTanh(var_17);
-void* var_19 = tensorPooling(var_18,0,2,2,0,0,2,2);
-void* var_22 = tensorGemmGPU(var_19, dense_1_w);
-void* var_23 = tensorAdd(var_22, dense_1_b);
-void* var_24 = tensorSoftmax(var_23);
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy2(labels, batch_size, var_24);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/cifar_keras/conv1.bin b/llvm/projects/hpvm-tensor-rt/model_params/cifar_keras/conv1.bin
deleted file mode 100644
index 6f71aade324ae87f604c62c4eb15769cfb0d5844..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/cifar_keras/conv1_bias.bin b/llvm/projects/hpvm-tensor-rt/model_params/cifar_keras/conv1_bias.bin
deleted file mode 100644
index a76d1cb8c50dc00f51379359e3bbfcf7709657d9..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/cifar_keras/conv1_bias.bin
+++ /dev/null
@@ -1 +0,0 @@
-Òkc<Ðn‰<QHÓ¼˜¾‚¼f€0:e$<«.¼™Ç£=3b< Ž½Öõ“»· =™r¼ŒN=¯¦ ¹=?=QÊÿ¼l¼¶<“‘/=Dç—=«Þ¦½EœË<™<›<_‹Å:_Jæ<èúeï
;yQ–»`dI½ Õ<|N0=ãÛ¸<
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/cifar_keras/conv2.bin b/llvm/projects/hpvm-tensor-rt/model_params/cifar_keras/conv2.bin
deleted file mode 100644
index 486dcacc3ecb15e2eb62b4b8354e79eebfbfd7dd..0000000000000000000000000000000000000000
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deleted file mode 100644
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deleted file mode 100644
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/cifar_keras/fc2.bin b/llvm/projects/hpvm-tensor-rt/model_params/cifar_keras/fc2.bin
deleted file mode 100644
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deleted file mode 100644
index 10e3aa2b3e50ec1f2211a6214a48e0ddc8345997..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/cifar_keras/fc2_bias.bin
+++ /dev/null
@@ -1,2 +0,0 @@
-w„ ½j‡½”x½›cb½‘*{½€%³½Îœ½
-™½!Én½À ›½
\ No newline at end of file
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-h4Q;¤ ù;34¼j0_½G½–h;ìz/½ðÇÊ:àk¥¼{l½t+O;u¼8™¨¼d »”½®¼}8›<íO’¼äÕ¿»¤#½„ö¼”u<¼¿l…¼f¢;Ð4½ŠO ½>Øž¼7K¼04½ ÎG:à'½ÔOF½M=;
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/lenet_relu/promise_src.cc b/llvm/projects/hpvm-tensor-rt/model_params/lenet_relu/promise_src.cc
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--- a/llvm/projects/hpvm-tensor-rt/model_params/lenet_relu/promise_src.cc
+++ /dev/null
@@ -1,58 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-
-std::string dir_prefix = std::string("lenet_relu/");
-std::string input_path = dir_prefix + std::string("input.bin");
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,1,28,28);
-std::string labels_path = dir_prefix + std::string("labels.bin");
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,1,5,5);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,5,5);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,64,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,3136,1024);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,1024,1,1);
-std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
-void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,1024,10);
-std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
-void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,10,1,1);
-
-
-void* var_0 = ConvLayer_PROMISE(input, 0.0, 1.0, conv2d_1_w, -0.2722561, 0.25817025, conv2d_1_b, -0.041063767, 0.031912163, 2, 2, 1, 1, 0, 2, 1, 0.0, 1.5512946, 9);
-void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 1.5512946, conv2d_2_w, -0.17580177, 0.16332611, conv2d_2_b, -0.041385915, 0.05869476, 2, 2, 1, 1, -1, 0, 1, 0.0, 4.916329, 9);
-void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 4.916329, conv2d_3_w, -0.20324017, 0.18275258, conv2d_3_b, -0.039915435, 0.04589232, 1, 1, 2, 2, -1, 0, 1, 0.0, 9.447418, 9);
-void* var_3 = FCLayer_PROMISE(var_2, 0.0, 9.447418, dense_1_w, -0.10757191, 0.123126, dense_1_b, -0.025070198, 0.027000334, 1, 0.0, 9.926857, 9);
-void* var_4 = FCLayer_PROMISE(var_3, 0.0, 9.926857, dense_2_w, -0.18867673, 0.16425411, dense_2_b, -0.012622595, 0.04586973, 1, 0.0, 42.018578, 9);
-void* var_5 = tensorSoftmax(var_4);
-
-computeAccuracy2(labels,10000,var_5);
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/lenet_relu/src.cc b/llvm/projects/hpvm-tensor-rt/model_params/lenet_relu/src.cc
deleted file mode 100644
index 5524c2edb5396ffd26e9907630b8514f9eb57e42..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/lenet_relu/src.cc
+++ /dev/null
@@ -1,68 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-std::string dir_prefix = std::string("lenet_relu/");
-std::string input_path = dir_prefix + std::string("input.bin");
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,1,28,28);
-std::string labels_path = dir_prefix + std::string("labels.bin");
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,1,5,5);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,5,5);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,64,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,3136,1024);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,1024,1,1);
-std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
-void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,1024,10);
-std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
-void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,10,1,1);
-
-
-void* var_0 = tensorConvolution(input, conv2d_1_w, 2, 2, 1, 1, 1, 0);
-void* var_1 = tensorAdd(var_0, conv2d_1_b);
-void* var_2 = tensorRelu(var_1);
-void* var_3 = tensorPooling(var_2,0,2,2,0,0,2,2);
-void* var_4 = tensorConvolution(var_3, conv2d_2_w, 2, 2, 1, 1, 1, 0);
-void* var_5 = tensorAdd(var_4, conv2d_2_b);
-void* var_6 = tensorRelu(var_5);
-void* var_8 = tensorConvolution(var_6, conv2d_3_w, 1, 1, 2, 2, 1, 0);
-void* var_9 = tensorAdd(var_8, conv2d_3_b);
-void* var_10 = tensorRelu(var_9);
-void* var_12 = tensorGemmGPU(var_10, dense_1_w);
-void* var_13 = tensorAdd(var_12, dense_1_b);
-void* var_14 = tensorRelu(var_13);
-void* var_15 = tensorGemmGPU(var_14, dense_2_w);
-void* var_16 = tensorAdd(var_15, dense_2_b);
-void* var_17 = tensorRelu(var_16);
-void* var_18 = tensorSoftmax(var_17);
-
-computeAccuracy2(labels,10000,var_18);
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh/conv1.bin b/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh/conv1.bin
deleted file mode 100644
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh/conv1_bias.bin b/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh/conv1_bias.bin
deleted file mode 100644
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh/conv2.bin b/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh/conv2.bin
deleted file mode 100644
index 2304c792451e65d7a6f4615060dfc0c90164dc29..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh/conv2_bias.bin b/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh/conv2_bias.bin
deleted file mode 100644
index 08c01b1586c269269d8dc8951afb7cd0c02606b2..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh/conv2_bias.bin
+++ /dev/null
@@ -1,2 +0,0 @@
- è¾#¬¿ÀIº¾¹¾ë ɾ¨9D¾èÍž¾£½fn»>_Ái>ú»«¾PØ >
-þv¾K,,¾az.¾&Ѿc…:¾EÂ¥>\Œ«>œöú½á†]½ÿ%]¾ö¸P>íi<>Ðû\¾¹ã¾åÀ)¾d€™>oÞ½«‡%>ŽÎ9½zNâ½È:>Ù˜\¾¦ºj>vP>ÿ›4> ‚¾ÖDà¾õ§Ð¾¶õ¬>qS¾Œ'á¾:é;zb$>Àƒu>í9w>¦˜n½+Ò¾»‘…¾“ÐS½ª
>јº½b¾#)¸¾’%e>=ä#<Í à¾Ã¾E‰2¾]«Y=r¦³½)*k¾ ,¿
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh/fc1.bin b/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh/fc1.bin
deleted file mode 100644
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deleted file mode 100644
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh/fc2.bin b/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh/fc2.bin
deleted file mode 100644
index 1b567de77acfc62b54ec4a676df8256b07a6b127..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh/fc2_bias.bin b/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh/fc2_bias.bin
deleted file mode 100644
index a7c0eae24cc844613f616fda43cd444c5f506ebf..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh/fc2_bias.bin
+++ /dev/null
@@ -1 +0,0 @@
-µs¾Ó˜>P5>ù>bëÈ=_Ïú½ƒA‚>Äô}> \¾+Nè<
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh/lenet_tanh/conv1.bin b/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh/lenet_tanh/conv1.bin
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deleted file mode 100644
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deleted file mode 100644
index 08c01b1586c269269d8dc8951afb7cd0c02606b2..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh/lenet_tanh/conv2_bias.bin
+++ /dev/null
@@ -1,2 +0,0 @@
- è¾#¬¿ÀIº¾¹¾ë ɾ¨9D¾èÍž¾£½fn»>_Ái>ú»«¾PØ >
-þv¾K,,¾az.¾&Ѿc…:¾EÂ¥>\Œ«>œöú½á†]½ÿ%]¾ö¸P>íi<>Ðû\¾¹ã¾åÀ)¾d€™>oÞ½«‡%>ŽÎ9½zNâ½È:>Ù˜\¾¦ºj>vP>ÿ›4> ‚¾ÖDà¾õ§Ð¾¶õ¬>qS¾Œ'á¾:é;zb$>Àƒu>í9w>¦˜n½+Ò¾»‘…¾“ÐS½ª
>јº½b¾#)¸¾’%e>=ä#<Í à¾Ã¾E‰2¾]«Y=r¦³½)*k¾ ,¿
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh/lenet_tanh/fc1.bin b/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh/lenet_tanh/fc1.bin
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deleted file mode 100644
index a7c0eae24cc844613f616fda43cd444c5f506ebf..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh/lenet_tanh/fc2_bias.bin
+++ /dev/null
@@ -1 +0,0 @@
-µs¾Ó˜>P5>ù>bëÈ=_Ïú½ƒA‚>Äô}> \¾+Nè<
\ No newline at end of file
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index 09c1e72f56a144675d48e5d4969e260100c35ada..0000000000000000000000000000000000000000
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deleted file mode 100644
index 3d4d6e388f22cf825a1e8b434fd34080fc8912e8..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh2/conv2_bias.bin
+++ /dev/null
@@ -1,3 +0,0 @@
-†¹œ½~Ä
-½Î"y½ô£½È ½ès½eªf½tN ¼ ¬½¨X½¨„?¼›þŸ¼ž¯½OÙ3½%§¼ß³H½†À
-½& ½JO0½¶wž½¥:o½ðе¼ý[Z½P½S‚³½ˆé?½Ä *½Š^[»`ì(½Æœ½þã½ÛV®¼¨ü˜½çtƽû¯¸º|Üv½®‰½ó®Ã¼@»¼ïÖŒ½
Hš¼ó‰»µh‹½ux½-N,½«Ú©½c†N»è¡Ž½µ¤@½ÈzÕ¼4™½.·!½ÐÊÀ¼ú® ½c¬Ûº>.½xཽá¼Oû½ý+;¼¿œ•½i‹š½éµ–¼ØGP½
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh2/fc1.bin b/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh2/fc1.bin
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deleted file mode 100644
index 4cb6d824d540d66502b5e7ab0157e567a2d1a300..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/lenet_tanh2/fc2_bias.bin
+++ /dev/null
@@ -1 +0,0 @@
-©|½8Ý;ÿÄ‚½!´½!ζ½ß[“½×ý–½$Ðm½0Œ¦½›V´½
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/lenet_test_params/conv1.bin b/llvm/projects/hpvm-tensor-rt/model_params/lenet_test_params/conv1.bin
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deleted file mode 100644
index bbb78f121d9c2a1ae219a45cc20539a990648186..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/lenet_test_params/conv1_bias.bin
+++ /dev/null
@@ -1 +0,0 @@
-éÃ?”è¿åSª?*?‡% ?’òJ¿a‰€¿CM]¾ ˆÎ>Íž¿Pú€?µ¿ûd»¾® y¿>‰?"™Õ?<ª¾|«ˆ¿Vr³¿~†?_!/?]Ú@B:L¿Ý¼Í>‡Å·¾<J„¾;–©?ek>rËß¾ "N?cPs¿st]¿
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/lenet_test_params/conv2.bin b/llvm/projects/hpvm-tensor-rt/model_params/lenet_test_params/conv2.bin
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--- a/llvm/projects/hpvm-tensor-rt/model_params/lenet_test_params/conv2_bias.bin
+++ /dev/null
@@ -1,2 +0,0 @@
-s+á¾ F§=´ïH¿J›@*G¿Uý±¿-ôA>»¢¿tƒô½q¾Ö¯ƒ>ÒáÙ½lÒ?é…&½æ¾cq}¿ö¼½‹@c÷o= òp?p–‡? ט?&j<ãR??&‚Ó¿w¥¿A…í¿%î>DXÁ¿ŽQ;@½¡£?µ1”?äßÀWp¿{è…¾Ù̦¾-G¿"©ˆ¿vË¥¾8Fd¿ãÔó½‚%?ZnÕ¾7ÍM¿a·K¿ó “<9 /?]/P?|‚H?y5˜¿.A?bï¿-øˆ¿A.Ù?<t¨½&òÀÅ3>óN’¿sÝ
-ÀF+è?‰;?,b«?©ýο‡ºæ>
\ No newline at end of file
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/lenet_test_params/fc2_bias.bin b/llvm/projects/hpvm-tensor-rt/model_params/lenet_test_params/fc2_bias.bin
deleted file mode 100644
index 94a9ea8487c42b6b27d411ab678f64085b12fef4..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/lenet_test_params/fc2_bias.bin
+++ /dev/null
@@ -1 +0,0 @@
-M‡“?#=e?N‹!¿‰ñs¿¸OÕ¿„@\?2±“¿j 3?sÆ¢½º—o¿
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/#layer_composition.txt# b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/#layer_composition.txt#
deleted file mode 100644
index 10692997a90e4490a91ad3d0e6e04285754144fd..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/#layer_composition.txt#
+++ /dev/null
@@ -1,83 +0,0 @@
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-pool
-dense add
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/#layers.txt# b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/#layers.txt#
deleted file mode 100644
index 0bd2b554374c10d748a652f52e5427c716be0084..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/#layers.txt#
+++ /dev/null
@@ -1,83 +0,0 @@
-Conv1,10000,3,32,32,32,3,3,3
-#tensorBatchNorm1
-#tensorRelu1
-#tensorDepthwiseConv1
-#tensorBatchNorm2
-#tensorRelu2
-Conv2,10000,32,32,32,64,32,1,1
-#tensorBatchNorm3
-#tensorRelu3
-#tensorDepthwiseConv2
-#tensorBatchNorm4
-#tensorRelu4
-Conv3,10000,64,16,16,128,64,1,1
-#tensorBatchNorm5
-#tensorRelu5
-#tensorDepthwiseConv3
-#tensorBatchNorm6
-#tensorRelu6
-Conv4,10000,128,16,16,128,128,1,1
-#tensorBatchNorm7
-#tensorRelu7
-#tensorDepthwiseConv4
-#tensorBatchNorm8
-#tensorRelu8
-Conv5,10000,128,8,8,256,128,1,1
-#tensorBatchNorm9
-#tensorRelu9
-#tensorDepthwiseConv5
-#tensorBatchNorm10
-#tensorRelu10
-Conv6,10000,256,8,8,256,256,1,1
-#tensorBatchNorm11
-#tensorRelu11
-#tensorDepthwiseConv6
-#tensorBatchNorm12
-#tensorRelu12
-Conv7,10000,256,4,4,512,256,1,1
-#tensorBatchNorm13
-#tensorRelu13
-#tensorDepthwiseConv7
-#tensorBatchNorm14
-#tensorRelu14
-Conv8,10000,512,4,4,512,512,1,1
-#tensorBatchNorm15
-#tensorRelu15
-#tensorDepthwiseConv8
-#tensorBatchNorm16
-#tensorRelu16
-Conv9,10000,512,4,4,512,512,1,1
-#tensorBatchNorm17
-#tensorRelu17
-#tensorDepthwiseConv9
-#tensorBatchNorm18
-#tensorRelu18
-Conv10,10000,512,4,4,512,512,1,1
-#tensorBatchNorm19
-#tensorRelu19
-#tensorDepthwiseConv10
-#tensorBatchNorm20
-#tensorRelu20
-Conv11,10000,512,4,4,512,512,1,1
-#tensorBatchNorm21
-#tensorRelu21
-#tensorDepthwiseConv11
-#tensorBatchNorm22
-#tensorRelu22
-Conv12,10000,512,4,4,512,512,1,1
-#tensorBatchNorm23
-#tensorRelu23
-#tensorDepthwiseConv12
-#tensorBatchNorm24
-#tensorRelu24
-Conv13,10000,512,2,2,1024,512,1,1
-#tensorBatchNorm25
-#tensorRelu25
-#tensorDepthwiseConv13
-#tensorBatchNorm26
-#tensorRelu26
-Conv14,10000,1024,2,2,1024,1024,1,1
-#tensorBatchNorm27
-#tensorRelu27
-#tensorPooling1
-FC1,10000,1024,1024,10
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/approxhpvm_src.cc b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/approxhpvm_src.cc
deleted file mode 100644
index 5089eb912bcb5335c96c04f6d98f5d17ab761c72..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/approxhpvm_src.cc
+++ /dev/null
@@ -1,2400 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/stat.h>
-#include <cstring>
-#include <visc.h>
-#include <tensorTypes.h>
-#include <tensorUtils.h>
-
-void var_0_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_1_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_2_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_3_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 32);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_4_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_5_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_6_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_7_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_8_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_9_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 2, 2, 1, 64);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_10_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_11_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_12_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_13_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_14_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_15_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 128);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_16_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_17_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_18_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_19_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_20_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_21_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 2, 2, 1, 128);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_22_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_23_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_24_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_25_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_26_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_27_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 256);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_28_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_29_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_30_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_31_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_32_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_33_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 2, 2, 1, 256);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_34_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_35_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_36_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_37_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_38_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_39_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 512);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_40_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_41_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_42_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_43_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_44_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_45_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 512);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_46_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_47_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_48_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_49_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_50_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_51_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 512);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_52_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_53_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_54_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_55_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_56_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_57_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 512);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_58_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_59_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_60_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_61_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_62_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_63_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 512);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_64_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_65_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_66_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_67_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_68_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_69_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 2, 2, 1, 512);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_70_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_71_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_72_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_73_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_74_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_75_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 1024);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_76_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_77_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_78_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_79_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_80_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_81_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_avg(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_82_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_mul(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_83_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_84_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_softmax(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void root(void* input, size_t input_bytes,
- void* conv2d_1_w, size_t conv2d_1_w_bytes,
- void* batch_normalization_1_gamma, size_t batch_normalization_1_gamma_bytes,
- void* batch_normalization_1_beta, size_t batch_normalization_1_beta_bytes,
- void* batch_normalization_1_mean, size_t batch_normalization_1_mean_bytes,
- void* batch_normalization_1_variance, size_t batch_normalization_1_variance_bytes,
- void* depthwise_conv2d_1_w, size_t depthwise_conv2d_1_w_bytes,
- void* batch_normalization_2_gamma, size_t batch_normalization_2_gamma_bytes,
- void* batch_normalization_2_beta, size_t batch_normalization_2_beta_bytes,
- void* batch_normalization_2_mean, size_t batch_normalization_2_mean_bytes,
- void* batch_normalization_2_variance, size_t batch_normalization_2_variance_bytes,
- void* conv2d_2_w, size_t conv2d_2_w_bytes,
- void* batch_normalization_3_gamma, size_t batch_normalization_3_gamma_bytes,
- void* batch_normalization_3_beta, size_t batch_normalization_3_beta_bytes,
- void* batch_normalization_3_mean, size_t batch_normalization_3_mean_bytes,
- void* batch_normalization_3_variance, size_t batch_normalization_3_variance_bytes,
- void* depthwise_conv2d_2_w, size_t depthwise_conv2d_2_w_bytes,
- void* batch_normalization_4_gamma, size_t batch_normalization_4_gamma_bytes,
- void* batch_normalization_4_beta, size_t batch_normalization_4_beta_bytes,
- void* batch_normalization_4_mean, size_t batch_normalization_4_mean_bytes,
- void* batch_normalization_4_variance, size_t batch_normalization_4_variance_bytes,
- void* conv2d_3_w, size_t conv2d_3_w_bytes,
- void* batch_normalization_5_gamma, size_t batch_normalization_5_gamma_bytes,
- void* batch_normalization_5_beta, size_t batch_normalization_5_beta_bytes,
- void* batch_normalization_5_mean, size_t batch_normalization_5_mean_bytes,
- void* batch_normalization_5_variance, size_t batch_normalization_5_variance_bytes,
- void* depthwise_conv2d_3_w, size_t depthwise_conv2d_3_w_bytes,
- void* batch_normalization_6_gamma, size_t batch_normalization_6_gamma_bytes,
- void* batch_normalization_6_beta, size_t batch_normalization_6_beta_bytes,
- void* batch_normalization_6_mean, size_t batch_normalization_6_mean_bytes,
- void* batch_normalization_6_variance, size_t batch_normalization_6_variance_bytes,
- void* conv2d_4_w, size_t conv2d_4_w_bytes,
- void* batch_normalization_7_gamma, size_t batch_normalization_7_gamma_bytes,
- void* batch_normalization_7_beta, size_t batch_normalization_7_beta_bytes,
- void* batch_normalization_7_mean, size_t batch_normalization_7_mean_bytes,
- void* batch_normalization_7_variance, size_t batch_normalization_7_variance_bytes,
- void* depthwise_conv2d_4_w, size_t depthwise_conv2d_4_w_bytes,
- void* batch_normalization_8_gamma, size_t batch_normalization_8_gamma_bytes,
- void* batch_normalization_8_beta, size_t batch_normalization_8_beta_bytes,
- void* batch_normalization_8_mean, size_t batch_normalization_8_mean_bytes,
- void* batch_normalization_8_variance, size_t batch_normalization_8_variance_bytes,
- void* conv2d_5_w, size_t conv2d_5_w_bytes,
- void* batch_normalization_9_gamma, size_t batch_normalization_9_gamma_bytes,
- void* batch_normalization_9_beta, size_t batch_normalization_9_beta_bytes,
- void* batch_normalization_9_mean, size_t batch_normalization_9_mean_bytes,
- void* batch_normalization_9_variance, size_t batch_normalization_9_variance_bytes,
- void* depthwise_conv2d_5_w, size_t depthwise_conv2d_5_w_bytes,
- void* batch_normalization_10_gamma, size_t batch_normalization_10_gamma_bytes,
- void* batch_normalization_10_beta, size_t batch_normalization_10_beta_bytes,
- void* batch_normalization_10_mean, size_t batch_normalization_10_mean_bytes,
- void* batch_normalization_10_variance, size_t batch_normalization_10_variance_bytes,
- void* conv2d_6_w, size_t conv2d_6_w_bytes,
- void* batch_normalization_11_gamma, size_t batch_normalization_11_gamma_bytes,
- void* batch_normalization_11_beta, size_t batch_normalization_11_beta_bytes,
- void* batch_normalization_11_mean, size_t batch_normalization_11_mean_bytes,
- void* batch_normalization_11_variance, size_t batch_normalization_11_variance_bytes,
- void* depthwise_conv2d_6_w, size_t depthwise_conv2d_6_w_bytes,
- void* batch_normalization_12_gamma, size_t batch_normalization_12_gamma_bytes,
- void* batch_normalization_12_beta, size_t batch_normalization_12_beta_bytes,
- void* batch_normalization_12_mean, size_t batch_normalization_12_mean_bytes,
- void* batch_normalization_12_variance, size_t batch_normalization_12_variance_bytes,
- void* conv2d_7_w, size_t conv2d_7_w_bytes,
- void* batch_normalization_13_gamma, size_t batch_normalization_13_gamma_bytes,
- void* batch_normalization_13_beta, size_t batch_normalization_13_beta_bytes,
- void* batch_normalization_13_mean, size_t batch_normalization_13_mean_bytes,
- void* batch_normalization_13_variance, size_t batch_normalization_13_variance_bytes,
- void* depthwise_conv2d_7_w, size_t depthwise_conv2d_7_w_bytes,
- void* batch_normalization_14_gamma, size_t batch_normalization_14_gamma_bytes,
- void* batch_normalization_14_beta, size_t batch_normalization_14_beta_bytes,
- void* batch_normalization_14_mean, size_t batch_normalization_14_mean_bytes,
- void* batch_normalization_14_variance, size_t batch_normalization_14_variance_bytes,
- void* conv2d_8_w, size_t conv2d_8_w_bytes,
- void* batch_normalization_15_gamma, size_t batch_normalization_15_gamma_bytes,
- void* batch_normalization_15_beta, size_t batch_normalization_15_beta_bytes,
- void* batch_normalization_15_mean, size_t batch_normalization_15_mean_bytes,
- void* batch_normalization_15_variance, size_t batch_normalization_15_variance_bytes,
- void* depthwise_conv2d_8_w, size_t depthwise_conv2d_8_w_bytes,
- void* batch_normalization_16_gamma, size_t batch_normalization_16_gamma_bytes,
- void* batch_normalization_16_beta, size_t batch_normalization_16_beta_bytes,
- void* batch_normalization_16_mean, size_t batch_normalization_16_mean_bytes,
- void* batch_normalization_16_variance, size_t batch_normalization_16_variance_bytes,
- void* conv2d_9_w, size_t conv2d_9_w_bytes,
- void* batch_normalization_17_gamma, size_t batch_normalization_17_gamma_bytes,
- void* batch_normalization_17_beta, size_t batch_normalization_17_beta_bytes,
- void* batch_normalization_17_mean, size_t batch_normalization_17_mean_bytes,
- void* batch_normalization_17_variance, size_t batch_normalization_17_variance_bytes,
- void* depthwise_conv2d_9_w, size_t depthwise_conv2d_9_w_bytes,
- void* batch_normalization_18_gamma, size_t batch_normalization_18_gamma_bytes,
- void* batch_normalization_18_beta, size_t batch_normalization_18_beta_bytes,
- void* batch_normalization_18_mean, size_t batch_normalization_18_mean_bytes,
- void* batch_normalization_18_variance, size_t batch_normalization_18_variance_bytes,
- void* conv2d_10_w, size_t conv2d_10_w_bytes,
- void* batch_normalization_19_gamma, size_t batch_normalization_19_gamma_bytes,
- void* batch_normalization_19_beta, size_t batch_normalization_19_beta_bytes,
- void* batch_normalization_19_mean, size_t batch_normalization_19_mean_bytes,
- void* batch_normalization_19_variance, size_t batch_normalization_19_variance_bytes,
- void* depthwise_conv2d_10_w, size_t depthwise_conv2d_10_w_bytes,
- void* batch_normalization_20_gamma, size_t batch_normalization_20_gamma_bytes,
- void* batch_normalization_20_beta, size_t batch_normalization_20_beta_bytes,
- void* batch_normalization_20_mean, size_t batch_normalization_20_mean_bytes,
- void* batch_normalization_20_variance, size_t batch_normalization_20_variance_bytes,
- void* conv2d_11_w, size_t conv2d_11_w_bytes,
- void* batch_normalization_21_gamma, size_t batch_normalization_21_gamma_bytes,
- void* batch_normalization_21_beta, size_t batch_normalization_21_beta_bytes,
- void* batch_normalization_21_mean, size_t batch_normalization_21_mean_bytes,
- void* batch_normalization_21_variance, size_t batch_normalization_21_variance_bytes,
- void* depthwise_conv2d_11_w, size_t depthwise_conv2d_11_w_bytes,
- void* batch_normalization_22_gamma, size_t batch_normalization_22_gamma_bytes,
- void* batch_normalization_22_beta, size_t batch_normalization_22_beta_bytes,
- void* batch_normalization_22_mean, size_t batch_normalization_22_mean_bytes,
- void* batch_normalization_22_variance, size_t batch_normalization_22_variance_bytes,
- void* conv2d_12_w, size_t conv2d_12_w_bytes,
- void* batch_normalization_23_gamma, size_t batch_normalization_23_gamma_bytes,
- void* batch_normalization_23_beta, size_t batch_normalization_23_beta_bytes,
- void* batch_normalization_23_mean, size_t batch_normalization_23_mean_bytes,
- void* batch_normalization_23_variance, size_t batch_normalization_23_variance_bytes,
- void* depthwise_conv2d_12_w, size_t depthwise_conv2d_12_w_bytes,
- void* batch_normalization_24_gamma, size_t batch_normalization_24_gamma_bytes,
- void* batch_normalization_24_beta, size_t batch_normalization_24_beta_bytes,
- void* batch_normalization_24_mean, size_t batch_normalization_24_mean_bytes,
- void* batch_normalization_24_variance, size_t batch_normalization_24_variance_bytes,
- void* conv2d_13_w, size_t conv2d_13_w_bytes,
- void* batch_normalization_25_gamma, size_t batch_normalization_25_gamma_bytes,
- void* batch_normalization_25_beta, size_t batch_normalization_25_beta_bytes,
- void* batch_normalization_25_mean, size_t batch_normalization_25_mean_bytes,
- void* batch_normalization_25_variance, size_t batch_normalization_25_variance_bytes,
- void* depthwise_conv2d_13_w, size_t depthwise_conv2d_13_w_bytes,
- void* batch_normalization_26_gamma, size_t batch_normalization_26_gamma_bytes,
- void* batch_normalization_26_beta, size_t batch_normalization_26_beta_bytes,
- void* batch_normalization_26_mean, size_t batch_normalization_26_mean_bytes,
- void* batch_normalization_26_variance, size_t batch_normalization_26_variance_bytes,
- void* conv2d_14_w, size_t conv2d_14_w_bytes,
- void* batch_normalization_27_gamma, size_t batch_normalization_27_gamma_bytes,
- void* batch_normalization_27_beta, size_t batch_normalization_27_beta_bytes,
- void* batch_normalization_27_mean, size_t batch_normalization_27_mean_bytes,
- void* batch_normalization_27_variance, size_t batch_normalization_27_variance_bytes,
- void* dense_1_w, size_t dense_1_w_bytes,
- void* dense_1_b, size_t dense_1_b_bytes){
-
-
- __visc__hint(visc::CPU_TARGET);
- __visc__attributes(138, input, conv2d_1_w, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, depthwise_conv2d_1_w, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, conv2d_2_w, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, depthwise_conv2d_2_w, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, conv2d_3_w, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, depthwise_conv2d_3_w, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, conv2d_4_w, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, depthwise_conv2d_4_w, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, conv2d_5_w, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, depthwise_conv2d_5_w, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, conv2d_6_w, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, depthwise_conv2d_6_w, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, conv2d_7_w, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, depthwise_conv2d_7_w, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, conv2d_8_w, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, depthwise_conv2d_8_w, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, conv2d_9_w, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, depthwise_conv2d_9_w, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, conv2d_10_w, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, depthwise_conv2d_10_w, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, conv2d_11_w, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, depthwise_conv2d_11_w, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, conv2d_12_w, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, depthwise_conv2d_12_w, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, conv2d_13_w, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, depthwise_conv2d_13_w, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, conv2d_14_w, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, dense_1_w, dense_1_b, 0);
-
-
- void* var_0 = __visc__createNodeND(0, var_0_node);
-
- __visc__bindIn(var_0, 0, 0, 0);
- __visc__bindIn(var_0, 1, 1, 0);
- __visc__bindIn(var_0, 2, 2, 0);
- __visc__bindIn(var_0, 3, 3, 0);
-
- void* var_1 = __visc__createNodeND(0, var_1_node);
-
- __visc__edge(var_0, var_1, 1, 0, 0, 0);
- __visc__edge(var_0, var_1, 1, 1, 1, 0);
- __visc__bindIn(var_1, 4, 2, 0);
- __visc__bindIn(var_1, 5, 3, 0);
- __visc__bindIn(var_1, 6, 4, 0);
- __visc__bindIn(var_1, 7, 5, 0);
- __visc__bindIn(var_1, 8, 6, 0);
- __visc__bindIn(var_1, 9, 7, 0);
- __visc__bindIn(var_1, 10, 8, 0);
- __visc__bindIn(var_1, 11, 9, 0);
-
- void* var_2 = __visc__createNodeND(0, var_2_node);
-
- __visc__edge(var_1, var_2, 1, 0, 0, 0);
- __visc__edge(var_1, var_2, 1, 1, 1, 0);
-
- void* var_3 = __visc__createNodeND(0, var_3_node);
-
- __visc__edge(var_2, var_3, 1, 0, 0, 0);
- __visc__edge(var_2, var_3, 1, 1, 1, 0);
- __visc__bindIn(var_3, 12, 2, 0);
- __visc__bindIn(var_3, 13, 3, 0);
-
- void* var_4 = __visc__createNodeND(0, var_4_node);
-
- __visc__edge(var_3, var_4, 1, 0, 0, 0);
- __visc__edge(var_3, var_4, 1, 1, 1, 0);
- __visc__bindIn(var_4, 14, 2, 0);
- __visc__bindIn(var_4, 15, 3, 0);
- __visc__bindIn(var_4, 16, 4, 0);
- __visc__bindIn(var_4, 17, 5, 0);
- __visc__bindIn(var_4, 18, 6, 0);
- __visc__bindIn(var_4, 19, 7, 0);
- __visc__bindIn(var_4, 20, 8, 0);
- __visc__bindIn(var_4, 21, 9, 0);
-
- void* var_5 = __visc__createNodeND(0, var_5_node);
-
- __visc__edge(var_4, var_5, 1, 0, 0, 0);
- __visc__edge(var_4, var_5, 1, 1, 1, 0);
-
- void* var_6 = __visc__createNodeND(0, var_6_node);
-
- __visc__edge(var_5, var_6, 1, 0, 0, 0);
- __visc__edge(var_5, var_6, 1, 1, 1, 0);
- __visc__bindIn(var_6, 22, 2, 0);
- __visc__bindIn(var_6, 23, 3, 0);
-
- void* var_7 = __visc__createNodeND(0, var_7_node);
-
- __visc__edge(var_6, var_7, 1, 0, 0, 0);
- __visc__edge(var_6, var_7, 1, 1, 1, 0);
- __visc__bindIn(var_7, 24, 2, 0);
- __visc__bindIn(var_7, 25, 3, 0);
- __visc__bindIn(var_7, 26, 4, 0);
- __visc__bindIn(var_7, 27, 5, 0);
- __visc__bindIn(var_7, 28, 6, 0);
- __visc__bindIn(var_7, 29, 7, 0);
- __visc__bindIn(var_7, 30, 8, 0);
- __visc__bindIn(var_7, 31, 9, 0);
-
- void* var_8 = __visc__createNodeND(0, var_8_node);
-
- __visc__edge(var_7, var_8, 1, 0, 0, 0);
- __visc__edge(var_7, var_8, 1, 1, 1, 0);
-
- void* var_9 = __visc__createNodeND(0, var_9_node);
-
- __visc__edge(var_8, var_9, 1, 0, 0, 0);
- __visc__edge(var_8, var_9, 1, 1, 1, 0);
- __visc__bindIn(var_9, 32, 2, 0);
- __visc__bindIn(var_9, 33, 3, 0);
-
- void* var_10 = __visc__createNodeND(0, var_10_node);
-
- __visc__edge(var_9, var_10, 1, 0, 0, 0);
- __visc__edge(var_9, var_10, 1, 1, 1, 0);
- __visc__bindIn(var_10, 34, 2, 0);
- __visc__bindIn(var_10, 35, 3, 0);
- __visc__bindIn(var_10, 36, 4, 0);
- __visc__bindIn(var_10, 37, 5, 0);
- __visc__bindIn(var_10, 38, 6, 0);
- __visc__bindIn(var_10, 39, 7, 0);
- __visc__bindIn(var_10, 40, 8, 0);
- __visc__bindIn(var_10, 41, 9, 0);
-
- void* var_11 = __visc__createNodeND(0, var_11_node);
-
- __visc__edge(var_10, var_11, 1, 0, 0, 0);
- __visc__edge(var_10, var_11, 1, 1, 1, 0);
-
- void* var_12 = __visc__createNodeND(0, var_12_node);
-
- __visc__edge(var_11, var_12, 1, 0, 0, 0);
- __visc__edge(var_11, var_12, 1, 1, 1, 0);
- __visc__bindIn(var_12, 42, 2, 0);
- __visc__bindIn(var_12, 43, 3, 0);
-
- void* var_13 = __visc__createNodeND(0, var_13_node);
-
- __visc__edge(var_12, var_13, 1, 0, 0, 0);
- __visc__edge(var_12, var_13, 1, 1, 1, 0);
- __visc__bindIn(var_13, 44, 2, 0);
- __visc__bindIn(var_13, 45, 3, 0);
- __visc__bindIn(var_13, 46, 4, 0);
- __visc__bindIn(var_13, 47, 5, 0);
- __visc__bindIn(var_13, 48, 6, 0);
- __visc__bindIn(var_13, 49, 7, 0);
- __visc__bindIn(var_13, 50, 8, 0);
- __visc__bindIn(var_13, 51, 9, 0);
-
- void* var_14 = __visc__createNodeND(0, var_14_node);
-
- __visc__edge(var_13, var_14, 1, 0, 0, 0);
- __visc__edge(var_13, var_14, 1, 1, 1, 0);
-
- void* var_15 = __visc__createNodeND(0, var_15_node);
-
- __visc__edge(var_14, var_15, 1, 0, 0, 0);
- __visc__edge(var_14, var_15, 1, 1, 1, 0);
- __visc__bindIn(var_15, 52, 2, 0);
- __visc__bindIn(var_15, 53, 3, 0);
-
- void* var_16 = __visc__createNodeND(0, var_16_node);
-
- __visc__edge(var_15, var_16, 1, 0, 0, 0);
- __visc__edge(var_15, var_16, 1, 1, 1, 0);
- __visc__bindIn(var_16, 54, 2, 0);
- __visc__bindIn(var_16, 55, 3, 0);
- __visc__bindIn(var_16, 56, 4, 0);
- __visc__bindIn(var_16, 57, 5, 0);
- __visc__bindIn(var_16, 58, 6, 0);
- __visc__bindIn(var_16, 59, 7, 0);
- __visc__bindIn(var_16, 60, 8, 0);
- __visc__bindIn(var_16, 61, 9, 0);
-
- void* var_17 = __visc__createNodeND(0, var_17_node);
-
- __visc__edge(var_16, var_17, 1, 0, 0, 0);
- __visc__edge(var_16, var_17, 1, 1, 1, 0);
-
- void* var_18 = __visc__createNodeND(0, var_18_node);
-
- __visc__edge(var_17, var_18, 1, 0, 0, 0);
- __visc__edge(var_17, var_18, 1, 1, 1, 0);
- __visc__bindIn(var_18, 62, 2, 0);
- __visc__bindIn(var_18, 63, 3, 0);
-
- void* var_19 = __visc__createNodeND(0, var_19_node);
-
- __visc__edge(var_18, var_19, 1, 0, 0, 0);
- __visc__edge(var_18, var_19, 1, 1, 1, 0);
- __visc__bindIn(var_19, 64, 2, 0);
- __visc__bindIn(var_19, 65, 3, 0);
- __visc__bindIn(var_19, 66, 4, 0);
- __visc__bindIn(var_19, 67, 5, 0);
- __visc__bindIn(var_19, 68, 6, 0);
- __visc__bindIn(var_19, 69, 7, 0);
- __visc__bindIn(var_19, 70, 8, 0);
- __visc__bindIn(var_19, 71, 9, 0);
-
- void* var_20 = __visc__createNodeND(0, var_20_node);
-
- __visc__edge(var_19, var_20, 1, 0, 0, 0);
- __visc__edge(var_19, var_20, 1, 1, 1, 0);
-
- void* var_21 = __visc__createNodeND(0, var_21_node);
-
- __visc__edge(var_20, var_21, 1, 0, 0, 0);
- __visc__edge(var_20, var_21, 1, 1, 1, 0);
- __visc__bindIn(var_21, 72, 2, 0);
- __visc__bindIn(var_21, 73, 3, 0);
-
- void* var_22 = __visc__createNodeND(0, var_22_node);
-
- __visc__edge(var_21, var_22, 1, 0, 0, 0);
- __visc__edge(var_21, var_22, 1, 1, 1, 0);
- __visc__bindIn(var_22, 74, 2, 0);
- __visc__bindIn(var_22, 75, 3, 0);
- __visc__bindIn(var_22, 76, 4, 0);
- __visc__bindIn(var_22, 77, 5, 0);
- __visc__bindIn(var_22, 78, 6, 0);
- __visc__bindIn(var_22, 79, 7, 0);
- __visc__bindIn(var_22, 80, 8, 0);
- __visc__bindIn(var_22, 81, 9, 0);
-
- void* var_23 = __visc__createNodeND(0, var_23_node);
-
- __visc__edge(var_22, var_23, 1, 0, 0, 0);
- __visc__edge(var_22, var_23, 1, 1, 1, 0);
-
- void* var_24 = __visc__createNodeND(0, var_24_node);
-
- __visc__edge(var_23, var_24, 1, 0, 0, 0);
- __visc__edge(var_23, var_24, 1, 1, 1, 0);
- __visc__bindIn(var_24, 82, 2, 0);
- __visc__bindIn(var_24, 83, 3, 0);
-
- void* var_25 = __visc__createNodeND(0, var_25_node);
-
- __visc__edge(var_24, var_25, 1, 0, 0, 0);
- __visc__edge(var_24, var_25, 1, 1, 1, 0);
- __visc__bindIn(var_25, 84, 2, 0);
- __visc__bindIn(var_25, 85, 3, 0);
- __visc__bindIn(var_25, 86, 4, 0);
- __visc__bindIn(var_25, 87, 5, 0);
- __visc__bindIn(var_25, 88, 6, 0);
- __visc__bindIn(var_25, 89, 7, 0);
- __visc__bindIn(var_25, 90, 8, 0);
- __visc__bindIn(var_25, 91, 9, 0);
-
- void* var_26 = __visc__createNodeND(0, var_26_node);
-
- __visc__edge(var_25, var_26, 1, 0, 0, 0);
- __visc__edge(var_25, var_26, 1, 1, 1, 0);
-
- void* var_27 = __visc__createNodeND(0, var_27_node);
-
- __visc__edge(var_26, var_27, 1, 0, 0, 0);
- __visc__edge(var_26, var_27, 1, 1, 1, 0);
- __visc__bindIn(var_27, 92, 2, 0);
- __visc__bindIn(var_27, 93, 3, 0);
-
- void* var_28 = __visc__createNodeND(0, var_28_node);
-
- __visc__edge(var_27, var_28, 1, 0, 0, 0);
- __visc__edge(var_27, var_28, 1, 1, 1, 0);
- __visc__bindIn(var_28, 94, 2, 0);
- __visc__bindIn(var_28, 95, 3, 0);
- __visc__bindIn(var_28, 96, 4, 0);
- __visc__bindIn(var_28, 97, 5, 0);
- __visc__bindIn(var_28, 98, 6, 0);
- __visc__bindIn(var_28, 99, 7, 0);
- __visc__bindIn(var_28, 100, 8, 0);
- __visc__bindIn(var_28, 101, 9, 0);
-
- void* var_29 = __visc__createNodeND(0, var_29_node);
-
- __visc__edge(var_28, var_29, 1, 0, 0, 0);
- __visc__edge(var_28, var_29, 1, 1, 1, 0);
-
- void* var_30 = __visc__createNodeND(0, var_30_node);
-
- __visc__edge(var_29, var_30, 1, 0, 0, 0);
- __visc__edge(var_29, var_30, 1, 1, 1, 0);
- __visc__bindIn(var_30, 102, 2, 0);
- __visc__bindIn(var_30, 103, 3, 0);
-
- void* var_31 = __visc__createNodeND(0, var_31_node);
-
- __visc__edge(var_30, var_31, 1, 0, 0, 0);
- __visc__edge(var_30, var_31, 1, 1, 1, 0);
- __visc__bindIn(var_31, 104, 2, 0);
- __visc__bindIn(var_31, 105, 3, 0);
- __visc__bindIn(var_31, 106, 4, 0);
- __visc__bindIn(var_31, 107, 5, 0);
- __visc__bindIn(var_31, 108, 6, 0);
- __visc__bindIn(var_31, 109, 7, 0);
- __visc__bindIn(var_31, 110, 8, 0);
- __visc__bindIn(var_31, 111, 9, 0);
-
- void* var_32 = __visc__createNodeND(0, var_32_node);
-
- __visc__edge(var_31, var_32, 1, 0, 0, 0);
- __visc__edge(var_31, var_32, 1, 1, 1, 0);
-
- void* var_33 = __visc__createNodeND(0, var_33_node);
-
- __visc__edge(var_32, var_33, 1, 0, 0, 0);
- __visc__edge(var_32, var_33, 1, 1, 1, 0);
- __visc__bindIn(var_33, 112, 2, 0);
- __visc__bindIn(var_33, 113, 3, 0);
-
- void* var_34 = __visc__createNodeND(0, var_34_node);
-
- __visc__edge(var_33, var_34, 1, 0, 0, 0);
- __visc__edge(var_33, var_34, 1, 1, 1, 0);
- __visc__bindIn(var_34, 114, 2, 0);
- __visc__bindIn(var_34, 115, 3, 0);
- __visc__bindIn(var_34, 116, 4, 0);
- __visc__bindIn(var_34, 117, 5, 0);
- __visc__bindIn(var_34, 118, 6, 0);
- __visc__bindIn(var_34, 119, 7, 0);
- __visc__bindIn(var_34, 120, 8, 0);
- __visc__bindIn(var_34, 121, 9, 0);
-
- void* var_35 = __visc__createNodeND(0, var_35_node);
-
- __visc__edge(var_34, var_35, 1, 0, 0, 0);
- __visc__edge(var_34, var_35, 1, 1, 1, 0);
-
- void* var_36 = __visc__createNodeND(0, var_36_node);
-
- __visc__edge(var_35, var_36, 1, 0, 0, 0);
- __visc__edge(var_35, var_36, 1, 1, 1, 0);
- __visc__bindIn(var_36, 122, 2, 0);
- __visc__bindIn(var_36, 123, 3, 0);
-
- void* var_37 = __visc__createNodeND(0, var_37_node);
-
- __visc__edge(var_36, var_37, 1, 0, 0, 0);
- __visc__edge(var_36, var_37, 1, 1, 1, 0);
- __visc__bindIn(var_37, 124, 2, 0);
- __visc__bindIn(var_37, 125, 3, 0);
- __visc__bindIn(var_37, 126, 4, 0);
- __visc__bindIn(var_37, 127, 5, 0);
- __visc__bindIn(var_37, 128, 6, 0);
- __visc__bindIn(var_37, 129, 7, 0);
- __visc__bindIn(var_37, 130, 8, 0);
- __visc__bindIn(var_37, 131, 9, 0);
-
- void* var_38 = __visc__createNodeND(0, var_38_node);
-
- __visc__edge(var_37, var_38, 1, 0, 0, 0);
- __visc__edge(var_37, var_38, 1, 1, 1, 0);
-
- void* var_39 = __visc__createNodeND(0, var_39_node);
-
- __visc__edge(var_38, var_39, 1, 0, 0, 0);
- __visc__edge(var_38, var_39, 1, 1, 1, 0);
- __visc__bindIn(var_39, 132, 2, 0);
- __visc__bindIn(var_39, 133, 3, 0);
-
- void* var_40 = __visc__createNodeND(0, var_40_node);
-
- __visc__edge(var_39, var_40, 1, 0, 0, 0);
- __visc__edge(var_39, var_40, 1, 1, 1, 0);
- __visc__bindIn(var_40, 134, 2, 0);
- __visc__bindIn(var_40, 135, 3, 0);
- __visc__bindIn(var_40, 136, 4, 0);
- __visc__bindIn(var_40, 137, 5, 0);
- __visc__bindIn(var_40, 138, 6, 0);
- __visc__bindIn(var_40, 139, 7, 0);
- __visc__bindIn(var_40, 140, 8, 0);
- __visc__bindIn(var_40, 141, 9, 0);
-
- void* var_41 = __visc__createNodeND(0, var_41_node);
-
- __visc__edge(var_40, var_41, 1, 0, 0, 0);
- __visc__edge(var_40, var_41, 1, 1, 1, 0);
-
- void* var_42 = __visc__createNodeND(0, var_42_node);
-
- __visc__edge(var_41, var_42, 1, 0, 0, 0);
- __visc__edge(var_41, var_42, 1, 1, 1, 0);
- __visc__bindIn(var_42, 142, 2, 0);
- __visc__bindIn(var_42, 143, 3, 0);
-
- void* var_43 = __visc__createNodeND(0, var_43_node);
-
- __visc__edge(var_42, var_43, 1, 0, 0, 0);
- __visc__edge(var_42, var_43, 1, 1, 1, 0);
- __visc__bindIn(var_43, 144, 2, 0);
- __visc__bindIn(var_43, 145, 3, 0);
- __visc__bindIn(var_43, 146, 4, 0);
- __visc__bindIn(var_43, 147, 5, 0);
- __visc__bindIn(var_43, 148, 6, 0);
- __visc__bindIn(var_43, 149, 7, 0);
- __visc__bindIn(var_43, 150, 8, 0);
- __visc__bindIn(var_43, 151, 9, 0);
-
- void* var_44 = __visc__createNodeND(0, var_44_node);
-
- __visc__edge(var_43, var_44, 1, 0, 0, 0);
- __visc__edge(var_43, var_44, 1, 1, 1, 0);
-
- void* var_45 = __visc__createNodeND(0, var_45_node);
-
- __visc__edge(var_44, var_45, 1, 0, 0, 0);
- __visc__edge(var_44, var_45, 1, 1, 1, 0);
- __visc__bindIn(var_45, 152, 2, 0);
- __visc__bindIn(var_45, 153, 3, 0);
-
- void* var_46 = __visc__createNodeND(0, var_46_node);
-
- __visc__edge(var_45, var_46, 1, 0, 0, 0);
- __visc__edge(var_45, var_46, 1, 1, 1, 0);
- __visc__bindIn(var_46, 154, 2, 0);
- __visc__bindIn(var_46, 155, 3, 0);
- __visc__bindIn(var_46, 156, 4, 0);
- __visc__bindIn(var_46, 157, 5, 0);
- __visc__bindIn(var_46, 158, 6, 0);
- __visc__bindIn(var_46, 159, 7, 0);
- __visc__bindIn(var_46, 160, 8, 0);
- __visc__bindIn(var_46, 161, 9, 0);
-
- void* var_47 = __visc__createNodeND(0, var_47_node);
-
- __visc__edge(var_46, var_47, 1, 0, 0, 0);
- __visc__edge(var_46, var_47, 1, 1, 1, 0);
-
- void* var_48 = __visc__createNodeND(0, var_48_node);
-
- __visc__edge(var_47, var_48, 1, 0, 0, 0);
- __visc__edge(var_47, var_48, 1, 1, 1, 0);
- __visc__bindIn(var_48, 162, 2, 0);
- __visc__bindIn(var_48, 163, 3, 0);
-
- void* var_49 = __visc__createNodeND(0, var_49_node);
-
- __visc__edge(var_48, var_49, 1, 0, 0, 0);
- __visc__edge(var_48, var_49, 1, 1, 1, 0);
- __visc__bindIn(var_49, 164, 2, 0);
- __visc__bindIn(var_49, 165, 3, 0);
- __visc__bindIn(var_49, 166, 4, 0);
- __visc__bindIn(var_49, 167, 5, 0);
- __visc__bindIn(var_49, 168, 6, 0);
- __visc__bindIn(var_49, 169, 7, 0);
- __visc__bindIn(var_49, 170, 8, 0);
- __visc__bindIn(var_49, 171, 9, 0);
-
- void* var_50 = __visc__createNodeND(0, var_50_node);
-
- __visc__edge(var_49, var_50, 1, 0, 0, 0);
- __visc__edge(var_49, var_50, 1, 1, 1, 0);
-
- void* var_51 = __visc__createNodeND(0, var_51_node);
-
- __visc__edge(var_50, var_51, 1, 0, 0, 0);
- __visc__edge(var_50, var_51, 1, 1, 1, 0);
- __visc__bindIn(var_51, 172, 2, 0);
- __visc__bindIn(var_51, 173, 3, 0);
-
- void* var_52 = __visc__createNodeND(0, var_52_node);
-
- __visc__edge(var_51, var_52, 1, 0, 0, 0);
- __visc__edge(var_51, var_52, 1, 1, 1, 0);
- __visc__bindIn(var_52, 174, 2, 0);
- __visc__bindIn(var_52, 175, 3, 0);
- __visc__bindIn(var_52, 176, 4, 0);
- __visc__bindIn(var_52, 177, 5, 0);
- __visc__bindIn(var_52, 178, 6, 0);
- __visc__bindIn(var_52, 179, 7, 0);
- __visc__bindIn(var_52, 180, 8, 0);
- __visc__bindIn(var_52, 181, 9, 0);
-
- void* var_53 = __visc__createNodeND(0, var_53_node);
-
- __visc__edge(var_52, var_53, 1, 0, 0, 0);
- __visc__edge(var_52, var_53, 1, 1, 1, 0);
-
- void* var_54 = __visc__createNodeND(0, var_54_node);
-
- __visc__edge(var_53, var_54, 1, 0, 0, 0);
- __visc__edge(var_53, var_54, 1, 1, 1, 0);
- __visc__bindIn(var_54, 182, 2, 0);
- __visc__bindIn(var_54, 183, 3, 0);
-
- void* var_55 = __visc__createNodeND(0, var_55_node);
-
- __visc__edge(var_54, var_55, 1, 0, 0, 0);
- __visc__edge(var_54, var_55, 1, 1, 1, 0);
- __visc__bindIn(var_55, 184, 2, 0);
- __visc__bindIn(var_55, 185, 3, 0);
- __visc__bindIn(var_55, 186, 4, 0);
- __visc__bindIn(var_55, 187, 5, 0);
- __visc__bindIn(var_55, 188, 6, 0);
- __visc__bindIn(var_55, 189, 7, 0);
- __visc__bindIn(var_55, 190, 8, 0);
- __visc__bindIn(var_55, 191, 9, 0);
-
- void* var_56 = __visc__createNodeND(0, var_56_node);
-
- __visc__edge(var_55, var_56, 1, 0, 0, 0);
- __visc__edge(var_55, var_56, 1, 1, 1, 0);
-
- void* var_57 = __visc__createNodeND(0, var_57_node);
-
- __visc__edge(var_56, var_57, 1, 0, 0, 0);
- __visc__edge(var_56, var_57, 1, 1, 1, 0);
- __visc__bindIn(var_57, 192, 2, 0);
- __visc__bindIn(var_57, 193, 3, 0);
-
- void* var_58 = __visc__createNodeND(0, var_58_node);
-
- __visc__edge(var_57, var_58, 1, 0, 0, 0);
- __visc__edge(var_57, var_58, 1, 1, 1, 0);
- __visc__bindIn(var_58, 194, 2, 0);
- __visc__bindIn(var_58, 195, 3, 0);
- __visc__bindIn(var_58, 196, 4, 0);
- __visc__bindIn(var_58, 197, 5, 0);
- __visc__bindIn(var_58, 198, 6, 0);
- __visc__bindIn(var_58, 199, 7, 0);
- __visc__bindIn(var_58, 200, 8, 0);
- __visc__bindIn(var_58, 201, 9, 0);
-
- void* var_59 = __visc__createNodeND(0, var_59_node);
-
- __visc__edge(var_58, var_59, 1, 0, 0, 0);
- __visc__edge(var_58, var_59, 1, 1, 1, 0);
-
- void* var_60 = __visc__createNodeND(0, var_60_node);
-
- __visc__edge(var_59, var_60, 1, 0, 0, 0);
- __visc__edge(var_59, var_60, 1, 1, 1, 0);
- __visc__bindIn(var_60, 202, 2, 0);
- __visc__bindIn(var_60, 203, 3, 0);
-
- void* var_61 = __visc__createNodeND(0, var_61_node);
-
- __visc__edge(var_60, var_61, 1, 0, 0, 0);
- __visc__edge(var_60, var_61, 1, 1, 1, 0);
- __visc__bindIn(var_61, 204, 2, 0);
- __visc__bindIn(var_61, 205, 3, 0);
- __visc__bindIn(var_61, 206, 4, 0);
- __visc__bindIn(var_61, 207, 5, 0);
- __visc__bindIn(var_61, 208, 6, 0);
- __visc__bindIn(var_61, 209, 7, 0);
- __visc__bindIn(var_61, 210, 8, 0);
- __visc__bindIn(var_61, 211, 9, 0);
-
- void* var_62 = __visc__createNodeND(0, var_62_node);
-
- __visc__edge(var_61, var_62, 1, 0, 0, 0);
- __visc__edge(var_61, var_62, 1, 1, 1, 0);
-
- void* var_63 = __visc__createNodeND(0, var_63_node);
-
- __visc__edge(var_62, var_63, 1, 0, 0, 0);
- __visc__edge(var_62, var_63, 1, 1, 1, 0);
- __visc__bindIn(var_63, 212, 2, 0);
- __visc__bindIn(var_63, 213, 3, 0);
-
- void* var_64 = __visc__createNodeND(0, var_64_node);
-
- __visc__edge(var_63, var_64, 1, 0, 0, 0);
- __visc__edge(var_63, var_64, 1, 1, 1, 0);
- __visc__bindIn(var_64, 214, 2, 0);
- __visc__bindIn(var_64, 215, 3, 0);
- __visc__bindIn(var_64, 216, 4, 0);
- __visc__bindIn(var_64, 217, 5, 0);
- __visc__bindIn(var_64, 218, 6, 0);
- __visc__bindIn(var_64, 219, 7, 0);
- __visc__bindIn(var_64, 220, 8, 0);
- __visc__bindIn(var_64, 221, 9, 0);
-
- void* var_65 = __visc__createNodeND(0, var_65_node);
-
- __visc__edge(var_64, var_65, 1, 0, 0, 0);
- __visc__edge(var_64, var_65, 1, 1, 1, 0);
-
- void* var_66 = __visc__createNodeND(0, var_66_node);
-
- __visc__edge(var_65, var_66, 1, 0, 0, 0);
- __visc__edge(var_65, var_66, 1, 1, 1, 0);
- __visc__bindIn(var_66, 222, 2, 0);
- __visc__bindIn(var_66, 223, 3, 0);
-
- void* var_67 = __visc__createNodeND(0, var_67_node);
-
- __visc__edge(var_66, var_67, 1, 0, 0, 0);
- __visc__edge(var_66, var_67, 1, 1, 1, 0);
- __visc__bindIn(var_67, 224, 2, 0);
- __visc__bindIn(var_67, 225, 3, 0);
- __visc__bindIn(var_67, 226, 4, 0);
- __visc__bindIn(var_67, 227, 5, 0);
- __visc__bindIn(var_67, 228, 6, 0);
- __visc__bindIn(var_67, 229, 7, 0);
- __visc__bindIn(var_67, 230, 8, 0);
- __visc__bindIn(var_67, 231, 9, 0);
-
- void* var_68 = __visc__createNodeND(0, var_68_node);
-
- __visc__edge(var_67, var_68, 1, 0, 0, 0);
- __visc__edge(var_67, var_68, 1, 1, 1, 0);
-
- void* var_69 = __visc__createNodeND(0, var_69_node);
-
- __visc__edge(var_68, var_69, 1, 0, 0, 0);
- __visc__edge(var_68, var_69, 1, 1, 1, 0);
- __visc__bindIn(var_69, 232, 2, 0);
- __visc__bindIn(var_69, 233, 3, 0);
-
- void* var_70 = __visc__createNodeND(0, var_70_node);
-
- __visc__edge(var_69, var_70, 1, 0, 0, 0);
- __visc__edge(var_69, var_70, 1, 1, 1, 0);
- __visc__bindIn(var_70, 234, 2, 0);
- __visc__bindIn(var_70, 235, 3, 0);
- __visc__bindIn(var_70, 236, 4, 0);
- __visc__bindIn(var_70, 237, 5, 0);
- __visc__bindIn(var_70, 238, 6, 0);
- __visc__bindIn(var_70, 239, 7, 0);
- __visc__bindIn(var_70, 240, 8, 0);
- __visc__bindIn(var_70, 241, 9, 0);
-
- void* var_71 = __visc__createNodeND(0, var_71_node);
-
- __visc__edge(var_70, var_71, 1, 0, 0, 0);
- __visc__edge(var_70, var_71, 1, 1, 1, 0);
-
- void* var_72 = __visc__createNodeND(0, var_72_node);
-
- __visc__edge(var_71, var_72, 1, 0, 0, 0);
- __visc__edge(var_71, var_72, 1, 1, 1, 0);
- __visc__bindIn(var_72, 242, 2, 0);
- __visc__bindIn(var_72, 243, 3, 0);
-
- void* var_73 = __visc__createNodeND(0, var_73_node);
-
- __visc__edge(var_72, var_73, 1, 0, 0, 0);
- __visc__edge(var_72, var_73, 1, 1, 1, 0);
- __visc__bindIn(var_73, 244, 2, 0);
- __visc__bindIn(var_73, 245, 3, 0);
- __visc__bindIn(var_73, 246, 4, 0);
- __visc__bindIn(var_73, 247, 5, 0);
- __visc__bindIn(var_73, 248, 6, 0);
- __visc__bindIn(var_73, 249, 7, 0);
- __visc__bindIn(var_73, 250, 8, 0);
- __visc__bindIn(var_73, 251, 9, 0);
-
- void* var_74 = __visc__createNodeND(0, var_74_node);
-
- __visc__edge(var_73, var_74, 1, 0, 0, 0);
- __visc__edge(var_73, var_74, 1, 1, 1, 0);
-
- void* var_75 = __visc__createNodeND(0, var_75_node);
-
- __visc__edge(var_74, var_75, 1, 0, 0, 0);
- __visc__edge(var_74, var_75, 1, 1, 1, 0);
- __visc__bindIn(var_75, 252, 2, 0);
- __visc__bindIn(var_75, 253, 3, 0);
-
- void* var_76 = __visc__createNodeND(0, var_76_node);
-
- __visc__edge(var_75, var_76, 1, 0, 0, 0);
- __visc__edge(var_75, var_76, 1, 1, 1, 0);
- __visc__bindIn(var_76, 254, 2, 0);
- __visc__bindIn(var_76, 255, 3, 0);
- __visc__bindIn(var_76, 256, 4, 0);
- __visc__bindIn(var_76, 257, 5, 0);
- __visc__bindIn(var_76, 258, 6, 0);
- __visc__bindIn(var_76, 259, 7, 0);
- __visc__bindIn(var_76, 260, 8, 0);
- __visc__bindIn(var_76, 261, 9, 0);
-
- void* var_77 = __visc__createNodeND(0, var_77_node);
-
- __visc__edge(var_76, var_77, 1, 0, 0, 0);
- __visc__edge(var_76, var_77, 1, 1, 1, 0);
-
- void* var_78 = __visc__createNodeND(0, var_78_node);
-
- __visc__edge(var_77, var_78, 1, 0, 0, 0);
- __visc__edge(var_77, var_78, 1, 1, 1, 0);
- __visc__bindIn(var_78, 262, 2, 0);
- __visc__bindIn(var_78, 263, 3, 0);
-
- void* var_79 = __visc__createNodeND(0, var_79_node);
-
- __visc__edge(var_78, var_79, 1, 0, 0, 0);
- __visc__edge(var_78, var_79, 1, 1, 1, 0);
- __visc__bindIn(var_79, 264, 2, 0);
- __visc__bindIn(var_79, 265, 3, 0);
- __visc__bindIn(var_79, 266, 4, 0);
- __visc__bindIn(var_79, 267, 5, 0);
- __visc__bindIn(var_79, 268, 6, 0);
- __visc__bindIn(var_79, 269, 7, 0);
- __visc__bindIn(var_79, 270, 8, 0);
- __visc__bindIn(var_79, 271, 9, 0);
-
- void* var_80 = __visc__createNodeND(0, var_80_node);
-
- __visc__edge(var_79, var_80, 1, 0, 0, 0);
- __visc__edge(var_79, var_80, 1, 1, 1, 0);
-
- void* var_81 = __visc__createNodeND(0, var_81_node);
-
- __visc__edge(var_80, var_81, 1, 0, 0, 0);
- __visc__edge(var_80, var_81, 1, 1, 1, 0);
-
- void* var_82 = __visc__createNodeND(0, var_82_node);
-
- __visc__edge(var_81, var_82, 1, 0, 0, 0);
- __visc__edge(var_81, var_82, 1, 1, 1, 0);
- __visc__bindIn(var_82, 272, 2, 0);
- __visc__bindIn(var_82, 273, 3, 0);
-
- void* var_83 = __visc__createNodeND(0, var_83_node);
-
- __visc__edge(var_82, var_83, 1, 0, 0, 0);
- __visc__edge(var_82, var_83, 1, 1, 1, 0);
- __visc__bindIn(var_83, 274, 2, 0);
- __visc__bindIn(var_83, 275, 3, 0);
-
- void* var_84 = __visc__createNodeND(0, var_84_node);
-
- __visc__edge(var_83, var_84, 1, 0, 0, 0);
- __visc__edge(var_83, var_84, 1, 1, 1, 0);
-
- __visc__bindOut(var_84, 0, 0, 0);
- __visc__bindOut(var_84, 1, 1, 0);
-
-}
-
-struct ret_t {
- void* tensor;
- size_t bytes;
-};
-
-typedef struct __attribute__((__packed__)) {
- void* input;
- size_t input_bytes;
- void* conv2d_1_w;
- size_t conv2d_1_w_bytes;
- void* batch_normalization_1_gamma;
- size_t batch_normalization_1_gamma_bytes;
- void* batch_normalization_1_beta;
- size_t batch_normalization_1_beta_bytes;
- void* batch_normalization_1_mean;
- size_t batch_normalization_1_mean_bytes;
- void* batch_normalization_1_variance;
- size_t batch_normalization_1_variance_bytes;
- void* depthwise_conv2d_1_w;
- size_t depthwise_conv2d_1_w_bytes;
- void* batch_normalization_2_gamma;
- size_t batch_normalization_2_gamma_bytes;
- void* batch_normalization_2_beta;
- size_t batch_normalization_2_beta_bytes;
- void* batch_normalization_2_mean;
- size_t batch_normalization_2_mean_bytes;
- void* batch_normalization_2_variance;
- size_t batch_normalization_2_variance_bytes;
- void* conv2d_2_w;
- size_t conv2d_2_w_bytes;
- void* batch_normalization_3_gamma;
- size_t batch_normalization_3_gamma_bytes;
- void* batch_normalization_3_beta;
- size_t batch_normalization_3_beta_bytes;
- void* batch_normalization_3_mean;
- size_t batch_normalization_3_mean_bytes;
- void* batch_normalization_3_variance;
- size_t batch_normalization_3_variance_bytes;
- void* depthwise_conv2d_2_w;
- size_t depthwise_conv2d_2_w_bytes;
- void* batch_normalization_4_gamma;
- size_t batch_normalization_4_gamma_bytes;
- void* batch_normalization_4_beta;
- size_t batch_normalization_4_beta_bytes;
- void* batch_normalization_4_mean;
- size_t batch_normalization_4_mean_bytes;
- void* batch_normalization_4_variance;
- size_t batch_normalization_4_variance_bytes;
- void* conv2d_3_w;
- size_t conv2d_3_w_bytes;
- void* batch_normalization_5_gamma;
- size_t batch_normalization_5_gamma_bytes;
- void* batch_normalization_5_beta;
- size_t batch_normalization_5_beta_bytes;
- void* batch_normalization_5_mean;
- size_t batch_normalization_5_mean_bytes;
- void* batch_normalization_5_variance;
- size_t batch_normalization_5_variance_bytes;
- void* depthwise_conv2d_3_w;
- size_t depthwise_conv2d_3_w_bytes;
- void* batch_normalization_6_gamma;
- size_t batch_normalization_6_gamma_bytes;
- void* batch_normalization_6_beta;
- size_t batch_normalization_6_beta_bytes;
- void* batch_normalization_6_mean;
- size_t batch_normalization_6_mean_bytes;
- void* batch_normalization_6_variance;
- size_t batch_normalization_6_variance_bytes;
- void* conv2d_4_w;
- size_t conv2d_4_w_bytes;
- void* batch_normalization_7_gamma;
- size_t batch_normalization_7_gamma_bytes;
- void* batch_normalization_7_beta;
- size_t batch_normalization_7_beta_bytes;
- void* batch_normalization_7_mean;
- size_t batch_normalization_7_mean_bytes;
- void* batch_normalization_7_variance;
- size_t batch_normalization_7_variance_bytes;
- void* depthwise_conv2d_4_w;
- size_t depthwise_conv2d_4_w_bytes;
- void* batch_normalization_8_gamma;
- size_t batch_normalization_8_gamma_bytes;
- void* batch_normalization_8_beta;
- size_t batch_normalization_8_beta_bytes;
- void* batch_normalization_8_mean;
- size_t batch_normalization_8_mean_bytes;
- void* batch_normalization_8_variance;
- size_t batch_normalization_8_variance_bytes;
- void* conv2d_5_w;
- size_t conv2d_5_w_bytes;
- void* batch_normalization_9_gamma;
- size_t batch_normalization_9_gamma_bytes;
- void* batch_normalization_9_beta;
- size_t batch_normalization_9_beta_bytes;
- void* batch_normalization_9_mean;
- size_t batch_normalization_9_mean_bytes;
- void* batch_normalization_9_variance;
- size_t batch_normalization_9_variance_bytes;
- void* depthwise_conv2d_5_w;
- size_t depthwise_conv2d_5_w_bytes;
- void* batch_normalization_10_gamma;
- size_t batch_normalization_10_gamma_bytes;
- void* batch_normalization_10_beta;
- size_t batch_normalization_10_beta_bytes;
- void* batch_normalization_10_mean;
- size_t batch_normalization_10_mean_bytes;
- void* batch_normalization_10_variance;
- size_t batch_normalization_10_variance_bytes;
- void* conv2d_6_w;
- size_t conv2d_6_w_bytes;
- void* batch_normalization_11_gamma;
- size_t batch_normalization_11_gamma_bytes;
- void* batch_normalization_11_beta;
- size_t batch_normalization_11_beta_bytes;
- void* batch_normalization_11_mean;
- size_t batch_normalization_11_mean_bytes;
- void* batch_normalization_11_variance;
- size_t batch_normalization_11_variance_bytes;
- void* depthwise_conv2d_6_w;
- size_t depthwise_conv2d_6_w_bytes;
- void* batch_normalization_12_gamma;
- size_t batch_normalization_12_gamma_bytes;
- void* batch_normalization_12_beta;
- size_t batch_normalization_12_beta_bytes;
- void* batch_normalization_12_mean;
- size_t batch_normalization_12_mean_bytes;
- void* batch_normalization_12_variance;
- size_t batch_normalization_12_variance_bytes;
- void* conv2d_7_w;
- size_t conv2d_7_w_bytes;
- void* batch_normalization_13_gamma;
- size_t batch_normalization_13_gamma_bytes;
- void* batch_normalization_13_beta;
- size_t batch_normalization_13_beta_bytes;
- void* batch_normalization_13_mean;
- size_t batch_normalization_13_mean_bytes;
- void* batch_normalization_13_variance;
- size_t batch_normalization_13_variance_bytes;
- void* depthwise_conv2d_7_w;
- size_t depthwise_conv2d_7_w_bytes;
- void* batch_normalization_14_gamma;
- size_t batch_normalization_14_gamma_bytes;
- void* batch_normalization_14_beta;
- size_t batch_normalization_14_beta_bytes;
- void* batch_normalization_14_mean;
- size_t batch_normalization_14_mean_bytes;
- void* batch_normalization_14_variance;
- size_t batch_normalization_14_variance_bytes;
- void* conv2d_8_w;
- size_t conv2d_8_w_bytes;
- void* batch_normalization_15_gamma;
- size_t batch_normalization_15_gamma_bytes;
- void* batch_normalization_15_beta;
- size_t batch_normalization_15_beta_bytes;
- void* batch_normalization_15_mean;
- size_t batch_normalization_15_mean_bytes;
- void* batch_normalization_15_variance;
- size_t batch_normalization_15_variance_bytes;
- void* depthwise_conv2d_8_w;
- size_t depthwise_conv2d_8_w_bytes;
- void* batch_normalization_16_gamma;
- size_t batch_normalization_16_gamma_bytes;
- void* batch_normalization_16_beta;
- size_t batch_normalization_16_beta_bytes;
- void* batch_normalization_16_mean;
- size_t batch_normalization_16_mean_bytes;
- void* batch_normalization_16_variance;
- size_t batch_normalization_16_variance_bytes;
- void* conv2d_9_w;
- size_t conv2d_9_w_bytes;
- void* batch_normalization_17_gamma;
- size_t batch_normalization_17_gamma_bytes;
- void* batch_normalization_17_beta;
- size_t batch_normalization_17_beta_bytes;
- void* batch_normalization_17_mean;
- size_t batch_normalization_17_mean_bytes;
- void* batch_normalization_17_variance;
- size_t batch_normalization_17_variance_bytes;
- void* depthwise_conv2d_9_w;
- size_t depthwise_conv2d_9_w_bytes;
- void* batch_normalization_18_gamma;
- size_t batch_normalization_18_gamma_bytes;
- void* batch_normalization_18_beta;
- size_t batch_normalization_18_beta_bytes;
- void* batch_normalization_18_mean;
- size_t batch_normalization_18_mean_bytes;
- void* batch_normalization_18_variance;
- size_t batch_normalization_18_variance_bytes;
- void* conv2d_10_w;
- size_t conv2d_10_w_bytes;
- void* batch_normalization_19_gamma;
- size_t batch_normalization_19_gamma_bytes;
- void* batch_normalization_19_beta;
- size_t batch_normalization_19_beta_bytes;
- void* batch_normalization_19_mean;
- size_t batch_normalization_19_mean_bytes;
- void* batch_normalization_19_variance;
- size_t batch_normalization_19_variance_bytes;
- void* depthwise_conv2d_10_w;
- size_t depthwise_conv2d_10_w_bytes;
- void* batch_normalization_20_gamma;
- size_t batch_normalization_20_gamma_bytes;
- void* batch_normalization_20_beta;
- size_t batch_normalization_20_beta_bytes;
- void* batch_normalization_20_mean;
- size_t batch_normalization_20_mean_bytes;
- void* batch_normalization_20_variance;
- size_t batch_normalization_20_variance_bytes;
- void* conv2d_11_w;
- size_t conv2d_11_w_bytes;
- void* batch_normalization_21_gamma;
- size_t batch_normalization_21_gamma_bytes;
- void* batch_normalization_21_beta;
- size_t batch_normalization_21_beta_bytes;
- void* batch_normalization_21_mean;
- size_t batch_normalization_21_mean_bytes;
- void* batch_normalization_21_variance;
- size_t batch_normalization_21_variance_bytes;
- void* depthwise_conv2d_11_w;
- size_t depthwise_conv2d_11_w_bytes;
- void* batch_normalization_22_gamma;
- size_t batch_normalization_22_gamma_bytes;
- void* batch_normalization_22_beta;
- size_t batch_normalization_22_beta_bytes;
- void* batch_normalization_22_mean;
- size_t batch_normalization_22_mean_bytes;
- void* batch_normalization_22_variance;
- size_t batch_normalization_22_variance_bytes;
- void* conv2d_12_w;
- size_t conv2d_12_w_bytes;
- void* batch_normalization_23_gamma;
- size_t batch_normalization_23_gamma_bytes;
- void* batch_normalization_23_beta;
- size_t batch_normalization_23_beta_bytes;
- void* batch_normalization_23_mean;
- size_t batch_normalization_23_mean_bytes;
- void* batch_normalization_23_variance;
- size_t batch_normalization_23_variance_bytes;
- void* depthwise_conv2d_12_w;
- size_t depthwise_conv2d_12_w_bytes;
- void* batch_normalization_24_gamma;
- size_t batch_normalization_24_gamma_bytes;
- void* batch_normalization_24_beta;
- size_t batch_normalization_24_beta_bytes;
- void* batch_normalization_24_mean;
- size_t batch_normalization_24_mean_bytes;
- void* batch_normalization_24_variance;
- size_t batch_normalization_24_variance_bytes;
- void* conv2d_13_w;
- size_t conv2d_13_w_bytes;
- void* batch_normalization_25_gamma;
- size_t batch_normalization_25_gamma_bytes;
- void* batch_normalization_25_beta;
- size_t batch_normalization_25_beta_bytes;
- void* batch_normalization_25_mean;
- size_t batch_normalization_25_mean_bytes;
- void* batch_normalization_25_variance;
- size_t batch_normalization_25_variance_bytes;
- void* depthwise_conv2d_13_w;
- size_t depthwise_conv2d_13_w_bytes;
- void* batch_normalization_26_gamma;
- size_t batch_normalization_26_gamma_bytes;
- void* batch_normalization_26_beta;
- size_t batch_normalization_26_beta_bytes;
- void* batch_normalization_26_mean;
- size_t batch_normalization_26_mean_bytes;
- void* batch_normalization_26_variance;
- size_t batch_normalization_26_variance_bytes;
- void* conv2d_14_w;
- size_t conv2d_14_w_bytes;
- void* batch_normalization_27_gamma;
- size_t batch_normalization_27_gamma_bytes;
- void* batch_normalization_27_beta;
- size_t batch_normalization_27_beta_bytes;
- void* batch_normalization_27_mean;
- size_t batch_normalization_27_mean_bytes;
- void* batch_normalization_27_variance;
- size_t batch_normalization_27_variance_bytes;
- void* dense_1_w;
- size_t dense_1_w_bytes;
- void* dense_1_b;
- size_t dense_1_b_bytes;
-
- struct ret_t r;
-}
-RootIn;
-
-int main(){
-
-std::string dir_prefix = std::string("data/mobilenet_quant/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
-void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
-void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
-void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
-void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
-std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
-void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
-std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
-void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
-void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
-void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
-void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
-std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
-void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
-void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
-void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
-void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
-std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
-void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
-std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
-void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
-void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
-void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
-void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
-std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
-void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
-void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
-void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
-void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
-void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
-void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
-void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
-void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
-void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
-std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
-void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
-void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
-void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
-void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
-void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
-void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
-void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
-void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
-void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
-std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
-void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
-void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
-void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
-void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
-void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
-void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
-void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
-void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
-void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
-std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
-void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
-void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
-void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
-void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
-void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
-void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
-void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
-void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
-void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
-std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
-void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
-void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
-void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
-void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
-void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
-void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
-void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
-void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
-void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
-void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
-void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
-void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
-void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
-void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
-void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
-void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
-void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
-void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
-void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
-void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
-void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
-void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
-void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
-void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
-void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
-void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
-void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
-void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
-void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
-void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
-void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
-void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
-void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
-void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
-void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
-void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
-void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
-void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
-void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
-void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
-void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
-void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
-void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
-void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
-void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
-void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
-void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
-void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
-void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
-void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
-void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
-void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
-void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
-void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1);
-std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
-void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
-void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
-void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
-void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1);
-std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
-void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3);
-std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
-void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
-void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
-void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
-void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1);
-std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
-void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1);
-std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
-void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
-void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
-void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
-void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-
-__visc__init();
-RootIn* args = static_cast<RootIn*>(malloc(sizeof(RootIn)));
-
-args->input = input;
-args->input_bytes = 0;
-args->conv2d_1_w = conv2d_1_w;
-args->conv2d_1_w_bytes = 0;
-args->batch_normalization_1_gamma = batch_normalization_1_gamma;
-args->batch_normalization_1_gamma_bytes = 0;
-args->batch_normalization_1_beta = batch_normalization_1_beta;
-args->batch_normalization_1_beta_bytes = 0;
-args->batch_normalization_1_mean = batch_normalization_1_mean;
-args->batch_normalization_1_mean_bytes = 0;
-args->batch_normalization_1_variance = batch_normalization_1_variance;
-args->batch_normalization_1_variance_bytes = 0;
-args->depthwise_conv2d_1_w = depthwise_conv2d_1_w;
-args->depthwise_conv2d_1_w_bytes = 0;
-args->batch_normalization_2_gamma = batch_normalization_2_gamma;
-args->batch_normalization_2_gamma_bytes = 0;
-args->batch_normalization_2_beta = batch_normalization_2_beta;
-args->batch_normalization_2_beta_bytes = 0;
-args->batch_normalization_2_mean = batch_normalization_2_mean;
-args->batch_normalization_2_mean_bytes = 0;
-args->batch_normalization_2_variance = batch_normalization_2_variance;
-args->batch_normalization_2_variance_bytes = 0;
-args->conv2d_2_w = conv2d_2_w;
-args->conv2d_2_w_bytes = 0;
-args->batch_normalization_3_gamma = batch_normalization_3_gamma;
-args->batch_normalization_3_gamma_bytes = 0;
-args->batch_normalization_3_beta = batch_normalization_3_beta;
-args->batch_normalization_3_beta_bytes = 0;
-args->batch_normalization_3_mean = batch_normalization_3_mean;
-args->batch_normalization_3_mean_bytes = 0;
-args->batch_normalization_3_variance = batch_normalization_3_variance;
-args->batch_normalization_3_variance_bytes = 0;
-args->depthwise_conv2d_2_w = depthwise_conv2d_2_w;
-args->depthwise_conv2d_2_w_bytes = 0;
-args->batch_normalization_4_gamma = batch_normalization_4_gamma;
-args->batch_normalization_4_gamma_bytes = 0;
-args->batch_normalization_4_beta = batch_normalization_4_beta;
-args->batch_normalization_4_beta_bytes = 0;
-args->batch_normalization_4_mean = batch_normalization_4_mean;
-args->batch_normalization_4_mean_bytes = 0;
-args->batch_normalization_4_variance = batch_normalization_4_variance;
-args->batch_normalization_4_variance_bytes = 0;
-args->conv2d_3_w = conv2d_3_w;
-args->conv2d_3_w_bytes = 0;
-args->batch_normalization_5_gamma = batch_normalization_5_gamma;
-args->batch_normalization_5_gamma_bytes = 0;
-args->batch_normalization_5_beta = batch_normalization_5_beta;
-args->batch_normalization_5_beta_bytes = 0;
-args->batch_normalization_5_mean = batch_normalization_5_mean;
-args->batch_normalization_5_mean_bytes = 0;
-args->batch_normalization_5_variance = batch_normalization_5_variance;
-args->batch_normalization_5_variance_bytes = 0;
-args->depthwise_conv2d_3_w = depthwise_conv2d_3_w;
-args->depthwise_conv2d_3_w_bytes = 0;
-args->batch_normalization_6_gamma = batch_normalization_6_gamma;
-args->batch_normalization_6_gamma_bytes = 0;
-args->batch_normalization_6_beta = batch_normalization_6_beta;
-args->batch_normalization_6_beta_bytes = 0;
-args->batch_normalization_6_mean = batch_normalization_6_mean;
-args->batch_normalization_6_mean_bytes = 0;
-args->batch_normalization_6_variance = batch_normalization_6_variance;
-args->batch_normalization_6_variance_bytes = 0;
-args->conv2d_4_w = conv2d_4_w;
-args->conv2d_4_w_bytes = 0;
-args->batch_normalization_7_gamma = batch_normalization_7_gamma;
-args->batch_normalization_7_gamma_bytes = 0;
-args->batch_normalization_7_beta = batch_normalization_7_beta;
-args->batch_normalization_7_beta_bytes = 0;
-args->batch_normalization_7_mean = batch_normalization_7_mean;
-args->batch_normalization_7_mean_bytes = 0;
-args->batch_normalization_7_variance = batch_normalization_7_variance;
-args->batch_normalization_7_variance_bytes = 0;
-args->depthwise_conv2d_4_w = depthwise_conv2d_4_w;
-args->depthwise_conv2d_4_w_bytes = 0;
-args->batch_normalization_8_gamma = batch_normalization_8_gamma;
-args->batch_normalization_8_gamma_bytes = 0;
-args->batch_normalization_8_beta = batch_normalization_8_beta;
-args->batch_normalization_8_beta_bytes = 0;
-args->batch_normalization_8_mean = batch_normalization_8_mean;
-args->batch_normalization_8_mean_bytes = 0;
-args->batch_normalization_8_variance = batch_normalization_8_variance;
-args->batch_normalization_8_variance_bytes = 0;
-args->conv2d_5_w = conv2d_5_w;
-args->conv2d_5_w_bytes = 0;
-args->batch_normalization_9_gamma = batch_normalization_9_gamma;
-args->batch_normalization_9_gamma_bytes = 0;
-args->batch_normalization_9_beta = batch_normalization_9_beta;
-args->batch_normalization_9_beta_bytes = 0;
-args->batch_normalization_9_mean = batch_normalization_9_mean;
-args->batch_normalization_9_mean_bytes = 0;
-args->batch_normalization_9_variance = batch_normalization_9_variance;
-args->batch_normalization_9_variance_bytes = 0;
-args->depthwise_conv2d_5_w = depthwise_conv2d_5_w;
-args->depthwise_conv2d_5_w_bytes = 0;
-args->batch_normalization_10_gamma = batch_normalization_10_gamma;
-args->batch_normalization_10_gamma_bytes = 0;
-args->batch_normalization_10_beta = batch_normalization_10_beta;
-args->batch_normalization_10_beta_bytes = 0;
-args->batch_normalization_10_mean = batch_normalization_10_mean;
-args->batch_normalization_10_mean_bytes = 0;
-args->batch_normalization_10_variance = batch_normalization_10_variance;
-args->batch_normalization_10_variance_bytes = 0;
-args->conv2d_6_w = conv2d_6_w;
-args->conv2d_6_w_bytes = 0;
-args->batch_normalization_11_gamma = batch_normalization_11_gamma;
-args->batch_normalization_11_gamma_bytes = 0;
-args->batch_normalization_11_beta = batch_normalization_11_beta;
-args->batch_normalization_11_beta_bytes = 0;
-args->batch_normalization_11_mean = batch_normalization_11_mean;
-args->batch_normalization_11_mean_bytes = 0;
-args->batch_normalization_11_variance = batch_normalization_11_variance;
-args->batch_normalization_11_variance_bytes = 0;
-args->depthwise_conv2d_6_w = depthwise_conv2d_6_w;
-args->depthwise_conv2d_6_w_bytes = 0;
-args->batch_normalization_12_gamma = batch_normalization_12_gamma;
-args->batch_normalization_12_gamma_bytes = 0;
-args->batch_normalization_12_beta = batch_normalization_12_beta;
-args->batch_normalization_12_beta_bytes = 0;
-args->batch_normalization_12_mean = batch_normalization_12_mean;
-args->batch_normalization_12_mean_bytes = 0;
-args->batch_normalization_12_variance = batch_normalization_12_variance;
-args->batch_normalization_12_variance_bytes = 0;
-args->conv2d_7_w = conv2d_7_w;
-args->conv2d_7_w_bytes = 0;
-args->batch_normalization_13_gamma = batch_normalization_13_gamma;
-args->batch_normalization_13_gamma_bytes = 0;
-args->batch_normalization_13_beta = batch_normalization_13_beta;
-args->batch_normalization_13_beta_bytes = 0;
-args->batch_normalization_13_mean = batch_normalization_13_mean;
-args->batch_normalization_13_mean_bytes = 0;
-args->batch_normalization_13_variance = batch_normalization_13_variance;
-args->batch_normalization_13_variance_bytes = 0;
-args->depthwise_conv2d_7_w = depthwise_conv2d_7_w;
-args->depthwise_conv2d_7_w_bytes = 0;
-args->batch_normalization_14_gamma = batch_normalization_14_gamma;
-args->batch_normalization_14_gamma_bytes = 0;
-args->batch_normalization_14_beta = batch_normalization_14_beta;
-args->batch_normalization_14_beta_bytes = 0;
-args->batch_normalization_14_mean = batch_normalization_14_mean;
-args->batch_normalization_14_mean_bytes = 0;
-args->batch_normalization_14_variance = batch_normalization_14_variance;
-args->batch_normalization_14_variance_bytes = 0;
-args->conv2d_8_w = conv2d_8_w;
-args->conv2d_8_w_bytes = 0;
-args->batch_normalization_15_gamma = batch_normalization_15_gamma;
-args->batch_normalization_15_gamma_bytes = 0;
-args->batch_normalization_15_beta = batch_normalization_15_beta;
-args->batch_normalization_15_beta_bytes = 0;
-args->batch_normalization_15_mean = batch_normalization_15_mean;
-args->batch_normalization_15_mean_bytes = 0;
-args->batch_normalization_15_variance = batch_normalization_15_variance;
-args->batch_normalization_15_variance_bytes = 0;
-args->depthwise_conv2d_8_w = depthwise_conv2d_8_w;
-args->depthwise_conv2d_8_w_bytes = 0;
-args->batch_normalization_16_gamma = batch_normalization_16_gamma;
-args->batch_normalization_16_gamma_bytes = 0;
-args->batch_normalization_16_beta = batch_normalization_16_beta;
-args->batch_normalization_16_beta_bytes = 0;
-args->batch_normalization_16_mean = batch_normalization_16_mean;
-args->batch_normalization_16_mean_bytes = 0;
-args->batch_normalization_16_variance = batch_normalization_16_variance;
-args->batch_normalization_16_variance_bytes = 0;
-args->conv2d_9_w = conv2d_9_w;
-args->conv2d_9_w_bytes = 0;
-args->batch_normalization_17_gamma = batch_normalization_17_gamma;
-args->batch_normalization_17_gamma_bytes = 0;
-args->batch_normalization_17_beta = batch_normalization_17_beta;
-args->batch_normalization_17_beta_bytes = 0;
-args->batch_normalization_17_mean = batch_normalization_17_mean;
-args->batch_normalization_17_mean_bytes = 0;
-args->batch_normalization_17_variance = batch_normalization_17_variance;
-args->batch_normalization_17_variance_bytes = 0;
-args->depthwise_conv2d_9_w = depthwise_conv2d_9_w;
-args->depthwise_conv2d_9_w_bytes = 0;
-args->batch_normalization_18_gamma = batch_normalization_18_gamma;
-args->batch_normalization_18_gamma_bytes = 0;
-args->batch_normalization_18_beta = batch_normalization_18_beta;
-args->batch_normalization_18_beta_bytes = 0;
-args->batch_normalization_18_mean = batch_normalization_18_mean;
-args->batch_normalization_18_mean_bytes = 0;
-args->batch_normalization_18_variance = batch_normalization_18_variance;
-args->batch_normalization_18_variance_bytes = 0;
-args->conv2d_10_w = conv2d_10_w;
-args->conv2d_10_w_bytes = 0;
-args->batch_normalization_19_gamma = batch_normalization_19_gamma;
-args->batch_normalization_19_gamma_bytes = 0;
-args->batch_normalization_19_beta = batch_normalization_19_beta;
-args->batch_normalization_19_beta_bytes = 0;
-args->batch_normalization_19_mean = batch_normalization_19_mean;
-args->batch_normalization_19_mean_bytes = 0;
-args->batch_normalization_19_variance = batch_normalization_19_variance;
-args->batch_normalization_19_variance_bytes = 0;
-args->depthwise_conv2d_10_w = depthwise_conv2d_10_w;
-args->depthwise_conv2d_10_w_bytes = 0;
-args->batch_normalization_20_gamma = batch_normalization_20_gamma;
-args->batch_normalization_20_gamma_bytes = 0;
-args->batch_normalization_20_beta = batch_normalization_20_beta;
-args->batch_normalization_20_beta_bytes = 0;
-args->batch_normalization_20_mean = batch_normalization_20_mean;
-args->batch_normalization_20_mean_bytes = 0;
-args->batch_normalization_20_variance = batch_normalization_20_variance;
-args->batch_normalization_20_variance_bytes = 0;
-args->conv2d_11_w = conv2d_11_w;
-args->conv2d_11_w_bytes = 0;
-args->batch_normalization_21_gamma = batch_normalization_21_gamma;
-args->batch_normalization_21_gamma_bytes = 0;
-args->batch_normalization_21_beta = batch_normalization_21_beta;
-args->batch_normalization_21_beta_bytes = 0;
-args->batch_normalization_21_mean = batch_normalization_21_mean;
-args->batch_normalization_21_mean_bytes = 0;
-args->batch_normalization_21_variance = batch_normalization_21_variance;
-args->batch_normalization_21_variance_bytes = 0;
-args->depthwise_conv2d_11_w = depthwise_conv2d_11_w;
-args->depthwise_conv2d_11_w_bytes = 0;
-args->batch_normalization_22_gamma = batch_normalization_22_gamma;
-args->batch_normalization_22_gamma_bytes = 0;
-args->batch_normalization_22_beta = batch_normalization_22_beta;
-args->batch_normalization_22_beta_bytes = 0;
-args->batch_normalization_22_mean = batch_normalization_22_mean;
-args->batch_normalization_22_mean_bytes = 0;
-args->batch_normalization_22_variance = batch_normalization_22_variance;
-args->batch_normalization_22_variance_bytes = 0;
-args->conv2d_12_w = conv2d_12_w;
-args->conv2d_12_w_bytes = 0;
-args->batch_normalization_23_gamma = batch_normalization_23_gamma;
-args->batch_normalization_23_gamma_bytes = 0;
-args->batch_normalization_23_beta = batch_normalization_23_beta;
-args->batch_normalization_23_beta_bytes = 0;
-args->batch_normalization_23_mean = batch_normalization_23_mean;
-args->batch_normalization_23_mean_bytes = 0;
-args->batch_normalization_23_variance = batch_normalization_23_variance;
-args->batch_normalization_23_variance_bytes = 0;
-args->depthwise_conv2d_12_w = depthwise_conv2d_12_w;
-args->depthwise_conv2d_12_w_bytes = 0;
-args->batch_normalization_24_gamma = batch_normalization_24_gamma;
-args->batch_normalization_24_gamma_bytes = 0;
-args->batch_normalization_24_beta = batch_normalization_24_beta;
-args->batch_normalization_24_beta_bytes = 0;
-args->batch_normalization_24_mean = batch_normalization_24_mean;
-args->batch_normalization_24_mean_bytes = 0;
-args->batch_normalization_24_variance = batch_normalization_24_variance;
-args->batch_normalization_24_variance_bytes = 0;
-args->conv2d_13_w = conv2d_13_w;
-args->conv2d_13_w_bytes = 0;
-args->batch_normalization_25_gamma = batch_normalization_25_gamma;
-args->batch_normalization_25_gamma_bytes = 0;
-args->batch_normalization_25_beta = batch_normalization_25_beta;
-args->batch_normalization_25_beta_bytes = 0;
-args->batch_normalization_25_mean = batch_normalization_25_mean;
-args->batch_normalization_25_mean_bytes = 0;
-args->batch_normalization_25_variance = batch_normalization_25_variance;
-args->batch_normalization_25_variance_bytes = 0;
-args->depthwise_conv2d_13_w = depthwise_conv2d_13_w;
-args->depthwise_conv2d_13_w_bytes = 0;
-args->batch_normalization_26_gamma = batch_normalization_26_gamma;
-args->batch_normalization_26_gamma_bytes = 0;
-args->batch_normalization_26_beta = batch_normalization_26_beta;
-args->batch_normalization_26_beta_bytes = 0;
-args->batch_normalization_26_mean = batch_normalization_26_mean;
-args->batch_normalization_26_mean_bytes = 0;
-args->batch_normalization_26_variance = batch_normalization_26_variance;
-args->batch_normalization_26_variance_bytes = 0;
-args->conv2d_14_w = conv2d_14_w;
-args->conv2d_14_w_bytes = 0;
-args->batch_normalization_27_gamma = batch_normalization_27_gamma;
-args->batch_normalization_27_gamma_bytes = 0;
-args->batch_normalization_27_beta = batch_normalization_27_beta;
-args->batch_normalization_27_beta_bytes = 0;
-args->batch_normalization_27_mean = batch_normalization_27_mean;
-args->batch_normalization_27_mean_bytes = 0;
-args->batch_normalization_27_variance = batch_normalization_27_variance;
-args->batch_normalization_27_variance_bytes = 0;
-args->dense_1_w = dense_1_w;
-args->dense_1_w_bytes = 0;
-args->dense_1_b = dense_1_b;
-args->dense_1_b_bytes = 0;
-
-void* dfg = __visc__launch(0, root, (void*) args);
-
-__visc__wait(dfg);
-
-void *result = static_cast<RootIn*>(args)->input;
-hpvm_request_tensor(result, 0);
-
-__visc__cleanup();
- computeAccuracy2(labels, 10000, result);
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_beta.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_beta.bin
deleted file mode 100644
index bb1eb07a8e262d2f4d941578fd4c19d6a90c7562..0000000000000000000000000000000000000000
Binary files a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_beta.bin and /dev/null differ
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_gamma.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_gamma.bin
deleted file mode 100644
index 931c8925b89f363a41d3cf81483bde60abafba61..0000000000000000000000000000000000000000
Binary files a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_gamma.bin and /dev/null differ
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_mean.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_mean.bin
deleted file mode 100644
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Binary files a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_mean.bin and /dev/null differ
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_variance.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_variance.bin
deleted file mode 100644
index f92c73f59eb5eb35ca94e3ce006e5f3c4f60ecef..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_10_variance.bin
+++ /dev/null
@@ -1,2 +0,0 @@
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_beta.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_beta.bin
deleted file mode 100644
index 5918477d3638e851c3fdfc47dc550cea3afa7d50..0000000000000000000000000000000000000000
Binary files a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_beta.bin and /dev/null differ
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_gamma.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_gamma.bin
deleted file mode 100644
index 6b3d705199383135bed811a6fdaa237d754487bd..0000000000000000000000000000000000000000
Binary files a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_gamma.bin and /dev/null differ
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_mean.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_mean.bin
deleted file mode 100644
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Binary files a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_mean.bin and /dev/null differ
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_variance.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_variance.bin
deleted file mode 100644
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Binary files a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_11_variance.bin and /dev/null differ
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_12_beta.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_12_beta.bin
deleted file mode 100644
index 8ade4cf080d7d3228e752d284ed500ba6300d261..0000000000000000000000000000000000000000
Binary files a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_12_beta.bin and /dev/null differ
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_12_gamma.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_12_gamma.bin
deleted file mode 100644
index 6dfb7c3833821b29f9230df806c4abc0c16a7b59..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_12_gamma.bin
+++ /dev/null
@@ -1,5 +0,0 @@
- «S?#ªI?ùàn? :g?o%P?ßR†?OŸP?û <?á6?†W?ÅM?ߣH?B»d? i_?Ã{W?ÅßC?
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m?¥Û@?P›L?ìÕ˜?'î?ò˜Y?Ä1?&±?$L?1¡¬?— G?ÚIw?˜ñ?z4? |K?ñN?,™@?a¦H? dZ?ÈóY?s´N?Ÿ)ˆ?°yd?³ù£?†\?<èX?ŽåO?¬N?²ÚO?™?4aQ?Xy?
-)Ÿ?›^?7uF?( X? hš?³?A3u?¸-“?«7P?=×a?œ‹C?ßøˆ?
qq?$ÚP?àß?Šì¨?ö^?%œp?kO?”Q?Š d?_G?ˆ??ïÞ@?½œk?<öV?¬<R?°>?.jO?„ Œ?2¬Q?¥ûª?µÊY?ÓÙD?L—f?EU?c²6?O©®?Z(H?‰Š?KX?p¦T?‚Jm?…;?ÇŸŠ?¶€?ým?Øp?¨@?~Ó^?;öC?/€[?ÃÑ©?zÅ‹?1éH?ìT>?p b?q9^?| K?
-ÞS?ÑE?[ô[?;ï’?/0?¬`?°µZ?FuX?o@c?eÑn?ÛvL?>š?êo”?ïèg?
-·q?2èF?AI™?j™=?7ÖZ?üó_?$ÆH?¹È_?øaW?jªƒ?ÅwT?^Ïm?•P?tZm?ns??°r^?]»U?@bk?†Ž?fŸ?ð'†?ñ´‘?ߤ?#Ñž?V'¢?[hM?fY]?
¤?’?q¼a?Ýþ[?g}¡?IJH?Š3”?ç\F?þ]?ÂS?w2D?—!V?¹IV?èõL?¡Œ_?øâ™?ÖxH?‘ùX?éH?+K8?†áV?ûR?–ß“?¶EI?˜'Z?Ñì^?¶~@?ö:–?ª¼_?
²ž?ÒÐ]?C¼·?!V?ÕK?Âc‘?9i”?X Y?$¸D??Å©?V#E?>Ž`?!Z?Éñ˜?c¯U?Öæš?v9?jfX?ÌvŸ?3aV?ÍðR?qt‘?ü
?ôýE?b{B?(ß7?{I\?& K?
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_12_mean.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/batch_normalization_12_mean.bin
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-conv
-
-activation
-
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-activation
-conv
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-activation
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-activation
-conv
-
-activation
-
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-activation
-conv
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-activation
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-dense add
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/layers.txt b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/layers.txt
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@@ -1,83 +0,0 @@
-Conv1,10000,3,32,32,32,3,3,3
-#tensorBatchNorm1
-#tensorRelu1
-#tensorDepthwiseConv1
-#tensorBatchNorm2
-#tensorRelu2
-Conv2,10000,32,32,32,64,32,1,1
-#tensorBatchNorm3
-#tensorRelu3
-#tensorDepthwiseConv2
-#tensorBatchNorm4
-#tensorRelu4
-Conv3,10000,64,16,16,128,64,1,1
-#tensorBatchNorm5
-#tensorRelu5
-#tensorDepthwiseConv3
-#tensorBatchNorm6
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-Conv4,10000,128,16,16,128,128,1,1
-#tensorBatchNorm7
-#tensorRelu7
-#tensorDepthwiseConv4
-#tensorBatchNorm8
-#tensorRelu8
-Conv5,10000,128,8,8,256,128,1,1
-#tensorBatchNorm9
-#tensorRelu9
-#tensorDepthwiseConv5
-#tensorBatchNorm10
-#tensorRelu10
-Conv6,10000,256,8,8,256,256,1,1
-#tensorBatchNorm11
-#tensorRelu11
-#tensorDepthwiseConv6
-#tensorBatchNorm12
-#tensorRelu12
-Conv7,10000,256,4,4,512,256,1,1
-#tensorBatchNorm13
-#tensorRelu13
-#tensorDepthwiseConv7
-#tensorBatchNorm14
-#tensorRelu14
-Conv8,10000,512,4,4,512,512,1,1
-#tensorBatchNorm15
-#tensorRelu15
-#tensorDepthwiseConv8
-#tensorBatchNorm16
-#tensorRelu16
-Conv9,10000,512,4,4,512,512,1,1
-#tensorBatchNorm17
-#tensorRelu17
-#tensorDepthwiseConv9
-#tensorBatchNorm18
-#tensorRelu18
-Conv10,10000,512,4,4,512,512,1,1
-#tensorBatchNorm19
-#tensorRelu19
-#tensorDepthwiseConv10
-#tensorBatchNorm20
-#tensorRelu20
-Conv11,10000,512,4,4,512,512,1,1
-#tensorBatchNorm21
-#tensorRelu21
-#tensorDepthwiseConv11
-#tensorBatchNorm22
-#tensorRelu22
-Conv12,10000,512,4,4,512,512,1,1
-#tensorBatchNorm23
-#tensorRelu23
-#tensorDepthwiseConv12
-#tensorBatchNorm24
-#tensorRelu24
-Conv13,10000,512,2,2,1024,512,1,1
-#tensorBatchNorm25
-#tensorRelu25
-#tensorDepthwiseConv13
-#tensorBatchNorm26
-#tensorRelu26
-Conv14,10000,1024,2,2,1024,1024,1,1
-#tensorBatchNorm27
-#tensorRelu27
-#tensorPooling1
-FC1,10000,1024,1024,10
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/mobilenet_layers.txt b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/mobilenet_layers.txt
deleted file mode 100644
index c2a4a29509ad89724905c869ff900f8ecaa5bf8c..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/mobilenet_layers.txt
+++ /dev/null
@@ -1,83 +0,0 @@
-Conv1,10000,3,32,32,32,3,3,3
-NML1
-NML2
-NML3
-NML4
-NML5
-Conv3,10000,32,32,32,64,32,1,1
-NML6
-NML7
-NML8
-NML9
-NML10
-Conv5,10000,64,16,16,128,64,1,1
-NML11
-NML12
-NML13
-NML14
-NML15
-Conv7,10000,128,16,16,128,128,1,1
-NML16
-NML17
-NML18
-NML19
-NML20
-Conv9,10000,128,8,8,256,128,1,1
-NML21
-NML22
-NML23
-NML24
-NML25
-Conv11,10000,256,8,8,256,256,1,1
-NML26
-NML27
-NML28
-NML29
-NML30
-Conv13,10000,256,4,4,512,256,1,1
-NML31
-NML32
-NML33
-NML34
-NML35
-Conv15,10000,512,4,4,512,512,1,1
-NML36
-NML37
-NML38
-NML39
-NML40
-Conv17,10000,512,4,4,512,512,1,1
-NML41
-NML42
-NML43
-NML44
-NML45
-Conv19,10000,512,4,4,512,512,1,1
-NML46
-NML47
-NML48
-NML49
-NML50
-Conv21,10000,512,4,4,512,512,1,1
-NML51
-NML52
-NML53
-NML54
-NML55
-Conv23,10000,512,4,4,512,512,1,1
-NML56
-NML57
-NML58
-NML59
-NML60
-Conv25,10000,512,2,2,1024,512,1,1
-NML61
-NML62
-NML63
-NML64
-NML65
-Conv27,10000,1024,2,2,1024,1024,1,1
-NML66
-NML67
-NML68
-FC1,10000,1024,1024,10
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/mobilenet_ops.txt b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/mobilenet_ops.txt
deleted file mode 100644
index 8e18f2ec58cddb9ab0251229b1e908b23b71d6bc..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/mobilenet_ops.txt
+++ /dev/null
@@ -1,165 +0,0 @@
-#Conv1,1
-Conv1
-#NML1,1
-BatchNorm1
-#NML2,1
-Relu1
-#NML3,1
-Conv2
-#NML4,1
-BatchNorm2
-#NML5,1
-Relu2
-#Conv3,1
-Conv3
-#NML6,1
-BatchNorm3
-#NML7,1
-Relu3
-#NML8,1
-Conv4
-#NML9,1
-BatchNorm4
-#NML10,1
-Relu4
-#Conv5,1
-Conv5
-#NML11,1
-BatchNorm5
-#NML12,1
-Relu5
-#NML13,1
-Conv6
-#NML14,1
-BatchNorm6
-#NML15,1
-Relu6
-#Conv7,1
-Conv7
-#NML16,1
-BatchNorm7
-#NML17,1
-Relu7
-#NML18,1
-Conv8
-#NML19,1
-BatchNorm8
-#NML20,1
-Relu8
-#Conv9,1
-Conv9
-#NML21,1
-BatchNorm9
-#NML22,1
-Relu9
-#NML23,1
-Conv10
-#NML24,1
-BatchNorm10
-#NML25,1
-Relu10
-#Conv11,1
-Conv11
-#NML26,1
-BatchNorm11
-#NML27,1
-Relu11
-#NML28,1
-Conv12
-#NML29,1
-BatchNorm12
-#NML30,1
-Relu12
-#Conv13,1
-Conv13
-#NML31,1
-BatchNorm13
-#NML32,1
-Relu13
-#NML33,1
-Conv14
-#NML34,1
-BatchNorm14
-#NML35,1
-Relu14
-#Conv15,1
-Conv15
-#NML36,1
-BatchNorm15
-#NML37,1
-Relu15
-#NML38,1
-Conv16
-#NML39,1
-BatchNorm16
-#NML40,1
-Relu16
-#Conv17,1
-Conv17
-#NML41,1
-BatchNorm17
-#NML42,1
-Relu17
-#NML43,1
-Conv18
-#NML44,1
-BatchNorm18
-#NML45,1
-Relu18
-#Conv19,1
-Conv19
-#NML46,1
-BatchNorm19
-#NML47,1
-Relu19
-#NML48,1
-Conv20
-#NML49,1
-BatchNorm20
-#NML50,1
-Relu20
-#Conv21,1
-conv21
-#NML51,1
-BatchNorm21
-#NML52,1
-Relu21
-#NML53,1
-Conv22
-#NML54,1
-BatchNorm22
-#NML55,1
-Relu22
-#Conv23,1
-Conv23
-#NML56,1
-BatchNorm23
-#NML57,1
-Relu23
-#NML58,1
-Conv24
-#NML59,1
-BatchNorm24
-#NML60,1
-Relu24
-#Conv25,1
-Conv25
-#NML61,1
-BatchNorm25
-#NML62,1
-Relu25
-#NML63,1
-Conv26
-#NML64,1
-BatchNorm26
-#NML65,1
-Relu26
-#Conv27,1
-Conv27
-#NML66,1
-BatchNorm27
-#NML67,1
-Relu27
-#NML68,1
-Pool1
-FC1,10000,1024,1024,10
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/promise_src.cc b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/promise_src.cc
deleted file mode 100644
index 146bc640cc4b1e8da65e3e7bb6cb5c7f2a007399..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/promise_src.cc
+++ /dev/null
@@ -1,420 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-int total_runs = 100;
-for (int i = 0 ; i < total_runs; i++){
-
-
-startMemTracking();
-
-int test_input_size = 10000;
-int batch_size = 10000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-
-
-std::string dir_prefix = std::string("data/mobilenet_quant/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
-void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
-void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
-void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
-void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
-std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
-void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
-std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
-void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
-void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
-void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
-void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
-std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
-void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
-void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
-void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
-void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
-std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
-void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
-std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
-void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
-void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
-void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
-void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
-std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
-void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
-void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
-void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
-void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
-void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
-void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
-void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
-void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
-void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
-std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
-void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
-void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
-void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
-void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
-void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
-void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
-void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
-void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
-void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
-std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
-void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
-void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
-void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
-void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
-void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
-void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
-void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
-void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
-void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
-std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
-void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
-void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
-void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
-void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
-void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
-void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
-void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
-void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
-void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
-std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
-void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
-void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
-void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
-void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
-void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
-void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
-void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
-void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
-void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
-void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
-void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
-void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
-void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
-void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
-void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
-void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
-void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
-void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
-void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
-void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
-void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
-void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
-void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
-void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
-void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
-void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
-void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
-void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
-void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
-void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
-void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
-void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
-void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
-void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
-void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
-void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
-void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
-void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
-void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
-void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
-void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
-void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
-void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
-void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
-void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
-void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
-void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
-void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
-void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
-void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
-void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
-void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
-void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
-void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1);
-std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
-void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
-void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
-void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
-void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1);
-std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
-void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3);
-std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
-void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
-void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
-void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
-void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1);
-std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
-void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1);
-std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
-void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
-void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
-void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
-void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = ConvLayer_PROMISE(input, -1.9892114, 2.126797, conv2d_1_w, -2.196306920051575, 1.347581704139706, NULL, 0, 0, 1, 1, 1, 1, -1, 0, -1, -60.89275047302246, 51.99256916046146, 9);
-void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
-void* var_2 = tensorRelu(var_1);
-void* var_3 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
-void* var_4 = tensorBatchNorm(var_3, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
-void* var_5 = tensorRelu(var_4);
-void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 5.713541553974245, conv2d_2_w, -0.9317721160650253, 1.0774258937835774, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.518589503288269, 6.810842518806449, 9);
-void* var_7 = tensorBatchNorm(var_6, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
-void* var_8 = tensorRelu(var_7);
-void* var_9 = tensorConvolution(var_8, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
-void* var_10 = tensorBatchNorm(var_9, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
-void* var_11 = tensorRelu(var_10);
-void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.932139402866376, conv2d_3_w, -0.5316544661521911, 0.5753790403604531, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.482631235122681, 3.96730119752885, 9);
-void* var_13 = tensorBatchNorm(var_12, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
-void* var_14 = tensorRelu(var_13);
-void* var_15 = tensorConvolution(var_14, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
-void* var_16 = tensorBatchNorm(var_15, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
-void* var_17 = tensorRelu(var_16);
-void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 4.103263397693674, conv2d_4_w, -0.36234098821878435, 0.4076913900375366, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.04261828327179, 3.88677932929993, 9);
-void* var_19 = tensorBatchNorm(var_18, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
-void* var_20 = tensorRelu(var_19);
-void* var_21 = tensorConvolution(var_20, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
-void* var_22 = tensorBatchNorm(var_21, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
-void* var_23 = tensorRelu(var_22);
-void* var_24 = ConvLayer_PROMISE(var_23, 0.0, 5.383221302509475, conv2d_5_w, -0.3131200549006462, 0.29357679939270065, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.921469215393066, 4.338679324150087, 9);
-void* var_25 = tensorBatchNorm(var_24, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
-void* var_26 = tensorRelu(var_25);
-void* var_27 = tensorConvolution(var_26, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
-void* var_28 = tensorBatchNorm(var_27, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
-void* var_29 = tensorRelu(var_28);
-void* var_30 = ConvLayer_PROMISE(var_29, 0.0, 4.316738154411368, conv2d_6_w, -0.23299247801303866, 0.2580290257930756, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.207789947509766, 3.932436970710759, 9);
-void* var_31 = tensorBatchNorm(var_30, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
-void* var_32 = tensorRelu(var_31);
-void* var_33 = tensorConvolution(var_32, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
-void* var_34 = tensorBatchNorm(var_33, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
-void* var_35 = tensorRelu(var_34);
-void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 5.830408106803901, conv2d_7_w, -0.20233777219057084, 0.18998308175802117, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.298286915779113, 4.848135117530843, 9);
-void* var_37 = tensorBatchNorm(var_36, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
-void* var_38 = tensorRelu(var_37);
-void* var_39 = tensorConvolution(var_38, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512);
-void* var_40 = tensorBatchNorm(var_39, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001);
-void* var_41 = tensorRelu(var_40);
-void* var_42 = ConvLayer_PROMISE(var_41, 0.0, 4.446417809963227, conv2d_8_w, -0.17442735651135444, 0.17695830866694454, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.347910885810852, 3.6144364695549145, 9);
-void* var_43 = tensorBatchNorm(var_42, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001);
-void* var_44 = tensorRelu(var_43);
-void* var_45 = tensorConvolution(var_44, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512);
-void* var_46 = tensorBatchNorm(var_45, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001);
-void* var_47 = tensorRelu(var_46);
-void* var_48 = ConvLayer_PROMISE(var_47, 0.0, 4.518095604896667, conv2d_9_w, -0.14546796187758446, 0.15256431668996823, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -3.0287702755928043, 2.9487365779876953, 9);
-void* var_49 = tensorBatchNorm(var_48, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001);
-void* var_50 = tensorRelu(var_49);
-void* var_51 = tensorConvolution(var_50, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512);
-void* var_52 = tensorBatchNorm(var_51, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001);
-void* var_53 = tensorRelu(var_52);
-void* var_54 = ConvLayer_PROMISE(var_53, 0.0, 6.348575634956407, conv2d_10_w, -0.13025874522328376, 0.13558243343234128, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.2293100805282595, 3.5315046372413645, 9);
-void* var_55 = tensorBatchNorm(var_54, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001);
-void* var_56 = tensorRelu(var_55);
-void* var_57 = tensorConvolution(var_56, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512);
-void* var_58 = tensorBatchNorm(var_57, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001);
-void* var_59 = tensorRelu(var_58);
-void* var_60 = ConvLayer_PROMISE(var_59, 0.0, 5.221003110408843, conv2d_11_w, -0.11900172759592534, 0.12536374783515936, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.038203780174255, 4.004009407043483, 9);
-void* var_61 = tensorBatchNorm(var_60, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001);
-void* var_62 = tensorRelu(var_61);
-void* var_63 = tensorConvolution(var_62, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512);
-void* var_64 = tensorBatchNorm(var_63, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001);
-void* var_65 = tensorRelu(var_64);
-void* var_66 = ConvLayer_PROMISE(var_65, 0.0, 5.732498347759442, conv2d_12_w, -0.10839721685647964, 0.11625668607652187, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -3.3111015114784244, 4.462933233261136, 9);
-void* var_67 = tensorBatchNorm(var_66, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001);
-void* var_68 = tensorRelu(var_67);
-void* var_69 = tensorConvolution(var_68, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512);
-void* var_70 = tensorBatchNorm(var_69, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001);
-void* var_71 = tensorRelu(var_70);
-void* var_72 = ConvLayer_PROMISE(var_71, 0.0, 7.240498211860681, conv2d_13_w, -0.08623744961619377, 0.08859449951350662, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.175431394577027, 6.2043294754027345, 9);
-void* var_73 = tensorBatchNorm(var_72, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001);
-void* var_74 = tensorRelu(var_73);
-void* var_75 = tensorConvolution(var_74, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024);
-void* var_76 = tensorBatchNorm(var_75, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001);
-void* var_77 = tensorRelu(var_76);
-void* var_78 = ConvLayer_PROMISE(var_77, 0.0, 7.813958834648251, conv2d_14_w, -0.06813025139272214, 0.07002027779817581, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -10.920566423416137, 2.6442912578582534, 9);
-void* var_79 = tensorBatchNorm(var_78, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001);
-void* var_80 = tensorRelu(var_79);
-void* var_81 = tensorPooling(var_80,1,2,2,0,0,2,2);
-void* var_82 = FCLayer_PROMISE(var_81, 0.0, 2.8692066650391013, dense_1_w, -0.22301019695401192, 0.1442659378200768, dense_1_b, -0.1654396, 0.23336112, -1, -12.245949958801269, 23.80532513427739, 9);
-void* var_83 = tensorSoftmax(var_82);
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy2(labels, batch_size, var_83);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-}
-
-dumpExecutionAccuracies();
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/src.cc b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/src.cc
deleted file mode 100644
index 25aec9bde3bc1aac157e2acc368dcddf866e455d..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet/src.cc
+++ /dev/null
@@ -1,413 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-std::string dir_prefix = std::string("data/mobilenet_quant/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
-void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
-void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
-void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
-void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
-std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
-void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
-std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
-void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
-void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
-void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
-void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
-std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
-void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
-void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
-void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
-void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
-std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
-void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
-std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
-void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
-void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
-void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
-void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
-std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
-void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
-void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
-void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
-void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
-void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
-void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
-void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
-void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
-void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
-std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
-void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
-void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
-void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
-void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
-void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
-void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
-void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
-void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
-void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
-std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
-void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
-void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
-void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
-void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
-void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
-void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
-void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
-void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
-void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
-std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
-void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
-void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
-void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
-void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
-void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
-void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
-void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
-void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
-void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
-std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
-void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
-void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
-void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
-void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
-void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
-void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
-void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
-void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
-void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
-void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
-void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
-void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
-void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
-void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
-void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
-void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
-void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
-void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
-void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
-void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
-void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
-void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
-void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
-void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
-void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
-void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
-void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
-void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
-void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
-void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
-void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
-void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
-void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
-void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
-void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
-void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
-void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
-void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
-void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
-void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
-void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
-void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
-void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
-void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
-void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
-void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
-void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
-void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
-void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
-void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
-void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
-void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
-void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
-void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1);
-std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
-void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
-void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
-void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
-void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1);
-std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
-void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3);
-std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
-void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
-void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
-void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
-void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1);
-std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
-void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1);
-std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
-void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
-void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
-void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
-void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-
-startMemTracking();
-
-int test_input_size = 10000;
-int batch_size = 10000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
-void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
-void* var_2 = tensorRelu(var_1);
-void* var_4 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
-void* var_5 = tensorBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
-void* var_6 = tensorRelu(var_5);
-void* var_7 = tensorConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
-void* var_8 = tensorBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
-void* var_9 = tensorRelu(var_8);
-void* var_11 = tensorConvolution(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
-void* var_12 = tensorBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
-void* var_13 = tensorRelu(var_12);
-void* var_14 = tensorConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1);
-void* var_15 = tensorBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
-void* var_16 = tensorRelu(var_15);
-void* var_18 = tensorConvolution(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
-void* var_19 = tensorBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
-void* var_20 = tensorRelu(var_19);
-void* var_21 = tensorConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1);
-void* var_22 = tensorBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
-void* var_23 = tensorRelu(var_22);
-void* var_26 = tensorConvolution(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
-void* var_27 = tensorBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
-void* var_28 = tensorRelu(var_27);
-void* var_29 = tensorConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1);
-void* var_30 = tensorBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
-void* var_31 = tensorRelu(var_30);
-void* var_33 = tensorConvolution(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
-void* var_34 = tensorBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
-void* var_35 = tensorRelu(var_34);
-void* var_36 = tensorConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1);
-void* var_37 = tensorBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
-void* var_38 = tensorRelu(var_37);
-void* var_41 = tensorConvolution(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
-void* var_42 = tensorBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
-void* var_43 = tensorRelu(var_42);
-void* var_44 = tensorConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1);
-void* var_45 = tensorBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
-void* var_46 = tensorRelu(var_45);
-void* var_48 = tensorConvolution(var_46, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512);
-void* var_49 = tensorBatchNorm(var_48, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001);
-void* var_50 = tensorRelu(var_49);
-void* var_51 = tensorConvolution(var_50, conv2d_8_w, 0, 0, 1, 1, 1, 1);
-void* var_52 = tensorBatchNorm(var_51, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001);
-void* var_53 = tensorRelu(var_52);
-void* var_55 = tensorConvolution(var_53, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512);
-void* var_56 = tensorBatchNorm(var_55, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001);
-void* var_57 = tensorRelu(var_56);
-void* var_58 = tensorConvolution(var_57, conv2d_9_w, 0, 0, 1, 1, 1, 1);
-void* var_59 = tensorBatchNorm(var_58, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001);
-void* var_60 = tensorRelu(var_59);
-void* var_63 = tensorConvolution(var_60, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512);
-void* var_64 = tensorBatchNorm(var_63, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001);
-void* var_65 = tensorRelu(var_64);
-void* var_66 = tensorConvolution(var_65, conv2d_10_w, 0, 0, 1, 1, 1, 1);
-void* var_67 = tensorBatchNorm(var_66, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001);
-void* var_68 = tensorRelu(var_67);
-void* var_70 = tensorConvolution(var_68, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512);
-void* var_71 = tensorBatchNorm(var_70, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001);
-void* var_72 = tensorRelu(var_71);
-void* var_73 = tensorConvolution(var_72, conv2d_11_w, 0, 0, 1, 1, 1, 1);
-void* var_74 = tensorBatchNorm(var_73, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001);
-void* var_75 = tensorRelu(var_74);
-void* var_77 = tensorConvolution(var_75, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512);
-void* var_78 = tensorBatchNorm(var_77, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001);
-void* var_79 = tensorRelu(var_78);
-void* var_80 = tensorConvolution(var_79, conv2d_12_w, 0, 0, 1, 1, 1, 1);
-void* var_81 = tensorBatchNorm(var_80, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001);
-void* var_82 = tensorRelu(var_81);
-void* var_85 = tensorConvolution(var_82, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512);
-void* var_86 = tensorBatchNorm(var_85, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001);
-void* var_87 = tensorRelu(var_86);
-void* var_88 = tensorConvolution(var_87, conv2d_13_w, 0, 0, 1, 1, 1, 1);
-void* var_89 = tensorBatchNorm(var_88, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001);
-void* var_90 = tensorRelu(var_89);
-void* var_92 = tensorConvolution(var_90, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024);
-void* var_93 = tensorBatchNorm(var_92, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001);
-void* var_94 = tensorRelu(var_93);
-void* var_95 = tensorConvolution(var_94, conv2d_14_w, 0, 0, 1, 1, 1, 1);
-void* var_96 = tensorBatchNorm(var_95, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001);
-void* var_97 = tensorRelu(var_96);
-void* var_99 = tensorPooling(var_97,1,2,2,0,0,2,2);
-void* var_101 = tensorGemmGPU(var_99, dense_1_w);
-void* var_102 = tensorAdd(var_101, dense_1_b);
-void* var_103 = tensorSoftmax(var_102);
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy2(labels, batch_size, var_103);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
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--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_data/src.cc
+++ /dev/null
@@ -1,413 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
- llvm_hpvm_initTensorRt(1);
-
-
- std::string dir_prefix = std::string("data/mobilenet_hpvm_5/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
- void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
- std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
- void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
- void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
- void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
- void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
- std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
- void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
- std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
- void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
- void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
- void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
- std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
- void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
- std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
- void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
- std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
- void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
- void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
- void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
- void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
- std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
- void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
- std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
- void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
- void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
- void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
- std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
- void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
- std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
- void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
- std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
- void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
- void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
- void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
- void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
- void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
- void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
- void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
- void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
- void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
- void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
- std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
- void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
- void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
- void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
- void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
- std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
- void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
- std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
- void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
- void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
- void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
- std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
- void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
- std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
- void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
- std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
- void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
- void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
- void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
- void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
- void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
- void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
- void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
- void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
- void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
- void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
- std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
- void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
- void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
- void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
- void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
- std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
- void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
- std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
- void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
- void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
- void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
- std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
- void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
- std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
- void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
- std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
- void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
- void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
- void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
- void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
- void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
- void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
- void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
- void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
- void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
- void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
- void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
- void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
- void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
- void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
- void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
- void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
- void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
- void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
- void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
- void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
- void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
- void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
- void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
- void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
- void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
- void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
- void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
- void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
- void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
- void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
- void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
- void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
- void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
- void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
- void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
- void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
- void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
- void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
- void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
- void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
- void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
- void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
- void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
- void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
- void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
- void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
- void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
- void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
- void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
- void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1);
- std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
- void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
- void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
- void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
- void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1);
- std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
- void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3);
- std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
- void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
- void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
- void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1);
- std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
- void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1);
- std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
- void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1);
- std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
- void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
- void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
- void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
- void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1);
- std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
- void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3);
- std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
- void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
- void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
- void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
- void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1);
- std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
- void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1);
- std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
- void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
- void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
- void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1);
- std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
- void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1);
- std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
- void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
- std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
- void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-
- startMemTracking();
-
- int test_input_size = 2000;
- int batch_size = 2000;
- int batch_count = test_input_size / batch_size;
- float final_accuracy = 0.0;
-
- for(int i = 0; i < batch_count; i++){
-
- int start = i * batch_size;
- int end = (i + 1) * batch_size;
-
- void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
- void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
- void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
- void* var_2 = tensorRelu(var_1);
- void* var_3 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
- void* var_4 = tensorBatchNorm(var_3, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
- void* var_5 = tensorRelu(var_4);
- void* var_6 = tensorConvolution(var_5, conv2d_2_w, 0, 0, 1, 1, 1, 1);
- void* var_7 = tensorBatchNorm(var_6, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
- void* var_8 = tensorRelu(var_7);
- void* var_9 = tensorConvolution(var_8, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
- void* var_10 = tensorBatchNorm(var_9, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
- void* var_11 = tensorRelu(var_10);
- void* var_12 = tensorConvolution(var_11, conv2d_3_w, 0, 0, 1, 1, 1, 1);
- void* var_13 = tensorBatchNorm(var_12, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
- void* var_14 = tensorRelu(var_13);
- void* var_15 = tensorConvolution(var_14, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
- void* var_16 = tensorBatchNorm(var_15, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
- void* var_17 = tensorRelu(var_16);
- void* var_18 = tensorConvolution(var_17, conv2d_4_w, 0, 0, 1, 1, 1, 1);
- void* var_19 = tensorBatchNorm(var_18, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
- void* var_20 = tensorRelu(var_19);
- void* var_22 = tensorConvolution(var_20, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
- void* var_23 = tensorBatchNorm(var_22, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
- void* var_24 = tensorRelu(var_23);
- void* var_25 = tensorConvolution(var_24, conv2d_5_w, 0, 0, 1, 1, 1, 1);
- void* var_26 = tensorBatchNorm(var_25, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
- void* var_27 = tensorRelu(var_26);
- void* var_28 = tensorConvolution(var_27, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
- void* var_29 = tensorBatchNorm(var_28, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
- void* var_30 = tensorRelu(var_29);
- void* var_31 = tensorConvolution(var_30, conv2d_6_w, 0, 0, 1, 1, 1, 1);
- void* var_32 = tensorBatchNorm(var_31, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
- void* var_33 = tensorRelu(var_32);
- void* var_35 = tensorConvolution(var_33, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
- void* var_36 = tensorBatchNorm(var_35, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
- void* var_37 = tensorRelu(var_36);
- void* var_38 = tensorConvolution(var_37, conv2d_7_w, 0, 0, 1, 1, 1, 1);
- void* var_39 = tensorBatchNorm(var_38, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
- void* var_40 = tensorRelu(var_39);
- void* var_41 = tensorConvolution(var_40, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512);
- void* var_42 = tensorBatchNorm(var_41, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001);
- void* var_43 = tensorRelu(var_42);
- void* var_44 = tensorConvolution(var_43, conv2d_8_w, 0, 0, 1, 1, 1, 1);
- void* var_45 = tensorBatchNorm(var_44, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001);
- void* var_46 = tensorRelu(var_45);
- void* var_47 = tensorConvolution(var_46, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512);
- void* var_48 = tensorBatchNorm(var_47, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001);
- void* var_49 = tensorRelu(var_48);
- void* var_50 = tensorConvolution(var_49, conv2d_9_w, 0, 0, 1, 1, 1, 1);
- void* var_51 = tensorBatchNorm(var_50, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001);
- void* var_52 = tensorRelu(var_51);
- void* var_54 = tensorConvolution(var_52, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512);
- void* var_55 = tensorBatchNorm(var_54, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001);
- void* var_56 = tensorRelu(var_55);
- void* var_57 = tensorConvolution(var_56, conv2d_10_w, 0, 0, 1, 1, 1, 1);
- void* var_58 = tensorBatchNorm(var_57, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001);
- void* var_59 = tensorRelu(var_58);
- void* var_60 = tensorConvolution(var_59, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512);
- void* var_61 = tensorBatchNorm(var_60, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001);
- void* var_62 = tensorRelu(var_61);
- void* var_63 = tensorConvolution(var_62, conv2d_11_w, 0, 0, 1, 1, 1, 1);
- void* var_64 = tensorBatchNorm(var_63, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001);
- void* var_65 = tensorRelu(var_64);
- void* var_66 = tensorConvolution(var_65, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512);
- void* var_67 = tensorBatchNorm(var_66, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001);
- void* var_68 = tensorRelu(var_67);
- void* var_69 = tensorConvolution(var_68, conv2d_12_w, 0, 0, 1, 1, 1, 1);
- void* var_70 = tensorBatchNorm(var_69, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001);
- void* var_71 = tensorRelu(var_70);
- void* var_73 = tensorConvolution(var_71, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512);
- void* var_74 = tensorBatchNorm(var_73, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001);
- void* var_75 = tensorRelu(var_74);
- void* var_76 = tensorConvolution(var_75, conv2d_13_w, 0, 0, 1, 1, 1, 1);
- void* var_77 = tensorBatchNorm(var_76, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001);
- void* var_78 = tensorRelu(var_77);
- void* var_79 = tensorConvolution(var_78, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024);
- void* var_80 = tensorBatchNorm(var_79, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001);
- void* var_81 = tensorRelu(var_80);
- void* var_82 = tensorConvolution(var_81, conv2d_14_w, 0, 0, 1, 1, 1, 1);
- void* var_83 = tensorBatchNorm(var_82, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001);
- void* var_84 = tensorRelu(var_83);
- void* var_86 = tensorPooling(var_84,1,2,2,0,0,2,2);
- void* var_88 = tensorGemmGPU(var_86, dense_1_w);
- void* var_89 = tensorAdd(var_88, dense_1_b);
- void* var_90 = tensorSoftmax(var_89);
-
- uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
- float accuracy = computeAccuracy2(labels, batch_size, var_90);
- final_accuracy += accuracy;
- freeBatchMemory();
-
- }
-
- final_accuracy = final_accuracy / batch_count;
- dumpFinalAccuracy(final_accuracy);
-
-
- llvm_hpvm_cleanupTensorRt();
-
- return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/approxhpvm_src.cc b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/approxhpvm_src.cc
deleted file mode 100644
index 5089eb912bcb5335c96c04f6d98f5d17ab761c72..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/approxhpvm_src.cc
+++ /dev/null
@@ -1,2400 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/stat.h>
-#include <cstring>
-#include <visc.h>
-#include <tensorTypes.h>
-#include <tensorUtils.h>
-
-void var_0_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_1_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_2_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_3_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 32);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_4_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_5_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_6_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_7_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_8_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_9_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 2, 2, 1, 64);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_10_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_11_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_12_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_13_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_14_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_15_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 128);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_16_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_17_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_18_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_19_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_20_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_21_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 2, 2, 1, 128);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_22_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_23_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_24_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_25_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_26_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_27_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 256);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_28_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_29_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_30_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_31_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_32_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_33_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 2, 2, 1, 256);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_34_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_35_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_36_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_37_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_38_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_39_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 512);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_40_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_41_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_42_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_43_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_44_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_45_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 512);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_46_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_47_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_48_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_49_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_50_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_51_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 512);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_52_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_53_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_54_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_55_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_56_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_57_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 512);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_58_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_59_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_60_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_61_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_62_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_63_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 512);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_64_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_65_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_66_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_67_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_68_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_69_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 2, 2, 1, 512);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_70_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_71_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_72_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_73_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_74_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_75_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 1024);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_76_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_77_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_78_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_79_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_80_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_81_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_avg(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_82_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_mul(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_83_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_84_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_softmax(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void root(void* input, size_t input_bytes,
- void* conv2d_1_w, size_t conv2d_1_w_bytes,
- void* batch_normalization_1_gamma, size_t batch_normalization_1_gamma_bytes,
- void* batch_normalization_1_beta, size_t batch_normalization_1_beta_bytes,
- void* batch_normalization_1_mean, size_t batch_normalization_1_mean_bytes,
- void* batch_normalization_1_variance, size_t batch_normalization_1_variance_bytes,
- void* depthwise_conv2d_1_w, size_t depthwise_conv2d_1_w_bytes,
- void* batch_normalization_2_gamma, size_t batch_normalization_2_gamma_bytes,
- void* batch_normalization_2_beta, size_t batch_normalization_2_beta_bytes,
- void* batch_normalization_2_mean, size_t batch_normalization_2_mean_bytes,
- void* batch_normalization_2_variance, size_t batch_normalization_2_variance_bytes,
- void* conv2d_2_w, size_t conv2d_2_w_bytes,
- void* batch_normalization_3_gamma, size_t batch_normalization_3_gamma_bytes,
- void* batch_normalization_3_beta, size_t batch_normalization_3_beta_bytes,
- void* batch_normalization_3_mean, size_t batch_normalization_3_mean_bytes,
- void* batch_normalization_3_variance, size_t batch_normalization_3_variance_bytes,
- void* depthwise_conv2d_2_w, size_t depthwise_conv2d_2_w_bytes,
- void* batch_normalization_4_gamma, size_t batch_normalization_4_gamma_bytes,
- void* batch_normalization_4_beta, size_t batch_normalization_4_beta_bytes,
- void* batch_normalization_4_mean, size_t batch_normalization_4_mean_bytes,
- void* batch_normalization_4_variance, size_t batch_normalization_4_variance_bytes,
- void* conv2d_3_w, size_t conv2d_3_w_bytes,
- void* batch_normalization_5_gamma, size_t batch_normalization_5_gamma_bytes,
- void* batch_normalization_5_beta, size_t batch_normalization_5_beta_bytes,
- void* batch_normalization_5_mean, size_t batch_normalization_5_mean_bytes,
- void* batch_normalization_5_variance, size_t batch_normalization_5_variance_bytes,
- void* depthwise_conv2d_3_w, size_t depthwise_conv2d_3_w_bytes,
- void* batch_normalization_6_gamma, size_t batch_normalization_6_gamma_bytes,
- void* batch_normalization_6_beta, size_t batch_normalization_6_beta_bytes,
- void* batch_normalization_6_mean, size_t batch_normalization_6_mean_bytes,
- void* batch_normalization_6_variance, size_t batch_normalization_6_variance_bytes,
- void* conv2d_4_w, size_t conv2d_4_w_bytes,
- void* batch_normalization_7_gamma, size_t batch_normalization_7_gamma_bytes,
- void* batch_normalization_7_beta, size_t batch_normalization_7_beta_bytes,
- void* batch_normalization_7_mean, size_t batch_normalization_7_mean_bytes,
- void* batch_normalization_7_variance, size_t batch_normalization_7_variance_bytes,
- void* depthwise_conv2d_4_w, size_t depthwise_conv2d_4_w_bytes,
- void* batch_normalization_8_gamma, size_t batch_normalization_8_gamma_bytes,
- void* batch_normalization_8_beta, size_t batch_normalization_8_beta_bytes,
- void* batch_normalization_8_mean, size_t batch_normalization_8_mean_bytes,
- void* batch_normalization_8_variance, size_t batch_normalization_8_variance_bytes,
- void* conv2d_5_w, size_t conv2d_5_w_bytes,
- void* batch_normalization_9_gamma, size_t batch_normalization_9_gamma_bytes,
- void* batch_normalization_9_beta, size_t batch_normalization_9_beta_bytes,
- void* batch_normalization_9_mean, size_t batch_normalization_9_mean_bytes,
- void* batch_normalization_9_variance, size_t batch_normalization_9_variance_bytes,
- void* depthwise_conv2d_5_w, size_t depthwise_conv2d_5_w_bytes,
- void* batch_normalization_10_gamma, size_t batch_normalization_10_gamma_bytes,
- void* batch_normalization_10_beta, size_t batch_normalization_10_beta_bytes,
- void* batch_normalization_10_mean, size_t batch_normalization_10_mean_bytes,
- void* batch_normalization_10_variance, size_t batch_normalization_10_variance_bytes,
- void* conv2d_6_w, size_t conv2d_6_w_bytes,
- void* batch_normalization_11_gamma, size_t batch_normalization_11_gamma_bytes,
- void* batch_normalization_11_beta, size_t batch_normalization_11_beta_bytes,
- void* batch_normalization_11_mean, size_t batch_normalization_11_mean_bytes,
- void* batch_normalization_11_variance, size_t batch_normalization_11_variance_bytes,
- void* depthwise_conv2d_6_w, size_t depthwise_conv2d_6_w_bytes,
- void* batch_normalization_12_gamma, size_t batch_normalization_12_gamma_bytes,
- void* batch_normalization_12_beta, size_t batch_normalization_12_beta_bytes,
- void* batch_normalization_12_mean, size_t batch_normalization_12_mean_bytes,
- void* batch_normalization_12_variance, size_t batch_normalization_12_variance_bytes,
- void* conv2d_7_w, size_t conv2d_7_w_bytes,
- void* batch_normalization_13_gamma, size_t batch_normalization_13_gamma_bytes,
- void* batch_normalization_13_beta, size_t batch_normalization_13_beta_bytes,
- void* batch_normalization_13_mean, size_t batch_normalization_13_mean_bytes,
- void* batch_normalization_13_variance, size_t batch_normalization_13_variance_bytes,
- void* depthwise_conv2d_7_w, size_t depthwise_conv2d_7_w_bytes,
- void* batch_normalization_14_gamma, size_t batch_normalization_14_gamma_bytes,
- void* batch_normalization_14_beta, size_t batch_normalization_14_beta_bytes,
- void* batch_normalization_14_mean, size_t batch_normalization_14_mean_bytes,
- void* batch_normalization_14_variance, size_t batch_normalization_14_variance_bytes,
- void* conv2d_8_w, size_t conv2d_8_w_bytes,
- void* batch_normalization_15_gamma, size_t batch_normalization_15_gamma_bytes,
- void* batch_normalization_15_beta, size_t batch_normalization_15_beta_bytes,
- void* batch_normalization_15_mean, size_t batch_normalization_15_mean_bytes,
- void* batch_normalization_15_variance, size_t batch_normalization_15_variance_bytes,
- void* depthwise_conv2d_8_w, size_t depthwise_conv2d_8_w_bytes,
- void* batch_normalization_16_gamma, size_t batch_normalization_16_gamma_bytes,
- void* batch_normalization_16_beta, size_t batch_normalization_16_beta_bytes,
- void* batch_normalization_16_mean, size_t batch_normalization_16_mean_bytes,
- void* batch_normalization_16_variance, size_t batch_normalization_16_variance_bytes,
- void* conv2d_9_w, size_t conv2d_9_w_bytes,
- void* batch_normalization_17_gamma, size_t batch_normalization_17_gamma_bytes,
- void* batch_normalization_17_beta, size_t batch_normalization_17_beta_bytes,
- void* batch_normalization_17_mean, size_t batch_normalization_17_mean_bytes,
- void* batch_normalization_17_variance, size_t batch_normalization_17_variance_bytes,
- void* depthwise_conv2d_9_w, size_t depthwise_conv2d_9_w_bytes,
- void* batch_normalization_18_gamma, size_t batch_normalization_18_gamma_bytes,
- void* batch_normalization_18_beta, size_t batch_normalization_18_beta_bytes,
- void* batch_normalization_18_mean, size_t batch_normalization_18_mean_bytes,
- void* batch_normalization_18_variance, size_t batch_normalization_18_variance_bytes,
- void* conv2d_10_w, size_t conv2d_10_w_bytes,
- void* batch_normalization_19_gamma, size_t batch_normalization_19_gamma_bytes,
- void* batch_normalization_19_beta, size_t batch_normalization_19_beta_bytes,
- void* batch_normalization_19_mean, size_t batch_normalization_19_mean_bytes,
- void* batch_normalization_19_variance, size_t batch_normalization_19_variance_bytes,
- void* depthwise_conv2d_10_w, size_t depthwise_conv2d_10_w_bytes,
- void* batch_normalization_20_gamma, size_t batch_normalization_20_gamma_bytes,
- void* batch_normalization_20_beta, size_t batch_normalization_20_beta_bytes,
- void* batch_normalization_20_mean, size_t batch_normalization_20_mean_bytes,
- void* batch_normalization_20_variance, size_t batch_normalization_20_variance_bytes,
- void* conv2d_11_w, size_t conv2d_11_w_bytes,
- void* batch_normalization_21_gamma, size_t batch_normalization_21_gamma_bytes,
- void* batch_normalization_21_beta, size_t batch_normalization_21_beta_bytes,
- void* batch_normalization_21_mean, size_t batch_normalization_21_mean_bytes,
- void* batch_normalization_21_variance, size_t batch_normalization_21_variance_bytes,
- void* depthwise_conv2d_11_w, size_t depthwise_conv2d_11_w_bytes,
- void* batch_normalization_22_gamma, size_t batch_normalization_22_gamma_bytes,
- void* batch_normalization_22_beta, size_t batch_normalization_22_beta_bytes,
- void* batch_normalization_22_mean, size_t batch_normalization_22_mean_bytes,
- void* batch_normalization_22_variance, size_t batch_normalization_22_variance_bytes,
- void* conv2d_12_w, size_t conv2d_12_w_bytes,
- void* batch_normalization_23_gamma, size_t batch_normalization_23_gamma_bytes,
- void* batch_normalization_23_beta, size_t batch_normalization_23_beta_bytes,
- void* batch_normalization_23_mean, size_t batch_normalization_23_mean_bytes,
- void* batch_normalization_23_variance, size_t batch_normalization_23_variance_bytes,
- void* depthwise_conv2d_12_w, size_t depthwise_conv2d_12_w_bytes,
- void* batch_normalization_24_gamma, size_t batch_normalization_24_gamma_bytes,
- void* batch_normalization_24_beta, size_t batch_normalization_24_beta_bytes,
- void* batch_normalization_24_mean, size_t batch_normalization_24_mean_bytes,
- void* batch_normalization_24_variance, size_t batch_normalization_24_variance_bytes,
- void* conv2d_13_w, size_t conv2d_13_w_bytes,
- void* batch_normalization_25_gamma, size_t batch_normalization_25_gamma_bytes,
- void* batch_normalization_25_beta, size_t batch_normalization_25_beta_bytes,
- void* batch_normalization_25_mean, size_t batch_normalization_25_mean_bytes,
- void* batch_normalization_25_variance, size_t batch_normalization_25_variance_bytes,
- void* depthwise_conv2d_13_w, size_t depthwise_conv2d_13_w_bytes,
- void* batch_normalization_26_gamma, size_t batch_normalization_26_gamma_bytes,
- void* batch_normalization_26_beta, size_t batch_normalization_26_beta_bytes,
- void* batch_normalization_26_mean, size_t batch_normalization_26_mean_bytes,
- void* batch_normalization_26_variance, size_t batch_normalization_26_variance_bytes,
- void* conv2d_14_w, size_t conv2d_14_w_bytes,
- void* batch_normalization_27_gamma, size_t batch_normalization_27_gamma_bytes,
- void* batch_normalization_27_beta, size_t batch_normalization_27_beta_bytes,
- void* batch_normalization_27_mean, size_t batch_normalization_27_mean_bytes,
- void* batch_normalization_27_variance, size_t batch_normalization_27_variance_bytes,
- void* dense_1_w, size_t dense_1_w_bytes,
- void* dense_1_b, size_t dense_1_b_bytes){
-
-
- __visc__hint(visc::CPU_TARGET);
- __visc__attributes(138, input, conv2d_1_w, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, depthwise_conv2d_1_w, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, conv2d_2_w, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, depthwise_conv2d_2_w, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, conv2d_3_w, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, depthwise_conv2d_3_w, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, conv2d_4_w, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, depthwise_conv2d_4_w, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, conv2d_5_w, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, depthwise_conv2d_5_w, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, conv2d_6_w, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, depthwise_conv2d_6_w, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, conv2d_7_w, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, depthwise_conv2d_7_w, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, conv2d_8_w, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, depthwise_conv2d_8_w, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, conv2d_9_w, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, depthwise_conv2d_9_w, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, conv2d_10_w, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, depthwise_conv2d_10_w, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, conv2d_11_w, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, depthwise_conv2d_11_w, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, conv2d_12_w, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, depthwise_conv2d_12_w, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, conv2d_13_w, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, depthwise_conv2d_13_w, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, conv2d_14_w, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, dense_1_w, dense_1_b, 0);
-
-
- void* var_0 = __visc__createNodeND(0, var_0_node);
-
- __visc__bindIn(var_0, 0, 0, 0);
- __visc__bindIn(var_0, 1, 1, 0);
- __visc__bindIn(var_0, 2, 2, 0);
- __visc__bindIn(var_0, 3, 3, 0);
-
- void* var_1 = __visc__createNodeND(0, var_1_node);
-
- __visc__edge(var_0, var_1, 1, 0, 0, 0);
- __visc__edge(var_0, var_1, 1, 1, 1, 0);
- __visc__bindIn(var_1, 4, 2, 0);
- __visc__bindIn(var_1, 5, 3, 0);
- __visc__bindIn(var_1, 6, 4, 0);
- __visc__bindIn(var_1, 7, 5, 0);
- __visc__bindIn(var_1, 8, 6, 0);
- __visc__bindIn(var_1, 9, 7, 0);
- __visc__bindIn(var_1, 10, 8, 0);
- __visc__bindIn(var_1, 11, 9, 0);
-
- void* var_2 = __visc__createNodeND(0, var_2_node);
-
- __visc__edge(var_1, var_2, 1, 0, 0, 0);
- __visc__edge(var_1, var_2, 1, 1, 1, 0);
-
- void* var_3 = __visc__createNodeND(0, var_3_node);
-
- __visc__edge(var_2, var_3, 1, 0, 0, 0);
- __visc__edge(var_2, var_3, 1, 1, 1, 0);
- __visc__bindIn(var_3, 12, 2, 0);
- __visc__bindIn(var_3, 13, 3, 0);
-
- void* var_4 = __visc__createNodeND(0, var_4_node);
-
- __visc__edge(var_3, var_4, 1, 0, 0, 0);
- __visc__edge(var_3, var_4, 1, 1, 1, 0);
- __visc__bindIn(var_4, 14, 2, 0);
- __visc__bindIn(var_4, 15, 3, 0);
- __visc__bindIn(var_4, 16, 4, 0);
- __visc__bindIn(var_4, 17, 5, 0);
- __visc__bindIn(var_4, 18, 6, 0);
- __visc__bindIn(var_4, 19, 7, 0);
- __visc__bindIn(var_4, 20, 8, 0);
- __visc__bindIn(var_4, 21, 9, 0);
-
- void* var_5 = __visc__createNodeND(0, var_5_node);
-
- __visc__edge(var_4, var_5, 1, 0, 0, 0);
- __visc__edge(var_4, var_5, 1, 1, 1, 0);
-
- void* var_6 = __visc__createNodeND(0, var_6_node);
-
- __visc__edge(var_5, var_6, 1, 0, 0, 0);
- __visc__edge(var_5, var_6, 1, 1, 1, 0);
- __visc__bindIn(var_6, 22, 2, 0);
- __visc__bindIn(var_6, 23, 3, 0);
-
- void* var_7 = __visc__createNodeND(0, var_7_node);
-
- __visc__edge(var_6, var_7, 1, 0, 0, 0);
- __visc__edge(var_6, var_7, 1, 1, 1, 0);
- __visc__bindIn(var_7, 24, 2, 0);
- __visc__bindIn(var_7, 25, 3, 0);
- __visc__bindIn(var_7, 26, 4, 0);
- __visc__bindIn(var_7, 27, 5, 0);
- __visc__bindIn(var_7, 28, 6, 0);
- __visc__bindIn(var_7, 29, 7, 0);
- __visc__bindIn(var_7, 30, 8, 0);
- __visc__bindIn(var_7, 31, 9, 0);
-
- void* var_8 = __visc__createNodeND(0, var_8_node);
-
- __visc__edge(var_7, var_8, 1, 0, 0, 0);
- __visc__edge(var_7, var_8, 1, 1, 1, 0);
-
- void* var_9 = __visc__createNodeND(0, var_9_node);
-
- __visc__edge(var_8, var_9, 1, 0, 0, 0);
- __visc__edge(var_8, var_9, 1, 1, 1, 0);
- __visc__bindIn(var_9, 32, 2, 0);
- __visc__bindIn(var_9, 33, 3, 0);
-
- void* var_10 = __visc__createNodeND(0, var_10_node);
-
- __visc__edge(var_9, var_10, 1, 0, 0, 0);
- __visc__edge(var_9, var_10, 1, 1, 1, 0);
- __visc__bindIn(var_10, 34, 2, 0);
- __visc__bindIn(var_10, 35, 3, 0);
- __visc__bindIn(var_10, 36, 4, 0);
- __visc__bindIn(var_10, 37, 5, 0);
- __visc__bindIn(var_10, 38, 6, 0);
- __visc__bindIn(var_10, 39, 7, 0);
- __visc__bindIn(var_10, 40, 8, 0);
- __visc__bindIn(var_10, 41, 9, 0);
-
- void* var_11 = __visc__createNodeND(0, var_11_node);
-
- __visc__edge(var_10, var_11, 1, 0, 0, 0);
- __visc__edge(var_10, var_11, 1, 1, 1, 0);
-
- void* var_12 = __visc__createNodeND(0, var_12_node);
-
- __visc__edge(var_11, var_12, 1, 0, 0, 0);
- __visc__edge(var_11, var_12, 1, 1, 1, 0);
- __visc__bindIn(var_12, 42, 2, 0);
- __visc__bindIn(var_12, 43, 3, 0);
-
- void* var_13 = __visc__createNodeND(0, var_13_node);
-
- __visc__edge(var_12, var_13, 1, 0, 0, 0);
- __visc__edge(var_12, var_13, 1, 1, 1, 0);
- __visc__bindIn(var_13, 44, 2, 0);
- __visc__bindIn(var_13, 45, 3, 0);
- __visc__bindIn(var_13, 46, 4, 0);
- __visc__bindIn(var_13, 47, 5, 0);
- __visc__bindIn(var_13, 48, 6, 0);
- __visc__bindIn(var_13, 49, 7, 0);
- __visc__bindIn(var_13, 50, 8, 0);
- __visc__bindIn(var_13, 51, 9, 0);
-
- void* var_14 = __visc__createNodeND(0, var_14_node);
-
- __visc__edge(var_13, var_14, 1, 0, 0, 0);
- __visc__edge(var_13, var_14, 1, 1, 1, 0);
-
- void* var_15 = __visc__createNodeND(0, var_15_node);
-
- __visc__edge(var_14, var_15, 1, 0, 0, 0);
- __visc__edge(var_14, var_15, 1, 1, 1, 0);
- __visc__bindIn(var_15, 52, 2, 0);
- __visc__bindIn(var_15, 53, 3, 0);
-
- void* var_16 = __visc__createNodeND(0, var_16_node);
-
- __visc__edge(var_15, var_16, 1, 0, 0, 0);
- __visc__edge(var_15, var_16, 1, 1, 1, 0);
- __visc__bindIn(var_16, 54, 2, 0);
- __visc__bindIn(var_16, 55, 3, 0);
- __visc__bindIn(var_16, 56, 4, 0);
- __visc__bindIn(var_16, 57, 5, 0);
- __visc__bindIn(var_16, 58, 6, 0);
- __visc__bindIn(var_16, 59, 7, 0);
- __visc__bindIn(var_16, 60, 8, 0);
- __visc__bindIn(var_16, 61, 9, 0);
-
- void* var_17 = __visc__createNodeND(0, var_17_node);
-
- __visc__edge(var_16, var_17, 1, 0, 0, 0);
- __visc__edge(var_16, var_17, 1, 1, 1, 0);
-
- void* var_18 = __visc__createNodeND(0, var_18_node);
-
- __visc__edge(var_17, var_18, 1, 0, 0, 0);
- __visc__edge(var_17, var_18, 1, 1, 1, 0);
- __visc__bindIn(var_18, 62, 2, 0);
- __visc__bindIn(var_18, 63, 3, 0);
-
- void* var_19 = __visc__createNodeND(0, var_19_node);
-
- __visc__edge(var_18, var_19, 1, 0, 0, 0);
- __visc__edge(var_18, var_19, 1, 1, 1, 0);
- __visc__bindIn(var_19, 64, 2, 0);
- __visc__bindIn(var_19, 65, 3, 0);
- __visc__bindIn(var_19, 66, 4, 0);
- __visc__bindIn(var_19, 67, 5, 0);
- __visc__bindIn(var_19, 68, 6, 0);
- __visc__bindIn(var_19, 69, 7, 0);
- __visc__bindIn(var_19, 70, 8, 0);
- __visc__bindIn(var_19, 71, 9, 0);
-
- void* var_20 = __visc__createNodeND(0, var_20_node);
-
- __visc__edge(var_19, var_20, 1, 0, 0, 0);
- __visc__edge(var_19, var_20, 1, 1, 1, 0);
-
- void* var_21 = __visc__createNodeND(0, var_21_node);
-
- __visc__edge(var_20, var_21, 1, 0, 0, 0);
- __visc__edge(var_20, var_21, 1, 1, 1, 0);
- __visc__bindIn(var_21, 72, 2, 0);
- __visc__bindIn(var_21, 73, 3, 0);
-
- void* var_22 = __visc__createNodeND(0, var_22_node);
-
- __visc__edge(var_21, var_22, 1, 0, 0, 0);
- __visc__edge(var_21, var_22, 1, 1, 1, 0);
- __visc__bindIn(var_22, 74, 2, 0);
- __visc__bindIn(var_22, 75, 3, 0);
- __visc__bindIn(var_22, 76, 4, 0);
- __visc__bindIn(var_22, 77, 5, 0);
- __visc__bindIn(var_22, 78, 6, 0);
- __visc__bindIn(var_22, 79, 7, 0);
- __visc__bindIn(var_22, 80, 8, 0);
- __visc__bindIn(var_22, 81, 9, 0);
-
- void* var_23 = __visc__createNodeND(0, var_23_node);
-
- __visc__edge(var_22, var_23, 1, 0, 0, 0);
- __visc__edge(var_22, var_23, 1, 1, 1, 0);
-
- void* var_24 = __visc__createNodeND(0, var_24_node);
-
- __visc__edge(var_23, var_24, 1, 0, 0, 0);
- __visc__edge(var_23, var_24, 1, 1, 1, 0);
- __visc__bindIn(var_24, 82, 2, 0);
- __visc__bindIn(var_24, 83, 3, 0);
-
- void* var_25 = __visc__createNodeND(0, var_25_node);
-
- __visc__edge(var_24, var_25, 1, 0, 0, 0);
- __visc__edge(var_24, var_25, 1, 1, 1, 0);
- __visc__bindIn(var_25, 84, 2, 0);
- __visc__bindIn(var_25, 85, 3, 0);
- __visc__bindIn(var_25, 86, 4, 0);
- __visc__bindIn(var_25, 87, 5, 0);
- __visc__bindIn(var_25, 88, 6, 0);
- __visc__bindIn(var_25, 89, 7, 0);
- __visc__bindIn(var_25, 90, 8, 0);
- __visc__bindIn(var_25, 91, 9, 0);
-
- void* var_26 = __visc__createNodeND(0, var_26_node);
-
- __visc__edge(var_25, var_26, 1, 0, 0, 0);
- __visc__edge(var_25, var_26, 1, 1, 1, 0);
-
- void* var_27 = __visc__createNodeND(0, var_27_node);
-
- __visc__edge(var_26, var_27, 1, 0, 0, 0);
- __visc__edge(var_26, var_27, 1, 1, 1, 0);
- __visc__bindIn(var_27, 92, 2, 0);
- __visc__bindIn(var_27, 93, 3, 0);
-
- void* var_28 = __visc__createNodeND(0, var_28_node);
-
- __visc__edge(var_27, var_28, 1, 0, 0, 0);
- __visc__edge(var_27, var_28, 1, 1, 1, 0);
- __visc__bindIn(var_28, 94, 2, 0);
- __visc__bindIn(var_28, 95, 3, 0);
- __visc__bindIn(var_28, 96, 4, 0);
- __visc__bindIn(var_28, 97, 5, 0);
- __visc__bindIn(var_28, 98, 6, 0);
- __visc__bindIn(var_28, 99, 7, 0);
- __visc__bindIn(var_28, 100, 8, 0);
- __visc__bindIn(var_28, 101, 9, 0);
-
- void* var_29 = __visc__createNodeND(0, var_29_node);
-
- __visc__edge(var_28, var_29, 1, 0, 0, 0);
- __visc__edge(var_28, var_29, 1, 1, 1, 0);
-
- void* var_30 = __visc__createNodeND(0, var_30_node);
-
- __visc__edge(var_29, var_30, 1, 0, 0, 0);
- __visc__edge(var_29, var_30, 1, 1, 1, 0);
- __visc__bindIn(var_30, 102, 2, 0);
- __visc__bindIn(var_30, 103, 3, 0);
-
- void* var_31 = __visc__createNodeND(0, var_31_node);
-
- __visc__edge(var_30, var_31, 1, 0, 0, 0);
- __visc__edge(var_30, var_31, 1, 1, 1, 0);
- __visc__bindIn(var_31, 104, 2, 0);
- __visc__bindIn(var_31, 105, 3, 0);
- __visc__bindIn(var_31, 106, 4, 0);
- __visc__bindIn(var_31, 107, 5, 0);
- __visc__bindIn(var_31, 108, 6, 0);
- __visc__bindIn(var_31, 109, 7, 0);
- __visc__bindIn(var_31, 110, 8, 0);
- __visc__bindIn(var_31, 111, 9, 0);
-
- void* var_32 = __visc__createNodeND(0, var_32_node);
-
- __visc__edge(var_31, var_32, 1, 0, 0, 0);
- __visc__edge(var_31, var_32, 1, 1, 1, 0);
-
- void* var_33 = __visc__createNodeND(0, var_33_node);
-
- __visc__edge(var_32, var_33, 1, 0, 0, 0);
- __visc__edge(var_32, var_33, 1, 1, 1, 0);
- __visc__bindIn(var_33, 112, 2, 0);
- __visc__bindIn(var_33, 113, 3, 0);
-
- void* var_34 = __visc__createNodeND(0, var_34_node);
-
- __visc__edge(var_33, var_34, 1, 0, 0, 0);
- __visc__edge(var_33, var_34, 1, 1, 1, 0);
- __visc__bindIn(var_34, 114, 2, 0);
- __visc__bindIn(var_34, 115, 3, 0);
- __visc__bindIn(var_34, 116, 4, 0);
- __visc__bindIn(var_34, 117, 5, 0);
- __visc__bindIn(var_34, 118, 6, 0);
- __visc__bindIn(var_34, 119, 7, 0);
- __visc__bindIn(var_34, 120, 8, 0);
- __visc__bindIn(var_34, 121, 9, 0);
-
- void* var_35 = __visc__createNodeND(0, var_35_node);
-
- __visc__edge(var_34, var_35, 1, 0, 0, 0);
- __visc__edge(var_34, var_35, 1, 1, 1, 0);
-
- void* var_36 = __visc__createNodeND(0, var_36_node);
-
- __visc__edge(var_35, var_36, 1, 0, 0, 0);
- __visc__edge(var_35, var_36, 1, 1, 1, 0);
- __visc__bindIn(var_36, 122, 2, 0);
- __visc__bindIn(var_36, 123, 3, 0);
-
- void* var_37 = __visc__createNodeND(0, var_37_node);
-
- __visc__edge(var_36, var_37, 1, 0, 0, 0);
- __visc__edge(var_36, var_37, 1, 1, 1, 0);
- __visc__bindIn(var_37, 124, 2, 0);
- __visc__bindIn(var_37, 125, 3, 0);
- __visc__bindIn(var_37, 126, 4, 0);
- __visc__bindIn(var_37, 127, 5, 0);
- __visc__bindIn(var_37, 128, 6, 0);
- __visc__bindIn(var_37, 129, 7, 0);
- __visc__bindIn(var_37, 130, 8, 0);
- __visc__bindIn(var_37, 131, 9, 0);
-
- void* var_38 = __visc__createNodeND(0, var_38_node);
-
- __visc__edge(var_37, var_38, 1, 0, 0, 0);
- __visc__edge(var_37, var_38, 1, 1, 1, 0);
-
- void* var_39 = __visc__createNodeND(0, var_39_node);
-
- __visc__edge(var_38, var_39, 1, 0, 0, 0);
- __visc__edge(var_38, var_39, 1, 1, 1, 0);
- __visc__bindIn(var_39, 132, 2, 0);
- __visc__bindIn(var_39, 133, 3, 0);
-
- void* var_40 = __visc__createNodeND(0, var_40_node);
-
- __visc__edge(var_39, var_40, 1, 0, 0, 0);
- __visc__edge(var_39, var_40, 1, 1, 1, 0);
- __visc__bindIn(var_40, 134, 2, 0);
- __visc__bindIn(var_40, 135, 3, 0);
- __visc__bindIn(var_40, 136, 4, 0);
- __visc__bindIn(var_40, 137, 5, 0);
- __visc__bindIn(var_40, 138, 6, 0);
- __visc__bindIn(var_40, 139, 7, 0);
- __visc__bindIn(var_40, 140, 8, 0);
- __visc__bindIn(var_40, 141, 9, 0);
-
- void* var_41 = __visc__createNodeND(0, var_41_node);
-
- __visc__edge(var_40, var_41, 1, 0, 0, 0);
- __visc__edge(var_40, var_41, 1, 1, 1, 0);
-
- void* var_42 = __visc__createNodeND(0, var_42_node);
-
- __visc__edge(var_41, var_42, 1, 0, 0, 0);
- __visc__edge(var_41, var_42, 1, 1, 1, 0);
- __visc__bindIn(var_42, 142, 2, 0);
- __visc__bindIn(var_42, 143, 3, 0);
-
- void* var_43 = __visc__createNodeND(0, var_43_node);
-
- __visc__edge(var_42, var_43, 1, 0, 0, 0);
- __visc__edge(var_42, var_43, 1, 1, 1, 0);
- __visc__bindIn(var_43, 144, 2, 0);
- __visc__bindIn(var_43, 145, 3, 0);
- __visc__bindIn(var_43, 146, 4, 0);
- __visc__bindIn(var_43, 147, 5, 0);
- __visc__bindIn(var_43, 148, 6, 0);
- __visc__bindIn(var_43, 149, 7, 0);
- __visc__bindIn(var_43, 150, 8, 0);
- __visc__bindIn(var_43, 151, 9, 0);
-
- void* var_44 = __visc__createNodeND(0, var_44_node);
-
- __visc__edge(var_43, var_44, 1, 0, 0, 0);
- __visc__edge(var_43, var_44, 1, 1, 1, 0);
-
- void* var_45 = __visc__createNodeND(0, var_45_node);
-
- __visc__edge(var_44, var_45, 1, 0, 0, 0);
- __visc__edge(var_44, var_45, 1, 1, 1, 0);
- __visc__bindIn(var_45, 152, 2, 0);
- __visc__bindIn(var_45, 153, 3, 0);
-
- void* var_46 = __visc__createNodeND(0, var_46_node);
-
- __visc__edge(var_45, var_46, 1, 0, 0, 0);
- __visc__edge(var_45, var_46, 1, 1, 1, 0);
- __visc__bindIn(var_46, 154, 2, 0);
- __visc__bindIn(var_46, 155, 3, 0);
- __visc__bindIn(var_46, 156, 4, 0);
- __visc__bindIn(var_46, 157, 5, 0);
- __visc__bindIn(var_46, 158, 6, 0);
- __visc__bindIn(var_46, 159, 7, 0);
- __visc__bindIn(var_46, 160, 8, 0);
- __visc__bindIn(var_46, 161, 9, 0);
-
- void* var_47 = __visc__createNodeND(0, var_47_node);
-
- __visc__edge(var_46, var_47, 1, 0, 0, 0);
- __visc__edge(var_46, var_47, 1, 1, 1, 0);
-
- void* var_48 = __visc__createNodeND(0, var_48_node);
-
- __visc__edge(var_47, var_48, 1, 0, 0, 0);
- __visc__edge(var_47, var_48, 1, 1, 1, 0);
- __visc__bindIn(var_48, 162, 2, 0);
- __visc__bindIn(var_48, 163, 3, 0);
-
- void* var_49 = __visc__createNodeND(0, var_49_node);
-
- __visc__edge(var_48, var_49, 1, 0, 0, 0);
- __visc__edge(var_48, var_49, 1, 1, 1, 0);
- __visc__bindIn(var_49, 164, 2, 0);
- __visc__bindIn(var_49, 165, 3, 0);
- __visc__bindIn(var_49, 166, 4, 0);
- __visc__bindIn(var_49, 167, 5, 0);
- __visc__bindIn(var_49, 168, 6, 0);
- __visc__bindIn(var_49, 169, 7, 0);
- __visc__bindIn(var_49, 170, 8, 0);
- __visc__bindIn(var_49, 171, 9, 0);
-
- void* var_50 = __visc__createNodeND(0, var_50_node);
-
- __visc__edge(var_49, var_50, 1, 0, 0, 0);
- __visc__edge(var_49, var_50, 1, 1, 1, 0);
-
- void* var_51 = __visc__createNodeND(0, var_51_node);
-
- __visc__edge(var_50, var_51, 1, 0, 0, 0);
- __visc__edge(var_50, var_51, 1, 1, 1, 0);
- __visc__bindIn(var_51, 172, 2, 0);
- __visc__bindIn(var_51, 173, 3, 0);
-
- void* var_52 = __visc__createNodeND(0, var_52_node);
-
- __visc__edge(var_51, var_52, 1, 0, 0, 0);
- __visc__edge(var_51, var_52, 1, 1, 1, 0);
- __visc__bindIn(var_52, 174, 2, 0);
- __visc__bindIn(var_52, 175, 3, 0);
- __visc__bindIn(var_52, 176, 4, 0);
- __visc__bindIn(var_52, 177, 5, 0);
- __visc__bindIn(var_52, 178, 6, 0);
- __visc__bindIn(var_52, 179, 7, 0);
- __visc__bindIn(var_52, 180, 8, 0);
- __visc__bindIn(var_52, 181, 9, 0);
-
- void* var_53 = __visc__createNodeND(0, var_53_node);
-
- __visc__edge(var_52, var_53, 1, 0, 0, 0);
- __visc__edge(var_52, var_53, 1, 1, 1, 0);
-
- void* var_54 = __visc__createNodeND(0, var_54_node);
-
- __visc__edge(var_53, var_54, 1, 0, 0, 0);
- __visc__edge(var_53, var_54, 1, 1, 1, 0);
- __visc__bindIn(var_54, 182, 2, 0);
- __visc__bindIn(var_54, 183, 3, 0);
-
- void* var_55 = __visc__createNodeND(0, var_55_node);
-
- __visc__edge(var_54, var_55, 1, 0, 0, 0);
- __visc__edge(var_54, var_55, 1, 1, 1, 0);
- __visc__bindIn(var_55, 184, 2, 0);
- __visc__bindIn(var_55, 185, 3, 0);
- __visc__bindIn(var_55, 186, 4, 0);
- __visc__bindIn(var_55, 187, 5, 0);
- __visc__bindIn(var_55, 188, 6, 0);
- __visc__bindIn(var_55, 189, 7, 0);
- __visc__bindIn(var_55, 190, 8, 0);
- __visc__bindIn(var_55, 191, 9, 0);
-
- void* var_56 = __visc__createNodeND(0, var_56_node);
-
- __visc__edge(var_55, var_56, 1, 0, 0, 0);
- __visc__edge(var_55, var_56, 1, 1, 1, 0);
-
- void* var_57 = __visc__createNodeND(0, var_57_node);
-
- __visc__edge(var_56, var_57, 1, 0, 0, 0);
- __visc__edge(var_56, var_57, 1, 1, 1, 0);
- __visc__bindIn(var_57, 192, 2, 0);
- __visc__bindIn(var_57, 193, 3, 0);
-
- void* var_58 = __visc__createNodeND(0, var_58_node);
-
- __visc__edge(var_57, var_58, 1, 0, 0, 0);
- __visc__edge(var_57, var_58, 1, 1, 1, 0);
- __visc__bindIn(var_58, 194, 2, 0);
- __visc__bindIn(var_58, 195, 3, 0);
- __visc__bindIn(var_58, 196, 4, 0);
- __visc__bindIn(var_58, 197, 5, 0);
- __visc__bindIn(var_58, 198, 6, 0);
- __visc__bindIn(var_58, 199, 7, 0);
- __visc__bindIn(var_58, 200, 8, 0);
- __visc__bindIn(var_58, 201, 9, 0);
-
- void* var_59 = __visc__createNodeND(0, var_59_node);
-
- __visc__edge(var_58, var_59, 1, 0, 0, 0);
- __visc__edge(var_58, var_59, 1, 1, 1, 0);
-
- void* var_60 = __visc__createNodeND(0, var_60_node);
-
- __visc__edge(var_59, var_60, 1, 0, 0, 0);
- __visc__edge(var_59, var_60, 1, 1, 1, 0);
- __visc__bindIn(var_60, 202, 2, 0);
- __visc__bindIn(var_60, 203, 3, 0);
-
- void* var_61 = __visc__createNodeND(0, var_61_node);
-
- __visc__edge(var_60, var_61, 1, 0, 0, 0);
- __visc__edge(var_60, var_61, 1, 1, 1, 0);
- __visc__bindIn(var_61, 204, 2, 0);
- __visc__bindIn(var_61, 205, 3, 0);
- __visc__bindIn(var_61, 206, 4, 0);
- __visc__bindIn(var_61, 207, 5, 0);
- __visc__bindIn(var_61, 208, 6, 0);
- __visc__bindIn(var_61, 209, 7, 0);
- __visc__bindIn(var_61, 210, 8, 0);
- __visc__bindIn(var_61, 211, 9, 0);
-
- void* var_62 = __visc__createNodeND(0, var_62_node);
-
- __visc__edge(var_61, var_62, 1, 0, 0, 0);
- __visc__edge(var_61, var_62, 1, 1, 1, 0);
-
- void* var_63 = __visc__createNodeND(0, var_63_node);
-
- __visc__edge(var_62, var_63, 1, 0, 0, 0);
- __visc__edge(var_62, var_63, 1, 1, 1, 0);
- __visc__bindIn(var_63, 212, 2, 0);
- __visc__bindIn(var_63, 213, 3, 0);
-
- void* var_64 = __visc__createNodeND(0, var_64_node);
-
- __visc__edge(var_63, var_64, 1, 0, 0, 0);
- __visc__edge(var_63, var_64, 1, 1, 1, 0);
- __visc__bindIn(var_64, 214, 2, 0);
- __visc__bindIn(var_64, 215, 3, 0);
- __visc__bindIn(var_64, 216, 4, 0);
- __visc__bindIn(var_64, 217, 5, 0);
- __visc__bindIn(var_64, 218, 6, 0);
- __visc__bindIn(var_64, 219, 7, 0);
- __visc__bindIn(var_64, 220, 8, 0);
- __visc__bindIn(var_64, 221, 9, 0);
-
- void* var_65 = __visc__createNodeND(0, var_65_node);
-
- __visc__edge(var_64, var_65, 1, 0, 0, 0);
- __visc__edge(var_64, var_65, 1, 1, 1, 0);
-
- void* var_66 = __visc__createNodeND(0, var_66_node);
-
- __visc__edge(var_65, var_66, 1, 0, 0, 0);
- __visc__edge(var_65, var_66, 1, 1, 1, 0);
- __visc__bindIn(var_66, 222, 2, 0);
- __visc__bindIn(var_66, 223, 3, 0);
-
- void* var_67 = __visc__createNodeND(0, var_67_node);
-
- __visc__edge(var_66, var_67, 1, 0, 0, 0);
- __visc__edge(var_66, var_67, 1, 1, 1, 0);
- __visc__bindIn(var_67, 224, 2, 0);
- __visc__bindIn(var_67, 225, 3, 0);
- __visc__bindIn(var_67, 226, 4, 0);
- __visc__bindIn(var_67, 227, 5, 0);
- __visc__bindIn(var_67, 228, 6, 0);
- __visc__bindIn(var_67, 229, 7, 0);
- __visc__bindIn(var_67, 230, 8, 0);
- __visc__bindIn(var_67, 231, 9, 0);
-
- void* var_68 = __visc__createNodeND(0, var_68_node);
-
- __visc__edge(var_67, var_68, 1, 0, 0, 0);
- __visc__edge(var_67, var_68, 1, 1, 1, 0);
-
- void* var_69 = __visc__createNodeND(0, var_69_node);
-
- __visc__edge(var_68, var_69, 1, 0, 0, 0);
- __visc__edge(var_68, var_69, 1, 1, 1, 0);
- __visc__bindIn(var_69, 232, 2, 0);
- __visc__bindIn(var_69, 233, 3, 0);
-
- void* var_70 = __visc__createNodeND(0, var_70_node);
-
- __visc__edge(var_69, var_70, 1, 0, 0, 0);
- __visc__edge(var_69, var_70, 1, 1, 1, 0);
- __visc__bindIn(var_70, 234, 2, 0);
- __visc__bindIn(var_70, 235, 3, 0);
- __visc__bindIn(var_70, 236, 4, 0);
- __visc__bindIn(var_70, 237, 5, 0);
- __visc__bindIn(var_70, 238, 6, 0);
- __visc__bindIn(var_70, 239, 7, 0);
- __visc__bindIn(var_70, 240, 8, 0);
- __visc__bindIn(var_70, 241, 9, 0);
-
- void* var_71 = __visc__createNodeND(0, var_71_node);
-
- __visc__edge(var_70, var_71, 1, 0, 0, 0);
- __visc__edge(var_70, var_71, 1, 1, 1, 0);
-
- void* var_72 = __visc__createNodeND(0, var_72_node);
-
- __visc__edge(var_71, var_72, 1, 0, 0, 0);
- __visc__edge(var_71, var_72, 1, 1, 1, 0);
- __visc__bindIn(var_72, 242, 2, 0);
- __visc__bindIn(var_72, 243, 3, 0);
-
- void* var_73 = __visc__createNodeND(0, var_73_node);
-
- __visc__edge(var_72, var_73, 1, 0, 0, 0);
- __visc__edge(var_72, var_73, 1, 1, 1, 0);
- __visc__bindIn(var_73, 244, 2, 0);
- __visc__bindIn(var_73, 245, 3, 0);
- __visc__bindIn(var_73, 246, 4, 0);
- __visc__bindIn(var_73, 247, 5, 0);
- __visc__bindIn(var_73, 248, 6, 0);
- __visc__bindIn(var_73, 249, 7, 0);
- __visc__bindIn(var_73, 250, 8, 0);
- __visc__bindIn(var_73, 251, 9, 0);
-
- void* var_74 = __visc__createNodeND(0, var_74_node);
-
- __visc__edge(var_73, var_74, 1, 0, 0, 0);
- __visc__edge(var_73, var_74, 1, 1, 1, 0);
-
- void* var_75 = __visc__createNodeND(0, var_75_node);
-
- __visc__edge(var_74, var_75, 1, 0, 0, 0);
- __visc__edge(var_74, var_75, 1, 1, 1, 0);
- __visc__bindIn(var_75, 252, 2, 0);
- __visc__bindIn(var_75, 253, 3, 0);
-
- void* var_76 = __visc__createNodeND(0, var_76_node);
-
- __visc__edge(var_75, var_76, 1, 0, 0, 0);
- __visc__edge(var_75, var_76, 1, 1, 1, 0);
- __visc__bindIn(var_76, 254, 2, 0);
- __visc__bindIn(var_76, 255, 3, 0);
- __visc__bindIn(var_76, 256, 4, 0);
- __visc__bindIn(var_76, 257, 5, 0);
- __visc__bindIn(var_76, 258, 6, 0);
- __visc__bindIn(var_76, 259, 7, 0);
- __visc__bindIn(var_76, 260, 8, 0);
- __visc__bindIn(var_76, 261, 9, 0);
-
- void* var_77 = __visc__createNodeND(0, var_77_node);
-
- __visc__edge(var_76, var_77, 1, 0, 0, 0);
- __visc__edge(var_76, var_77, 1, 1, 1, 0);
-
- void* var_78 = __visc__createNodeND(0, var_78_node);
-
- __visc__edge(var_77, var_78, 1, 0, 0, 0);
- __visc__edge(var_77, var_78, 1, 1, 1, 0);
- __visc__bindIn(var_78, 262, 2, 0);
- __visc__bindIn(var_78, 263, 3, 0);
-
- void* var_79 = __visc__createNodeND(0, var_79_node);
-
- __visc__edge(var_78, var_79, 1, 0, 0, 0);
- __visc__edge(var_78, var_79, 1, 1, 1, 0);
- __visc__bindIn(var_79, 264, 2, 0);
- __visc__bindIn(var_79, 265, 3, 0);
- __visc__bindIn(var_79, 266, 4, 0);
- __visc__bindIn(var_79, 267, 5, 0);
- __visc__bindIn(var_79, 268, 6, 0);
- __visc__bindIn(var_79, 269, 7, 0);
- __visc__bindIn(var_79, 270, 8, 0);
- __visc__bindIn(var_79, 271, 9, 0);
-
- void* var_80 = __visc__createNodeND(0, var_80_node);
-
- __visc__edge(var_79, var_80, 1, 0, 0, 0);
- __visc__edge(var_79, var_80, 1, 1, 1, 0);
-
- void* var_81 = __visc__createNodeND(0, var_81_node);
-
- __visc__edge(var_80, var_81, 1, 0, 0, 0);
- __visc__edge(var_80, var_81, 1, 1, 1, 0);
-
- void* var_82 = __visc__createNodeND(0, var_82_node);
-
- __visc__edge(var_81, var_82, 1, 0, 0, 0);
- __visc__edge(var_81, var_82, 1, 1, 1, 0);
- __visc__bindIn(var_82, 272, 2, 0);
- __visc__bindIn(var_82, 273, 3, 0);
-
- void* var_83 = __visc__createNodeND(0, var_83_node);
-
- __visc__edge(var_82, var_83, 1, 0, 0, 0);
- __visc__edge(var_82, var_83, 1, 1, 1, 0);
- __visc__bindIn(var_83, 274, 2, 0);
- __visc__bindIn(var_83, 275, 3, 0);
-
- void* var_84 = __visc__createNodeND(0, var_84_node);
-
- __visc__edge(var_83, var_84, 1, 0, 0, 0);
- __visc__edge(var_83, var_84, 1, 1, 1, 0);
-
- __visc__bindOut(var_84, 0, 0, 0);
- __visc__bindOut(var_84, 1, 1, 0);
-
-}
-
-struct ret_t {
- void* tensor;
- size_t bytes;
-};
-
-typedef struct __attribute__((__packed__)) {
- void* input;
- size_t input_bytes;
- void* conv2d_1_w;
- size_t conv2d_1_w_bytes;
- void* batch_normalization_1_gamma;
- size_t batch_normalization_1_gamma_bytes;
- void* batch_normalization_1_beta;
- size_t batch_normalization_1_beta_bytes;
- void* batch_normalization_1_mean;
- size_t batch_normalization_1_mean_bytes;
- void* batch_normalization_1_variance;
- size_t batch_normalization_1_variance_bytes;
- void* depthwise_conv2d_1_w;
- size_t depthwise_conv2d_1_w_bytes;
- void* batch_normalization_2_gamma;
- size_t batch_normalization_2_gamma_bytes;
- void* batch_normalization_2_beta;
- size_t batch_normalization_2_beta_bytes;
- void* batch_normalization_2_mean;
- size_t batch_normalization_2_mean_bytes;
- void* batch_normalization_2_variance;
- size_t batch_normalization_2_variance_bytes;
- void* conv2d_2_w;
- size_t conv2d_2_w_bytes;
- void* batch_normalization_3_gamma;
- size_t batch_normalization_3_gamma_bytes;
- void* batch_normalization_3_beta;
- size_t batch_normalization_3_beta_bytes;
- void* batch_normalization_3_mean;
- size_t batch_normalization_3_mean_bytes;
- void* batch_normalization_3_variance;
- size_t batch_normalization_3_variance_bytes;
- void* depthwise_conv2d_2_w;
- size_t depthwise_conv2d_2_w_bytes;
- void* batch_normalization_4_gamma;
- size_t batch_normalization_4_gamma_bytes;
- void* batch_normalization_4_beta;
- size_t batch_normalization_4_beta_bytes;
- void* batch_normalization_4_mean;
- size_t batch_normalization_4_mean_bytes;
- void* batch_normalization_4_variance;
- size_t batch_normalization_4_variance_bytes;
- void* conv2d_3_w;
- size_t conv2d_3_w_bytes;
- void* batch_normalization_5_gamma;
- size_t batch_normalization_5_gamma_bytes;
- void* batch_normalization_5_beta;
- size_t batch_normalization_5_beta_bytes;
- void* batch_normalization_5_mean;
- size_t batch_normalization_5_mean_bytes;
- void* batch_normalization_5_variance;
- size_t batch_normalization_5_variance_bytes;
- void* depthwise_conv2d_3_w;
- size_t depthwise_conv2d_3_w_bytes;
- void* batch_normalization_6_gamma;
- size_t batch_normalization_6_gamma_bytes;
- void* batch_normalization_6_beta;
- size_t batch_normalization_6_beta_bytes;
- void* batch_normalization_6_mean;
- size_t batch_normalization_6_mean_bytes;
- void* batch_normalization_6_variance;
- size_t batch_normalization_6_variance_bytes;
- void* conv2d_4_w;
- size_t conv2d_4_w_bytes;
- void* batch_normalization_7_gamma;
- size_t batch_normalization_7_gamma_bytes;
- void* batch_normalization_7_beta;
- size_t batch_normalization_7_beta_bytes;
- void* batch_normalization_7_mean;
- size_t batch_normalization_7_mean_bytes;
- void* batch_normalization_7_variance;
- size_t batch_normalization_7_variance_bytes;
- void* depthwise_conv2d_4_w;
- size_t depthwise_conv2d_4_w_bytes;
- void* batch_normalization_8_gamma;
- size_t batch_normalization_8_gamma_bytes;
- void* batch_normalization_8_beta;
- size_t batch_normalization_8_beta_bytes;
- void* batch_normalization_8_mean;
- size_t batch_normalization_8_mean_bytes;
- void* batch_normalization_8_variance;
- size_t batch_normalization_8_variance_bytes;
- void* conv2d_5_w;
- size_t conv2d_5_w_bytes;
- void* batch_normalization_9_gamma;
- size_t batch_normalization_9_gamma_bytes;
- void* batch_normalization_9_beta;
- size_t batch_normalization_9_beta_bytes;
- void* batch_normalization_9_mean;
- size_t batch_normalization_9_mean_bytes;
- void* batch_normalization_9_variance;
- size_t batch_normalization_9_variance_bytes;
- void* depthwise_conv2d_5_w;
- size_t depthwise_conv2d_5_w_bytes;
- void* batch_normalization_10_gamma;
- size_t batch_normalization_10_gamma_bytes;
- void* batch_normalization_10_beta;
- size_t batch_normalization_10_beta_bytes;
- void* batch_normalization_10_mean;
- size_t batch_normalization_10_mean_bytes;
- void* batch_normalization_10_variance;
- size_t batch_normalization_10_variance_bytes;
- void* conv2d_6_w;
- size_t conv2d_6_w_bytes;
- void* batch_normalization_11_gamma;
- size_t batch_normalization_11_gamma_bytes;
- void* batch_normalization_11_beta;
- size_t batch_normalization_11_beta_bytes;
- void* batch_normalization_11_mean;
- size_t batch_normalization_11_mean_bytes;
- void* batch_normalization_11_variance;
- size_t batch_normalization_11_variance_bytes;
- void* depthwise_conv2d_6_w;
- size_t depthwise_conv2d_6_w_bytes;
- void* batch_normalization_12_gamma;
- size_t batch_normalization_12_gamma_bytes;
- void* batch_normalization_12_beta;
- size_t batch_normalization_12_beta_bytes;
- void* batch_normalization_12_mean;
- size_t batch_normalization_12_mean_bytes;
- void* batch_normalization_12_variance;
- size_t batch_normalization_12_variance_bytes;
- void* conv2d_7_w;
- size_t conv2d_7_w_bytes;
- void* batch_normalization_13_gamma;
- size_t batch_normalization_13_gamma_bytes;
- void* batch_normalization_13_beta;
- size_t batch_normalization_13_beta_bytes;
- void* batch_normalization_13_mean;
- size_t batch_normalization_13_mean_bytes;
- void* batch_normalization_13_variance;
- size_t batch_normalization_13_variance_bytes;
- void* depthwise_conv2d_7_w;
- size_t depthwise_conv2d_7_w_bytes;
- void* batch_normalization_14_gamma;
- size_t batch_normalization_14_gamma_bytes;
- void* batch_normalization_14_beta;
- size_t batch_normalization_14_beta_bytes;
- void* batch_normalization_14_mean;
- size_t batch_normalization_14_mean_bytes;
- void* batch_normalization_14_variance;
- size_t batch_normalization_14_variance_bytes;
- void* conv2d_8_w;
- size_t conv2d_8_w_bytes;
- void* batch_normalization_15_gamma;
- size_t batch_normalization_15_gamma_bytes;
- void* batch_normalization_15_beta;
- size_t batch_normalization_15_beta_bytes;
- void* batch_normalization_15_mean;
- size_t batch_normalization_15_mean_bytes;
- void* batch_normalization_15_variance;
- size_t batch_normalization_15_variance_bytes;
- void* depthwise_conv2d_8_w;
- size_t depthwise_conv2d_8_w_bytes;
- void* batch_normalization_16_gamma;
- size_t batch_normalization_16_gamma_bytes;
- void* batch_normalization_16_beta;
- size_t batch_normalization_16_beta_bytes;
- void* batch_normalization_16_mean;
- size_t batch_normalization_16_mean_bytes;
- void* batch_normalization_16_variance;
- size_t batch_normalization_16_variance_bytes;
- void* conv2d_9_w;
- size_t conv2d_9_w_bytes;
- void* batch_normalization_17_gamma;
- size_t batch_normalization_17_gamma_bytes;
- void* batch_normalization_17_beta;
- size_t batch_normalization_17_beta_bytes;
- void* batch_normalization_17_mean;
- size_t batch_normalization_17_mean_bytes;
- void* batch_normalization_17_variance;
- size_t batch_normalization_17_variance_bytes;
- void* depthwise_conv2d_9_w;
- size_t depthwise_conv2d_9_w_bytes;
- void* batch_normalization_18_gamma;
- size_t batch_normalization_18_gamma_bytes;
- void* batch_normalization_18_beta;
- size_t batch_normalization_18_beta_bytes;
- void* batch_normalization_18_mean;
- size_t batch_normalization_18_mean_bytes;
- void* batch_normalization_18_variance;
- size_t batch_normalization_18_variance_bytes;
- void* conv2d_10_w;
- size_t conv2d_10_w_bytes;
- void* batch_normalization_19_gamma;
- size_t batch_normalization_19_gamma_bytes;
- void* batch_normalization_19_beta;
- size_t batch_normalization_19_beta_bytes;
- void* batch_normalization_19_mean;
- size_t batch_normalization_19_mean_bytes;
- void* batch_normalization_19_variance;
- size_t batch_normalization_19_variance_bytes;
- void* depthwise_conv2d_10_w;
- size_t depthwise_conv2d_10_w_bytes;
- void* batch_normalization_20_gamma;
- size_t batch_normalization_20_gamma_bytes;
- void* batch_normalization_20_beta;
- size_t batch_normalization_20_beta_bytes;
- void* batch_normalization_20_mean;
- size_t batch_normalization_20_mean_bytes;
- void* batch_normalization_20_variance;
- size_t batch_normalization_20_variance_bytes;
- void* conv2d_11_w;
- size_t conv2d_11_w_bytes;
- void* batch_normalization_21_gamma;
- size_t batch_normalization_21_gamma_bytes;
- void* batch_normalization_21_beta;
- size_t batch_normalization_21_beta_bytes;
- void* batch_normalization_21_mean;
- size_t batch_normalization_21_mean_bytes;
- void* batch_normalization_21_variance;
- size_t batch_normalization_21_variance_bytes;
- void* depthwise_conv2d_11_w;
- size_t depthwise_conv2d_11_w_bytes;
- void* batch_normalization_22_gamma;
- size_t batch_normalization_22_gamma_bytes;
- void* batch_normalization_22_beta;
- size_t batch_normalization_22_beta_bytes;
- void* batch_normalization_22_mean;
- size_t batch_normalization_22_mean_bytes;
- void* batch_normalization_22_variance;
- size_t batch_normalization_22_variance_bytes;
- void* conv2d_12_w;
- size_t conv2d_12_w_bytes;
- void* batch_normalization_23_gamma;
- size_t batch_normalization_23_gamma_bytes;
- void* batch_normalization_23_beta;
- size_t batch_normalization_23_beta_bytes;
- void* batch_normalization_23_mean;
- size_t batch_normalization_23_mean_bytes;
- void* batch_normalization_23_variance;
- size_t batch_normalization_23_variance_bytes;
- void* depthwise_conv2d_12_w;
- size_t depthwise_conv2d_12_w_bytes;
- void* batch_normalization_24_gamma;
- size_t batch_normalization_24_gamma_bytes;
- void* batch_normalization_24_beta;
- size_t batch_normalization_24_beta_bytes;
- void* batch_normalization_24_mean;
- size_t batch_normalization_24_mean_bytes;
- void* batch_normalization_24_variance;
- size_t batch_normalization_24_variance_bytes;
- void* conv2d_13_w;
- size_t conv2d_13_w_bytes;
- void* batch_normalization_25_gamma;
- size_t batch_normalization_25_gamma_bytes;
- void* batch_normalization_25_beta;
- size_t batch_normalization_25_beta_bytes;
- void* batch_normalization_25_mean;
- size_t batch_normalization_25_mean_bytes;
- void* batch_normalization_25_variance;
- size_t batch_normalization_25_variance_bytes;
- void* depthwise_conv2d_13_w;
- size_t depthwise_conv2d_13_w_bytes;
- void* batch_normalization_26_gamma;
- size_t batch_normalization_26_gamma_bytes;
- void* batch_normalization_26_beta;
- size_t batch_normalization_26_beta_bytes;
- void* batch_normalization_26_mean;
- size_t batch_normalization_26_mean_bytes;
- void* batch_normalization_26_variance;
- size_t batch_normalization_26_variance_bytes;
- void* conv2d_14_w;
- size_t conv2d_14_w_bytes;
- void* batch_normalization_27_gamma;
- size_t batch_normalization_27_gamma_bytes;
- void* batch_normalization_27_beta;
- size_t batch_normalization_27_beta_bytes;
- void* batch_normalization_27_mean;
- size_t batch_normalization_27_mean_bytes;
- void* batch_normalization_27_variance;
- size_t batch_normalization_27_variance_bytes;
- void* dense_1_w;
- size_t dense_1_w_bytes;
- void* dense_1_b;
- size_t dense_1_b_bytes;
-
- struct ret_t r;
-}
-RootIn;
-
-int main(){
-
-std::string dir_prefix = std::string("data/mobilenet_quant/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
-void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
-void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
-void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
-void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
-std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
-void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
-std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
-void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
-void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
-void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
-void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
-std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
-void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
-void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
-void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
-void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
-std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
-void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
-std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
-void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
-void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
-void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
-void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
-std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
-void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
-void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
-void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
-void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
-void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
-void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
-void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
-void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
-void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
-std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
-void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
-void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
-void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
-void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
-void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
-void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
-void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
-void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
-void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
-std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
-void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
-void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
-void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
-void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
-void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
-void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
-void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
-void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
-void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
-std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
-void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
-void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
-void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
-void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
-void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
-void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
-void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
-void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
-void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
-std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
-void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
-void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
-void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
-void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
-void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
-void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
-void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
-void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
-void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
-void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
-void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
-void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
-void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
-void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
-void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
-void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
-void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
-void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
-void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
-void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
-void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
-void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
-void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
-void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
-void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
-void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
-void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
-void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
-void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
-void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
-void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
-void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
-void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
-void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
-void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
-void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
-void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
-void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
-void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
-void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
-void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
-void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
-void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
-void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
-void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
-void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
-void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
-void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
-void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
-void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
-void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
-void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
-void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
-void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1);
-std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
-void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
-void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
-void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
-void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1);
-std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
-void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3);
-std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
-void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
-void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
-void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
-void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1);
-std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
-void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1);
-std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
-void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
-void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
-void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
-void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-
-__visc__init();
-RootIn* args = static_cast<RootIn*>(malloc(sizeof(RootIn)));
-
-args->input = input;
-args->input_bytes = 0;
-args->conv2d_1_w = conv2d_1_w;
-args->conv2d_1_w_bytes = 0;
-args->batch_normalization_1_gamma = batch_normalization_1_gamma;
-args->batch_normalization_1_gamma_bytes = 0;
-args->batch_normalization_1_beta = batch_normalization_1_beta;
-args->batch_normalization_1_beta_bytes = 0;
-args->batch_normalization_1_mean = batch_normalization_1_mean;
-args->batch_normalization_1_mean_bytes = 0;
-args->batch_normalization_1_variance = batch_normalization_1_variance;
-args->batch_normalization_1_variance_bytes = 0;
-args->depthwise_conv2d_1_w = depthwise_conv2d_1_w;
-args->depthwise_conv2d_1_w_bytes = 0;
-args->batch_normalization_2_gamma = batch_normalization_2_gamma;
-args->batch_normalization_2_gamma_bytes = 0;
-args->batch_normalization_2_beta = batch_normalization_2_beta;
-args->batch_normalization_2_beta_bytes = 0;
-args->batch_normalization_2_mean = batch_normalization_2_mean;
-args->batch_normalization_2_mean_bytes = 0;
-args->batch_normalization_2_variance = batch_normalization_2_variance;
-args->batch_normalization_2_variance_bytes = 0;
-args->conv2d_2_w = conv2d_2_w;
-args->conv2d_2_w_bytes = 0;
-args->batch_normalization_3_gamma = batch_normalization_3_gamma;
-args->batch_normalization_3_gamma_bytes = 0;
-args->batch_normalization_3_beta = batch_normalization_3_beta;
-args->batch_normalization_3_beta_bytes = 0;
-args->batch_normalization_3_mean = batch_normalization_3_mean;
-args->batch_normalization_3_mean_bytes = 0;
-args->batch_normalization_3_variance = batch_normalization_3_variance;
-args->batch_normalization_3_variance_bytes = 0;
-args->depthwise_conv2d_2_w = depthwise_conv2d_2_w;
-args->depthwise_conv2d_2_w_bytes = 0;
-args->batch_normalization_4_gamma = batch_normalization_4_gamma;
-args->batch_normalization_4_gamma_bytes = 0;
-args->batch_normalization_4_beta = batch_normalization_4_beta;
-args->batch_normalization_4_beta_bytes = 0;
-args->batch_normalization_4_mean = batch_normalization_4_mean;
-args->batch_normalization_4_mean_bytes = 0;
-args->batch_normalization_4_variance = batch_normalization_4_variance;
-args->batch_normalization_4_variance_bytes = 0;
-args->conv2d_3_w = conv2d_3_w;
-args->conv2d_3_w_bytes = 0;
-args->batch_normalization_5_gamma = batch_normalization_5_gamma;
-args->batch_normalization_5_gamma_bytes = 0;
-args->batch_normalization_5_beta = batch_normalization_5_beta;
-args->batch_normalization_5_beta_bytes = 0;
-args->batch_normalization_5_mean = batch_normalization_5_mean;
-args->batch_normalization_5_mean_bytes = 0;
-args->batch_normalization_5_variance = batch_normalization_5_variance;
-args->batch_normalization_5_variance_bytes = 0;
-args->depthwise_conv2d_3_w = depthwise_conv2d_3_w;
-args->depthwise_conv2d_3_w_bytes = 0;
-args->batch_normalization_6_gamma = batch_normalization_6_gamma;
-args->batch_normalization_6_gamma_bytes = 0;
-args->batch_normalization_6_beta = batch_normalization_6_beta;
-args->batch_normalization_6_beta_bytes = 0;
-args->batch_normalization_6_mean = batch_normalization_6_mean;
-args->batch_normalization_6_mean_bytes = 0;
-args->batch_normalization_6_variance = batch_normalization_6_variance;
-args->batch_normalization_6_variance_bytes = 0;
-args->conv2d_4_w = conv2d_4_w;
-args->conv2d_4_w_bytes = 0;
-args->batch_normalization_7_gamma = batch_normalization_7_gamma;
-args->batch_normalization_7_gamma_bytes = 0;
-args->batch_normalization_7_beta = batch_normalization_7_beta;
-args->batch_normalization_7_beta_bytes = 0;
-args->batch_normalization_7_mean = batch_normalization_7_mean;
-args->batch_normalization_7_mean_bytes = 0;
-args->batch_normalization_7_variance = batch_normalization_7_variance;
-args->batch_normalization_7_variance_bytes = 0;
-args->depthwise_conv2d_4_w = depthwise_conv2d_4_w;
-args->depthwise_conv2d_4_w_bytes = 0;
-args->batch_normalization_8_gamma = batch_normalization_8_gamma;
-args->batch_normalization_8_gamma_bytes = 0;
-args->batch_normalization_8_beta = batch_normalization_8_beta;
-args->batch_normalization_8_beta_bytes = 0;
-args->batch_normalization_8_mean = batch_normalization_8_mean;
-args->batch_normalization_8_mean_bytes = 0;
-args->batch_normalization_8_variance = batch_normalization_8_variance;
-args->batch_normalization_8_variance_bytes = 0;
-args->conv2d_5_w = conv2d_5_w;
-args->conv2d_5_w_bytes = 0;
-args->batch_normalization_9_gamma = batch_normalization_9_gamma;
-args->batch_normalization_9_gamma_bytes = 0;
-args->batch_normalization_9_beta = batch_normalization_9_beta;
-args->batch_normalization_9_beta_bytes = 0;
-args->batch_normalization_9_mean = batch_normalization_9_mean;
-args->batch_normalization_9_mean_bytes = 0;
-args->batch_normalization_9_variance = batch_normalization_9_variance;
-args->batch_normalization_9_variance_bytes = 0;
-args->depthwise_conv2d_5_w = depthwise_conv2d_5_w;
-args->depthwise_conv2d_5_w_bytes = 0;
-args->batch_normalization_10_gamma = batch_normalization_10_gamma;
-args->batch_normalization_10_gamma_bytes = 0;
-args->batch_normalization_10_beta = batch_normalization_10_beta;
-args->batch_normalization_10_beta_bytes = 0;
-args->batch_normalization_10_mean = batch_normalization_10_mean;
-args->batch_normalization_10_mean_bytes = 0;
-args->batch_normalization_10_variance = batch_normalization_10_variance;
-args->batch_normalization_10_variance_bytes = 0;
-args->conv2d_6_w = conv2d_6_w;
-args->conv2d_6_w_bytes = 0;
-args->batch_normalization_11_gamma = batch_normalization_11_gamma;
-args->batch_normalization_11_gamma_bytes = 0;
-args->batch_normalization_11_beta = batch_normalization_11_beta;
-args->batch_normalization_11_beta_bytes = 0;
-args->batch_normalization_11_mean = batch_normalization_11_mean;
-args->batch_normalization_11_mean_bytes = 0;
-args->batch_normalization_11_variance = batch_normalization_11_variance;
-args->batch_normalization_11_variance_bytes = 0;
-args->depthwise_conv2d_6_w = depthwise_conv2d_6_w;
-args->depthwise_conv2d_6_w_bytes = 0;
-args->batch_normalization_12_gamma = batch_normalization_12_gamma;
-args->batch_normalization_12_gamma_bytes = 0;
-args->batch_normalization_12_beta = batch_normalization_12_beta;
-args->batch_normalization_12_beta_bytes = 0;
-args->batch_normalization_12_mean = batch_normalization_12_mean;
-args->batch_normalization_12_mean_bytes = 0;
-args->batch_normalization_12_variance = batch_normalization_12_variance;
-args->batch_normalization_12_variance_bytes = 0;
-args->conv2d_7_w = conv2d_7_w;
-args->conv2d_7_w_bytes = 0;
-args->batch_normalization_13_gamma = batch_normalization_13_gamma;
-args->batch_normalization_13_gamma_bytes = 0;
-args->batch_normalization_13_beta = batch_normalization_13_beta;
-args->batch_normalization_13_beta_bytes = 0;
-args->batch_normalization_13_mean = batch_normalization_13_mean;
-args->batch_normalization_13_mean_bytes = 0;
-args->batch_normalization_13_variance = batch_normalization_13_variance;
-args->batch_normalization_13_variance_bytes = 0;
-args->depthwise_conv2d_7_w = depthwise_conv2d_7_w;
-args->depthwise_conv2d_7_w_bytes = 0;
-args->batch_normalization_14_gamma = batch_normalization_14_gamma;
-args->batch_normalization_14_gamma_bytes = 0;
-args->batch_normalization_14_beta = batch_normalization_14_beta;
-args->batch_normalization_14_beta_bytes = 0;
-args->batch_normalization_14_mean = batch_normalization_14_mean;
-args->batch_normalization_14_mean_bytes = 0;
-args->batch_normalization_14_variance = batch_normalization_14_variance;
-args->batch_normalization_14_variance_bytes = 0;
-args->conv2d_8_w = conv2d_8_w;
-args->conv2d_8_w_bytes = 0;
-args->batch_normalization_15_gamma = batch_normalization_15_gamma;
-args->batch_normalization_15_gamma_bytes = 0;
-args->batch_normalization_15_beta = batch_normalization_15_beta;
-args->batch_normalization_15_beta_bytes = 0;
-args->batch_normalization_15_mean = batch_normalization_15_mean;
-args->batch_normalization_15_mean_bytes = 0;
-args->batch_normalization_15_variance = batch_normalization_15_variance;
-args->batch_normalization_15_variance_bytes = 0;
-args->depthwise_conv2d_8_w = depthwise_conv2d_8_w;
-args->depthwise_conv2d_8_w_bytes = 0;
-args->batch_normalization_16_gamma = batch_normalization_16_gamma;
-args->batch_normalization_16_gamma_bytes = 0;
-args->batch_normalization_16_beta = batch_normalization_16_beta;
-args->batch_normalization_16_beta_bytes = 0;
-args->batch_normalization_16_mean = batch_normalization_16_mean;
-args->batch_normalization_16_mean_bytes = 0;
-args->batch_normalization_16_variance = batch_normalization_16_variance;
-args->batch_normalization_16_variance_bytes = 0;
-args->conv2d_9_w = conv2d_9_w;
-args->conv2d_9_w_bytes = 0;
-args->batch_normalization_17_gamma = batch_normalization_17_gamma;
-args->batch_normalization_17_gamma_bytes = 0;
-args->batch_normalization_17_beta = batch_normalization_17_beta;
-args->batch_normalization_17_beta_bytes = 0;
-args->batch_normalization_17_mean = batch_normalization_17_mean;
-args->batch_normalization_17_mean_bytes = 0;
-args->batch_normalization_17_variance = batch_normalization_17_variance;
-args->batch_normalization_17_variance_bytes = 0;
-args->depthwise_conv2d_9_w = depthwise_conv2d_9_w;
-args->depthwise_conv2d_9_w_bytes = 0;
-args->batch_normalization_18_gamma = batch_normalization_18_gamma;
-args->batch_normalization_18_gamma_bytes = 0;
-args->batch_normalization_18_beta = batch_normalization_18_beta;
-args->batch_normalization_18_beta_bytes = 0;
-args->batch_normalization_18_mean = batch_normalization_18_mean;
-args->batch_normalization_18_mean_bytes = 0;
-args->batch_normalization_18_variance = batch_normalization_18_variance;
-args->batch_normalization_18_variance_bytes = 0;
-args->conv2d_10_w = conv2d_10_w;
-args->conv2d_10_w_bytes = 0;
-args->batch_normalization_19_gamma = batch_normalization_19_gamma;
-args->batch_normalization_19_gamma_bytes = 0;
-args->batch_normalization_19_beta = batch_normalization_19_beta;
-args->batch_normalization_19_beta_bytes = 0;
-args->batch_normalization_19_mean = batch_normalization_19_mean;
-args->batch_normalization_19_mean_bytes = 0;
-args->batch_normalization_19_variance = batch_normalization_19_variance;
-args->batch_normalization_19_variance_bytes = 0;
-args->depthwise_conv2d_10_w = depthwise_conv2d_10_w;
-args->depthwise_conv2d_10_w_bytes = 0;
-args->batch_normalization_20_gamma = batch_normalization_20_gamma;
-args->batch_normalization_20_gamma_bytes = 0;
-args->batch_normalization_20_beta = batch_normalization_20_beta;
-args->batch_normalization_20_beta_bytes = 0;
-args->batch_normalization_20_mean = batch_normalization_20_mean;
-args->batch_normalization_20_mean_bytes = 0;
-args->batch_normalization_20_variance = batch_normalization_20_variance;
-args->batch_normalization_20_variance_bytes = 0;
-args->conv2d_11_w = conv2d_11_w;
-args->conv2d_11_w_bytes = 0;
-args->batch_normalization_21_gamma = batch_normalization_21_gamma;
-args->batch_normalization_21_gamma_bytes = 0;
-args->batch_normalization_21_beta = batch_normalization_21_beta;
-args->batch_normalization_21_beta_bytes = 0;
-args->batch_normalization_21_mean = batch_normalization_21_mean;
-args->batch_normalization_21_mean_bytes = 0;
-args->batch_normalization_21_variance = batch_normalization_21_variance;
-args->batch_normalization_21_variance_bytes = 0;
-args->depthwise_conv2d_11_w = depthwise_conv2d_11_w;
-args->depthwise_conv2d_11_w_bytes = 0;
-args->batch_normalization_22_gamma = batch_normalization_22_gamma;
-args->batch_normalization_22_gamma_bytes = 0;
-args->batch_normalization_22_beta = batch_normalization_22_beta;
-args->batch_normalization_22_beta_bytes = 0;
-args->batch_normalization_22_mean = batch_normalization_22_mean;
-args->batch_normalization_22_mean_bytes = 0;
-args->batch_normalization_22_variance = batch_normalization_22_variance;
-args->batch_normalization_22_variance_bytes = 0;
-args->conv2d_12_w = conv2d_12_w;
-args->conv2d_12_w_bytes = 0;
-args->batch_normalization_23_gamma = batch_normalization_23_gamma;
-args->batch_normalization_23_gamma_bytes = 0;
-args->batch_normalization_23_beta = batch_normalization_23_beta;
-args->batch_normalization_23_beta_bytes = 0;
-args->batch_normalization_23_mean = batch_normalization_23_mean;
-args->batch_normalization_23_mean_bytes = 0;
-args->batch_normalization_23_variance = batch_normalization_23_variance;
-args->batch_normalization_23_variance_bytes = 0;
-args->depthwise_conv2d_12_w = depthwise_conv2d_12_w;
-args->depthwise_conv2d_12_w_bytes = 0;
-args->batch_normalization_24_gamma = batch_normalization_24_gamma;
-args->batch_normalization_24_gamma_bytes = 0;
-args->batch_normalization_24_beta = batch_normalization_24_beta;
-args->batch_normalization_24_beta_bytes = 0;
-args->batch_normalization_24_mean = batch_normalization_24_mean;
-args->batch_normalization_24_mean_bytes = 0;
-args->batch_normalization_24_variance = batch_normalization_24_variance;
-args->batch_normalization_24_variance_bytes = 0;
-args->conv2d_13_w = conv2d_13_w;
-args->conv2d_13_w_bytes = 0;
-args->batch_normalization_25_gamma = batch_normalization_25_gamma;
-args->batch_normalization_25_gamma_bytes = 0;
-args->batch_normalization_25_beta = batch_normalization_25_beta;
-args->batch_normalization_25_beta_bytes = 0;
-args->batch_normalization_25_mean = batch_normalization_25_mean;
-args->batch_normalization_25_mean_bytes = 0;
-args->batch_normalization_25_variance = batch_normalization_25_variance;
-args->batch_normalization_25_variance_bytes = 0;
-args->depthwise_conv2d_13_w = depthwise_conv2d_13_w;
-args->depthwise_conv2d_13_w_bytes = 0;
-args->batch_normalization_26_gamma = batch_normalization_26_gamma;
-args->batch_normalization_26_gamma_bytes = 0;
-args->batch_normalization_26_beta = batch_normalization_26_beta;
-args->batch_normalization_26_beta_bytes = 0;
-args->batch_normalization_26_mean = batch_normalization_26_mean;
-args->batch_normalization_26_mean_bytes = 0;
-args->batch_normalization_26_variance = batch_normalization_26_variance;
-args->batch_normalization_26_variance_bytes = 0;
-args->conv2d_14_w = conv2d_14_w;
-args->conv2d_14_w_bytes = 0;
-args->batch_normalization_27_gamma = batch_normalization_27_gamma;
-args->batch_normalization_27_gamma_bytes = 0;
-args->batch_normalization_27_beta = batch_normalization_27_beta;
-args->batch_normalization_27_beta_bytes = 0;
-args->batch_normalization_27_mean = batch_normalization_27_mean;
-args->batch_normalization_27_mean_bytes = 0;
-args->batch_normalization_27_variance = batch_normalization_27_variance;
-args->batch_normalization_27_variance_bytes = 0;
-args->dense_1_w = dense_1_w;
-args->dense_1_w_bytes = 0;
-args->dense_1_b = dense_1_b;
-args->dense_1_b_bytes = 0;
-
-void* dfg = __visc__launch(0, root, (void*) args);
-
-__visc__wait(dfg);
-
-void *result = static_cast<RootIn*>(args)->input;
-hpvm_request_tensor(result, 0);
-
-__visc__cleanup();
- computeAccuracy2(labels, 10000, result);
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_10_beta.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_10_beta.bin
deleted file mode 100644
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_10_gamma.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_10_gamma.bin
deleted file mode 100644
index 931c8925b89f363a41d3cf81483bde60abafba61..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_10_mean.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_10_mean.bin
deleted file mode 100644
index 633bdc9fd4a9ef052ca8b6ab488a156002e3d4b5..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_10_variance.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_10_variance.bin
deleted file mode 100644
index f92c73f59eb5eb35ca94e3ce006e5f3c4f60ecef..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_10_variance.bin
+++ /dev/null
@@ -1,2 +0,0 @@
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_11_beta.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_11_beta.bin
deleted file mode 100644
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_11_gamma.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_11_gamma.bin
deleted file mode 100644
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_11_mean.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_11_mean.bin
deleted file mode 100644
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_11_variance.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_11_variance.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_12_beta.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_12_beta.bin
deleted file mode 100644
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_12_gamma.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_12_gamma.bin
deleted file mode 100644
index 6dfb7c3833821b29f9230df806c4abc0c16a7b59..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_12_gamma.bin
+++ /dev/null
@@ -1,5 +0,0 @@
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-)Ÿ?›^?7uF?( X? hš?³?A3u?¸-“?«7P?=×a?œ‹C?ßøˆ?
qq?$ÚP?àß?Šì¨?ö^?%œp?kO?”Q?Š d?_G?ˆ??ïÞ@?½œk?<öV?¬<R?°>?.jO?„ Œ?2¬Q?¥ûª?µÊY?ÓÙD?L—f?EU?c²6?O©®?Z(H?‰Š?KX?p¦T?‚Jm?…;?ÇŸŠ?¶€?ým?Øp?¨@?~Ó^?;öC?/€[?ÃÑ©?zÅ‹?1éH?ìT>?p b?q9^?| K?
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¤?’?q¼a?Ýþ[?g}¡?IJH?Š3”?ç\F?þ]?ÂS?w2D?—!V?¹IV?èõL?¡Œ_?øâ™?ÖxH?‘ùX?éH?+K8?†áV?ûR?–ß“?¶EI?˜'Z?Ñì^?¶~@?ö:–?ª¼_?
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?ôýE?b{B?(ß7?{I\?& K?
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_12_mean.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_12_mean.bin
deleted file mode 100644
index 8899c2ad8395a98c752b1777095018cc90ca693b..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_12_variance.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_12_variance.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_13_beta.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_13_beta.bin
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index 0f5fe8656435b28ec4b928af599b0a63915a651a..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_13_gamma.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_13_gamma.bin
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index c79d7d0b02b65ea9953bfd1fa164773f96e5ade0..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_13_mean.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_13_mean.bin
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index 2a6d471779cb2634718545d33827ca1d8d023c07..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_13_variance.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/batch_normalization_13_variance.bin
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--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/layer_composition.txt
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@@ -1,83 +0,0 @@
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
-
-activation
-conv
-
-activation
-
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-activation
-conv
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-activation
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-conv
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-activation
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-activation
-conv
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-activation
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-activation
-conv
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-activation
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-
-activation
-conv
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-activation
-pool
-dense add
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/layers.txt b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/layers.txt
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index 0bd2b554374c10d748a652f52e5427c716be0084..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/layers.txt
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@@ -1,83 +0,0 @@
-Conv1,10000,3,32,32,32,3,3,3
-#tensorBatchNorm1
-#tensorRelu1
-#tensorDepthwiseConv1
-#tensorBatchNorm2
-#tensorRelu2
-Conv2,10000,32,32,32,64,32,1,1
-#tensorBatchNorm3
-#tensorRelu3
-#tensorDepthwiseConv2
-#tensorBatchNorm4
-#tensorRelu4
-Conv3,10000,64,16,16,128,64,1,1
-#tensorBatchNorm5
-#tensorRelu5
-#tensorDepthwiseConv3
-#tensorBatchNorm6
-#tensorRelu6
-Conv4,10000,128,16,16,128,128,1,1
-#tensorBatchNorm7
-#tensorRelu7
-#tensorDepthwiseConv4
-#tensorBatchNorm8
-#tensorRelu8
-Conv5,10000,128,8,8,256,128,1,1
-#tensorBatchNorm9
-#tensorRelu9
-#tensorDepthwiseConv5
-#tensorBatchNorm10
-#tensorRelu10
-Conv6,10000,256,8,8,256,256,1,1
-#tensorBatchNorm11
-#tensorRelu11
-#tensorDepthwiseConv6
-#tensorBatchNorm12
-#tensorRelu12
-Conv7,10000,256,4,4,512,256,1,1
-#tensorBatchNorm13
-#tensorRelu13
-#tensorDepthwiseConv7
-#tensorBatchNorm14
-#tensorRelu14
-Conv8,10000,512,4,4,512,512,1,1
-#tensorBatchNorm15
-#tensorRelu15
-#tensorDepthwiseConv8
-#tensorBatchNorm16
-#tensorRelu16
-Conv9,10000,512,4,4,512,512,1,1
-#tensorBatchNorm17
-#tensorRelu17
-#tensorDepthwiseConv9
-#tensorBatchNorm18
-#tensorRelu18
-Conv10,10000,512,4,4,512,512,1,1
-#tensorBatchNorm19
-#tensorRelu19
-#tensorDepthwiseConv10
-#tensorBatchNorm20
-#tensorRelu20
-Conv11,10000,512,4,4,512,512,1,1
-#tensorBatchNorm21
-#tensorRelu21
-#tensorDepthwiseConv11
-#tensorBatchNorm22
-#tensorRelu22
-Conv12,10000,512,4,4,512,512,1,1
-#tensorBatchNorm23
-#tensorRelu23
-#tensorDepthwiseConv12
-#tensorBatchNorm24
-#tensorRelu24
-Conv13,10000,512,2,2,1024,512,1,1
-#tensorBatchNorm25
-#tensorRelu25
-#tensorDepthwiseConv13
-#tensorBatchNorm26
-#tensorRelu26
-Conv14,10000,1024,2,2,1024,1024,1,1
-#tensorBatchNorm27
-#tensorRelu27
-#tensorPooling1
-FC1,10000,1024,1024,10
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/promise_src.cc b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/promise_src.cc
deleted file mode 100644
index 146bc640cc4b1e8da65e3e7bb6cb5c7f2a007399..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/promise_src.cc
+++ /dev/null
@@ -1,420 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-int total_runs = 100;
-for (int i = 0 ; i < total_runs; i++){
-
-
-startMemTracking();
-
-int test_input_size = 10000;
-int batch_size = 10000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-
-
-std::string dir_prefix = std::string("data/mobilenet_quant/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
-void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
-void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
-void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
-void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
-std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
-void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
-std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
-void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
-void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
-void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
-void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
-std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
-void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
-void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
-void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
-void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
-std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
-void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
-std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
-void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
-void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
-void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
-void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
-std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
-void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
-void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
-void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
-void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
-void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
-void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
-void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
-void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
-void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
-std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
-void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
-void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
-void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
-void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
-void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
-void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
-void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
-void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
-void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
-std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
-void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
-void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
-void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
-void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
-void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
-void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
-void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
-void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
-void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
-std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
-void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
-void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
-void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
-void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
-void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
-void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
-void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
-void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
-void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
-std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
-void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
-void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
-void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
-void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
-void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
-void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
-void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
-void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
-void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
-void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
-void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
-void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
-void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
-void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
-void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
-void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
-void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
-void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
-void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
-void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
-void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
-void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
-void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
-void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
-void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
-void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
-void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
-void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
-void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
-void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
-void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
-void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
-void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
-void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
-void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
-void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
-void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
-void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
-void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
-void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
-void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
-void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
-void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
-void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
-void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
-void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
-void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
-void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
-void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
-void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
-void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
-void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
-void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
-void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1);
-std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
-void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
-void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
-void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
-void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1);
-std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
-void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3);
-std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
-void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
-void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
-void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
-void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1);
-std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
-void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1);
-std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
-void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
-void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
-void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
-void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = ConvLayer_PROMISE(input, -1.9892114, 2.126797, conv2d_1_w, -2.196306920051575, 1.347581704139706, NULL, 0, 0, 1, 1, 1, 1, -1, 0, -1, -60.89275047302246, 51.99256916046146, 9);
-void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
-void* var_2 = tensorRelu(var_1);
-void* var_3 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
-void* var_4 = tensorBatchNorm(var_3, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
-void* var_5 = tensorRelu(var_4);
-void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 5.713541553974245, conv2d_2_w, -0.9317721160650253, 1.0774258937835774, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.518589503288269, 6.810842518806449, 9);
-void* var_7 = tensorBatchNorm(var_6, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
-void* var_8 = tensorRelu(var_7);
-void* var_9 = tensorConvolution(var_8, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
-void* var_10 = tensorBatchNorm(var_9, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
-void* var_11 = tensorRelu(var_10);
-void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.932139402866376, conv2d_3_w, -0.5316544661521911, 0.5753790403604531, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.482631235122681, 3.96730119752885, 9);
-void* var_13 = tensorBatchNorm(var_12, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
-void* var_14 = tensorRelu(var_13);
-void* var_15 = tensorConvolution(var_14, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
-void* var_16 = tensorBatchNorm(var_15, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
-void* var_17 = tensorRelu(var_16);
-void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 4.103263397693674, conv2d_4_w, -0.36234098821878435, 0.4076913900375366, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.04261828327179, 3.88677932929993, 9);
-void* var_19 = tensorBatchNorm(var_18, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
-void* var_20 = tensorRelu(var_19);
-void* var_21 = tensorConvolution(var_20, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
-void* var_22 = tensorBatchNorm(var_21, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
-void* var_23 = tensorRelu(var_22);
-void* var_24 = ConvLayer_PROMISE(var_23, 0.0, 5.383221302509475, conv2d_5_w, -0.3131200549006462, 0.29357679939270065, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.921469215393066, 4.338679324150087, 9);
-void* var_25 = tensorBatchNorm(var_24, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
-void* var_26 = tensorRelu(var_25);
-void* var_27 = tensorConvolution(var_26, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
-void* var_28 = tensorBatchNorm(var_27, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
-void* var_29 = tensorRelu(var_28);
-void* var_30 = ConvLayer_PROMISE(var_29, 0.0, 4.316738154411368, conv2d_6_w, -0.23299247801303866, 0.2580290257930756, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.207789947509766, 3.932436970710759, 9);
-void* var_31 = tensorBatchNorm(var_30, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
-void* var_32 = tensorRelu(var_31);
-void* var_33 = tensorConvolution(var_32, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
-void* var_34 = tensorBatchNorm(var_33, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
-void* var_35 = tensorRelu(var_34);
-void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 5.830408106803901, conv2d_7_w, -0.20233777219057084, 0.18998308175802117, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.298286915779113, 4.848135117530843, 9);
-void* var_37 = tensorBatchNorm(var_36, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
-void* var_38 = tensorRelu(var_37);
-void* var_39 = tensorConvolution(var_38, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512);
-void* var_40 = tensorBatchNorm(var_39, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001);
-void* var_41 = tensorRelu(var_40);
-void* var_42 = ConvLayer_PROMISE(var_41, 0.0, 4.446417809963227, conv2d_8_w, -0.17442735651135444, 0.17695830866694454, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.347910885810852, 3.6144364695549145, 9);
-void* var_43 = tensorBatchNorm(var_42, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001);
-void* var_44 = tensorRelu(var_43);
-void* var_45 = tensorConvolution(var_44, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512);
-void* var_46 = tensorBatchNorm(var_45, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001);
-void* var_47 = tensorRelu(var_46);
-void* var_48 = ConvLayer_PROMISE(var_47, 0.0, 4.518095604896667, conv2d_9_w, -0.14546796187758446, 0.15256431668996823, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -3.0287702755928043, 2.9487365779876953, 9);
-void* var_49 = tensorBatchNorm(var_48, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001);
-void* var_50 = tensorRelu(var_49);
-void* var_51 = tensorConvolution(var_50, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512);
-void* var_52 = tensorBatchNorm(var_51, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001);
-void* var_53 = tensorRelu(var_52);
-void* var_54 = ConvLayer_PROMISE(var_53, 0.0, 6.348575634956407, conv2d_10_w, -0.13025874522328376, 0.13558243343234128, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.2293100805282595, 3.5315046372413645, 9);
-void* var_55 = tensorBatchNorm(var_54, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001);
-void* var_56 = tensorRelu(var_55);
-void* var_57 = tensorConvolution(var_56, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512);
-void* var_58 = tensorBatchNorm(var_57, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001);
-void* var_59 = tensorRelu(var_58);
-void* var_60 = ConvLayer_PROMISE(var_59, 0.0, 5.221003110408843, conv2d_11_w, -0.11900172759592534, 0.12536374783515936, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.038203780174255, 4.004009407043483, 9);
-void* var_61 = tensorBatchNorm(var_60, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001);
-void* var_62 = tensorRelu(var_61);
-void* var_63 = tensorConvolution(var_62, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512);
-void* var_64 = tensorBatchNorm(var_63, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001);
-void* var_65 = tensorRelu(var_64);
-void* var_66 = ConvLayer_PROMISE(var_65, 0.0, 5.732498347759442, conv2d_12_w, -0.10839721685647964, 0.11625668607652187, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -3.3111015114784244, 4.462933233261136, 9);
-void* var_67 = tensorBatchNorm(var_66, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001);
-void* var_68 = tensorRelu(var_67);
-void* var_69 = tensorConvolution(var_68, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512);
-void* var_70 = tensorBatchNorm(var_69, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001);
-void* var_71 = tensorRelu(var_70);
-void* var_72 = ConvLayer_PROMISE(var_71, 0.0, 7.240498211860681, conv2d_13_w, -0.08623744961619377, 0.08859449951350662, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.175431394577027, 6.2043294754027345, 9);
-void* var_73 = tensorBatchNorm(var_72, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001);
-void* var_74 = tensorRelu(var_73);
-void* var_75 = tensorConvolution(var_74, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024);
-void* var_76 = tensorBatchNorm(var_75, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001);
-void* var_77 = tensorRelu(var_76);
-void* var_78 = ConvLayer_PROMISE(var_77, 0.0, 7.813958834648251, conv2d_14_w, -0.06813025139272214, 0.07002027779817581, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -10.920566423416137, 2.6442912578582534, 9);
-void* var_79 = tensorBatchNorm(var_78, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001);
-void* var_80 = tensorRelu(var_79);
-void* var_81 = tensorPooling(var_80,1,2,2,0,0,2,2);
-void* var_82 = FCLayer_PROMISE(var_81, 0.0, 2.8692066650391013, dense_1_w, -0.22301019695401192, 0.1442659378200768, dense_1_b, -0.1654396, 0.23336112, -1, -12.245949958801269, 23.80532513427739, 9);
-void* var_83 = tensorSoftmax(var_82);
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy2(labels, batch_size, var_83);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-}
-
-dumpExecutionAccuracies();
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/src.cc b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/src.cc
deleted file mode 100644
index 25aec9bde3bc1aac157e2acc368dcddf866e455d..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_quant/src.cc
+++ /dev/null
@@ -1,413 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-std::string dir_prefix = std::string("data/mobilenet_quant/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
-void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
-void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
-void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
-void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
-std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
-void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
-std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
-void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
-void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
-void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
-void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
-std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
-void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
-void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
-void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
-void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
-std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
-void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
-std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
-void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
-void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
-void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
-void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
-std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
-void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
-void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
-void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
-void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
-void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
-void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
-void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
-void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
-void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
-std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
-void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
-void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
-void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
-void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
-void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
-void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
-void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
-void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
-void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
-std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
-void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
-void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
-void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
-void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
-void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
-void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
-void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
-void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
-void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
-std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
-void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
-void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
-void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
-void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
-void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
-void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
-void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
-void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
-void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
-std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
-void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
-void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
-void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
-void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
-void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
-void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
-void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
-void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
-void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
-void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
-void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
-void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
-void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
-void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
-void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
-void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
-void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
-void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
-void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
-void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
-void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
-void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
-void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
-void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
-void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
-void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
-void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
-void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
-void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
-void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
-void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
-void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
-void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
-void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
-void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
-void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
-void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
-void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
-void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
-void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
-void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
-void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
-void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
-void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
-void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,1,1);
-std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
-void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
-void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
-void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
-void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,512,1,1);
-std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
-void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,512,1,3,3);
-std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
-void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
-void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
-void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
-void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,512,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,1024,512,1,1);
-std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
-void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
-void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
-void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
-void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,1024,1,1);
-std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
-void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,1024,1,3,3);
-std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
-void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
-void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
-void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
-void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,1024,1,1);
-std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
-void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,1024,1024,1,1);
-std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
-void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
-void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
-void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,1024,1,1);
-std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
-void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,1024,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1024,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-
-startMemTracking();
-
-int test_input_size = 10000;
-int batch_size = 10000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
-void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
-void* var_2 = tensorRelu(var_1);
-void* var_4 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
-void* var_5 = tensorBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
-void* var_6 = tensorRelu(var_5);
-void* var_7 = tensorConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
-void* var_8 = tensorBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
-void* var_9 = tensorRelu(var_8);
-void* var_11 = tensorConvolution(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
-void* var_12 = tensorBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
-void* var_13 = tensorRelu(var_12);
-void* var_14 = tensorConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1);
-void* var_15 = tensorBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
-void* var_16 = tensorRelu(var_15);
-void* var_18 = tensorConvolution(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
-void* var_19 = tensorBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
-void* var_20 = tensorRelu(var_19);
-void* var_21 = tensorConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1);
-void* var_22 = tensorBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
-void* var_23 = tensorRelu(var_22);
-void* var_26 = tensorConvolution(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
-void* var_27 = tensorBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
-void* var_28 = tensorRelu(var_27);
-void* var_29 = tensorConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1);
-void* var_30 = tensorBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
-void* var_31 = tensorRelu(var_30);
-void* var_33 = tensorConvolution(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
-void* var_34 = tensorBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
-void* var_35 = tensorRelu(var_34);
-void* var_36 = tensorConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1);
-void* var_37 = tensorBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
-void* var_38 = tensorRelu(var_37);
-void* var_41 = tensorConvolution(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
-void* var_42 = tensorBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
-void* var_43 = tensorRelu(var_42);
-void* var_44 = tensorConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1);
-void* var_45 = tensorBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
-void* var_46 = tensorRelu(var_45);
-void* var_48 = tensorConvolution(var_46, depthwise_conv2d_7_w, 1, 1, 1, 1, 1, 512);
-void* var_49 = tensorBatchNorm(var_48, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001);
-void* var_50 = tensorRelu(var_49);
-void* var_51 = tensorConvolution(var_50, conv2d_8_w, 0, 0, 1, 1, 1, 1);
-void* var_52 = tensorBatchNorm(var_51, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001);
-void* var_53 = tensorRelu(var_52);
-void* var_55 = tensorConvolution(var_53, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 512);
-void* var_56 = tensorBatchNorm(var_55, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001);
-void* var_57 = tensorRelu(var_56);
-void* var_58 = tensorConvolution(var_57, conv2d_9_w, 0, 0, 1, 1, 1, 1);
-void* var_59 = tensorBatchNorm(var_58, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001);
-void* var_60 = tensorRelu(var_59);
-void* var_63 = tensorConvolution(var_60, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 512);
-void* var_64 = tensorBatchNorm(var_63, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001);
-void* var_65 = tensorRelu(var_64);
-void* var_66 = tensorConvolution(var_65, conv2d_10_w, 0, 0, 1, 1, 1, 1);
-void* var_67 = tensorBatchNorm(var_66, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001);
-void* var_68 = tensorRelu(var_67);
-void* var_70 = tensorConvolution(var_68, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 512);
-void* var_71 = tensorBatchNorm(var_70, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001);
-void* var_72 = tensorRelu(var_71);
-void* var_73 = tensorConvolution(var_72, conv2d_11_w, 0, 0, 1, 1, 1, 1);
-void* var_74 = tensorBatchNorm(var_73, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001);
-void* var_75 = tensorRelu(var_74);
-void* var_77 = tensorConvolution(var_75, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 512);
-void* var_78 = tensorBatchNorm(var_77, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001);
-void* var_79 = tensorRelu(var_78);
-void* var_80 = tensorConvolution(var_79, conv2d_12_w, 0, 0, 1, 1, 1, 1);
-void* var_81 = tensorBatchNorm(var_80, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001);
-void* var_82 = tensorRelu(var_81);
-void* var_85 = tensorConvolution(var_82, depthwise_conv2d_12_w, 1, 1, 2, 2, 1, 512);
-void* var_86 = tensorBatchNorm(var_85, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001);
-void* var_87 = tensorRelu(var_86);
-void* var_88 = tensorConvolution(var_87, conv2d_13_w, 0, 0, 1, 1, 1, 1);
-void* var_89 = tensorBatchNorm(var_88, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001);
-void* var_90 = tensorRelu(var_89);
-void* var_92 = tensorConvolution(var_90, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 1024);
-void* var_93 = tensorBatchNorm(var_92, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001);
-void* var_94 = tensorRelu(var_93);
-void* var_95 = tensorConvolution(var_94, conv2d_14_w, 0, 0, 1, 1, 1, 1);
-void* var_96 = tensorBatchNorm(var_95, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001);
-void* var_97 = tensorRelu(var_96);
-void* var_99 = tensorPooling(var_97,1,2,2,0,0,2,2);
-void* var_101 = tensorGemmGPU(var_99, dense_1_w);
-void* var_102 = tensorAdd(var_101, dense_1_b);
-void* var_103 = tensorSoftmax(var_102);
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy2(labels, batch_size, var_103);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/approxhpvm_src.cc b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/approxhpvm_src.cc
deleted file mode 100644
index dc0c873c63333299981591cb5654cb38be9d4092..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/approxhpvm_src.cc
+++ /dev/null
@@ -1,1224 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/stat.h>
-#include <cstring>
-#include <visc.h>
-#include <tensorTypes.h>
-#include <tensorUtils.h>
-
-void var_0_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_1_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_2_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_3_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 32);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_4_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_5_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_6_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_7_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_8_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_9_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 2, 2, 1, 64);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_10_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_11_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_12_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_13_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_14_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_15_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 128);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_16_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_17_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_18_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_19_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_20_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_21_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 2, 2, 1, 128);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_22_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_23_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_24_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_25_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_26_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_27_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 256);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_28_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_29_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_30_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_31_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_32_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_33_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 2, 2, 1, 256);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_34_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_35_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_36_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_37_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_38_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_39_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_avg(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_40_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_mul(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_41_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_42_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_softmax(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void root(void* input, size_t input_bytes,
- void* conv2d_1_w, size_t conv2d_1_w_bytes,
- void* batch_normalization_1_gamma, size_t batch_normalization_1_gamma_bytes,
- void* batch_normalization_1_beta, size_t batch_normalization_1_beta_bytes,
- void* batch_normalization_1_mean, size_t batch_normalization_1_mean_bytes,
- void* batch_normalization_1_variance, size_t batch_normalization_1_variance_bytes,
- void* depthwise_conv2d_1_w, size_t depthwise_conv2d_1_w_bytes,
- void* batch_normalization_2_gamma, size_t batch_normalization_2_gamma_bytes,
- void* batch_normalization_2_beta, size_t batch_normalization_2_beta_bytes,
- void* batch_normalization_2_mean, size_t batch_normalization_2_mean_bytes,
- void* batch_normalization_2_variance, size_t batch_normalization_2_variance_bytes,
- void* conv2d_2_w, size_t conv2d_2_w_bytes,
- void* batch_normalization_3_gamma, size_t batch_normalization_3_gamma_bytes,
- void* batch_normalization_3_beta, size_t batch_normalization_3_beta_bytes,
- void* batch_normalization_3_mean, size_t batch_normalization_3_mean_bytes,
- void* batch_normalization_3_variance, size_t batch_normalization_3_variance_bytes,
- void* depthwise_conv2d_2_w, size_t depthwise_conv2d_2_w_bytes,
- void* batch_normalization_4_gamma, size_t batch_normalization_4_gamma_bytes,
- void* batch_normalization_4_beta, size_t batch_normalization_4_beta_bytes,
- void* batch_normalization_4_mean, size_t batch_normalization_4_mean_bytes,
- void* batch_normalization_4_variance, size_t batch_normalization_4_variance_bytes,
- void* conv2d_3_w, size_t conv2d_3_w_bytes,
- void* batch_normalization_5_gamma, size_t batch_normalization_5_gamma_bytes,
- void* batch_normalization_5_beta, size_t batch_normalization_5_beta_bytes,
- void* batch_normalization_5_mean, size_t batch_normalization_5_mean_bytes,
- void* batch_normalization_5_variance, size_t batch_normalization_5_variance_bytes,
- void* depthwise_conv2d_3_w, size_t depthwise_conv2d_3_w_bytes,
- void* batch_normalization_6_gamma, size_t batch_normalization_6_gamma_bytes,
- void* batch_normalization_6_beta, size_t batch_normalization_6_beta_bytes,
- void* batch_normalization_6_mean, size_t batch_normalization_6_mean_bytes,
- void* batch_normalization_6_variance, size_t batch_normalization_6_variance_bytes,
- void* conv2d_4_w, size_t conv2d_4_w_bytes,
- void* batch_normalization_7_gamma, size_t batch_normalization_7_gamma_bytes,
- void* batch_normalization_7_beta, size_t batch_normalization_7_beta_bytes,
- void* batch_normalization_7_mean, size_t batch_normalization_7_mean_bytes,
- void* batch_normalization_7_variance, size_t batch_normalization_7_variance_bytes,
- void* depthwise_conv2d_4_w, size_t depthwise_conv2d_4_w_bytes,
- void* batch_normalization_8_gamma, size_t batch_normalization_8_gamma_bytes,
- void* batch_normalization_8_beta, size_t batch_normalization_8_beta_bytes,
- void* batch_normalization_8_mean, size_t batch_normalization_8_mean_bytes,
- void* batch_normalization_8_variance, size_t batch_normalization_8_variance_bytes,
- void* conv2d_5_w, size_t conv2d_5_w_bytes,
- void* batch_normalization_9_gamma, size_t batch_normalization_9_gamma_bytes,
- void* batch_normalization_9_beta, size_t batch_normalization_9_beta_bytes,
- void* batch_normalization_9_mean, size_t batch_normalization_9_mean_bytes,
- void* batch_normalization_9_variance, size_t batch_normalization_9_variance_bytes,
- void* depthwise_conv2d_5_w, size_t depthwise_conv2d_5_w_bytes,
- void* batch_normalization_10_gamma, size_t batch_normalization_10_gamma_bytes,
- void* batch_normalization_10_beta, size_t batch_normalization_10_beta_bytes,
- void* batch_normalization_10_mean, size_t batch_normalization_10_mean_bytes,
- void* batch_normalization_10_variance, size_t batch_normalization_10_variance_bytes,
- void* conv2d_6_w, size_t conv2d_6_w_bytes,
- void* batch_normalization_11_gamma, size_t batch_normalization_11_gamma_bytes,
- void* batch_normalization_11_beta, size_t batch_normalization_11_beta_bytes,
- void* batch_normalization_11_mean, size_t batch_normalization_11_mean_bytes,
- void* batch_normalization_11_variance, size_t batch_normalization_11_variance_bytes,
- void* depthwise_conv2d_6_w, size_t depthwise_conv2d_6_w_bytes,
- void* batch_normalization_12_gamma, size_t batch_normalization_12_gamma_bytes,
- void* batch_normalization_12_beta, size_t batch_normalization_12_beta_bytes,
- void* batch_normalization_12_mean, size_t batch_normalization_12_mean_bytes,
- void* batch_normalization_12_variance, size_t batch_normalization_12_variance_bytes,
- void* conv2d_7_w, size_t conv2d_7_w_bytes,
- void* batch_normalization_13_gamma, size_t batch_normalization_13_gamma_bytes,
- void* batch_normalization_13_beta, size_t batch_normalization_13_beta_bytes,
- void* batch_normalization_13_mean, size_t batch_normalization_13_mean_bytes,
- void* batch_normalization_13_variance, size_t batch_normalization_13_variance_bytes,
- void* dense_1_w, size_t dense_1_w_bytes,
- void* dense_1_b, size_t dense_1_b_bytes){
-
-
- __visc__hint(visc::CPU_TARGET);
- __visc__attributes(68, input, conv2d_1_w, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, depthwise_conv2d_1_w, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, conv2d_2_w, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, depthwise_conv2d_2_w, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, conv2d_3_w, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, depthwise_conv2d_3_w, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, conv2d_4_w, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, depthwise_conv2d_4_w, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, conv2d_5_w, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, depthwise_conv2d_5_w, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, conv2d_6_w, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, depthwise_conv2d_6_w, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, conv2d_7_w, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, dense_1_w, dense_1_b, 0);
-
-
- void* var_0 = __visc__createNodeND(0, var_0_node);
-
- __visc__bindIn(var_0, 0, 0, 0);
- __visc__bindIn(var_0, 1, 1, 0);
- __visc__bindIn(var_0, 2, 2, 0);
- __visc__bindIn(var_0, 3, 3, 0);
-
- void* var_1 = __visc__createNodeND(0, var_1_node);
-
- __visc__edge(var_0, var_1, 1, 0, 0, 0);
- __visc__edge(var_0, var_1, 1, 1, 1, 0);
- __visc__bindIn(var_1, 4, 2, 0);
- __visc__bindIn(var_1, 5, 3, 0);
- __visc__bindIn(var_1, 6, 4, 0);
- __visc__bindIn(var_1, 7, 5, 0);
- __visc__bindIn(var_1, 8, 6, 0);
- __visc__bindIn(var_1, 9, 7, 0);
- __visc__bindIn(var_1, 10, 8, 0);
- __visc__bindIn(var_1, 11, 9, 0);
-
- void* var_2 = __visc__createNodeND(0, var_2_node);
-
- __visc__edge(var_1, var_2, 1, 0, 0, 0);
- __visc__edge(var_1, var_2, 1, 1, 1, 0);
-
- void* var_3 = __visc__createNodeND(0, var_3_node);
-
- __visc__edge(var_2, var_3, 1, 0, 0, 0);
- __visc__edge(var_2, var_3, 1, 1, 1, 0);
- __visc__bindIn(var_3, 12, 2, 0);
- __visc__bindIn(var_3, 13, 3, 0);
-
- void* var_4 = __visc__createNodeND(0, var_4_node);
-
- __visc__edge(var_3, var_4, 1, 0, 0, 0);
- __visc__edge(var_3, var_4, 1, 1, 1, 0);
- __visc__bindIn(var_4, 14, 2, 0);
- __visc__bindIn(var_4, 15, 3, 0);
- __visc__bindIn(var_4, 16, 4, 0);
- __visc__bindIn(var_4, 17, 5, 0);
- __visc__bindIn(var_4, 18, 6, 0);
- __visc__bindIn(var_4, 19, 7, 0);
- __visc__bindIn(var_4, 20, 8, 0);
- __visc__bindIn(var_4, 21, 9, 0);
-
- void* var_5 = __visc__createNodeND(0, var_5_node);
-
- __visc__edge(var_4, var_5, 1, 0, 0, 0);
- __visc__edge(var_4, var_5, 1, 1, 1, 0);
-
- void* var_6 = __visc__createNodeND(0, var_6_node);
-
- __visc__edge(var_5, var_6, 1, 0, 0, 0);
- __visc__edge(var_5, var_6, 1, 1, 1, 0);
- __visc__bindIn(var_6, 22, 2, 0);
- __visc__bindIn(var_6, 23, 3, 0);
-
- void* var_7 = __visc__createNodeND(0, var_7_node);
-
- __visc__edge(var_6, var_7, 1, 0, 0, 0);
- __visc__edge(var_6, var_7, 1, 1, 1, 0);
- __visc__bindIn(var_7, 24, 2, 0);
- __visc__bindIn(var_7, 25, 3, 0);
- __visc__bindIn(var_7, 26, 4, 0);
- __visc__bindIn(var_7, 27, 5, 0);
- __visc__bindIn(var_7, 28, 6, 0);
- __visc__bindIn(var_7, 29, 7, 0);
- __visc__bindIn(var_7, 30, 8, 0);
- __visc__bindIn(var_7, 31, 9, 0);
-
- void* var_8 = __visc__createNodeND(0, var_8_node);
-
- __visc__edge(var_7, var_8, 1, 0, 0, 0);
- __visc__edge(var_7, var_8, 1, 1, 1, 0);
-
- void* var_9 = __visc__createNodeND(0, var_9_node);
-
- __visc__edge(var_8, var_9, 1, 0, 0, 0);
- __visc__edge(var_8, var_9, 1, 1, 1, 0);
- __visc__bindIn(var_9, 32, 2, 0);
- __visc__bindIn(var_9, 33, 3, 0);
-
- void* var_10 = __visc__createNodeND(0, var_10_node);
-
- __visc__edge(var_9, var_10, 1, 0, 0, 0);
- __visc__edge(var_9, var_10, 1, 1, 1, 0);
- __visc__bindIn(var_10, 34, 2, 0);
- __visc__bindIn(var_10, 35, 3, 0);
- __visc__bindIn(var_10, 36, 4, 0);
- __visc__bindIn(var_10, 37, 5, 0);
- __visc__bindIn(var_10, 38, 6, 0);
- __visc__bindIn(var_10, 39, 7, 0);
- __visc__bindIn(var_10, 40, 8, 0);
- __visc__bindIn(var_10, 41, 9, 0);
-
- void* var_11 = __visc__createNodeND(0, var_11_node);
-
- __visc__edge(var_10, var_11, 1, 0, 0, 0);
- __visc__edge(var_10, var_11, 1, 1, 1, 0);
-
- void* var_12 = __visc__createNodeND(0, var_12_node);
-
- __visc__edge(var_11, var_12, 1, 0, 0, 0);
- __visc__edge(var_11, var_12, 1, 1, 1, 0);
- __visc__bindIn(var_12, 42, 2, 0);
- __visc__bindIn(var_12, 43, 3, 0);
-
- void* var_13 = __visc__createNodeND(0, var_13_node);
-
- __visc__edge(var_12, var_13, 1, 0, 0, 0);
- __visc__edge(var_12, var_13, 1, 1, 1, 0);
- __visc__bindIn(var_13, 44, 2, 0);
- __visc__bindIn(var_13, 45, 3, 0);
- __visc__bindIn(var_13, 46, 4, 0);
- __visc__bindIn(var_13, 47, 5, 0);
- __visc__bindIn(var_13, 48, 6, 0);
- __visc__bindIn(var_13, 49, 7, 0);
- __visc__bindIn(var_13, 50, 8, 0);
- __visc__bindIn(var_13, 51, 9, 0);
-
- void* var_14 = __visc__createNodeND(0, var_14_node);
-
- __visc__edge(var_13, var_14, 1, 0, 0, 0);
- __visc__edge(var_13, var_14, 1, 1, 1, 0);
-
- void* var_15 = __visc__createNodeND(0, var_15_node);
-
- __visc__edge(var_14, var_15, 1, 0, 0, 0);
- __visc__edge(var_14, var_15, 1, 1, 1, 0);
- __visc__bindIn(var_15, 52, 2, 0);
- __visc__bindIn(var_15, 53, 3, 0);
-
- void* var_16 = __visc__createNodeND(0, var_16_node);
-
- __visc__edge(var_15, var_16, 1, 0, 0, 0);
- __visc__edge(var_15, var_16, 1, 1, 1, 0);
- __visc__bindIn(var_16, 54, 2, 0);
- __visc__bindIn(var_16, 55, 3, 0);
- __visc__bindIn(var_16, 56, 4, 0);
- __visc__bindIn(var_16, 57, 5, 0);
- __visc__bindIn(var_16, 58, 6, 0);
- __visc__bindIn(var_16, 59, 7, 0);
- __visc__bindIn(var_16, 60, 8, 0);
- __visc__bindIn(var_16, 61, 9, 0);
-
- void* var_17 = __visc__createNodeND(0, var_17_node);
-
- __visc__edge(var_16, var_17, 1, 0, 0, 0);
- __visc__edge(var_16, var_17, 1, 1, 1, 0);
-
- void* var_18 = __visc__createNodeND(0, var_18_node);
-
- __visc__edge(var_17, var_18, 1, 0, 0, 0);
- __visc__edge(var_17, var_18, 1, 1, 1, 0);
- __visc__bindIn(var_18, 62, 2, 0);
- __visc__bindIn(var_18, 63, 3, 0);
-
- void* var_19 = __visc__createNodeND(0, var_19_node);
-
- __visc__edge(var_18, var_19, 1, 0, 0, 0);
- __visc__edge(var_18, var_19, 1, 1, 1, 0);
- __visc__bindIn(var_19, 64, 2, 0);
- __visc__bindIn(var_19, 65, 3, 0);
- __visc__bindIn(var_19, 66, 4, 0);
- __visc__bindIn(var_19, 67, 5, 0);
- __visc__bindIn(var_19, 68, 6, 0);
- __visc__bindIn(var_19, 69, 7, 0);
- __visc__bindIn(var_19, 70, 8, 0);
- __visc__bindIn(var_19, 71, 9, 0);
-
- void* var_20 = __visc__createNodeND(0, var_20_node);
-
- __visc__edge(var_19, var_20, 1, 0, 0, 0);
- __visc__edge(var_19, var_20, 1, 1, 1, 0);
-
- void* var_21 = __visc__createNodeND(0, var_21_node);
-
- __visc__edge(var_20, var_21, 1, 0, 0, 0);
- __visc__edge(var_20, var_21, 1, 1, 1, 0);
- __visc__bindIn(var_21, 72, 2, 0);
- __visc__bindIn(var_21, 73, 3, 0);
-
- void* var_22 = __visc__createNodeND(0, var_22_node);
-
- __visc__edge(var_21, var_22, 1, 0, 0, 0);
- __visc__edge(var_21, var_22, 1, 1, 1, 0);
- __visc__bindIn(var_22, 74, 2, 0);
- __visc__bindIn(var_22, 75, 3, 0);
- __visc__bindIn(var_22, 76, 4, 0);
- __visc__bindIn(var_22, 77, 5, 0);
- __visc__bindIn(var_22, 78, 6, 0);
- __visc__bindIn(var_22, 79, 7, 0);
- __visc__bindIn(var_22, 80, 8, 0);
- __visc__bindIn(var_22, 81, 9, 0);
-
- void* var_23 = __visc__createNodeND(0, var_23_node);
-
- __visc__edge(var_22, var_23, 1, 0, 0, 0);
- __visc__edge(var_22, var_23, 1, 1, 1, 0);
-
- void* var_24 = __visc__createNodeND(0, var_24_node);
-
- __visc__edge(var_23, var_24, 1, 0, 0, 0);
- __visc__edge(var_23, var_24, 1, 1, 1, 0);
- __visc__bindIn(var_24, 82, 2, 0);
- __visc__bindIn(var_24, 83, 3, 0);
-
- void* var_25 = __visc__createNodeND(0, var_25_node);
-
- __visc__edge(var_24, var_25, 1, 0, 0, 0);
- __visc__edge(var_24, var_25, 1, 1, 1, 0);
- __visc__bindIn(var_25, 84, 2, 0);
- __visc__bindIn(var_25, 85, 3, 0);
- __visc__bindIn(var_25, 86, 4, 0);
- __visc__bindIn(var_25, 87, 5, 0);
- __visc__bindIn(var_25, 88, 6, 0);
- __visc__bindIn(var_25, 89, 7, 0);
- __visc__bindIn(var_25, 90, 8, 0);
- __visc__bindIn(var_25, 91, 9, 0);
-
- void* var_26 = __visc__createNodeND(0, var_26_node);
-
- __visc__edge(var_25, var_26, 1, 0, 0, 0);
- __visc__edge(var_25, var_26, 1, 1, 1, 0);
-
- void* var_27 = __visc__createNodeND(0, var_27_node);
-
- __visc__edge(var_26, var_27, 1, 0, 0, 0);
- __visc__edge(var_26, var_27, 1, 1, 1, 0);
- __visc__bindIn(var_27, 92, 2, 0);
- __visc__bindIn(var_27, 93, 3, 0);
-
- void* var_28 = __visc__createNodeND(0, var_28_node);
-
- __visc__edge(var_27, var_28, 1, 0, 0, 0);
- __visc__edge(var_27, var_28, 1, 1, 1, 0);
- __visc__bindIn(var_28, 94, 2, 0);
- __visc__bindIn(var_28, 95, 3, 0);
- __visc__bindIn(var_28, 96, 4, 0);
- __visc__bindIn(var_28, 97, 5, 0);
- __visc__bindIn(var_28, 98, 6, 0);
- __visc__bindIn(var_28, 99, 7, 0);
- __visc__bindIn(var_28, 100, 8, 0);
- __visc__bindIn(var_28, 101, 9, 0);
-
- void* var_29 = __visc__createNodeND(0, var_29_node);
-
- __visc__edge(var_28, var_29, 1, 0, 0, 0);
- __visc__edge(var_28, var_29, 1, 1, 1, 0);
-
- void* var_30 = __visc__createNodeND(0, var_30_node);
-
- __visc__edge(var_29, var_30, 1, 0, 0, 0);
- __visc__edge(var_29, var_30, 1, 1, 1, 0);
- __visc__bindIn(var_30, 102, 2, 0);
- __visc__bindIn(var_30, 103, 3, 0);
-
- void* var_31 = __visc__createNodeND(0, var_31_node);
-
- __visc__edge(var_30, var_31, 1, 0, 0, 0);
- __visc__edge(var_30, var_31, 1, 1, 1, 0);
- __visc__bindIn(var_31, 104, 2, 0);
- __visc__bindIn(var_31, 105, 3, 0);
- __visc__bindIn(var_31, 106, 4, 0);
- __visc__bindIn(var_31, 107, 5, 0);
- __visc__bindIn(var_31, 108, 6, 0);
- __visc__bindIn(var_31, 109, 7, 0);
- __visc__bindIn(var_31, 110, 8, 0);
- __visc__bindIn(var_31, 111, 9, 0);
-
- void* var_32 = __visc__createNodeND(0, var_32_node);
-
- __visc__edge(var_31, var_32, 1, 0, 0, 0);
- __visc__edge(var_31, var_32, 1, 1, 1, 0);
-
- void* var_33 = __visc__createNodeND(0, var_33_node);
-
- __visc__edge(var_32, var_33, 1, 0, 0, 0);
- __visc__edge(var_32, var_33, 1, 1, 1, 0);
- __visc__bindIn(var_33, 112, 2, 0);
- __visc__bindIn(var_33, 113, 3, 0);
-
- void* var_34 = __visc__createNodeND(0, var_34_node);
-
- __visc__edge(var_33, var_34, 1, 0, 0, 0);
- __visc__edge(var_33, var_34, 1, 1, 1, 0);
- __visc__bindIn(var_34, 114, 2, 0);
- __visc__bindIn(var_34, 115, 3, 0);
- __visc__bindIn(var_34, 116, 4, 0);
- __visc__bindIn(var_34, 117, 5, 0);
- __visc__bindIn(var_34, 118, 6, 0);
- __visc__bindIn(var_34, 119, 7, 0);
- __visc__bindIn(var_34, 120, 8, 0);
- __visc__bindIn(var_34, 121, 9, 0);
-
- void* var_35 = __visc__createNodeND(0, var_35_node);
-
- __visc__edge(var_34, var_35, 1, 0, 0, 0);
- __visc__edge(var_34, var_35, 1, 1, 1, 0);
-
- void* var_36 = __visc__createNodeND(0, var_36_node);
-
- __visc__edge(var_35, var_36, 1, 0, 0, 0);
- __visc__edge(var_35, var_36, 1, 1, 1, 0);
- __visc__bindIn(var_36, 122, 2, 0);
- __visc__bindIn(var_36, 123, 3, 0);
-
- void* var_37 = __visc__createNodeND(0, var_37_node);
-
- __visc__edge(var_36, var_37, 1, 0, 0, 0);
- __visc__edge(var_36, var_37, 1, 1, 1, 0);
- __visc__bindIn(var_37, 124, 2, 0);
- __visc__bindIn(var_37, 125, 3, 0);
- __visc__bindIn(var_37, 126, 4, 0);
- __visc__bindIn(var_37, 127, 5, 0);
- __visc__bindIn(var_37, 128, 6, 0);
- __visc__bindIn(var_37, 129, 7, 0);
- __visc__bindIn(var_37, 130, 8, 0);
- __visc__bindIn(var_37, 131, 9, 0);
-
- void* var_38 = __visc__createNodeND(0, var_38_node);
-
- __visc__edge(var_37, var_38, 1, 0, 0, 0);
- __visc__edge(var_37, var_38, 1, 1, 1, 0);
-
- void* var_39 = __visc__createNodeND(0, var_39_node);
-
- __visc__edge(var_38, var_39, 1, 0, 0, 0);
- __visc__edge(var_38, var_39, 1, 1, 1, 0);
-
- void* var_40 = __visc__createNodeND(0, var_40_node);
-
- __visc__edge(var_39, var_40, 1, 0, 0, 0);
- __visc__edge(var_39, var_40, 1, 1, 1, 0);
- __visc__bindIn(var_40, 132, 2, 0);
- __visc__bindIn(var_40, 133, 3, 0);
-
- void* var_41 = __visc__createNodeND(0, var_41_node);
-
- __visc__edge(var_40, var_41, 1, 0, 0, 0);
- __visc__edge(var_40, var_41, 1, 1, 1, 0);
- __visc__bindIn(var_41, 134, 2, 0);
- __visc__bindIn(var_41, 135, 3, 0);
-
- void* var_42 = __visc__createNodeND(0, var_42_node);
-
- __visc__edge(var_41, var_42, 1, 0, 0, 0);
- __visc__edge(var_41, var_42, 1, 1, 1, 0);
-
- __visc__bindOut(var_42, 0, 0, 0);
- __visc__bindOut(var_42, 1, 1, 0);
-
-}
-
-struct ret_t {
- void* tensor;
- size_t bytes;
-};
-
-typedef struct __attribute__((__packed__)) {
- void* input;
- size_t input_bytes;
- void* conv2d_1_w;
- size_t conv2d_1_w_bytes;
- void* batch_normalization_1_gamma;
- size_t batch_normalization_1_gamma_bytes;
- void* batch_normalization_1_beta;
- size_t batch_normalization_1_beta_bytes;
- void* batch_normalization_1_mean;
- size_t batch_normalization_1_mean_bytes;
- void* batch_normalization_1_variance;
- size_t batch_normalization_1_variance_bytes;
- void* depthwise_conv2d_1_w;
- size_t depthwise_conv2d_1_w_bytes;
- void* batch_normalization_2_gamma;
- size_t batch_normalization_2_gamma_bytes;
- void* batch_normalization_2_beta;
- size_t batch_normalization_2_beta_bytes;
- void* batch_normalization_2_mean;
- size_t batch_normalization_2_mean_bytes;
- void* batch_normalization_2_variance;
- size_t batch_normalization_2_variance_bytes;
- void* conv2d_2_w;
- size_t conv2d_2_w_bytes;
- void* batch_normalization_3_gamma;
- size_t batch_normalization_3_gamma_bytes;
- void* batch_normalization_3_beta;
- size_t batch_normalization_3_beta_bytes;
- void* batch_normalization_3_mean;
- size_t batch_normalization_3_mean_bytes;
- void* batch_normalization_3_variance;
- size_t batch_normalization_3_variance_bytes;
- void* depthwise_conv2d_2_w;
- size_t depthwise_conv2d_2_w_bytes;
- void* batch_normalization_4_gamma;
- size_t batch_normalization_4_gamma_bytes;
- void* batch_normalization_4_beta;
- size_t batch_normalization_4_beta_bytes;
- void* batch_normalization_4_mean;
- size_t batch_normalization_4_mean_bytes;
- void* batch_normalization_4_variance;
- size_t batch_normalization_4_variance_bytes;
- void* conv2d_3_w;
- size_t conv2d_3_w_bytes;
- void* batch_normalization_5_gamma;
- size_t batch_normalization_5_gamma_bytes;
- void* batch_normalization_5_beta;
- size_t batch_normalization_5_beta_bytes;
- void* batch_normalization_5_mean;
- size_t batch_normalization_5_mean_bytes;
- void* batch_normalization_5_variance;
- size_t batch_normalization_5_variance_bytes;
- void* depthwise_conv2d_3_w;
- size_t depthwise_conv2d_3_w_bytes;
- void* batch_normalization_6_gamma;
- size_t batch_normalization_6_gamma_bytes;
- void* batch_normalization_6_beta;
- size_t batch_normalization_6_beta_bytes;
- void* batch_normalization_6_mean;
- size_t batch_normalization_6_mean_bytes;
- void* batch_normalization_6_variance;
- size_t batch_normalization_6_variance_bytes;
- void* conv2d_4_w;
- size_t conv2d_4_w_bytes;
- void* batch_normalization_7_gamma;
- size_t batch_normalization_7_gamma_bytes;
- void* batch_normalization_7_beta;
- size_t batch_normalization_7_beta_bytes;
- void* batch_normalization_7_mean;
- size_t batch_normalization_7_mean_bytes;
- void* batch_normalization_7_variance;
- size_t batch_normalization_7_variance_bytes;
- void* depthwise_conv2d_4_w;
- size_t depthwise_conv2d_4_w_bytes;
- void* batch_normalization_8_gamma;
- size_t batch_normalization_8_gamma_bytes;
- void* batch_normalization_8_beta;
- size_t batch_normalization_8_beta_bytes;
- void* batch_normalization_8_mean;
- size_t batch_normalization_8_mean_bytes;
- void* batch_normalization_8_variance;
- size_t batch_normalization_8_variance_bytes;
- void* conv2d_5_w;
- size_t conv2d_5_w_bytes;
- void* batch_normalization_9_gamma;
- size_t batch_normalization_9_gamma_bytes;
- void* batch_normalization_9_beta;
- size_t batch_normalization_9_beta_bytes;
- void* batch_normalization_9_mean;
- size_t batch_normalization_9_mean_bytes;
- void* batch_normalization_9_variance;
- size_t batch_normalization_9_variance_bytes;
- void* depthwise_conv2d_5_w;
- size_t depthwise_conv2d_5_w_bytes;
- void* batch_normalization_10_gamma;
- size_t batch_normalization_10_gamma_bytes;
- void* batch_normalization_10_beta;
- size_t batch_normalization_10_beta_bytes;
- void* batch_normalization_10_mean;
- size_t batch_normalization_10_mean_bytes;
- void* batch_normalization_10_variance;
- size_t batch_normalization_10_variance_bytes;
- void* conv2d_6_w;
- size_t conv2d_6_w_bytes;
- void* batch_normalization_11_gamma;
- size_t batch_normalization_11_gamma_bytes;
- void* batch_normalization_11_beta;
- size_t batch_normalization_11_beta_bytes;
- void* batch_normalization_11_mean;
- size_t batch_normalization_11_mean_bytes;
- void* batch_normalization_11_variance;
- size_t batch_normalization_11_variance_bytes;
- void* depthwise_conv2d_6_w;
- size_t depthwise_conv2d_6_w_bytes;
- void* batch_normalization_12_gamma;
- size_t batch_normalization_12_gamma_bytes;
- void* batch_normalization_12_beta;
- size_t batch_normalization_12_beta_bytes;
- void* batch_normalization_12_mean;
- size_t batch_normalization_12_mean_bytes;
- void* batch_normalization_12_variance;
- size_t batch_normalization_12_variance_bytes;
- void* conv2d_7_w;
- size_t conv2d_7_w_bytes;
- void* batch_normalization_13_gamma;
- size_t batch_normalization_13_gamma_bytes;
- void* batch_normalization_13_beta;
- size_t batch_normalization_13_beta_bytes;
- void* batch_normalization_13_mean;
- size_t batch_normalization_13_mean_bytes;
- void* batch_normalization_13_variance;
- size_t batch_normalization_13_variance_bytes;
- void* dense_1_w;
- size_t dense_1_w_bytes;
- void* dense_1_b;
- size_t dense_1_b_bytes;
-
- struct ret_t r;
-}
-RootIn;
-
-int main(){
-
-std::string dir_prefix = std::string("data/mobilenet_shallow_nathan/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
-void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
-void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
-void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
-void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
-std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
-void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
-std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
-void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
-void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
-void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
-void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
-std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
-void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
-void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
-void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
-void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
-std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
-void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
-std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
-void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
-void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
-void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
-void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
-std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
-void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
-void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
-void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
-void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
-void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
-void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
-void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
-void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
-void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
-std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
-void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
-void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
-void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
-void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
-void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
-void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
-void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
-void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
-void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
-std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
-void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
-void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
-void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
-void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
-void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
-void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
-void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
-void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
-void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
-std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
-void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
-void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
-void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
-void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
-void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
-void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
-void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
-void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
-void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
-std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
-void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
-void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
-void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
-void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,2048,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-
-__visc__init();
-RootIn* args = static_cast<RootIn*>(malloc(sizeof(RootIn)));
-
-args->input = input;
-args->input_bytes = 0;
-args->conv2d_1_w = conv2d_1_w;
-args->conv2d_1_w_bytes = 0;
-args->batch_normalization_1_gamma = batch_normalization_1_gamma;
-args->batch_normalization_1_gamma_bytes = 0;
-args->batch_normalization_1_beta = batch_normalization_1_beta;
-args->batch_normalization_1_beta_bytes = 0;
-args->batch_normalization_1_mean = batch_normalization_1_mean;
-args->batch_normalization_1_mean_bytes = 0;
-args->batch_normalization_1_variance = batch_normalization_1_variance;
-args->batch_normalization_1_variance_bytes = 0;
-args->depthwise_conv2d_1_w = depthwise_conv2d_1_w;
-args->depthwise_conv2d_1_w_bytes = 0;
-args->batch_normalization_2_gamma = batch_normalization_2_gamma;
-args->batch_normalization_2_gamma_bytes = 0;
-args->batch_normalization_2_beta = batch_normalization_2_beta;
-args->batch_normalization_2_beta_bytes = 0;
-args->batch_normalization_2_mean = batch_normalization_2_mean;
-args->batch_normalization_2_mean_bytes = 0;
-args->batch_normalization_2_variance = batch_normalization_2_variance;
-args->batch_normalization_2_variance_bytes = 0;
-args->conv2d_2_w = conv2d_2_w;
-args->conv2d_2_w_bytes = 0;
-args->batch_normalization_3_gamma = batch_normalization_3_gamma;
-args->batch_normalization_3_gamma_bytes = 0;
-args->batch_normalization_3_beta = batch_normalization_3_beta;
-args->batch_normalization_3_beta_bytes = 0;
-args->batch_normalization_3_mean = batch_normalization_3_mean;
-args->batch_normalization_3_mean_bytes = 0;
-args->batch_normalization_3_variance = batch_normalization_3_variance;
-args->batch_normalization_3_variance_bytes = 0;
-args->depthwise_conv2d_2_w = depthwise_conv2d_2_w;
-args->depthwise_conv2d_2_w_bytes = 0;
-args->batch_normalization_4_gamma = batch_normalization_4_gamma;
-args->batch_normalization_4_gamma_bytes = 0;
-args->batch_normalization_4_beta = batch_normalization_4_beta;
-args->batch_normalization_4_beta_bytes = 0;
-args->batch_normalization_4_mean = batch_normalization_4_mean;
-args->batch_normalization_4_mean_bytes = 0;
-args->batch_normalization_4_variance = batch_normalization_4_variance;
-args->batch_normalization_4_variance_bytes = 0;
-args->conv2d_3_w = conv2d_3_w;
-args->conv2d_3_w_bytes = 0;
-args->batch_normalization_5_gamma = batch_normalization_5_gamma;
-args->batch_normalization_5_gamma_bytes = 0;
-args->batch_normalization_5_beta = batch_normalization_5_beta;
-args->batch_normalization_5_beta_bytes = 0;
-args->batch_normalization_5_mean = batch_normalization_5_mean;
-args->batch_normalization_5_mean_bytes = 0;
-args->batch_normalization_5_variance = batch_normalization_5_variance;
-args->batch_normalization_5_variance_bytes = 0;
-args->depthwise_conv2d_3_w = depthwise_conv2d_3_w;
-args->depthwise_conv2d_3_w_bytes = 0;
-args->batch_normalization_6_gamma = batch_normalization_6_gamma;
-args->batch_normalization_6_gamma_bytes = 0;
-args->batch_normalization_6_beta = batch_normalization_6_beta;
-args->batch_normalization_6_beta_bytes = 0;
-args->batch_normalization_6_mean = batch_normalization_6_mean;
-args->batch_normalization_6_mean_bytes = 0;
-args->batch_normalization_6_variance = batch_normalization_6_variance;
-args->batch_normalization_6_variance_bytes = 0;
-args->conv2d_4_w = conv2d_4_w;
-args->conv2d_4_w_bytes = 0;
-args->batch_normalization_7_gamma = batch_normalization_7_gamma;
-args->batch_normalization_7_gamma_bytes = 0;
-args->batch_normalization_7_beta = batch_normalization_7_beta;
-args->batch_normalization_7_beta_bytes = 0;
-args->batch_normalization_7_mean = batch_normalization_7_mean;
-args->batch_normalization_7_mean_bytes = 0;
-args->batch_normalization_7_variance = batch_normalization_7_variance;
-args->batch_normalization_7_variance_bytes = 0;
-args->depthwise_conv2d_4_w = depthwise_conv2d_4_w;
-args->depthwise_conv2d_4_w_bytes = 0;
-args->batch_normalization_8_gamma = batch_normalization_8_gamma;
-args->batch_normalization_8_gamma_bytes = 0;
-args->batch_normalization_8_beta = batch_normalization_8_beta;
-args->batch_normalization_8_beta_bytes = 0;
-args->batch_normalization_8_mean = batch_normalization_8_mean;
-args->batch_normalization_8_mean_bytes = 0;
-args->batch_normalization_8_variance = batch_normalization_8_variance;
-args->batch_normalization_8_variance_bytes = 0;
-args->conv2d_5_w = conv2d_5_w;
-args->conv2d_5_w_bytes = 0;
-args->batch_normalization_9_gamma = batch_normalization_9_gamma;
-args->batch_normalization_9_gamma_bytes = 0;
-args->batch_normalization_9_beta = batch_normalization_9_beta;
-args->batch_normalization_9_beta_bytes = 0;
-args->batch_normalization_9_mean = batch_normalization_9_mean;
-args->batch_normalization_9_mean_bytes = 0;
-args->batch_normalization_9_variance = batch_normalization_9_variance;
-args->batch_normalization_9_variance_bytes = 0;
-args->depthwise_conv2d_5_w = depthwise_conv2d_5_w;
-args->depthwise_conv2d_5_w_bytes = 0;
-args->batch_normalization_10_gamma = batch_normalization_10_gamma;
-args->batch_normalization_10_gamma_bytes = 0;
-args->batch_normalization_10_beta = batch_normalization_10_beta;
-args->batch_normalization_10_beta_bytes = 0;
-args->batch_normalization_10_mean = batch_normalization_10_mean;
-args->batch_normalization_10_mean_bytes = 0;
-args->batch_normalization_10_variance = batch_normalization_10_variance;
-args->batch_normalization_10_variance_bytes = 0;
-args->conv2d_6_w = conv2d_6_w;
-args->conv2d_6_w_bytes = 0;
-args->batch_normalization_11_gamma = batch_normalization_11_gamma;
-args->batch_normalization_11_gamma_bytes = 0;
-args->batch_normalization_11_beta = batch_normalization_11_beta;
-args->batch_normalization_11_beta_bytes = 0;
-args->batch_normalization_11_mean = batch_normalization_11_mean;
-args->batch_normalization_11_mean_bytes = 0;
-args->batch_normalization_11_variance = batch_normalization_11_variance;
-args->batch_normalization_11_variance_bytes = 0;
-args->depthwise_conv2d_6_w = depthwise_conv2d_6_w;
-args->depthwise_conv2d_6_w_bytes = 0;
-args->batch_normalization_12_gamma = batch_normalization_12_gamma;
-args->batch_normalization_12_gamma_bytes = 0;
-args->batch_normalization_12_beta = batch_normalization_12_beta;
-args->batch_normalization_12_beta_bytes = 0;
-args->batch_normalization_12_mean = batch_normalization_12_mean;
-args->batch_normalization_12_mean_bytes = 0;
-args->batch_normalization_12_variance = batch_normalization_12_variance;
-args->batch_normalization_12_variance_bytes = 0;
-args->conv2d_7_w = conv2d_7_w;
-args->conv2d_7_w_bytes = 0;
-args->batch_normalization_13_gamma = batch_normalization_13_gamma;
-args->batch_normalization_13_gamma_bytes = 0;
-args->batch_normalization_13_beta = batch_normalization_13_beta;
-args->batch_normalization_13_beta_bytes = 0;
-args->batch_normalization_13_mean = batch_normalization_13_mean;
-args->batch_normalization_13_mean_bytes = 0;
-args->batch_normalization_13_variance = batch_normalization_13_variance;
-args->batch_normalization_13_variance_bytes = 0;
-args->dense_1_w = dense_1_w;
-args->dense_1_w_bytes = 0;
-args->dense_1_b = dense_1_b;
-args->dense_1_b_bytes = 0;
-
-void* dfg = __visc__launch(0, root, (void*) args);
-
-__visc__wait(dfg);
-
-void *result = static_cast<RootIn*>(args)->input;
-hpvm_request_tensor(result, 0);
-
-__visc__cleanup();
- computeAccuracy2(labels, 10000, result);
-return 0;
-
-}
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_1_beta.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_1_beta.bin
deleted file mode 100644
index 05d61c8e00f196b83dde7de794cc9feff2929582..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_1_beta.bin
+++ /dev/null
@@ -1,2 +0,0 @@
-êâ®>@Hx>„jb¾Çå>:Ù*>YR¡>nÍ>ù}u?ýæ>¤ÌB?|¾bç&?ÇO?e=”?¿‹¸¾f'e¾8ƒ¸½è‹Î;Éï:?Ó
-‹?ˆþ„>½»>ŸÞ>î‚?˜5A?|6žàÒ>²•Ý¼Þ>?Ä1?nŸ¾u‡‚>
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_1_gamma.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_1_gamma.bin
deleted file mode 100644
index 1aaaeaa110d8b9eb8108a1546302f8d5c1c12c35..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_1_gamma.bin
+++ /dev/null
@@ -1,2 +0,0 @@
-³]ƒ?Ç/?®0€?]…o?ƒ?ߌ?Vň?`Z?Gk„?±*`?žÐƒ?pÈ~?
*?+g ?4
-u?Òü?Qƒ?|?š O??ä?K{?^5Œ?ÃÜ‚?£ ?…ØA?žTr?1 y?÷€?!56?&ñƒ?HWv?§^‚?
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_1_mean.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_1_mean.bin
deleted file mode 100644
index ffcd3adfeac9b601872fa59caa42601fdc10494c..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_1_mean.bin
+++ /dev/null
@@ -1 +0,0 @@
-ˆG¢¹$ =ã®Ý;9Oü¼ø-'<p;K<
à»Þâ€;žcé¼K!¼“¿œ<õön¼M9 ;¬…;’½î3ì<¯Á ;¨`…<|¾¼þæ:¼;ÿ%=÷»L¶Z»)z·»ªfºS:Œ<j*>»¾M<¨u» û ;%;þ;ñ&J:
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_1_variance.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_1_variance.bin
deleted file mode 100644
index f29dc5a9db7e4fe9783917749bd151ce80e40702..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_1_variance.bin
+++ /dev/null
@@ -1 +0,0 @@
-ÃÅn@éA²£š?ä +@ "@9ÛÞ@áÀ(@•¹÷>Ò¢Á@ƒ'E@)¡@øˆ+@œZž>«Ç?A?¤A˜x°A0ªª?®¯#AÿΕ@«Vì>~ÑÅAg«“@VúAÿ>;j @š”@j¯ø?.AB¾>œê;@ø#û?Q ~@
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_2_beta.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_2_beta.bin
deleted file mode 100644
index ba12532332cec1d6ee20d16d04be81575a8f0802..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_2_beta.bin
+++ /dev/null
@@ -1 +0,0 @@
-N ?¶k©¾¬r>{_?kÙy¾ R?fÀ?ä%Q?“k¾åœ?—õ^½g o?=9L>A†?ím½Ôm ¿†Ç½R?²¾íO‡?àhv?ìt4¾ÙN?cá~?i«Ÿ?¹[ ?•ï¾<M_ >Êõö¾>ðn½rÞ¾? Ž?¼êªE=
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_2_gamma.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_2_gamma.bin
deleted file mode 100644
index bf0dd075d19280dfcda711fd95eeab6fb429b8f5..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_2_gamma.bin
+++ /dev/null
@@ -1 +0,0 @@
-L¾ö>üiQ?-)]?!0?Lw_?`Ë…?ö%4?sÚ¢?²È7?œé`?Ò¿b?¬Y?hv?MC @\ÞY?ñ8P?”Ü\?»QI? ë8?ʤ‘?Ëîl?`¥&?,S?&›?wxr?Ãl_?@8l?k<,?6Ä…?Y={?YjC?2L>?
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_2_mean.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_2_mean.bin
deleted file mode 100644
index faec424f63fab99e4ba00101a005c1b84cb2f8f3..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_2_mean.bin
+++ /dev/null
@@ -1,2 +0,0 @@
-’D´¿h J¿úVN¿ŠÊ>?Ú%8¿¶œf?U_C¾]º¤½†Ã?çv=Ü"¾Ï6¾g½&¾òß[>`"¿0Õ8>•¿Fô¦¾i³?Z?=鯿‰\TÀ
5[?&Ñ6>Ÿÿ.¿ýkn¾Ú4•>p%5¿Î(>" ×>Ù
- ¿p²M>
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_2_variance.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_2_variance.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_3_beta.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_3_beta.bin
deleted file mode 100644
index 0a7e5127f93e4f3e77893c02209dd34f92fcde00..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_3_beta.bin
+++ /dev/null
@@ -1,2 +0,0 @@
-ùEͽý¨½õž9¾pÊ>Ûàÿ½›³>Ÿÿ²>£f>’ë¾Ó‹Ý¹"å>¼½–¾
-ð~¾½¼¾Üöý==Á‹=ä!>D >°©>’çn>£ç;(+Ö=Ÿ‡?Üÿl? m¾$0`>¥¡<¸Ïy?Vál?‚ ?(Ò'½Y ?o>uƒ@> €q>…ö>ë}î>fÓW?K>¿»
?) ?F¿¾Ÿ¯Y¾Ý¯ ?·°—>YdL¾–ZC?þÕ÷=?pUV?þV]>›Â©¾å=9{/>É’Ž½«U>¸ŠÖ½*Ï&>1Í;Mj1¾B‡A=™¾ü½æ…)?! 3?
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_3_gamma.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_3_gamma.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_8_beta.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_8_beta.bin
deleted file mode 100644
index 5f92c58a7c47c207a98a77f6961410d08e8446f0..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_8_gamma.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_8_gamma.bin
deleted file mode 100644
index 6ab36ce54740e9e4a4e4948684e0e4fbbd71b1cb..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_8_gamma.bin
+++ /dev/null
@@ -1,3 +0,0 @@
-œ®?œË?®m‚?…4?¸µ?µ¦?ã ?‡F'?nÑ#?
-=?sˆ.?V>“?m¹ ?æÓD? SÁ? §7?Š›¼?
‡?É&?qä™?§§?Ä'?"ZN?aég?"ò*?&.)?ÕM ?%??Ы?%Œ1?Å:?æ! ?”O:?ÐѨ?9½*?õ¬? w¨?ÎÁT?HÆ
-?%„?ª)?‚e&?x °?‡Ë?µ«±?ÏÜ ?C1?w¯?kì>? ?uD?£òˆ?²’Ÿ?µ¢G?!0?Ñãœ?eŠ2?!b$?úK¬? d?Ü6¨?è‰?2À¹?÷f?ñ?ﾫ?»g?zT@?<e,?Å@7?z‚0?W*:?)‘+?£?(?«Ø;?ùI±?·Œ?Œ ?²Í?Ä2?o¶ª?¾)? ?N³V?ßHž?Ç-?ü,-?? #?HÄ?¾À}? pG?Yù#?HÁ ?0è?„U? '%?u‘?£Í? O«?©é¨?ÉN›?£ED?@œ?¨À˜?v™?>²?G²?”?¶?‡ ?Y·¬?j ?´ØP?^À?Xܘ?…ì ?°bG?¨¨?ã§?r¨+?绤?ŸG£?¿¬E?&6%?áˆ?ô²?"
£?¿®›?-4?
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_8_mean.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_8_mean.bin
deleted file mode 100644
index 5d093813a0926dd3c1c67b6f50e092465101fde9..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/batch_normalization_8_mean.bin
+++ /dev/null
@@ -1,4 +0,0 @@
-¡‰>- ¿Â¿ZF%>Õ#
-¿7æ¾*~>Šß;>€Ó3>P^P>¯ÓX>íÝ¿2 Ë=Æô”>¡o"¿©àÕ>EX$¿30 >œË©<tÐ ¿h{W>LÕˆ>¨Â>KZâ>+_>;™>€g¾>ÒjÇ> ô ¿C‘>í>I“#¿2‹Ï>üfÁ¾‡ø+>4rc>•—˾‘÷´>ZœÕ=V±ì=Ü^é=ƒv‘>[;û¾sˆ=ÓŒ¿Ë“>@AI>¸:¿ÿäD>c˜Z>Žè–>'Š¿t7¿¬?~>gÈ{=ê© ¿9½>Jr'>ðg¿(=d>X)ÿ¾œÉL>)I¿{>ÛfC>ÅS ¿”š¥>ô„Ï>í?¹>O> => >¥>ˆ§‡>¿]&=¡q…>‘)¿Î
-¿-N>Cü=H÷>Úªø¾9>êü>+Ó>Û ¿ÀDB>xÈn>²ï9>h›[>Æ•ì¾ôn–>ušp>|Zi>ûÊ—>ÕY>|‚7>îÓ‹=‹Ú÷=ðŠ´¾=—¿ µÓ¾Œ_^>Òr!¿bâ¾ÏÍÞ¾Ô>#é¿ŒrH¿¾îƒ>ñ÷ ¿fP >åB>â:/¿I ƾà(r>[ïa>`
-¿O£ ¿c@à=A¯Û¾ Y'¿‚°> 3>P2F> ¿Ä ¿ò¿&;>
\ No newline at end of file
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/dense_1_b.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/dense_1_b.bin
deleted file mode 100644
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--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/dense_1_b.bin
+++ /dev/null
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-êÞ^>ÂX`¾q =Ï·‡>Hp‚>°¾¾B—b>6ÁU¾$ƒt¾½M¾
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/labels32.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/labels32.bin
deleted file mode 100644
index 24b800b7002207fa05a7976e08c05cf5f72aa91d..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/layer_composition.txt b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/layer_composition.txt
deleted file mode 100644
index 9b8b3f7e11a428a28fecbde2c204bf39b7e02703..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/layer_composition.txt
+++ /dev/null
@@ -1,41 +0,0 @@
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-pool
-dense add
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/layers.txt b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/layers.txt
deleted file mode 100644
index a9415755180a7ebdceb89b7e3e6d6cee258b18c4..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/layers.txt
+++ /dev/null
@@ -1,41 +0,0 @@
-Conv1,10000,3,32,32,32,3,3,3
-#tensorBatchNorm1
-#tensorRelu1
-#tensorDepthwiseConv1
-#tensorBatchNorm2
-#tensorRelu2
-Conv2,10000,32,32,32,64,32,1,1
-#tensorBatchNorm3
-#tensorRelu3
-#tensorDepthwiseConv2
-#tensorBatchNorm4
-#tensorRelu4
-Conv3,10000,64,16,16,128,64,1,1
-#tensorBatchNorm5
-#tensorRelu5
-#tensorDepthwiseConv3
-#tensorBatchNorm6
-#tensorRelu6
-Conv4,10000,128,16,16,128,128,1,1
-#tensorBatchNorm7
-#tensorRelu7
-#tensorDepthwiseConv4
-#tensorBatchNorm8
-#tensorRelu8
-Conv5,10000,128,8,8,256,128,1,1
-#tensorBatchNorm9
-#tensorRelu9
-#tensorDepthwiseConv5
-#tensorBatchNorm10
-#tensorRelu10
-Conv6,10000,256,8,8,256,256,1,1
-#tensorBatchNorm11
-#tensorRelu11
-#tensorDepthwiseConv6
-#tensorBatchNorm12
-#tensorRelu12
-Conv7,10000,256,4,4,512,256,1,1
-#tensorBatchNorm13
-#tensorRelu13
-#tensorPooling1
-FC1,10000,2048,2048,10
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/promise_src.cc b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/promise_src.cc
deleted file mode 100644
index c5fd3606da51281bc2c583e98f024bd2f54f837b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/promise_src.cc
+++ /dev/null
@@ -1,238 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-int total_runs = 100;
-for (int i = 0 ; i < total_runs; i++){
-
-
-startMemTracking();
-
-int test_input_size = 10000;
-int batch_size = 10000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-
-
-std::string dir_prefix = std::string("data/mobilenet_shallow_nathan/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
-void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
-void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
-void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
-void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
-std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
-void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
-std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
-void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
-void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
-void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
-void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
-std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
-void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
-void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
-void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
-void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
-std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
-void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
-std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
-void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
-void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
-void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
-void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
-std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
-void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
-void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
-void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
-void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
-void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
-void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
-void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
-void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
-void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
-std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
-void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
-void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
-void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
-void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
-void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
-void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
-void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
-void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
-void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
-std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
-void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
-void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
-void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
-void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
-void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
-void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
-void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
-void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
-void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
-std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
-void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
-void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
-void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
-void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
-void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
-void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
-void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
-void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
-void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
-std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
-void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
-void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
-void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
-void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,2048,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = ConvLayer_PROMISE(input, -1.9892114, 2.126797, conv2d_1_w, -1.5164621164798737, 1.6472081774473288, NULL, 0, 0, 1, 1, 1, 1, -1, 0, -1, -9.868980642318725, 10.560956018447879, 9);
-void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
-void* var_2 = tensorRelu(var_1);
-void* var_3 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
-void* var_4 = tensorBatchNorm(var_3, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
-void* var_5 = tensorRelu(var_4);
-void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 6.821381127357554, conv2d_2_w, -1.1834390873908995, 1.2731596627235617, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -9.875998497009277, 7.51305247974393, 9);
-void* var_7 = tensorBatchNorm(var_6, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
-void* var_8 = tensorRelu(var_7);
-void* var_9 = tensorConvolution(var_8, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
-void* var_10 = tensorBatchNorm(var_9, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
-void* var_11 = tensorRelu(var_10);
-void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.826067455768602, conv2d_3_w, -0.599876856982708, 0.6812073457241064, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.633289833068848, 5.177892235755925, 9);
-void* var_13 = tensorBatchNorm(var_12, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
-void* var_14 = tensorRelu(var_13);
-void* var_15 = tensorConvolution(var_14, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
-void* var_16 = tensorBatchNorm(var_15, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
-void* var_17 = tensorRelu(var_16);
-void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 4.02646304416659, conv2d_4_w, -0.4555967862010002, 0.4942613914608956, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.316803941726685, 4.605850250244146, 9);
-void* var_19 = tensorBatchNorm(var_18, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
-void* var_20 = tensorRelu(var_19);
-void* var_21 = tensorConvolution(var_20, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
-void* var_22 = tensorBatchNorm(var_21, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
-void* var_23 = tensorRelu(var_22);
-void* var_24 = ConvLayer_PROMISE(var_23, 0.0, 4.532649063110355, conv2d_5_w, -0.35657615590095515, 0.3382165088057521, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.1012511816024775, 4.3630500688553, 9);
-void* var_25 = tensorBatchNorm(var_24, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
-void* var_26 = tensorRelu(var_25);
-void* var_27 = tensorConvolution(var_26, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
-void* var_28 = tensorBatchNorm(var_27, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
-void* var_29 = tensorRelu(var_28);
-void* var_30 = ConvLayer_PROMISE(var_29, 0.0, 3.9874704387188977, conv2d_6_w, -0.28502783328294756, 0.28604640334844594, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.243851703643799, 3.486250406742097, 9);
-void* var_31 = tensorBatchNorm(var_30, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
-void* var_32 = tensorRelu(var_31);
-void* var_33 = tensorConvolution(var_32, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
-void* var_34 = tensorBatchNorm(var_33, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
-void* var_35 = tensorRelu(var_34);
-void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 6.563065901756522, conv2d_7_w, -0.18946402323246003, 0.19012390717864017, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.938115713119507, 3.538363476753238, 9);
-void* var_37 = tensorBatchNorm(var_36, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
-void* var_38 = tensorRelu(var_37);
-void* var_39 = tensorPooling(var_38,1,2,2,0,0,2,2);
-void* var_40 = FCLayer_PROMISE(var_39, 0.0, 1.8908388000727185, dense_1_w, -0.35140394401550296, 0.422872786462307, dense_1_b, -0.23878151, 0.26507422, -1, -14.630816223144532, 27.27252123260504, 9);
-void* var_41 = tensorSoftmax(var_40);
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy2(labels, batch_size, var_41);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-}
-
-dumpExecutionAccuracies();
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/src.cc b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/src.cc
deleted file mode 100644
index 6599f7d0ea0be6a76c4154d25b3a7be2c6724115..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow/src.cc
+++ /dev/null
@@ -1,231 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-std::string dir_prefix = std::string("data/mobilenet_shallow_nathan/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
-void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
-void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
-void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
-void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
-std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
-void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
-std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
-void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
-void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
-void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
-void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
-std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
-void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
-void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
-void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
-void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
-std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
-void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
-std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
-void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
-void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
-void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
-void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
-std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
-void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
-void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
-void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
-void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
-void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
-void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
-void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
-void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
-void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
-std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
-void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
-void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
-void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
-void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
-void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
-void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
-void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
-void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
-void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
-std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
-void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
-void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
-void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
-void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
-void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
-void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
-void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
-void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
-void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
-std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
-void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
-void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
-void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
-void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
-void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
-void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
-void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
-void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
-void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
-std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
-void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
-void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
-void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
-void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,2048,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-
-startMemTracking();
-
-int test_input_size = 10000;
-int batch_size = 10000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
-void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
-void* var_2 = tensorRelu(var_1);
-void* var_4 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
-void* var_5 = tensorBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
-void* var_6 = tensorRelu(var_5);
-void* var_7 = tensorConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
-void* var_8 = tensorBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
-void* var_9 = tensorRelu(var_8);
-void* var_11 = tensorConvolution(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
-void* var_12 = tensorBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
-void* var_13 = tensorRelu(var_12);
-void* var_14 = tensorConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1);
-void* var_15 = tensorBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
-void* var_16 = tensorRelu(var_15);
-void* var_18 = tensorConvolution(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
-void* var_19 = tensorBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
-void* var_20 = tensorRelu(var_19);
-void* var_21 = tensorConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1);
-void* var_22 = tensorBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
-void* var_23 = tensorRelu(var_22);
-void* var_26 = tensorConvolution(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
-void* var_27 = tensorBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
-void* var_28 = tensorRelu(var_27);
-void* var_29 = tensorConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1);
-void* var_30 = tensorBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
-void* var_31 = tensorRelu(var_30);
-void* var_33 = tensorConvolution(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
-void* var_34 = tensorBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
-void* var_35 = tensorRelu(var_34);
-void* var_36 = tensorConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1);
-void* var_37 = tensorBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
-void* var_38 = tensorRelu(var_37);
-void* var_41 = tensorConvolution(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
-void* var_42 = tensorBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
-void* var_43 = tensorRelu(var_42);
-void* var_44 = tensorConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1);
-void* var_45 = tensorBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
-void* var_46 = tensorRelu(var_45);
-void* var_47 = tensorPooling(var_46,1,2,2,0,0,2,2);
-void* var_49 = tensorGemmGPU(var_47, dense_1_w);
-void* var_50 = tensorAdd(var_49, dense_1_b);
-void* var_51 = tensorSoftmax(var_50);
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy2(labels, batch_size, var_51);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/approxhpvm_src.cc b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/approxhpvm_src.cc
deleted file mode 100644
index dc0c873c63333299981591cb5654cb38be9d4092..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/approxhpvm_src.cc
+++ /dev/null
@@ -1,1224 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/stat.h>
-#include <cstring>
-#include <visc.h>
-#include <tensorTypes.h>
-#include <tensorUtils.h>
-
-void var_0_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_1_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_2_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_3_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 32);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_4_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_5_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_6_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_7_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_8_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_9_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 2, 2, 1, 64);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_10_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_11_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_12_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_13_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_14_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_15_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 128);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_16_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_17_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_18_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_19_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_20_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_21_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 2, 2, 1, 128);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_22_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_23_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_24_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_25_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_26_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_27_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 256);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_28_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_29_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_30_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_31_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_32_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_33_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 2, 2, 1, 256);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_34_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_35_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_36_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_37_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_38_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_39_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_avg(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_40_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_mul(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_41_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_42_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_softmax(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void root(void* input, size_t input_bytes,
- void* conv2d_1_w, size_t conv2d_1_w_bytes,
- void* batch_normalization_1_gamma, size_t batch_normalization_1_gamma_bytes,
- void* batch_normalization_1_beta, size_t batch_normalization_1_beta_bytes,
- void* batch_normalization_1_mean, size_t batch_normalization_1_mean_bytes,
- void* batch_normalization_1_variance, size_t batch_normalization_1_variance_bytes,
- void* depthwise_conv2d_1_w, size_t depthwise_conv2d_1_w_bytes,
- void* batch_normalization_2_gamma, size_t batch_normalization_2_gamma_bytes,
- void* batch_normalization_2_beta, size_t batch_normalization_2_beta_bytes,
- void* batch_normalization_2_mean, size_t batch_normalization_2_mean_bytes,
- void* batch_normalization_2_variance, size_t batch_normalization_2_variance_bytes,
- void* conv2d_2_w, size_t conv2d_2_w_bytes,
- void* batch_normalization_3_gamma, size_t batch_normalization_3_gamma_bytes,
- void* batch_normalization_3_beta, size_t batch_normalization_3_beta_bytes,
- void* batch_normalization_3_mean, size_t batch_normalization_3_mean_bytes,
- void* batch_normalization_3_variance, size_t batch_normalization_3_variance_bytes,
- void* depthwise_conv2d_2_w, size_t depthwise_conv2d_2_w_bytes,
- void* batch_normalization_4_gamma, size_t batch_normalization_4_gamma_bytes,
- void* batch_normalization_4_beta, size_t batch_normalization_4_beta_bytes,
- void* batch_normalization_4_mean, size_t batch_normalization_4_mean_bytes,
- void* batch_normalization_4_variance, size_t batch_normalization_4_variance_bytes,
- void* conv2d_3_w, size_t conv2d_3_w_bytes,
- void* batch_normalization_5_gamma, size_t batch_normalization_5_gamma_bytes,
- void* batch_normalization_5_beta, size_t batch_normalization_5_beta_bytes,
- void* batch_normalization_5_mean, size_t batch_normalization_5_mean_bytes,
- void* batch_normalization_5_variance, size_t batch_normalization_5_variance_bytes,
- void* depthwise_conv2d_3_w, size_t depthwise_conv2d_3_w_bytes,
- void* batch_normalization_6_gamma, size_t batch_normalization_6_gamma_bytes,
- void* batch_normalization_6_beta, size_t batch_normalization_6_beta_bytes,
- void* batch_normalization_6_mean, size_t batch_normalization_6_mean_bytes,
- void* batch_normalization_6_variance, size_t batch_normalization_6_variance_bytes,
- void* conv2d_4_w, size_t conv2d_4_w_bytes,
- void* batch_normalization_7_gamma, size_t batch_normalization_7_gamma_bytes,
- void* batch_normalization_7_beta, size_t batch_normalization_7_beta_bytes,
- void* batch_normalization_7_mean, size_t batch_normalization_7_mean_bytes,
- void* batch_normalization_7_variance, size_t batch_normalization_7_variance_bytes,
- void* depthwise_conv2d_4_w, size_t depthwise_conv2d_4_w_bytes,
- void* batch_normalization_8_gamma, size_t batch_normalization_8_gamma_bytes,
- void* batch_normalization_8_beta, size_t batch_normalization_8_beta_bytes,
- void* batch_normalization_8_mean, size_t batch_normalization_8_mean_bytes,
- void* batch_normalization_8_variance, size_t batch_normalization_8_variance_bytes,
- void* conv2d_5_w, size_t conv2d_5_w_bytes,
- void* batch_normalization_9_gamma, size_t batch_normalization_9_gamma_bytes,
- void* batch_normalization_9_beta, size_t batch_normalization_9_beta_bytes,
- void* batch_normalization_9_mean, size_t batch_normalization_9_mean_bytes,
- void* batch_normalization_9_variance, size_t batch_normalization_9_variance_bytes,
- void* depthwise_conv2d_5_w, size_t depthwise_conv2d_5_w_bytes,
- void* batch_normalization_10_gamma, size_t batch_normalization_10_gamma_bytes,
- void* batch_normalization_10_beta, size_t batch_normalization_10_beta_bytes,
- void* batch_normalization_10_mean, size_t batch_normalization_10_mean_bytes,
- void* batch_normalization_10_variance, size_t batch_normalization_10_variance_bytes,
- void* conv2d_6_w, size_t conv2d_6_w_bytes,
- void* batch_normalization_11_gamma, size_t batch_normalization_11_gamma_bytes,
- void* batch_normalization_11_beta, size_t batch_normalization_11_beta_bytes,
- void* batch_normalization_11_mean, size_t batch_normalization_11_mean_bytes,
- void* batch_normalization_11_variance, size_t batch_normalization_11_variance_bytes,
- void* depthwise_conv2d_6_w, size_t depthwise_conv2d_6_w_bytes,
- void* batch_normalization_12_gamma, size_t batch_normalization_12_gamma_bytes,
- void* batch_normalization_12_beta, size_t batch_normalization_12_beta_bytes,
- void* batch_normalization_12_mean, size_t batch_normalization_12_mean_bytes,
- void* batch_normalization_12_variance, size_t batch_normalization_12_variance_bytes,
- void* conv2d_7_w, size_t conv2d_7_w_bytes,
- void* batch_normalization_13_gamma, size_t batch_normalization_13_gamma_bytes,
- void* batch_normalization_13_beta, size_t batch_normalization_13_beta_bytes,
- void* batch_normalization_13_mean, size_t batch_normalization_13_mean_bytes,
- void* batch_normalization_13_variance, size_t batch_normalization_13_variance_bytes,
- void* dense_1_w, size_t dense_1_w_bytes,
- void* dense_1_b, size_t dense_1_b_bytes){
-
-
- __visc__hint(visc::CPU_TARGET);
- __visc__attributes(68, input, conv2d_1_w, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, depthwise_conv2d_1_w, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, conv2d_2_w, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, depthwise_conv2d_2_w, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, conv2d_3_w, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, depthwise_conv2d_3_w, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, conv2d_4_w, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, depthwise_conv2d_4_w, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, conv2d_5_w, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, depthwise_conv2d_5_w, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, conv2d_6_w, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, depthwise_conv2d_6_w, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, conv2d_7_w, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, dense_1_w, dense_1_b, 0);
-
-
- void* var_0 = __visc__createNodeND(0, var_0_node);
-
- __visc__bindIn(var_0, 0, 0, 0);
- __visc__bindIn(var_0, 1, 1, 0);
- __visc__bindIn(var_0, 2, 2, 0);
- __visc__bindIn(var_0, 3, 3, 0);
-
- void* var_1 = __visc__createNodeND(0, var_1_node);
-
- __visc__edge(var_0, var_1, 1, 0, 0, 0);
- __visc__edge(var_0, var_1, 1, 1, 1, 0);
- __visc__bindIn(var_1, 4, 2, 0);
- __visc__bindIn(var_1, 5, 3, 0);
- __visc__bindIn(var_1, 6, 4, 0);
- __visc__bindIn(var_1, 7, 5, 0);
- __visc__bindIn(var_1, 8, 6, 0);
- __visc__bindIn(var_1, 9, 7, 0);
- __visc__bindIn(var_1, 10, 8, 0);
- __visc__bindIn(var_1, 11, 9, 0);
-
- void* var_2 = __visc__createNodeND(0, var_2_node);
-
- __visc__edge(var_1, var_2, 1, 0, 0, 0);
- __visc__edge(var_1, var_2, 1, 1, 1, 0);
-
- void* var_3 = __visc__createNodeND(0, var_3_node);
-
- __visc__edge(var_2, var_3, 1, 0, 0, 0);
- __visc__edge(var_2, var_3, 1, 1, 1, 0);
- __visc__bindIn(var_3, 12, 2, 0);
- __visc__bindIn(var_3, 13, 3, 0);
-
- void* var_4 = __visc__createNodeND(0, var_4_node);
-
- __visc__edge(var_3, var_4, 1, 0, 0, 0);
- __visc__edge(var_3, var_4, 1, 1, 1, 0);
- __visc__bindIn(var_4, 14, 2, 0);
- __visc__bindIn(var_4, 15, 3, 0);
- __visc__bindIn(var_4, 16, 4, 0);
- __visc__bindIn(var_4, 17, 5, 0);
- __visc__bindIn(var_4, 18, 6, 0);
- __visc__bindIn(var_4, 19, 7, 0);
- __visc__bindIn(var_4, 20, 8, 0);
- __visc__bindIn(var_4, 21, 9, 0);
-
- void* var_5 = __visc__createNodeND(0, var_5_node);
-
- __visc__edge(var_4, var_5, 1, 0, 0, 0);
- __visc__edge(var_4, var_5, 1, 1, 1, 0);
-
- void* var_6 = __visc__createNodeND(0, var_6_node);
-
- __visc__edge(var_5, var_6, 1, 0, 0, 0);
- __visc__edge(var_5, var_6, 1, 1, 1, 0);
- __visc__bindIn(var_6, 22, 2, 0);
- __visc__bindIn(var_6, 23, 3, 0);
-
- void* var_7 = __visc__createNodeND(0, var_7_node);
-
- __visc__edge(var_6, var_7, 1, 0, 0, 0);
- __visc__edge(var_6, var_7, 1, 1, 1, 0);
- __visc__bindIn(var_7, 24, 2, 0);
- __visc__bindIn(var_7, 25, 3, 0);
- __visc__bindIn(var_7, 26, 4, 0);
- __visc__bindIn(var_7, 27, 5, 0);
- __visc__bindIn(var_7, 28, 6, 0);
- __visc__bindIn(var_7, 29, 7, 0);
- __visc__bindIn(var_7, 30, 8, 0);
- __visc__bindIn(var_7, 31, 9, 0);
-
- void* var_8 = __visc__createNodeND(0, var_8_node);
-
- __visc__edge(var_7, var_8, 1, 0, 0, 0);
- __visc__edge(var_7, var_8, 1, 1, 1, 0);
-
- void* var_9 = __visc__createNodeND(0, var_9_node);
-
- __visc__edge(var_8, var_9, 1, 0, 0, 0);
- __visc__edge(var_8, var_9, 1, 1, 1, 0);
- __visc__bindIn(var_9, 32, 2, 0);
- __visc__bindIn(var_9, 33, 3, 0);
-
- void* var_10 = __visc__createNodeND(0, var_10_node);
-
- __visc__edge(var_9, var_10, 1, 0, 0, 0);
- __visc__edge(var_9, var_10, 1, 1, 1, 0);
- __visc__bindIn(var_10, 34, 2, 0);
- __visc__bindIn(var_10, 35, 3, 0);
- __visc__bindIn(var_10, 36, 4, 0);
- __visc__bindIn(var_10, 37, 5, 0);
- __visc__bindIn(var_10, 38, 6, 0);
- __visc__bindIn(var_10, 39, 7, 0);
- __visc__bindIn(var_10, 40, 8, 0);
- __visc__bindIn(var_10, 41, 9, 0);
-
- void* var_11 = __visc__createNodeND(0, var_11_node);
-
- __visc__edge(var_10, var_11, 1, 0, 0, 0);
- __visc__edge(var_10, var_11, 1, 1, 1, 0);
-
- void* var_12 = __visc__createNodeND(0, var_12_node);
-
- __visc__edge(var_11, var_12, 1, 0, 0, 0);
- __visc__edge(var_11, var_12, 1, 1, 1, 0);
- __visc__bindIn(var_12, 42, 2, 0);
- __visc__bindIn(var_12, 43, 3, 0);
-
- void* var_13 = __visc__createNodeND(0, var_13_node);
-
- __visc__edge(var_12, var_13, 1, 0, 0, 0);
- __visc__edge(var_12, var_13, 1, 1, 1, 0);
- __visc__bindIn(var_13, 44, 2, 0);
- __visc__bindIn(var_13, 45, 3, 0);
- __visc__bindIn(var_13, 46, 4, 0);
- __visc__bindIn(var_13, 47, 5, 0);
- __visc__bindIn(var_13, 48, 6, 0);
- __visc__bindIn(var_13, 49, 7, 0);
- __visc__bindIn(var_13, 50, 8, 0);
- __visc__bindIn(var_13, 51, 9, 0);
-
- void* var_14 = __visc__createNodeND(0, var_14_node);
-
- __visc__edge(var_13, var_14, 1, 0, 0, 0);
- __visc__edge(var_13, var_14, 1, 1, 1, 0);
-
- void* var_15 = __visc__createNodeND(0, var_15_node);
-
- __visc__edge(var_14, var_15, 1, 0, 0, 0);
- __visc__edge(var_14, var_15, 1, 1, 1, 0);
- __visc__bindIn(var_15, 52, 2, 0);
- __visc__bindIn(var_15, 53, 3, 0);
-
- void* var_16 = __visc__createNodeND(0, var_16_node);
-
- __visc__edge(var_15, var_16, 1, 0, 0, 0);
- __visc__edge(var_15, var_16, 1, 1, 1, 0);
- __visc__bindIn(var_16, 54, 2, 0);
- __visc__bindIn(var_16, 55, 3, 0);
- __visc__bindIn(var_16, 56, 4, 0);
- __visc__bindIn(var_16, 57, 5, 0);
- __visc__bindIn(var_16, 58, 6, 0);
- __visc__bindIn(var_16, 59, 7, 0);
- __visc__bindIn(var_16, 60, 8, 0);
- __visc__bindIn(var_16, 61, 9, 0);
-
- void* var_17 = __visc__createNodeND(0, var_17_node);
-
- __visc__edge(var_16, var_17, 1, 0, 0, 0);
- __visc__edge(var_16, var_17, 1, 1, 1, 0);
-
- void* var_18 = __visc__createNodeND(0, var_18_node);
-
- __visc__edge(var_17, var_18, 1, 0, 0, 0);
- __visc__edge(var_17, var_18, 1, 1, 1, 0);
- __visc__bindIn(var_18, 62, 2, 0);
- __visc__bindIn(var_18, 63, 3, 0);
-
- void* var_19 = __visc__createNodeND(0, var_19_node);
-
- __visc__edge(var_18, var_19, 1, 0, 0, 0);
- __visc__edge(var_18, var_19, 1, 1, 1, 0);
- __visc__bindIn(var_19, 64, 2, 0);
- __visc__bindIn(var_19, 65, 3, 0);
- __visc__bindIn(var_19, 66, 4, 0);
- __visc__bindIn(var_19, 67, 5, 0);
- __visc__bindIn(var_19, 68, 6, 0);
- __visc__bindIn(var_19, 69, 7, 0);
- __visc__bindIn(var_19, 70, 8, 0);
- __visc__bindIn(var_19, 71, 9, 0);
-
- void* var_20 = __visc__createNodeND(0, var_20_node);
-
- __visc__edge(var_19, var_20, 1, 0, 0, 0);
- __visc__edge(var_19, var_20, 1, 1, 1, 0);
-
- void* var_21 = __visc__createNodeND(0, var_21_node);
-
- __visc__edge(var_20, var_21, 1, 0, 0, 0);
- __visc__edge(var_20, var_21, 1, 1, 1, 0);
- __visc__bindIn(var_21, 72, 2, 0);
- __visc__bindIn(var_21, 73, 3, 0);
-
- void* var_22 = __visc__createNodeND(0, var_22_node);
-
- __visc__edge(var_21, var_22, 1, 0, 0, 0);
- __visc__edge(var_21, var_22, 1, 1, 1, 0);
- __visc__bindIn(var_22, 74, 2, 0);
- __visc__bindIn(var_22, 75, 3, 0);
- __visc__bindIn(var_22, 76, 4, 0);
- __visc__bindIn(var_22, 77, 5, 0);
- __visc__bindIn(var_22, 78, 6, 0);
- __visc__bindIn(var_22, 79, 7, 0);
- __visc__bindIn(var_22, 80, 8, 0);
- __visc__bindIn(var_22, 81, 9, 0);
-
- void* var_23 = __visc__createNodeND(0, var_23_node);
-
- __visc__edge(var_22, var_23, 1, 0, 0, 0);
- __visc__edge(var_22, var_23, 1, 1, 1, 0);
-
- void* var_24 = __visc__createNodeND(0, var_24_node);
-
- __visc__edge(var_23, var_24, 1, 0, 0, 0);
- __visc__edge(var_23, var_24, 1, 1, 1, 0);
- __visc__bindIn(var_24, 82, 2, 0);
- __visc__bindIn(var_24, 83, 3, 0);
-
- void* var_25 = __visc__createNodeND(0, var_25_node);
-
- __visc__edge(var_24, var_25, 1, 0, 0, 0);
- __visc__edge(var_24, var_25, 1, 1, 1, 0);
- __visc__bindIn(var_25, 84, 2, 0);
- __visc__bindIn(var_25, 85, 3, 0);
- __visc__bindIn(var_25, 86, 4, 0);
- __visc__bindIn(var_25, 87, 5, 0);
- __visc__bindIn(var_25, 88, 6, 0);
- __visc__bindIn(var_25, 89, 7, 0);
- __visc__bindIn(var_25, 90, 8, 0);
- __visc__bindIn(var_25, 91, 9, 0);
-
- void* var_26 = __visc__createNodeND(0, var_26_node);
-
- __visc__edge(var_25, var_26, 1, 0, 0, 0);
- __visc__edge(var_25, var_26, 1, 1, 1, 0);
-
- void* var_27 = __visc__createNodeND(0, var_27_node);
-
- __visc__edge(var_26, var_27, 1, 0, 0, 0);
- __visc__edge(var_26, var_27, 1, 1, 1, 0);
- __visc__bindIn(var_27, 92, 2, 0);
- __visc__bindIn(var_27, 93, 3, 0);
-
- void* var_28 = __visc__createNodeND(0, var_28_node);
-
- __visc__edge(var_27, var_28, 1, 0, 0, 0);
- __visc__edge(var_27, var_28, 1, 1, 1, 0);
- __visc__bindIn(var_28, 94, 2, 0);
- __visc__bindIn(var_28, 95, 3, 0);
- __visc__bindIn(var_28, 96, 4, 0);
- __visc__bindIn(var_28, 97, 5, 0);
- __visc__bindIn(var_28, 98, 6, 0);
- __visc__bindIn(var_28, 99, 7, 0);
- __visc__bindIn(var_28, 100, 8, 0);
- __visc__bindIn(var_28, 101, 9, 0);
-
- void* var_29 = __visc__createNodeND(0, var_29_node);
-
- __visc__edge(var_28, var_29, 1, 0, 0, 0);
- __visc__edge(var_28, var_29, 1, 1, 1, 0);
-
- void* var_30 = __visc__createNodeND(0, var_30_node);
-
- __visc__edge(var_29, var_30, 1, 0, 0, 0);
- __visc__edge(var_29, var_30, 1, 1, 1, 0);
- __visc__bindIn(var_30, 102, 2, 0);
- __visc__bindIn(var_30, 103, 3, 0);
-
- void* var_31 = __visc__createNodeND(0, var_31_node);
-
- __visc__edge(var_30, var_31, 1, 0, 0, 0);
- __visc__edge(var_30, var_31, 1, 1, 1, 0);
- __visc__bindIn(var_31, 104, 2, 0);
- __visc__bindIn(var_31, 105, 3, 0);
- __visc__bindIn(var_31, 106, 4, 0);
- __visc__bindIn(var_31, 107, 5, 0);
- __visc__bindIn(var_31, 108, 6, 0);
- __visc__bindIn(var_31, 109, 7, 0);
- __visc__bindIn(var_31, 110, 8, 0);
- __visc__bindIn(var_31, 111, 9, 0);
-
- void* var_32 = __visc__createNodeND(0, var_32_node);
-
- __visc__edge(var_31, var_32, 1, 0, 0, 0);
- __visc__edge(var_31, var_32, 1, 1, 1, 0);
-
- void* var_33 = __visc__createNodeND(0, var_33_node);
-
- __visc__edge(var_32, var_33, 1, 0, 0, 0);
- __visc__edge(var_32, var_33, 1, 1, 1, 0);
- __visc__bindIn(var_33, 112, 2, 0);
- __visc__bindIn(var_33, 113, 3, 0);
-
- void* var_34 = __visc__createNodeND(0, var_34_node);
-
- __visc__edge(var_33, var_34, 1, 0, 0, 0);
- __visc__edge(var_33, var_34, 1, 1, 1, 0);
- __visc__bindIn(var_34, 114, 2, 0);
- __visc__bindIn(var_34, 115, 3, 0);
- __visc__bindIn(var_34, 116, 4, 0);
- __visc__bindIn(var_34, 117, 5, 0);
- __visc__bindIn(var_34, 118, 6, 0);
- __visc__bindIn(var_34, 119, 7, 0);
- __visc__bindIn(var_34, 120, 8, 0);
- __visc__bindIn(var_34, 121, 9, 0);
-
- void* var_35 = __visc__createNodeND(0, var_35_node);
-
- __visc__edge(var_34, var_35, 1, 0, 0, 0);
- __visc__edge(var_34, var_35, 1, 1, 1, 0);
-
- void* var_36 = __visc__createNodeND(0, var_36_node);
-
- __visc__edge(var_35, var_36, 1, 0, 0, 0);
- __visc__edge(var_35, var_36, 1, 1, 1, 0);
- __visc__bindIn(var_36, 122, 2, 0);
- __visc__bindIn(var_36, 123, 3, 0);
-
- void* var_37 = __visc__createNodeND(0, var_37_node);
-
- __visc__edge(var_36, var_37, 1, 0, 0, 0);
- __visc__edge(var_36, var_37, 1, 1, 1, 0);
- __visc__bindIn(var_37, 124, 2, 0);
- __visc__bindIn(var_37, 125, 3, 0);
- __visc__bindIn(var_37, 126, 4, 0);
- __visc__bindIn(var_37, 127, 5, 0);
- __visc__bindIn(var_37, 128, 6, 0);
- __visc__bindIn(var_37, 129, 7, 0);
- __visc__bindIn(var_37, 130, 8, 0);
- __visc__bindIn(var_37, 131, 9, 0);
-
- void* var_38 = __visc__createNodeND(0, var_38_node);
-
- __visc__edge(var_37, var_38, 1, 0, 0, 0);
- __visc__edge(var_37, var_38, 1, 1, 1, 0);
-
- void* var_39 = __visc__createNodeND(0, var_39_node);
-
- __visc__edge(var_38, var_39, 1, 0, 0, 0);
- __visc__edge(var_38, var_39, 1, 1, 1, 0);
-
- void* var_40 = __visc__createNodeND(0, var_40_node);
-
- __visc__edge(var_39, var_40, 1, 0, 0, 0);
- __visc__edge(var_39, var_40, 1, 1, 1, 0);
- __visc__bindIn(var_40, 132, 2, 0);
- __visc__bindIn(var_40, 133, 3, 0);
-
- void* var_41 = __visc__createNodeND(0, var_41_node);
-
- __visc__edge(var_40, var_41, 1, 0, 0, 0);
- __visc__edge(var_40, var_41, 1, 1, 1, 0);
- __visc__bindIn(var_41, 134, 2, 0);
- __visc__bindIn(var_41, 135, 3, 0);
-
- void* var_42 = __visc__createNodeND(0, var_42_node);
-
- __visc__edge(var_41, var_42, 1, 0, 0, 0);
- __visc__edge(var_41, var_42, 1, 1, 1, 0);
-
- __visc__bindOut(var_42, 0, 0, 0);
- __visc__bindOut(var_42, 1, 1, 0);
-
-}
-
-struct ret_t {
- void* tensor;
- size_t bytes;
-};
-
-typedef struct __attribute__((__packed__)) {
- void* input;
- size_t input_bytes;
- void* conv2d_1_w;
- size_t conv2d_1_w_bytes;
- void* batch_normalization_1_gamma;
- size_t batch_normalization_1_gamma_bytes;
- void* batch_normalization_1_beta;
- size_t batch_normalization_1_beta_bytes;
- void* batch_normalization_1_mean;
- size_t batch_normalization_1_mean_bytes;
- void* batch_normalization_1_variance;
- size_t batch_normalization_1_variance_bytes;
- void* depthwise_conv2d_1_w;
- size_t depthwise_conv2d_1_w_bytes;
- void* batch_normalization_2_gamma;
- size_t batch_normalization_2_gamma_bytes;
- void* batch_normalization_2_beta;
- size_t batch_normalization_2_beta_bytes;
- void* batch_normalization_2_mean;
- size_t batch_normalization_2_mean_bytes;
- void* batch_normalization_2_variance;
- size_t batch_normalization_2_variance_bytes;
- void* conv2d_2_w;
- size_t conv2d_2_w_bytes;
- void* batch_normalization_3_gamma;
- size_t batch_normalization_3_gamma_bytes;
- void* batch_normalization_3_beta;
- size_t batch_normalization_3_beta_bytes;
- void* batch_normalization_3_mean;
- size_t batch_normalization_3_mean_bytes;
- void* batch_normalization_3_variance;
- size_t batch_normalization_3_variance_bytes;
- void* depthwise_conv2d_2_w;
- size_t depthwise_conv2d_2_w_bytes;
- void* batch_normalization_4_gamma;
- size_t batch_normalization_4_gamma_bytes;
- void* batch_normalization_4_beta;
- size_t batch_normalization_4_beta_bytes;
- void* batch_normalization_4_mean;
- size_t batch_normalization_4_mean_bytes;
- void* batch_normalization_4_variance;
- size_t batch_normalization_4_variance_bytes;
- void* conv2d_3_w;
- size_t conv2d_3_w_bytes;
- void* batch_normalization_5_gamma;
- size_t batch_normalization_5_gamma_bytes;
- void* batch_normalization_5_beta;
- size_t batch_normalization_5_beta_bytes;
- void* batch_normalization_5_mean;
- size_t batch_normalization_5_mean_bytes;
- void* batch_normalization_5_variance;
- size_t batch_normalization_5_variance_bytes;
- void* depthwise_conv2d_3_w;
- size_t depthwise_conv2d_3_w_bytes;
- void* batch_normalization_6_gamma;
- size_t batch_normalization_6_gamma_bytes;
- void* batch_normalization_6_beta;
- size_t batch_normalization_6_beta_bytes;
- void* batch_normalization_6_mean;
- size_t batch_normalization_6_mean_bytes;
- void* batch_normalization_6_variance;
- size_t batch_normalization_6_variance_bytes;
- void* conv2d_4_w;
- size_t conv2d_4_w_bytes;
- void* batch_normalization_7_gamma;
- size_t batch_normalization_7_gamma_bytes;
- void* batch_normalization_7_beta;
- size_t batch_normalization_7_beta_bytes;
- void* batch_normalization_7_mean;
- size_t batch_normalization_7_mean_bytes;
- void* batch_normalization_7_variance;
- size_t batch_normalization_7_variance_bytes;
- void* depthwise_conv2d_4_w;
- size_t depthwise_conv2d_4_w_bytes;
- void* batch_normalization_8_gamma;
- size_t batch_normalization_8_gamma_bytes;
- void* batch_normalization_8_beta;
- size_t batch_normalization_8_beta_bytes;
- void* batch_normalization_8_mean;
- size_t batch_normalization_8_mean_bytes;
- void* batch_normalization_8_variance;
- size_t batch_normalization_8_variance_bytes;
- void* conv2d_5_w;
- size_t conv2d_5_w_bytes;
- void* batch_normalization_9_gamma;
- size_t batch_normalization_9_gamma_bytes;
- void* batch_normalization_9_beta;
- size_t batch_normalization_9_beta_bytes;
- void* batch_normalization_9_mean;
- size_t batch_normalization_9_mean_bytes;
- void* batch_normalization_9_variance;
- size_t batch_normalization_9_variance_bytes;
- void* depthwise_conv2d_5_w;
- size_t depthwise_conv2d_5_w_bytes;
- void* batch_normalization_10_gamma;
- size_t batch_normalization_10_gamma_bytes;
- void* batch_normalization_10_beta;
- size_t batch_normalization_10_beta_bytes;
- void* batch_normalization_10_mean;
- size_t batch_normalization_10_mean_bytes;
- void* batch_normalization_10_variance;
- size_t batch_normalization_10_variance_bytes;
- void* conv2d_6_w;
- size_t conv2d_6_w_bytes;
- void* batch_normalization_11_gamma;
- size_t batch_normalization_11_gamma_bytes;
- void* batch_normalization_11_beta;
- size_t batch_normalization_11_beta_bytes;
- void* batch_normalization_11_mean;
- size_t batch_normalization_11_mean_bytes;
- void* batch_normalization_11_variance;
- size_t batch_normalization_11_variance_bytes;
- void* depthwise_conv2d_6_w;
- size_t depthwise_conv2d_6_w_bytes;
- void* batch_normalization_12_gamma;
- size_t batch_normalization_12_gamma_bytes;
- void* batch_normalization_12_beta;
- size_t batch_normalization_12_beta_bytes;
- void* batch_normalization_12_mean;
- size_t batch_normalization_12_mean_bytes;
- void* batch_normalization_12_variance;
- size_t batch_normalization_12_variance_bytes;
- void* conv2d_7_w;
- size_t conv2d_7_w_bytes;
- void* batch_normalization_13_gamma;
- size_t batch_normalization_13_gamma_bytes;
- void* batch_normalization_13_beta;
- size_t batch_normalization_13_beta_bytes;
- void* batch_normalization_13_mean;
- size_t batch_normalization_13_mean_bytes;
- void* batch_normalization_13_variance;
- size_t batch_normalization_13_variance_bytes;
- void* dense_1_w;
- size_t dense_1_w_bytes;
- void* dense_1_b;
- size_t dense_1_b_bytes;
-
- struct ret_t r;
-}
-RootIn;
-
-int main(){
-
-std::string dir_prefix = std::string("data/mobilenet_shallow_nathan/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
-void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
-void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
-void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
-void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
-std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
-void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
-std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
-void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
-void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
-void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
-void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
-std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
-void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
-void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
-void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
-void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
-std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
-void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
-std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
-void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
-void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
-void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
-void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
-std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
-void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
-void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
-void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
-void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
-void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
-void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
-void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
-void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
-void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
-std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
-void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
-void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
-void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
-void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
-void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
-void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
-void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
-void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
-void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
-std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
-void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
-void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
-void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
-void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
-void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
-void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
-void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
-void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
-void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
-std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
-void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
-void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
-void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
-void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
-void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
-void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
-void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
-void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
-void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
-std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
-void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
-void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
-void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
-void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,2048,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-
-__visc__init();
-RootIn* args = static_cast<RootIn*>(malloc(sizeof(RootIn)));
-
-args->input = input;
-args->input_bytes = 0;
-args->conv2d_1_w = conv2d_1_w;
-args->conv2d_1_w_bytes = 0;
-args->batch_normalization_1_gamma = batch_normalization_1_gamma;
-args->batch_normalization_1_gamma_bytes = 0;
-args->batch_normalization_1_beta = batch_normalization_1_beta;
-args->batch_normalization_1_beta_bytes = 0;
-args->batch_normalization_1_mean = batch_normalization_1_mean;
-args->batch_normalization_1_mean_bytes = 0;
-args->batch_normalization_1_variance = batch_normalization_1_variance;
-args->batch_normalization_1_variance_bytes = 0;
-args->depthwise_conv2d_1_w = depthwise_conv2d_1_w;
-args->depthwise_conv2d_1_w_bytes = 0;
-args->batch_normalization_2_gamma = batch_normalization_2_gamma;
-args->batch_normalization_2_gamma_bytes = 0;
-args->batch_normalization_2_beta = batch_normalization_2_beta;
-args->batch_normalization_2_beta_bytes = 0;
-args->batch_normalization_2_mean = batch_normalization_2_mean;
-args->batch_normalization_2_mean_bytes = 0;
-args->batch_normalization_2_variance = batch_normalization_2_variance;
-args->batch_normalization_2_variance_bytes = 0;
-args->conv2d_2_w = conv2d_2_w;
-args->conv2d_2_w_bytes = 0;
-args->batch_normalization_3_gamma = batch_normalization_3_gamma;
-args->batch_normalization_3_gamma_bytes = 0;
-args->batch_normalization_3_beta = batch_normalization_3_beta;
-args->batch_normalization_3_beta_bytes = 0;
-args->batch_normalization_3_mean = batch_normalization_3_mean;
-args->batch_normalization_3_mean_bytes = 0;
-args->batch_normalization_3_variance = batch_normalization_3_variance;
-args->batch_normalization_3_variance_bytes = 0;
-args->depthwise_conv2d_2_w = depthwise_conv2d_2_w;
-args->depthwise_conv2d_2_w_bytes = 0;
-args->batch_normalization_4_gamma = batch_normalization_4_gamma;
-args->batch_normalization_4_gamma_bytes = 0;
-args->batch_normalization_4_beta = batch_normalization_4_beta;
-args->batch_normalization_4_beta_bytes = 0;
-args->batch_normalization_4_mean = batch_normalization_4_mean;
-args->batch_normalization_4_mean_bytes = 0;
-args->batch_normalization_4_variance = batch_normalization_4_variance;
-args->batch_normalization_4_variance_bytes = 0;
-args->conv2d_3_w = conv2d_3_w;
-args->conv2d_3_w_bytes = 0;
-args->batch_normalization_5_gamma = batch_normalization_5_gamma;
-args->batch_normalization_5_gamma_bytes = 0;
-args->batch_normalization_5_beta = batch_normalization_5_beta;
-args->batch_normalization_5_beta_bytes = 0;
-args->batch_normalization_5_mean = batch_normalization_5_mean;
-args->batch_normalization_5_mean_bytes = 0;
-args->batch_normalization_5_variance = batch_normalization_5_variance;
-args->batch_normalization_5_variance_bytes = 0;
-args->depthwise_conv2d_3_w = depthwise_conv2d_3_w;
-args->depthwise_conv2d_3_w_bytes = 0;
-args->batch_normalization_6_gamma = batch_normalization_6_gamma;
-args->batch_normalization_6_gamma_bytes = 0;
-args->batch_normalization_6_beta = batch_normalization_6_beta;
-args->batch_normalization_6_beta_bytes = 0;
-args->batch_normalization_6_mean = batch_normalization_6_mean;
-args->batch_normalization_6_mean_bytes = 0;
-args->batch_normalization_6_variance = batch_normalization_6_variance;
-args->batch_normalization_6_variance_bytes = 0;
-args->conv2d_4_w = conv2d_4_w;
-args->conv2d_4_w_bytes = 0;
-args->batch_normalization_7_gamma = batch_normalization_7_gamma;
-args->batch_normalization_7_gamma_bytes = 0;
-args->batch_normalization_7_beta = batch_normalization_7_beta;
-args->batch_normalization_7_beta_bytes = 0;
-args->batch_normalization_7_mean = batch_normalization_7_mean;
-args->batch_normalization_7_mean_bytes = 0;
-args->batch_normalization_7_variance = batch_normalization_7_variance;
-args->batch_normalization_7_variance_bytes = 0;
-args->depthwise_conv2d_4_w = depthwise_conv2d_4_w;
-args->depthwise_conv2d_4_w_bytes = 0;
-args->batch_normalization_8_gamma = batch_normalization_8_gamma;
-args->batch_normalization_8_gamma_bytes = 0;
-args->batch_normalization_8_beta = batch_normalization_8_beta;
-args->batch_normalization_8_beta_bytes = 0;
-args->batch_normalization_8_mean = batch_normalization_8_mean;
-args->batch_normalization_8_mean_bytes = 0;
-args->batch_normalization_8_variance = batch_normalization_8_variance;
-args->batch_normalization_8_variance_bytes = 0;
-args->conv2d_5_w = conv2d_5_w;
-args->conv2d_5_w_bytes = 0;
-args->batch_normalization_9_gamma = batch_normalization_9_gamma;
-args->batch_normalization_9_gamma_bytes = 0;
-args->batch_normalization_9_beta = batch_normalization_9_beta;
-args->batch_normalization_9_beta_bytes = 0;
-args->batch_normalization_9_mean = batch_normalization_9_mean;
-args->batch_normalization_9_mean_bytes = 0;
-args->batch_normalization_9_variance = batch_normalization_9_variance;
-args->batch_normalization_9_variance_bytes = 0;
-args->depthwise_conv2d_5_w = depthwise_conv2d_5_w;
-args->depthwise_conv2d_5_w_bytes = 0;
-args->batch_normalization_10_gamma = batch_normalization_10_gamma;
-args->batch_normalization_10_gamma_bytes = 0;
-args->batch_normalization_10_beta = batch_normalization_10_beta;
-args->batch_normalization_10_beta_bytes = 0;
-args->batch_normalization_10_mean = batch_normalization_10_mean;
-args->batch_normalization_10_mean_bytes = 0;
-args->batch_normalization_10_variance = batch_normalization_10_variance;
-args->batch_normalization_10_variance_bytes = 0;
-args->conv2d_6_w = conv2d_6_w;
-args->conv2d_6_w_bytes = 0;
-args->batch_normalization_11_gamma = batch_normalization_11_gamma;
-args->batch_normalization_11_gamma_bytes = 0;
-args->batch_normalization_11_beta = batch_normalization_11_beta;
-args->batch_normalization_11_beta_bytes = 0;
-args->batch_normalization_11_mean = batch_normalization_11_mean;
-args->batch_normalization_11_mean_bytes = 0;
-args->batch_normalization_11_variance = batch_normalization_11_variance;
-args->batch_normalization_11_variance_bytes = 0;
-args->depthwise_conv2d_6_w = depthwise_conv2d_6_w;
-args->depthwise_conv2d_6_w_bytes = 0;
-args->batch_normalization_12_gamma = batch_normalization_12_gamma;
-args->batch_normalization_12_gamma_bytes = 0;
-args->batch_normalization_12_beta = batch_normalization_12_beta;
-args->batch_normalization_12_beta_bytes = 0;
-args->batch_normalization_12_mean = batch_normalization_12_mean;
-args->batch_normalization_12_mean_bytes = 0;
-args->batch_normalization_12_variance = batch_normalization_12_variance;
-args->batch_normalization_12_variance_bytes = 0;
-args->conv2d_7_w = conv2d_7_w;
-args->conv2d_7_w_bytes = 0;
-args->batch_normalization_13_gamma = batch_normalization_13_gamma;
-args->batch_normalization_13_gamma_bytes = 0;
-args->batch_normalization_13_beta = batch_normalization_13_beta;
-args->batch_normalization_13_beta_bytes = 0;
-args->batch_normalization_13_mean = batch_normalization_13_mean;
-args->batch_normalization_13_mean_bytes = 0;
-args->batch_normalization_13_variance = batch_normalization_13_variance;
-args->batch_normalization_13_variance_bytes = 0;
-args->dense_1_w = dense_1_w;
-args->dense_1_w_bytes = 0;
-args->dense_1_b = dense_1_b;
-args->dense_1_b_bytes = 0;
-
-void* dfg = __visc__launch(0, root, (void*) args);
-
-__visc__wait(dfg);
-
-void *result = static_cast<RootIn*>(args)->input;
-hpvm_request_tensor(result, 0);
-
-__visc__cleanup();
- computeAccuracy2(labels, 10000, result);
-return 0;
-
-}
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/batch_normalization_1_beta.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/batch_normalization_1_beta.bin
deleted file mode 100644
index 05d61c8e00f196b83dde7de794cc9feff2929582..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/batch_normalization_1_beta.bin
+++ /dev/null
@@ -1,2 +0,0 @@
-êâ®>@Hx>„jb¾Çå>:Ù*>YR¡>nÍ>ù}u?ýæ>¤ÌB?|¾bç&?ÇO?e=”?¿‹¸¾f'e¾8ƒ¸½è‹Î;Éï:?Ó
-‹?ˆþ„>½»>ŸÞ>î‚?˜5A?|6žàÒ>²•Ý¼Þ>?Ä1?nŸ¾u‡‚>
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/batch_normalization_1_gamma.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/batch_normalization_1_gamma.bin
deleted file mode 100644
index 1aaaeaa110d8b9eb8108a1546302f8d5c1c12c35..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/batch_normalization_1_gamma.bin
+++ /dev/null
@@ -1,2 +0,0 @@
-³]ƒ?Ç/?®0€?]…o?ƒ?ߌ?Vň?`Z?Gk„?±*`?žÐƒ?pÈ~?
*?+g ?4
-u?Òü?Qƒ?|?š O??ä?K{?^5Œ?ÃÜ‚?£ ?…ØA?žTr?1 y?÷€?!56?&ñƒ?HWv?§^‚?
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/batch_normalization_1_mean.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/batch_normalization_1_mean.bin
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--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/batch_normalization_1_mean.bin
+++ /dev/null
@@ -1 +0,0 @@
-ˆG¢¹$ =ã®Ý;9Oü¼ø-'<p;K<
à»Þâ€;žcé¼K!¼“¿œ<õön¼M9 ;¬…;’½î3ì<¯Á ;¨`…<|¾¼þæ:¼;ÿ%=÷»L¶Z»)z·»ªfºS:Œ<j*>»¾M<¨u» û ;%;þ;ñ&J:
\ No newline at end of file
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+++ /dev/null
@@ -1 +0,0 @@
-ÃÅn@éA²£š?ä +@ "@9ÛÞ@áÀ(@•¹÷>Ò¢Á@ƒ'E@)¡@øˆ+@œZž>«Ç?A?¤A˜x°A0ªª?®¯#AÿΕ@«Vì>~ÑÅAg«“@VúAÿ>;j @š”@j¯ø?.AB¾>œê;@ø#û?Q ~@
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/batch_normalization_2_beta.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/batch_normalization_2_beta.bin
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--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/batch_normalization_2_beta.bin
+++ /dev/null
@@ -1 +0,0 @@
-N ?¶k©¾¬r>{_?kÙy¾ R?fÀ?ä%Q?“k¾åœ?—õ^½g o?=9L>A†?ím½Ôm ¿†Ç½R?²¾íO‡?àhv?ìt4¾ÙN?cá~?i«Ÿ?¹[ ?•ï¾<M_ >Êõö¾>ðn½rÞ¾? Ž?¼êªE=
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/batch_normalization_2_gamma.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/batch_normalization_2_gamma.bin
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+++ /dev/null
@@ -1 +0,0 @@
-L¾ö>üiQ?-)]?!0?Lw_?`Ë…?ö%4?sÚ¢?²È7?œé`?Ò¿b?¬Y?hv?MC @\ÞY?ñ8P?”Ü\?»QI? ë8?ʤ‘?Ëîl?`¥&?,S?&›?wxr?Ãl_?@8l?k<,?6Ä…?Y={?YjC?2L>?
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/batch_normalization_2_mean.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/batch_normalization_2_mean.bin
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+++ /dev/null
@@ -1,2 +0,0 @@
-’D´¿h J¿úVN¿ŠÊ>?Ú%8¿¶œf?U_C¾]º¤½†Ã?çv=Ü"¾Ï6¾g½&¾òß[>`"¿0Õ8>•¿Fô¦¾i³?Z?=鯿‰\TÀ
5[?&Ñ6>Ÿÿ.¿ýkn¾Ú4•>p%5¿Î(>" ×>Ù
- ¿p²M>
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+++ /dev/null
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-ùEͽý¨½õž9¾pÊ>Ûàÿ½›³>Ÿÿ²>£f>’ë¾Ó‹Ý¹"å>¼½–¾
-ð~¾½¼¾Üöý==Á‹=ä!>D >°©>’çn>£ç;(+Ö=Ÿ‡?Üÿl? m¾$0`>¥¡<¸Ïy?Vál?‚ ?(Ò'½Y ?o>uƒ@> €q>…ö>ë}î>fÓW?K>¿»
?) ?F¿¾Ÿ¯Y¾Ý¯ ?·°—>YdL¾–ZC?þÕ÷=?pUV?þV]>›Â©¾å=9{/>É’Ž½«U>¸ŠÖ½*Ï&>1Í;Mj1¾B‡A=™¾ü½æ…)?! 3?
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/batch_normalization_8_beta.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/batch_normalization_8_beta.bin
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index 5f92c58a7c47c207a98a77f6961410d08e8446f0..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/batch_normalization_8_gamma.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/batch_normalization_8_gamma.bin
deleted file mode 100644
index 6ab36ce54740e9e4a4e4948684e0e4fbbd71b1cb..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/batch_normalization_8_gamma.bin
+++ /dev/null
@@ -1,3 +0,0 @@
-œ®?œË?®m‚?…4?¸µ?µ¦?ã ?‡F'?nÑ#?
-=?sˆ.?V>“?m¹ ?æÓD? SÁ? §7?Š›¼?
‡?É&?qä™?§§?Ä'?"ZN?aég?"ò*?&.)?ÕM ?%??Ы?%Œ1?Å:?æ! ?”O:?ÐѨ?9½*?õ¬? w¨?ÎÁT?HÆ
-?%„?ª)?‚e&?x °?‡Ë?µ«±?ÏÜ ?C1?w¯?kì>? ?uD?£òˆ?²’Ÿ?µ¢G?!0?Ñãœ?eŠ2?!b$?úK¬? d?Ü6¨?è‰?2À¹?÷f?ñ?ﾫ?»g?zT@?<e,?Å@7?z‚0?W*:?)‘+?£?(?«Ø;?ùI±?·Œ?Œ ?²Í?Ä2?o¶ª?¾)? ?N³V?ßHž?Ç-?ü,-?? #?HÄ?¾À}? pG?Yù#?HÁ ?0è?„U? '%?u‘?£Í? O«?©é¨?ÉN›?£ED?@œ?¨À˜?v™?>²?G²?”?¶?‡ ?Y·¬?j ?´ØP?^À?Xܘ?…ì ?°bG?¨¨?ã§?r¨+?绤?ŸG£?¿¬E?&6%?áˆ?ô²?"
£?¿®›?-4?
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/batch_normalization_8_mean.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/batch_normalization_8_mean.bin
deleted file mode 100644
index 5d093813a0926dd3c1c67b6f50e092465101fde9..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/batch_normalization_8_mean.bin
+++ /dev/null
@@ -1,4 +0,0 @@
-¡‰>- ¿Â¿ZF%>Õ#
-¿7æ¾*~>Šß;>€Ó3>P^P>¯ÓX>íÝ¿2 Ë=Æô”>¡o"¿©àÕ>EX$¿30 >œË©<tÐ ¿h{W>LÕˆ>¨Â>KZâ>+_>;™>€g¾>ÒjÇ> ô ¿C‘>í>I“#¿2‹Ï>üfÁ¾‡ø+>4rc>•—˾‘÷´>ZœÕ=V±ì=Ü^é=ƒv‘>[;û¾sˆ=ÓŒ¿Ë“>@AI>¸:¿ÿäD>c˜Z>Žè–>'Š¿t7¿¬?~>gÈ{=ê© ¿9½>Jr'>ðg¿(=d>X)ÿ¾œÉL>)I¿{>ÛfC>ÅS ¿”š¥>ô„Ï>í?¹>O> => >¥>ˆ§‡>¿]&=¡q…>‘)¿Î
-¿-N>Cü=H÷>Úªø¾9>êü>+Ó>Û ¿ÀDB>xÈn>²ï9>h›[>Æ•ì¾ôn–>ušp>|Zi>ûÊ—>ÕY>|‚7>îÓ‹=‹Ú÷=ðŠ´¾=—¿ µÓ¾Œ_^>Òr!¿bâ¾ÏÍÞ¾Ô>#é¿ŒrH¿¾îƒ>ñ÷ ¿fP >åB>â:/¿I ƾà(r>[ïa>`
-¿O£ ¿c@à=A¯Û¾ Y'¿‚°> 3>P2F> ¿Ä ¿ò¿&;>
\ No newline at end of file
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/dense_1_b.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/dense_1_b.bin
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--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/dense_1_b.bin
+++ /dev/null
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-êÞ^>ÂX`¾q =Ï·‡>Hp‚>°¾¾B—b>6ÁU¾$ƒt¾½M¾
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/labels.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/labels.bin
deleted file mode 100644
index 7172750913a297f331af9ba88bce0d3e49968d47..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/layer_composition.txt b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/layer_composition.txt
deleted file mode 100644
index 9b8b3f7e11a428a28fecbde2c204bf39b7e02703..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/layer_composition.txt
+++ /dev/null
@@ -1,41 +0,0 @@
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-depthwise_conv
-batchnorm
-activation
-conv
-batchnorm
-activation
-pool
-dense add
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/layers.txt b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/layers.txt
deleted file mode 100644
index a9415755180a7ebdceb89b7e3e6d6cee258b18c4..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/layers.txt
+++ /dev/null
@@ -1,41 +0,0 @@
-Conv1,10000,3,32,32,32,3,3,3
-#tensorBatchNorm1
-#tensorRelu1
-#tensorDepthwiseConv1
-#tensorBatchNorm2
-#tensorRelu2
-Conv2,10000,32,32,32,64,32,1,1
-#tensorBatchNorm3
-#tensorRelu3
-#tensorDepthwiseConv2
-#tensorBatchNorm4
-#tensorRelu4
-Conv3,10000,64,16,16,128,64,1,1
-#tensorBatchNorm5
-#tensorRelu5
-#tensorDepthwiseConv3
-#tensorBatchNorm6
-#tensorRelu6
-Conv4,10000,128,16,16,128,128,1,1
-#tensorBatchNorm7
-#tensorRelu7
-#tensorDepthwiseConv4
-#tensorBatchNorm8
-#tensorRelu8
-Conv5,10000,128,8,8,256,128,1,1
-#tensorBatchNorm9
-#tensorRelu9
-#tensorDepthwiseConv5
-#tensorBatchNorm10
-#tensorRelu10
-Conv6,10000,256,8,8,256,256,1,1
-#tensorBatchNorm11
-#tensorRelu11
-#tensorDepthwiseConv6
-#tensorBatchNorm12
-#tensorRelu12
-Conv7,10000,256,4,4,512,256,1,1
-#tensorBatchNorm13
-#tensorRelu13
-#tensorPooling1
-FC1,10000,2048,2048,10
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/promise_src.cc b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/promise_src.cc
deleted file mode 100644
index c5fd3606da51281bc2c583e98f024bd2f54f837b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/promise_src.cc
+++ /dev/null
@@ -1,238 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-int total_runs = 100;
-for (int i = 0 ; i < total_runs; i++){
-
-
-startMemTracking();
-
-int test_input_size = 10000;
-int batch_size = 10000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-
-
-std::string dir_prefix = std::string("data/mobilenet_shallow_nathan/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
-void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
-void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
-void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
-void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
-std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
-void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
-std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
-void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
-void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
-void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
-void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
-std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
-void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
-void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
-void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
-void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
-std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
-void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
-std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
-void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
-void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
-void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
-void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
-std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
-void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
-void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
-void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
-void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
-void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
-void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
-void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
-void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
-void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
-std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
-void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
-void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
-void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
-void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
-void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
-void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
-void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
-void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
-void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
-std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
-void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
-void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
-void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
-void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
-void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
-void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
-void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
-void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
-void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
-std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
-void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
-void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
-void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
-void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
-void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
-void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
-void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
-void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
-void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
-std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
-void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
-void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
-void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
-void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,2048,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = ConvLayer_PROMISE(input, -1.9892114, 2.126797, conv2d_1_w, -1.5164621164798737, 1.6472081774473288, NULL, 0, 0, 1, 1, 1, 1, -1, 0, -1, -9.868980642318725, 10.560956018447879, 9);
-void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
-void* var_2 = tensorRelu(var_1);
-void* var_3 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
-void* var_4 = tensorBatchNorm(var_3, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
-void* var_5 = tensorRelu(var_4);
-void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 6.821381127357554, conv2d_2_w, -1.1834390873908995, 1.2731596627235617, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -9.875998497009277, 7.51305247974393, 9);
-void* var_7 = tensorBatchNorm(var_6, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
-void* var_8 = tensorRelu(var_7);
-void* var_9 = tensorConvolution(var_8, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
-void* var_10 = tensorBatchNorm(var_9, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
-void* var_11 = tensorRelu(var_10);
-void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.826067455768602, conv2d_3_w, -0.599876856982708, 0.6812073457241064, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.633289833068848, 5.177892235755925, 9);
-void* var_13 = tensorBatchNorm(var_12, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
-void* var_14 = tensorRelu(var_13);
-void* var_15 = tensorConvolution(var_14, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
-void* var_16 = tensorBatchNorm(var_15, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
-void* var_17 = tensorRelu(var_16);
-void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 4.02646304416659, conv2d_4_w, -0.4555967862010002, 0.4942613914608956, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -5.316803941726685, 4.605850250244146, 9);
-void* var_19 = tensorBatchNorm(var_18, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
-void* var_20 = tensorRelu(var_19);
-void* var_21 = tensorConvolution(var_20, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
-void* var_22 = tensorBatchNorm(var_21, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
-void* var_23 = tensorRelu(var_22);
-void* var_24 = ConvLayer_PROMISE(var_23, 0.0, 4.532649063110355, conv2d_5_w, -0.35657615590095515, 0.3382165088057521, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.1012511816024775, 4.3630500688553, 9);
-void* var_25 = tensorBatchNorm(var_24, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
-void* var_26 = tensorRelu(var_25);
-void* var_27 = tensorConvolution(var_26, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
-void* var_28 = tensorBatchNorm(var_27, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
-void* var_29 = tensorRelu(var_28);
-void* var_30 = ConvLayer_PROMISE(var_29, 0.0, 3.9874704387188977, conv2d_6_w, -0.28502783328294756, 0.28604640334844594, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.243851703643799, 3.486250406742097, 9);
-void* var_31 = tensorBatchNorm(var_30, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
-void* var_32 = tensorRelu(var_31);
-void* var_33 = tensorConvolution(var_32, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
-void* var_34 = tensorBatchNorm(var_33, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
-void* var_35 = tensorRelu(var_34);
-void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 6.563065901756522, conv2d_7_w, -0.18946402323246003, 0.19012390717864017, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.938115713119507, 3.538363476753238, 9);
-void* var_37 = tensorBatchNorm(var_36, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
-void* var_38 = tensorRelu(var_37);
-void* var_39 = tensorPooling(var_38,1,2,2,0,0,2,2);
-void* var_40 = FCLayer_PROMISE(var_39, 0.0, 1.8908388000727185, dense_1_w, -0.35140394401550296, 0.422872786462307, dense_1_b, -0.23878151, 0.26507422, -1, -14.630816223144532, 27.27252123260504, 9);
-void* var_41 = tensorSoftmax(var_40);
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy2(labels, batch_size, var_41);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-}
-
-dumpExecutionAccuracies();
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/src.cc b/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/src.cc
deleted file mode 100644
index 6599f7d0ea0be6a76c4154d25b3a7be2c6724115..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenet_shallow2/src.cc
+++ /dev/null
@@ -1,231 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-std::string dir_prefix = std::string("data/mobilenet_shallow_nathan/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
-void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
-void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
-void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
-void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
-std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
-void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
-std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
-void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
-void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
-void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
-void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,32,1,1);
-std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
-void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
-void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
-void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
-void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,64,1,1);
-std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
-void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,64,1,3,3);
-std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
-void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
-void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
-void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
-void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,1,1);
-std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
-void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
-void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
-void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
-void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
-void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
-void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
-void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
-void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
-void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,1,1);
-std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
-void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
-void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
-void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
-void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,128,1,1);
-std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
-void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,128,1,3,3);
-std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
-void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
-void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
-void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,128,1,1);
-std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
-void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,1,1);
-std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
-void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
-void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
-void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
-void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
-void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
-void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
-void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
-void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
-void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,1,1);
-std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
-void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
-void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
-void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
-void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,256,1,1);
-std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
-void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,256,1,3,3);
-std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
-void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
-void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
-void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,256,1,1);
-std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
-void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,512,256,1,1);
-std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
-void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
-void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
-void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,512,1,1);
-std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
-void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,512,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,2048,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-
-startMemTracking();
-
-int test_input_size = 10000;
-int batch_size = 10000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
-void* var_1 = tensorBatchNorm(var_0, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
-void* var_2 = tensorRelu(var_1);
-void* var_4 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
-void* var_5 = tensorBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
-void* var_6 = tensorRelu(var_5);
-void* var_7 = tensorConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
-void* var_8 = tensorBatchNorm(var_7, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
-void* var_9 = tensorRelu(var_8);
-void* var_11 = tensorConvolution(var_9, depthwise_conv2d_2_w, 1, 1, 2, 2, 1, 64);
-void* var_12 = tensorBatchNorm(var_11, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
-void* var_13 = tensorRelu(var_12);
-void* var_14 = tensorConvolution(var_13, conv2d_3_w, 0, 0, 1, 1, 1, 1);
-void* var_15 = tensorBatchNorm(var_14, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
-void* var_16 = tensorRelu(var_15);
-void* var_18 = tensorConvolution(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 128);
-void* var_19 = tensorBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
-void* var_20 = tensorRelu(var_19);
-void* var_21 = tensorConvolution(var_20, conv2d_4_w, 0, 0, 1, 1, 1, 1);
-void* var_22 = tensorBatchNorm(var_21, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
-void* var_23 = tensorRelu(var_22);
-void* var_26 = tensorConvolution(var_23, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 128);
-void* var_27 = tensorBatchNorm(var_26, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
-void* var_28 = tensorRelu(var_27);
-void* var_29 = tensorConvolution(var_28, conv2d_5_w, 0, 0, 1, 1, 1, 1);
-void* var_30 = tensorBatchNorm(var_29, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
-void* var_31 = tensorRelu(var_30);
-void* var_33 = tensorConvolution(var_31, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 256);
-void* var_34 = tensorBatchNorm(var_33, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
-void* var_35 = tensorRelu(var_34);
-void* var_36 = tensorConvolution(var_35, conv2d_6_w, 0, 0, 1, 1, 1, 1);
-void* var_37 = tensorBatchNorm(var_36, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
-void* var_38 = tensorRelu(var_37);
-void* var_41 = tensorConvolution(var_38, depthwise_conv2d_6_w, 1, 1, 2, 2, 1, 256);
-void* var_42 = tensorBatchNorm(var_41, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
-void* var_43 = tensorRelu(var_42);
-void* var_44 = tensorConvolution(var_43, conv2d_7_w, 0, 0, 1, 1, 1, 1);
-void* var_45 = tensorBatchNorm(var_44, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
-void* var_46 = tensorRelu(var_45);
-void* var_47 = tensorPooling(var_46,1,2,2,0,0,2,2);
-void* var_49 = tensorGemmGPU(var_47, dense_1_w);
-void* var_50 = tensorAdd(var_49, dense_1_b);
-void* var_51 = tensorSoftmax(var_50);
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy2(labels, batch_size, var_51);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/approxhpvm_src.cc b/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/approxhpvm_src.cc
deleted file mode 100644
index d880493c5417e14cd15e5acfadc03b7a7ecd170a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/approxhpvm_src.cc
+++ /dev/null
@@ -1,4367 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/stat.h>
-#include <cstring>
-#include <visc.h>
-#include <tensorTypes.h>
-#include <tensorUtils.h>
-
-void var_0_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_1_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 32);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_2_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_3_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_4_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_5_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_6_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_7_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_8_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_9_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 96);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_10_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_11_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_12_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_13_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_14_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_15_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_16_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_17_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 144);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_18_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_19_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_20_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_21_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_22_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_23_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_24_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_25_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_26_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 2, 2, 1, 144);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_27_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_28_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_29_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_30_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_31_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_32_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_33_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_34_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 192);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_35_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_36_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_37_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_38_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_39_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_40_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_41_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_42_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_43_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 192);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_44_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_45_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_46_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_47_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_48_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_49_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_50_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_51_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_52_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 2, 2, 1, 192);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_53_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_54_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_55_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_56_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_57_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_58_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_59_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_60_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 384);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_61_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_62_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_63_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_64_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_65_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_66_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_67_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_68_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_69_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 384);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_70_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_71_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_72_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_73_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_74_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_75_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_76_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_77_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_78_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 384);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_79_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_80_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_81_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_82_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_83_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_84_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_85_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_86_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_87_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 384);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_88_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_89_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_90_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_91_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_92_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_93_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_94_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_95_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 576);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_96_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_97_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_98_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_99_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_100_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_101_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_102_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_103_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_104_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 576);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_105_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_106_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_107_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_108_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_109_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_110_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_111_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_112_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_113_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 2, 2, 1, 576);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_114_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_115_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_116_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_117_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_118_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_119_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_120_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_121_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 960);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_122_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_123_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_124_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_125_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_126_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_127_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_128_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_129_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_130_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 960);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_131_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_132_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_133_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_134_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_135_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_136_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_137_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_138_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_139_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_group_convolution(t1, t2, 1, 1, 1, 1, 1, 960);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_140_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_141_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_142_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_143_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_144_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_145_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2, void* t3, size_t bytes_t3, void* t4, size_t bytes_t4, void* t5, size_t bytes_t5) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(5, t1, t2, t3, t4, t5, 0);
-
- void *r = __visc__tensor_batchnorm(t1, t2, t3, t4, t5, 0.001);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_146_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_147_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_avg(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_148_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_mul(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_149_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_150_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_softmax(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void root(void* input, size_t input_bytes,
- void* conv2d_1_w, size_t conv2d_1_w_bytes,
- void* depthwise_conv2d_1_w, size_t depthwise_conv2d_1_w_bytes,
- void* batch_normalization_1_gamma, size_t batch_normalization_1_gamma_bytes,
- void* batch_normalization_1_beta, size_t batch_normalization_1_beta_bytes,
- void* batch_normalization_1_mean, size_t batch_normalization_1_mean_bytes,
- void* batch_normalization_1_variance, size_t batch_normalization_1_variance_bytes,
- void* conv2d_2_w, size_t conv2d_2_w_bytes,
- void* batch_normalization_2_gamma, size_t batch_normalization_2_gamma_bytes,
- void* batch_normalization_2_beta, size_t batch_normalization_2_beta_bytes,
- void* batch_normalization_2_mean, size_t batch_normalization_2_mean_bytes,
- void* batch_normalization_2_variance, size_t batch_normalization_2_variance_bytes,
- void* conv2d_3_w, size_t conv2d_3_w_bytes,
- void* batch_normalization_3_gamma, size_t batch_normalization_3_gamma_bytes,
- void* batch_normalization_3_beta, size_t batch_normalization_3_beta_bytes,
- void* batch_normalization_3_mean, size_t batch_normalization_3_mean_bytes,
- void* batch_normalization_3_variance, size_t batch_normalization_3_variance_bytes,
- void* depthwise_conv2d_2_w, size_t depthwise_conv2d_2_w_bytes,
- void* batch_normalization_4_gamma, size_t batch_normalization_4_gamma_bytes,
- void* batch_normalization_4_beta, size_t batch_normalization_4_beta_bytes,
- void* batch_normalization_4_mean, size_t batch_normalization_4_mean_bytes,
- void* batch_normalization_4_variance, size_t batch_normalization_4_variance_bytes,
- void* conv2d_4_w, size_t conv2d_4_w_bytes,
- void* batch_normalization_5_gamma, size_t batch_normalization_5_gamma_bytes,
- void* batch_normalization_5_beta, size_t batch_normalization_5_beta_bytes,
- void* batch_normalization_5_mean, size_t batch_normalization_5_mean_bytes,
- void* batch_normalization_5_variance, size_t batch_normalization_5_variance_bytes,
- void* conv2d_5_w, size_t conv2d_5_w_bytes,
- void* batch_normalization_6_gamma, size_t batch_normalization_6_gamma_bytes,
- void* batch_normalization_6_beta, size_t batch_normalization_6_beta_bytes,
- void* batch_normalization_6_mean, size_t batch_normalization_6_mean_bytes,
- void* batch_normalization_6_variance, size_t batch_normalization_6_variance_bytes,
- void* depthwise_conv2d_3_w, size_t depthwise_conv2d_3_w_bytes,
- void* batch_normalization_7_gamma, size_t batch_normalization_7_gamma_bytes,
- void* batch_normalization_7_beta, size_t batch_normalization_7_beta_bytes,
- void* batch_normalization_7_mean, size_t batch_normalization_7_mean_bytes,
- void* batch_normalization_7_variance, size_t batch_normalization_7_variance_bytes,
- void* conv2d_6_w, size_t conv2d_6_w_bytes,
- void* batch_normalization_8_gamma, size_t batch_normalization_8_gamma_bytes,
- void* batch_normalization_8_beta, size_t batch_normalization_8_beta_bytes,
- void* batch_normalization_8_mean, size_t batch_normalization_8_mean_bytes,
- void* batch_normalization_8_variance, size_t batch_normalization_8_variance_bytes,
- void* conv2d_7_w, size_t conv2d_7_w_bytes,
- void* batch_normalization_9_gamma, size_t batch_normalization_9_gamma_bytes,
- void* batch_normalization_9_beta, size_t batch_normalization_9_beta_bytes,
- void* batch_normalization_9_mean, size_t batch_normalization_9_mean_bytes,
- void* batch_normalization_9_variance, size_t batch_normalization_9_variance_bytes,
- void* depthwise_conv2d_4_w, size_t depthwise_conv2d_4_w_bytes,
- void* batch_normalization_10_gamma, size_t batch_normalization_10_gamma_bytes,
- void* batch_normalization_10_beta, size_t batch_normalization_10_beta_bytes,
- void* batch_normalization_10_mean, size_t batch_normalization_10_mean_bytes,
- void* batch_normalization_10_variance, size_t batch_normalization_10_variance_bytes,
- void* conv2d_8_w, size_t conv2d_8_w_bytes,
- void* batch_normalization_11_gamma, size_t batch_normalization_11_gamma_bytes,
- void* batch_normalization_11_beta, size_t batch_normalization_11_beta_bytes,
- void* batch_normalization_11_mean, size_t batch_normalization_11_mean_bytes,
- void* batch_normalization_11_variance, size_t batch_normalization_11_variance_bytes,
- void* conv2d_9_w, size_t conv2d_9_w_bytes,
- void* batch_normalization_12_gamma, size_t batch_normalization_12_gamma_bytes,
- void* batch_normalization_12_beta, size_t batch_normalization_12_beta_bytes,
- void* batch_normalization_12_mean, size_t batch_normalization_12_mean_bytes,
- void* batch_normalization_12_variance, size_t batch_normalization_12_variance_bytes,
- void* depthwise_conv2d_5_w, size_t depthwise_conv2d_5_w_bytes,
- void* batch_normalization_13_gamma, size_t batch_normalization_13_gamma_bytes,
- void* batch_normalization_13_beta, size_t batch_normalization_13_beta_bytes,
- void* batch_normalization_13_mean, size_t batch_normalization_13_mean_bytes,
- void* batch_normalization_13_variance, size_t batch_normalization_13_variance_bytes,
- void* conv2d_10_w, size_t conv2d_10_w_bytes,
- void* batch_normalization_14_gamma, size_t batch_normalization_14_gamma_bytes,
- void* batch_normalization_14_beta, size_t batch_normalization_14_beta_bytes,
- void* batch_normalization_14_mean, size_t batch_normalization_14_mean_bytes,
- void* batch_normalization_14_variance, size_t batch_normalization_14_variance_bytes,
- void* conv2d_11_w, size_t conv2d_11_w_bytes,
- void* batch_normalization_15_gamma, size_t batch_normalization_15_gamma_bytes,
- void* batch_normalization_15_beta, size_t batch_normalization_15_beta_bytes,
- void* batch_normalization_15_mean, size_t batch_normalization_15_mean_bytes,
- void* batch_normalization_15_variance, size_t batch_normalization_15_variance_bytes,
- void* depthwise_conv2d_6_w, size_t depthwise_conv2d_6_w_bytes,
- void* batch_normalization_16_gamma, size_t batch_normalization_16_gamma_bytes,
- void* batch_normalization_16_beta, size_t batch_normalization_16_beta_bytes,
- void* batch_normalization_16_mean, size_t batch_normalization_16_mean_bytes,
- void* batch_normalization_16_variance, size_t batch_normalization_16_variance_bytes,
- void* conv2d_12_w, size_t conv2d_12_w_bytes,
- void* batch_normalization_17_gamma, size_t batch_normalization_17_gamma_bytes,
- void* batch_normalization_17_beta, size_t batch_normalization_17_beta_bytes,
- void* batch_normalization_17_mean, size_t batch_normalization_17_mean_bytes,
- void* batch_normalization_17_variance, size_t batch_normalization_17_variance_bytes,
- void* conv2d_13_w, size_t conv2d_13_w_bytes,
- void* batch_normalization_18_gamma, size_t batch_normalization_18_gamma_bytes,
- void* batch_normalization_18_beta, size_t batch_normalization_18_beta_bytes,
- void* batch_normalization_18_mean, size_t batch_normalization_18_mean_bytes,
- void* batch_normalization_18_variance, size_t batch_normalization_18_variance_bytes,
- void* depthwise_conv2d_7_w, size_t depthwise_conv2d_7_w_bytes,
- void* batch_normalization_19_gamma, size_t batch_normalization_19_gamma_bytes,
- void* batch_normalization_19_beta, size_t batch_normalization_19_beta_bytes,
- void* batch_normalization_19_mean, size_t batch_normalization_19_mean_bytes,
- void* batch_normalization_19_variance, size_t batch_normalization_19_variance_bytes,
- void* conv2d_14_w, size_t conv2d_14_w_bytes,
- void* batch_normalization_20_gamma, size_t batch_normalization_20_gamma_bytes,
- void* batch_normalization_20_beta, size_t batch_normalization_20_beta_bytes,
- void* batch_normalization_20_mean, size_t batch_normalization_20_mean_bytes,
- void* batch_normalization_20_variance, size_t batch_normalization_20_variance_bytes,
- void* conv2d_15_w, size_t conv2d_15_w_bytes,
- void* batch_normalization_21_gamma, size_t batch_normalization_21_gamma_bytes,
- void* batch_normalization_21_beta, size_t batch_normalization_21_beta_bytes,
- void* batch_normalization_21_mean, size_t batch_normalization_21_mean_bytes,
- void* batch_normalization_21_variance, size_t batch_normalization_21_variance_bytes,
- void* depthwise_conv2d_8_w, size_t depthwise_conv2d_8_w_bytes,
- void* batch_normalization_22_gamma, size_t batch_normalization_22_gamma_bytes,
- void* batch_normalization_22_beta, size_t batch_normalization_22_beta_bytes,
- void* batch_normalization_22_mean, size_t batch_normalization_22_mean_bytes,
- void* batch_normalization_22_variance, size_t batch_normalization_22_variance_bytes,
- void* conv2d_16_w, size_t conv2d_16_w_bytes,
- void* batch_normalization_23_gamma, size_t batch_normalization_23_gamma_bytes,
- void* batch_normalization_23_beta, size_t batch_normalization_23_beta_bytes,
- void* batch_normalization_23_mean, size_t batch_normalization_23_mean_bytes,
- void* batch_normalization_23_variance, size_t batch_normalization_23_variance_bytes,
- void* conv2d_17_w, size_t conv2d_17_w_bytes,
- void* batch_normalization_24_gamma, size_t batch_normalization_24_gamma_bytes,
- void* batch_normalization_24_beta, size_t batch_normalization_24_beta_bytes,
- void* batch_normalization_24_mean, size_t batch_normalization_24_mean_bytes,
- void* batch_normalization_24_variance, size_t batch_normalization_24_variance_bytes,
- void* depthwise_conv2d_9_w, size_t depthwise_conv2d_9_w_bytes,
- void* batch_normalization_25_gamma, size_t batch_normalization_25_gamma_bytes,
- void* batch_normalization_25_beta, size_t batch_normalization_25_beta_bytes,
- void* batch_normalization_25_mean, size_t batch_normalization_25_mean_bytes,
- void* batch_normalization_25_variance, size_t batch_normalization_25_variance_bytes,
- void* conv2d_18_w, size_t conv2d_18_w_bytes,
- void* batch_normalization_26_gamma, size_t batch_normalization_26_gamma_bytes,
- void* batch_normalization_26_beta, size_t batch_normalization_26_beta_bytes,
- void* batch_normalization_26_mean, size_t batch_normalization_26_mean_bytes,
- void* batch_normalization_26_variance, size_t batch_normalization_26_variance_bytes,
- void* conv2d_19_w, size_t conv2d_19_w_bytes,
- void* batch_normalization_27_gamma, size_t batch_normalization_27_gamma_bytes,
- void* batch_normalization_27_beta, size_t batch_normalization_27_beta_bytes,
- void* batch_normalization_27_mean, size_t batch_normalization_27_mean_bytes,
- void* batch_normalization_27_variance, size_t batch_normalization_27_variance_bytes,
- void* depthwise_conv2d_10_w, size_t depthwise_conv2d_10_w_bytes,
- void* batch_normalization_28_gamma, size_t batch_normalization_28_gamma_bytes,
- void* batch_normalization_28_beta, size_t batch_normalization_28_beta_bytes,
- void* batch_normalization_28_mean, size_t batch_normalization_28_mean_bytes,
- void* batch_normalization_28_variance, size_t batch_normalization_28_variance_bytes,
- void* conv2d_20_w, size_t conv2d_20_w_bytes,
- void* batch_normalization_29_gamma, size_t batch_normalization_29_gamma_bytes,
- void* batch_normalization_29_beta, size_t batch_normalization_29_beta_bytes,
- void* batch_normalization_29_mean, size_t batch_normalization_29_mean_bytes,
- void* batch_normalization_29_variance, size_t batch_normalization_29_variance_bytes,
- void* conv2d_21_w, size_t conv2d_21_w_bytes,
- void* batch_normalization_30_gamma, size_t batch_normalization_30_gamma_bytes,
- void* batch_normalization_30_beta, size_t batch_normalization_30_beta_bytes,
- void* batch_normalization_30_mean, size_t batch_normalization_30_mean_bytes,
- void* batch_normalization_30_variance, size_t batch_normalization_30_variance_bytes,
- void* depthwise_conv2d_11_w, size_t depthwise_conv2d_11_w_bytes,
- void* batch_normalization_31_gamma, size_t batch_normalization_31_gamma_bytes,
- void* batch_normalization_31_beta, size_t batch_normalization_31_beta_bytes,
- void* batch_normalization_31_mean, size_t batch_normalization_31_mean_bytes,
- void* batch_normalization_31_variance, size_t batch_normalization_31_variance_bytes,
- void* conv2d_22_w, size_t conv2d_22_w_bytes,
- void* batch_normalization_32_gamma, size_t batch_normalization_32_gamma_bytes,
- void* batch_normalization_32_beta, size_t batch_normalization_32_beta_bytes,
- void* batch_normalization_32_mean, size_t batch_normalization_32_mean_bytes,
- void* batch_normalization_32_variance, size_t batch_normalization_32_variance_bytes,
- void* conv2d_23_w, size_t conv2d_23_w_bytes,
- void* batch_normalization_33_gamma, size_t batch_normalization_33_gamma_bytes,
- void* batch_normalization_33_beta, size_t batch_normalization_33_beta_bytes,
- void* batch_normalization_33_mean, size_t batch_normalization_33_mean_bytes,
- void* batch_normalization_33_variance, size_t batch_normalization_33_variance_bytes,
- void* depthwise_conv2d_12_w, size_t depthwise_conv2d_12_w_bytes,
- void* batch_normalization_34_gamma, size_t batch_normalization_34_gamma_bytes,
- void* batch_normalization_34_beta, size_t batch_normalization_34_beta_bytes,
- void* batch_normalization_34_mean, size_t batch_normalization_34_mean_bytes,
- void* batch_normalization_34_variance, size_t batch_normalization_34_variance_bytes,
- void* conv2d_24_w, size_t conv2d_24_w_bytes,
- void* batch_normalization_35_gamma, size_t batch_normalization_35_gamma_bytes,
- void* batch_normalization_35_beta, size_t batch_normalization_35_beta_bytes,
- void* batch_normalization_35_mean, size_t batch_normalization_35_mean_bytes,
- void* batch_normalization_35_variance, size_t batch_normalization_35_variance_bytes,
- void* conv2d_25_w, size_t conv2d_25_w_bytes,
- void* batch_normalization_36_gamma, size_t batch_normalization_36_gamma_bytes,
- void* batch_normalization_36_beta, size_t batch_normalization_36_beta_bytes,
- void* batch_normalization_36_mean, size_t batch_normalization_36_mean_bytes,
- void* batch_normalization_36_variance, size_t batch_normalization_36_variance_bytes,
- void* depthwise_conv2d_13_w, size_t depthwise_conv2d_13_w_bytes,
- void* batch_normalization_37_gamma, size_t batch_normalization_37_gamma_bytes,
- void* batch_normalization_37_beta, size_t batch_normalization_37_beta_bytes,
- void* batch_normalization_37_mean, size_t batch_normalization_37_mean_bytes,
- void* batch_normalization_37_variance, size_t batch_normalization_37_variance_bytes,
- void* conv2d_26_w, size_t conv2d_26_w_bytes,
- void* batch_normalization_38_gamma, size_t batch_normalization_38_gamma_bytes,
- void* batch_normalization_38_beta, size_t batch_normalization_38_beta_bytes,
- void* batch_normalization_38_mean, size_t batch_normalization_38_mean_bytes,
- void* batch_normalization_38_variance, size_t batch_normalization_38_variance_bytes,
- void* conv2d_27_w, size_t conv2d_27_w_bytes,
- void* batch_normalization_39_gamma, size_t batch_normalization_39_gamma_bytes,
- void* batch_normalization_39_beta, size_t batch_normalization_39_beta_bytes,
- void* batch_normalization_39_mean, size_t batch_normalization_39_mean_bytes,
- void* batch_normalization_39_variance, size_t batch_normalization_39_variance_bytes,
- void* depthwise_conv2d_14_w, size_t depthwise_conv2d_14_w_bytes,
- void* batch_normalization_40_gamma, size_t batch_normalization_40_gamma_bytes,
- void* batch_normalization_40_beta, size_t batch_normalization_40_beta_bytes,
- void* batch_normalization_40_mean, size_t batch_normalization_40_mean_bytes,
- void* batch_normalization_40_variance, size_t batch_normalization_40_variance_bytes,
- void* conv2d_28_w, size_t conv2d_28_w_bytes,
- void* batch_normalization_41_gamma, size_t batch_normalization_41_gamma_bytes,
- void* batch_normalization_41_beta, size_t batch_normalization_41_beta_bytes,
- void* batch_normalization_41_mean, size_t batch_normalization_41_mean_bytes,
- void* batch_normalization_41_variance, size_t batch_normalization_41_variance_bytes,
- void* conv2d_29_w, size_t conv2d_29_w_bytes,
- void* batch_normalization_42_gamma, size_t batch_normalization_42_gamma_bytes,
- void* batch_normalization_42_beta, size_t batch_normalization_42_beta_bytes,
- void* batch_normalization_42_mean, size_t batch_normalization_42_mean_bytes,
- void* batch_normalization_42_variance, size_t batch_normalization_42_variance_bytes,
- void* depthwise_conv2d_15_w, size_t depthwise_conv2d_15_w_bytes,
- void* batch_normalization_43_gamma, size_t batch_normalization_43_gamma_bytes,
- void* batch_normalization_43_beta, size_t batch_normalization_43_beta_bytes,
- void* batch_normalization_43_mean, size_t batch_normalization_43_mean_bytes,
- void* batch_normalization_43_variance, size_t batch_normalization_43_variance_bytes,
- void* conv2d_30_w, size_t conv2d_30_w_bytes,
- void* batch_normalization_44_gamma, size_t batch_normalization_44_gamma_bytes,
- void* batch_normalization_44_beta, size_t batch_normalization_44_beta_bytes,
- void* batch_normalization_44_mean, size_t batch_normalization_44_mean_bytes,
- void* batch_normalization_44_variance, size_t batch_normalization_44_variance_bytes,
- void* conv2d_31_w, size_t conv2d_31_w_bytes,
- void* batch_normalization_45_gamma, size_t batch_normalization_45_gamma_bytes,
- void* batch_normalization_45_beta, size_t batch_normalization_45_beta_bytes,
- void* batch_normalization_45_mean, size_t batch_normalization_45_mean_bytes,
- void* batch_normalization_45_variance, size_t batch_normalization_45_variance_bytes,
- void* depthwise_conv2d_16_w, size_t depthwise_conv2d_16_w_bytes,
- void* batch_normalization_46_gamma, size_t batch_normalization_46_gamma_bytes,
- void* batch_normalization_46_beta, size_t batch_normalization_46_beta_bytes,
- void* batch_normalization_46_mean, size_t batch_normalization_46_mean_bytes,
- void* batch_normalization_46_variance, size_t batch_normalization_46_variance_bytes,
- void* conv2d_32_w, size_t conv2d_32_w_bytes,
- void* batch_normalization_47_gamma, size_t batch_normalization_47_gamma_bytes,
- void* batch_normalization_47_beta, size_t batch_normalization_47_beta_bytes,
- void* batch_normalization_47_mean, size_t batch_normalization_47_mean_bytes,
- void* batch_normalization_47_variance, size_t batch_normalization_47_variance_bytes,
- void* conv2d_33_w, size_t conv2d_33_w_bytes,
- void* batch_normalization_48_gamma, size_t batch_normalization_48_gamma_bytes,
- void* batch_normalization_48_beta, size_t batch_normalization_48_beta_bytes,
- void* batch_normalization_48_mean, size_t batch_normalization_48_mean_bytes,
- void* batch_normalization_48_variance, size_t batch_normalization_48_variance_bytes,
- void* depthwise_conv2d_17_w, size_t depthwise_conv2d_17_w_bytes,
- void* batch_normalization_49_gamma, size_t batch_normalization_49_gamma_bytes,
- void* batch_normalization_49_beta, size_t batch_normalization_49_beta_bytes,
- void* batch_normalization_49_mean, size_t batch_normalization_49_mean_bytes,
- void* batch_normalization_49_variance, size_t batch_normalization_49_variance_bytes,
- void* conv2d_34_w, size_t conv2d_34_w_bytes,
- void* batch_normalization_50_gamma, size_t batch_normalization_50_gamma_bytes,
- void* batch_normalization_50_beta, size_t batch_normalization_50_beta_bytes,
- void* batch_normalization_50_mean, size_t batch_normalization_50_mean_bytes,
- void* batch_normalization_50_variance, size_t batch_normalization_50_variance_bytes,
- void* conv2d_35_w, size_t conv2d_35_w_bytes,
- void* batch_normalization_51_gamma, size_t batch_normalization_51_gamma_bytes,
- void* batch_normalization_51_beta, size_t batch_normalization_51_beta_bytes,
- void* batch_normalization_51_mean, size_t batch_normalization_51_mean_bytes,
- void* batch_normalization_51_variance, size_t batch_normalization_51_variance_bytes,
- void* dense_1_w, size_t dense_1_w_bytes,
- void* dense_1_b, size_t dense_1_b_bytes){
-
-
- __visc__hint(visc::CPU_TARGET);
- __visc__attributes(259, input, conv2d_1_w, depthwise_conv2d_1_w, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, conv2d_2_w, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, conv2d_3_w, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, depthwise_conv2d_2_w, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, conv2d_4_w, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, conv2d_5_w, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, depthwise_conv2d_3_w, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, conv2d_6_w, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, conv2d_7_w, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, depthwise_conv2d_4_w, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, conv2d_8_w, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, conv2d_9_w, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, depthwise_conv2d_5_w, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, conv2d_10_w, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, conv2d_11_w, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, depthwise_conv2d_6_w, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, conv2d_12_w, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, conv2d_13_w, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, depthwise_conv2d_7_w, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, conv2d_14_w, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, conv2d_15_w, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, depthwise_conv2d_8_w, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, conv2d_16_w, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, conv2d_17_w, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, depthwise_conv2d_9_w, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, conv2d_18_w, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, conv2d_19_w, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, depthwise_conv2d_10_w, batch_normalization_28_gamma, batch_normalization_28_beta, batch_normalization_28_mean, batch_normalization_28_variance, conv2d_20_w, batch_normalization_29_gamma, batch_normalization_29_beta, batch_normalization_29_mean, batch_normalization_29_variance, conv2d_21_w, batch_normalization_30_gamma, batch_normalization_30_beta, batch_normalization_30_mean, batch_normalization_30_variance, depthwise_conv2d_11_w, batch_normalization_31_gamma, batch_normalization_31_beta, batch_normalization_31_mean, batch_normalization_31_variance, conv2d_22_w, batch_normalization_32_gamma, batch_normalization_32_beta, batch_normalization_32_mean, batch_normalization_32_variance, conv2d_23_w, batch_normalization_33_gamma, batch_normalization_33_beta, batch_normalization_33_mean, batch_normalization_33_variance, depthwise_conv2d_12_w, batch_normalization_34_gamma, batch_normalization_34_beta, batch_normalization_34_mean, batch_normalization_34_variance, conv2d_24_w, batch_normalization_35_gamma, batch_normalization_35_beta, batch_normalization_35_mean, batch_normalization_35_variance, conv2d_25_w, batch_normalization_36_gamma, batch_normalization_36_beta, batch_normalization_36_mean, batch_normalization_36_variance, depthwise_conv2d_13_w, batch_normalization_37_gamma, batch_normalization_37_beta, batch_normalization_37_mean, batch_normalization_37_variance, conv2d_26_w, batch_normalization_38_gamma, batch_normalization_38_beta, batch_normalization_38_mean, batch_normalization_38_variance, conv2d_27_w, batch_normalization_39_gamma, batch_normalization_39_beta, batch_normalization_39_mean, batch_normalization_39_variance, depthwise_conv2d_14_w, batch_normalization_40_gamma, batch_normalization_40_beta, batch_normalization_40_mean, batch_normalization_40_variance, conv2d_28_w, batch_normalization_41_gamma, batch_normalization_41_beta, batch_normalization_41_mean, batch_normalization_41_variance, conv2d_29_w, batch_normalization_42_gamma, batch_normalization_42_beta, batch_normalization_42_mean, batch_normalization_42_variance, depthwise_conv2d_15_w, batch_normalization_43_gamma, batch_normalization_43_beta, batch_normalization_43_mean, batch_normalization_43_variance, conv2d_30_w, batch_normalization_44_gamma, batch_normalization_44_beta, batch_normalization_44_mean, batch_normalization_44_variance, conv2d_31_w, batch_normalization_45_gamma, batch_normalization_45_beta, batch_normalization_45_mean, batch_normalization_45_variance, depthwise_conv2d_16_w, batch_normalization_46_gamma, batch_normalization_46_beta, batch_normalization_46_mean, batch_normalization_46_variance, conv2d_32_w, batch_normalization_47_gamma, batch_normalization_47_beta, batch_normalization_47_mean, batch_normalization_47_variance, conv2d_33_w, batch_normalization_48_gamma, batch_normalization_48_beta, batch_normalization_48_mean, batch_normalization_48_variance, depthwise_conv2d_17_w, batch_normalization_49_gamma, batch_normalization_49_beta, batch_normalization_49_mean, batch_normalization_49_variance, conv2d_34_w, batch_normalization_50_gamma, batch_normalization_50_beta, batch_normalization_50_mean, batch_normalization_50_variance, conv2d_35_w, batch_normalization_51_gamma, batch_normalization_51_beta, batch_normalization_51_mean, batch_normalization_51_variance, dense_1_w, dense_1_b, 0);
-
-
- void* var_0 = __visc__createNodeND(0, var_0_node);
-
- __visc__bindIn(var_0, 0, 0, 0);
- __visc__bindIn(var_0, 1, 1, 0);
- __visc__bindIn(var_0, 2, 2, 0);
- __visc__bindIn(var_0, 3, 3, 0);
-
- void* var_1 = __visc__createNodeND(0, var_1_node);
-
- __visc__edge(var_0, var_1, 1, 0, 0, 0);
- __visc__edge(var_0, var_1, 1, 1, 1, 0);
- __visc__bindIn(var_1, 4, 2, 0);
- __visc__bindIn(var_1, 5, 3, 0);
-
- void* var_2 = __visc__createNodeND(0, var_2_node);
-
- __visc__edge(var_1, var_2, 1, 0, 0, 0);
- __visc__edge(var_1, var_2, 1, 1, 1, 0);
- __visc__bindIn(var_2, 6, 2, 0);
- __visc__bindIn(var_2, 7, 3, 0);
- __visc__bindIn(var_2, 8, 4, 0);
- __visc__bindIn(var_2, 9, 5, 0);
- __visc__bindIn(var_2, 10, 6, 0);
- __visc__bindIn(var_2, 11, 7, 0);
- __visc__bindIn(var_2, 12, 8, 0);
- __visc__bindIn(var_2, 13, 9, 0);
-
- void* var_3 = __visc__createNodeND(0, var_3_node);
-
- __visc__edge(var_2, var_3, 1, 0, 0, 0);
- __visc__edge(var_2, var_3, 1, 1, 1, 0);
-
- void* var_4 = __visc__createNodeND(0, var_4_node);
-
- __visc__edge(var_3, var_4, 1, 0, 0, 0);
- __visc__edge(var_3, var_4, 1, 1, 1, 0);
- __visc__bindIn(var_4, 14, 2, 0);
- __visc__bindIn(var_4, 15, 3, 0);
-
- void* var_5 = __visc__createNodeND(0, var_5_node);
-
- __visc__edge(var_4, var_5, 1, 0, 0, 0);
- __visc__edge(var_4, var_5, 1, 1, 1, 0);
- __visc__bindIn(var_5, 16, 2, 0);
- __visc__bindIn(var_5, 17, 3, 0);
- __visc__bindIn(var_5, 18, 4, 0);
- __visc__bindIn(var_5, 19, 5, 0);
- __visc__bindIn(var_5, 20, 6, 0);
- __visc__bindIn(var_5, 21, 7, 0);
- __visc__bindIn(var_5, 22, 8, 0);
- __visc__bindIn(var_5, 23, 9, 0);
-
- void* var_6 = __visc__createNodeND(0, var_6_node);
-
- __visc__edge(var_5, var_6, 1, 0, 0, 0);
- __visc__edge(var_5, var_6, 1, 1, 1, 0);
- __visc__bindIn(var_6, 24, 2, 0);
- __visc__bindIn(var_6, 25, 3, 0);
-
- void* var_7 = __visc__createNodeND(0, var_7_node);
-
- __visc__edge(var_6, var_7, 1, 0, 0, 0);
- __visc__edge(var_6, var_7, 1, 1, 1, 0);
- __visc__bindIn(var_7, 26, 2, 0);
- __visc__bindIn(var_7, 27, 3, 0);
- __visc__bindIn(var_7, 28, 4, 0);
- __visc__bindIn(var_7, 29, 5, 0);
- __visc__bindIn(var_7, 30, 6, 0);
- __visc__bindIn(var_7, 31, 7, 0);
- __visc__bindIn(var_7, 32, 8, 0);
- __visc__bindIn(var_7, 33, 9, 0);
-
- void* var_8 = __visc__createNodeND(0, var_8_node);
-
- __visc__edge(var_7, var_8, 1, 0, 0, 0);
- __visc__edge(var_7, var_8, 1, 1, 1, 0);
-
- void* var_9 = __visc__createNodeND(0, var_9_node);
-
- __visc__edge(var_8, var_9, 1, 0, 0, 0);
- __visc__edge(var_8, var_9, 1, 1, 1, 0);
- __visc__bindIn(var_9, 34, 2, 0);
- __visc__bindIn(var_9, 35, 3, 0);
-
- void* var_10 = __visc__createNodeND(0, var_10_node);
-
- __visc__edge(var_9, var_10, 1, 0, 0, 0);
- __visc__edge(var_9, var_10, 1, 1, 1, 0);
- __visc__bindIn(var_10, 36, 2, 0);
- __visc__bindIn(var_10, 37, 3, 0);
- __visc__bindIn(var_10, 38, 4, 0);
- __visc__bindIn(var_10, 39, 5, 0);
- __visc__bindIn(var_10, 40, 6, 0);
- __visc__bindIn(var_10, 41, 7, 0);
- __visc__bindIn(var_10, 42, 8, 0);
- __visc__bindIn(var_10, 43, 9, 0);
-
- void* var_11 = __visc__createNodeND(0, var_11_node);
-
- __visc__edge(var_10, var_11, 1, 0, 0, 0);
- __visc__edge(var_10, var_11, 1, 1, 1, 0);
-
- void* var_12 = __visc__createNodeND(0, var_12_node);
-
- __visc__edge(var_11, var_12, 1, 0, 0, 0);
- __visc__edge(var_11, var_12, 1, 1, 1, 0);
- __visc__bindIn(var_12, 44, 2, 0);
- __visc__bindIn(var_12, 45, 3, 0);
-
- void* var_13 = __visc__createNodeND(0, var_13_node);
-
- __visc__edge(var_12, var_13, 1, 0, 0, 0);
- __visc__edge(var_12, var_13, 1, 1, 1, 0);
- __visc__bindIn(var_13, 46, 2, 0);
- __visc__bindIn(var_13, 47, 3, 0);
- __visc__bindIn(var_13, 48, 4, 0);
- __visc__bindIn(var_13, 49, 5, 0);
- __visc__bindIn(var_13, 50, 6, 0);
- __visc__bindIn(var_13, 51, 7, 0);
- __visc__bindIn(var_13, 52, 8, 0);
- __visc__bindIn(var_13, 53, 9, 0);
-
- void* var_14 = __visc__createNodeND(0, var_14_node);
-
- __visc__edge(var_13, var_14, 1, 0, 0, 0);
- __visc__edge(var_13, var_14, 1, 1, 1, 0);
- __visc__bindIn(var_14, 54, 2, 0);
- __visc__bindIn(var_14, 55, 3, 0);
-
- void* var_15 = __visc__createNodeND(0, var_15_node);
-
- __visc__edge(var_14, var_15, 1, 0, 0, 0);
- __visc__edge(var_14, var_15, 1, 1, 1, 0);
- __visc__bindIn(var_15, 56, 2, 0);
- __visc__bindIn(var_15, 57, 3, 0);
- __visc__bindIn(var_15, 58, 4, 0);
- __visc__bindIn(var_15, 59, 5, 0);
- __visc__bindIn(var_15, 60, 6, 0);
- __visc__bindIn(var_15, 61, 7, 0);
- __visc__bindIn(var_15, 62, 8, 0);
- __visc__bindIn(var_15, 63, 9, 0);
-
- void* var_16 = __visc__createNodeND(0, var_16_node);
-
- __visc__edge(var_15, var_16, 1, 0, 0, 0);
- __visc__edge(var_15, var_16, 1, 1, 1, 0);
-
- void* var_17 = __visc__createNodeND(0, var_17_node);
-
- __visc__edge(var_16, var_17, 1, 0, 0, 0);
- __visc__edge(var_16, var_17, 1, 1, 1, 0);
- __visc__bindIn(var_17, 64, 2, 0);
- __visc__bindIn(var_17, 65, 3, 0);
-
- void* var_18 = __visc__createNodeND(0, var_18_node);
-
- __visc__edge(var_17, var_18, 1, 0, 0, 0);
- __visc__edge(var_17, var_18, 1, 1, 1, 0);
- __visc__bindIn(var_18, 66, 2, 0);
- __visc__bindIn(var_18, 67, 3, 0);
- __visc__bindIn(var_18, 68, 4, 0);
- __visc__bindIn(var_18, 69, 5, 0);
- __visc__bindIn(var_18, 70, 6, 0);
- __visc__bindIn(var_18, 71, 7, 0);
- __visc__bindIn(var_18, 72, 8, 0);
- __visc__bindIn(var_18, 73, 9, 0);
-
- void* var_19 = __visc__createNodeND(0, var_19_node);
-
- __visc__edge(var_18, var_19, 1, 0, 0, 0);
- __visc__edge(var_18, var_19, 1, 1, 1, 0);
-
- void* var_20 = __visc__createNodeND(0, var_20_node);
-
- __visc__edge(var_19, var_20, 1, 0, 0, 0);
- __visc__edge(var_19, var_20, 1, 1, 1, 0);
- __visc__bindIn(var_20, 74, 2, 0);
- __visc__bindIn(var_20, 75, 3, 0);
-
- void* var_21 = __visc__createNodeND(0, var_21_node);
-
- __visc__edge(var_20, var_21, 1, 0, 0, 0);
- __visc__edge(var_20, var_21, 1, 1, 1, 0);
- __visc__bindIn(var_21, 76, 2, 0);
- __visc__bindIn(var_21, 77, 3, 0);
- __visc__bindIn(var_21, 78, 4, 0);
- __visc__bindIn(var_21, 79, 5, 0);
- __visc__bindIn(var_21, 80, 6, 0);
- __visc__bindIn(var_21, 81, 7, 0);
- __visc__bindIn(var_21, 82, 8, 0);
- __visc__bindIn(var_21, 83, 9, 0);
-
- void* var_22 = __visc__createNodeND(0, var_22_node);
-
- __visc__edge(var_13, var_22, 1, 0, 0, 0);
- __visc__edge(var_13, var_22, 1, 1, 1, 0);
- __visc__edge(var_21, var_22, 1, 0, 2, 0);
- __visc__edge(var_21, var_22, 1, 1, 3, 0);
-
- void* var_23 = __visc__createNodeND(0, var_23_node);
-
- __visc__edge(var_22, var_23, 1, 0, 0, 0);
- __visc__edge(var_22, var_23, 1, 1, 1, 0);
- __visc__bindIn(var_23, 84, 2, 0);
- __visc__bindIn(var_23, 85, 3, 0);
-
- void* var_24 = __visc__createNodeND(0, var_24_node);
-
- __visc__edge(var_23, var_24, 1, 0, 0, 0);
- __visc__edge(var_23, var_24, 1, 1, 1, 0);
- __visc__bindIn(var_24, 86, 2, 0);
- __visc__bindIn(var_24, 87, 3, 0);
- __visc__bindIn(var_24, 88, 4, 0);
- __visc__bindIn(var_24, 89, 5, 0);
- __visc__bindIn(var_24, 90, 6, 0);
- __visc__bindIn(var_24, 91, 7, 0);
- __visc__bindIn(var_24, 92, 8, 0);
- __visc__bindIn(var_24, 93, 9, 0);
-
- void* var_25 = __visc__createNodeND(0, var_25_node);
-
- __visc__edge(var_24, var_25, 1, 0, 0, 0);
- __visc__edge(var_24, var_25, 1, 1, 1, 0);
-
- void* var_26 = __visc__createNodeND(0, var_26_node);
-
- __visc__edge(var_25, var_26, 1, 0, 0, 0);
- __visc__edge(var_25, var_26, 1, 1, 1, 0);
- __visc__bindIn(var_26, 94, 2, 0);
- __visc__bindIn(var_26, 95, 3, 0);
-
- void* var_27 = __visc__createNodeND(0, var_27_node);
-
- __visc__edge(var_26, var_27, 1, 0, 0, 0);
- __visc__edge(var_26, var_27, 1, 1, 1, 0);
- __visc__bindIn(var_27, 96, 2, 0);
- __visc__bindIn(var_27, 97, 3, 0);
- __visc__bindIn(var_27, 98, 4, 0);
- __visc__bindIn(var_27, 99, 5, 0);
- __visc__bindIn(var_27, 100, 6, 0);
- __visc__bindIn(var_27, 101, 7, 0);
- __visc__bindIn(var_27, 102, 8, 0);
- __visc__bindIn(var_27, 103, 9, 0);
-
- void* var_28 = __visc__createNodeND(0, var_28_node);
-
- __visc__edge(var_27, var_28, 1, 0, 0, 0);
- __visc__edge(var_27, var_28, 1, 1, 1, 0);
-
- void* var_29 = __visc__createNodeND(0, var_29_node);
-
- __visc__edge(var_28, var_29, 1, 0, 0, 0);
- __visc__edge(var_28, var_29, 1, 1, 1, 0);
- __visc__bindIn(var_29, 104, 2, 0);
- __visc__bindIn(var_29, 105, 3, 0);
-
- void* var_30 = __visc__createNodeND(0, var_30_node);
-
- __visc__edge(var_29, var_30, 1, 0, 0, 0);
- __visc__edge(var_29, var_30, 1, 1, 1, 0);
- __visc__bindIn(var_30, 106, 2, 0);
- __visc__bindIn(var_30, 107, 3, 0);
- __visc__bindIn(var_30, 108, 4, 0);
- __visc__bindIn(var_30, 109, 5, 0);
- __visc__bindIn(var_30, 110, 6, 0);
- __visc__bindIn(var_30, 111, 7, 0);
- __visc__bindIn(var_30, 112, 8, 0);
- __visc__bindIn(var_30, 113, 9, 0);
-
- void* var_31 = __visc__createNodeND(0, var_31_node);
-
- __visc__edge(var_30, var_31, 1, 0, 0, 0);
- __visc__edge(var_30, var_31, 1, 1, 1, 0);
- __visc__bindIn(var_31, 114, 2, 0);
- __visc__bindIn(var_31, 115, 3, 0);
-
- void* var_32 = __visc__createNodeND(0, var_32_node);
-
- __visc__edge(var_31, var_32, 1, 0, 0, 0);
- __visc__edge(var_31, var_32, 1, 1, 1, 0);
- __visc__bindIn(var_32, 116, 2, 0);
- __visc__bindIn(var_32, 117, 3, 0);
- __visc__bindIn(var_32, 118, 4, 0);
- __visc__bindIn(var_32, 119, 5, 0);
- __visc__bindIn(var_32, 120, 6, 0);
- __visc__bindIn(var_32, 121, 7, 0);
- __visc__bindIn(var_32, 122, 8, 0);
- __visc__bindIn(var_32, 123, 9, 0);
-
- void* var_33 = __visc__createNodeND(0, var_33_node);
-
- __visc__edge(var_32, var_33, 1, 0, 0, 0);
- __visc__edge(var_32, var_33, 1, 1, 1, 0);
-
- void* var_34 = __visc__createNodeND(0, var_34_node);
-
- __visc__edge(var_33, var_34, 1, 0, 0, 0);
- __visc__edge(var_33, var_34, 1, 1, 1, 0);
- __visc__bindIn(var_34, 124, 2, 0);
- __visc__bindIn(var_34, 125, 3, 0);
-
- void* var_35 = __visc__createNodeND(0, var_35_node);
-
- __visc__edge(var_34, var_35, 1, 0, 0, 0);
- __visc__edge(var_34, var_35, 1, 1, 1, 0);
- __visc__bindIn(var_35, 126, 2, 0);
- __visc__bindIn(var_35, 127, 3, 0);
- __visc__bindIn(var_35, 128, 4, 0);
- __visc__bindIn(var_35, 129, 5, 0);
- __visc__bindIn(var_35, 130, 6, 0);
- __visc__bindIn(var_35, 131, 7, 0);
- __visc__bindIn(var_35, 132, 8, 0);
- __visc__bindIn(var_35, 133, 9, 0);
-
- void* var_36 = __visc__createNodeND(0, var_36_node);
-
- __visc__edge(var_35, var_36, 1, 0, 0, 0);
- __visc__edge(var_35, var_36, 1, 1, 1, 0);
-
- void* var_37 = __visc__createNodeND(0, var_37_node);
-
- __visc__edge(var_36, var_37, 1, 0, 0, 0);
- __visc__edge(var_36, var_37, 1, 1, 1, 0);
- __visc__bindIn(var_37, 134, 2, 0);
- __visc__bindIn(var_37, 135, 3, 0);
-
- void* var_38 = __visc__createNodeND(0, var_38_node);
-
- __visc__edge(var_37, var_38, 1, 0, 0, 0);
- __visc__edge(var_37, var_38, 1, 1, 1, 0);
- __visc__bindIn(var_38, 136, 2, 0);
- __visc__bindIn(var_38, 137, 3, 0);
- __visc__bindIn(var_38, 138, 4, 0);
- __visc__bindIn(var_38, 139, 5, 0);
- __visc__bindIn(var_38, 140, 6, 0);
- __visc__bindIn(var_38, 141, 7, 0);
- __visc__bindIn(var_38, 142, 8, 0);
- __visc__bindIn(var_38, 143, 9, 0);
-
- void* var_39 = __visc__createNodeND(0, var_39_node);
-
- __visc__edge(var_30, var_39, 1, 0, 0, 0);
- __visc__edge(var_30, var_39, 1, 1, 1, 0);
- __visc__edge(var_38, var_39, 1, 0, 2, 0);
- __visc__edge(var_38, var_39, 1, 1, 3, 0);
-
- void* var_40 = __visc__createNodeND(0, var_40_node);
-
- __visc__edge(var_39, var_40, 1, 0, 0, 0);
- __visc__edge(var_39, var_40, 1, 1, 1, 0);
- __visc__bindIn(var_40, 144, 2, 0);
- __visc__bindIn(var_40, 145, 3, 0);
-
- void* var_41 = __visc__createNodeND(0, var_41_node);
-
- __visc__edge(var_40, var_41, 1, 0, 0, 0);
- __visc__edge(var_40, var_41, 1, 1, 1, 0);
- __visc__bindIn(var_41, 146, 2, 0);
- __visc__bindIn(var_41, 147, 3, 0);
- __visc__bindIn(var_41, 148, 4, 0);
- __visc__bindIn(var_41, 149, 5, 0);
- __visc__bindIn(var_41, 150, 6, 0);
- __visc__bindIn(var_41, 151, 7, 0);
- __visc__bindIn(var_41, 152, 8, 0);
- __visc__bindIn(var_41, 153, 9, 0);
-
- void* var_42 = __visc__createNodeND(0, var_42_node);
-
- __visc__edge(var_41, var_42, 1, 0, 0, 0);
- __visc__edge(var_41, var_42, 1, 1, 1, 0);
-
- void* var_43 = __visc__createNodeND(0, var_43_node);
-
- __visc__edge(var_42, var_43, 1, 0, 0, 0);
- __visc__edge(var_42, var_43, 1, 1, 1, 0);
- __visc__bindIn(var_43, 154, 2, 0);
- __visc__bindIn(var_43, 155, 3, 0);
-
- void* var_44 = __visc__createNodeND(0, var_44_node);
-
- __visc__edge(var_43, var_44, 1, 0, 0, 0);
- __visc__edge(var_43, var_44, 1, 1, 1, 0);
- __visc__bindIn(var_44, 156, 2, 0);
- __visc__bindIn(var_44, 157, 3, 0);
- __visc__bindIn(var_44, 158, 4, 0);
- __visc__bindIn(var_44, 159, 5, 0);
- __visc__bindIn(var_44, 160, 6, 0);
- __visc__bindIn(var_44, 161, 7, 0);
- __visc__bindIn(var_44, 162, 8, 0);
- __visc__bindIn(var_44, 163, 9, 0);
-
- void* var_45 = __visc__createNodeND(0, var_45_node);
-
- __visc__edge(var_44, var_45, 1, 0, 0, 0);
- __visc__edge(var_44, var_45, 1, 1, 1, 0);
-
- void* var_46 = __visc__createNodeND(0, var_46_node);
-
- __visc__edge(var_45, var_46, 1, 0, 0, 0);
- __visc__edge(var_45, var_46, 1, 1, 1, 0);
- __visc__bindIn(var_46, 164, 2, 0);
- __visc__bindIn(var_46, 165, 3, 0);
-
- void* var_47 = __visc__createNodeND(0, var_47_node);
-
- __visc__edge(var_46, var_47, 1, 0, 0, 0);
- __visc__edge(var_46, var_47, 1, 1, 1, 0);
- __visc__bindIn(var_47, 166, 2, 0);
- __visc__bindIn(var_47, 167, 3, 0);
- __visc__bindIn(var_47, 168, 4, 0);
- __visc__bindIn(var_47, 169, 5, 0);
- __visc__bindIn(var_47, 170, 6, 0);
- __visc__bindIn(var_47, 171, 7, 0);
- __visc__bindIn(var_47, 172, 8, 0);
- __visc__bindIn(var_47, 173, 9, 0);
-
- void* var_48 = __visc__createNodeND(0, var_48_node);
-
- __visc__edge(var_39, var_48, 1, 0, 0, 0);
- __visc__edge(var_39, var_48, 1, 1, 1, 0);
- __visc__edge(var_47, var_48, 1, 0, 2, 0);
- __visc__edge(var_47, var_48, 1, 1, 3, 0);
-
- void* var_49 = __visc__createNodeND(0, var_49_node);
-
- __visc__edge(var_48, var_49, 1, 0, 0, 0);
- __visc__edge(var_48, var_49, 1, 1, 1, 0);
- __visc__bindIn(var_49, 174, 2, 0);
- __visc__bindIn(var_49, 175, 3, 0);
-
- void* var_50 = __visc__createNodeND(0, var_50_node);
-
- __visc__edge(var_49, var_50, 1, 0, 0, 0);
- __visc__edge(var_49, var_50, 1, 1, 1, 0);
- __visc__bindIn(var_50, 176, 2, 0);
- __visc__bindIn(var_50, 177, 3, 0);
- __visc__bindIn(var_50, 178, 4, 0);
- __visc__bindIn(var_50, 179, 5, 0);
- __visc__bindIn(var_50, 180, 6, 0);
- __visc__bindIn(var_50, 181, 7, 0);
- __visc__bindIn(var_50, 182, 8, 0);
- __visc__bindIn(var_50, 183, 9, 0);
-
- void* var_51 = __visc__createNodeND(0, var_51_node);
-
- __visc__edge(var_50, var_51, 1, 0, 0, 0);
- __visc__edge(var_50, var_51, 1, 1, 1, 0);
-
- void* var_52 = __visc__createNodeND(0, var_52_node);
-
- __visc__edge(var_51, var_52, 1, 0, 0, 0);
- __visc__edge(var_51, var_52, 1, 1, 1, 0);
- __visc__bindIn(var_52, 184, 2, 0);
- __visc__bindIn(var_52, 185, 3, 0);
-
- void* var_53 = __visc__createNodeND(0, var_53_node);
-
- __visc__edge(var_52, var_53, 1, 0, 0, 0);
- __visc__edge(var_52, var_53, 1, 1, 1, 0);
- __visc__bindIn(var_53, 186, 2, 0);
- __visc__bindIn(var_53, 187, 3, 0);
- __visc__bindIn(var_53, 188, 4, 0);
- __visc__bindIn(var_53, 189, 5, 0);
- __visc__bindIn(var_53, 190, 6, 0);
- __visc__bindIn(var_53, 191, 7, 0);
- __visc__bindIn(var_53, 192, 8, 0);
- __visc__bindIn(var_53, 193, 9, 0);
-
- void* var_54 = __visc__createNodeND(0, var_54_node);
-
- __visc__edge(var_53, var_54, 1, 0, 0, 0);
- __visc__edge(var_53, var_54, 1, 1, 1, 0);
-
- void* var_55 = __visc__createNodeND(0, var_55_node);
-
- __visc__edge(var_54, var_55, 1, 0, 0, 0);
- __visc__edge(var_54, var_55, 1, 1, 1, 0);
- __visc__bindIn(var_55, 194, 2, 0);
- __visc__bindIn(var_55, 195, 3, 0);
-
- void* var_56 = __visc__createNodeND(0, var_56_node);
-
- __visc__edge(var_55, var_56, 1, 0, 0, 0);
- __visc__edge(var_55, var_56, 1, 1, 1, 0);
- __visc__bindIn(var_56, 196, 2, 0);
- __visc__bindIn(var_56, 197, 3, 0);
- __visc__bindIn(var_56, 198, 4, 0);
- __visc__bindIn(var_56, 199, 5, 0);
- __visc__bindIn(var_56, 200, 6, 0);
- __visc__bindIn(var_56, 201, 7, 0);
- __visc__bindIn(var_56, 202, 8, 0);
- __visc__bindIn(var_56, 203, 9, 0);
-
- void* var_57 = __visc__createNodeND(0, var_57_node);
-
- __visc__edge(var_56, var_57, 1, 0, 0, 0);
- __visc__edge(var_56, var_57, 1, 1, 1, 0);
- __visc__bindIn(var_57, 204, 2, 0);
- __visc__bindIn(var_57, 205, 3, 0);
-
- void* var_58 = __visc__createNodeND(0, var_58_node);
-
- __visc__edge(var_57, var_58, 1, 0, 0, 0);
- __visc__edge(var_57, var_58, 1, 1, 1, 0);
- __visc__bindIn(var_58, 206, 2, 0);
- __visc__bindIn(var_58, 207, 3, 0);
- __visc__bindIn(var_58, 208, 4, 0);
- __visc__bindIn(var_58, 209, 5, 0);
- __visc__bindIn(var_58, 210, 6, 0);
- __visc__bindIn(var_58, 211, 7, 0);
- __visc__bindIn(var_58, 212, 8, 0);
- __visc__bindIn(var_58, 213, 9, 0);
-
- void* var_59 = __visc__createNodeND(0, var_59_node);
-
- __visc__edge(var_58, var_59, 1, 0, 0, 0);
- __visc__edge(var_58, var_59, 1, 1, 1, 0);
-
- void* var_60 = __visc__createNodeND(0, var_60_node);
-
- __visc__edge(var_59, var_60, 1, 0, 0, 0);
- __visc__edge(var_59, var_60, 1, 1, 1, 0);
- __visc__bindIn(var_60, 214, 2, 0);
- __visc__bindIn(var_60, 215, 3, 0);
-
- void* var_61 = __visc__createNodeND(0, var_61_node);
-
- __visc__edge(var_60, var_61, 1, 0, 0, 0);
- __visc__edge(var_60, var_61, 1, 1, 1, 0);
- __visc__bindIn(var_61, 216, 2, 0);
- __visc__bindIn(var_61, 217, 3, 0);
- __visc__bindIn(var_61, 218, 4, 0);
- __visc__bindIn(var_61, 219, 5, 0);
- __visc__bindIn(var_61, 220, 6, 0);
- __visc__bindIn(var_61, 221, 7, 0);
- __visc__bindIn(var_61, 222, 8, 0);
- __visc__bindIn(var_61, 223, 9, 0);
-
- void* var_62 = __visc__createNodeND(0, var_62_node);
-
- __visc__edge(var_61, var_62, 1, 0, 0, 0);
- __visc__edge(var_61, var_62, 1, 1, 1, 0);
-
- void* var_63 = __visc__createNodeND(0, var_63_node);
-
- __visc__edge(var_62, var_63, 1, 0, 0, 0);
- __visc__edge(var_62, var_63, 1, 1, 1, 0);
- __visc__bindIn(var_63, 224, 2, 0);
- __visc__bindIn(var_63, 225, 3, 0);
-
- void* var_64 = __visc__createNodeND(0, var_64_node);
-
- __visc__edge(var_63, var_64, 1, 0, 0, 0);
- __visc__edge(var_63, var_64, 1, 1, 1, 0);
- __visc__bindIn(var_64, 226, 2, 0);
- __visc__bindIn(var_64, 227, 3, 0);
- __visc__bindIn(var_64, 228, 4, 0);
- __visc__bindIn(var_64, 229, 5, 0);
- __visc__bindIn(var_64, 230, 6, 0);
- __visc__bindIn(var_64, 231, 7, 0);
- __visc__bindIn(var_64, 232, 8, 0);
- __visc__bindIn(var_64, 233, 9, 0);
-
- void* var_65 = __visc__createNodeND(0, var_65_node);
-
- __visc__edge(var_56, var_65, 1, 0, 0, 0);
- __visc__edge(var_56, var_65, 1, 1, 1, 0);
- __visc__edge(var_64, var_65, 1, 0, 2, 0);
- __visc__edge(var_64, var_65, 1, 1, 3, 0);
-
- void* var_66 = __visc__createNodeND(0, var_66_node);
-
- __visc__edge(var_65, var_66, 1, 0, 0, 0);
- __visc__edge(var_65, var_66, 1, 1, 1, 0);
- __visc__bindIn(var_66, 234, 2, 0);
- __visc__bindIn(var_66, 235, 3, 0);
-
- void* var_67 = __visc__createNodeND(0, var_67_node);
-
- __visc__edge(var_66, var_67, 1, 0, 0, 0);
- __visc__edge(var_66, var_67, 1, 1, 1, 0);
- __visc__bindIn(var_67, 236, 2, 0);
- __visc__bindIn(var_67, 237, 3, 0);
- __visc__bindIn(var_67, 238, 4, 0);
- __visc__bindIn(var_67, 239, 5, 0);
- __visc__bindIn(var_67, 240, 6, 0);
- __visc__bindIn(var_67, 241, 7, 0);
- __visc__bindIn(var_67, 242, 8, 0);
- __visc__bindIn(var_67, 243, 9, 0);
-
- void* var_68 = __visc__createNodeND(0, var_68_node);
-
- __visc__edge(var_67, var_68, 1, 0, 0, 0);
- __visc__edge(var_67, var_68, 1, 1, 1, 0);
-
- void* var_69 = __visc__createNodeND(0, var_69_node);
-
- __visc__edge(var_68, var_69, 1, 0, 0, 0);
- __visc__edge(var_68, var_69, 1, 1, 1, 0);
- __visc__bindIn(var_69, 244, 2, 0);
- __visc__bindIn(var_69, 245, 3, 0);
-
- void* var_70 = __visc__createNodeND(0, var_70_node);
-
- __visc__edge(var_69, var_70, 1, 0, 0, 0);
- __visc__edge(var_69, var_70, 1, 1, 1, 0);
- __visc__bindIn(var_70, 246, 2, 0);
- __visc__bindIn(var_70, 247, 3, 0);
- __visc__bindIn(var_70, 248, 4, 0);
- __visc__bindIn(var_70, 249, 5, 0);
- __visc__bindIn(var_70, 250, 6, 0);
- __visc__bindIn(var_70, 251, 7, 0);
- __visc__bindIn(var_70, 252, 8, 0);
- __visc__bindIn(var_70, 253, 9, 0);
-
- void* var_71 = __visc__createNodeND(0, var_71_node);
-
- __visc__edge(var_70, var_71, 1, 0, 0, 0);
- __visc__edge(var_70, var_71, 1, 1, 1, 0);
-
- void* var_72 = __visc__createNodeND(0, var_72_node);
-
- __visc__edge(var_71, var_72, 1, 0, 0, 0);
- __visc__edge(var_71, var_72, 1, 1, 1, 0);
- __visc__bindIn(var_72, 254, 2, 0);
- __visc__bindIn(var_72, 255, 3, 0);
-
- void* var_73 = __visc__createNodeND(0, var_73_node);
-
- __visc__edge(var_72, var_73, 1, 0, 0, 0);
- __visc__edge(var_72, var_73, 1, 1, 1, 0);
- __visc__bindIn(var_73, 256, 2, 0);
- __visc__bindIn(var_73, 257, 3, 0);
- __visc__bindIn(var_73, 258, 4, 0);
- __visc__bindIn(var_73, 259, 5, 0);
- __visc__bindIn(var_73, 260, 6, 0);
- __visc__bindIn(var_73, 261, 7, 0);
- __visc__bindIn(var_73, 262, 8, 0);
- __visc__bindIn(var_73, 263, 9, 0);
-
- void* var_74 = __visc__createNodeND(0, var_74_node);
-
- __visc__edge(var_65, var_74, 1, 0, 0, 0);
- __visc__edge(var_65, var_74, 1, 1, 1, 0);
- __visc__edge(var_73, var_74, 1, 0, 2, 0);
- __visc__edge(var_73, var_74, 1, 1, 3, 0);
-
- void* var_75 = __visc__createNodeND(0, var_75_node);
-
- __visc__edge(var_74, var_75, 1, 0, 0, 0);
- __visc__edge(var_74, var_75, 1, 1, 1, 0);
- __visc__bindIn(var_75, 264, 2, 0);
- __visc__bindIn(var_75, 265, 3, 0);
-
- void* var_76 = __visc__createNodeND(0, var_76_node);
-
- __visc__edge(var_75, var_76, 1, 0, 0, 0);
- __visc__edge(var_75, var_76, 1, 1, 1, 0);
- __visc__bindIn(var_76, 266, 2, 0);
- __visc__bindIn(var_76, 267, 3, 0);
- __visc__bindIn(var_76, 268, 4, 0);
- __visc__bindIn(var_76, 269, 5, 0);
- __visc__bindIn(var_76, 270, 6, 0);
- __visc__bindIn(var_76, 271, 7, 0);
- __visc__bindIn(var_76, 272, 8, 0);
- __visc__bindIn(var_76, 273, 9, 0);
-
- void* var_77 = __visc__createNodeND(0, var_77_node);
-
- __visc__edge(var_76, var_77, 1, 0, 0, 0);
- __visc__edge(var_76, var_77, 1, 1, 1, 0);
-
- void* var_78 = __visc__createNodeND(0, var_78_node);
-
- __visc__edge(var_77, var_78, 1, 0, 0, 0);
- __visc__edge(var_77, var_78, 1, 1, 1, 0);
- __visc__bindIn(var_78, 274, 2, 0);
- __visc__bindIn(var_78, 275, 3, 0);
-
- void* var_79 = __visc__createNodeND(0, var_79_node);
-
- __visc__edge(var_78, var_79, 1, 0, 0, 0);
- __visc__edge(var_78, var_79, 1, 1, 1, 0);
- __visc__bindIn(var_79, 276, 2, 0);
- __visc__bindIn(var_79, 277, 3, 0);
- __visc__bindIn(var_79, 278, 4, 0);
- __visc__bindIn(var_79, 279, 5, 0);
- __visc__bindIn(var_79, 280, 6, 0);
- __visc__bindIn(var_79, 281, 7, 0);
- __visc__bindIn(var_79, 282, 8, 0);
- __visc__bindIn(var_79, 283, 9, 0);
-
- void* var_80 = __visc__createNodeND(0, var_80_node);
-
- __visc__edge(var_79, var_80, 1, 0, 0, 0);
- __visc__edge(var_79, var_80, 1, 1, 1, 0);
-
- void* var_81 = __visc__createNodeND(0, var_81_node);
-
- __visc__edge(var_80, var_81, 1, 0, 0, 0);
- __visc__edge(var_80, var_81, 1, 1, 1, 0);
- __visc__bindIn(var_81, 284, 2, 0);
- __visc__bindIn(var_81, 285, 3, 0);
-
- void* var_82 = __visc__createNodeND(0, var_82_node);
-
- __visc__edge(var_81, var_82, 1, 0, 0, 0);
- __visc__edge(var_81, var_82, 1, 1, 1, 0);
- __visc__bindIn(var_82, 286, 2, 0);
- __visc__bindIn(var_82, 287, 3, 0);
- __visc__bindIn(var_82, 288, 4, 0);
- __visc__bindIn(var_82, 289, 5, 0);
- __visc__bindIn(var_82, 290, 6, 0);
- __visc__bindIn(var_82, 291, 7, 0);
- __visc__bindIn(var_82, 292, 8, 0);
- __visc__bindIn(var_82, 293, 9, 0);
-
- void* var_83 = __visc__createNodeND(0, var_83_node);
-
- __visc__edge(var_74, var_83, 1, 0, 0, 0);
- __visc__edge(var_74, var_83, 1, 1, 1, 0);
- __visc__edge(var_82, var_83, 1, 0, 2, 0);
- __visc__edge(var_82, var_83, 1, 1, 3, 0);
-
- void* var_84 = __visc__createNodeND(0, var_84_node);
-
- __visc__edge(var_83, var_84, 1, 0, 0, 0);
- __visc__edge(var_83, var_84, 1, 1, 1, 0);
- __visc__bindIn(var_84, 294, 2, 0);
- __visc__bindIn(var_84, 295, 3, 0);
-
- void* var_85 = __visc__createNodeND(0, var_85_node);
-
- __visc__edge(var_84, var_85, 1, 0, 0, 0);
- __visc__edge(var_84, var_85, 1, 1, 1, 0);
- __visc__bindIn(var_85, 296, 2, 0);
- __visc__bindIn(var_85, 297, 3, 0);
- __visc__bindIn(var_85, 298, 4, 0);
- __visc__bindIn(var_85, 299, 5, 0);
- __visc__bindIn(var_85, 300, 6, 0);
- __visc__bindIn(var_85, 301, 7, 0);
- __visc__bindIn(var_85, 302, 8, 0);
- __visc__bindIn(var_85, 303, 9, 0);
-
- void* var_86 = __visc__createNodeND(0, var_86_node);
-
- __visc__edge(var_85, var_86, 1, 0, 0, 0);
- __visc__edge(var_85, var_86, 1, 1, 1, 0);
-
- void* var_87 = __visc__createNodeND(0, var_87_node);
-
- __visc__edge(var_86, var_87, 1, 0, 0, 0);
- __visc__edge(var_86, var_87, 1, 1, 1, 0);
- __visc__bindIn(var_87, 304, 2, 0);
- __visc__bindIn(var_87, 305, 3, 0);
-
- void* var_88 = __visc__createNodeND(0, var_88_node);
-
- __visc__edge(var_87, var_88, 1, 0, 0, 0);
- __visc__edge(var_87, var_88, 1, 1, 1, 0);
- __visc__bindIn(var_88, 306, 2, 0);
- __visc__bindIn(var_88, 307, 3, 0);
- __visc__bindIn(var_88, 308, 4, 0);
- __visc__bindIn(var_88, 309, 5, 0);
- __visc__bindIn(var_88, 310, 6, 0);
- __visc__bindIn(var_88, 311, 7, 0);
- __visc__bindIn(var_88, 312, 8, 0);
- __visc__bindIn(var_88, 313, 9, 0);
-
- void* var_89 = __visc__createNodeND(0, var_89_node);
-
- __visc__edge(var_88, var_89, 1, 0, 0, 0);
- __visc__edge(var_88, var_89, 1, 1, 1, 0);
-
- void* var_90 = __visc__createNodeND(0, var_90_node);
-
- __visc__edge(var_89, var_90, 1, 0, 0, 0);
- __visc__edge(var_89, var_90, 1, 1, 1, 0);
- __visc__bindIn(var_90, 314, 2, 0);
- __visc__bindIn(var_90, 315, 3, 0);
-
- void* var_91 = __visc__createNodeND(0, var_91_node);
-
- __visc__edge(var_90, var_91, 1, 0, 0, 0);
- __visc__edge(var_90, var_91, 1, 1, 1, 0);
- __visc__bindIn(var_91, 316, 2, 0);
- __visc__bindIn(var_91, 317, 3, 0);
- __visc__bindIn(var_91, 318, 4, 0);
- __visc__bindIn(var_91, 319, 5, 0);
- __visc__bindIn(var_91, 320, 6, 0);
- __visc__bindIn(var_91, 321, 7, 0);
- __visc__bindIn(var_91, 322, 8, 0);
- __visc__bindIn(var_91, 323, 9, 0);
-
- void* var_92 = __visc__createNodeND(0, var_92_node);
-
- __visc__edge(var_91, var_92, 1, 0, 0, 0);
- __visc__edge(var_91, var_92, 1, 1, 1, 0);
- __visc__bindIn(var_92, 324, 2, 0);
- __visc__bindIn(var_92, 325, 3, 0);
-
- void* var_93 = __visc__createNodeND(0, var_93_node);
-
- __visc__edge(var_92, var_93, 1, 0, 0, 0);
- __visc__edge(var_92, var_93, 1, 1, 1, 0);
- __visc__bindIn(var_93, 326, 2, 0);
- __visc__bindIn(var_93, 327, 3, 0);
- __visc__bindIn(var_93, 328, 4, 0);
- __visc__bindIn(var_93, 329, 5, 0);
- __visc__bindIn(var_93, 330, 6, 0);
- __visc__bindIn(var_93, 331, 7, 0);
- __visc__bindIn(var_93, 332, 8, 0);
- __visc__bindIn(var_93, 333, 9, 0);
-
- void* var_94 = __visc__createNodeND(0, var_94_node);
-
- __visc__edge(var_93, var_94, 1, 0, 0, 0);
- __visc__edge(var_93, var_94, 1, 1, 1, 0);
-
- void* var_95 = __visc__createNodeND(0, var_95_node);
-
- __visc__edge(var_94, var_95, 1, 0, 0, 0);
- __visc__edge(var_94, var_95, 1, 1, 1, 0);
- __visc__bindIn(var_95, 334, 2, 0);
- __visc__bindIn(var_95, 335, 3, 0);
-
- void* var_96 = __visc__createNodeND(0, var_96_node);
-
- __visc__edge(var_95, var_96, 1, 0, 0, 0);
- __visc__edge(var_95, var_96, 1, 1, 1, 0);
- __visc__bindIn(var_96, 336, 2, 0);
- __visc__bindIn(var_96, 337, 3, 0);
- __visc__bindIn(var_96, 338, 4, 0);
- __visc__bindIn(var_96, 339, 5, 0);
- __visc__bindIn(var_96, 340, 6, 0);
- __visc__bindIn(var_96, 341, 7, 0);
- __visc__bindIn(var_96, 342, 8, 0);
- __visc__bindIn(var_96, 343, 9, 0);
-
- void* var_97 = __visc__createNodeND(0, var_97_node);
-
- __visc__edge(var_96, var_97, 1, 0, 0, 0);
- __visc__edge(var_96, var_97, 1, 1, 1, 0);
-
- void* var_98 = __visc__createNodeND(0, var_98_node);
-
- __visc__edge(var_97, var_98, 1, 0, 0, 0);
- __visc__edge(var_97, var_98, 1, 1, 1, 0);
- __visc__bindIn(var_98, 344, 2, 0);
- __visc__bindIn(var_98, 345, 3, 0);
-
- void* var_99 = __visc__createNodeND(0, var_99_node);
-
- __visc__edge(var_98, var_99, 1, 0, 0, 0);
- __visc__edge(var_98, var_99, 1, 1, 1, 0);
- __visc__bindIn(var_99, 346, 2, 0);
- __visc__bindIn(var_99, 347, 3, 0);
- __visc__bindIn(var_99, 348, 4, 0);
- __visc__bindIn(var_99, 349, 5, 0);
- __visc__bindIn(var_99, 350, 6, 0);
- __visc__bindIn(var_99, 351, 7, 0);
- __visc__bindIn(var_99, 352, 8, 0);
- __visc__bindIn(var_99, 353, 9, 0);
-
- void* var_100 = __visc__createNodeND(0, var_100_node);
-
- __visc__edge(var_91, var_100, 1, 0, 0, 0);
- __visc__edge(var_91, var_100, 1, 1, 1, 0);
- __visc__edge(var_99, var_100, 1, 0, 2, 0);
- __visc__edge(var_99, var_100, 1, 1, 3, 0);
-
- void* var_101 = __visc__createNodeND(0, var_101_node);
-
- __visc__edge(var_100, var_101, 1, 0, 0, 0);
- __visc__edge(var_100, var_101, 1, 1, 1, 0);
- __visc__bindIn(var_101, 354, 2, 0);
- __visc__bindIn(var_101, 355, 3, 0);
-
- void* var_102 = __visc__createNodeND(0, var_102_node);
-
- __visc__edge(var_101, var_102, 1, 0, 0, 0);
- __visc__edge(var_101, var_102, 1, 1, 1, 0);
- __visc__bindIn(var_102, 356, 2, 0);
- __visc__bindIn(var_102, 357, 3, 0);
- __visc__bindIn(var_102, 358, 4, 0);
- __visc__bindIn(var_102, 359, 5, 0);
- __visc__bindIn(var_102, 360, 6, 0);
- __visc__bindIn(var_102, 361, 7, 0);
- __visc__bindIn(var_102, 362, 8, 0);
- __visc__bindIn(var_102, 363, 9, 0);
-
- void* var_103 = __visc__createNodeND(0, var_103_node);
-
- __visc__edge(var_102, var_103, 1, 0, 0, 0);
- __visc__edge(var_102, var_103, 1, 1, 1, 0);
-
- void* var_104 = __visc__createNodeND(0, var_104_node);
-
- __visc__edge(var_103, var_104, 1, 0, 0, 0);
- __visc__edge(var_103, var_104, 1, 1, 1, 0);
- __visc__bindIn(var_104, 364, 2, 0);
- __visc__bindIn(var_104, 365, 3, 0);
-
- void* var_105 = __visc__createNodeND(0, var_105_node);
-
- __visc__edge(var_104, var_105, 1, 0, 0, 0);
- __visc__edge(var_104, var_105, 1, 1, 1, 0);
- __visc__bindIn(var_105, 366, 2, 0);
- __visc__bindIn(var_105, 367, 3, 0);
- __visc__bindIn(var_105, 368, 4, 0);
- __visc__bindIn(var_105, 369, 5, 0);
- __visc__bindIn(var_105, 370, 6, 0);
- __visc__bindIn(var_105, 371, 7, 0);
- __visc__bindIn(var_105, 372, 8, 0);
- __visc__bindIn(var_105, 373, 9, 0);
-
- void* var_106 = __visc__createNodeND(0, var_106_node);
-
- __visc__edge(var_105, var_106, 1, 0, 0, 0);
- __visc__edge(var_105, var_106, 1, 1, 1, 0);
-
- void* var_107 = __visc__createNodeND(0, var_107_node);
-
- __visc__edge(var_106, var_107, 1, 0, 0, 0);
- __visc__edge(var_106, var_107, 1, 1, 1, 0);
- __visc__bindIn(var_107, 374, 2, 0);
- __visc__bindIn(var_107, 375, 3, 0);
-
- void* var_108 = __visc__createNodeND(0, var_108_node);
-
- __visc__edge(var_107, var_108, 1, 0, 0, 0);
- __visc__edge(var_107, var_108, 1, 1, 1, 0);
- __visc__bindIn(var_108, 376, 2, 0);
- __visc__bindIn(var_108, 377, 3, 0);
- __visc__bindIn(var_108, 378, 4, 0);
- __visc__bindIn(var_108, 379, 5, 0);
- __visc__bindIn(var_108, 380, 6, 0);
- __visc__bindIn(var_108, 381, 7, 0);
- __visc__bindIn(var_108, 382, 8, 0);
- __visc__bindIn(var_108, 383, 9, 0);
-
- void* var_109 = __visc__createNodeND(0, var_109_node);
-
- __visc__edge(var_100, var_109, 1, 0, 0, 0);
- __visc__edge(var_100, var_109, 1, 1, 1, 0);
- __visc__edge(var_108, var_109, 1, 0, 2, 0);
- __visc__edge(var_108, var_109, 1, 1, 3, 0);
-
- void* var_110 = __visc__createNodeND(0, var_110_node);
-
- __visc__edge(var_109, var_110, 1, 0, 0, 0);
- __visc__edge(var_109, var_110, 1, 1, 1, 0);
- __visc__bindIn(var_110, 384, 2, 0);
- __visc__bindIn(var_110, 385, 3, 0);
-
- void* var_111 = __visc__createNodeND(0, var_111_node);
-
- __visc__edge(var_110, var_111, 1, 0, 0, 0);
- __visc__edge(var_110, var_111, 1, 1, 1, 0);
- __visc__bindIn(var_111, 386, 2, 0);
- __visc__bindIn(var_111, 387, 3, 0);
- __visc__bindIn(var_111, 388, 4, 0);
- __visc__bindIn(var_111, 389, 5, 0);
- __visc__bindIn(var_111, 390, 6, 0);
- __visc__bindIn(var_111, 391, 7, 0);
- __visc__bindIn(var_111, 392, 8, 0);
- __visc__bindIn(var_111, 393, 9, 0);
-
- void* var_112 = __visc__createNodeND(0, var_112_node);
-
- __visc__edge(var_111, var_112, 1, 0, 0, 0);
- __visc__edge(var_111, var_112, 1, 1, 1, 0);
-
- void* var_113 = __visc__createNodeND(0, var_113_node);
-
- __visc__edge(var_112, var_113, 1, 0, 0, 0);
- __visc__edge(var_112, var_113, 1, 1, 1, 0);
- __visc__bindIn(var_113, 394, 2, 0);
- __visc__bindIn(var_113, 395, 3, 0);
-
- void* var_114 = __visc__createNodeND(0, var_114_node);
-
- __visc__edge(var_113, var_114, 1, 0, 0, 0);
- __visc__edge(var_113, var_114, 1, 1, 1, 0);
- __visc__bindIn(var_114, 396, 2, 0);
- __visc__bindIn(var_114, 397, 3, 0);
- __visc__bindIn(var_114, 398, 4, 0);
- __visc__bindIn(var_114, 399, 5, 0);
- __visc__bindIn(var_114, 400, 6, 0);
- __visc__bindIn(var_114, 401, 7, 0);
- __visc__bindIn(var_114, 402, 8, 0);
- __visc__bindIn(var_114, 403, 9, 0);
-
- void* var_115 = __visc__createNodeND(0, var_115_node);
-
- __visc__edge(var_114, var_115, 1, 0, 0, 0);
- __visc__edge(var_114, var_115, 1, 1, 1, 0);
-
- void* var_116 = __visc__createNodeND(0, var_116_node);
-
- __visc__edge(var_115, var_116, 1, 0, 0, 0);
- __visc__edge(var_115, var_116, 1, 1, 1, 0);
- __visc__bindIn(var_116, 404, 2, 0);
- __visc__bindIn(var_116, 405, 3, 0);
-
- void* var_117 = __visc__createNodeND(0, var_117_node);
-
- __visc__edge(var_116, var_117, 1, 0, 0, 0);
- __visc__edge(var_116, var_117, 1, 1, 1, 0);
- __visc__bindIn(var_117, 406, 2, 0);
- __visc__bindIn(var_117, 407, 3, 0);
- __visc__bindIn(var_117, 408, 4, 0);
- __visc__bindIn(var_117, 409, 5, 0);
- __visc__bindIn(var_117, 410, 6, 0);
- __visc__bindIn(var_117, 411, 7, 0);
- __visc__bindIn(var_117, 412, 8, 0);
- __visc__bindIn(var_117, 413, 9, 0);
-
- void* var_118 = __visc__createNodeND(0, var_118_node);
-
- __visc__edge(var_117, var_118, 1, 0, 0, 0);
- __visc__edge(var_117, var_118, 1, 1, 1, 0);
- __visc__bindIn(var_118, 414, 2, 0);
- __visc__bindIn(var_118, 415, 3, 0);
-
- void* var_119 = __visc__createNodeND(0, var_119_node);
-
- __visc__edge(var_118, var_119, 1, 0, 0, 0);
- __visc__edge(var_118, var_119, 1, 1, 1, 0);
- __visc__bindIn(var_119, 416, 2, 0);
- __visc__bindIn(var_119, 417, 3, 0);
- __visc__bindIn(var_119, 418, 4, 0);
- __visc__bindIn(var_119, 419, 5, 0);
- __visc__bindIn(var_119, 420, 6, 0);
- __visc__bindIn(var_119, 421, 7, 0);
- __visc__bindIn(var_119, 422, 8, 0);
- __visc__bindIn(var_119, 423, 9, 0);
-
- void* var_120 = __visc__createNodeND(0, var_120_node);
-
- __visc__edge(var_119, var_120, 1, 0, 0, 0);
- __visc__edge(var_119, var_120, 1, 1, 1, 0);
-
- void* var_121 = __visc__createNodeND(0, var_121_node);
-
- __visc__edge(var_120, var_121, 1, 0, 0, 0);
- __visc__edge(var_120, var_121, 1, 1, 1, 0);
- __visc__bindIn(var_121, 424, 2, 0);
- __visc__bindIn(var_121, 425, 3, 0);
-
- void* var_122 = __visc__createNodeND(0, var_122_node);
-
- __visc__edge(var_121, var_122, 1, 0, 0, 0);
- __visc__edge(var_121, var_122, 1, 1, 1, 0);
- __visc__bindIn(var_122, 426, 2, 0);
- __visc__bindIn(var_122, 427, 3, 0);
- __visc__bindIn(var_122, 428, 4, 0);
- __visc__bindIn(var_122, 429, 5, 0);
- __visc__bindIn(var_122, 430, 6, 0);
- __visc__bindIn(var_122, 431, 7, 0);
- __visc__bindIn(var_122, 432, 8, 0);
- __visc__bindIn(var_122, 433, 9, 0);
-
- void* var_123 = __visc__createNodeND(0, var_123_node);
-
- __visc__edge(var_122, var_123, 1, 0, 0, 0);
- __visc__edge(var_122, var_123, 1, 1, 1, 0);
-
- void* var_124 = __visc__createNodeND(0, var_124_node);
-
- __visc__edge(var_123, var_124, 1, 0, 0, 0);
- __visc__edge(var_123, var_124, 1, 1, 1, 0);
- __visc__bindIn(var_124, 434, 2, 0);
- __visc__bindIn(var_124, 435, 3, 0);
-
- void* var_125 = __visc__createNodeND(0, var_125_node);
-
- __visc__edge(var_124, var_125, 1, 0, 0, 0);
- __visc__edge(var_124, var_125, 1, 1, 1, 0);
- __visc__bindIn(var_125, 436, 2, 0);
- __visc__bindIn(var_125, 437, 3, 0);
- __visc__bindIn(var_125, 438, 4, 0);
- __visc__bindIn(var_125, 439, 5, 0);
- __visc__bindIn(var_125, 440, 6, 0);
- __visc__bindIn(var_125, 441, 7, 0);
- __visc__bindIn(var_125, 442, 8, 0);
- __visc__bindIn(var_125, 443, 9, 0);
-
- void* var_126 = __visc__createNodeND(0, var_126_node);
-
- __visc__edge(var_117, var_126, 1, 0, 0, 0);
- __visc__edge(var_117, var_126, 1, 1, 1, 0);
- __visc__edge(var_125, var_126, 1, 0, 2, 0);
- __visc__edge(var_125, var_126, 1, 1, 3, 0);
-
- void* var_127 = __visc__createNodeND(0, var_127_node);
-
- __visc__edge(var_126, var_127, 1, 0, 0, 0);
- __visc__edge(var_126, var_127, 1, 1, 1, 0);
- __visc__bindIn(var_127, 444, 2, 0);
- __visc__bindIn(var_127, 445, 3, 0);
-
- void* var_128 = __visc__createNodeND(0, var_128_node);
-
- __visc__edge(var_127, var_128, 1, 0, 0, 0);
- __visc__edge(var_127, var_128, 1, 1, 1, 0);
- __visc__bindIn(var_128, 446, 2, 0);
- __visc__bindIn(var_128, 447, 3, 0);
- __visc__bindIn(var_128, 448, 4, 0);
- __visc__bindIn(var_128, 449, 5, 0);
- __visc__bindIn(var_128, 450, 6, 0);
- __visc__bindIn(var_128, 451, 7, 0);
- __visc__bindIn(var_128, 452, 8, 0);
- __visc__bindIn(var_128, 453, 9, 0);
-
- void* var_129 = __visc__createNodeND(0, var_129_node);
-
- __visc__edge(var_128, var_129, 1, 0, 0, 0);
- __visc__edge(var_128, var_129, 1, 1, 1, 0);
-
- void* var_130 = __visc__createNodeND(0, var_130_node);
-
- __visc__edge(var_129, var_130, 1, 0, 0, 0);
- __visc__edge(var_129, var_130, 1, 1, 1, 0);
- __visc__bindIn(var_130, 454, 2, 0);
- __visc__bindIn(var_130, 455, 3, 0);
-
- void* var_131 = __visc__createNodeND(0, var_131_node);
-
- __visc__edge(var_130, var_131, 1, 0, 0, 0);
- __visc__edge(var_130, var_131, 1, 1, 1, 0);
- __visc__bindIn(var_131, 456, 2, 0);
- __visc__bindIn(var_131, 457, 3, 0);
- __visc__bindIn(var_131, 458, 4, 0);
- __visc__bindIn(var_131, 459, 5, 0);
- __visc__bindIn(var_131, 460, 6, 0);
- __visc__bindIn(var_131, 461, 7, 0);
- __visc__bindIn(var_131, 462, 8, 0);
- __visc__bindIn(var_131, 463, 9, 0);
-
- void* var_132 = __visc__createNodeND(0, var_132_node);
-
- __visc__edge(var_131, var_132, 1, 0, 0, 0);
- __visc__edge(var_131, var_132, 1, 1, 1, 0);
-
- void* var_133 = __visc__createNodeND(0, var_133_node);
-
- __visc__edge(var_132, var_133, 1, 0, 0, 0);
- __visc__edge(var_132, var_133, 1, 1, 1, 0);
- __visc__bindIn(var_133, 464, 2, 0);
- __visc__bindIn(var_133, 465, 3, 0);
-
- void* var_134 = __visc__createNodeND(0, var_134_node);
-
- __visc__edge(var_133, var_134, 1, 0, 0, 0);
- __visc__edge(var_133, var_134, 1, 1, 1, 0);
- __visc__bindIn(var_134, 466, 2, 0);
- __visc__bindIn(var_134, 467, 3, 0);
- __visc__bindIn(var_134, 468, 4, 0);
- __visc__bindIn(var_134, 469, 5, 0);
- __visc__bindIn(var_134, 470, 6, 0);
- __visc__bindIn(var_134, 471, 7, 0);
- __visc__bindIn(var_134, 472, 8, 0);
- __visc__bindIn(var_134, 473, 9, 0);
-
- void* var_135 = __visc__createNodeND(0, var_135_node);
-
- __visc__edge(var_126, var_135, 1, 0, 0, 0);
- __visc__edge(var_126, var_135, 1, 1, 1, 0);
- __visc__edge(var_134, var_135, 1, 0, 2, 0);
- __visc__edge(var_134, var_135, 1, 1, 3, 0);
-
- void* var_136 = __visc__createNodeND(0, var_136_node);
-
- __visc__edge(var_135, var_136, 1, 0, 0, 0);
- __visc__edge(var_135, var_136, 1, 1, 1, 0);
- __visc__bindIn(var_136, 474, 2, 0);
- __visc__bindIn(var_136, 475, 3, 0);
-
- void* var_137 = __visc__createNodeND(0, var_137_node);
-
- __visc__edge(var_136, var_137, 1, 0, 0, 0);
- __visc__edge(var_136, var_137, 1, 1, 1, 0);
- __visc__bindIn(var_137, 476, 2, 0);
- __visc__bindIn(var_137, 477, 3, 0);
- __visc__bindIn(var_137, 478, 4, 0);
- __visc__bindIn(var_137, 479, 5, 0);
- __visc__bindIn(var_137, 480, 6, 0);
- __visc__bindIn(var_137, 481, 7, 0);
- __visc__bindIn(var_137, 482, 8, 0);
- __visc__bindIn(var_137, 483, 9, 0);
-
- void* var_138 = __visc__createNodeND(0, var_138_node);
-
- __visc__edge(var_137, var_138, 1, 0, 0, 0);
- __visc__edge(var_137, var_138, 1, 1, 1, 0);
-
- void* var_139 = __visc__createNodeND(0, var_139_node);
-
- __visc__edge(var_138, var_139, 1, 0, 0, 0);
- __visc__edge(var_138, var_139, 1, 1, 1, 0);
- __visc__bindIn(var_139, 484, 2, 0);
- __visc__bindIn(var_139, 485, 3, 0);
-
- void* var_140 = __visc__createNodeND(0, var_140_node);
-
- __visc__edge(var_139, var_140, 1, 0, 0, 0);
- __visc__edge(var_139, var_140, 1, 1, 1, 0);
- __visc__bindIn(var_140, 486, 2, 0);
- __visc__bindIn(var_140, 487, 3, 0);
- __visc__bindIn(var_140, 488, 4, 0);
- __visc__bindIn(var_140, 489, 5, 0);
- __visc__bindIn(var_140, 490, 6, 0);
- __visc__bindIn(var_140, 491, 7, 0);
- __visc__bindIn(var_140, 492, 8, 0);
- __visc__bindIn(var_140, 493, 9, 0);
-
- void* var_141 = __visc__createNodeND(0, var_141_node);
-
- __visc__edge(var_140, var_141, 1, 0, 0, 0);
- __visc__edge(var_140, var_141, 1, 1, 1, 0);
-
- void* var_142 = __visc__createNodeND(0, var_142_node);
-
- __visc__edge(var_141, var_142, 1, 0, 0, 0);
- __visc__edge(var_141, var_142, 1, 1, 1, 0);
- __visc__bindIn(var_142, 494, 2, 0);
- __visc__bindIn(var_142, 495, 3, 0);
-
- void* var_143 = __visc__createNodeND(0, var_143_node);
-
- __visc__edge(var_142, var_143, 1, 0, 0, 0);
- __visc__edge(var_142, var_143, 1, 1, 1, 0);
- __visc__bindIn(var_143, 496, 2, 0);
- __visc__bindIn(var_143, 497, 3, 0);
- __visc__bindIn(var_143, 498, 4, 0);
- __visc__bindIn(var_143, 499, 5, 0);
- __visc__bindIn(var_143, 500, 6, 0);
- __visc__bindIn(var_143, 501, 7, 0);
- __visc__bindIn(var_143, 502, 8, 0);
- __visc__bindIn(var_143, 503, 9, 0);
-
- void* var_144 = __visc__createNodeND(0, var_144_node);
-
- __visc__edge(var_143, var_144, 1, 0, 0, 0);
- __visc__edge(var_143, var_144, 1, 1, 1, 0);
- __visc__bindIn(var_144, 504, 2, 0);
- __visc__bindIn(var_144, 505, 3, 0);
-
- void* var_145 = __visc__createNodeND(0, var_145_node);
-
- __visc__edge(var_144, var_145, 1, 0, 0, 0);
- __visc__edge(var_144, var_145, 1, 1, 1, 0);
- __visc__bindIn(var_145, 506, 2, 0);
- __visc__bindIn(var_145, 507, 3, 0);
- __visc__bindIn(var_145, 508, 4, 0);
- __visc__bindIn(var_145, 509, 5, 0);
- __visc__bindIn(var_145, 510, 6, 0);
- __visc__bindIn(var_145, 511, 7, 0);
- __visc__bindIn(var_145, 512, 8, 0);
- __visc__bindIn(var_145, 513, 9, 0);
-
- void* var_146 = __visc__createNodeND(0, var_146_node);
-
- __visc__edge(var_145, var_146, 1, 0, 0, 0);
- __visc__edge(var_145, var_146, 1, 1, 1, 0);
-
- void* var_147 = __visc__createNodeND(0, var_147_node);
-
- __visc__edge(var_146, var_147, 1, 0, 0, 0);
- __visc__edge(var_146, var_147, 1, 1, 1, 0);
-
- void* var_148 = __visc__createNodeND(0, var_148_node);
-
- __visc__edge(var_147, var_148, 1, 0, 0, 0);
- __visc__edge(var_147, var_148, 1, 1, 1, 0);
- __visc__bindIn(var_148, 514, 2, 0);
- __visc__bindIn(var_148, 515, 3, 0);
-
- void* var_149 = __visc__createNodeND(0, var_149_node);
-
- __visc__edge(var_148, var_149, 1, 0, 0, 0);
- __visc__edge(var_148, var_149, 1, 1, 1, 0);
- __visc__bindIn(var_149, 516, 2, 0);
- __visc__bindIn(var_149, 517, 3, 0);
-
- void* var_150 = __visc__createNodeND(0, var_150_node);
-
- __visc__edge(var_149, var_150, 1, 0, 0, 0);
- __visc__edge(var_149, var_150, 1, 1, 1, 0);
-
- __visc__bindOut(var_150, 0, 0, 0);
- __visc__bindOut(var_150, 1, 1, 0);
-
-}
-
-struct ret_t {
- void* tensor;
- size_t bytes;
-};
-
-typedef struct __attribute__((__packed__)) {
- void* input;
- size_t input_bytes;
- void* conv2d_1_w;
- size_t conv2d_1_w_bytes;
- void* depthwise_conv2d_1_w;
- size_t depthwise_conv2d_1_w_bytes;
- void* batch_normalization_1_gamma;
- size_t batch_normalization_1_gamma_bytes;
- void* batch_normalization_1_beta;
- size_t batch_normalization_1_beta_bytes;
- void* batch_normalization_1_mean;
- size_t batch_normalization_1_mean_bytes;
- void* batch_normalization_1_variance;
- size_t batch_normalization_1_variance_bytes;
- void* conv2d_2_w;
- size_t conv2d_2_w_bytes;
- void* batch_normalization_2_gamma;
- size_t batch_normalization_2_gamma_bytes;
- void* batch_normalization_2_beta;
- size_t batch_normalization_2_beta_bytes;
- void* batch_normalization_2_mean;
- size_t batch_normalization_2_mean_bytes;
- void* batch_normalization_2_variance;
- size_t batch_normalization_2_variance_bytes;
- void* conv2d_3_w;
- size_t conv2d_3_w_bytes;
- void* batch_normalization_3_gamma;
- size_t batch_normalization_3_gamma_bytes;
- void* batch_normalization_3_beta;
- size_t batch_normalization_3_beta_bytes;
- void* batch_normalization_3_mean;
- size_t batch_normalization_3_mean_bytes;
- void* batch_normalization_3_variance;
- size_t batch_normalization_3_variance_bytes;
- void* depthwise_conv2d_2_w;
- size_t depthwise_conv2d_2_w_bytes;
- void* batch_normalization_4_gamma;
- size_t batch_normalization_4_gamma_bytes;
- void* batch_normalization_4_beta;
- size_t batch_normalization_4_beta_bytes;
- void* batch_normalization_4_mean;
- size_t batch_normalization_4_mean_bytes;
- void* batch_normalization_4_variance;
- size_t batch_normalization_4_variance_bytes;
- void* conv2d_4_w;
- size_t conv2d_4_w_bytes;
- void* batch_normalization_5_gamma;
- size_t batch_normalization_5_gamma_bytes;
- void* batch_normalization_5_beta;
- size_t batch_normalization_5_beta_bytes;
- void* batch_normalization_5_mean;
- size_t batch_normalization_5_mean_bytes;
- void* batch_normalization_5_variance;
- size_t batch_normalization_5_variance_bytes;
- void* conv2d_5_w;
- size_t conv2d_5_w_bytes;
- void* batch_normalization_6_gamma;
- size_t batch_normalization_6_gamma_bytes;
- void* batch_normalization_6_beta;
- size_t batch_normalization_6_beta_bytes;
- void* batch_normalization_6_mean;
- size_t batch_normalization_6_mean_bytes;
- void* batch_normalization_6_variance;
- size_t batch_normalization_6_variance_bytes;
- void* depthwise_conv2d_3_w;
- size_t depthwise_conv2d_3_w_bytes;
- void* batch_normalization_7_gamma;
- size_t batch_normalization_7_gamma_bytes;
- void* batch_normalization_7_beta;
- size_t batch_normalization_7_beta_bytes;
- void* batch_normalization_7_mean;
- size_t batch_normalization_7_mean_bytes;
- void* batch_normalization_7_variance;
- size_t batch_normalization_7_variance_bytes;
- void* conv2d_6_w;
- size_t conv2d_6_w_bytes;
- void* batch_normalization_8_gamma;
- size_t batch_normalization_8_gamma_bytes;
- void* batch_normalization_8_beta;
- size_t batch_normalization_8_beta_bytes;
- void* batch_normalization_8_mean;
- size_t batch_normalization_8_mean_bytes;
- void* batch_normalization_8_variance;
- size_t batch_normalization_8_variance_bytes;
- void* conv2d_7_w;
- size_t conv2d_7_w_bytes;
- void* batch_normalization_9_gamma;
- size_t batch_normalization_9_gamma_bytes;
- void* batch_normalization_9_beta;
- size_t batch_normalization_9_beta_bytes;
- void* batch_normalization_9_mean;
- size_t batch_normalization_9_mean_bytes;
- void* batch_normalization_9_variance;
- size_t batch_normalization_9_variance_bytes;
- void* depthwise_conv2d_4_w;
- size_t depthwise_conv2d_4_w_bytes;
- void* batch_normalization_10_gamma;
- size_t batch_normalization_10_gamma_bytes;
- void* batch_normalization_10_beta;
- size_t batch_normalization_10_beta_bytes;
- void* batch_normalization_10_mean;
- size_t batch_normalization_10_mean_bytes;
- void* batch_normalization_10_variance;
- size_t batch_normalization_10_variance_bytes;
- void* conv2d_8_w;
- size_t conv2d_8_w_bytes;
- void* batch_normalization_11_gamma;
- size_t batch_normalization_11_gamma_bytes;
- void* batch_normalization_11_beta;
- size_t batch_normalization_11_beta_bytes;
- void* batch_normalization_11_mean;
- size_t batch_normalization_11_mean_bytes;
- void* batch_normalization_11_variance;
- size_t batch_normalization_11_variance_bytes;
- void* conv2d_9_w;
- size_t conv2d_9_w_bytes;
- void* batch_normalization_12_gamma;
- size_t batch_normalization_12_gamma_bytes;
- void* batch_normalization_12_beta;
- size_t batch_normalization_12_beta_bytes;
- void* batch_normalization_12_mean;
- size_t batch_normalization_12_mean_bytes;
- void* batch_normalization_12_variance;
- size_t batch_normalization_12_variance_bytes;
- void* depthwise_conv2d_5_w;
- size_t depthwise_conv2d_5_w_bytes;
- void* batch_normalization_13_gamma;
- size_t batch_normalization_13_gamma_bytes;
- void* batch_normalization_13_beta;
- size_t batch_normalization_13_beta_bytes;
- void* batch_normalization_13_mean;
- size_t batch_normalization_13_mean_bytes;
- void* batch_normalization_13_variance;
- size_t batch_normalization_13_variance_bytes;
- void* conv2d_10_w;
- size_t conv2d_10_w_bytes;
- void* batch_normalization_14_gamma;
- size_t batch_normalization_14_gamma_bytes;
- void* batch_normalization_14_beta;
- size_t batch_normalization_14_beta_bytes;
- void* batch_normalization_14_mean;
- size_t batch_normalization_14_mean_bytes;
- void* batch_normalization_14_variance;
- size_t batch_normalization_14_variance_bytes;
- void* conv2d_11_w;
- size_t conv2d_11_w_bytes;
- void* batch_normalization_15_gamma;
- size_t batch_normalization_15_gamma_bytes;
- void* batch_normalization_15_beta;
- size_t batch_normalization_15_beta_bytes;
- void* batch_normalization_15_mean;
- size_t batch_normalization_15_mean_bytes;
- void* batch_normalization_15_variance;
- size_t batch_normalization_15_variance_bytes;
- void* depthwise_conv2d_6_w;
- size_t depthwise_conv2d_6_w_bytes;
- void* batch_normalization_16_gamma;
- size_t batch_normalization_16_gamma_bytes;
- void* batch_normalization_16_beta;
- size_t batch_normalization_16_beta_bytes;
- void* batch_normalization_16_mean;
- size_t batch_normalization_16_mean_bytes;
- void* batch_normalization_16_variance;
- size_t batch_normalization_16_variance_bytes;
- void* conv2d_12_w;
- size_t conv2d_12_w_bytes;
- void* batch_normalization_17_gamma;
- size_t batch_normalization_17_gamma_bytes;
- void* batch_normalization_17_beta;
- size_t batch_normalization_17_beta_bytes;
- void* batch_normalization_17_mean;
- size_t batch_normalization_17_mean_bytes;
- void* batch_normalization_17_variance;
- size_t batch_normalization_17_variance_bytes;
- void* conv2d_13_w;
- size_t conv2d_13_w_bytes;
- void* batch_normalization_18_gamma;
- size_t batch_normalization_18_gamma_bytes;
- void* batch_normalization_18_beta;
- size_t batch_normalization_18_beta_bytes;
- void* batch_normalization_18_mean;
- size_t batch_normalization_18_mean_bytes;
- void* batch_normalization_18_variance;
- size_t batch_normalization_18_variance_bytes;
- void* depthwise_conv2d_7_w;
- size_t depthwise_conv2d_7_w_bytes;
- void* batch_normalization_19_gamma;
- size_t batch_normalization_19_gamma_bytes;
- void* batch_normalization_19_beta;
- size_t batch_normalization_19_beta_bytes;
- void* batch_normalization_19_mean;
- size_t batch_normalization_19_mean_bytes;
- void* batch_normalization_19_variance;
- size_t batch_normalization_19_variance_bytes;
- void* conv2d_14_w;
- size_t conv2d_14_w_bytes;
- void* batch_normalization_20_gamma;
- size_t batch_normalization_20_gamma_bytes;
- void* batch_normalization_20_beta;
- size_t batch_normalization_20_beta_bytes;
- void* batch_normalization_20_mean;
- size_t batch_normalization_20_mean_bytes;
- void* batch_normalization_20_variance;
- size_t batch_normalization_20_variance_bytes;
- void* conv2d_15_w;
- size_t conv2d_15_w_bytes;
- void* batch_normalization_21_gamma;
- size_t batch_normalization_21_gamma_bytes;
- void* batch_normalization_21_beta;
- size_t batch_normalization_21_beta_bytes;
- void* batch_normalization_21_mean;
- size_t batch_normalization_21_mean_bytes;
- void* batch_normalization_21_variance;
- size_t batch_normalization_21_variance_bytes;
- void* depthwise_conv2d_8_w;
- size_t depthwise_conv2d_8_w_bytes;
- void* batch_normalization_22_gamma;
- size_t batch_normalization_22_gamma_bytes;
- void* batch_normalization_22_beta;
- size_t batch_normalization_22_beta_bytes;
- void* batch_normalization_22_mean;
- size_t batch_normalization_22_mean_bytes;
- void* batch_normalization_22_variance;
- size_t batch_normalization_22_variance_bytes;
- void* conv2d_16_w;
- size_t conv2d_16_w_bytes;
- void* batch_normalization_23_gamma;
- size_t batch_normalization_23_gamma_bytes;
- void* batch_normalization_23_beta;
- size_t batch_normalization_23_beta_bytes;
- void* batch_normalization_23_mean;
- size_t batch_normalization_23_mean_bytes;
- void* batch_normalization_23_variance;
- size_t batch_normalization_23_variance_bytes;
- void* conv2d_17_w;
- size_t conv2d_17_w_bytes;
- void* batch_normalization_24_gamma;
- size_t batch_normalization_24_gamma_bytes;
- void* batch_normalization_24_beta;
- size_t batch_normalization_24_beta_bytes;
- void* batch_normalization_24_mean;
- size_t batch_normalization_24_mean_bytes;
- void* batch_normalization_24_variance;
- size_t batch_normalization_24_variance_bytes;
- void* depthwise_conv2d_9_w;
- size_t depthwise_conv2d_9_w_bytes;
- void* batch_normalization_25_gamma;
- size_t batch_normalization_25_gamma_bytes;
- void* batch_normalization_25_beta;
- size_t batch_normalization_25_beta_bytes;
- void* batch_normalization_25_mean;
- size_t batch_normalization_25_mean_bytes;
- void* batch_normalization_25_variance;
- size_t batch_normalization_25_variance_bytes;
- void* conv2d_18_w;
- size_t conv2d_18_w_bytes;
- void* batch_normalization_26_gamma;
- size_t batch_normalization_26_gamma_bytes;
- void* batch_normalization_26_beta;
- size_t batch_normalization_26_beta_bytes;
- void* batch_normalization_26_mean;
- size_t batch_normalization_26_mean_bytes;
- void* batch_normalization_26_variance;
- size_t batch_normalization_26_variance_bytes;
- void* conv2d_19_w;
- size_t conv2d_19_w_bytes;
- void* batch_normalization_27_gamma;
- size_t batch_normalization_27_gamma_bytes;
- void* batch_normalization_27_beta;
- size_t batch_normalization_27_beta_bytes;
- void* batch_normalization_27_mean;
- size_t batch_normalization_27_mean_bytes;
- void* batch_normalization_27_variance;
- size_t batch_normalization_27_variance_bytes;
- void* depthwise_conv2d_10_w;
- size_t depthwise_conv2d_10_w_bytes;
- void* batch_normalization_28_gamma;
- size_t batch_normalization_28_gamma_bytes;
- void* batch_normalization_28_beta;
- size_t batch_normalization_28_beta_bytes;
- void* batch_normalization_28_mean;
- size_t batch_normalization_28_mean_bytes;
- void* batch_normalization_28_variance;
- size_t batch_normalization_28_variance_bytes;
- void* conv2d_20_w;
- size_t conv2d_20_w_bytes;
- void* batch_normalization_29_gamma;
- size_t batch_normalization_29_gamma_bytes;
- void* batch_normalization_29_beta;
- size_t batch_normalization_29_beta_bytes;
- void* batch_normalization_29_mean;
- size_t batch_normalization_29_mean_bytes;
- void* batch_normalization_29_variance;
- size_t batch_normalization_29_variance_bytes;
- void* conv2d_21_w;
- size_t conv2d_21_w_bytes;
- void* batch_normalization_30_gamma;
- size_t batch_normalization_30_gamma_bytes;
- void* batch_normalization_30_beta;
- size_t batch_normalization_30_beta_bytes;
- void* batch_normalization_30_mean;
- size_t batch_normalization_30_mean_bytes;
- void* batch_normalization_30_variance;
- size_t batch_normalization_30_variance_bytes;
- void* depthwise_conv2d_11_w;
- size_t depthwise_conv2d_11_w_bytes;
- void* batch_normalization_31_gamma;
- size_t batch_normalization_31_gamma_bytes;
- void* batch_normalization_31_beta;
- size_t batch_normalization_31_beta_bytes;
- void* batch_normalization_31_mean;
- size_t batch_normalization_31_mean_bytes;
- void* batch_normalization_31_variance;
- size_t batch_normalization_31_variance_bytes;
- void* conv2d_22_w;
- size_t conv2d_22_w_bytes;
- void* batch_normalization_32_gamma;
- size_t batch_normalization_32_gamma_bytes;
- void* batch_normalization_32_beta;
- size_t batch_normalization_32_beta_bytes;
- void* batch_normalization_32_mean;
- size_t batch_normalization_32_mean_bytes;
- void* batch_normalization_32_variance;
- size_t batch_normalization_32_variance_bytes;
- void* conv2d_23_w;
- size_t conv2d_23_w_bytes;
- void* batch_normalization_33_gamma;
- size_t batch_normalization_33_gamma_bytes;
- void* batch_normalization_33_beta;
- size_t batch_normalization_33_beta_bytes;
- void* batch_normalization_33_mean;
- size_t batch_normalization_33_mean_bytes;
- void* batch_normalization_33_variance;
- size_t batch_normalization_33_variance_bytes;
- void* depthwise_conv2d_12_w;
- size_t depthwise_conv2d_12_w_bytes;
- void* batch_normalization_34_gamma;
- size_t batch_normalization_34_gamma_bytes;
- void* batch_normalization_34_beta;
- size_t batch_normalization_34_beta_bytes;
- void* batch_normalization_34_mean;
- size_t batch_normalization_34_mean_bytes;
- void* batch_normalization_34_variance;
- size_t batch_normalization_34_variance_bytes;
- void* conv2d_24_w;
- size_t conv2d_24_w_bytes;
- void* batch_normalization_35_gamma;
- size_t batch_normalization_35_gamma_bytes;
- void* batch_normalization_35_beta;
- size_t batch_normalization_35_beta_bytes;
- void* batch_normalization_35_mean;
- size_t batch_normalization_35_mean_bytes;
- void* batch_normalization_35_variance;
- size_t batch_normalization_35_variance_bytes;
- void* conv2d_25_w;
- size_t conv2d_25_w_bytes;
- void* batch_normalization_36_gamma;
- size_t batch_normalization_36_gamma_bytes;
- void* batch_normalization_36_beta;
- size_t batch_normalization_36_beta_bytes;
- void* batch_normalization_36_mean;
- size_t batch_normalization_36_mean_bytes;
- void* batch_normalization_36_variance;
- size_t batch_normalization_36_variance_bytes;
- void* depthwise_conv2d_13_w;
- size_t depthwise_conv2d_13_w_bytes;
- void* batch_normalization_37_gamma;
- size_t batch_normalization_37_gamma_bytes;
- void* batch_normalization_37_beta;
- size_t batch_normalization_37_beta_bytes;
- void* batch_normalization_37_mean;
- size_t batch_normalization_37_mean_bytes;
- void* batch_normalization_37_variance;
- size_t batch_normalization_37_variance_bytes;
- void* conv2d_26_w;
- size_t conv2d_26_w_bytes;
- void* batch_normalization_38_gamma;
- size_t batch_normalization_38_gamma_bytes;
- void* batch_normalization_38_beta;
- size_t batch_normalization_38_beta_bytes;
- void* batch_normalization_38_mean;
- size_t batch_normalization_38_mean_bytes;
- void* batch_normalization_38_variance;
- size_t batch_normalization_38_variance_bytes;
- void* conv2d_27_w;
- size_t conv2d_27_w_bytes;
- void* batch_normalization_39_gamma;
- size_t batch_normalization_39_gamma_bytes;
- void* batch_normalization_39_beta;
- size_t batch_normalization_39_beta_bytes;
- void* batch_normalization_39_mean;
- size_t batch_normalization_39_mean_bytes;
- void* batch_normalization_39_variance;
- size_t batch_normalization_39_variance_bytes;
- void* depthwise_conv2d_14_w;
- size_t depthwise_conv2d_14_w_bytes;
- void* batch_normalization_40_gamma;
- size_t batch_normalization_40_gamma_bytes;
- void* batch_normalization_40_beta;
- size_t batch_normalization_40_beta_bytes;
- void* batch_normalization_40_mean;
- size_t batch_normalization_40_mean_bytes;
- void* batch_normalization_40_variance;
- size_t batch_normalization_40_variance_bytes;
- void* conv2d_28_w;
- size_t conv2d_28_w_bytes;
- void* batch_normalization_41_gamma;
- size_t batch_normalization_41_gamma_bytes;
- void* batch_normalization_41_beta;
- size_t batch_normalization_41_beta_bytes;
- void* batch_normalization_41_mean;
- size_t batch_normalization_41_mean_bytes;
- void* batch_normalization_41_variance;
- size_t batch_normalization_41_variance_bytes;
- void* conv2d_29_w;
- size_t conv2d_29_w_bytes;
- void* batch_normalization_42_gamma;
- size_t batch_normalization_42_gamma_bytes;
- void* batch_normalization_42_beta;
- size_t batch_normalization_42_beta_bytes;
- void* batch_normalization_42_mean;
- size_t batch_normalization_42_mean_bytes;
- void* batch_normalization_42_variance;
- size_t batch_normalization_42_variance_bytes;
- void* depthwise_conv2d_15_w;
- size_t depthwise_conv2d_15_w_bytes;
- void* batch_normalization_43_gamma;
- size_t batch_normalization_43_gamma_bytes;
- void* batch_normalization_43_beta;
- size_t batch_normalization_43_beta_bytes;
- void* batch_normalization_43_mean;
- size_t batch_normalization_43_mean_bytes;
- void* batch_normalization_43_variance;
- size_t batch_normalization_43_variance_bytes;
- void* conv2d_30_w;
- size_t conv2d_30_w_bytes;
- void* batch_normalization_44_gamma;
- size_t batch_normalization_44_gamma_bytes;
- void* batch_normalization_44_beta;
- size_t batch_normalization_44_beta_bytes;
- void* batch_normalization_44_mean;
- size_t batch_normalization_44_mean_bytes;
- void* batch_normalization_44_variance;
- size_t batch_normalization_44_variance_bytes;
- void* conv2d_31_w;
- size_t conv2d_31_w_bytes;
- void* batch_normalization_45_gamma;
- size_t batch_normalization_45_gamma_bytes;
- void* batch_normalization_45_beta;
- size_t batch_normalization_45_beta_bytes;
- void* batch_normalization_45_mean;
- size_t batch_normalization_45_mean_bytes;
- void* batch_normalization_45_variance;
- size_t batch_normalization_45_variance_bytes;
- void* depthwise_conv2d_16_w;
- size_t depthwise_conv2d_16_w_bytes;
- void* batch_normalization_46_gamma;
- size_t batch_normalization_46_gamma_bytes;
- void* batch_normalization_46_beta;
- size_t batch_normalization_46_beta_bytes;
- void* batch_normalization_46_mean;
- size_t batch_normalization_46_mean_bytes;
- void* batch_normalization_46_variance;
- size_t batch_normalization_46_variance_bytes;
- void* conv2d_32_w;
- size_t conv2d_32_w_bytes;
- void* batch_normalization_47_gamma;
- size_t batch_normalization_47_gamma_bytes;
- void* batch_normalization_47_beta;
- size_t batch_normalization_47_beta_bytes;
- void* batch_normalization_47_mean;
- size_t batch_normalization_47_mean_bytes;
- void* batch_normalization_47_variance;
- size_t batch_normalization_47_variance_bytes;
- void* conv2d_33_w;
- size_t conv2d_33_w_bytes;
- void* batch_normalization_48_gamma;
- size_t batch_normalization_48_gamma_bytes;
- void* batch_normalization_48_beta;
- size_t batch_normalization_48_beta_bytes;
- void* batch_normalization_48_mean;
- size_t batch_normalization_48_mean_bytes;
- void* batch_normalization_48_variance;
- size_t batch_normalization_48_variance_bytes;
- void* depthwise_conv2d_17_w;
- size_t depthwise_conv2d_17_w_bytes;
- void* batch_normalization_49_gamma;
- size_t batch_normalization_49_gamma_bytes;
- void* batch_normalization_49_beta;
- size_t batch_normalization_49_beta_bytes;
- void* batch_normalization_49_mean;
- size_t batch_normalization_49_mean_bytes;
- void* batch_normalization_49_variance;
- size_t batch_normalization_49_variance_bytes;
- void* conv2d_34_w;
- size_t conv2d_34_w_bytes;
- void* batch_normalization_50_gamma;
- size_t batch_normalization_50_gamma_bytes;
- void* batch_normalization_50_beta;
- size_t batch_normalization_50_beta_bytes;
- void* batch_normalization_50_mean;
- size_t batch_normalization_50_mean_bytes;
- void* batch_normalization_50_variance;
- size_t batch_normalization_50_variance_bytes;
- void* conv2d_35_w;
- size_t conv2d_35_w_bytes;
- void* batch_normalization_51_gamma;
- size_t batch_normalization_51_gamma_bytes;
- void* batch_normalization_51_beta;
- size_t batch_normalization_51_beta_bytes;
- void* batch_normalization_51_mean;
- size_t batch_normalization_51_mean_bytes;
- void* batch_normalization_51_variance;
- size_t batch_normalization_51_variance_bytes;
- void* dense_1_w;
- size_t dense_1_w_bytes;
- void* dense_1_b;
- size_t dense_1_b_bytes;
-
- struct ret_t r;
-}
-RootIn;
-
-int main(){
-
-std::string dir_prefix = std::string("data/mobilenetv2_quant/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
-void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
-std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
-void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
-void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
-void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
-void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,16,32,1,1);
-std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
-void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,16,1,1);
-std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
-void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,16,1,1);
-std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
-void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,16,1,1);
-std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
-void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,16,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,96,16,1,1);
-std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
-void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
-void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
-void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
-void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,96,1,1);
-std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
-void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,96,1,3,3);
-std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
-void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
-void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
-void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
-void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,96,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,24,96,1,1);
-std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
-void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,24,1,1);
-std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
-void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,24,1,1);
-std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
-void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,24,1,1);
-std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
-void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,24,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,144,24,1,1);
-std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
-void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
-void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
-void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
-void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,144,1,1);
-std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
-void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,144,1,3,3);
-std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
-void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
-void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
-void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
-void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,144,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,24,144,1,1);
-std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
-void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,24,1,1);
-std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
-void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,24,1,1);
-std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
-void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,24,1,1);
-std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
-void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,24,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,144,24,1,1);
-std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
-void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
-void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
-void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
-void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,144,1,1);
-std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
-void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,144,1,3,3);
-std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
-void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
-void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
-void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
-void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,144,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,144,1,1);
-std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
-void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
-void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
-void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
-void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,192,32,1,1);
-std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
-void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
-void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
-void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
-void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,192,1,1);
-std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
-void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,192,1,3,3);
-std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
-void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
-void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
-void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
-void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,192,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,192,1,1);
-std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
-void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
-void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
-void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
-void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,192,32,1,1);
-std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
-void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
-void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
-void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
-void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,192,1,1);
-std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
-void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,192,1,3,3);
-std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
-void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
-void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
-void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
-void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,192,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,192,1,1);
-std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
-void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
-void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
-void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
-void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,192,32,1,1);
-std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
-void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
-void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
-void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
-void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,192,1,1);
-std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
-void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,192,1,3,3);
-std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
-void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
-void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
-void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
-void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,192,1,1);
-std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
-void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,64,192,1,1);
-std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
-void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
-void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
-void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
-void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_15_w_path = dir_prefix + std::string("conv2d_15_w.bin");
-void* conv2d_15_w = readTrainedWeights(conv2d_15_w_path.c_str(), 0,384,64,1,1);
-std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
-void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
-void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
-void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
-void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,384,1,1);
-std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
-void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,384,1,3,3);
-std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
-void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
-void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
-void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
-void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,384,1,1);
-std::string conv2d_16_w_path = dir_prefix + std::string("conv2d_16_w.bin");
-void* conv2d_16_w = readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,384,1,1);
-std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
-void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
-void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
-void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
-void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_17_w_path = dir_prefix + std::string("conv2d_17_w.bin");
-void* conv2d_17_w = readTrainedWeights(conv2d_17_w_path.c_str(), 0,384,64,1,1);
-std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
-void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
-void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
-void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
-void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,384,1,1);
-std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
-void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,384,1,3,3);
-std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
-void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
-void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
-void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
-void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,384,1,1);
-std::string conv2d_18_w_path = dir_prefix + std::string("conv2d_18_w.bin");
-void* conv2d_18_w = readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,384,1,1);
-std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
-void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
-void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
-void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
-void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_19_w_path = dir_prefix + std::string("conv2d_19_w.bin");
-void* conv2d_19_w = readTrainedWeights(conv2d_19_w_path.c_str(), 0,384,64,1,1);
-std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
-void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
-void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
-void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
-void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,384,1,1);
-std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
-void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,384,1,3,3);
-std::string batch_normalization_28_gamma_path = dir_prefix + std::string("batch_normalization_28_gamma.bin");
-void* batch_normalization_28_gamma = readTrainedWeights(batch_normalization_28_gamma_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_28_beta_path = dir_prefix + std::string("batch_normalization_28_beta.bin");
-void* batch_normalization_28_beta = readTrainedWeights(batch_normalization_28_beta_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_28_mean_path = dir_prefix + std::string("batch_normalization_28_mean.bin");
-void* batch_normalization_28_mean = readTrainedWeights(batch_normalization_28_mean_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_28_variance_path = dir_prefix + std::string("batch_normalization_28_variance.bin");
-void* batch_normalization_28_variance = readTrainedWeights(batch_normalization_28_variance_path.c_str(), 0,1,384,1,1);
-std::string conv2d_20_w_path = dir_prefix + std::string("conv2d_20_w.bin");
-void* conv2d_20_w = readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,384,1,1);
-std::string batch_normalization_29_gamma_path = dir_prefix + std::string("batch_normalization_29_gamma.bin");
-void* batch_normalization_29_gamma = readTrainedWeights(batch_normalization_29_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_29_beta_path = dir_prefix + std::string("batch_normalization_29_beta.bin");
-void* batch_normalization_29_beta = readTrainedWeights(batch_normalization_29_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_29_mean_path = dir_prefix + std::string("batch_normalization_29_mean.bin");
-void* batch_normalization_29_mean = readTrainedWeights(batch_normalization_29_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_29_variance_path = dir_prefix + std::string("batch_normalization_29_variance.bin");
-void* batch_normalization_29_variance = readTrainedWeights(batch_normalization_29_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_21_w_path = dir_prefix + std::string("conv2d_21_w.bin");
-void* conv2d_21_w = readTrainedWeights(conv2d_21_w_path.c_str(), 0,384,64,1,1);
-std::string batch_normalization_30_gamma_path = dir_prefix + std::string("batch_normalization_30_gamma.bin");
-void* batch_normalization_30_gamma = readTrainedWeights(batch_normalization_30_gamma_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_30_beta_path = dir_prefix + std::string("batch_normalization_30_beta.bin");
-void* batch_normalization_30_beta = readTrainedWeights(batch_normalization_30_beta_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_30_mean_path = dir_prefix + std::string("batch_normalization_30_mean.bin");
-void* batch_normalization_30_mean = readTrainedWeights(batch_normalization_30_mean_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_30_variance_path = dir_prefix + std::string("batch_normalization_30_variance.bin");
-void* batch_normalization_30_variance = readTrainedWeights(batch_normalization_30_variance_path.c_str(), 0,1,384,1,1);
-std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
-void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,384,1,3,3);
-std::string batch_normalization_31_gamma_path = dir_prefix + std::string("batch_normalization_31_gamma.bin");
-void* batch_normalization_31_gamma = readTrainedWeights(batch_normalization_31_gamma_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_31_beta_path = dir_prefix + std::string("batch_normalization_31_beta.bin");
-void* batch_normalization_31_beta = readTrainedWeights(batch_normalization_31_beta_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_31_mean_path = dir_prefix + std::string("batch_normalization_31_mean.bin");
-void* batch_normalization_31_mean = readTrainedWeights(batch_normalization_31_mean_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_31_variance_path = dir_prefix + std::string("batch_normalization_31_variance.bin");
-void* batch_normalization_31_variance = readTrainedWeights(batch_normalization_31_variance_path.c_str(), 0,1,384,1,1);
-std::string conv2d_22_w_path = dir_prefix + std::string("conv2d_22_w.bin");
-void* conv2d_22_w = readTrainedWeights(conv2d_22_w_path.c_str(), 0,96,384,1,1);
-std::string batch_normalization_32_gamma_path = dir_prefix + std::string("batch_normalization_32_gamma.bin");
-void* batch_normalization_32_gamma = readTrainedWeights(batch_normalization_32_gamma_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_32_beta_path = dir_prefix + std::string("batch_normalization_32_beta.bin");
-void* batch_normalization_32_beta = readTrainedWeights(batch_normalization_32_beta_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_32_mean_path = dir_prefix + std::string("batch_normalization_32_mean.bin");
-void* batch_normalization_32_mean = readTrainedWeights(batch_normalization_32_mean_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_32_variance_path = dir_prefix + std::string("batch_normalization_32_variance.bin");
-void* batch_normalization_32_variance = readTrainedWeights(batch_normalization_32_variance_path.c_str(), 0,1,96,1,1);
-std::string conv2d_23_w_path = dir_prefix + std::string("conv2d_23_w.bin");
-void* conv2d_23_w = readTrainedWeights(conv2d_23_w_path.c_str(), 0,576,96,1,1);
-std::string batch_normalization_33_gamma_path = dir_prefix + std::string("batch_normalization_33_gamma.bin");
-void* batch_normalization_33_gamma = readTrainedWeights(batch_normalization_33_gamma_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_33_beta_path = dir_prefix + std::string("batch_normalization_33_beta.bin");
-void* batch_normalization_33_beta = readTrainedWeights(batch_normalization_33_beta_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_33_mean_path = dir_prefix + std::string("batch_normalization_33_mean.bin");
-void* batch_normalization_33_mean = readTrainedWeights(batch_normalization_33_mean_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_33_variance_path = dir_prefix + std::string("batch_normalization_33_variance.bin");
-void* batch_normalization_33_variance = readTrainedWeights(batch_normalization_33_variance_path.c_str(), 0,1,576,1,1);
-std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
-void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,576,1,3,3);
-std::string batch_normalization_34_gamma_path = dir_prefix + std::string("batch_normalization_34_gamma.bin");
-void* batch_normalization_34_gamma = readTrainedWeights(batch_normalization_34_gamma_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_34_beta_path = dir_prefix + std::string("batch_normalization_34_beta.bin");
-void* batch_normalization_34_beta = readTrainedWeights(batch_normalization_34_beta_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_34_mean_path = dir_prefix + std::string("batch_normalization_34_mean.bin");
-void* batch_normalization_34_mean = readTrainedWeights(batch_normalization_34_mean_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_34_variance_path = dir_prefix + std::string("batch_normalization_34_variance.bin");
-void* batch_normalization_34_variance = readTrainedWeights(batch_normalization_34_variance_path.c_str(), 0,1,576,1,1);
-std::string conv2d_24_w_path = dir_prefix + std::string("conv2d_24_w.bin");
-void* conv2d_24_w = readTrainedWeights(conv2d_24_w_path.c_str(), 0,96,576,1,1);
-std::string batch_normalization_35_gamma_path = dir_prefix + std::string("batch_normalization_35_gamma.bin");
-void* batch_normalization_35_gamma = readTrainedWeights(batch_normalization_35_gamma_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_35_beta_path = dir_prefix + std::string("batch_normalization_35_beta.bin");
-void* batch_normalization_35_beta = readTrainedWeights(batch_normalization_35_beta_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_35_mean_path = dir_prefix + std::string("batch_normalization_35_mean.bin");
-void* batch_normalization_35_mean = readTrainedWeights(batch_normalization_35_mean_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_35_variance_path = dir_prefix + std::string("batch_normalization_35_variance.bin");
-void* batch_normalization_35_variance = readTrainedWeights(batch_normalization_35_variance_path.c_str(), 0,1,96,1,1);
-std::string conv2d_25_w_path = dir_prefix + std::string("conv2d_25_w.bin");
-void* conv2d_25_w = readTrainedWeights(conv2d_25_w_path.c_str(), 0,576,96,1,1);
-std::string batch_normalization_36_gamma_path = dir_prefix + std::string("batch_normalization_36_gamma.bin");
-void* batch_normalization_36_gamma = readTrainedWeights(batch_normalization_36_gamma_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_36_beta_path = dir_prefix + std::string("batch_normalization_36_beta.bin");
-void* batch_normalization_36_beta = readTrainedWeights(batch_normalization_36_beta_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_36_mean_path = dir_prefix + std::string("batch_normalization_36_mean.bin");
-void* batch_normalization_36_mean = readTrainedWeights(batch_normalization_36_mean_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_36_variance_path = dir_prefix + std::string("batch_normalization_36_variance.bin");
-void* batch_normalization_36_variance = readTrainedWeights(batch_normalization_36_variance_path.c_str(), 0,1,576,1,1);
-std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
-void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,576,1,3,3);
-std::string batch_normalization_37_gamma_path = dir_prefix + std::string("batch_normalization_37_gamma.bin");
-void* batch_normalization_37_gamma = readTrainedWeights(batch_normalization_37_gamma_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_37_beta_path = dir_prefix + std::string("batch_normalization_37_beta.bin");
-void* batch_normalization_37_beta = readTrainedWeights(batch_normalization_37_beta_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_37_mean_path = dir_prefix + std::string("batch_normalization_37_mean.bin");
-void* batch_normalization_37_mean = readTrainedWeights(batch_normalization_37_mean_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_37_variance_path = dir_prefix + std::string("batch_normalization_37_variance.bin");
-void* batch_normalization_37_variance = readTrainedWeights(batch_normalization_37_variance_path.c_str(), 0,1,576,1,1);
-std::string conv2d_26_w_path = dir_prefix + std::string("conv2d_26_w.bin");
-void* conv2d_26_w = readTrainedWeights(conv2d_26_w_path.c_str(), 0,96,576,1,1);
-std::string batch_normalization_38_gamma_path = dir_prefix + std::string("batch_normalization_38_gamma.bin");
-void* batch_normalization_38_gamma = readTrainedWeights(batch_normalization_38_gamma_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_38_beta_path = dir_prefix + std::string("batch_normalization_38_beta.bin");
-void* batch_normalization_38_beta = readTrainedWeights(batch_normalization_38_beta_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_38_mean_path = dir_prefix + std::string("batch_normalization_38_mean.bin");
-void* batch_normalization_38_mean = readTrainedWeights(batch_normalization_38_mean_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_38_variance_path = dir_prefix + std::string("batch_normalization_38_variance.bin");
-void* batch_normalization_38_variance = readTrainedWeights(batch_normalization_38_variance_path.c_str(), 0,1,96,1,1);
-std::string conv2d_27_w_path = dir_prefix + std::string("conv2d_27_w.bin");
-void* conv2d_27_w = readTrainedWeights(conv2d_27_w_path.c_str(), 0,576,96,1,1);
-std::string batch_normalization_39_gamma_path = dir_prefix + std::string("batch_normalization_39_gamma.bin");
-void* batch_normalization_39_gamma = readTrainedWeights(batch_normalization_39_gamma_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_39_beta_path = dir_prefix + std::string("batch_normalization_39_beta.bin");
-void* batch_normalization_39_beta = readTrainedWeights(batch_normalization_39_beta_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_39_mean_path = dir_prefix + std::string("batch_normalization_39_mean.bin");
-void* batch_normalization_39_mean = readTrainedWeights(batch_normalization_39_mean_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_39_variance_path = dir_prefix + std::string("batch_normalization_39_variance.bin");
-void* batch_normalization_39_variance = readTrainedWeights(batch_normalization_39_variance_path.c_str(), 0,1,576,1,1);
-std::string depthwise_conv2d_14_w_path = dir_prefix + std::string("depthwise_conv2d_14_w.bin");
-void* depthwise_conv2d_14_w = readTrainedWeights(depthwise_conv2d_14_w_path.c_str(), 0,576,1,3,3);
-std::string batch_normalization_40_gamma_path = dir_prefix + std::string("batch_normalization_40_gamma.bin");
-void* batch_normalization_40_gamma = readTrainedWeights(batch_normalization_40_gamma_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_40_beta_path = dir_prefix + std::string("batch_normalization_40_beta.bin");
-void* batch_normalization_40_beta = readTrainedWeights(batch_normalization_40_beta_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_40_mean_path = dir_prefix + std::string("batch_normalization_40_mean.bin");
-void* batch_normalization_40_mean = readTrainedWeights(batch_normalization_40_mean_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_40_variance_path = dir_prefix + std::string("batch_normalization_40_variance.bin");
-void* batch_normalization_40_variance = readTrainedWeights(batch_normalization_40_variance_path.c_str(), 0,1,576,1,1);
-std::string conv2d_28_w_path = dir_prefix + std::string("conv2d_28_w.bin");
-void* conv2d_28_w = readTrainedWeights(conv2d_28_w_path.c_str(), 0,160,576,1,1);
-std::string batch_normalization_41_gamma_path = dir_prefix + std::string("batch_normalization_41_gamma.bin");
-void* batch_normalization_41_gamma = readTrainedWeights(batch_normalization_41_gamma_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_41_beta_path = dir_prefix + std::string("batch_normalization_41_beta.bin");
-void* batch_normalization_41_beta = readTrainedWeights(batch_normalization_41_beta_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_41_mean_path = dir_prefix + std::string("batch_normalization_41_mean.bin");
-void* batch_normalization_41_mean = readTrainedWeights(batch_normalization_41_mean_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_41_variance_path = dir_prefix + std::string("batch_normalization_41_variance.bin");
-void* batch_normalization_41_variance = readTrainedWeights(batch_normalization_41_variance_path.c_str(), 0,1,160,1,1);
-std::string conv2d_29_w_path = dir_prefix + std::string("conv2d_29_w.bin");
-void* conv2d_29_w = readTrainedWeights(conv2d_29_w_path.c_str(), 0,960,160,1,1);
-std::string batch_normalization_42_gamma_path = dir_prefix + std::string("batch_normalization_42_gamma.bin");
-void* batch_normalization_42_gamma = readTrainedWeights(batch_normalization_42_gamma_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_42_beta_path = dir_prefix + std::string("batch_normalization_42_beta.bin");
-void* batch_normalization_42_beta = readTrainedWeights(batch_normalization_42_beta_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_42_mean_path = dir_prefix + std::string("batch_normalization_42_mean.bin");
-void* batch_normalization_42_mean = readTrainedWeights(batch_normalization_42_mean_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_42_variance_path = dir_prefix + std::string("batch_normalization_42_variance.bin");
-void* batch_normalization_42_variance = readTrainedWeights(batch_normalization_42_variance_path.c_str(), 0,1,960,1,1);
-std::string depthwise_conv2d_15_w_path = dir_prefix + std::string("depthwise_conv2d_15_w.bin");
-void* depthwise_conv2d_15_w = readTrainedWeights(depthwise_conv2d_15_w_path.c_str(), 0,960,1,3,3);
-std::string batch_normalization_43_gamma_path = dir_prefix + std::string("batch_normalization_43_gamma.bin");
-void* batch_normalization_43_gamma = readTrainedWeights(batch_normalization_43_gamma_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_43_beta_path = dir_prefix + std::string("batch_normalization_43_beta.bin");
-void* batch_normalization_43_beta = readTrainedWeights(batch_normalization_43_beta_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_43_mean_path = dir_prefix + std::string("batch_normalization_43_mean.bin");
-void* batch_normalization_43_mean = readTrainedWeights(batch_normalization_43_mean_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_43_variance_path = dir_prefix + std::string("batch_normalization_43_variance.bin");
-void* batch_normalization_43_variance = readTrainedWeights(batch_normalization_43_variance_path.c_str(), 0,1,960,1,1);
-std::string conv2d_30_w_path = dir_prefix + std::string("conv2d_30_w.bin");
-void* conv2d_30_w = readTrainedWeights(conv2d_30_w_path.c_str(), 0,160,960,1,1);
-std::string batch_normalization_44_gamma_path = dir_prefix + std::string("batch_normalization_44_gamma.bin");
-void* batch_normalization_44_gamma = readTrainedWeights(batch_normalization_44_gamma_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_44_beta_path = dir_prefix + std::string("batch_normalization_44_beta.bin");
-void* batch_normalization_44_beta = readTrainedWeights(batch_normalization_44_beta_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_44_mean_path = dir_prefix + std::string("batch_normalization_44_mean.bin");
-void* batch_normalization_44_mean = readTrainedWeights(batch_normalization_44_mean_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_44_variance_path = dir_prefix + std::string("batch_normalization_44_variance.bin");
-void* batch_normalization_44_variance = readTrainedWeights(batch_normalization_44_variance_path.c_str(), 0,1,160,1,1);
-std::string conv2d_31_w_path = dir_prefix + std::string("conv2d_31_w.bin");
-void* conv2d_31_w = readTrainedWeights(conv2d_31_w_path.c_str(), 0,960,160,1,1);
-std::string batch_normalization_45_gamma_path = dir_prefix + std::string("batch_normalization_45_gamma.bin");
-void* batch_normalization_45_gamma = readTrainedWeights(batch_normalization_45_gamma_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_45_beta_path = dir_prefix + std::string("batch_normalization_45_beta.bin");
-void* batch_normalization_45_beta = readTrainedWeights(batch_normalization_45_beta_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_45_mean_path = dir_prefix + std::string("batch_normalization_45_mean.bin");
-void* batch_normalization_45_mean = readTrainedWeights(batch_normalization_45_mean_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_45_variance_path = dir_prefix + std::string("batch_normalization_45_variance.bin");
-void* batch_normalization_45_variance = readTrainedWeights(batch_normalization_45_variance_path.c_str(), 0,1,960,1,1);
-std::string depthwise_conv2d_16_w_path = dir_prefix + std::string("depthwise_conv2d_16_w.bin");
-void* depthwise_conv2d_16_w = readTrainedWeights(depthwise_conv2d_16_w_path.c_str(), 0,960,1,3,3);
-std::string batch_normalization_46_gamma_path = dir_prefix + std::string("batch_normalization_46_gamma.bin");
-void* batch_normalization_46_gamma = readTrainedWeights(batch_normalization_46_gamma_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_46_beta_path = dir_prefix + std::string("batch_normalization_46_beta.bin");
-void* batch_normalization_46_beta = readTrainedWeights(batch_normalization_46_beta_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_46_mean_path = dir_prefix + std::string("batch_normalization_46_mean.bin");
-void* batch_normalization_46_mean = readTrainedWeights(batch_normalization_46_mean_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_46_variance_path = dir_prefix + std::string("batch_normalization_46_variance.bin");
-void* batch_normalization_46_variance = readTrainedWeights(batch_normalization_46_variance_path.c_str(), 0,1,960,1,1);
-std::string conv2d_32_w_path = dir_prefix + std::string("conv2d_32_w.bin");
-void* conv2d_32_w = readTrainedWeights(conv2d_32_w_path.c_str(), 0,160,960,1,1);
-std::string batch_normalization_47_gamma_path = dir_prefix + std::string("batch_normalization_47_gamma.bin");
-void* batch_normalization_47_gamma = readTrainedWeights(batch_normalization_47_gamma_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_47_beta_path = dir_prefix + std::string("batch_normalization_47_beta.bin");
-void* batch_normalization_47_beta = readTrainedWeights(batch_normalization_47_beta_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_47_mean_path = dir_prefix + std::string("batch_normalization_47_mean.bin");
-void* batch_normalization_47_mean = readTrainedWeights(batch_normalization_47_mean_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_47_variance_path = dir_prefix + std::string("batch_normalization_47_variance.bin");
-void* batch_normalization_47_variance = readTrainedWeights(batch_normalization_47_variance_path.c_str(), 0,1,160,1,1);
-std::string conv2d_33_w_path = dir_prefix + std::string("conv2d_33_w.bin");
-void* conv2d_33_w = readTrainedWeights(conv2d_33_w_path.c_str(), 0,960,160,1,1);
-std::string batch_normalization_48_gamma_path = dir_prefix + std::string("batch_normalization_48_gamma.bin");
-void* batch_normalization_48_gamma = readTrainedWeights(batch_normalization_48_gamma_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_48_beta_path = dir_prefix + std::string("batch_normalization_48_beta.bin");
-void* batch_normalization_48_beta = readTrainedWeights(batch_normalization_48_beta_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_48_mean_path = dir_prefix + std::string("batch_normalization_48_mean.bin");
-void* batch_normalization_48_mean = readTrainedWeights(batch_normalization_48_mean_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_48_variance_path = dir_prefix + std::string("batch_normalization_48_variance.bin");
-void* batch_normalization_48_variance = readTrainedWeights(batch_normalization_48_variance_path.c_str(), 0,1,960,1,1);
-std::string depthwise_conv2d_17_w_path = dir_prefix + std::string("depthwise_conv2d_17_w.bin");
-void* depthwise_conv2d_17_w = readTrainedWeights(depthwise_conv2d_17_w_path.c_str(), 0,960,1,3,3);
-std::string batch_normalization_49_gamma_path = dir_prefix + std::string("batch_normalization_49_gamma.bin");
-void* batch_normalization_49_gamma = readTrainedWeights(batch_normalization_49_gamma_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_49_beta_path = dir_prefix + std::string("batch_normalization_49_beta.bin");
-void* batch_normalization_49_beta = readTrainedWeights(batch_normalization_49_beta_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_49_mean_path = dir_prefix + std::string("batch_normalization_49_mean.bin");
-void* batch_normalization_49_mean = readTrainedWeights(batch_normalization_49_mean_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_49_variance_path = dir_prefix + std::string("batch_normalization_49_variance.bin");
-void* batch_normalization_49_variance = readTrainedWeights(batch_normalization_49_variance_path.c_str(), 0,1,960,1,1);
-std::string conv2d_34_w_path = dir_prefix + std::string("conv2d_34_w.bin");
-void* conv2d_34_w = readTrainedWeights(conv2d_34_w_path.c_str(), 0,320,960,1,1);
-std::string batch_normalization_50_gamma_path = dir_prefix + std::string("batch_normalization_50_gamma.bin");
-void* batch_normalization_50_gamma = readTrainedWeights(batch_normalization_50_gamma_path.c_str(), 0,1,320,1,1);
-std::string batch_normalization_50_beta_path = dir_prefix + std::string("batch_normalization_50_beta.bin");
-void* batch_normalization_50_beta = readTrainedWeights(batch_normalization_50_beta_path.c_str(), 0,1,320,1,1);
-std::string batch_normalization_50_mean_path = dir_prefix + std::string("batch_normalization_50_mean.bin");
-void* batch_normalization_50_mean = readTrainedWeights(batch_normalization_50_mean_path.c_str(), 0,1,320,1,1);
-std::string batch_normalization_50_variance_path = dir_prefix + std::string("batch_normalization_50_variance.bin");
-void* batch_normalization_50_variance = readTrainedWeights(batch_normalization_50_variance_path.c_str(), 0,1,320,1,1);
-std::string conv2d_35_w_path = dir_prefix + std::string("conv2d_35_w.bin");
-void* conv2d_35_w = readTrainedWeights(conv2d_35_w_path.c_str(), 0,1280,320,1,1);
-std::string batch_normalization_51_gamma_path = dir_prefix + std::string("batch_normalization_51_gamma.bin");
-void* batch_normalization_51_gamma = readTrainedWeights(batch_normalization_51_gamma_path.c_str(), 0,1,1280,1,1);
-std::string batch_normalization_51_beta_path = dir_prefix + std::string("batch_normalization_51_beta.bin");
-void* batch_normalization_51_beta = readTrainedWeights(batch_normalization_51_beta_path.c_str(), 0,1,1280,1,1);
-std::string batch_normalization_51_mean_path = dir_prefix + std::string("batch_normalization_51_mean.bin");
-void* batch_normalization_51_mean = readTrainedWeights(batch_normalization_51_mean_path.c_str(), 0,1,1280,1,1);
-std::string batch_normalization_51_variance_path = dir_prefix + std::string("batch_normalization_51_variance.bin");
-void* batch_normalization_51_variance = readTrainedWeights(batch_normalization_51_variance_path.c_str(), 0,1,1280,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,5120,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-
-__visc__init();
-RootIn* args = static_cast<RootIn*>(malloc(sizeof(RootIn)));
-
-args->input = input;
-args->input_bytes = 0;
-args->conv2d_1_w = conv2d_1_w;
-args->conv2d_1_w_bytes = 0;
-args->depthwise_conv2d_1_w = depthwise_conv2d_1_w;
-args->depthwise_conv2d_1_w_bytes = 0;
-args->batch_normalization_1_gamma = batch_normalization_1_gamma;
-args->batch_normalization_1_gamma_bytes = 0;
-args->batch_normalization_1_beta = batch_normalization_1_beta;
-args->batch_normalization_1_beta_bytes = 0;
-args->batch_normalization_1_mean = batch_normalization_1_mean;
-args->batch_normalization_1_mean_bytes = 0;
-args->batch_normalization_1_variance = batch_normalization_1_variance;
-args->batch_normalization_1_variance_bytes = 0;
-args->conv2d_2_w = conv2d_2_w;
-args->conv2d_2_w_bytes = 0;
-args->batch_normalization_2_gamma = batch_normalization_2_gamma;
-args->batch_normalization_2_gamma_bytes = 0;
-args->batch_normalization_2_beta = batch_normalization_2_beta;
-args->batch_normalization_2_beta_bytes = 0;
-args->batch_normalization_2_mean = batch_normalization_2_mean;
-args->batch_normalization_2_mean_bytes = 0;
-args->batch_normalization_2_variance = batch_normalization_2_variance;
-args->batch_normalization_2_variance_bytes = 0;
-args->conv2d_3_w = conv2d_3_w;
-args->conv2d_3_w_bytes = 0;
-args->batch_normalization_3_gamma = batch_normalization_3_gamma;
-args->batch_normalization_3_gamma_bytes = 0;
-args->batch_normalization_3_beta = batch_normalization_3_beta;
-args->batch_normalization_3_beta_bytes = 0;
-args->batch_normalization_3_mean = batch_normalization_3_mean;
-args->batch_normalization_3_mean_bytes = 0;
-args->batch_normalization_3_variance = batch_normalization_3_variance;
-args->batch_normalization_3_variance_bytes = 0;
-args->depthwise_conv2d_2_w = depthwise_conv2d_2_w;
-args->depthwise_conv2d_2_w_bytes = 0;
-args->batch_normalization_4_gamma = batch_normalization_4_gamma;
-args->batch_normalization_4_gamma_bytes = 0;
-args->batch_normalization_4_beta = batch_normalization_4_beta;
-args->batch_normalization_4_beta_bytes = 0;
-args->batch_normalization_4_mean = batch_normalization_4_mean;
-args->batch_normalization_4_mean_bytes = 0;
-args->batch_normalization_4_variance = batch_normalization_4_variance;
-args->batch_normalization_4_variance_bytes = 0;
-args->conv2d_4_w = conv2d_4_w;
-args->conv2d_4_w_bytes = 0;
-args->batch_normalization_5_gamma = batch_normalization_5_gamma;
-args->batch_normalization_5_gamma_bytes = 0;
-args->batch_normalization_5_beta = batch_normalization_5_beta;
-args->batch_normalization_5_beta_bytes = 0;
-args->batch_normalization_5_mean = batch_normalization_5_mean;
-args->batch_normalization_5_mean_bytes = 0;
-args->batch_normalization_5_variance = batch_normalization_5_variance;
-args->batch_normalization_5_variance_bytes = 0;
-args->conv2d_5_w = conv2d_5_w;
-args->conv2d_5_w_bytes = 0;
-args->batch_normalization_6_gamma = batch_normalization_6_gamma;
-args->batch_normalization_6_gamma_bytes = 0;
-args->batch_normalization_6_beta = batch_normalization_6_beta;
-args->batch_normalization_6_beta_bytes = 0;
-args->batch_normalization_6_mean = batch_normalization_6_mean;
-args->batch_normalization_6_mean_bytes = 0;
-args->batch_normalization_6_variance = batch_normalization_6_variance;
-args->batch_normalization_6_variance_bytes = 0;
-args->depthwise_conv2d_3_w = depthwise_conv2d_3_w;
-args->depthwise_conv2d_3_w_bytes = 0;
-args->batch_normalization_7_gamma = batch_normalization_7_gamma;
-args->batch_normalization_7_gamma_bytes = 0;
-args->batch_normalization_7_beta = batch_normalization_7_beta;
-args->batch_normalization_7_beta_bytes = 0;
-args->batch_normalization_7_mean = batch_normalization_7_mean;
-args->batch_normalization_7_mean_bytes = 0;
-args->batch_normalization_7_variance = batch_normalization_7_variance;
-args->batch_normalization_7_variance_bytes = 0;
-args->conv2d_6_w = conv2d_6_w;
-args->conv2d_6_w_bytes = 0;
-args->batch_normalization_8_gamma = batch_normalization_8_gamma;
-args->batch_normalization_8_gamma_bytes = 0;
-args->batch_normalization_8_beta = batch_normalization_8_beta;
-args->batch_normalization_8_beta_bytes = 0;
-args->batch_normalization_8_mean = batch_normalization_8_mean;
-args->batch_normalization_8_mean_bytes = 0;
-args->batch_normalization_8_variance = batch_normalization_8_variance;
-args->batch_normalization_8_variance_bytes = 0;
-args->conv2d_7_w = conv2d_7_w;
-args->conv2d_7_w_bytes = 0;
-args->batch_normalization_9_gamma = batch_normalization_9_gamma;
-args->batch_normalization_9_gamma_bytes = 0;
-args->batch_normalization_9_beta = batch_normalization_9_beta;
-args->batch_normalization_9_beta_bytes = 0;
-args->batch_normalization_9_mean = batch_normalization_9_mean;
-args->batch_normalization_9_mean_bytes = 0;
-args->batch_normalization_9_variance = batch_normalization_9_variance;
-args->batch_normalization_9_variance_bytes = 0;
-args->depthwise_conv2d_4_w = depthwise_conv2d_4_w;
-args->depthwise_conv2d_4_w_bytes = 0;
-args->batch_normalization_10_gamma = batch_normalization_10_gamma;
-args->batch_normalization_10_gamma_bytes = 0;
-args->batch_normalization_10_beta = batch_normalization_10_beta;
-args->batch_normalization_10_beta_bytes = 0;
-args->batch_normalization_10_mean = batch_normalization_10_mean;
-args->batch_normalization_10_mean_bytes = 0;
-args->batch_normalization_10_variance = batch_normalization_10_variance;
-args->batch_normalization_10_variance_bytes = 0;
-args->conv2d_8_w = conv2d_8_w;
-args->conv2d_8_w_bytes = 0;
-args->batch_normalization_11_gamma = batch_normalization_11_gamma;
-args->batch_normalization_11_gamma_bytes = 0;
-args->batch_normalization_11_beta = batch_normalization_11_beta;
-args->batch_normalization_11_beta_bytes = 0;
-args->batch_normalization_11_mean = batch_normalization_11_mean;
-args->batch_normalization_11_mean_bytes = 0;
-args->batch_normalization_11_variance = batch_normalization_11_variance;
-args->batch_normalization_11_variance_bytes = 0;
-args->conv2d_9_w = conv2d_9_w;
-args->conv2d_9_w_bytes = 0;
-args->batch_normalization_12_gamma = batch_normalization_12_gamma;
-args->batch_normalization_12_gamma_bytes = 0;
-args->batch_normalization_12_beta = batch_normalization_12_beta;
-args->batch_normalization_12_beta_bytes = 0;
-args->batch_normalization_12_mean = batch_normalization_12_mean;
-args->batch_normalization_12_mean_bytes = 0;
-args->batch_normalization_12_variance = batch_normalization_12_variance;
-args->batch_normalization_12_variance_bytes = 0;
-args->depthwise_conv2d_5_w = depthwise_conv2d_5_w;
-args->depthwise_conv2d_5_w_bytes = 0;
-args->batch_normalization_13_gamma = batch_normalization_13_gamma;
-args->batch_normalization_13_gamma_bytes = 0;
-args->batch_normalization_13_beta = batch_normalization_13_beta;
-args->batch_normalization_13_beta_bytes = 0;
-args->batch_normalization_13_mean = batch_normalization_13_mean;
-args->batch_normalization_13_mean_bytes = 0;
-args->batch_normalization_13_variance = batch_normalization_13_variance;
-args->batch_normalization_13_variance_bytes = 0;
-args->conv2d_10_w = conv2d_10_w;
-args->conv2d_10_w_bytes = 0;
-args->batch_normalization_14_gamma = batch_normalization_14_gamma;
-args->batch_normalization_14_gamma_bytes = 0;
-args->batch_normalization_14_beta = batch_normalization_14_beta;
-args->batch_normalization_14_beta_bytes = 0;
-args->batch_normalization_14_mean = batch_normalization_14_mean;
-args->batch_normalization_14_mean_bytes = 0;
-args->batch_normalization_14_variance = batch_normalization_14_variance;
-args->batch_normalization_14_variance_bytes = 0;
-args->conv2d_11_w = conv2d_11_w;
-args->conv2d_11_w_bytes = 0;
-args->batch_normalization_15_gamma = batch_normalization_15_gamma;
-args->batch_normalization_15_gamma_bytes = 0;
-args->batch_normalization_15_beta = batch_normalization_15_beta;
-args->batch_normalization_15_beta_bytes = 0;
-args->batch_normalization_15_mean = batch_normalization_15_mean;
-args->batch_normalization_15_mean_bytes = 0;
-args->batch_normalization_15_variance = batch_normalization_15_variance;
-args->batch_normalization_15_variance_bytes = 0;
-args->depthwise_conv2d_6_w = depthwise_conv2d_6_w;
-args->depthwise_conv2d_6_w_bytes = 0;
-args->batch_normalization_16_gamma = batch_normalization_16_gamma;
-args->batch_normalization_16_gamma_bytes = 0;
-args->batch_normalization_16_beta = batch_normalization_16_beta;
-args->batch_normalization_16_beta_bytes = 0;
-args->batch_normalization_16_mean = batch_normalization_16_mean;
-args->batch_normalization_16_mean_bytes = 0;
-args->batch_normalization_16_variance = batch_normalization_16_variance;
-args->batch_normalization_16_variance_bytes = 0;
-args->conv2d_12_w = conv2d_12_w;
-args->conv2d_12_w_bytes = 0;
-args->batch_normalization_17_gamma = batch_normalization_17_gamma;
-args->batch_normalization_17_gamma_bytes = 0;
-args->batch_normalization_17_beta = batch_normalization_17_beta;
-args->batch_normalization_17_beta_bytes = 0;
-args->batch_normalization_17_mean = batch_normalization_17_mean;
-args->batch_normalization_17_mean_bytes = 0;
-args->batch_normalization_17_variance = batch_normalization_17_variance;
-args->batch_normalization_17_variance_bytes = 0;
-args->conv2d_13_w = conv2d_13_w;
-args->conv2d_13_w_bytes = 0;
-args->batch_normalization_18_gamma = batch_normalization_18_gamma;
-args->batch_normalization_18_gamma_bytes = 0;
-args->batch_normalization_18_beta = batch_normalization_18_beta;
-args->batch_normalization_18_beta_bytes = 0;
-args->batch_normalization_18_mean = batch_normalization_18_mean;
-args->batch_normalization_18_mean_bytes = 0;
-args->batch_normalization_18_variance = batch_normalization_18_variance;
-args->batch_normalization_18_variance_bytes = 0;
-args->depthwise_conv2d_7_w = depthwise_conv2d_7_w;
-args->depthwise_conv2d_7_w_bytes = 0;
-args->batch_normalization_19_gamma = batch_normalization_19_gamma;
-args->batch_normalization_19_gamma_bytes = 0;
-args->batch_normalization_19_beta = batch_normalization_19_beta;
-args->batch_normalization_19_beta_bytes = 0;
-args->batch_normalization_19_mean = batch_normalization_19_mean;
-args->batch_normalization_19_mean_bytes = 0;
-args->batch_normalization_19_variance = batch_normalization_19_variance;
-args->batch_normalization_19_variance_bytes = 0;
-args->conv2d_14_w = conv2d_14_w;
-args->conv2d_14_w_bytes = 0;
-args->batch_normalization_20_gamma = batch_normalization_20_gamma;
-args->batch_normalization_20_gamma_bytes = 0;
-args->batch_normalization_20_beta = batch_normalization_20_beta;
-args->batch_normalization_20_beta_bytes = 0;
-args->batch_normalization_20_mean = batch_normalization_20_mean;
-args->batch_normalization_20_mean_bytes = 0;
-args->batch_normalization_20_variance = batch_normalization_20_variance;
-args->batch_normalization_20_variance_bytes = 0;
-args->conv2d_15_w = conv2d_15_w;
-args->conv2d_15_w_bytes = 0;
-args->batch_normalization_21_gamma = batch_normalization_21_gamma;
-args->batch_normalization_21_gamma_bytes = 0;
-args->batch_normalization_21_beta = batch_normalization_21_beta;
-args->batch_normalization_21_beta_bytes = 0;
-args->batch_normalization_21_mean = batch_normalization_21_mean;
-args->batch_normalization_21_mean_bytes = 0;
-args->batch_normalization_21_variance = batch_normalization_21_variance;
-args->batch_normalization_21_variance_bytes = 0;
-args->depthwise_conv2d_8_w = depthwise_conv2d_8_w;
-args->depthwise_conv2d_8_w_bytes = 0;
-args->batch_normalization_22_gamma = batch_normalization_22_gamma;
-args->batch_normalization_22_gamma_bytes = 0;
-args->batch_normalization_22_beta = batch_normalization_22_beta;
-args->batch_normalization_22_beta_bytes = 0;
-args->batch_normalization_22_mean = batch_normalization_22_mean;
-args->batch_normalization_22_mean_bytes = 0;
-args->batch_normalization_22_variance = batch_normalization_22_variance;
-args->batch_normalization_22_variance_bytes = 0;
-args->conv2d_16_w = conv2d_16_w;
-args->conv2d_16_w_bytes = 0;
-args->batch_normalization_23_gamma = batch_normalization_23_gamma;
-args->batch_normalization_23_gamma_bytes = 0;
-args->batch_normalization_23_beta = batch_normalization_23_beta;
-args->batch_normalization_23_beta_bytes = 0;
-args->batch_normalization_23_mean = batch_normalization_23_mean;
-args->batch_normalization_23_mean_bytes = 0;
-args->batch_normalization_23_variance = batch_normalization_23_variance;
-args->batch_normalization_23_variance_bytes = 0;
-args->conv2d_17_w = conv2d_17_w;
-args->conv2d_17_w_bytes = 0;
-args->batch_normalization_24_gamma = batch_normalization_24_gamma;
-args->batch_normalization_24_gamma_bytes = 0;
-args->batch_normalization_24_beta = batch_normalization_24_beta;
-args->batch_normalization_24_beta_bytes = 0;
-args->batch_normalization_24_mean = batch_normalization_24_mean;
-args->batch_normalization_24_mean_bytes = 0;
-args->batch_normalization_24_variance = batch_normalization_24_variance;
-args->batch_normalization_24_variance_bytes = 0;
-args->depthwise_conv2d_9_w = depthwise_conv2d_9_w;
-args->depthwise_conv2d_9_w_bytes = 0;
-args->batch_normalization_25_gamma = batch_normalization_25_gamma;
-args->batch_normalization_25_gamma_bytes = 0;
-args->batch_normalization_25_beta = batch_normalization_25_beta;
-args->batch_normalization_25_beta_bytes = 0;
-args->batch_normalization_25_mean = batch_normalization_25_mean;
-args->batch_normalization_25_mean_bytes = 0;
-args->batch_normalization_25_variance = batch_normalization_25_variance;
-args->batch_normalization_25_variance_bytes = 0;
-args->conv2d_18_w = conv2d_18_w;
-args->conv2d_18_w_bytes = 0;
-args->batch_normalization_26_gamma = batch_normalization_26_gamma;
-args->batch_normalization_26_gamma_bytes = 0;
-args->batch_normalization_26_beta = batch_normalization_26_beta;
-args->batch_normalization_26_beta_bytes = 0;
-args->batch_normalization_26_mean = batch_normalization_26_mean;
-args->batch_normalization_26_mean_bytes = 0;
-args->batch_normalization_26_variance = batch_normalization_26_variance;
-args->batch_normalization_26_variance_bytes = 0;
-args->conv2d_19_w = conv2d_19_w;
-args->conv2d_19_w_bytes = 0;
-args->batch_normalization_27_gamma = batch_normalization_27_gamma;
-args->batch_normalization_27_gamma_bytes = 0;
-args->batch_normalization_27_beta = batch_normalization_27_beta;
-args->batch_normalization_27_beta_bytes = 0;
-args->batch_normalization_27_mean = batch_normalization_27_mean;
-args->batch_normalization_27_mean_bytes = 0;
-args->batch_normalization_27_variance = batch_normalization_27_variance;
-args->batch_normalization_27_variance_bytes = 0;
-args->depthwise_conv2d_10_w = depthwise_conv2d_10_w;
-args->depthwise_conv2d_10_w_bytes = 0;
-args->batch_normalization_28_gamma = batch_normalization_28_gamma;
-args->batch_normalization_28_gamma_bytes = 0;
-args->batch_normalization_28_beta = batch_normalization_28_beta;
-args->batch_normalization_28_beta_bytes = 0;
-args->batch_normalization_28_mean = batch_normalization_28_mean;
-args->batch_normalization_28_mean_bytes = 0;
-args->batch_normalization_28_variance = batch_normalization_28_variance;
-args->batch_normalization_28_variance_bytes = 0;
-args->conv2d_20_w = conv2d_20_w;
-args->conv2d_20_w_bytes = 0;
-args->batch_normalization_29_gamma = batch_normalization_29_gamma;
-args->batch_normalization_29_gamma_bytes = 0;
-args->batch_normalization_29_beta = batch_normalization_29_beta;
-args->batch_normalization_29_beta_bytes = 0;
-args->batch_normalization_29_mean = batch_normalization_29_mean;
-args->batch_normalization_29_mean_bytes = 0;
-args->batch_normalization_29_variance = batch_normalization_29_variance;
-args->batch_normalization_29_variance_bytes = 0;
-args->conv2d_21_w = conv2d_21_w;
-args->conv2d_21_w_bytes = 0;
-args->batch_normalization_30_gamma = batch_normalization_30_gamma;
-args->batch_normalization_30_gamma_bytes = 0;
-args->batch_normalization_30_beta = batch_normalization_30_beta;
-args->batch_normalization_30_beta_bytes = 0;
-args->batch_normalization_30_mean = batch_normalization_30_mean;
-args->batch_normalization_30_mean_bytes = 0;
-args->batch_normalization_30_variance = batch_normalization_30_variance;
-args->batch_normalization_30_variance_bytes = 0;
-args->depthwise_conv2d_11_w = depthwise_conv2d_11_w;
-args->depthwise_conv2d_11_w_bytes = 0;
-args->batch_normalization_31_gamma = batch_normalization_31_gamma;
-args->batch_normalization_31_gamma_bytes = 0;
-args->batch_normalization_31_beta = batch_normalization_31_beta;
-args->batch_normalization_31_beta_bytes = 0;
-args->batch_normalization_31_mean = batch_normalization_31_mean;
-args->batch_normalization_31_mean_bytes = 0;
-args->batch_normalization_31_variance = batch_normalization_31_variance;
-args->batch_normalization_31_variance_bytes = 0;
-args->conv2d_22_w = conv2d_22_w;
-args->conv2d_22_w_bytes = 0;
-args->batch_normalization_32_gamma = batch_normalization_32_gamma;
-args->batch_normalization_32_gamma_bytes = 0;
-args->batch_normalization_32_beta = batch_normalization_32_beta;
-args->batch_normalization_32_beta_bytes = 0;
-args->batch_normalization_32_mean = batch_normalization_32_mean;
-args->batch_normalization_32_mean_bytes = 0;
-args->batch_normalization_32_variance = batch_normalization_32_variance;
-args->batch_normalization_32_variance_bytes = 0;
-args->conv2d_23_w = conv2d_23_w;
-args->conv2d_23_w_bytes = 0;
-args->batch_normalization_33_gamma = batch_normalization_33_gamma;
-args->batch_normalization_33_gamma_bytes = 0;
-args->batch_normalization_33_beta = batch_normalization_33_beta;
-args->batch_normalization_33_beta_bytes = 0;
-args->batch_normalization_33_mean = batch_normalization_33_mean;
-args->batch_normalization_33_mean_bytes = 0;
-args->batch_normalization_33_variance = batch_normalization_33_variance;
-args->batch_normalization_33_variance_bytes = 0;
-args->depthwise_conv2d_12_w = depthwise_conv2d_12_w;
-args->depthwise_conv2d_12_w_bytes = 0;
-args->batch_normalization_34_gamma = batch_normalization_34_gamma;
-args->batch_normalization_34_gamma_bytes = 0;
-args->batch_normalization_34_beta = batch_normalization_34_beta;
-args->batch_normalization_34_beta_bytes = 0;
-args->batch_normalization_34_mean = batch_normalization_34_mean;
-args->batch_normalization_34_mean_bytes = 0;
-args->batch_normalization_34_variance = batch_normalization_34_variance;
-args->batch_normalization_34_variance_bytes = 0;
-args->conv2d_24_w = conv2d_24_w;
-args->conv2d_24_w_bytes = 0;
-args->batch_normalization_35_gamma = batch_normalization_35_gamma;
-args->batch_normalization_35_gamma_bytes = 0;
-args->batch_normalization_35_beta = batch_normalization_35_beta;
-args->batch_normalization_35_beta_bytes = 0;
-args->batch_normalization_35_mean = batch_normalization_35_mean;
-args->batch_normalization_35_mean_bytes = 0;
-args->batch_normalization_35_variance = batch_normalization_35_variance;
-args->batch_normalization_35_variance_bytes = 0;
-args->conv2d_25_w = conv2d_25_w;
-args->conv2d_25_w_bytes = 0;
-args->batch_normalization_36_gamma = batch_normalization_36_gamma;
-args->batch_normalization_36_gamma_bytes = 0;
-args->batch_normalization_36_beta = batch_normalization_36_beta;
-args->batch_normalization_36_beta_bytes = 0;
-args->batch_normalization_36_mean = batch_normalization_36_mean;
-args->batch_normalization_36_mean_bytes = 0;
-args->batch_normalization_36_variance = batch_normalization_36_variance;
-args->batch_normalization_36_variance_bytes = 0;
-args->depthwise_conv2d_13_w = depthwise_conv2d_13_w;
-args->depthwise_conv2d_13_w_bytes = 0;
-args->batch_normalization_37_gamma = batch_normalization_37_gamma;
-args->batch_normalization_37_gamma_bytes = 0;
-args->batch_normalization_37_beta = batch_normalization_37_beta;
-args->batch_normalization_37_beta_bytes = 0;
-args->batch_normalization_37_mean = batch_normalization_37_mean;
-args->batch_normalization_37_mean_bytes = 0;
-args->batch_normalization_37_variance = batch_normalization_37_variance;
-args->batch_normalization_37_variance_bytes = 0;
-args->conv2d_26_w = conv2d_26_w;
-args->conv2d_26_w_bytes = 0;
-args->batch_normalization_38_gamma = batch_normalization_38_gamma;
-args->batch_normalization_38_gamma_bytes = 0;
-args->batch_normalization_38_beta = batch_normalization_38_beta;
-args->batch_normalization_38_beta_bytes = 0;
-args->batch_normalization_38_mean = batch_normalization_38_mean;
-args->batch_normalization_38_mean_bytes = 0;
-args->batch_normalization_38_variance = batch_normalization_38_variance;
-args->batch_normalization_38_variance_bytes = 0;
-args->conv2d_27_w = conv2d_27_w;
-args->conv2d_27_w_bytes = 0;
-args->batch_normalization_39_gamma = batch_normalization_39_gamma;
-args->batch_normalization_39_gamma_bytes = 0;
-args->batch_normalization_39_beta = batch_normalization_39_beta;
-args->batch_normalization_39_beta_bytes = 0;
-args->batch_normalization_39_mean = batch_normalization_39_mean;
-args->batch_normalization_39_mean_bytes = 0;
-args->batch_normalization_39_variance = batch_normalization_39_variance;
-args->batch_normalization_39_variance_bytes = 0;
-args->depthwise_conv2d_14_w = depthwise_conv2d_14_w;
-args->depthwise_conv2d_14_w_bytes = 0;
-args->batch_normalization_40_gamma = batch_normalization_40_gamma;
-args->batch_normalization_40_gamma_bytes = 0;
-args->batch_normalization_40_beta = batch_normalization_40_beta;
-args->batch_normalization_40_beta_bytes = 0;
-args->batch_normalization_40_mean = batch_normalization_40_mean;
-args->batch_normalization_40_mean_bytes = 0;
-args->batch_normalization_40_variance = batch_normalization_40_variance;
-args->batch_normalization_40_variance_bytes = 0;
-args->conv2d_28_w = conv2d_28_w;
-args->conv2d_28_w_bytes = 0;
-args->batch_normalization_41_gamma = batch_normalization_41_gamma;
-args->batch_normalization_41_gamma_bytes = 0;
-args->batch_normalization_41_beta = batch_normalization_41_beta;
-args->batch_normalization_41_beta_bytes = 0;
-args->batch_normalization_41_mean = batch_normalization_41_mean;
-args->batch_normalization_41_mean_bytes = 0;
-args->batch_normalization_41_variance = batch_normalization_41_variance;
-args->batch_normalization_41_variance_bytes = 0;
-args->conv2d_29_w = conv2d_29_w;
-args->conv2d_29_w_bytes = 0;
-args->batch_normalization_42_gamma = batch_normalization_42_gamma;
-args->batch_normalization_42_gamma_bytes = 0;
-args->batch_normalization_42_beta = batch_normalization_42_beta;
-args->batch_normalization_42_beta_bytes = 0;
-args->batch_normalization_42_mean = batch_normalization_42_mean;
-args->batch_normalization_42_mean_bytes = 0;
-args->batch_normalization_42_variance = batch_normalization_42_variance;
-args->batch_normalization_42_variance_bytes = 0;
-args->depthwise_conv2d_15_w = depthwise_conv2d_15_w;
-args->depthwise_conv2d_15_w_bytes = 0;
-args->batch_normalization_43_gamma = batch_normalization_43_gamma;
-args->batch_normalization_43_gamma_bytes = 0;
-args->batch_normalization_43_beta = batch_normalization_43_beta;
-args->batch_normalization_43_beta_bytes = 0;
-args->batch_normalization_43_mean = batch_normalization_43_mean;
-args->batch_normalization_43_mean_bytes = 0;
-args->batch_normalization_43_variance = batch_normalization_43_variance;
-args->batch_normalization_43_variance_bytes = 0;
-args->conv2d_30_w = conv2d_30_w;
-args->conv2d_30_w_bytes = 0;
-args->batch_normalization_44_gamma = batch_normalization_44_gamma;
-args->batch_normalization_44_gamma_bytes = 0;
-args->batch_normalization_44_beta = batch_normalization_44_beta;
-args->batch_normalization_44_beta_bytes = 0;
-args->batch_normalization_44_mean = batch_normalization_44_mean;
-args->batch_normalization_44_mean_bytes = 0;
-args->batch_normalization_44_variance = batch_normalization_44_variance;
-args->batch_normalization_44_variance_bytes = 0;
-args->conv2d_31_w = conv2d_31_w;
-args->conv2d_31_w_bytes = 0;
-args->batch_normalization_45_gamma = batch_normalization_45_gamma;
-args->batch_normalization_45_gamma_bytes = 0;
-args->batch_normalization_45_beta = batch_normalization_45_beta;
-args->batch_normalization_45_beta_bytes = 0;
-args->batch_normalization_45_mean = batch_normalization_45_mean;
-args->batch_normalization_45_mean_bytes = 0;
-args->batch_normalization_45_variance = batch_normalization_45_variance;
-args->batch_normalization_45_variance_bytes = 0;
-args->depthwise_conv2d_16_w = depthwise_conv2d_16_w;
-args->depthwise_conv2d_16_w_bytes = 0;
-args->batch_normalization_46_gamma = batch_normalization_46_gamma;
-args->batch_normalization_46_gamma_bytes = 0;
-args->batch_normalization_46_beta = batch_normalization_46_beta;
-args->batch_normalization_46_beta_bytes = 0;
-args->batch_normalization_46_mean = batch_normalization_46_mean;
-args->batch_normalization_46_mean_bytes = 0;
-args->batch_normalization_46_variance = batch_normalization_46_variance;
-args->batch_normalization_46_variance_bytes = 0;
-args->conv2d_32_w = conv2d_32_w;
-args->conv2d_32_w_bytes = 0;
-args->batch_normalization_47_gamma = batch_normalization_47_gamma;
-args->batch_normalization_47_gamma_bytes = 0;
-args->batch_normalization_47_beta = batch_normalization_47_beta;
-args->batch_normalization_47_beta_bytes = 0;
-args->batch_normalization_47_mean = batch_normalization_47_mean;
-args->batch_normalization_47_mean_bytes = 0;
-args->batch_normalization_47_variance = batch_normalization_47_variance;
-args->batch_normalization_47_variance_bytes = 0;
-args->conv2d_33_w = conv2d_33_w;
-args->conv2d_33_w_bytes = 0;
-args->batch_normalization_48_gamma = batch_normalization_48_gamma;
-args->batch_normalization_48_gamma_bytes = 0;
-args->batch_normalization_48_beta = batch_normalization_48_beta;
-args->batch_normalization_48_beta_bytes = 0;
-args->batch_normalization_48_mean = batch_normalization_48_mean;
-args->batch_normalization_48_mean_bytes = 0;
-args->batch_normalization_48_variance = batch_normalization_48_variance;
-args->batch_normalization_48_variance_bytes = 0;
-args->depthwise_conv2d_17_w = depthwise_conv2d_17_w;
-args->depthwise_conv2d_17_w_bytes = 0;
-args->batch_normalization_49_gamma = batch_normalization_49_gamma;
-args->batch_normalization_49_gamma_bytes = 0;
-args->batch_normalization_49_beta = batch_normalization_49_beta;
-args->batch_normalization_49_beta_bytes = 0;
-args->batch_normalization_49_mean = batch_normalization_49_mean;
-args->batch_normalization_49_mean_bytes = 0;
-args->batch_normalization_49_variance = batch_normalization_49_variance;
-args->batch_normalization_49_variance_bytes = 0;
-args->conv2d_34_w = conv2d_34_w;
-args->conv2d_34_w_bytes = 0;
-args->batch_normalization_50_gamma = batch_normalization_50_gamma;
-args->batch_normalization_50_gamma_bytes = 0;
-args->batch_normalization_50_beta = batch_normalization_50_beta;
-args->batch_normalization_50_beta_bytes = 0;
-args->batch_normalization_50_mean = batch_normalization_50_mean;
-args->batch_normalization_50_mean_bytes = 0;
-args->batch_normalization_50_variance = batch_normalization_50_variance;
-args->batch_normalization_50_variance_bytes = 0;
-args->conv2d_35_w = conv2d_35_w;
-args->conv2d_35_w_bytes = 0;
-args->batch_normalization_51_gamma = batch_normalization_51_gamma;
-args->batch_normalization_51_gamma_bytes = 0;
-args->batch_normalization_51_beta = batch_normalization_51_beta;
-args->batch_normalization_51_beta_bytes = 0;
-args->batch_normalization_51_mean = batch_normalization_51_mean;
-args->batch_normalization_51_mean_bytes = 0;
-args->batch_normalization_51_variance = batch_normalization_51_variance;
-args->batch_normalization_51_variance_bytes = 0;
-args->dense_1_w = dense_1_w;
-args->dense_1_w_bytes = 0;
-args->dense_1_b = dense_1_b;
-args->dense_1_b_bytes = 0;
-
-void* dfg = __visc__launch(0, root, (void*) args);
-
-__visc__wait(dfg);
-
-void *result = static_cast<RootIn*>(args)->input;
-hpvm_request_tensor(result, 0);
-
-__visc__cleanup();
- computeAccuracy2(labels, 10000, result);
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_10_beta.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_10_beta.bin
deleted file mode 100644
index ac0a7aa06c6a9cac74c19ed43fa4fc4114e978ae..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_10_gamma.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_10_gamma.bin
deleted file mode 100644
index f4fe5c8b9aa77833eae869f47a7de1bc30445d7b..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_10_mean.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_10_mean.bin
deleted file mode 100644
index 887ec6e2f7b5547aac9f08ebb26bb46424fef360..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_10_variance.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_10_variance.bin
deleted file mode 100644
index 623d1458dc4978b1196b6e65a1be6e1f9f1de23a..0000000000000000000000000000000000000000
Binary files a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_10_variance.bin and /dev/null differ
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_11_beta.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_11_beta.bin
deleted file mode 100644
index 7657f258c2856a685d057f6e3792438d79fb1a60..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_11_gamma.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_11_gamma.bin
deleted file mode 100644
index 0d68deb069e67e85a282b3ef204ba3847e57b141..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_11_mean.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_11_mean.bin
deleted file mode 100644
index b0ea894f9c04a5106c2c7e592f791e7ebac6d804..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_11_mean.bin
+++ /dev/null
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_11_variance.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_11_variance.bin
deleted file mode 100644
index f3095ba68312b1217fd9defa267aebe650e0823a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_11_variance.bin
+++ /dev/null
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_12_beta.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_12_beta.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_12_gamma.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_12_gamma.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_12_mean.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_12_mean.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_12_variance.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_12_variance.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_13_beta.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_13_beta.bin
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index fe943abe2bd464fd36e6d6b370e2b891fb462dca..0000000000000000000000000000000000000000
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_13_gamma.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_13_gamma.bin
deleted file mode 100644
index b43f0af0c535c991c3c80d5be2f8e37c930d4ab1..0000000000000000000000000000000000000000
Binary files a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_13_gamma.bin and /dev/null differ
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_13_mean.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_13_mean.bin
deleted file mode 100644
index 8271b08f2d37294ae1a84af55728fc0b3cf874b7..0000000000000000000000000000000000000000
Binary files a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_13_mean.bin and /dev/null differ
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_13_variance.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_13_variance.bin
deleted file mode 100644
index c68118085bcf02c38b5633d84e6ea6d12cd144e7..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_13_variance.bin
+++ /dev/null
@@ -1,6 +0,0 @@
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d?F¹=70«=>j¹<3K£=ÿ“8>¯à>šÿÀ<é5>EÚ=&k=ðË‹>ÄsD=½îQ>°:->ñÖ´=VÖ>²ñ >ø}·>G\u>!=9o
->SLp>M>J‘ú>
P6>×E+=OHr=%°>]±?ª ¦>Î?Î}Z>¥WW>" B>õ¶Ä>–L ?g¸>FÍ=€Lo>w—˜=‚™>U»ƒ=¬½M>“½=“Á·=´°>C-=¨–¨>-6Â>º»´=ž5b=•Ï>Èÿ>¤Ô =~ª>Ù.>†eÇ=ë׋=ýH«>I¹¼=a¿Â=™ Œ>žc<=ˆ Š>å?>šªL>kJÓ=7ü;=Ùs`> M>m?x >°¤û=P±“>‡TË= Â?9`§=™Å5>ûàï=âµ= kÿ>°¥>ù%=é…r?Š1>¡L=Loi=©ïG>‹0= >†˜i>9ð>M#¾=.«£<Áʲ>œè)>4*->(½~=ãi?ä¦~>2/ >ÝÍ>ŒÐš>Cï³>P>dˆŠ= ¯y=‡´?&0> Á=¸º>h;¯=<s
->7Ð>ð?k@d>æ÷Ü=
I"> ©?=Áù=)n=Qm.=:®–=iŒj=KIH>£õ0=K:Æ=èv">T‚=5†?‘zÅ= Û>D¿=v<>8É1?I·ã=åµ= >ÚM=QÛ >×,?øSÕ>ËäU>
-€ö=SŠ= õÍ<˜ä >ÌOµ=8Ê ?-WÓ=Þ=Î!?_~H>Vs=W > fé= Ì‚=yY=
-Õ >v(ž=šw‘>"®„>jI‰>æa?2Þ$>¯ý,>9ÂE>ž
-Ô=€‰>Nv¥>]‡?BÁ¤>®(=k¶R>|×ñ=
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_14_beta.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_14_beta.bin
deleted file mode 100644
index 4507e0cdd399dd91cb1f339804fecf8d3943a363..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_14_beta.bin
+++ /dev/null
@@ -1,2 +0,0 @@
-6ÝÑ3|ï² gÊ3Œ¡O³½˜D±ã<?³–Ü´êöH´CY´ˆ}Ò²eQ°3ñçX´tŒö³bŒ¼²•¡'2oD´¹Â ³B
`3»Ú÷3(
-³wxR´’f¸1ƒ¥a´<.4ìg2„f³Ã×´öMr´tó±8Hh´¶“'1— 1
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_14_gamma.bin b/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_14_gamma.bin
deleted file mode 100644
index 264a6ea84b68e7cdc10f2f5a07595b4f8fef88b7..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/batch_normalization_14_gamma.bin
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@@ -1 +0,0 @@
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-conv
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-Conv1,10000,3,32,32,32,3,3,3
-#tensorDepthwiseConv1
-#tensorBatchNorm1
-#tensorRelu1
-Conv2,10000,32,32,32,16,32,1,1
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-Conv3,10000,16,32,32,96,16,1,1
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-Conv4,10000,96,32,32,24,96,1,1
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-Conv5,10000,24,32,32,144,24,1,1
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-Conv6,10000,144,32,32,24,144,1,1
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-Conv7,10000,24,32,32,144,24,1,1
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-Conv9,10000,32,16,16,192,32,1,1
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-Conv10,10000,192,16,16,32,192,1,1
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-Conv11,10000,32,16,16,192,32,1,1
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-Conv12,10000,192,16,16,32,192,1,1
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-Conv14,10000,192,8,8,64,192,1,1
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-Conv15,10000,64,8,8,384,64,1,1
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-Conv16,10000,384,8,8,64,384,1,1
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-Conv17,10000,64,8,8,384,64,1,1
-#tensorBatchNorm24
-#tensorRelu16
-#tensorDepthwiseConv9
-#tensorBatchNorm25
-#tensorRelu17
-Conv18,10000,384,8,8,64,384,1,1
-#tensorBatchNorm26
-#tensorAdd5
-Conv19,10000,64,8,8,384,64,1,1
-#tensorBatchNorm27
-#tensorRelu18
-#tensorDepthwiseConv10
-#tensorBatchNorm28
-#tensorRelu19
-Conv20,10000,384,8,8,64,384,1,1
-#tensorBatchNorm29
-#tensorAdd6
-Conv21,10000,64,8,8,384,64,1,1
-#tensorBatchNorm30
-#tensorRelu20
-#tensorDepthwiseConv11
-#tensorBatchNorm31
-#tensorRelu21
-Conv22,10000,384,8,8,96,384,1,1
-#tensorBatchNorm32
-Conv23,10000,96,8,8,576,96,1,1
-#tensorBatchNorm33
-#tensorRelu22
-#tensorDepthwiseConv12
-#tensorBatchNorm34
-#tensorRelu23
-Conv24,10000,576,8,8,96,576,1,1
-#tensorBatchNorm35
-#tensorAdd7
-Conv25,10000,96,8,8,576,96,1,1
-#tensorBatchNorm36
-#tensorRelu24
-#tensorDepthwiseConv13
-#tensorBatchNorm37
-#tensorRelu25
-Conv26,10000,576,8,8,96,576,1,1
-#tensorBatchNorm38
-#tensorAdd8
-Conv27,10000,96,8,8,576,96,1,1
-#tensorBatchNorm39
-#tensorRelu26
-#tensorDepthwiseConv14
-#tensorBatchNorm40
-#tensorRelu27
-Conv28,10000,576,4,4,160,576,1,1
-#tensorBatchNorm41
-Conv29,10000,160,4,4,960,160,1,1
-#tensorBatchNorm42
-#tensorRelu28
-#tensorDepthwiseConv15
-#tensorBatchNorm43
-#tensorRelu29
-Conv30,10000,960,4,4,160,960,1,1
-#tensorBatchNorm44
-#tensorAdd9
-Conv31,10000,160,4,4,960,160,1,1
-#tensorBatchNorm45
-#tensorRelu30
-#tensorDepthwiseConv16
-#tensorBatchNorm46
-#tensorRelu31
-Conv32,10000,960,4,4,160,960,1,1
-#tensorBatchNorm47
-#tensorAdd10
-Conv33,10000,160,4,4,960,160,1,1
-#tensorBatchNorm48
-#tensorRelu32
-#tensorDepthwiseConv17
-#tensorBatchNorm49
-#tensorRelu33
-Conv34,10000,960,4,4,320,960,1,1
-#tensorBatchNorm50
-Conv35,10000,320,4,4,1280,320,1,1
-#tensorBatchNorm51
-#tensorRelu34
-#tensorPooling1
-FC1,10000,5120,5120,10
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/promise_src.cc b/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/promise_src.cc
deleted file mode 100644
index 430b8afddf640fe3ff251a734ac315e430947618..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/promise_src.cc
+++ /dev/null
@@ -1,726 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(1);
-
-int total_runs = 1;
-for (int i = 0 ; i < total_runs; i++){
-
-
-startMemTracking();
-
-int test_input_size = 10000;
-int batch_size = 2000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-std::string dir_prefix = std::string("../../keras/data/mobilenetv2_quant/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
-void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
-std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
-void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
-void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
-void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
-void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,16,32,1,1);
-std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
-void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,16,1,1);
-std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
-void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,16,1,1);
-std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
-void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,16,1,1);
-std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
-void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,16,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,96,16,1,1);
-std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
-void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
-void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
-void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
-void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,96,1,1);
-std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
-void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,96,1,3,3);
-std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
-void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
-void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
-void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
-void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,96,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,24,96,1,1);
-std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
-void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,24,1,1);
-std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
-void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,24,1,1);
-std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
-void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,24,1,1);
-std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
-void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,24,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,144,24,1,1);
-std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
-void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
-void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
-void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
-void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,144,1,1);
-std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
-void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,144,1,3,3);
-std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
-void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
-void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
-void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
-void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,144,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,24,144,1,1);
-std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
-void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,24,1,1);
-std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
-void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,24,1,1);
-std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
-void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,24,1,1);
-std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
-void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,24,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,144,24,1,1);
-std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
-void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
-void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
-void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
-void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,144,1,1);
-std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
-void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,144,1,3,3);
-std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
-void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
-void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
-void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
-void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,144,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,144,1,1);
-std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
-void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
-void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
-void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
-void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,192,32,1,1);
-std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
-void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
-void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
-void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
-void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,192,1,1);
-std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
-void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,192,1,3,3);
-std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
-void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
-void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
-void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
-void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,192,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,192,1,1);
-std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
-void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
-void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
-void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
-void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,192,32,1,1);
-std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
-void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
-void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
-void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
-void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,192,1,1);
-std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
-void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,192,1,3,3);
-std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
-void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
-void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
-void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
-void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,192,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,192,1,1);
-std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
-void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
-void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
-void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
-void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,192,32,1,1);
-std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
-void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
-void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
-void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
-void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,192,1,1);
-std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
-void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,192,1,3,3);
-std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
-void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
-void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
-void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
-void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,192,1,1);
-std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
-void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,64,192,1,1);
-std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
-void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
-void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
-void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
-void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_15_w_path = dir_prefix + std::string("conv2d_15_w.bin");
-void* conv2d_15_w = readTrainedWeights(conv2d_15_w_path.c_str(), 0,384,64,1,1);
-std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
-void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
-void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
-void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
-void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,384,1,1);
-std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
-void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,384,1,3,3);
-std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
-void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
-void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
-void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
-void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,384,1,1);
-std::string conv2d_16_w_path = dir_prefix + std::string("conv2d_16_w.bin");
-void* conv2d_16_w = readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,384,1,1);
-std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
-void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
-void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
-void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
-void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_17_w_path = dir_prefix + std::string("conv2d_17_w.bin");
-void* conv2d_17_w = readTrainedWeights(conv2d_17_w_path.c_str(), 0,384,64,1,1);
-std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
-void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
-void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
-void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
-void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,384,1,1);
-std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
-void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,384,1,3,3);
-std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
-void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
-void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
-void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
-void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,384,1,1);
-std::string conv2d_18_w_path = dir_prefix + std::string("conv2d_18_w.bin");
-void* conv2d_18_w = readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,384,1,1);
-std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
-void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
-void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
-void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
-void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_19_w_path = dir_prefix + std::string("conv2d_19_w.bin");
-void* conv2d_19_w = readTrainedWeights(conv2d_19_w_path.c_str(), 0,384,64,1,1);
-std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
-void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
-void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
-void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
-void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,384,1,1);
-std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
-void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,384,1,3,3);
-std::string batch_normalization_28_gamma_path = dir_prefix + std::string("batch_normalization_28_gamma.bin");
-void* batch_normalization_28_gamma = readTrainedWeights(batch_normalization_28_gamma_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_28_beta_path = dir_prefix + std::string("batch_normalization_28_beta.bin");
-void* batch_normalization_28_beta = readTrainedWeights(batch_normalization_28_beta_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_28_mean_path = dir_prefix + std::string("batch_normalization_28_mean.bin");
-void* batch_normalization_28_mean = readTrainedWeights(batch_normalization_28_mean_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_28_variance_path = dir_prefix + std::string("batch_normalization_28_variance.bin");
-void* batch_normalization_28_variance = readTrainedWeights(batch_normalization_28_variance_path.c_str(), 0,1,384,1,1);
-std::string conv2d_20_w_path = dir_prefix + std::string("conv2d_20_w.bin");
-void* conv2d_20_w = readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,384,1,1);
-std::string batch_normalization_29_gamma_path = dir_prefix + std::string("batch_normalization_29_gamma.bin");
-void* batch_normalization_29_gamma = readTrainedWeights(batch_normalization_29_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_29_beta_path = dir_prefix + std::string("batch_normalization_29_beta.bin");
-void* batch_normalization_29_beta = readTrainedWeights(batch_normalization_29_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_29_mean_path = dir_prefix + std::string("batch_normalization_29_mean.bin");
-void* batch_normalization_29_mean = readTrainedWeights(batch_normalization_29_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_29_variance_path = dir_prefix + std::string("batch_normalization_29_variance.bin");
-void* batch_normalization_29_variance = readTrainedWeights(batch_normalization_29_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_21_w_path = dir_prefix + std::string("conv2d_21_w.bin");
-void* conv2d_21_w = readTrainedWeights(conv2d_21_w_path.c_str(), 0,384,64,1,1);
-std::string batch_normalization_30_gamma_path = dir_prefix + std::string("batch_normalization_30_gamma.bin");
-void* batch_normalization_30_gamma = readTrainedWeights(batch_normalization_30_gamma_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_30_beta_path = dir_prefix + std::string("batch_normalization_30_beta.bin");
-void* batch_normalization_30_beta = readTrainedWeights(batch_normalization_30_beta_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_30_mean_path = dir_prefix + std::string("batch_normalization_30_mean.bin");
-void* batch_normalization_30_mean = readTrainedWeights(batch_normalization_30_mean_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_30_variance_path = dir_prefix + std::string("batch_normalization_30_variance.bin");
-void* batch_normalization_30_variance = readTrainedWeights(batch_normalization_30_variance_path.c_str(), 0,1,384,1,1);
-std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
-void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,384,1,3,3);
-std::string batch_normalization_31_gamma_path = dir_prefix + std::string("batch_normalization_31_gamma.bin");
-void* batch_normalization_31_gamma = readTrainedWeights(batch_normalization_31_gamma_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_31_beta_path = dir_prefix + std::string("batch_normalization_31_beta.bin");
-void* batch_normalization_31_beta = readTrainedWeights(batch_normalization_31_beta_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_31_mean_path = dir_prefix + std::string("batch_normalization_31_mean.bin");
-void* batch_normalization_31_mean = readTrainedWeights(batch_normalization_31_mean_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_31_variance_path = dir_prefix + std::string("batch_normalization_31_variance.bin");
-void* batch_normalization_31_variance = readTrainedWeights(batch_normalization_31_variance_path.c_str(), 0,1,384,1,1);
-std::string conv2d_22_w_path = dir_prefix + std::string("conv2d_22_w.bin");
-void* conv2d_22_w = readTrainedWeights(conv2d_22_w_path.c_str(), 0,96,384,1,1);
-std::string batch_normalization_32_gamma_path = dir_prefix + std::string("batch_normalization_32_gamma.bin");
-void* batch_normalization_32_gamma = readTrainedWeights(batch_normalization_32_gamma_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_32_beta_path = dir_prefix + std::string("batch_normalization_32_beta.bin");
-void* batch_normalization_32_beta = readTrainedWeights(batch_normalization_32_beta_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_32_mean_path = dir_prefix + std::string("batch_normalization_32_mean.bin");
-void* batch_normalization_32_mean = readTrainedWeights(batch_normalization_32_mean_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_32_variance_path = dir_prefix + std::string("batch_normalization_32_variance.bin");
-void* batch_normalization_32_variance = readTrainedWeights(batch_normalization_32_variance_path.c_str(), 0,1,96,1,1);
-std::string conv2d_23_w_path = dir_prefix + std::string("conv2d_23_w.bin");
-void* conv2d_23_w = readTrainedWeights(conv2d_23_w_path.c_str(), 0,576,96,1,1);
-std::string batch_normalization_33_gamma_path = dir_prefix + std::string("batch_normalization_33_gamma.bin");
-void* batch_normalization_33_gamma = readTrainedWeights(batch_normalization_33_gamma_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_33_beta_path = dir_prefix + std::string("batch_normalization_33_beta.bin");
-void* batch_normalization_33_beta = readTrainedWeights(batch_normalization_33_beta_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_33_mean_path = dir_prefix + std::string("batch_normalization_33_mean.bin");
-void* batch_normalization_33_mean = readTrainedWeights(batch_normalization_33_mean_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_33_variance_path = dir_prefix + std::string("batch_normalization_33_variance.bin");
-void* batch_normalization_33_variance = readTrainedWeights(batch_normalization_33_variance_path.c_str(), 0,1,576,1,1);
-std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
-void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,576,1,3,3);
-std::string batch_normalization_34_gamma_path = dir_prefix + std::string("batch_normalization_34_gamma.bin");
-void* batch_normalization_34_gamma = readTrainedWeights(batch_normalization_34_gamma_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_34_beta_path = dir_prefix + std::string("batch_normalization_34_beta.bin");
-void* batch_normalization_34_beta = readTrainedWeights(batch_normalization_34_beta_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_34_mean_path = dir_prefix + std::string("batch_normalization_34_mean.bin");
-void* batch_normalization_34_mean = readTrainedWeights(batch_normalization_34_mean_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_34_variance_path = dir_prefix + std::string("batch_normalization_34_variance.bin");
-void* batch_normalization_34_variance = readTrainedWeights(batch_normalization_34_variance_path.c_str(), 0,1,576,1,1);
-std::string conv2d_24_w_path = dir_prefix + std::string("conv2d_24_w.bin");
-void* conv2d_24_w = readTrainedWeights(conv2d_24_w_path.c_str(), 0,96,576,1,1);
-std::string batch_normalization_35_gamma_path = dir_prefix + std::string("batch_normalization_35_gamma.bin");
-void* batch_normalization_35_gamma = readTrainedWeights(batch_normalization_35_gamma_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_35_beta_path = dir_prefix + std::string("batch_normalization_35_beta.bin");
-void* batch_normalization_35_beta = readTrainedWeights(batch_normalization_35_beta_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_35_mean_path = dir_prefix + std::string("batch_normalization_35_mean.bin");
-void* batch_normalization_35_mean = readTrainedWeights(batch_normalization_35_mean_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_35_variance_path = dir_prefix + std::string("batch_normalization_35_variance.bin");
-void* batch_normalization_35_variance = readTrainedWeights(batch_normalization_35_variance_path.c_str(), 0,1,96,1,1);
-std::string conv2d_25_w_path = dir_prefix + std::string("conv2d_25_w.bin");
-void* conv2d_25_w = readTrainedWeights(conv2d_25_w_path.c_str(), 0,576,96,1,1);
-std::string batch_normalization_36_gamma_path = dir_prefix + std::string("batch_normalization_36_gamma.bin");
-void* batch_normalization_36_gamma = readTrainedWeights(batch_normalization_36_gamma_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_36_beta_path = dir_prefix + std::string("batch_normalization_36_beta.bin");
-void* batch_normalization_36_beta = readTrainedWeights(batch_normalization_36_beta_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_36_mean_path = dir_prefix + std::string("batch_normalization_36_mean.bin");
-void* batch_normalization_36_mean = readTrainedWeights(batch_normalization_36_mean_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_36_variance_path = dir_prefix + std::string("batch_normalization_36_variance.bin");
-void* batch_normalization_36_variance = readTrainedWeights(batch_normalization_36_variance_path.c_str(), 0,1,576,1,1);
-std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
-void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,576,1,3,3);
-std::string batch_normalization_37_gamma_path = dir_prefix + std::string("batch_normalization_37_gamma.bin");
-void* batch_normalization_37_gamma = readTrainedWeights(batch_normalization_37_gamma_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_37_beta_path = dir_prefix + std::string("batch_normalization_37_beta.bin");
-void* batch_normalization_37_beta = readTrainedWeights(batch_normalization_37_beta_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_37_mean_path = dir_prefix + std::string("batch_normalization_37_mean.bin");
-void* batch_normalization_37_mean = readTrainedWeights(batch_normalization_37_mean_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_37_variance_path = dir_prefix + std::string("batch_normalization_37_variance.bin");
-void* batch_normalization_37_variance = readTrainedWeights(batch_normalization_37_variance_path.c_str(), 0,1,576,1,1);
-std::string conv2d_26_w_path = dir_prefix + std::string("conv2d_26_w.bin");
-void* conv2d_26_w = readTrainedWeights(conv2d_26_w_path.c_str(), 0,96,576,1,1);
-std::string batch_normalization_38_gamma_path = dir_prefix + std::string("batch_normalization_38_gamma.bin");
-void* batch_normalization_38_gamma = readTrainedWeights(batch_normalization_38_gamma_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_38_beta_path = dir_prefix + std::string("batch_normalization_38_beta.bin");
-void* batch_normalization_38_beta = readTrainedWeights(batch_normalization_38_beta_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_38_mean_path = dir_prefix + std::string("batch_normalization_38_mean.bin");
-void* batch_normalization_38_mean = readTrainedWeights(batch_normalization_38_mean_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_38_variance_path = dir_prefix + std::string("batch_normalization_38_variance.bin");
-void* batch_normalization_38_variance = readTrainedWeights(batch_normalization_38_variance_path.c_str(), 0,1,96,1,1);
-std::string conv2d_27_w_path = dir_prefix + std::string("conv2d_27_w.bin");
-void* conv2d_27_w = readTrainedWeights(conv2d_27_w_path.c_str(), 0,576,96,1,1);
-std::string batch_normalization_39_gamma_path = dir_prefix + std::string("batch_normalization_39_gamma.bin");
-void* batch_normalization_39_gamma = readTrainedWeights(batch_normalization_39_gamma_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_39_beta_path = dir_prefix + std::string("batch_normalization_39_beta.bin");
-void* batch_normalization_39_beta = readTrainedWeights(batch_normalization_39_beta_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_39_mean_path = dir_prefix + std::string("batch_normalization_39_mean.bin");
-void* batch_normalization_39_mean = readTrainedWeights(batch_normalization_39_mean_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_39_variance_path = dir_prefix + std::string("batch_normalization_39_variance.bin");
-void* batch_normalization_39_variance = readTrainedWeights(batch_normalization_39_variance_path.c_str(), 0,1,576,1,1);
-std::string depthwise_conv2d_14_w_path = dir_prefix + std::string("depthwise_conv2d_14_w.bin");
-void* depthwise_conv2d_14_w = readTrainedWeights(depthwise_conv2d_14_w_path.c_str(), 0,576,1,3,3);
-std::string batch_normalization_40_gamma_path = dir_prefix + std::string("batch_normalization_40_gamma.bin");
-void* batch_normalization_40_gamma = readTrainedWeights(batch_normalization_40_gamma_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_40_beta_path = dir_prefix + std::string("batch_normalization_40_beta.bin");
-void* batch_normalization_40_beta = readTrainedWeights(batch_normalization_40_beta_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_40_mean_path = dir_prefix + std::string("batch_normalization_40_mean.bin");
-void* batch_normalization_40_mean = readTrainedWeights(batch_normalization_40_mean_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_40_variance_path = dir_prefix + std::string("batch_normalization_40_variance.bin");
-void* batch_normalization_40_variance = readTrainedWeights(batch_normalization_40_variance_path.c_str(), 0,1,576,1,1);
-std::string conv2d_28_w_path = dir_prefix + std::string("conv2d_28_w.bin");
-void* conv2d_28_w = readTrainedWeights(conv2d_28_w_path.c_str(), 0,160,576,1,1);
-std::string batch_normalization_41_gamma_path = dir_prefix + std::string("batch_normalization_41_gamma.bin");
-void* batch_normalization_41_gamma = readTrainedWeights(batch_normalization_41_gamma_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_41_beta_path = dir_prefix + std::string("batch_normalization_41_beta.bin");
-void* batch_normalization_41_beta = readTrainedWeights(batch_normalization_41_beta_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_41_mean_path = dir_prefix + std::string("batch_normalization_41_mean.bin");
-void* batch_normalization_41_mean = readTrainedWeights(batch_normalization_41_mean_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_41_variance_path = dir_prefix + std::string("batch_normalization_41_variance.bin");
-void* batch_normalization_41_variance = readTrainedWeights(batch_normalization_41_variance_path.c_str(), 0,1,160,1,1);
-std::string conv2d_29_w_path = dir_prefix + std::string("conv2d_29_w.bin");
-void* conv2d_29_w = readTrainedWeights(conv2d_29_w_path.c_str(), 0,960,160,1,1);
-std::string batch_normalization_42_gamma_path = dir_prefix + std::string("batch_normalization_42_gamma.bin");
-void* batch_normalization_42_gamma = readTrainedWeights(batch_normalization_42_gamma_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_42_beta_path = dir_prefix + std::string("batch_normalization_42_beta.bin");
-void* batch_normalization_42_beta = readTrainedWeights(batch_normalization_42_beta_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_42_mean_path = dir_prefix + std::string("batch_normalization_42_mean.bin");
-void* batch_normalization_42_mean = readTrainedWeights(batch_normalization_42_mean_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_42_variance_path = dir_prefix + std::string("batch_normalization_42_variance.bin");
-void* batch_normalization_42_variance = readTrainedWeights(batch_normalization_42_variance_path.c_str(), 0,1,960,1,1);
-std::string depthwise_conv2d_15_w_path = dir_prefix + std::string("depthwise_conv2d_15_w.bin");
-void* depthwise_conv2d_15_w = readTrainedWeights(depthwise_conv2d_15_w_path.c_str(), 0,960,1,3,3);
-std::string batch_normalization_43_gamma_path = dir_prefix + std::string("batch_normalization_43_gamma.bin");
-void* batch_normalization_43_gamma = readTrainedWeights(batch_normalization_43_gamma_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_43_beta_path = dir_prefix + std::string("batch_normalization_43_beta.bin");
-void* batch_normalization_43_beta = readTrainedWeights(batch_normalization_43_beta_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_43_mean_path = dir_prefix + std::string("batch_normalization_43_mean.bin");
-void* batch_normalization_43_mean = readTrainedWeights(batch_normalization_43_mean_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_43_variance_path = dir_prefix + std::string("batch_normalization_43_variance.bin");
-void* batch_normalization_43_variance = readTrainedWeights(batch_normalization_43_variance_path.c_str(), 0,1,960,1,1);
-std::string conv2d_30_w_path = dir_prefix + std::string("conv2d_30_w.bin");
-void* conv2d_30_w = readTrainedWeights(conv2d_30_w_path.c_str(), 0,160,960,1,1);
-std::string batch_normalization_44_gamma_path = dir_prefix + std::string("batch_normalization_44_gamma.bin");
-void* batch_normalization_44_gamma = readTrainedWeights(batch_normalization_44_gamma_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_44_beta_path = dir_prefix + std::string("batch_normalization_44_beta.bin");
-void* batch_normalization_44_beta = readTrainedWeights(batch_normalization_44_beta_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_44_mean_path = dir_prefix + std::string("batch_normalization_44_mean.bin");
-void* batch_normalization_44_mean = readTrainedWeights(batch_normalization_44_mean_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_44_variance_path = dir_prefix + std::string("batch_normalization_44_variance.bin");
-void* batch_normalization_44_variance = readTrainedWeights(batch_normalization_44_variance_path.c_str(), 0,1,160,1,1);
-std::string conv2d_31_w_path = dir_prefix + std::string("conv2d_31_w.bin");
-void* conv2d_31_w = readTrainedWeights(conv2d_31_w_path.c_str(), 0,960,160,1,1);
-std::string batch_normalization_45_gamma_path = dir_prefix + std::string("batch_normalization_45_gamma.bin");
-void* batch_normalization_45_gamma = readTrainedWeights(batch_normalization_45_gamma_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_45_beta_path = dir_prefix + std::string("batch_normalization_45_beta.bin");
-void* batch_normalization_45_beta = readTrainedWeights(batch_normalization_45_beta_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_45_mean_path = dir_prefix + std::string("batch_normalization_45_mean.bin");
-void* batch_normalization_45_mean = readTrainedWeights(batch_normalization_45_mean_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_45_variance_path = dir_prefix + std::string("batch_normalization_45_variance.bin");
-void* batch_normalization_45_variance = readTrainedWeights(batch_normalization_45_variance_path.c_str(), 0,1,960,1,1);
-std::string depthwise_conv2d_16_w_path = dir_prefix + std::string("depthwise_conv2d_16_w.bin");
-void* depthwise_conv2d_16_w = readTrainedWeights(depthwise_conv2d_16_w_path.c_str(), 0,960,1,3,3);
-std::string batch_normalization_46_gamma_path = dir_prefix + std::string("batch_normalization_46_gamma.bin");
-void* batch_normalization_46_gamma = readTrainedWeights(batch_normalization_46_gamma_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_46_beta_path = dir_prefix + std::string("batch_normalization_46_beta.bin");
-void* batch_normalization_46_beta = readTrainedWeights(batch_normalization_46_beta_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_46_mean_path = dir_prefix + std::string("batch_normalization_46_mean.bin");
-void* batch_normalization_46_mean = readTrainedWeights(batch_normalization_46_mean_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_46_variance_path = dir_prefix + std::string("batch_normalization_46_variance.bin");
-void* batch_normalization_46_variance = readTrainedWeights(batch_normalization_46_variance_path.c_str(), 0,1,960,1,1);
-std::string conv2d_32_w_path = dir_prefix + std::string("conv2d_32_w.bin");
-void* conv2d_32_w = readTrainedWeights(conv2d_32_w_path.c_str(), 0,160,960,1,1);
-std::string batch_normalization_47_gamma_path = dir_prefix + std::string("batch_normalization_47_gamma.bin");
-void* batch_normalization_47_gamma = readTrainedWeights(batch_normalization_47_gamma_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_47_beta_path = dir_prefix + std::string("batch_normalization_47_beta.bin");
-void* batch_normalization_47_beta = readTrainedWeights(batch_normalization_47_beta_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_47_mean_path = dir_prefix + std::string("batch_normalization_47_mean.bin");
-void* batch_normalization_47_mean = readTrainedWeights(batch_normalization_47_mean_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_47_variance_path = dir_prefix + std::string("batch_normalization_47_variance.bin");
-void* batch_normalization_47_variance = readTrainedWeights(batch_normalization_47_variance_path.c_str(), 0,1,160,1,1);
-std::string conv2d_33_w_path = dir_prefix + std::string("conv2d_33_w.bin");
-void* conv2d_33_w = readTrainedWeights(conv2d_33_w_path.c_str(), 0,960,160,1,1);
-std::string batch_normalization_48_gamma_path = dir_prefix + std::string("batch_normalization_48_gamma.bin");
-void* batch_normalization_48_gamma = readTrainedWeights(batch_normalization_48_gamma_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_48_beta_path = dir_prefix + std::string("batch_normalization_48_beta.bin");
-void* batch_normalization_48_beta = readTrainedWeights(batch_normalization_48_beta_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_48_mean_path = dir_prefix + std::string("batch_normalization_48_mean.bin");
-void* batch_normalization_48_mean = readTrainedWeights(batch_normalization_48_mean_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_48_variance_path = dir_prefix + std::string("batch_normalization_48_variance.bin");
-void* batch_normalization_48_variance = readTrainedWeights(batch_normalization_48_variance_path.c_str(), 0,1,960,1,1);
-std::string depthwise_conv2d_17_w_path = dir_prefix + std::string("depthwise_conv2d_17_w.bin");
-void* depthwise_conv2d_17_w = readTrainedWeights(depthwise_conv2d_17_w_path.c_str(), 0,960,1,3,3);
-std::string batch_normalization_49_gamma_path = dir_prefix + std::string("batch_normalization_49_gamma.bin");
-void* batch_normalization_49_gamma = readTrainedWeights(batch_normalization_49_gamma_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_49_beta_path = dir_prefix + std::string("batch_normalization_49_beta.bin");
-void* batch_normalization_49_beta = readTrainedWeights(batch_normalization_49_beta_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_49_mean_path = dir_prefix + std::string("batch_normalization_49_mean.bin");
-void* batch_normalization_49_mean = readTrainedWeights(batch_normalization_49_mean_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_49_variance_path = dir_prefix + std::string("batch_normalization_49_variance.bin");
-void* batch_normalization_49_variance = readTrainedWeights(batch_normalization_49_variance_path.c_str(), 0,1,960,1,1);
-std::string conv2d_34_w_path = dir_prefix + std::string("conv2d_34_w.bin");
-void* conv2d_34_w = readTrainedWeights(conv2d_34_w_path.c_str(), 0,320,960,1,1);
-std::string batch_normalization_50_gamma_path = dir_prefix + std::string("batch_normalization_50_gamma.bin");
-void* batch_normalization_50_gamma = readTrainedWeights(batch_normalization_50_gamma_path.c_str(), 0,1,320,1,1);
-std::string batch_normalization_50_beta_path = dir_prefix + std::string("batch_normalization_50_beta.bin");
-void* batch_normalization_50_beta = readTrainedWeights(batch_normalization_50_beta_path.c_str(), 0,1,320,1,1);
-std::string batch_normalization_50_mean_path = dir_prefix + std::string("batch_normalization_50_mean.bin");
-void* batch_normalization_50_mean = readTrainedWeights(batch_normalization_50_mean_path.c_str(), 0,1,320,1,1);
-std::string batch_normalization_50_variance_path = dir_prefix + std::string("batch_normalization_50_variance.bin");
-void* batch_normalization_50_variance = readTrainedWeights(batch_normalization_50_variance_path.c_str(), 0,1,320,1,1);
-std::string conv2d_35_w_path = dir_prefix + std::string("conv2d_35_w.bin");
-void* conv2d_35_w = readTrainedWeights(conv2d_35_w_path.c_str(), 0,1280,320,1,1);
-std::string batch_normalization_51_gamma_path = dir_prefix + std::string("batch_normalization_51_gamma.bin");
-void* batch_normalization_51_gamma = readTrainedWeights(batch_normalization_51_gamma_path.c_str(), 0,1,1280,1,1);
-std::string batch_normalization_51_beta_path = dir_prefix + std::string("batch_normalization_51_beta.bin");
-void* batch_normalization_51_beta = readTrainedWeights(batch_normalization_51_beta_path.c_str(), 0,1,1280,1,1);
-std::string batch_normalization_51_mean_path = dir_prefix + std::string("batch_normalization_51_mean.bin");
-void* batch_normalization_51_mean = readTrainedWeights(batch_normalization_51_mean_path.c_str(), 0,1,1280,1,1);
-std::string batch_normalization_51_variance_path = dir_prefix + std::string("batch_normalization_51_variance.bin");
-void* batch_normalization_51_variance = readTrainedWeights(batch_normalization_51_variance_path.c_str(), 0,1,1280,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,5120,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = ConvLayer_PROMISE(input, -1.9105923, 2.145039, conv2d_1_w, -1.6180872917175293, 1.117160677909851, NULL, 0, 0, 1, 1, 1, 1, -1, 0, -1, -30.515915058135988, 30.680313323974644, 9);
-void* var_1 = tensorConvolution(var_0, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
-void* var_2 = tensorBatchNorm(var_1, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
-void* var_3 = tensorRelu(var_2);
-void* var_4 = ConvLayer_PROMISE(var_3, 0.0, 5.59976006364824, conv2d_2_w, -1.1370596212148665, 1.3073962382078181, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -14.505918518066407, 13.629034059524539, 9);
-void* var_5 = tensorBatchNorm(var_4, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
-void* var_6 = ConvLayer_PROMISE(var_5, -4.309355854988098, 3.900550650119789, conv2d_3_w, -1.6225290149450302, 1.2627512609958669, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -18.078887981414795, 15.030233385086063, 9);
-void* var_7 = tensorBatchNorm(var_6, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
-void* var_8 = tensorRelu(var_7);
-void* var_9 = tensorConvolution(var_8, depthwise_conv2d_2_w, 1, 1, 1, 1, 1, 96);
-void* var_10 = tensorBatchNorm(var_9, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
-void* var_11 = tensorRelu(var_10);
-void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.8726992659569675, conv2d_4_w, -0.7004256679415704, 0.7406479438543322, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -12.122967027664185, 12.041194002151496, 9);
-void* var_13 = tensorBatchNorm(var_12, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
-void* var_14 = ConvLayer_PROMISE(var_13, -5.584836505889893, 5.1076841955185035, conv2d_5_w, -0.5288015562295914, 0.6439660099148974, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -7.6197896194458, 9.064867986679104, 9);
-void* var_15 = tensorBatchNorm(var_14, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
-void* var_16 = tensorRelu(var_15);
-void* var_17 = tensorConvolution(var_16, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 144);
-void* var_18 = tensorBatchNorm(var_17, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
-void* var_19 = tensorRelu(var_18);
-void* var_20 = ConvLayer_PROMISE(var_19, 0.0, 5.017239574909283, conv2d_6_w, -0.41820720136165623, 0.5334677529335075, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -14.035128602981567, 15.852058460235604, 9);
-void* var_21 = tensorBatchNorm(var_20, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
-void* var_22 = tensorAdd(var_13, var_21);
-void* var_23 = ConvLayer_PROMISE(var_22, -6.516137770652771, 6.882242226600651, conv2d_7_w, -0.6695078402757645, 0.7230790755152725, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -12.456909196853637, 13.537109403610287, 9);
-void* var_24 = tensorBatchNorm(var_23, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
-void* var_25 = tensorRelu(var_24);
-void* var_26 = tensorConvolution(var_25, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 144);
-void* var_27 = tensorBatchNorm(var_26, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
-void* var_28 = tensorRelu(var_27);
-void* var_29 = ConvLayer_PROMISE(var_28, 0.0, 4.6885352153778115, conv2d_8_w, -0.515219985961914, 0.4966081013679511, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -10.365011343002319, 13.858240459442193, 9);
-void* var_30 = tensorBatchNorm(var_29, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
-void* var_31 = ConvLayer_PROMISE(var_30, -5.659313384056091, 5.353823287963884, conv2d_9_w, -0.38776333206892016, 0.4313740559220367, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -7.494747163772583, 9.150955280303819, 9);
-void* var_32 = tensorBatchNorm(var_31, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
-void* var_33 = tensorRelu(var_32);
-void* var_34 = tensorConvolution(var_33, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 192);
-void* var_35 = tensorBatchNorm(var_34, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
-void* var_36 = tensorRelu(var_35);
-void* var_37 = ConvLayer_PROMISE(var_36, 0.0, 5.240342163562822, conv2d_10_w, -0.3396388011574745, 0.3658320212364196, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -13.428524814605714, 14.668315940856942, 9);
-void* var_38 = tensorBatchNorm(var_37, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001);
-void* var_39 = tensorAdd(var_30, var_38);
-void* var_40 = ConvLayer_PROMISE(var_39, -7.257712150573731, 7.189542776107789, conv2d_11_w, -0.4041371369659901, 0.34401592910289736, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -10.049096151351927, 9.779580131530736, 9);
-void* var_41 = tensorBatchNorm(var_40, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001);
-void* var_42 = tensorRelu(var_41);
-void* var_43 = tensorConvolution(var_42, depthwise_conv2d_6_w, 1, 1, 1, 1, 1, 192);
-void* var_44 = tensorBatchNorm(var_43, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001);
-void* var_45 = tensorRelu(var_44);
-void* var_46 = ConvLayer_PROMISE(var_45, 0.0, 5.171829322815142, conv2d_12_w, -0.3318945516943932, 0.3025509023666382, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -10.416078590393067, 8.875564914703425, 9);
-void* var_47 = tensorBatchNorm(var_46, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001);
-void* var_48 = tensorAdd(var_39, var_47);
-void* var_49 = ConvLayer_PROMISE(var_48, -7.8144073562622065, 7.714953693390015, conv2d_13_w, -0.4585379458963871, 0.48376197892427397, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -11.66812041282654, 13.606535158157008, 9);
-void* var_50 = tensorBatchNorm(var_49, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001);
-void* var_51 = tensorRelu(var_50);
-void* var_52 = tensorConvolution(var_51, depthwise_conv2d_7_w, 1, 1, 2, 2, 1, 192);
-void* var_53 = tensorBatchNorm(var_52, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001);
-void* var_54 = tensorRelu(var_53);
-void* var_55 = ConvLayer_PROMISE(var_54, 0.0, 4.76878218793879, conv2d_14_w, -0.3252002420425415, 0.33277199989557193, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.83636313343048, 5.8209967956542945, 9);
-void* var_56 = tensorBatchNorm(var_55, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001);
-void* var_57 = ConvLayer_PROMISE(var_56, -4.2116189937591555, 3.667763746261561, conv2d_15_w, -0.21767465770244598, 0.20771052688360275, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.7635746021270755, 4.714454175949097, 9);
-void* var_58 = tensorBatchNorm(var_57, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001);
-void* var_59 = tensorRelu(var_58);
-void* var_60 = tensorConvolution(var_59, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 384);
-void* var_61 = tensorBatchNorm(var_60, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001);
-void* var_62 = tensorRelu(var_61);
-void* var_63 = ConvLayer_PROMISE(var_62, 0.0, 4.830589411258934, conv2d_16_w, -0.20011539459228517, 0.18881031423807182, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -9.757593467712402, 9.28659096336362, 9);
-void* var_64 = tensorBatchNorm(var_63, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001);
-void* var_65 = tensorAdd(var_56, var_64);
-void* var_66 = ConvLayer_PROMISE(var_65, -6.0708443632125855, 5.517500228881749, conv2d_17_w, -0.19379180744290353, 0.202173922583461, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.838886291503906, 6.482348596572901, 9);
-void* var_67 = tensorBatchNorm(var_66, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001);
-void* var_68 = tensorRelu(var_67);
-void* var_69 = tensorConvolution(var_68, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 384);
-void* var_70 = tensorBatchNorm(var_69, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001);
-void* var_71 = tensorRelu(var_70);
-void* var_72 = ConvLayer_PROMISE(var_71, 0.0, 5.407182216644287, conv2d_18_w, -0.1867049880325794, 0.17993023470044128, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -12.412525278091431, 12.208400741577147, 9);
-void* var_73 = tensorBatchNorm(var_72, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001);
-void* var_74 = tensorAdd(var_65, var_73);
-void* var_75 = ConvLayer_PROMISE(var_74, -7.759848834037781, 7.189491007804856, conv2d_19_w, -0.18295864015817642, 0.1774685539305213, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.372270188331604, 8.098179874420175, 9);
-void* var_76 = tensorBatchNorm(var_75, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001);
-void* var_77 = tensorRelu(var_76);
-void* var_78 = tensorConvolution(var_77, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 384);
-void* var_79 = tensorBatchNorm(var_78, batch_normalization_28_gamma, batch_normalization_28_beta, batch_normalization_28_mean, batch_normalization_28_variance, 0.001);
-void* var_80 = tensorRelu(var_79);
-void* var_81 = ConvLayer_PROMISE(var_80, 0.0, 5.629057416915913, conv2d_20_w, -0.16942347586154938, 0.17993337959051173, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -7.798038047790526, 8.919698917388772, 9);
-void* var_82 = tensorBatchNorm(var_81, batch_normalization_29_gamma, batch_normalization_29_beta, batch_normalization_29_mean, batch_normalization_29_variance, 0.001);
-void* var_83 = tensorAdd(var_74, var_82);
-void* var_84 = ConvLayer_PROMISE(var_83, -9.430036806106568, 8.332214206695502, conv2d_21_w, -0.24210206493735315, 0.2529735609889023, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -11.151032608032226, 14.833457197189365, 9);
-void* var_85 = tensorBatchNorm(var_84, batch_normalization_30_gamma, batch_normalization_30_beta, batch_normalization_30_mean, batch_normalization_30_variance, 0.001);
-void* var_86 = tensorRelu(var_85);
-void* var_87 = tensorConvolution(var_86, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 384);
-void* var_88 = tensorBatchNorm(var_87, batch_normalization_31_gamma, batch_normalization_31_beta, batch_normalization_31_mean, batch_normalization_31_variance, 0.001);
-void* var_89 = tensorRelu(var_88);
-void* var_90 = ConvLayer_PROMISE(var_89, 0.0, 5.798735237121775, conv2d_22_w, -0.1911477698981762, 0.19145021069049817, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.568199612617493, 9.707792984008904, 9);
-void* var_91 = tensorBatchNorm(var_90, batch_normalization_32_gamma, batch_normalization_32_beta, batch_normalization_32_mean, batch_normalization_32_variance, 0.001);
-void* var_92 = ConvLayer_PROMISE(var_91, -3.6258800530433657, 4.978443423271225, conv2d_23_w, -0.14561383947730064, 0.14606056764721925, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.058748731613159, 5.207366097450073, 9);
-void* var_93 = tensorBatchNorm(var_92, batch_normalization_33_gamma, batch_normalization_33_beta, batch_normalization_33_mean, batch_normalization_33_variance, 0.001);
-void* var_94 = tensorRelu(var_93);
-void* var_95 = tensorConvolution(var_94, depthwise_conv2d_12_w, 1, 1, 1, 1, 1, 576);
-void* var_96 = tensorBatchNorm(var_95, batch_normalization_34_gamma, batch_normalization_34_beta, batch_normalization_34_mean, batch_normalization_34_variance, 0.001);
-void* var_97 = tensorRelu(var_96);
-void* var_98 = ConvLayer_PROMISE(var_97, 0.0, 6.51658540868771, conv2d_24_w, -0.14010273113846777, 0.133546221256256, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -16.38492376708984, 12.431352186203089, 9);
-void* var_99 = tensorBatchNorm(var_98, batch_normalization_35_gamma, batch_normalization_35_beta, batch_normalization_35_mean, batch_normalization_35_variance, 0.001);
-void* var_100 = tensorAdd(var_91, var_99);
-void* var_101 = ConvLayer_PROMISE(var_100, -6.121079467773438, 6.870752349853518, conv2d_25_w, -0.13994703620672228, 0.14077512532472705, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.201230587005615, 7.182718342780955, 9);
-void* var_102 = tensorBatchNorm(var_101, batch_normalization_36_gamma, batch_normalization_36_beta, batch_normalization_36_mean, batch_normalization_36_variance, 0.001);
-void* var_103 = tensorRelu(var_102);
-void* var_104 = tensorConvolution(var_103, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 576);
-void* var_105 = tensorBatchNorm(var_104, batch_normalization_37_gamma, batch_normalization_37_beta, batch_normalization_37_mean, batch_normalization_37_variance, 0.001);
-void* var_106 = tensorRelu(var_105);
-void* var_107 = ConvLayer_PROMISE(var_106, 0.0, 6.78510052871718, conv2d_26_w, -0.1351969686150551, 0.12828950628638264, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -12.989627714157105, 12.089402362823513, 9);
-void* var_108 = tensorBatchNorm(var_107, batch_normalization_38_gamma, batch_normalization_38_beta, batch_normalization_38_mean, batch_normalization_38_variance, 0.001);
-void* var_109 = tensorAdd(var_100, var_108);
-void* var_110 = ConvLayer_PROMISE(var_109, -7.789269973754883, 7.7248824281693, conv2d_27_w, -0.1802013835310936, 0.17725828483700806, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -8.86290655517578, 11.057712678909091, 9);
-void* var_111 = tensorBatchNorm(var_110, batch_normalization_39_gamma, batch_normalization_39_beta, batch_normalization_39_mean, batch_normalization_39_variance, 0.001);
-void* var_112 = tensorRelu(var_111);
-void* var_113 = tensorConvolution(var_112, depthwise_conv2d_14_w, 1, 1, 2, 2, 1, 576);
-void* var_114 = tensorBatchNorm(var_113, batch_normalization_40_gamma, batch_normalization_40_beta, batch_normalization_40_mean, batch_normalization_40_variance, 0.001);
-void* var_115 = tensorRelu(var_114);
-void* var_116 = ConvLayer_PROMISE(var_115, 0.0, 7.198854037761778, conv2d_28_w, -0.1413962979018688, 0.13931855550408365, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -9.692179286956787, 7.801771405220015, 9);
-void* var_117 = tensorBatchNorm(var_116, batch_normalization_41_gamma, batch_normalization_41_beta, batch_normalization_41_mean, batch_normalization_41_variance, 0.001);
-void* var_118 = ConvLayer_PROMISE(var_117, -5.154439496040343, 4.208310750007554, conv2d_29_w, -0.10713684476912022, 0.10857602393627158, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -3.840223340034485, 3.906086678028145, 9);
-void* var_119 = tensorBatchNorm(var_118, batch_normalization_42_gamma, batch_normalization_42_beta, batch_normalization_42_mean, batch_normalization_42_variance, 0.001);
-void* var_120 = tensorRelu(var_119);
-void* var_121 = tensorConvolution(var_120, depthwise_conv2d_15_w, 1, 1, 1, 1, 1, 960);
-void* var_122 = tensorBatchNorm(var_121, batch_normalization_43_gamma, batch_normalization_43_beta, batch_normalization_43_mean, batch_normalization_43_variance, 0.001);
-void* var_123 = tensorRelu(var_122);
-void* var_124 = ConvLayer_PROMISE(var_123, 0.0, 5.625304239749944, conv2d_30_w, -0.10913688711822034, 0.10753180256485936, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -7.80785062122345, 9.989215379714604, 9);
-void* var_125 = tensorBatchNorm(var_124, batch_normalization_44_gamma, batch_normalization_44_beta, batch_normalization_44_mean, batch_normalization_44_variance, 0.001);
-void* var_126 = tensorAdd(var_117, var_125);
-void* var_127 = ConvLayer_PROMISE(var_126, -4.683857499122619, 4.920808605194038, conv2d_31_w, -0.11377229495346546, 0.10955536790192154, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -4.129921095848084, 3.6074319915773216, 9);
-void* var_128 = tensorBatchNorm(var_127, batch_normalization_45_gamma, batch_normalization_45_beta, batch_normalization_45_mean, batch_normalization_45_variance, 0.001);
-void* var_129 = tensorRelu(var_128);
-void* var_130 = tensorConvolution(var_129, depthwise_conv2d_16_w, 1, 1, 1, 1, 1, 960);
-void* var_131 = tensorBatchNorm(var_130, batch_normalization_46_gamma, batch_normalization_46_beta, batch_normalization_46_mean, batch_normalization_46_variance, 0.001);
-void* var_132 = tensorRelu(var_131);
-void* var_133 = ConvLayer_PROMISE(var_132, 0.0, 4.8969989051828975, conv2d_32_w, -0.10555544766783714, 0.10780695463716974, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -2.947932963848114, 3.134355350017495, 9);
-void* var_134 = tensorBatchNorm(var_133, batch_normalization_47_gamma, batch_normalization_47_beta, batch_normalization_47_mean, batch_normalization_47_variance, 0.001);
-void* var_135 = tensorAdd(var_126, var_134);
-void* var_136 = ConvLayer_PROMISE(var_135, -5.496015277862549, 5.680448228836048, conv2d_33_w, -0.11888585649430752, 0.11954810336232179, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -6.086169843673707, 5.468563261032134, 9);
-void* var_137 = tensorBatchNorm(var_136, batch_normalization_48_gamma, batch_normalization_48_beta, batch_normalization_48_mean, batch_normalization_48_variance, 0.001);
-void* var_138 = tensorRelu(var_137);
-void* var_139 = tensorConvolution(var_138, depthwise_conv2d_17_w, 1, 1, 1, 1, 1, 960);
-void* var_140 = tensorBatchNorm(var_139, batch_normalization_49_gamma, batch_normalization_49_beta, batch_normalization_49_mean, batch_normalization_49_variance, 0.001);
-void* var_141 = tensorRelu(var_140);
-void* var_142 = ConvLayer_PROMISE(var_141, 0.0, 6.855439195632954, conv2d_34_w, -0.09301538287103175, 0.09316299571096909, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -3.198445457458496, 3.939841930866389, 9);
-void* var_143 = tensorBatchNorm(var_142, batch_normalization_50_gamma, batch_normalization_50_beta, batch_normalization_50_mean, batch_normalization_50_variance, 0.001);
-void* var_144 = ConvLayer_PROMISE(var_143, -2.4313668818473815, 2.9796178574562893, conv2d_35_w, -0.07986876694858074, 0.07952085809409648, NULL, 0, 0, 0, 0, 1, 1, -1, 0, -1, -13.93901468849182, 1.7606397964954392, 9);
-void* var_145 = tensorBatchNorm(var_144, batch_normalization_51_gamma, batch_normalization_51_beta, batch_normalization_51_mean, batch_normalization_51_variance, 0.001);
-void* var_146 = tensorRelu(var_145);
-void* var_147 = tensorPooling(var_146,1,2,2,0,0,2,2);
-void* var_148 = FCLayer_PROMISE(var_147, 0.0, 0.8575248373746913, dense_1_w, -0.12000246234238149, 0.12751513716578475, dense_1_b, -0.15739505, 0.23266713, -1, -9.031755617141723, 13.669795604705882, 9);
-void* var_149 = tensorSoftmax(var_148);
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy2(labels, batch_size, var_149);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-}
-
-dumpExecutionAccuracies();
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/src.cc b/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/src.cc
deleted file mode 100644
index 47fdea27a8376b9e68365c674ee09419c763c651..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/mobilenetv2_quant/src.cc
+++ /dev/null
@@ -1,721 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-std::string dir_prefix = std::string("data/mobilenetv2_quant/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string depthwise_conv2d_1_w_path = dir_prefix + std::string("depthwise_conv2d_1_w.bin");
-void* depthwise_conv2d_1_w = readTrainedWeights(depthwise_conv2d_1_w_path.c_str(), 0,32,1,3,3);
-std::string batch_normalization_1_gamma_path = dir_prefix + std::string("batch_normalization_1_gamma.bin");
-void* batch_normalization_1_gamma = readTrainedWeights(batch_normalization_1_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_beta_path = dir_prefix + std::string("batch_normalization_1_beta.bin");
-void* batch_normalization_1_beta = readTrainedWeights(batch_normalization_1_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_mean_path = dir_prefix + std::string("batch_normalization_1_mean.bin");
-void* batch_normalization_1_mean = readTrainedWeights(batch_normalization_1_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_1_variance_path = dir_prefix + std::string("batch_normalization_1_variance.bin");
-void* batch_normalization_1_variance = readTrainedWeights(batch_normalization_1_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,16,32,1,1);
-std::string batch_normalization_2_gamma_path = dir_prefix + std::string("batch_normalization_2_gamma.bin");
-void* batch_normalization_2_gamma = readTrainedWeights(batch_normalization_2_gamma_path.c_str(), 0,1,16,1,1);
-std::string batch_normalization_2_beta_path = dir_prefix + std::string("batch_normalization_2_beta.bin");
-void* batch_normalization_2_beta = readTrainedWeights(batch_normalization_2_beta_path.c_str(), 0,1,16,1,1);
-std::string batch_normalization_2_mean_path = dir_prefix + std::string("batch_normalization_2_mean.bin");
-void* batch_normalization_2_mean = readTrainedWeights(batch_normalization_2_mean_path.c_str(), 0,1,16,1,1);
-std::string batch_normalization_2_variance_path = dir_prefix + std::string("batch_normalization_2_variance.bin");
-void* batch_normalization_2_variance = readTrainedWeights(batch_normalization_2_variance_path.c_str(), 0,1,16,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,96,16,1,1);
-std::string batch_normalization_3_gamma_path = dir_prefix + std::string("batch_normalization_3_gamma.bin");
-void* batch_normalization_3_gamma = readTrainedWeights(batch_normalization_3_gamma_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_3_beta_path = dir_prefix + std::string("batch_normalization_3_beta.bin");
-void* batch_normalization_3_beta = readTrainedWeights(batch_normalization_3_beta_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_3_mean_path = dir_prefix + std::string("batch_normalization_3_mean.bin");
-void* batch_normalization_3_mean = readTrainedWeights(batch_normalization_3_mean_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_3_variance_path = dir_prefix + std::string("batch_normalization_3_variance.bin");
-void* batch_normalization_3_variance = readTrainedWeights(batch_normalization_3_variance_path.c_str(), 0,1,96,1,1);
-std::string depthwise_conv2d_2_w_path = dir_prefix + std::string("depthwise_conv2d_2_w.bin");
-void* depthwise_conv2d_2_w = readTrainedWeights(depthwise_conv2d_2_w_path.c_str(), 0,96,1,3,3);
-std::string batch_normalization_4_gamma_path = dir_prefix + std::string("batch_normalization_4_gamma.bin");
-void* batch_normalization_4_gamma = readTrainedWeights(batch_normalization_4_gamma_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_4_beta_path = dir_prefix + std::string("batch_normalization_4_beta.bin");
-void* batch_normalization_4_beta = readTrainedWeights(batch_normalization_4_beta_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_4_mean_path = dir_prefix + std::string("batch_normalization_4_mean.bin");
-void* batch_normalization_4_mean = readTrainedWeights(batch_normalization_4_mean_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_4_variance_path = dir_prefix + std::string("batch_normalization_4_variance.bin");
-void* batch_normalization_4_variance = readTrainedWeights(batch_normalization_4_variance_path.c_str(), 0,1,96,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,24,96,1,1);
-std::string batch_normalization_5_gamma_path = dir_prefix + std::string("batch_normalization_5_gamma.bin");
-void* batch_normalization_5_gamma = readTrainedWeights(batch_normalization_5_gamma_path.c_str(), 0,1,24,1,1);
-std::string batch_normalization_5_beta_path = dir_prefix + std::string("batch_normalization_5_beta.bin");
-void* batch_normalization_5_beta = readTrainedWeights(batch_normalization_5_beta_path.c_str(), 0,1,24,1,1);
-std::string batch_normalization_5_mean_path = dir_prefix + std::string("batch_normalization_5_mean.bin");
-void* batch_normalization_5_mean = readTrainedWeights(batch_normalization_5_mean_path.c_str(), 0,1,24,1,1);
-std::string batch_normalization_5_variance_path = dir_prefix + std::string("batch_normalization_5_variance.bin");
-void* batch_normalization_5_variance = readTrainedWeights(batch_normalization_5_variance_path.c_str(), 0,1,24,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,144,24,1,1);
-std::string batch_normalization_6_gamma_path = dir_prefix + std::string("batch_normalization_6_gamma.bin");
-void* batch_normalization_6_gamma = readTrainedWeights(batch_normalization_6_gamma_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_6_beta_path = dir_prefix + std::string("batch_normalization_6_beta.bin");
-void* batch_normalization_6_beta = readTrainedWeights(batch_normalization_6_beta_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_6_mean_path = dir_prefix + std::string("batch_normalization_6_mean.bin");
-void* batch_normalization_6_mean = readTrainedWeights(batch_normalization_6_mean_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_6_variance_path = dir_prefix + std::string("batch_normalization_6_variance.bin");
-void* batch_normalization_6_variance = readTrainedWeights(batch_normalization_6_variance_path.c_str(), 0,1,144,1,1);
-std::string depthwise_conv2d_3_w_path = dir_prefix + std::string("depthwise_conv2d_3_w.bin");
-void* depthwise_conv2d_3_w = readTrainedWeights(depthwise_conv2d_3_w_path.c_str(), 0,144,1,3,3);
-std::string batch_normalization_7_gamma_path = dir_prefix + std::string("batch_normalization_7_gamma.bin");
-void* batch_normalization_7_gamma = readTrainedWeights(batch_normalization_7_gamma_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_7_beta_path = dir_prefix + std::string("batch_normalization_7_beta.bin");
-void* batch_normalization_7_beta = readTrainedWeights(batch_normalization_7_beta_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_7_mean_path = dir_prefix + std::string("batch_normalization_7_mean.bin");
-void* batch_normalization_7_mean = readTrainedWeights(batch_normalization_7_mean_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_7_variance_path = dir_prefix + std::string("batch_normalization_7_variance.bin");
-void* batch_normalization_7_variance = readTrainedWeights(batch_normalization_7_variance_path.c_str(), 0,1,144,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,24,144,1,1);
-std::string batch_normalization_8_gamma_path = dir_prefix + std::string("batch_normalization_8_gamma.bin");
-void* batch_normalization_8_gamma = readTrainedWeights(batch_normalization_8_gamma_path.c_str(), 0,1,24,1,1);
-std::string batch_normalization_8_beta_path = dir_prefix + std::string("batch_normalization_8_beta.bin");
-void* batch_normalization_8_beta = readTrainedWeights(batch_normalization_8_beta_path.c_str(), 0,1,24,1,1);
-std::string batch_normalization_8_mean_path = dir_prefix + std::string("batch_normalization_8_mean.bin");
-void* batch_normalization_8_mean = readTrainedWeights(batch_normalization_8_mean_path.c_str(), 0,1,24,1,1);
-std::string batch_normalization_8_variance_path = dir_prefix + std::string("batch_normalization_8_variance.bin");
-void* batch_normalization_8_variance = readTrainedWeights(batch_normalization_8_variance_path.c_str(), 0,1,24,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,144,24,1,1);
-std::string batch_normalization_9_gamma_path = dir_prefix + std::string("batch_normalization_9_gamma.bin");
-void* batch_normalization_9_gamma = readTrainedWeights(batch_normalization_9_gamma_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_9_beta_path = dir_prefix + std::string("batch_normalization_9_beta.bin");
-void* batch_normalization_9_beta = readTrainedWeights(batch_normalization_9_beta_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_9_mean_path = dir_prefix + std::string("batch_normalization_9_mean.bin");
-void* batch_normalization_9_mean = readTrainedWeights(batch_normalization_9_mean_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_9_variance_path = dir_prefix + std::string("batch_normalization_9_variance.bin");
-void* batch_normalization_9_variance = readTrainedWeights(batch_normalization_9_variance_path.c_str(), 0,1,144,1,1);
-std::string depthwise_conv2d_4_w_path = dir_prefix + std::string("depthwise_conv2d_4_w.bin");
-void* depthwise_conv2d_4_w = readTrainedWeights(depthwise_conv2d_4_w_path.c_str(), 0,144,1,3,3);
-std::string batch_normalization_10_gamma_path = dir_prefix + std::string("batch_normalization_10_gamma.bin");
-void* batch_normalization_10_gamma = readTrainedWeights(batch_normalization_10_gamma_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_10_beta_path = dir_prefix + std::string("batch_normalization_10_beta.bin");
-void* batch_normalization_10_beta = readTrainedWeights(batch_normalization_10_beta_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_10_mean_path = dir_prefix + std::string("batch_normalization_10_mean.bin");
-void* batch_normalization_10_mean = readTrainedWeights(batch_normalization_10_mean_path.c_str(), 0,1,144,1,1);
-std::string batch_normalization_10_variance_path = dir_prefix + std::string("batch_normalization_10_variance.bin");
-void* batch_normalization_10_variance = readTrainedWeights(batch_normalization_10_variance_path.c_str(), 0,1,144,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,144,1,1);
-std::string batch_normalization_11_gamma_path = dir_prefix + std::string("batch_normalization_11_gamma.bin");
-void* batch_normalization_11_gamma = readTrainedWeights(batch_normalization_11_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_11_beta_path = dir_prefix + std::string("batch_normalization_11_beta.bin");
-void* batch_normalization_11_beta = readTrainedWeights(batch_normalization_11_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_11_mean_path = dir_prefix + std::string("batch_normalization_11_mean.bin");
-void* batch_normalization_11_mean = readTrainedWeights(batch_normalization_11_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_11_variance_path = dir_prefix + std::string("batch_normalization_11_variance.bin");
-void* batch_normalization_11_variance = readTrainedWeights(batch_normalization_11_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,192,32,1,1);
-std::string batch_normalization_12_gamma_path = dir_prefix + std::string("batch_normalization_12_gamma.bin");
-void* batch_normalization_12_gamma = readTrainedWeights(batch_normalization_12_gamma_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_12_beta_path = dir_prefix + std::string("batch_normalization_12_beta.bin");
-void* batch_normalization_12_beta = readTrainedWeights(batch_normalization_12_beta_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_12_mean_path = dir_prefix + std::string("batch_normalization_12_mean.bin");
-void* batch_normalization_12_mean = readTrainedWeights(batch_normalization_12_mean_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_12_variance_path = dir_prefix + std::string("batch_normalization_12_variance.bin");
-void* batch_normalization_12_variance = readTrainedWeights(batch_normalization_12_variance_path.c_str(), 0,1,192,1,1);
-std::string depthwise_conv2d_5_w_path = dir_prefix + std::string("depthwise_conv2d_5_w.bin");
-void* depthwise_conv2d_5_w = readTrainedWeights(depthwise_conv2d_5_w_path.c_str(), 0,192,1,3,3);
-std::string batch_normalization_13_gamma_path = dir_prefix + std::string("batch_normalization_13_gamma.bin");
-void* batch_normalization_13_gamma = readTrainedWeights(batch_normalization_13_gamma_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_13_beta_path = dir_prefix + std::string("batch_normalization_13_beta.bin");
-void* batch_normalization_13_beta = readTrainedWeights(batch_normalization_13_beta_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_13_mean_path = dir_prefix + std::string("batch_normalization_13_mean.bin");
-void* batch_normalization_13_mean = readTrainedWeights(batch_normalization_13_mean_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_13_variance_path = dir_prefix + std::string("batch_normalization_13_variance.bin");
-void* batch_normalization_13_variance = readTrainedWeights(batch_normalization_13_variance_path.c_str(), 0,1,192,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,192,1,1);
-std::string batch_normalization_14_gamma_path = dir_prefix + std::string("batch_normalization_14_gamma.bin");
-void* batch_normalization_14_gamma = readTrainedWeights(batch_normalization_14_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_14_beta_path = dir_prefix + std::string("batch_normalization_14_beta.bin");
-void* batch_normalization_14_beta = readTrainedWeights(batch_normalization_14_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_14_mean_path = dir_prefix + std::string("batch_normalization_14_mean.bin");
-void* batch_normalization_14_mean = readTrainedWeights(batch_normalization_14_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_14_variance_path = dir_prefix + std::string("batch_normalization_14_variance.bin");
-void* batch_normalization_14_variance = readTrainedWeights(batch_normalization_14_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,192,32,1,1);
-std::string batch_normalization_15_gamma_path = dir_prefix + std::string("batch_normalization_15_gamma.bin");
-void* batch_normalization_15_gamma = readTrainedWeights(batch_normalization_15_gamma_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_15_beta_path = dir_prefix + std::string("batch_normalization_15_beta.bin");
-void* batch_normalization_15_beta = readTrainedWeights(batch_normalization_15_beta_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_15_mean_path = dir_prefix + std::string("batch_normalization_15_mean.bin");
-void* batch_normalization_15_mean = readTrainedWeights(batch_normalization_15_mean_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_15_variance_path = dir_prefix + std::string("batch_normalization_15_variance.bin");
-void* batch_normalization_15_variance = readTrainedWeights(batch_normalization_15_variance_path.c_str(), 0,1,192,1,1);
-std::string depthwise_conv2d_6_w_path = dir_prefix + std::string("depthwise_conv2d_6_w.bin");
-void* depthwise_conv2d_6_w = readTrainedWeights(depthwise_conv2d_6_w_path.c_str(), 0,192,1,3,3);
-std::string batch_normalization_16_gamma_path = dir_prefix + std::string("batch_normalization_16_gamma.bin");
-void* batch_normalization_16_gamma = readTrainedWeights(batch_normalization_16_gamma_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_16_beta_path = dir_prefix + std::string("batch_normalization_16_beta.bin");
-void* batch_normalization_16_beta = readTrainedWeights(batch_normalization_16_beta_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_16_mean_path = dir_prefix + std::string("batch_normalization_16_mean.bin");
-void* batch_normalization_16_mean = readTrainedWeights(batch_normalization_16_mean_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_16_variance_path = dir_prefix + std::string("batch_normalization_16_variance.bin");
-void* batch_normalization_16_variance = readTrainedWeights(batch_normalization_16_variance_path.c_str(), 0,1,192,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,192,1,1);
-std::string batch_normalization_17_gamma_path = dir_prefix + std::string("batch_normalization_17_gamma.bin");
-void* batch_normalization_17_gamma = readTrainedWeights(batch_normalization_17_gamma_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_17_beta_path = dir_prefix + std::string("batch_normalization_17_beta.bin");
-void* batch_normalization_17_beta = readTrainedWeights(batch_normalization_17_beta_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_17_mean_path = dir_prefix + std::string("batch_normalization_17_mean.bin");
-void* batch_normalization_17_mean = readTrainedWeights(batch_normalization_17_mean_path.c_str(), 0,1,32,1,1);
-std::string batch_normalization_17_variance_path = dir_prefix + std::string("batch_normalization_17_variance.bin");
-void* batch_normalization_17_variance = readTrainedWeights(batch_normalization_17_variance_path.c_str(), 0,1,32,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,192,32,1,1);
-std::string batch_normalization_18_gamma_path = dir_prefix + std::string("batch_normalization_18_gamma.bin");
-void* batch_normalization_18_gamma = readTrainedWeights(batch_normalization_18_gamma_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_18_beta_path = dir_prefix + std::string("batch_normalization_18_beta.bin");
-void* batch_normalization_18_beta = readTrainedWeights(batch_normalization_18_beta_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_18_mean_path = dir_prefix + std::string("batch_normalization_18_mean.bin");
-void* batch_normalization_18_mean = readTrainedWeights(batch_normalization_18_mean_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_18_variance_path = dir_prefix + std::string("batch_normalization_18_variance.bin");
-void* batch_normalization_18_variance = readTrainedWeights(batch_normalization_18_variance_path.c_str(), 0,1,192,1,1);
-std::string depthwise_conv2d_7_w_path = dir_prefix + std::string("depthwise_conv2d_7_w.bin");
-void* depthwise_conv2d_7_w = readTrainedWeights(depthwise_conv2d_7_w_path.c_str(), 0,192,1,3,3);
-std::string batch_normalization_19_gamma_path = dir_prefix + std::string("batch_normalization_19_gamma.bin");
-void* batch_normalization_19_gamma = readTrainedWeights(batch_normalization_19_gamma_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_19_beta_path = dir_prefix + std::string("batch_normalization_19_beta.bin");
-void* batch_normalization_19_beta = readTrainedWeights(batch_normalization_19_beta_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_19_mean_path = dir_prefix + std::string("batch_normalization_19_mean.bin");
-void* batch_normalization_19_mean = readTrainedWeights(batch_normalization_19_mean_path.c_str(), 0,1,192,1,1);
-std::string batch_normalization_19_variance_path = dir_prefix + std::string("batch_normalization_19_variance.bin");
-void* batch_normalization_19_variance = readTrainedWeights(batch_normalization_19_variance_path.c_str(), 0,1,192,1,1);
-std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
-void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,64,192,1,1);
-std::string batch_normalization_20_gamma_path = dir_prefix + std::string("batch_normalization_20_gamma.bin");
-void* batch_normalization_20_gamma = readTrainedWeights(batch_normalization_20_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_20_beta_path = dir_prefix + std::string("batch_normalization_20_beta.bin");
-void* batch_normalization_20_beta = readTrainedWeights(batch_normalization_20_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_20_mean_path = dir_prefix + std::string("batch_normalization_20_mean.bin");
-void* batch_normalization_20_mean = readTrainedWeights(batch_normalization_20_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_20_variance_path = dir_prefix + std::string("batch_normalization_20_variance.bin");
-void* batch_normalization_20_variance = readTrainedWeights(batch_normalization_20_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_15_w_path = dir_prefix + std::string("conv2d_15_w.bin");
-void* conv2d_15_w = readTrainedWeights(conv2d_15_w_path.c_str(), 0,384,64,1,1);
-std::string batch_normalization_21_gamma_path = dir_prefix + std::string("batch_normalization_21_gamma.bin");
-void* batch_normalization_21_gamma = readTrainedWeights(batch_normalization_21_gamma_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_21_beta_path = dir_prefix + std::string("batch_normalization_21_beta.bin");
-void* batch_normalization_21_beta = readTrainedWeights(batch_normalization_21_beta_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_21_mean_path = dir_prefix + std::string("batch_normalization_21_mean.bin");
-void* batch_normalization_21_mean = readTrainedWeights(batch_normalization_21_mean_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_21_variance_path = dir_prefix + std::string("batch_normalization_21_variance.bin");
-void* batch_normalization_21_variance = readTrainedWeights(batch_normalization_21_variance_path.c_str(), 0,1,384,1,1);
-std::string depthwise_conv2d_8_w_path = dir_prefix + std::string("depthwise_conv2d_8_w.bin");
-void* depthwise_conv2d_8_w = readTrainedWeights(depthwise_conv2d_8_w_path.c_str(), 0,384,1,3,3);
-std::string batch_normalization_22_gamma_path = dir_prefix + std::string("batch_normalization_22_gamma.bin");
-void* batch_normalization_22_gamma = readTrainedWeights(batch_normalization_22_gamma_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_22_beta_path = dir_prefix + std::string("batch_normalization_22_beta.bin");
-void* batch_normalization_22_beta = readTrainedWeights(batch_normalization_22_beta_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_22_mean_path = dir_prefix + std::string("batch_normalization_22_mean.bin");
-void* batch_normalization_22_mean = readTrainedWeights(batch_normalization_22_mean_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_22_variance_path = dir_prefix + std::string("batch_normalization_22_variance.bin");
-void* batch_normalization_22_variance = readTrainedWeights(batch_normalization_22_variance_path.c_str(), 0,1,384,1,1);
-std::string conv2d_16_w_path = dir_prefix + std::string("conv2d_16_w.bin");
-void* conv2d_16_w = readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,384,1,1);
-std::string batch_normalization_23_gamma_path = dir_prefix + std::string("batch_normalization_23_gamma.bin");
-void* batch_normalization_23_gamma = readTrainedWeights(batch_normalization_23_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_23_beta_path = dir_prefix + std::string("batch_normalization_23_beta.bin");
-void* batch_normalization_23_beta = readTrainedWeights(batch_normalization_23_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_23_mean_path = dir_prefix + std::string("batch_normalization_23_mean.bin");
-void* batch_normalization_23_mean = readTrainedWeights(batch_normalization_23_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_23_variance_path = dir_prefix + std::string("batch_normalization_23_variance.bin");
-void* batch_normalization_23_variance = readTrainedWeights(batch_normalization_23_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_17_w_path = dir_prefix + std::string("conv2d_17_w.bin");
-void* conv2d_17_w = readTrainedWeights(conv2d_17_w_path.c_str(), 0,384,64,1,1);
-std::string batch_normalization_24_gamma_path = dir_prefix + std::string("batch_normalization_24_gamma.bin");
-void* batch_normalization_24_gamma = readTrainedWeights(batch_normalization_24_gamma_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_24_beta_path = dir_prefix + std::string("batch_normalization_24_beta.bin");
-void* batch_normalization_24_beta = readTrainedWeights(batch_normalization_24_beta_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_24_mean_path = dir_prefix + std::string("batch_normalization_24_mean.bin");
-void* batch_normalization_24_mean = readTrainedWeights(batch_normalization_24_mean_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_24_variance_path = dir_prefix + std::string("batch_normalization_24_variance.bin");
-void* batch_normalization_24_variance = readTrainedWeights(batch_normalization_24_variance_path.c_str(), 0,1,384,1,1);
-std::string depthwise_conv2d_9_w_path = dir_prefix + std::string("depthwise_conv2d_9_w.bin");
-void* depthwise_conv2d_9_w = readTrainedWeights(depthwise_conv2d_9_w_path.c_str(), 0,384,1,3,3);
-std::string batch_normalization_25_gamma_path = dir_prefix + std::string("batch_normalization_25_gamma.bin");
-void* batch_normalization_25_gamma = readTrainedWeights(batch_normalization_25_gamma_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_25_beta_path = dir_prefix + std::string("batch_normalization_25_beta.bin");
-void* batch_normalization_25_beta = readTrainedWeights(batch_normalization_25_beta_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_25_mean_path = dir_prefix + std::string("batch_normalization_25_mean.bin");
-void* batch_normalization_25_mean = readTrainedWeights(batch_normalization_25_mean_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_25_variance_path = dir_prefix + std::string("batch_normalization_25_variance.bin");
-void* batch_normalization_25_variance = readTrainedWeights(batch_normalization_25_variance_path.c_str(), 0,1,384,1,1);
-std::string conv2d_18_w_path = dir_prefix + std::string("conv2d_18_w.bin");
-void* conv2d_18_w = readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,384,1,1);
-std::string batch_normalization_26_gamma_path = dir_prefix + std::string("batch_normalization_26_gamma.bin");
-void* batch_normalization_26_gamma = readTrainedWeights(batch_normalization_26_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_26_beta_path = dir_prefix + std::string("batch_normalization_26_beta.bin");
-void* batch_normalization_26_beta = readTrainedWeights(batch_normalization_26_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_26_mean_path = dir_prefix + std::string("batch_normalization_26_mean.bin");
-void* batch_normalization_26_mean = readTrainedWeights(batch_normalization_26_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_26_variance_path = dir_prefix + std::string("batch_normalization_26_variance.bin");
-void* batch_normalization_26_variance = readTrainedWeights(batch_normalization_26_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_19_w_path = dir_prefix + std::string("conv2d_19_w.bin");
-void* conv2d_19_w = readTrainedWeights(conv2d_19_w_path.c_str(), 0,384,64,1,1);
-std::string batch_normalization_27_gamma_path = dir_prefix + std::string("batch_normalization_27_gamma.bin");
-void* batch_normalization_27_gamma = readTrainedWeights(batch_normalization_27_gamma_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_27_beta_path = dir_prefix + std::string("batch_normalization_27_beta.bin");
-void* batch_normalization_27_beta = readTrainedWeights(batch_normalization_27_beta_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_27_mean_path = dir_prefix + std::string("batch_normalization_27_mean.bin");
-void* batch_normalization_27_mean = readTrainedWeights(batch_normalization_27_mean_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_27_variance_path = dir_prefix + std::string("batch_normalization_27_variance.bin");
-void* batch_normalization_27_variance = readTrainedWeights(batch_normalization_27_variance_path.c_str(), 0,1,384,1,1);
-std::string depthwise_conv2d_10_w_path = dir_prefix + std::string("depthwise_conv2d_10_w.bin");
-void* depthwise_conv2d_10_w = readTrainedWeights(depthwise_conv2d_10_w_path.c_str(), 0,384,1,3,3);
-std::string batch_normalization_28_gamma_path = dir_prefix + std::string("batch_normalization_28_gamma.bin");
-void* batch_normalization_28_gamma = readTrainedWeights(batch_normalization_28_gamma_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_28_beta_path = dir_prefix + std::string("batch_normalization_28_beta.bin");
-void* batch_normalization_28_beta = readTrainedWeights(batch_normalization_28_beta_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_28_mean_path = dir_prefix + std::string("batch_normalization_28_mean.bin");
-void* batch_normalization_28_mean = readTrainedWeights(batch_normalization_28_mean_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_28_variance_path = dir_prefix + std::string("batch_normalization_28_variance.bin");
-void* batch_normalization_28_variance = readTrainedWeights(batch_normalization_28_variance_path.c_str(), 0,1,384,1,1);
-std::string conv2d_20_w_path = dir_prefix + std::string("conv2d_20_w.bin");
-void* conv2d_20_w = readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,384,1,1);
-std::string batch_normalization_29_gamma_path = dir_prefix + std::string("batch_normalization_29_gamma.bin");
-void* batch_normalization_29_gamma = readTrainedWeights(batch_normalization_29_gamma_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_29_beta_path = dir_prefix + std::string("batch_normalization_29_beta.bin");
-void* batch_normalization_29_beta = readTrainedWeights(batch_normalization_29_beta_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_29_mean_path = dir_prefix + std::string("batch_normalization_29_mean.bin");
-void* batch_normalization_29_mean = readTrainedWeights(batch_normalization_29_mean_path.c_str(), 0,1,64,1,1);
-std::string batch_normalization_29_variance_path = dir_prefix + std::string("batch_normalization_29_variance.bin");
-void* batch_normalization_29_variance = readTrainedWeights(batch_normalization_29_variance_path.c_str(), 0,1,64,1,1);
-std::string conv2d_21_w_path = dir_prefix + std::string("conv2d_21_w.bin");
-void* conv2d_21_w = readTrainedWeights(conv2d_21_w_path.c_str(), 0,384,64,1,1);
-std::string batch_normalization_30_gamma_path = dir_prefix + std::string("batch_normalization_30_gamma.bin");
-void* batch_normalization_30_gamma = readTrainedWeights(batch_normalization_30_gamma_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_30_beta_path = dir_prefix + std::string("batch_normalization_30_beta.bin");
-void* batch_normalization_30_beta = readTrainedWeights(batch_normalization_30_beta_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_30_mean_path = dir_prefix + std::string("batch_normalization_30_mean.bin");
-void* batch_normalization_30_mean = readTrainedWeights(batch_normalization_30_mean_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_30_variance_path = dir_prefix + std::string("batch_normalization_30_variance.bin");
-void* batch_normalization_30_variance = readTrainedWeights(batch_normalization_30_variance_path.c_str(), 0,1,384,1,1);
-std::string depthwise_conv2d_11_w_path = dir_prefix + std::string("depthwise_conv2d_11_w.bin");
-void* depthwise_conv2d_11_w = readTrainedWeights(depthwise_conv2d_11_w_path.c_str(), 0,384,1,3,3);
-std::string batch_normalization_31_gamma_path = dir_prefix + std::string("batch_normalization_31_gamma.bin");
-void* batch_normalization_31_gamma = readTrainedWeights(batch_normalization_31_gamma_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_31_beta_path = dir_prefix + std::string("batch_normalization_31_beta.bin");
-void* batch_normalization_31_beta = readTrainedWeights(batch_normalization_31_beta_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_31_mean_path = dir_prefix + std::string("batch_normalization_31_mean.bin");
-void* batch_normalization_31_mean = readTrainedWeights(batch_normalization_31_mean_path.c_str(), 0,1,384,1,1);
-std::string batch_normalization_31_variance_path = dir_prefix + std::string("batch_normalization_31_variance.bin");
-void* batch_normalization_31_variance = readTrainedWeights(batch_normalization_31_variance_path.c_str(), 0,1,384,1,1);
-std::string conv2d_22_w_path = dir_prefix + std::string("conv2d_22_w.bin");
-void* conv2d_22_w = readTrainedWeights(conv2d_22_w_path.c_str(), 0,96,384,1,1);
-std::string batch_normalization_32_gamma_path = dir_prefix + std::string("batch_normalization_32_gamma.bin");
-void* batch_normalization_32_gamma = readTrainedWeights(batch_normalization_32_gamma_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_32_beta_path = dir_prefix + std::string("batch_normalization_32_beta.bin");
-void* batch_normalization_32_beta = readTrainedWeights(batch_normalization_32_beta_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_32_mean_path = dir_prefix + std::string("batch_normalization_32_mean.bin");
-void* batch_normalization_32_mean = readTrainedWeights(batch_normalization_32_mean_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_32_variance_path = dir_prefix + std::string("batch_normalization_32_variance.bin");
-void* batch_normalization_32_variance = readTrainedWeights(batch_normalization_32_variance_path.c_str(), 0,1,96,1,1);
-std::string conv2d_23_w_path = dir_prefix + std::string("conv2d_23_w.bin");
-void* conv2d_23_w = readTrainedWeights(conv2d_23_w_path.c_str(), 0,576,96,1,1);
-std::string batch_normalization_33_gamma_path = dir_prefix + std::string("batch_normalization_33_gamma.bin");
-void* batch_normalization_33_gamma = readTrainedWeights(batch_normalization_33_gamma_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_33_beta_path = dir_prefix + std::string("batch_normalization_33_beta.bin");
-void* batch_normalization_33_beta = readTrainedWeights(batch_normalization_33_beta_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_33_mean_path = dir_prefix + std::string("batch_normalization_33_mean.bin");
-void* batch_normalization_33_mean = readTrainedWeights(batch_normalization_33_mean_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_33_variance_path = dir_prefix + std::string("batch_normalization_33_variance.bin");
-void* batch_normalization_33_variance = readTrainedWeights(batch_normalization_33_variance_path.c_str(), 0,1,576,1,1);
-std::string depthwise_conv2d_12_w_path = dir_prefix + std::string("depthwise_conv2d_12_w.bin");
-void* depthwise_conv2d_12_w = readTrainedWeights(depthwise_conv2d_12_w_path.c_str(), 0,576,1,3,3);
-std::string batch_normalization_34_gamma_path = dir_prefix + std::string("batch_normalization_34_gamma.bin");
-void* batch_normalization_34_gamma = readTrainedWeights(batch_normalization_34_gamma_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_34_beta_path = dir_prefix + std::string("batch_normalization_34_beta.bin");
-void* batch_normalization_34_beta = readTrainedWeights(batch_normalization_34_beta_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_34_mean_path = dir_prefix + std::string("batch_normalization_34_mean.bin");
-void* batch_normalization_34_mean = readTrainedWeights(batch_normalization_34_mean_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_34_variance_path = dir_prefix + std::string("batch_normalization_34_variance.bin");
-void* batch_normalization_34_variance = readTrainedWeights(batch_normalization_34_variance_path.c_str(), 0,1,576,1,1);
-std::string conv2d_24_w_path = dir_prefix + std::string("conv2d_24_w.bin");
-void* conv2d_24_w = readTrainedWeights(conv2d_24_w_path.c_str(), 0,96,576,1,1);
-std::string batch_normalization_35_gamma_path = dir_prefix + std::string("batch_normalization_35_gamma.bin");
-void* batch_normalization_35_gamma = readTrainedWeights(batch_normalization_35_gamma_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_35_beta_path = dir_prefix + std::string("batch_normalization_35_beta.bin");
-void* batch_normalization_35_beta = readTrainedWeights(batch_normalization_35_beta_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_35_mean_path = dir_prefix + std::string("batch_normalization_35_mean.bin");
-void* batch_normalization_35_mean = readTrainedWeights(batch_normalization_35_mean_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_35_variance_path = dir_prefix + std::string("batch_normalization_35_variance.bin");
-void* batch_normalization_35_variance = readTrainedWeights(batch_normalization_35_variance_path.c_str(), 0,1,96,1,1);
-std::string conv2d_25_w_path = dir_prefix + std::string("conv2d_25_w.bin");
-void* conv2d_25_w = readTrainedWeights(conv2d_25_w_path.c_str(), 0,576,96,1,1);
-std::string batch_normalization_36_gamma_path = dir_prefix + std::string("batch_normalization_36_gamma.bin");
-void* batch_normalization_36_gamma = readTrainedWeights(batch_normalization_36_gamma_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_36_beta_path = dir_prefix + std::string("batch_normalization_36_beta.bin");
-void* batch_normalization_36_beta = readTrainedWeights(batch_normalization_36_beta_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_36_mean_path = dir_prefix + std::string("batch_normalization_36_mean.bin");
-void* batch_normalization_36_mean = readTrainedWeights(batch_normalization_36_mean_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_36_variance_path = dir_prefix + std::string("batch_normalization_36_variance.bin");
-void* batch_normalization_36_variance = readTrainedWeights(batch_normalization_36_variance_path.c_str(), 0,1,576,1,1);
-std::string depthwise_conv2d_13_w_path = dir_prefix + std::string("depthwise_conv2d_13_w.bin");
-void* depthwise_conv2d_13_w = readTrainedWeights(depthwise_conv2d_13_w_path.c_str(), 0,576,1,3,3);
-std::string batch_normalization_37_gamma_path = dir_prefix + std::string("batch_normalization_37_gamma.bin");
-void* batch_normalization_37_gamma = readTrainedWeights(batch_normalization_37_gamma_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_37_beta_path = dir_prefix + std::string("batch_normalization_37_beta.bin");
-void* batch_normalization_37_beta = readTrainedWeights(batch_normalization_37_beta_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_37_mean_path = dir_prefix + std::string("batch_normalization_37_mean.bin");
-void* batch_normalization_37_mean = readTrainedWeights(batch_normalization_37_mean_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_37_variance_path = dir_prefix + std::string("batch_normalization_37_variance.bin");
-void* batch_normalization_37_variance = readTrainedWeights(batch_normalization_37_variance_path.c_str(), 0,1,576,1,1);
-std::string conv2d_26_w_path = dir_prefix + std::string("conv2d_26_w.bin");
-void* conv2d_26_w = readTrainedWeights(conv2d_26_w_path.c_str(), 0,96,576,1,1);
-std::string batch_normalization_38_gamma_path = dir_prefix + std::string("batch_normalization_38_gamma.bin");
-void* batch_normalization_38_gamma = readTrainedWeights(batch_normalization_38_gamma_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_38_beta_path = dir_prefix + std::string("batch_normalization_38_beta.bin");
-void* batch_normalization_38_beta = readTrainedWeights(batch_normalization_38_beta_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_38_mean_path = dir_prefix + std::string("batch_normalization_38_mean.bin");
-void* batch_normalization_38_mean = readTrainedWeights(batch_normalization_38_mean_path.c_str(), 0,1,96,1,1);
-std::string batch_normalization_38_variance_path = dir_prefix + std::string("batch_normalization_38_variance.bin");
-void* batch_normalization_38_variance = readTrainedWeights(batch_normalization_38_variance_path.c_str(), 0,1,96,1,1);
-std::string conv2d_27_w_path = dir_prefix + std::string("conv2d_27_w.bin");
-void* conv2d_27_w = readTrainedWeights(conv2d_27_w_path.c_str(), 0,576,96,1,1);
-std::string batch_normalization_39_gamma_path = dir_prefix + std::string("batch_normalization_39_gamma.bin");
-void* batch_normalization_39_gamma = readTrainedWeights(batch_normalization_39_gamma_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_39_beta_path = dir_prefix + std::string("batch_normalization_39_beta.bin");
-void* batch_normalization_39_beta = readTrainedWeights(batch_normalization_39_beta_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_39_mean_path = dir_prefix + std::string("batch_normalization_39_mean.bin");
-void* batch_normalization_39_mean = readTrainedWeights(batch_normalization_39_mean_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_39_variance_path = dir_prefix + std::string("batch_normalization_39_variance.bin");
-void* batch_normalization_39_variance = readTrainedWeights(batch_normalization_39_variance_path.c_str(), 0,1,576,1,1);
-std::string depthwise_conv2d_14_w_path = dir_prefix + std::string("depthwise_conv2d_14_w.bin");
-void* depthwise_conv2d_14_w = readTrainedWeights(depthwise_conv2d_14_w_path.c_str(), 0,576,1,3,3);
-std::string batch_normalization_40_gamma_path = dir_prefix + std::string("batch_normalization_40_gamma.bin");
-void* batch_normalization_40_gamma = readTrainedWeights(batch_normalization_40_gamma_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_40_beta_path = dir_prefix + std::string("batch_normalization_40_beta.bin");
-void* batch_normalization_40_beta = readTrainedWeights(batch_normalization_40_beta_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_40_mean_path = dir_prefix + std::string("batch_normalization_40_mean.bin");
-void* batch_normalization_40_mean = readTrainedWeights(batch_normalization_40_mean_path.c_str(), 0,1,576,1,1);
-std::string batch_normalization_40_variance_path = dir_prefix + std::string("batch_normalization_40_variance.bin");
-void* batch_normalization_40_variance = readTrainedWeights(batch_normalization_40_variance_path.c_str(), 0,1,576,1,1);
-std::string conv2d_28_w_path = dir_prefix + std::string("conv2d_28_w.bin");
-void* conv2d_28_w = readTrainedWeights(conv2d_28_w_path.c_str(), 0,160,576,1,1);
-std::string batch_normalization_41_gamma_path = dir_prefix + std::string("batch_normalization_41_gamma.bin");
-void* batch_normalization_41_gamma = readTrainedWeights(batch_normalization_41_gamma_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_41_beta_path = dir_prefix + std::string("batch_normalization_41_beta.bin");
-void* batch_normalization_41_beta = readTrainedWeights(batch_normalization_41_beta_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_41_mean_path = dir_prefix + std::string("batch_normalization_41_mean.bin");
-void* batch_normalization_41_mean = readTrainedWeights(batch_normalization_41_mean_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_41_variance_path = dir_prefix + std::string("batch_normalization_41_variance.bin");
-void* batch_normalization_41_variance = readTrainedWeights(batch_normalization_41_variance_path.c_str(), 0,1,160,1,1);
-std::string conv2d_29_w_path = dir_prefix + std::string("conv2d_29_w.bin");
-void* conv2d_29_w = readTrainedWeights(conv2d_29_w_path.c_str(), 0,960,160,1,1);
-std::string batch_normalization_42_gamma_path = dir_prefix + std::string("batch_normalization_42_gamma.bin");
-void* batch_normalization_42_gamma = readTrainedWeights(batch_normalization_42_gamma_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_42_beta_path = dir_prefix + std::string("batch_normalization_42_beta.bin");
-void* batch_normalization_42_beta = readTrainedWeights(batch_normalization_42_beta_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_42_mean_path = dir_prefix + std::string("batch_normalization_42_mean.bin");
-void* batch_normalization_42_mean = readTrainedWeights(batch_normalization_42_mean_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_42_variance_path = dir_prefix + std::string("batch_normalization_42_variance.bin");
-void* batch_normalization_42_variance = readTrainedWeights(batch_normalization_42_variance_path.c_str(), 0,1,960,1,1);
-std::string depthwise_conv2d_15_w_path = dir_prefix + std::string("depthwise_conv2d_15_w.bin");
-void* depthwise_conv2d_15_w = readTrainedWeights(depthwise_conv2d_15_w_path.c_str(), 0,960,1,3,3);
-std::string batch_normalization_43_gamma_path = dir_prefix + std::string("batch_normalization_43_gamma.bin");
-void* batch_normalization_43_gamma = readTrainedWeights(batch_normalization_43_gamma_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_43_beta_path = dir_prefix + std::string("batch_normalization_43_beta.bin");
-void* batch_normalization_43_beta = readTrainedWeights(batch_normalization_43_beta_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_43_mean_path = dir_prefix + std::string("batch_normalization_43_mean.bin");
-void* batch_normalization_43_mean = readTrainedWeights(batch_normalization_43_mean_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_43_variance_path = dir_prefix + std::string("batch_normalization_43_variance.bin");
-void* batch_normalization_43_variance = readTrainedWeights(batch_normalization_43_variance_path.c_str(), 0,1,960,1,1);
-std::string conv2d_30_w_path = dir_prefix + std::string("conv2d_30_w.bin");
-void* conv2d_30_w = readTrainedWeights(conv2d_30_w_path.c_str(), 0,160,960,1,1);
-std::string batch_normalization_44_gamma_path = dir_prefix + std::string("batch_normalization_44_gamma.bin");
-void* batch_normalization_44_gamma = readTrainedWeights(batch_normalization_44_gamma_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_44_beta_path = dir_prefix + std::string("batch_normalization_44_beta.bin");
-void* batch_normalization_44_beta = readTrainedWeights(batch_normalization_44_beta_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_44_mean_path = dir_prefix + std::string("batch_normalization_44_mean.bin");
-void* batch_normalization_44_mean = readTrainedWeights(batch_normalization_44_mean_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_44_variance_path = dir_prefix + std::string("batch_normalization_44_variance.bin");
-void* batch_normalization_44_variance = readTrainedWeights(batch_normalization_44_variance_path.c_str(), 0,1,160,1,1);
-std::string conv2d_31_w_path = dir_prefix + std::string("conv2d_31_w.bin");
-void* conv2d_31_w = readTrainedWeights(conv2d_31_w_path.c_str(), 0,960,160,1,1);
-std::string batch_normalization_45_gamma_path = dir_prefix + std::string("batch_normalization_45_gamma.bin");
-void* batch_normalization_45_gamma = readTrainedWeights(batch_normalization_45_gamma_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_45_beta_path = dir_prefix + std::string("batch_normalization_45_beta.bin");
-void* batch_normalization_45_beta = readTrainedWeights(batch_normalization_45_beta_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_45_mean_path = dir_prefix + std::string("batch_normalization_45_mean.bin");
-void* batch_normalization_45_mean = readTrainedWeights(batch_normalization_45_mean_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_45_variance_path = dir_prefix + std::string("batch_normalization_45_variance.bin");
-void* batch_normalization_45_variance = readTrainedWeights(batch_normalization_45_variance_path.c_str(), 0,1,960,1,1);
-std::string depthwise_conv2d_16_w_path = dir_prefix + std::string("depthwise_conv2d_16_w.bin");
-void* depthwise_conv2d_16_w = readTrainedWeights(depthwise_conv2d_16_w_path.c_str(), 0,960,1,3,3);
-std::string batch_normalization_46_gamma_path = dir_prefix + std::string("batch_normalization_46_gamma.bin");
-void* batch_normalization_46_gamma = readTrainedWeights(batch_normalization_46_gamma_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_46_beta_path = dir_prefix + std::string("batch_normalization_46_beta.bin");
-void* batch_normalization_46_beta = readTrainedWeights(batch_normalization_46_beta_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_46_mean_path = dir_prefix + std::string("batch_normalization_46_mean.bin");
-void* batch_normalization_46_mean = readTrainedWeights(batch_normalization_46_mean_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_46_variance_path = dir_prefix + std::string("batch_normalization_46_variance.bin");
-void* batch_normalization_46_variance = readTrainedWeights(batch_normalization_46_variance_path.c_str(), 0,1,960,1,1);
-std::string conv2d_32_w_path = dir_prefix + std::string("conv2d_32_w.bin");
-void* conv2d_32_w = readTrainedWeights(conv2d_32_w_path.c_str(), 0,160,960,1,1);
-std::string batch_normalization_47_gamma_path = dir_prefix + std::string("batch_normalization_47_gamma.bin");
-void* batch_normalization_47_gamma = readTrainedWeights(batch_normalization_47_gamma_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_47_beta_path = dir_prefix + std::string("batch_normalization_47_beta.bin");
-void* batch_normalization_47_beta = readTrainedWeights(batch_normalization_47_beta_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_47_mean_path = dir_prefix + std::string("batch_normalization_47_mean.bin");
-void* batch_normalization_47_mean = readTrainedWeights(batch_normalization_47_mean_path.c_str(), 0,1,160,1,1);
-std::string batch_normalization_47_variance_path = dir_prefix + std::string("batch_normalization_47_variance.bin");
-void* batch_normalization_47_variance = readTrainedWeights(batch_normalization_47_variance_path.c_str(), 0,1,160,1,1);
-std::string conv2d_33_w_path = dir_prefix + std::string("conv2d_33_w.bin");
-void* conv2d_33_w = readTrainedWeights(conv2d_33_w_path.c_str(), 0,960,160,1,1);
-std::string batch_normalization_48_gamma_path = dir_prefix + std::string("batch_normalization_48_gamma.bin");
-void* batch_normalization_48_gamma = readTrainedWeights(batch_normalization_48_gamma_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_48_beta_path = dir_prefix + std::string("batch_normalization_48_beta.bin");
-void* batch_normalization_48_beta = readTrainedWeights(batch_normalization_48_beta_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_48_mean_path = dir_prefix + std::string("batch_normalization_48_mean.bin");
-void* batch_normalization_48_mean = readTrainedWeights(batch_normalization_48_mean_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_48_variance_path = dir_prefix + std::string("batch_normalization_48_variance.bin");
-void* batch_normalization_48_variance = readTrainedWeights(batch_normalization_48_variance_path.c_str(), 0,1,960,1,1);
-std::string depthwise_conv2d_17_w_path = dir_prefix + std::string("depthwise_conv2d_17_w.bin");
-void* depthwise_conv2d_17_w = readTrainedWeights(depthwise_conv2d_17_w_path.c_str(), 0,960,1,3,3);
-std::string batch_normalization_49_gamma_path = dir_prefix + std::string("batch_normalization_49_gamma.bin");
-void* batch_normalization_49_gamma = readTrainedWeights(batch_normalization_49_gamma_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_49_beta_path = dir_prefix + std::string("batch_normalization_49_beta.bin");
-void* batch_normalization_49_beta = readTrainedWeights(batch_normalization_49_beta_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_49_mean_path = dir_prefix + std::string("batch_normalization_49_mean.bin");
-void* batch_normalization_49_mean = readTrainedWeights(batch_normalization_49_mean_path.c_str(), 0,1,960,1,1);
-std::string batch_normalization_49_variance_path = dir_prefix + std::string("batch_normalization_49_variance.bin");
-void* batch_normalization_49_variance = readTrainedWeights(batch_normalization_49_variance_path.c_str(), 0,1,960,1,1);
-std::string conv2d_34_w_path = dir_prefix + std::string("conv2d_34_w.bin");
-void* conv2d_34_w = readTrainedWeights(conv2d_34_w_path.c_str(), 0,320,960,1,1);
-std::string batch_normalization_50_gamma_path = dir_prefix + std::string("batch_normalization_50_gamma.bin");
-void* batch_normalization_50_gamma = readTrainedWeights(batch_normalization_50_gamma_path.c_str(), 0,1,320,1,1);
-std::string batch_normalization_50_beta_path = dir_prefix + std::string("batch_normalization_50_beta.bin");
-void* batch_normalization_50_beta = readTrainedWeights(batch_normalization_50_beta_path.c_str(), 0,1,320,1,1);
-std::string batch_normalization_50_mean_path = dir_prefix + std::string("batch_normalization_50_mean.bin");
-void* batch_normalization_50_mean = readTrainedWeights(batch_normalization_50_mean_path.c_str(), 0,1,320,1,1);
-std::string batch_normalization_50_variance_path = dir_prefix + std::string("batch_normalization_50_variance.bin");
-void* batch_normalization_50_variance = readTrainedWeights(batch_normalization_50_variance_path.c_str(), 0,1,320,1,1);
-std::string conv2d_35_w_path = dir_prefix + std::string("conv2d_35_w.bin");
-void* conv2d_35_w = readTrainedWeights(conv2d_35_w_path.c_str(), 0,1280,320,1,1);
-std::string batch_normalization_51_gamma_path = dir_prefix + std::string("batch_normalization_51_gamma.bin");
-void* batch_normalization_51_gamma = readTrainedWeights(batch_normalization_51_gamma_path.c_str(), 0,1,1280,1,1);
-std::string batch_normalization_51_beta_path = dir_prefix + std::string("batch_normalization_51_beta.bin");
-void* batch_normalization_51_beta = readTrainedWeights(batch_normalization_51_beta_path.c_str(), 0,1,1280,1,1);
-std::string batch_normalization_51_mean_path = dir_prefix + std::string("batch_normalization_51_mean.bin");
-void* batch_normalization_51_mean = readTrainedWeights(batch_normalization_51_mean_path.c_str(), 0,1,1280,1,1);
-std::string batch_normalization_51_variance_path = dir_prefix + std::string("batch_normalization_51_variance.bin");
-void* batch_normalization_51_variance = readTrainedWeights(batch_normalization_51_variance_path.c_str(), 0,1,1280,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,5120,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-
-startMemTracking();
-
-int test_input_size = 10000;
-int batch_size = 10000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_2 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 1);
-void* var_4 = tensorConvolution(var_2, depthwise_conv2d_1_w, 1, 1, 1, 1, 1, 32);
-void* var_5 = tensorBatchNorm(var_4, batch_normalization_1_gamma, batch_normalization_1_beta, batch_normalization_1_mean, batch_normalization_1_variance, 0.001);
-void* var_6 = tensorRelu(var_5);
-void* var_7 = tensorConvolution(var_6, conv2d_2_w, 0, 0, 1, 1, 1, 1);
-void* var_8 = tensorBatchNorm(var_7, batch_normalization_2_gamma, batch_normalization_2_beta, batch_normalization_2_mean, batch_normalization_2_variance, 0.001);
-void* var_9 = tensorConvolution(var_8, conv2d_3_w, 0, 0, 1, 1, 1, 1);
-void* var_10 = tensorBatchNorm(var_9, batch_normalization_3_gamma, batch_normalization_3_beta, batch_normalization_3_mean, batch_normalization_3_variance, 0.001);
-void* var_11 = tensorRelu(var_10);
-void* var_13 = tensorConvolution(var_11, depthwise_conv2d_2_w, 1, 1, 1, 1, 1, 96);
-void* var_14 = tensorBatchNorm(var_13, batch_normalization_4_gamma, batch_normalization_4_beta, batch_normalization_4_mean, batch_normalization_4_variance, 0.001);
-void* var_15 = tensorRelu(var_14);
-void* var_16 = tensorConvolution(var_15, conv2d_4_w, 0, 0, 1, 1, 1, 1);
-void* var_17 = tensorBatchNorm(var_16, batch_normalization_5_gamma, batch_normalization_5_beta, batch_normalization_5_mean, batch_normalization_5_variance, 0.001);
-void* var_18 = tensorConvolution(var_17, conv2d_5_w, 0, 0, 1, 1, 1, 1);
-void* var_19 = tensorBatchNorm(var_18, batch_normalization_6_gamma, batch_normalization_6_beta, batch_normalization_6_mean, batch_normalization_6_variance, 0.001);
-void* var_20 = tensorRelu(var_19);
-void* var_22 = tensorConvolution(var_20, depthwise_conv2d_3_w, 1, 1, 1, 1, 1, 144);
-void* var_23 = tensorBatchNorm(var_22, batch_normalization_7_gamma, batch_normalization_7_beta, batch_normalization_7_mean, batch_normalization_7_variance, 0.001);
-void* var_24 = tensorRelu(var_23);
-void* var_25 = tensorConvolution(var_24, conv2d_6_w, 0, 0, 1, 1, 1, 1);
-void* var_26 = tensorBatchNorm(var_25, batch_normalization_8_gamma, batch_normalization_8_beta, batch_normalization_8_mean, batch_normalization_8_variance, 0.001);
-void* var_27 = tensorAdd(var_17, var_26);
-void* var_28 = tensorConvolution(var_27, conv2d_7_w, 0, 0, 1, 1, 1, 1);
-void* var_29 = tensorBatchNorm(var_28, batch_normalization_9_gamma, batch_normalization_9_beta, batch_normalization_9_mean, batch_normalization_9_variance, 0.001);
-void* var_30 = tensorRelu(var_29);
-void* var_32 = tensorConvolution(var_30, depthwise_conv2d_4_w, 1, 1, 2, 2, 1, 144);
-void* var_33 = tensorBatchNorm(var_32, batch_normalization_10_gamma, batch_normalization_10_beta, batch_normalization_10_mean, batch_normalization_10_variance, 0.001);
-void* var_34 = tensorRelu(var_33);
-void* var_35 = tensorConvolution(var_34, conv2d_8_w, 0, 0, 1, 1, 1, 1);
-void* var_36 = tensorBatchNorm(var_35, batch_normalization_11_gamma, batch_normalization_11_beta, batch_normalization_11_mean, batch_normalization_11_variance, 0.001);
-void* var_37 = tensorConvolution(var_36, conv2d_9_w, 0, 0, 1, 1, 1, 1);
-void* var_38 = tensorBatchNorm(var_37, batch_normalization_12_gamma, batch_normalization_12_beta, batch_normalization_12_mean, batch_normalization_12_variance, 0.001);
-void* var_39 = tensorRelu(var_38);
-void* var_41 = tensorConvolution(var_39, depthwise_conv2d_5_w, 1, 1, 1, 1, 1, 192);
-void* var_42 = tensorBatchNorm(var_41, batch_normalization_13_gamma, batch_normalization_13_beta, batch_normalization_13_mean, batch_normalization_13_variance, 0.001);
-void* var_43 = tensorRelu(var_42);
-void* var_44 = tensorConvolution(var_43, conv2d_10_w, 0, 0, 1, 1, 1, 1);
-void* var_45 = tensorBatchNorm(var_44, batch_normalization_14_gamma, batch_normalization_14_beta, batch_normalization_14_mean, batch_normalization_14_variance, 0.001);
-void* var_46 = tensorAdd(var_36, var_45);
-void* var_47 = tensorConvolution(var_46, conv2d_11_w, 0, 0, 1, 1, 1, 1);
-void* var_48 = tensorBatchNorm(var_47, batch_normalization_15_gamma, batch_normalization_15_beta, batch_normalization_15_mean, batch_normalization_15_variance, 0.001);
-void* var_49 = tensorRelu(var_48);
-void* var_51 = tensorConvolution(var_49, depthwise_conv2d_6_w, 1, 1, 1, 1, 1, 192);
-void* var_52 = tensorBatchNorm(var_51, batch_normalization_16_gamma, batch_normalization_16_beta, batch_normalization_16_mean, batch_normalization_16_variance, 0.001);
-void* var_53 = tensorRelu(var_52);
-void* var_54 = tensorConvolution(var_53, conv2d_12_w, 0, 0, 1, 1, 1, 1);
-void* var_55 = tensorBatchNorm(var_54, batch_normalization_17_gamma, batch_normalization_17_beta, batch_normalization_17_mean, batch_normalization_17_variance, 0.001);
-void* var_56 = tensorAdd(var_46, var_55);
-void* var_57 = tensorConvolution(var_56, conv2d_13_w, 0, 0, 1, 1, 1, 1);
-void* var_58 = tensorBatchNorm(var_57, batch_normalization_18_gamma, batch_normalization_18_beta, batch_normalization_18_mean, batch_normalization_18_variance, 0.001);
-void* var_59 = tensorRelu(var_58);
-void* var_61 = tensorConvolution(var_59, depthwise_conv2d_7_w, 1, 1, 2, 2, 1, 192);
-void* var_62 = tensorBatchNorm(var_61, batch_normalization_19_gamma, batch_normalization_19_beta, batch_normalization_19_mean, batch_normalization_19_variance, 0.001);
-void* var_63 = tensorRelu(var_62);
-void* var_64 = tensorConvolution(var_63, conv2d_14_w, 0, 0, 1, 1, 1, 1);
-void* var_65 = tensorBatchNorm(var_64, batch_normalization_20_gamma, batch_normalization_20_beta, batch_normalization_20_mean, batch_normalization_20_variance, 0.001);
-void* var_66 = tensorConvolution(var_65, conv2d_15_w, 0, 0, 1, 1, 1, 1);
-void* var_67 = tensorBatchNorm(var_66, batch_normalization_21_gamma, batch_normalization_21_beta, batch_normalization_21_mean, batch_normalization_21_variance, 0.001);
-void* var_68 = tensorRelu(var_67);
-void* var_70 = tensorConvolution(var_68, depthwise_conv2d_8_w, 1, 1, 1, 1, 1, 384);
-void* var_71 = tensorBatchNorm(var_70, batch_normalization_22_gamma, batch_normalization_22_beta, batch_normalization_22_mean, batch_normalization_22_variance, 0.001);
-void* var_72 = tensorRelu(var_71);
-void* var_73 = tensorConvolution(var_72, conv2d_16_w, 0, 0, 1, 1, 1, 1);
-void* var_74 = tensorBatchNorm(var_73, batch_normalization_23_gamma, batch_normalization_23_beta, batch_normalization_23_mean, batch_normalization_23_variance, 0.001);
-void* var_75 = tensorAdd(var_65, var_74);
-void* var_76 = tensorConvolution(var_75, conv2d_17_w, 0, 0, 1, 1, 1, 1);
-void* var_77 = tensorBatchNorm(var_76, batch_normalization_24_gamma, batch_normalization_24_beta, batch_normalization_24_mean, batch_normalization_24_variance, 0.001);
-void* var_78 = tensorRelu(var_77);
-void* var_80 = tensorConvolution(var_78, depthwise_conv2d_9_w, 1, 1, 1, 1, 1, 384);
-void* var_81 = tensorBatchNorm(var_80, batch_normalization_25_gamma, batch_normalization_25_beta, batch_normalization_25_mean, batch_normalization_25_variance, 0.001);
-void* var_82 = tensorRelu(var_81);
-void* var_83 = tensorConvolution(var_82, conv2d_18_w, 0, 0, 1, 1, 1, 1);
-void* var_84 = tensorBatchNorm(var_83, batch_normalization_26_gamma, batch_normalization_26_beta, batch_normalization_26_mean, batch_normalization_26_variance, 0.001);
-void* var_85 = tensorAdd(var_75, var_84);
-void* var_86 = tensorConvolution(var_85, conv2d_19_w, 0, 0, 1, 1, 1, 1);
-void* var_87 = tensorBatchNorm(var_86, batch_normalization_27_gamma, batch_normalization_27_beta, batch_normalization_27_mean, batch_normalization_27_variance, 0.001);
-void* var_88 = tensorRelu(var_87);
-void* var_90 = tensorConvolution(var_88, depthwise_conv2d_10_w, 1, 1, 1, 1, 1, 384);
-void* var_91 = tensorBatchNorm(var_90, batch_normalization_28_gamma, batch_normalization_28_beta, batch_normalization_28_mean, batch_normalization_28_variance, 0.001);
-void* var_92 = tensorRelu(var_91);
-void* var_93 = tensorConvolution(var_92, conv2d_20_w, 0, 0, 1, 1, 1, 1);
-void* var_94 = tensorBatchNorm(var_93, batch_normalization_29_gamma, batch_normalization_29_beta, batch_normalization_29_mean, batch_normalization_29_variance, 0.001);
-void* var_95 = tensorAdd(var_85, var_94);
-void* var_97 = tensorConvolution(var_95, conv2d_21_w, 0, 0, 1, 1, 1, 1);
-void* var_98 = tensorBatchNorm(var_97, batch_normalization_30_gamma, batch_normalization_30_beta, batch_normalization_30_mean, batch_normalization_30_variance, 0.001);
-void* var_99 = tensorRelu(var_98);
-void* var_101 = tensorConvolution(var_99, depthwise_conv2d_11_w, 1, 1, 1, 1, 1, 384);
-void* var_102 = tensorBatchNorm(var_101, batch_normalization_31_gamma, batch_normalization_31_beta, batch_normalization_31_mean, batch_normalization_31_variance, 0.001);
-void* var_103 = tensorRelu(var_102);
-void* var_104 = tensorConvolution(var_103, conv2d_22_w, 0, 0, 1, 1, 1, 1);
-void* var_105 = tensorBatchNorm(var_104, batch_normalization_32_gamma, batch_normalization_32_beta, batch_normalization_32_mean, batch_normalization_32_variance, 0.001);
-void* var_106 = tensorConvolution(var_105, conv2d_23_w, 0, 0, 1, 1, 1, 1);
-void* var_107 = tensorBatchNorm(var_106, batch_normalization_33_gamma, batch_normalization_33_beta, batch_normalization_33_mean, batch_normalization_33_variance, 0.001);
-void* var_108 = tensorRelu(var_107);
-void* var_110 = tensorConvolution(var_108, depthwise_conv2d_12_w, 1, 1, 1, 1, 1, 576);
-void* var_111 = tensorBatchNorm(var_110, batch_normalization_34_gamma, batch_normalization_34_beta, batch_normalization_34_mean, batch_normalization_34_variance, 0.001);
-void* var_112 = tensorRelu(var_111);
-void* var_113 = tensorConvolution(var_112, conv2d_24_w, 0, 0, 1, 1, 1, 1);
-void* var_114 = tensorBatchNorm(var_113, batch_normalization_35_gamma, batch_normalization_35_beta, batch_normalization_35_mean, batch_normalization_35_variance, 0.001);
-void* var_115 = tensorAdd(var_105, var_114);
-void* var_116 = tensorConvolution(var_115, conv2d_25_w, 0, 0, 1, 1, 1, 1);
-void* var_117 = tensorBatchNorm(var_116, batch_normalization_36_gamma, batch_normalization_36_beta, batch_normalization_36_mean, batch_normalization_36_variance, 0.001);
-void* var_118 = tensorRelu(var_117);
-void* var_120 = tensorConvolution(var_118, depthwise_conv2d_13_w, 1, 1, 1, 1, 1, 576);
-void* var_121 = tensorBatchNorm(var_120, batch_normalization_37_gamma, batch_normalization_37_beta, batch_normalization_37_mean, batch_normalization_37_variance, 0.001);
-void* var_122 = tensorRelu(var_121);
-void* var_123 = tensorConvolution(var_122, conv2d_26_w, 0, 0, 1, 1, 1, 1);
-void* var_124 = tensorBatchNorm(var_123, batch_normalization_38_gamma, batch_normalization_38_beta, batch_normalization_38_mean, batch_normalization_38_variance, 0.001);
-void* var_125 = tensorAdd(var_115, var_124);
-void* var_127 = tensorConvolution(var_125, conv2d_27_w, 0, 0, 1, 1, 1, 1);
-void* var_128 = tensorBatchNorm(var_127, batch_normalization_39_gamma, batch_normalization_39_beta, batch_normalization_39_mean, batch_normalization_39_variance, 0.001);
-void* var_129 = tensorRelu(var_128);
-void* var_131 = tensorConvolution(var_129, depthwise_conv2d_14_w, 1, 1, 2, 2, 1, 576);
-void* var_132 = tensorBatchNorm(var_131, batch_normalization_40_gamma, batch_normalization_40_beta, batch_normalization_40_mean, batch_normalization_40_variance, 0.001);
-void* var_133 = tensorRelu(var_132);
-void* var_134 = tensorConvolution(var_133, conv2d_28_w, 0, 0, 1, 1, 1, 1);
-void* var_135 = tensorBatchNorm(var_134, batch_normalization_41_gamma, batch_normalization_41_beta, batch_normalization_41_mean, batch_normalization_41_variance, 0.001);
-void* var_136 = tensorConvolution(var_135, conv2d_29_w, 0, 0, 1, 1, 1, 1);
-void* var_137 = tensorBatchNorm(var_136, batch_normalization_42_gamma, batch_normalization_42_beta, batch_normalization_42_mean, batch_normalization_42_variance, 0.001);
-void* var_138 = tensorRelu(var_137);
-void* var_140 = tensorConvolution(var_138, depthwise_conv2d_15_w, 1, 1, 1, 1, 1, 960);
-void* var_141 = tensorBatchNorm(var_140, batch_normalization_43_gamma, batch_normalization_43_beta, batch_normalization_43_mean, batch_normalization_43_variance, 0.001);
-void* var_142 = tensorRelu(var_141);
-void* var_143 = tensorConvolution(var_142, conv2d_30_w, 0, 0, 1, 1, 1, 1);
-void* var_144 = tensorBatchNorm(var_143, batch_normalization_44_gamma, batch_normalization_44_beta, batch_normalization_44_mean, batch_normalization_44_variance, 0.001);
-void* var_145 = tensorAdd(var_135, var_144);
-void* var_146 = tensorConvolution(var_145, conv2d_31_w, 0, 0, 1, 1, 1, 1);
-void* var_147 = tensorBatchNorm(var_146, batch_normalization_45_gamma, batch_normalization_45_beta, batch_normalization_45_mean, batch_normalization_45_variance, 0.001);
-void* var_148 = tensorRelu(var_147);
-void* var_150 = tensorConvolution(var_148, depthwise_conv2d_16_w, 1, 1, 1, 1, 1, 960);
-void* var_151 = tensorBatchNorm(var_150, batch_normalization_46_gamma, batch_normalization_46_beta, batch_normalization_46_mean, batch_normalization_46_variance, 0.001);
-void* var_152 = tensorRelu(var_151);
-void* var_153 = tensorConvolution(var_152, conv2d_32_w, 0, 0, 1, 1, 1, 1);
-void* var_154 = tensorBatchNorm(var_153, batch_normalization_47_gamma, batch_normalization_47_beta, batch_normalization_47_mean, batch_normalization_47_variance, 0.001);
-void* var_155 = tensorAdd(var_145, var_154);
-void* var_157 = tensorConvolution(var_155, conv2d_33_w, 0, 0, 1, 1, 1, 1);
-void* var_158 = tensorBatchNorm(var_157, batch_normalization_48_gamma, batch_normalization_48_beta, batch_normalization_48_mean, batch_normalization_48_variance, 0.001);
-void* var_159 = tensorRelu(var_158);
-void* var_161 = tensorConvolution(var_159, depthwise_conv2d_17_w, 1, 1, 1, 1, 1, 960);
-void* var_162 = tensorBatchNorm(var_161, batch_normalization_49_gamma, batch_normalization_49_beta, batch_normalization_49_mean, batch_normalization_49_variance, 0.001);
-void* var_163 = tensorRelu(var_162);
-void* var_164 = tensorConvolution(var_163, conv2d_34_w, 0, 0, 1, 1, 1, 1);
-void* var_165 = tensorBatchNorm(var_164, batch_normalization_50_gamma, batch_normalization_50_beta, batch_normalization_50_mean, batch_normalization_50_variance, 0.001);
-void* var_167 = tensorConvolution(var_165, conv2d_35_w, 0, 0, 1, 1, 1, 1);
-void* var_168 = tensorBatchNorm(var_167, batch_normalization_51_gamma, batch_normalization_51_beta, batch_normalization_51_mean, batch_normalization_51_variance, 0.001);
-void* var_169 = tensorRelu(var_168);
-void* var_170 = tensorPooling(var_169,1,2,2,0,0,2,2);
-void* var_172 = tensorGemmGPU(var_170, dense_1_w);
-void* var_173 = tensorAdd(var_172, dense_1_b);
-void* var_174 = tensorSoftmax(var_173);
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy2(labels, batch_size, var_174);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/pipeline/Pipeline.py b/llvm/projects/hpvm-tensor-rt/model_params/pipeline/Pipeline.py
deleted file mode 100644
index 0b07a503d237965956801b410fd16fc5a128eb46..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/pipeline/Pipeline.py
+++ /dev/null
@@ -1,207 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# arg 1: 0/1, to denote whether to show outputs on screen
-# arg 2: 0/1, to denote whether to save outputs
-# arg 3: input image
-
-import numpy as np
-import os
-import sys
-import matplotlib.pyplot as plt
-import cv2
-from scipy.ndimage import gaussian_filter
-#from skimage import io
-import struct
-
-
-N = 9145
-H = 240
-W = 300
-
-
-images = np.fromfile("dataset/caltech101_255_float32.bin", dtype=np.float32)
-images = images.reshape(N, H, W)
-
-
-kernel_gaussian = np.fromfile("filters/GaussianFilter.bin", dtype = np.float32).reshape(9,9)
-kernel_outline = np.fromfile("filters/OutlineFilter.bin", dtype = np.float32).reshape(3,3)
-kernel_sharpen = np.fromfile("filters/SharpenFilter.bin", dtype = np.float32).reshape(3,3)
-kernel_motionblur = np.fromfile("filters/MotionblurFilter.bin", dtype = np.float32).reshape(9,9)
-kernel_emboss = np.fromfile("filters/EmbossFilter.bin", dtype = np.float32).reshape(5,5)
-bias_emboss = np.fromfile("filters/EmbossBias.bin", dtype = np.float32)
-
-
-print(kernel_gaussian.min(), kernel_gaussian.max())
-print(kernel_outline.min(), kernel_outline.max())
-print(kernel_sharpen.min(), kernel_sharpen.max())
-print(kernel_motionblur.min(), kernel_motionblur.max())
-print(kernel_emboss.min(), kernel_emboss.max())
-print(bias_emboss.min(), bias_emboss.max())
-
-
-# Gaussian Blurring
-def Gaussian(img):
-# size = 9
-# sigma = 1.
-# G = generate_gaussian_kernel(size,sigma)
- output=cv2.filter2D(img,-1,kernel_gaussian)
- output = np.clip(output, 0, 255)
-# G.astype(np.float32).tofile("GaussianFilter.bin")
- return output
-
-
-# Keeps edges of image
-def Outline(img):
-# kernel_outline = np.array([[-1, -1, -1],
-# [-1, 8, -1],
-# [-1, -1, -1]])
- output=cv2.filter2D(img,-1,kernel_outline)
-# kernel_outline.astype(np.float32).tofile("OutlineFilter.bin")
- output = np.clip(output, 0, 255)
- return output
-
-
-def Sharpen(img):
-# kernel_sharpen = np.array([[0, -1, 0],
-# [-1, 5, -1],
-# [0, -1, 0]])
- output=cv2.filter2D(img,-1,kernel_sharpen)
-# kernel_sharpen.astype(np.float32).tofile("SharpenFilter.bin")
- output = np.clip(output, 0, 255)
- return output
-
-
-# Motion blur, from the top left to the bottom right
-def MotionBlur(img):
-# kernel_motionblur = np.array([[1, 0, 0, 0, 0, 0, 0, 0, 0],
-# [0, 1, 0, 0, 0, 0, 0, 0, 0],
-# [0, 0, 1, 0, 0, 0, 0, 0, 0],
-# [0, 0, 0, 1, 0, 0, 0, 0, 0],
-# [0, 0, 0, 0, 1, 0, 0, 0, 0],
-# [0, 0, 0, 0, 0, 1, 0, 0, 0],
-# [0, 0, 0, 0, 0, 0, 1, 0, 0],
-# [0, 0, 0, 0, 0, 0, 0, 1, 0],
-# [0, 0, 0, 0, 0, 0, 0, 0, 1]]) / 9.0
- output=cv2.filter2D(img,-1,kernel_motionblur)
-# kernel_motionblur.astype(np.float32).tofile("MotionblurFilter.bin")
- output = np.clip(output, 0, 255)
- return output
-
-
-# 5x5 45 degrees emboss, for an engraving effect
-def Emboss5x5(img):
-# kernel_emboss = np.array([[-1, -1, -1, -1, 0],
-# [-1, -1, -1, 0, 1],
-# [-1, -1, 0, 1, 1],
-# [-1, 0, 1, 1, 1],
-# [ 0, 1, 1, 1, 1]])
-# bias_emboss = np.full(1,128.0)
- output=cv2.filter2D(img,-1,kernel_emboss) + bias_emboss
-# output=cv2.filter2D(img,-1,kernel_emboss)
-# kernel_emboss = kernel_emboss + 128.0
-# kernel_emboss.astype(np.float32).tofile("EmbossFilter.bin")
-# bias_emboss.astype(np.float32).tofile("EmbossBias.bin")
- output = np.clip(output, 0, 255)
- return output
-
-
-def dumpData(file_name, X_test):
-
- N = X_test.shape[0]
- C = X_test.shape[1]
- H = X_test.shape[2]
- W = X_test.shape[3]
-
- print ("*DumpData")
- print("-min_val = ", np.amin(X_test))
- print("-max_val = ", np.amax(X_test))
-
- f = open(file_name, "wb")
- for i in range(N):
- for j in range(C):
- for k in range(H):
- for l in range(W):
- val = struct.unpack("f", struct.pack("f", X_test[i][j][k][l]))
- f.write(np.float32(val[0]))
-
- f.close()
-
-
-def dumpConvWeights(file_name, weights, N, C, H, W):
-
- print (weights.shape)
- print ("*DumpConvWeights")
- print("-min_val = ", np.amin(weights))
- print("-max_val = ", np.amax(weights))
-
- f = open(file_name, "wb")
- for i in range(N):
- for j in range(C):
- for k in range(H):
- for l in range(W):
- f.write(weights[k][l][j][i])
-
- f.close()
-
-
-def pipeline_G_O_M_E(img):
- img = Gaussian(img)
- img = Outline(img)
- img = MotionBlur(img)
- img = Emboss5x5(img)
- return img
-
-
-def pipeline_G_S_M_E(img):
- img = Gaussian(img)
- img = Sharpen(img)
- img = MotionBlur(img)
- img = Emboss5x5(img)
- return img
-
-
-def pipeline_G_E_O_M(img):
- img = Gaussian(img)
- img = Emboss5x5(img)
- img = Outline(img)
- img = MotionBlur(img)
- return img
-
-
-def pipeline_G_E_S_M(img):
- img = Gaussian(img)
- img = Emboss5x5(img)
- img = Sharpen(img)
- img = MotionBlur(img)
- return img
-
-
-def runPipeline(pipeline, images):
- output = np.zeros_like(images)
- for i in range(N):
- output[i] = pipeline(images[i])
- return output
-
-
-
-output_G_S_M_E = runPipeline(pipeline_G_S_M_E, images)
-output_G_S_M_E = output_G_S_M_E.reshape(N, 1, H, W)
-output_G_S_M_E.astype(np.float32).tofile("caltech-gaussian-sharpen-motionblur-emboss.bin")
-
-
-output_G_O_M_E = runPipeline(pipeline_G_O_M_E, images)
-output_G_O_M_E = output_G_O_M_E.reshape(N, 1, H, W)
-output_G_O_M_E.astype(np.float32).tofile("caltech-gaussian-outline-motionblur-emboss.bin")
-
-
-output_G_E_O_M = runPipeline(pipeline_G_E_O_M, images)
-output_G_E_O_M.shape
-output_G_E_O_M.astype(np.float32).tofile("caltech-gaussian-emboss-outline-motionblur.bin")
-
-
-output_G_E_S_M = runPipeline(pipeline_G_E_S_M, images)
-output_G_E_S_M.shape
-output_G_E_S_M.astype(np.float32).tofile("caltech-gaussian-emboss-sharpen-motionblur.bin")
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/pipeline/dataset/.gitkeep b/llvm/projects/hpvm-tensor-rt/model_params/pipeline/dataset/.gitkeep
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/pipeline/filters/.gitkeep b/llvm/projects/hpvm-tensor-rt/model_params/pipeline/filters/.gitkeep
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/pipeline/getImage.py b/llvm/projects/hpvm-tensor-rt/model_params/pipeline/getImage.py
deleted file mode 100644
index 7b4984024e9c0188cab43705ae816c2ab60316c3..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/pipeline/getImage.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import sys
-import matplotlib.pyplot as plt
-import numpy as np
-
-num_args = len(sys.argv)
-if(num_args < 3 or num_args > 4):
- print("Usage:\n\t python getImage.py <binary_file> <image_index> <total_images = 9145 (default)>\n")
- sys.exit(0)
-
-#print(sys.argv[1])
-#print(sys.argv[2])
-N = 9145
-if (num_args == 4):
- N = int(sys.argv[3])
-
-H = 240
-W = 300
-
-filename = str(sys.argv[1])
-index = int(sys.argv[2])
-
-images = np.fromfile(filename, dtype=np.float32)
-images = images.reshape(N, H, W)
-
-plt.imshow(images[index], cmap = 'gray')
-plt.savefig("output.jpeg")
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/pipeline/golden_output/.gitkeep b/llvm/projects/hpvm-tensor-rt/model_params/pipeline/golden_output/.gitkeep
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_2/conv2d_10_b.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_2/conv2d_10_b.bin
deleted file mode 100644
index d79b2e67bad72c6ebdb19c941b37756b263ef0a4..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_2/conv2d_10_b.bin
+++ /dev/null
@@ -1,3 +0,0 @@
-ŧ%>„¶½é
-4½Égæ½¼R <T#m>|u>.v¾\{•½…HZ>ÕŽ½öÈݽÓø½4l™>D•"½¶hß½;R¾œlȼ =†Ý >ðéõ¼Ã3×=ôpã¼hÎ<¹ãžÛo<[·‹¾9
->2 G>ÿ …> ½
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_2/conv2d_12_b.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_2/conv2d_12_b.bin
deleted file mode 100644
index bb5b9987af846d46bbfe488685c374f88dd7e4c5..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_2/conv2d_12_b.bin
+++ /dev/null
@@ -1 +0,0 @@
-)¼ø½³hà½Wོ;½„ï »ã ˜=Ë«¾Øm ¾È`;™u±¾¬{¾xŽ½?E¾D:B½dל½xt¾¾>ï¼)^½ ¾”¼Á ¾b}½ èb½üéÖ½3õľž®p½=âõ½ªžš<䧾¼¹§
¼?nǽ
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_2/conv2d_12_w.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_2/conv2d_12_w.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_2/conv2d_13_b.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_2/conv2d_13_b.bin
deleted file mode 100644
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+++ /dev/null
@@ -1 +0,0 @@
-˜y ¾Î×½~·1½‹¤9>Õs½5¬½¥±»:++¾’㽧¯z½É4¾”ëm¼í¿ ñ‚¾_²ž½=æ!½\æC½ÎØ¿ l=›,½e¤K¾÷˜o=þz¾°,]½Š9^½+é¾{^J>š™¼¹ÆÛ½µ[¾(AŠ¾Gã6¾
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+++ /dev/null
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-Y7»½çÎr¾Nä1¾J5¾#F½F°½õÈ“=€ƒA¾bŠ$¾$ìU¼Ü–>;z¾Q‰ó½ðr=¾È::Í̺½šL¾pl’¾q÷½)³Î½o*ˆ½ _¾¶Ï@¾›Å¿½É¨Y¾§ïµ¾ˆG1¾ÝÀ¾ëò‚=•H»»Ðe=k
-¾
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+++ /dev/null
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-Œó½P•=Á4½«¼B¾~
X> \œ<ôg¾«Aª>÷@¤>Z·=“,Ÿ>÷ð“½qG¬>Ö7¿¼úº6>Ç+Ž½^Æ€< Áé¼…½¼¼ñ¡=Lö]>ƒ²‡¾1¦·½-Lø½w4°=IÌ>ð<K¾`µ¯¼I#+¾‘錾<+½{é›=`s“½I†E>ßö¤¾Á™>(j7¾²`A½ ×> yò½Š ¿B¾ñ<Ð5Á¾æ>>ó²‰=àÐy=zk¾ öͽk³½B>(½Ý×G¼$a>û;4½„ÃG¼‚>ƒ‚U>Éö:½Ú)¾ê´d=Mw=ç«‘¾v¡Ö½
-´¾ìÍž>
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+++ /dev/null
@@ -1 +0,0 @@
- ¦Å½’»¤¼I]à=Xù<p¬Ê=›ÿ’=³Gt¾CÏŠ½ù9“¼Ò.*>“Ë=ÏÌ=}µK>‘Òd½HP=N4ö=
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-/n#>†ßá½wÿŠ½ ;yÿ¼&°œ½ -M>*¨q¾µû¤=§¤4>VZ÷<}ž> ,J½}#½ e¨¾3ºQ¾Øfé¼àÂ>®Ü;9Ä5=ˆý€½Š¦—½¼ºø¼wHŒ>÷ë_½$R ½‚4m>ÜŸ#>gc ¼; >ôÄ,½Ûð>Lp(½Ù©½Ž£p>çy¢>á‚Î=T F>ÞO™¼p3b¼’s°>WÃ$>‚쎾rë\½SþB>I®>ìÅ…½ww ½¼†™½Eù ½£L>9A½h¹‡¾ý™>¾zÇ>ôm#½µE¸½¯3=K×½V†¥½Nw¿>¾´¡½«n> ßǽ
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-¨8¸=ê|ݽ!¯=€Ø%>HÌ¥>ó„ ?Ÿƒ³¼ ó>Šð!¾”À=- ýHqÚ>ç–€=ÍÙ9>Ñ E>¿¾ <çÖˆ¾*«¾y%‹¼jyõ;=#@>ì\÷=$†‰¼¯!î>âŒx¾¿mþ¼T*æ=ÓæD¼tîÞ:tÝw¾ØÍ>~7 ½Nt½›ç+>\Þÿ½H>Ü ‹>ýY7¾X¶$>qË„>kí¾!öè=ks ¾jXõ¾/G}>]¤î½iq>‹º¯>ñ¢œ=5wϽX©>Ãé³>¢¢= h>3q>ì¹8½—ál=ø0Q>ÍÜ¥=ÏDé=*s>ÿ7?>ôÿk½—‡=
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-ïË°=ü=Ú=Þä½¥‡=ªÞ=¸;ü½4&½Å> ¼aÉà;¡_‡=%u> …ú=Åš.=@ˆ=þý×¼
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-=–½G#Ò»HëÕ½ÓÔ=^j ¾Dí¡½ž‡ˆ½"íU½ƒ4>PÜ= Z>=btŒ½à•>…„÷¼v6½Ù·,=
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-‘e =,i†¼‡%¾h²½µùX>£ôý;_©¾oË)>ÕŽ¥¾,9G½ÕÖ¾Ÿ†Ä¼Ù$ ¾Y V<D5¾õt>¾
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-¯D2½ès€¼<¦ ½Óµþ;‘É&½šÉ:=ko¼]‘=°Šç=÷?>`ð,=É W¾KË4>@i"½)¢½>îì=¾1-=ÄÃB¾4üô;ûra=d–½[ŠG½©e =ý÷>½1âÄ=B#a½ªi)>}õ–½9>½6ÚܽÝ@½þ ν
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-4½Égæ½¼R <T#m>|u>.v¾\{•½…HZ>ÕŽ½öÈݽÓø½4l™>D•"½¶hß½;R¾œlȼ =†Ý >ðéõ¼Ã3×=ôpã¼hÎ<¹ãžÛo<[·‹¾9
->2 G>ÿ …> ½
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_2/input.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_2/input.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_2/labels.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_2/labels.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_2/src.cc b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_2/src.cc
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index 2ba7ab5fd6ed7d8edac77ad1507c395b04b5328c..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_2/src.cc
+++ /dev/null
@@ -1,193 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-std::string dir_prefix = std::string("resnet18_cifar10/");
-std::string input_path = dir_prefix + std::string("input.bin");
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
-std::string labels_path = dir_prefix + std::string("labels.bin");
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,16,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
-void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
-void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,16,3,3);
-std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
-void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,16,1,1);
-std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
-void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
-void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
-void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
-void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
-void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
-void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_14_b_path = dir_prefix + std::string("conv2d_14_b.bin");
-void* conv2d_14_b = readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_15_w_path = dir_prefix + std::string("conv2d_15_w.bin");
-void* conv2d_15_w = readTrainedWeights(conv2d_15_w_path.c_str(), 0,64,32,3,3);
-std::string conv2d_15_b_path = dir_prefix + std::string("conv2d_15_b.bin");
-void* conv2d_15_b = readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_17_w_path = dir_prefix + std::string("conv2d_17_w.bin");
-void* conv2d_17_w = readTrainedWeights(conv2d_17_w_path.c_str(), 0,64,32,1,1);
-std::string conv2d_17_b_path = dir_prefix + std::string("conv2d_17_b.bin");
-void* conv2d_17_b = readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_16_w_path = dir_prefix + std::string("conv2d_16_w.bin");
-void* conv2d_16_w = readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_16_b_path = dir_prefix + std::string("conv2d_16_b.bin");
-void* conv2d_16_b = readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_18_w_path = dir_prefix + std::string("conv2d_18_w.bin");
-void* conv2d_18_w = readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_18_b_path = dir_prefix + std::string("conv2d_18_b.bin");
-void* conv2d_18_b = readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_19_w_path = dir_prefix + std::string("conv2d_19_w.bin");
-void* conv2d_19_w = readTrainedWeights(conv2d_19_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_19_b_path = dir_prefix + std::string("conv2d_19_b.bin");
-void* conv2d_19_b = readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_20_w_path = dir_prefix + std::string("conv2d_20_w.bin");
-void* conv2d_20_w = readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_20_b_path = dir_prefix + std::string("conv2d_20_b.bin");
-void* conv2d_20_b = readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_21_w_path = dir_prefix + std::string("conv2d_21_w.bin");
-void* conv2d_21_w = readTrainedWeights(conv2d_21_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_21_b_path = dir_prefix + std::string("conv2d_21_b.bin");
-void* conv2d_21_b = readTrainedWeights(conv2d_21_b_path.c_str(), 0,1,64,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,64,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-void* var_2 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
-void* var_3 = tensorAdd(var_2, conv2d_1_b);
-void* var_4 = tensorRelu(var_3);
-void* var_6 = tensorConvolution(var_4, conv2d_2_w, 1, 1, 1, 1, 1, 0);
-void* var_7 = tensorAdd(var_6, conv2d_2_b);
-void* var_8 = tensorRelu(var_7);
-void* var_10 = tensorConvolution(var_8, conv2d_3_w, 1, 1, 1, 1, 1, 0);
-void* var_11 = tensorAdd(var_10, conv2d_3_b);
-void* var_12 = tensorAdd(var_4, var_11);
-void* var_13 = tensorRelu(var_12);
-void* var_15 = tensorConvolution(var_13, conv2d_4_w, 1, 1, 1, 1, 1, 0);
-void* var_16 = tensorAdd(var_15, conv2d_4_b);
-void* var_17 = tensorRelu(var_16);
-void* var_19 = tensorConvolution(var_17, conv2d_5_w, 1, 1, 1, 1, 1, 0);
-void* var_20 = tensorAdd(var_19, conv2d_5_b);
-void* var_21 = tensorAdd(var_13, var_20);
-void* var_22 = tensorRelu(var_21);
-void* var_24 = tensorConvolution(var_22, conv2d_6_w, 1, 1, 1, 1, 1, 0);
-void* var_25 = tensorAdd(var_24, conv2d_6_b);
-void* var_26 = tensorRelu(var_25);
-void* var_28 = tensorConvolution(var_26, conv2d_7_w, 1, 1, 1, 1, 1, 0);
-void* var_29 = tensorAdd(var_28, conv2d_7_b);
-void* var_30 = tensorAdd(var_22, var_29);
-void* var_31 = tensorRelu(var_30);
-void* var_33 = tensorConvolution(var_31, conv2d_8_w, 1, 1, 2, 2, 1, 0);
-void* var_34 = tensorAdd(var_33, conv2d_8_b);
-void* var_35 = tensorRelu(var_34);
-void* var_37 = tensorConvolution(var_35, conv2d_9_w, 1, 1, 1, 1, 1, 0);
-void* var_38 = tensorAdd(var_37, conv2d_9_b);
-void* var_40 = tensorConvolution(var_31, conv2d_10_w, 0, 0, 2, 2, 1, 0);
-void* var_41 = tensorAdd(var_40, conv2d_10_b);
-void* var_42 = tensorAdd(var_41, var_38);
-void* var_43 = tensorRelu(var_42);
-void* var_45 = tensorConvolution(var_43, conv2d_11_w, 1, 1, 1, 1, 1, 0);
-void* var_46 = tensorAdd(var_45, conv2d_11_b);
-void* var_47 = tensorRelu(var_46);
-void* var_49 = tensorConvolution(var_47, conv2d_12_w, 1, 1, 1, 1, 1, 0);
-void* var_50 = tensorAdd(var_49, conv2d_12_b);
-void* var_51 = tensorAdd(var_43, var_50);
-void* var_52 = tensorRelu(var_51);
-void* var_54 = tensorConvolution(var_52, conv2d_13_w, 1, 1, 1, 1, 1, 0);
-void* var_55 = tensorAdd(var_54, conv2d_13_b);
-void* var_56 = tensorRelu(var_55);
-void* var_58 = tensorConvolution(var_56, conv2d_14_w, 1, 1, 1, 1, 1, 0);
-void* var_59 = tensorAdd(var_58, conv2d_14_b);
-void* var_60 = tensorAdd(var_52, var_59);
-void* var_61 = tensorRelu(var_60);
-void* var_63 = tensorConvolution(var_61, conv2d_15_w, 1, 1, 2, 2, 1, 0);
-void* var_64 = tensorAdd(var_63, conv2d_15_b);
-void* var_65 = tensorRelu(var_64);
-void* var_67 = tensorConvolution(var_65, conv2d_16_w, 1, 1, 1, 1, 1, 0);
-void* var_68 = tensorAdd(var_67, conv2d_16_b);
-void* var_70 = tensorConvolution(var_61, conv2d_17_w, 0, 0, 2, 2, 1, 0);
-void* var_71 = tensorAdd(var_70, conv2d_17_b);
-void* var_72 = tensorAdd(var_71, var_68);
-void* var_73 = tensorRelu(var_72);
-void* var_75 = tensorConvolution(var_73, conv2d_18_w, 1, 1, 1, 1, 1, 0);
-void* var_76 = tensorAdd(var_75, conv2d_18_b);
-void* var_77 = tensorRelu(var_76);
-void* var_79 = tensorConvolution(var_77, conv2d_19_w, 1, 1, 1, 1, 1, 0);
-void* var_80 = tensorAdd(var_79, conv2d_19_b);
-void* var_81 = tensorAdd(var_73, var_80);
-void* var_82 = tensorRelu(var_81);
-void* var_84 = tensorConvolution(var_82, conv2d_20_w, 1, 1, 1, 1, 1, 0);
-void* var_85 = tensorAdd(var_84, conv2d_20_b);
-void* var_86 = tensorRelu(var_85);
-void* var_88 = tensorConvolution(var_86, conv2d_21_w, 1, 1, 1, 1, 1, 0);
-void* var_89 = tensorAdd(var_88, conv2d_21_b);
-void* var_90 = tensorAdd(var_82, var_89);
-void* var_91 = tensorRelu(var_90);
-void* var_92 = tensorPooling(var_91,1,8,8,0,0,8,8);
-void* var_94 = tensorGemmGPU(var_92, dense_1_w);
-void* var_95 = tensorAdd(var_94, dense_1_b);
-void* var_96 = tensorSoftmax(var_95);
-
-computeAccuracy2(labels,10000,var_96);
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_3/conv2d_10_b.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_3/conv2d_10_b.bin
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--- a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_3/conv2d_10_b.bin
+++ /dev/null
@@ -1,3 +0,0 @@
-¹ãe>ñºŒ¾ E ¾ÄÑ>º©:?
-1Ò>œ²>Jkj>
¿>ª³ê=‹C?Oqß>Ô"¬>Bˆ<îüKºCæ>+ƒ-=sdÂ=ë
-˜¾ÍQ5½4½á0½<'/>¬Ô&?f“û> %–>O_ ?¼Zð=<è©»…W=@X€>s T¿
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_3/conv2d_10_w.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_3/conv2d_10_w.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_3/conv2d_11_b.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_3/conv2d_11_b.bin
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--- a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_3/conv2d_11_b.bin
+++ /dev/null
@@ -1 +0,0 @@
-)ú>‰£¿¡œ¾„€L¾ÚV¿m̈½ê8½U;ÄDƾņ¡¾×ˆü¾ÏD˾Ùü˜»þ€1½uK¿A–¾E¥‘¾K?¿@ôé½þ ¿Â-¾qç¥=. Ò¾±Æª¾™Q½¤3̼ÑËƾàá4¿Æß›¾hf®¾ùì¾ù•¾
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_3/conv2d_13_b.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_3/conv2d_13_b.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_3/conv2d_14_b.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_3/conv2d_14_b.bin
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+++ /dev/null
@@ -1 +0,0 @@
-8OL¾\¾D'4¾Ô© ¼høÜ<¤ù‘¾xI¾QŽŒ¾z›¾<
¿ ý<·`¾×#A¾è³¾FÍ;N0¾6gw¾
D¾“8v¾{Ù:¾Á2¿Ï«¾°Bo¾Q”V¼Ç4y¾qaｎöˆ>Ky¾Ò¸¨¾\°–¾Úµ¾ªÅ ¿
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-áÚ£>'8Q¿¦qd½w0¾´xÙ¾}JM¿*¡Ð½º÷=H•¾$E?0v&¿'±f> º7>™\[?¢Ø¾bˆ·½ñé+¿’Þ÷< /‡¾ï$¾ä-?Ô$]¿ð ¿¤(æ>Ua³¾f¢›¾üR⾬|ø½Ñ³`¿§0·¾GVž¿‘‹>
-)¬> µÝ¾ ç¾O‹¾+æì½T]ô¾”f½œiÚ>@º¾J=®>úX™<6>—>G ½Ã£n>.ƒ{¾ûš³¼õwf¿o
ß¾‰3ƒ?Fh¿æÿ¿?'¿e{÷¾t¨7>-—ʽW›…½“e¸>ÿfT>b^=“#k¾ÜE»ÑÐØ=
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-sÊJ>=?œ>—¦¾v†ñ¾‚Ê“>D£®:½Ší¾F” >v ˆ¾ÂQ>EÀó<E&Ô> &Œ>Ž^¿ÈR´=Ùt>
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-˜¾ÍQ5½4½á0½<'/>¬Ô&?f“û> %–>O_ ?¼Zð=<è©»…W=@X€>s T¿
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_3/src.cc b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_3/src.cc
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--- a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_3/src.cc
+++ /dev/null
@@ -1,193 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-std::string dir_prefix = std::string("resnet18_cifar10/");
-std::string input_path = dir_prefix + std::string("input.bin");
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
-std::string labels_path = dir_prefix + std::string("labels.bin");
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,16,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
-void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
-void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,16,3,3);
-std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
-void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,16,1,1);
-std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
-void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
-void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
-void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
-void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
-void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
-void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_14_b_path = dir_prefix + std::string("conv2d_14_b.bin");
-void* conv2d_14_b = readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_15_w_path = dir_prefix + std::string("conv2d_15_w.bin");
-void* conv2d_15_w = readTrainedWeights(conv2d_15_w_path.c_str(), 0,64,32,3,3);
-std::string conv2d_15_b_path = dir_prefix + std::string("conv2d_15_b.bin");
-void* conv2d_15_b = readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_17_w_path = dir_prefix + std::string("conv2d_17_w.bin");
-void* conv2d_17_w = readTrainedWeights(conv2d_17_w_path.c_str(), 0,64,32,1,1);
-std::string conv2d_17_b_path = dir_prefix + std::string("conv2d_17_b.bin");
-void* conv2d_17_b = readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_16_w_path = dir_prefix + std::string("conv2d_16_w.bin");
-void* conv2d_16_w = readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_16_b_path = dir_prefix + std::string("conv2d_16_b.bin");
-void* conv2d_16_b = readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_18_w_path = dir_prefix + std::string("conv2d_18_w.bin");
-void* conv2d_18_w = readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_18_b_path = dir_prefix + std::string("conv2d_18_b.bin");
-void* conv2d_18_b = readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_19_w_path = dir_prefix + std::string("conv2d_19_w.bin");
-void* conv2d_19_w = readTrainedWeights(conv2d_19_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_19_b_path = dir_prefix + std::string("conv2d_19_b.bin");
-void* conv2d_19_b = readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_20_w_path = dir_prefix + std::string("conv2d_20_w.bin");
-void* conv2d_20_w = readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_20_b_path = dir_prefix + std::string("conv2d_20_b.bin");
-void* conv2d_20_b = readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_21_w_path = dir_prefix + std::string("conv2d_21_w.bin");
-void* conv2d_21_w = readTrainedWeights(conv2d_21_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_21_b_path = dir_prefix + std::string("conv2d_21_b.bin");
-void* conv2d_21_b = readTrainedWeights(conv2d_21_b_path.c_str(), 0,1,64,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,64,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-void* var_2 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
-void* var_3 = tensorAdd(var_2, conv2d_1_b);
-void* var_4 = tensorRelu(var_3);
-void* var_6 = tensorConvolution(var_4, conv2d_2_w, 1, 1, 1, 1, 1, 0);
-void* var_7 = tensorAdd(var_6, conv2d_2_b);
-void* var_8 = tensorRelu(var_7);
-void* var_10 = tensorConvolution(var_8, conv2d_3_w, 1, 1, 1, 1, 1, 0);
-void* var_11 = tensorAdd(var_10, conv2d_3_b);
-void* var_12 = tensorAdd(var_4, var_11);
-void* var_13 = tensorRelu(var_12);
-void* var_15 = tensorConvolution(var_13, conv2d_4_w, 1, 1, 1, 1, 1, 0);
-void* var_16 = tensorAdd(var_15, conv2d_4_b);
-void* var_17 = tensorRelu(var_16);
-void* var_19 = tensorConvolution(var_17, conv2d_5_w, 1, 1, 1, 1, 1, 0);
-void* var_20 = tensorAdd(var_19, conv2d_5_b);
-void* var_21 = tensorAdd(var_13, var_20);
-void* var_22 = tensorRelu(var_21);
-void* var_24 = tensorConvolution(var_22, conv2d_6_w, 1, 1, 1, 1, 1, 0);
-void* var_25 = tensorAdd(var_24, conv2d_6_b);
-void* var_26 = tensorRelu(var_25);
-void* var_28 = tensorConvolution(var_26, conv2d_7_w, 1, 1, 1, 1, 1, 0);
-void* var_29 = tensorAdd(var_28, conv2d_7_b);
-void* var_30 = tensorAdd(var_22, var_29);
-void* var_31 = tensorRelu(var_30);
-void* var_33 = tensorConvolution(var_31, conv2d_8_w, 1, 1, 2, 2, 1, 0);
-void* var_34 = tensorAdd(var_33, conv2d_8_b);
-void* var_35 = tensorRelu(var_34);
-void* var_37 = tensorConvolution(var_35, conv2d_9_w, 1, 1, 1, 1, 1, 0);
-void* var_38 = tensorAdd(var_37, conv2d_9_b);
-void* var_40 = tensorConvolution(var_31, conv2d_10_w, 0, 0, 2, 2, 1, 0);
-void* var_41 = tensorAdd(var_40, conv2d_10_b);
-void* var_42 = tensorAdd(var_41, var_38);
-void* var_43 = tensorRelu(var_42);
-void* var_45 = tensorConvolution(var_43, conv2d_11_w, 1, 1, 1, 1, 1, 0);
-void* var_46 = tensorAdd(var_45, conv2d_11_b);
-void* var_47 = tensorRelu(var_46);
-void* var_49 = tensorConvolution(var_47, conv2d_12_w, 1, 1, 1, 1, 1, 0);
-void* var_50 = tensorAdd(var_49, conv2d_12_b);
-void* var_51 = tensorAdd(var_43, var_50);
-void* var_52 = tensorRelu(var_51);
-void* var_54 = tensorConvolution(var_52, conv2d_13_w, 1, 1, 1, 1, 1, 0);
-void* var_55 = tensorAdd(var_54, conv2d_13_b);
-void* var_56 = tensorRelu(var_55);
-void* var_58 = tensorConvolution(var_56, conv2d_14_w, 1, 1, 1, 1, 1, 0);
-void* var_59 = tensorAdd(var_58, conv2d_14_b);
-void* var_60 = tensorAdd(var_52, var_59);
-void* var_61 = tensorRelu(var_60);
-void* var_63 = tensorConvolution(var_61, conv2d_15_w, 1, 1, 2, 2, 1, 0);
-void* var_64 = tensorAdd(var_63, conv2d_15_b);
-void* var_65 = tensorRelu(var_64);
-void* var_67 = tensorConvolution(var_65, conv2d_16_w, 1, 1, 1, 1, 1, 0);
-void* var_68 = tensorAdd(var_67, conv2d_16_b);
-void* var_70 = tensorConvolution(var_61, conv2d_17_w, 0, 0, 2, 2, 1, 0);
-void* var_71 = tensorAdd(var_70, conv2d_17_b);
-void* var_72 = tensorAdd(var_71, var_68);
-void* var_73 = tensorRelu(var_72);
-void* var_75 = tensorConvolution(var_73, conv2d_18_w, 1, 1, 1, 1, 1, 0);
-void* var_76 = tensorAdd(var_75, conv2d_18_b);
-void* var_77 = tensorRelu(var_76);
-void* var_79 = tensorConvolution(var_77, conv2d_19_w, 1, 1, 1, 1, 1, 0);
-void* var_80 = tensorAdd(var_79, conv2d_19_b);
-void* var_81 = tensorAdd(var_73, var_80);
-void* var_82 = tensorRelu(var_81);
-void* var_84 = tensorConvolution(var_82, conv2d_20_w, 1, 1, 1, 1, 1, 0);
-void* var_85 = tensorAdd(var_84, conv2d_20_b);
-void* var_86 = tensorRelu(var_85);
-void* var_88 = tensorConvolution(var_86, conv2d_21_w, 1, 1, 1, 1, 1, 0);
-void* var_89 = tensorAdd(var_88, conv2d_21_b);
-void* var_90 = tensorAdd(var_82, var_89);
-void* var_91 = tensorRelu(var_90);
-void* var_92 = tensorPooling(var_91,1,8,8,0,0,8,8);
-void* var_94 = tensorGemmGPU(var_92, dense_1_w);
-void* var_95 = tensorAdd(var_94, dense_1_b);
-void* var_96 = tensorSoftmax(var_95);
-
-computeAccuracy2(labels,10000,var_96);
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
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-)¯¼YÅG¾^þ™½-‚ä¾R¾Ý¾‰c¾l2&?D¾
-"q<~øb¾ÌîY¾LmÞ½hÍ$¾ÌÅg¾]db¾V&¾ÿ
p¾À’`¾M·4½Õø=š× ¾õ: ½Ž“½_ºN½µf¼Û]K¾à·õ½>°ù¼“(½®èྮb<
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-¶‡“¼ž®Y¾$,Ö¼Y'm½"0Ž*„6¿(Ö¿äšÌ¾§þ¿¾IX/¾Û^l¼VÌú¼'P‹¾B‹[½·õa½:n¿î@¾F•¼¿áVè¼#…¿'©›¿¸{j¾~º½ÿŽ¾PXi¾ñs¹½ÿƒ›¿ÂV ¿¸Žà½BD¾eò™¾Ø¥½
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-Dz\¾—ý(¾n¬½ãå¾0
-¾h.¾á"Œ¾â%ˆ>†Ÿ¾BÁ½B¦¿_ V¾e“ѽD„¾EàϽUIf¾Ç3N¾zì7¾7¨a¾–Ž‚¾3í¼ Ã?¾Ðl¶½üß…¾Ss¡½µ
Š¾ïÞ¾WŸ¾ ô¾í÷³¾³Õ½
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-Л>g܇?5þ½ì¹¼a¢';7Ð+¼t¾¤h¿è1”>ª ª¾èêl?ÅŠ>n >ý «=w2¾Uf?k&?ñã!> ã‘>ø!½ß‹½âuD=¦d¾!6Á¾Cb?ûÄ輆¤¼¸Ñ¿ j½–B·=œœY½Æ+®>9ø]½Š
-î<ÂQ¾ˆlO½»®9È“¾´$>³Ð ½q( ?S)¾ T¾Œ'>€ù…>¡^¾U2 ?µ”œ>Ï ˜¾…]¿‹úZ½ƒNá½U½ÔY>qÅ#>›bä>B"?J˜ ?m+ý¾¹zɾ“½½Œ*z¼mc?Ç-x=
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_21_b.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_21_b.bin
deleted file mode 100644
index f8e4509e626f855566c430560c33501c741d82cd..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_21_b.bin
+++ /dev/null
@@ -1,2 +0,0 @@
-oD??êÙ€?àkü½žHÝ>Ô61¾½< =¡¶(>Ž—¿œˆ>"W«¿¨^B½F[$¿:’ì>¾ýë¼Í
-'¿]3¡>Üx!¿¿N?}Þ=oCÀ”=¾é½*¢Ÿ>í#œ¿Gk?ŠxÞ>YÌ¢¾$l¢¼)Ó¿² ¿6¸q¿ä —¿,à>? ÿѽ£ïξFlü¾%»¾ò—X¿³¶:>IZ
À±@q½bõ‰½‚yŸ¾Ü&,¿÷‰µ¿‹8 ¿qÞD?*ƒ;¿û뽚ۗ½‰>ô'*¿pÑ¡¾—Á>Âý\>*Y5¾åsP½Êè’>E¥¿—1Q¿¢*)¾ç©–¿GSµ¿›JÔ¾
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_21_w.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_21_w.bin
deleted file mode 100644
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_2_b.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_2_b.bin
deleted file mode 100644
index 66f62621d56c6d4713819980a3fd8511c6a01dcf..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_2_b.bin
+++ /dev/null
@@ -1 +0,0 @@
-ç‹=߇˜>Ä@Y>žÁ9>âË»Sé>“4W>Ó€"=.ÊU½l‘¬>¬ÅÕ>!ì=\¿V>µ¤> 2ô¼
eŽ¾
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_2_w.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_2_w.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_3_b.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_3_b.bin
deleted file mode 100644
index 7e9cb4e6ec2336a8230354a1ff90f45bf2f29d66..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_3_b.bin
+++ /dev/null
@@ -1 +0,0 @@
-¹¥¶=MÖt¾À,J>Ê;\¿éX‹½4û;=–šF>«+¾i¬¦½Õ¹ß=G¹=Ø)3½–c&½>bμž±>}¬°½
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_3_w.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_3_w.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_4_b.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_4_b.bin
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--- a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_4_b.bin
+++ /dev/null
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-Äa>½ `‹>!Æ½ß >¾¶½}K›>!Ã3¿õc>vLN>ÊU½Ö!>ÍÙ5>U]¾`þ¸¾úÈ= lq¾
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_4_w.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_4_w.bin
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--- a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_5_b.bin
+++ /dev/null
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-”-¾‰óݽD b½DF½œX“½¦ÑŠ½l®¡½Ï•S¾[6>œÃ–½Ï¤.½PÖÜ<\mN½ÓrI½æX™½êG¢¼
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_5_w.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_5_w.bin
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index a00bc8b1ffc5ca67d5aa827e9d74b34123e2f483..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_6_b.bin
+++ /dev/null
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-iA¾vÿ= ‡<¿™ˆ•=ÉÎG¿šö&¿žVò½° ñ½'<¿ìûw½ÕÍQ¿¥`Å>óÁ½¼t¾â¨¾=7UK=
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_6_w.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_6_w.bin
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index 201aea2f09a83bf5ad755304aa63342a036b659d..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_7_b.bin
+++ /dev/null
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-A8µ½ ˆ¼IÂr¾÷·*¼¥¾“½ m¶¾ Y¾Že>Í޽ܺ ¾Š(ü¼y.6¾Ð‹lºÏ̽
ž¾
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_7_w.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/conv2d_7_w.bin
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deleted file mode 100644
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-Øwã>ëÒé>Ý4F?ýÜ;=Ô\¾‚\ô<[fõ¼¶Ðè»w@-¾2C¹½ƒßk>Gã½ê ½#t6> Ô¼=Ø9 ×®=Sï¾>œ6?Õ@
>aý >é?}#’>Ñ8Ç>Y«¬<UÀ>A ¾ªð—¾ìª=FR¸½ãI> D>
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/dense_1_b.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/dense_1_b.bin
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index 5df6e08172e8ad71bf72131d8e7afa8389c77514..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/dense_1_b.bin
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-×Ö¦½Óü¾ôeŽ¾ƒ‘>[> ?°Ã¾„ƒ:>Aù½>n‰>>¸ >
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/input.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/input.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/labels.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/labels.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/labels32.bin b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/labels32.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/promise_src.cc b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/promise_src.cc
deleted file mode 100644
index f6e7e32153a5e89a68798f809cf4166285b408ea..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/promise_src.cc
+++ /dev/null
@@ -1,162 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-
-std::string dir_prefix = std::string("resnet18_cifar10_promise/");
-std::string input_path = dir_prefix + std::string("input.bin");
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
-std::string labels_path = dir_prefix + std::string("labels.bin");
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,16,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
-void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
-void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,16,3,3);
-std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
-void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,16,1,1);
-std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
-void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
-void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
-void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
-void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
-void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
-void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_14_b_path = dir_prefix + std::string("conv2d_14_b.bin");
-void* conv2d_14_b = readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_15_w_path = dir_prefix + std::string("conv2d_15_w.bin");
-void* conv2d_15_w = readTrainedWeights(conv2d_15_w_path.c_str(), 0,64,32,3,3);
-std::string conv2d_15_b_path = dir_prefix + std::string("conv2d_15_b.bin");
-void* conv2d_15_b = readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_17_w_path = dir_prefix + std::string("conv2d_17_w.bin");
-void* conv2d_17_w = readTrainedWeights(conv2d_17_w_path.c_str(), 0,64,32,1,1);
-std::string conv2d_17_b_path = dir_prefix + std::string("conv2d_17_b.bin");
-void* conv2d_17_b = readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_16_w_path = dir_prefix + std::string("conv2d_16_w.bin");
-void* conv2d_16_w = readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_16_b_path = dir_prefix + std::string("conv2d_16_b.bin");
-void* conv2d_16_b = readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_18_w_path = dir_prefix + std::string("conv2d_18_w.bin");
-void* conv2d_18_w = readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_18_b_path = dir_prefix + std::string("conv2d_18_b.bin");
-void* conv2d_18_b = readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_19_w_path = dir_prefix + std::string("conv2d_19_w.bin");
-void* conv2d_19_w = readTrainedWeights(conv2d_19_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_19_b_path = dir_prefix + std::string("conv2d_19_b.bin");
-void* conv2d_19_b = readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_20_w_path = dir_prefix + std::string("conv2d_20_w.bin");
-void* conv2d_20_w = readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_20_b_path = dir_prefix + std::string("conv2d_20_b.bin");
-void* conv2d_20_b = readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_21_w_path = dir_prefix + std::string("conv2d_21_w.bin");
-void* conv2d_21_w = readTrainedWeights(conv2d_21_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_21_b_path = dir_prefix + std::string("conv2d_21_b.bin");
-void* conv2d_21_b = readTrainedWeights(conv2d_21_b_path.c_str(), 0,1,64,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,64,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-void* var_0 = ConvLayer_PROMISE(input, -0.5500815, 0.60786617, conv2d_1_w, -1.0248864, 1.2929907, conv2d_1_b, -0.36291853, 0.2533059, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.9356618, 9);
-void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 2.9356618, conv2d_2_w, -0.69884616, 0.71849966, conv2d_2_b, -0.2781147, 0.45571187, 1, 1, 1, 1, -1, 0, 1, 0.0, 4.0425158, 9);
-void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 4.0425158, conv2d_3_w, -0.59568167, 0.7714691, conv2d_3_b, -0.8602873, 0.19743633, 1, 1, 1, 1, -1, 0, -1, -10.203314, 9.055045, 9);
-void* var_3 = tensorAdd(var_0, var_2);
-void* var_4 = tensorRelu(var_3);
-void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 9.734258, conv2d_4_w, -0.41976976, 0.43748936, conv2d_4_b, -0.7021962, 0.3033103, 1, 1, 1, 1, -1, 0, 1, 0.0, 7.0053105, 9);
-void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 7.0053105, conv2d_5_w, -0.46757826, 0.4635873, conv2d_5_b, -0.20662616, 0.1778044, 1, 1, 1, 1, -1, 0, -1, -4.8778534, 6.7311873, 9);
-void* var_7 = tensorAdd(var_4, var_6);
-void* var_8 = tensorRelu(var_7);
-void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 10.858562, conv2d_6_w, -0.64404047, 0.45383143, conv2d_6_b, -0.819547, 0.38550296, 1, 1, 1, 1, -1, 0, 1, 0.0, 8.843336, 9);
-void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 8.843336, conv2d_7_w, -0.41986948, 0.33654243, conv2d_7_b, -0.3563013, 0.22371122, 1, 1, 1, 1, -1, 0, -1, -10.204111, 5.4952374, 9);
-void* var_11 = tensorAdd(var_8, var_10);
-void* var_12 = tensorRelu(var_11);
-void* var_13 = ConvLayer_PROMISE(var_12, 0.0, 11.359337, conv2d_8_w, -0.4805263, 0.50655717, conv2d_8_b, -0.296758, 0.7742441, 1, 1, 2, 2, -1, 0, 1, 0.0, 19.303282, 9);
-void* var_14 = ConvLayer_PROMISE(var_13, 0.0, 19.303282, conv2d_9_w, -0.52083415, 0.45517674, conv2d_9_b, -0.20242067, 0.8236838, 1, 1, 1, 1, -1, 0, -1, -24.404322, 24.37361, 9);
-void* var_15 = ConvLayer_PROMISE(var_12, 0.0, 11.359337, conv2d_10_w, -0.5338656, 1.3395424, conv2d_10_b, -0.20242067, 0.8236838, 0, 0, 2, 2, -1, 0, -1, -6.497986, 12.8968935, 9);
-void* var_16 = tensorAdd(var_15, var_14);
-void* var_17 = tensorRelu(var_16);
-void* var_18 = ConvLayer_PROMISE(var_17, 0.0, 29.462997, conv2d_11_w, -0.34429058, 0.43629733, conv2d_11_b, -1.0744808, 0.056708273, 1, 1, 1, 1, -1, 0, 1, 0.0, 24.329395, 9);
-void* var_19 = ConvLayer_PROMISE(var_18, 0.0, 24.329395, conv2d_12_w, -0.30342352, 0.39493486, conv2d_12_b, -0.44630566, 0.6492069, 1, 1, 1, 1, -1, 0, -1, -9.780206, 20.320444, 9);
-void* var_20 = tensorAdd(var_17, var_19);
-void* var_21 = tensorRelu(var_20);
-void* var_22 = ConvLayer_PROMISE(var_21, 0.0, 29.647404, conv2d_13_w, -0.38351893, 0.45775774, conv2d_13_b, -1.4733055, -0.014426912, 1, 1, 1, 1, -1, 0, 1, 0.0, 25.600573, 9);
-void* var_23 = ConvLayer_PROMISE(var_22, 0.0, 25.600573, conv2d_14_w, -0.25695276, 0.45372736, conv2d_14_b, -0.5259744, 0.26591402, 1, 1, 1, 1, -1, 0, -1, -10.179335, 27.598986, 9);
-void* var_24 = tensorAdd(var_21, var_23);
-void* var_25 = tensorRelu(var_24);
-void* var_26 = ConvLayer_PROMISE(var_25, 0.0, 42.450073, conv2d_15_w, -0.55299705, 0.5443531, conv2d_15_b, -0.71790683, 1.2730768, 1, 1, 2, 2, -1, 0, 1, 0.0, 48.943645, 9);
-void* var_27 = ConvLayer_PROMISE(var_26, 0.0, 48.943645, conv2d_16_w, -0.4203967, 0.48641303, conv2d_16_b, -0.90653443, 1.3546854, 1, 1, 1, 1, -1, 0, -1, -75.016396, 112.3873, 9);
-void* var_28 = ConvLayer_PROMISE(var_25, 0.0, 42.450073, conv2d_17_w, -0.4365755, 0.84913826, conv2d_17_b, -0.90653443, 1.3546851, 0, 0, 2, 2, -1, 0, -1, -13.93596, 24.614315, 9);
-void* var_29 = tensorAdd(var_28, var_27);
-void* var_30 = tensorRelu(var_29);
-void* var_31 = ConvLayer_PROMISE(var_30, 0.0, 126.758545, conv2d_18_w, -0.38657624, 0.5228989, conv2d_18_b, -1.2083547, 0.76361173, 1, 1, 1, 1, -1, 0, 1, 0.0, 130.02768, 9);
-void* var_32 = ConvLayer_PROMISE(var_31, 0.0, 130.02768, conv2d_19_w, -0.40857902, 0.575035, conv2d_19_b, -1.8731614, 1.0960501, 1, 1, 1, 1, -1, 0, -1, -97.91927, 130.89008, 9);
-void* var_33 = tensorAdd(var_30, var_32);
-void* var_34 = tensorRelu(var_33);
-void* var_35 = ConvLayer_PROMISE(var_34, 0.0, 220.4094, conv2d_20_w, -0.33079496, 0.5893278, conv2d_20_b, -1.0234511, 1.0016295, 1, 1, 1, 1, -1, 0, 1, 0.0, 268.19254, 9);
-void* var_36 = ConvLayer_PROMISE(var_35, 0.0, 268.19254, conv2d_21_w, -0.27897888, 0.38280907, conv2d_21_b, -2.2086356, 1.0066502, 1, 1, 1, 1, -1, 0, -1, -235.08034, 264.33008, 9);
-void* var_37 = tensorAdd(var_34, var_36);
-void* var_38 = tensorRelu(var_37);
-void* var_39 = tensorPooling(var_38,1,8,8,0,0,8,8);
-void* var_40 = FCLayer_PROMISE(var_39, 0.0, 39.821262, dense_1_w, -1.5092047, 1.0279838, dense_1_b, -0.49379802, 0.61032647, -1, -84.49565, 60.312202, 9);
-void* var_41 = tensorSoftmax(var_40);
-
-computeAccuracy2(labels,10000,var_41);
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/src.cc b/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/src.cc
deleted file mode 100644
index e82c54651aaa8b8df2ab34b65490aa79b730c327..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/resnet18_cifar10_promise/src.cc
+++ /dev/null
@@ -1,193 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-std::string dir_prefix = std::string("resnet18_cifar10_promise/");
-std::string input_path = dir_prefix + std::string("input.bin");
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
-std::string labels_path = dir_prefix + std::string("labels.bin");
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,16,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
-void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,16,16,3,3);
-std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
-void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,16,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,32,16,3,3);
-std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
-void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,32,16,1,1);
-std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
-void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
-void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
-void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
-void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
-void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_14_w_path = dir_prefix + std::string("conv2d_14_w.bin");
-void* conv2d_14_w = readTrainedWeights(conv2d_14_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_14_b_path = dir_prefix + std::string("conv2d_14_b.bin");
-void* conv2d_14_b = readTrainedWeights(conv2d_14_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_15_w_path = dir_prefix + std::string("conv2d_15_w.bin");
-void* conv2d_15_w = readTrainedWeights(conv2d_15_w_path.c_str(), 0,64,32,3,3);
-std::string conv2d_15_b_path = dir_prefix + std::string("conv2d_15_b.bin");
-void* conv2d_15_b = readTrainedWeights(conv2d_15_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_17_w_path = dir_prefix + std::string("conv2d_17_w.bin");
-void* conv2d_17_w = readTrainedWeights(conv2d_17_w_path.c_str(), 0,64,32,1,1);
-std::string conv2d_17_b_path = dir_prefix + std::string("conv2d_17_b.bin");
-void* conv2d_17_b = readTrainedWeights(conv2d_17_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_16_w_path = dir_prefix + std::string("conv2d_16_w.bin");
-void* conv2d_16_w = readTrainedWeights(conv2d_16_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_16_b_path = dir_prefix + std::string("conv2d_16_b.bin");
-void* conv2d_16_b = readTrainedWeights(conv2d_16_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_18_w_path = dir_prefix + std::string("conv2d_18_w.bin");
-void* conv2d_18_w = readTrainedWeights(conv2d_18_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_18_b_path = dir_prefix + std::string("conv2d_18_b.bin");
-void* conv2d_18_b = readTrainedWeights(conv2d_18_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_19_w_path = dir_prefix + std::string("conv2d_19_w.bin");
-void* conv2d_19_w = readTrainedWeights(conv2d_19_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_19_b_path = dir_prefix + std::string("conv2d_19_b.bin");
-void* conv2d_19_b = readTrainedWeights(conv2d_19_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_20_w_path = dir_prefix + std::string("conv2d_20_w.bin");
-void* conv2d_20_w = readTrainedWeights(conv2d_20_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_20_b_path = dir_prefix + std::string("conv2d_20_b.bin");
-void* conv2d_20_b = readTrainedWeights(conv2d_20_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_21_w_path = dir_prefix + std::string("conv2d_21_w.bin");
-void* conv2d_21_w = readTrainedWeights(conv2d_21_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_21_b_path = dir_prefix + std::string("conv2d_21_b.bin");
-void* conv2d_21_b = readTrainedWeights(conv2d_21_b_path.c_str(), 0,1,64,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,64,10);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1);
-
-
-void* var_2 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
-void* var_3 = tensorAdd(var_2, conv2d_1_b);
-void* var_4 = tensorRelu(var_3);
-void* var_6 = tensorConvolution(var_4, conv2d_2_w, 1, 1, 1, 1, 1, 0);
-void* var_7 = tensorAdd(var_6, conv2d_2_b);
-void* var_8 = tensorRelu(var_7);
-void* var_10 = tensorConvolution(var_8, conv2d_3_w, 1, 1, 1, 1, 1, 0);
-void* var_11 = tensorAdd(var_10, conv2d_3_b);
-void* var_12 = tensorAdd(var_4, var_11);
-void* var_13 = tensorRelu(var_12);
-void* var_15 = tensorConvolution(var_13, conv2d_4_w, 1, 1, 1, 1, 1, 0);
-void* var_16 = tensorAdd(var_15, conv2d_4_b);
-void* var_17 = tensorRelu(var_16);
-void* var_19 = tensorConvolution(var_17, conv2d_5_w, 1, 1, 1, 1, 1, 0);
-void* var_20 = tensorAdd(var_19, conv2d_5_b);
-void* var_21 = tensorAdd(var_13, var_20);
-void* var_22 = tensorRelu(var_21);
-void* var_24 = tensorConvolution(var_22, conv2d_6_w, 1, 1, 1, 1, 1, 0);
-void* var_25 = tensorAdd(var_24, conv2d_6_b);
-void* var_26 = tensorRelu(var_25);
-void* var_28 = tensorConvolution(var_26, conv2d_7_w, 1, 1, 1, 1, 1, 0);
-void* var_29 = tensorAdd(var_28, conv2d_7_b);
-void* var_30 = tensorAdd(var_22, var_29);
-void* var_31 = tensorRelu(var_30);
-void* var_33 = tensorConvolution(var_31, conv2d_8_w, 1, 1, 2, 2, 1, 0);
-void* var_34 = tensorAdd(var_33, conv2d_8_b);
-void* var_35 = tensorRelu(var_34);
-void* var_37 = tensorConvolution(var_35, conv2d_9_w, 1, 1, 1, 1, 1, 0);
-void* var_38 = tensorAdd(var_37, conv2d_9_b);
-void* var_40 = tensorConvolution(var_31, conv2d_10_w, 0, 0, 2, 2, 1, 0);
-void* var_41 = tensorAdd(var_40, conv2d_10_b);
-void* var_42 = tensorAdd(var_41, var_38);
-void* var_43 = tensorRelu(var_42);
-void* var_45 = tensorConvolution(var_43, conv2d_11_w, 1, 1, 1, 1, 1, 0);
-void* var_46 = tensorAdd(var_45, conv2d_11_b);
-void* var_47 = tensorRelu(var_46);
-void* var_49 = tensorConvolution(var_47, conv2d_12_w, 1, 1, 1, 1, 1, 0);
-void* var_50 = tensorAdd(var_49, conv2d_12_b);
-void* var_51 = tensorAdd(var_43, var_50);
-void* var_52 = tensorRelu(var_51);
-void* var_54 = tensorConvolution(var_52, conv2d_13_w, 1, 1, 1, 1, 1, 0);
-void* var_55 = tensorAdd(var_54, conv2d_13_b);
-void* var_56 = tensorRelu(var_55);
-void* var_58 = tensorConvolution(var_56, conv2d_14_w, 1, 1, 1, 1, 1, 0);
-void* var_59 = tensorAdd(var_58, conv2d_14_b);
-void* var_60 = tensorAdd(var_52, var_59);
-void* var_61 = tensorRelu(var_60);
-void* var_63 = tensorConvolution(var_61, conv2d_15_w, 1, 1, 2, 2, 1, 0);
-void* var_64 = tensorAdd(var_63, conv2d_15_b);
-void* var_65 = tensorRelu(var_64);
-void* var_67 = tensorConvolution(var_65, conv2d_16_w, 1, 1, 1, 1, 1, 0);
-void* var_68 = tensorAdd(var_67, conv2d_16_b);
-void* var_70 = tensorConvolution(var_61, conv2d_17_w, 0, 0, 2, 2, 1, 0);
-void* var_71 = tensorAdd(var_70, conv2d_17_b);
-void* var_72 = tensorAdd(var_71, var_68);
-void* var_73 = tensorRelu(var_72);
-void* var_75 = tensorConvolution(var_73, conv2d_18_w, 1, 1, 1, 1, 1, 0);
-void* var_76 = tensorAdd(var_75, conv2d_18_b);
-void* var_77 = tensorRelu(var_76);
-void* var_79 = tensorConvolution(var_77, conv2d_19_w, 1, 1, 1, 1, 1, 0);
-void* var_80 = tensorAdd(var_79, conv2d_19_b);
-void* var_81 = tensorAdd(var_73, var_80);
-void* var_82 = tensorRelu(var_81);
-void* var_84 = tensorConvolution(var_82, conv2d_20_w, 1, 1, 1, 1, 1, 0);
-void* var_85 = tensorAdd(var_84, conv2d_20_b);
-void* var_86 = tensorRelu(var_85);
-void* var_88 = tensorConvolution(var_86, conv2d_21_w, 1, 1, 1, 1, 1, 0);
-void* var_89 = tensorAdd(var_88, conv2d_21_b);
-void* var_90 = tensorAdd(var_82, var_89);
-void* var_91 = tensorRelu(var_90);
-void* var_92 = tensorPooling(var_91,1,8,8,0,0,8,8);
-void* var_94 = tensorGemmGPU(var_92, dense_1_w);
-void* var_95 = tensorAdd(var_94, dense_1_b);
-void* var_96 = tensorSoftmax(var_95);
-
-computeAccuracy2(labels,10000,var_96);
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100_front/approxhpvm_src.cc b/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100_front/approxhpvm_src.cc
deleted file mode 100644
index 8084e3723a6141ac0e99729b8455bcc529ac7a0f..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100_front/approxhpvm_src.cc
+++ /dev/null
@@ -1,982 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/stat.h>
-#include <cstring>
-#include <visc.h>
-#include <tensorTypes.h>
-#include <tensorUtils.h>
-
-void var_0_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_1_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_2_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_3_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_4_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_5_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_6_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_7_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_8_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_9_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_10_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_11_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_12_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_13_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_14_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_15_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_16_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_17_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_18_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_19_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_20_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_21_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_22_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_23_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_24_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_25_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_26_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_27_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_28_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_29_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_30_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_31_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_32_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_33_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_34_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_35_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_36_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_37_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_38_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_39_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_40_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 1, 1, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_41_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_42_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_43_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_44_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_mul(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_45_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_46_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_47_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_mul(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_48_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_49_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_softmax(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void root(void* input, size_t input_bytes,
- void* conv2d_1_w, size_t conv2d_1_w_bytes,
- void* conv2d_1_b, size_t conv2d_1_b_bytes,
- void* conv2d_2_w, size_t conv2d_2_w_bytes,
- void* conv2d_2_b, size_t conv2d_2_b_bytes,
- void* conv2d_3_w, size_t conv2d_3_w_bytes,
- void* conv2d_3_b, size_t conv2d_3_b_bytes,
- void* conv2d_4_w, size_t conv2d_4_w_bytes,
- void* conv2d_4_b, size_t conv2d_4_b_bytes,
- void* conv2d_5_w, size_t conv2d_5_w_bytes,
- void* conv2d_5_b, size_t conv2d_5_b_bytes,
- void* conv2d_6_w, size_t conv2d_6_w_bytes,
- void* conv2d_6_b, size_t conv2d_6_b_bytes,
- void* conv2d_7_w, size_t conv2d_7_w_bytes,
- void* conv2d_7_b, size_t conv2d_7_b_bytes,
- void* conv2d_8_w, size_t conv2d_8_w_bytes,
- void* conv2d_8_b, size_t conv2d_8_b_bytes,
- void* conv2d_9_w, size_t conv2d_9_w_bytes,
- void* conv2d_9_b, size_t conv2d_9_b_bytes,
- void* conv2d_10_w, size_t conv2d_10_w_bytes,
- void* conv2d_10_b, size_t conv2d_10_b_bytes,
- void* conv2d_11_w, size_t conv2d_11_w_bytes,
- void* conv2d_11_b, size_t conv2d_11_b_bytes,
- void* conv2d_12_w, size_t conv2d_12_w_bytes,
- void* conv2d_12_b, size_t conv2d_12_b_bytes,
- void* conv2d_13_w, size_t conv2d_13_w_bytes,
- void* conv2d_13_b, size_t conv2d_13_b_bytes,
- void* dense_1_w, size_t dense_1_w_bytes,
- void* dense_1_b, size_t dense_1_b_bytes,
- void* dense_2_w, size_t dense_2_w_bytes,
- void* dense_2_b, size_t dense_2_b_bytes){
-
-
- __visc__hint(visc::CPU_TARGET);
- __visc__attributes(31, input, conv2d_1_w, conv2d_1_b, conv2d_2_w, conv2d_2_b, conv2d_3_w, conv2d_3_b, conv2d_4_w, conv2d_4_b, conv2d_5_w, conv2d_5_b, conv2d_6_w, conv2d_6_b, conv2d_7_w, conv2d_7_b, conv2d_8_w, conv2d_8_b, conv2d_9_w, conv2d_9_b, conv2d_10_w, conv2d_10_b, conv2d_11_w, conv2d_11_b, conv2d_12_w, conv2d_12_b, conv2d_13_w, conv2d_13_b, dense_1_w, dense_1_b, dense_2_w, dense_2_b, 0);
-
-
- void* var_0 = __visc__createNodeND(0, var_0_node);
-
- __visc__bindIn(var_0, 0, 0, 0);
- __visc__bindIn(var_0, 1, 1, 0);
- __visc__bindIn(var_0, 2, 2, 0);
- __visc__bindIn(var_0, 3, 3, 0);
-
- void* var_1 = __visc__createNodeND(0, var_1_node);
-
- __visc__edge(var_0, var_1, 1, 0, 0, 0);
- __visc__edge(var_0, var_1, 1, 1, 1, 0);
- __visc__bindIn(var_1, 4, 2, 0);
- __visc__bindIn(var_1, 5, 3, 0);
-
- void* var_2 = __visc__createNodeND(0, var_2_node);
-
- __visc__edge(var_1, var_2, 1, 0, 0, 0);
- __visc__edge(var_1, var_2, 1, 1, 1, 0);
-
- void* var_3 = __visc__createNodeND(0, var_3_node);
-
- __visc__edge(var_2, var_3, 1, 0, 0, 0);
- __visc__edge(var_2, var_3, 1, 1, 1, 0);
- __visc__bindIn(var_3, 6, 2, 0);
- __visc__bindIn(var_3, 7, 3, 0);
-
- void* var_4 = __visc__createNodeND(0, var_4_node);
-
- __visc__edge(var_3, var_4, 1, 0, 0, 0);
- __visc__edge(var_3, var_4, 1, 1, 1, 0);
- __visc__bindIn(var_4, 8, 2, 0);
- __visc__bindIn(var_4, 9, 3, 0);
-
- void* var_5 = __visc__createNodeND(0, var_5_node);
-
- __visc__edge(var_4, var_5, 1, 0, 0, 0);
- __visc__edge(var_4, var_5, 1, 1, 1, 0);
-
- void* var_6 = __visc__createNodeND(0, var_6_node);
-
- __visc__edge(var_5, var_6, 1, 0, 0, 0);
- __visc__edge(var_5, var_6, 1, 1, 1, 0);
-
- void* var_7 = __visc__createNodeND(0, var_7_node);
-
- __visc__edge(var_6, var_7, 1, 0, 0, 0);
- __visc__edge(var_6, var_7, 1, 1, 1, 0);
- __visc__bindIn(var_7, 10, 2, 0);
- __visc__bindIn(var_7, 11, 3, 0);
-
- void* var_8 = __visc__createNodeND(0, var_8_node);
-
- __visc__edge(var_7, var_8, 1, 0, 0, 0);
- __visc__edge(var_7, var_8, 1, 1, 1, 0);
- __visc__bindIn(var_8, 12, 2, 0);
- __visc__bindIn(var_8, 13, 3, 0);
-
- void* var_9 = __visc__createNodeND(0, var_9_node);
-
- __visc__edge(var_8, var_9, 1, 0, 0, 0);
- __visc__edge(var_8, var_9, 1, 1, 1, 0);
-
- void* var_10 = __visc__createNodeND(0, var_10_node);
-
- __visc__edge(var_9, var_10, 1, 0, 0, 0);
- __visc__edge(var_9, var_10, 1, 1, 1, 0);
- __visc__bindIn(var_10, 14, 2, 0);
- __visc__bindIn(var_10, 15, 3, 0);
-
- void* var_11 = __visc__createNodeND(0, var_11_node);
-
- __visc__edge(var_10, var_11, 1, 0, 0, 0);
- __visc__edge(var_10, var_11, 1, 1, 1, 0);
- __visc__bindIn(var_11, 16, 2, 0);
- __visc__bindIn(var_11, 17, 3, 0);
-
- void* var_12 = __visc__createNodeND(0, var_12_node);
-
- __visc__edge(var_11, var_12, 1, 0, 0, 0);
- __visc__edge(var_11, var_12, 1, 1, 1, 0);
-
- void* var_13 = __visc__createNodeND(0, var_13_node);
-
- __visc__edge(var_12, var_13, 1, 0, 0, 0);
- __visc__edge(var_12, var_13, 1, 1, 1, 0);
-
- void* var_14 = __visc__createNodeND(0, var_14_node);
-
- __visc__edge(var_13, var_14, 1, 0, 0, 0);
- __visc__edge(var_13, var_14, 1, 1, 1, 0);
- __visc__bindIn(var_14, 18, 2, 0);
- __visc__bindIn(var_14, 19, 3, 0);
-
- void* var_15 = __visc__createNodeND(0, var_15_node);
-
- __visc__edge(var_14, var_15, 1, 0, 0, 0);
- __visc__edge(var_14, var_15, 1, 1, 1, 0);
- __visc__bindIn(var_15, 20, 2, 0);
- __visc__bindIn(var_15, 21, 3, 0);
-
- void* var_16 = __visc__createNodeND(0, var_16_node);
-
- __visc__edge(var_15, var_16, 1, 0, 0, 0);
- __visc__edge(var_15, var_16, 1, 1, 1, 0);
-
- void* var_17 = __visc__createNodeND(0, var_17_node);
-
- __visc__edge(var_16, var_17, 1, 0, 0, 0);
- __visc__edge(var_16, var_17, 1, 1, 1, 0);
- __visc__bindIn(var_17, 22, 2, 0);
- __visc__bindIn(var_17, 23, 3, 0);
-
- void* var_18 = __visc__createNodeND(0, var_18_node);
-
- __visc__edge(var_17, var_18, 1, 0, 0, 0);
- __visc__edge(var_17, var_18, 1, 1, 1, 0);
- __visc__bindIn(var_18, 24, 2, 0);
- __visc__bindIn(var_18, 25, 3, 0);
-
- void* var_19 = __visc__createNodeND(0, var_19_node);
-
- __visc__edge(var_18, var_19, 1, 0, 0, 0);
- __visc__edge(var_18, var_19, 1, 1, 1, 0);
-
- void* var_20 = __visc__createNodeND(0, var_20_node);
-
- __visc__edge(var_19, var_20, 1, 0, 0, 0);
- __visc__edge(var_19, var_20, 1, 1, 1, 0);
- __visc__bindIn(var_20, 26, 2, 0);
- __visc__bindIn(var_20, 27, 3, 0);
-
- void* var_21 = __visc__createNodeND(0, var_21_node);
-
- __visc__edge(var_20, var_21, 1, 0, 0, 0);
- __visc__edge(var_20, var_21, 1, 1, 1, 0);
- __visc__bindIn(var_21, 28, 2, 0);
- __visc__bindIn(var_21, 29, 3, 0);
-
- void* var_22 = __visc__createNodeND(0, var_22_node);
-
- __visc__edge(var_21, var_22, 1, 0, 0, 0);
- __visc__edge(var_21, var_22, 1, 1, 1, 0);
-
- void* var_23 = __visc__createNodeND(0, var_23_node);
-
- __visc__edge(var_22, var_23, 1, 0, 0, 0);
- __visc__edge(var_22, var_23, 1, 1, 1, 0);
-
- void* var_24 = __visc__createNodeND(0, var_24_node);
-
- __visc__edge(var_23, var_24, 1, 0, 0, 0);
- __visc__edge(var_23, var_24, 1, 1, 1, 0);
- __visc__bindIn(var_24, 30, 2, 0);
- __visc__bindIn(var_24, 31, 3, 0);
-
- void* var_25 = __visc__createNodeND(0, var_25_node);
-
- __visc__edge(var_24, var_25, 1, 0, 0, 0);
- __visc__edge(var_24, var_25, 1, 1, 1, 0);
- __visc__bindIn(var_25, 32, 2, 0);
- __visc__bindIn(var_25, 33, 3, 0);
-
- void* var_26 = __visc__createNodeND(0, var_26_node);
-
- __visc__edge(var_25, var_26, 1, 0, 0, 0);
- __visc__edge(var_25, var_26, 1, 1, 1, 0);
-
- void* var_27 = __visc__createNodeND(0, var_27_node);
-
- __visc__edge(var_26, var_27, 1, 0, 0, 0);
- __visc__edge(var_26, var_27, 1, 1, 1, 0);
- __visc__bindIn(var_27, 34, 2, 0);
- __visc__bindIn(var_27, 35, 3, 0);
-
- void* var_28 = __visc__createNodeND(0, var_28_node);
-
- __visc__edge(var_27, var_28, 1, 0, 0, 0);
- __visc__edge(var_27, var_28, 1, 1, 1, 0);
- __visc__bindIn(var_28, 36, 2, 0);
- __visc__bindIn(var_28, 37, 3, 0);
-
- void* var_29 = __visc__createNodeND(0, var_29_node);
-
- __visc__edge(var_28, var_29, 1, 0, 0, 0);
- __visc__edge(var_28, var_29, 1, 1, 1, 0);
-
- void* var_30 = __visc__createNodeND(0, var_30_node);
-
- __visc__edge(var_29, var_30, 1, 0, 0, 0);
- __visc__edge(var_29, var_30, 1, 1, 1, 0);
- __visc__bindIn(var_30, 38, 2, 0);
- __visc__bindIn(var_30, 39, 3, 0);
-
- void* var_31 = __visc__createNodeND(0, var_31_node);
-
- __visc__edge(var_30, var_31, 1, 0, 0, 0);
- __visc__edge(var_30, var_31, 1, 1, 1, 0);
- __visc__bindIn(var_31, 40, 2, 0);
- __visc__bindIn(var_31, 41, 3, 0);
-
- void* var_32 = __visc__createNodeND(0, var_32_node);
-
- __visc__edge(var_31, var_32, 1, 0, 0, 0);
- __visc__edge(var_31, var_32, 1, 1, 1, 0);
-
- void* var_33 = __visc__createNodeND(0, var_33_node);
-
- __visc__edge(var_32, var_33, 1, 0, 0, 0);
- __visc__edge(var_32, var_33, 1, 1, 1, 0);
-
- void* var_34 = __visc__createNodeND(0, var_34_node);
-
- __visc__edge(var_33, var_34, 1, 0, 0, 0);
- __visc__edge(var_33, var_34, 1, 1, 1, 0);
- __visc__bindIn(var_34, 42, 2, 0);
- __visc__bindIn(var_34, 43, 3, 0);
-
- void* var_35 = __visc__createNodeND(0, var_35_node);
-
- __visc__edge(var_34, var_35, 1, 0, 0, 0);
- __visc__edge(var_34, var_35, 1, 1, 1, 0);
- __visc__bindIn(var_35, 44, 2, 0);
- __visc__bindIn(var_35, 45, 3, 0);
-
- void* var_36 = __visc__createNodeND(0, var_36_node);
-
- __visc__edge(var_35, var_36, 1, 0, 0, 0);
- __visc__edge(var_35, var_36, 1, 1, 1, 0);
-
- void* var_37 = __visc__createNodeND(0, var_37_node);
-
- __visc__edge(var_36, var_37, 1, 0, 0, 0);
- __visc__edge(var_36, var_37, 1, 1, 1, 0);
- __visc__bindIn(var_37, 46, 2, 0);
- __visc__bindIn(var_37, 47, 3, 0);
-
- void* var_38 = __visc__createNodeND(0, var_38_node);
-
- __visc__edge(var_37, var_38, 1, 0, 0, 0);
- __visc__edge(var_37, var_38, 1, 1, 1, 0);
- __visc__bindIn(var_38, 48, 2, 0);
- __visc__bindIn(var_38, 49, 3, 0);
-
- void* var_39 = __visc__createNodeND(0, var_39_node);
-
- __visc__edge(var_38, var_39, 1, 0, 0, 0);
- __visc__edge(var_38, var_39, 1, 1, 1, 0);
-
- void* var_40 = __visc__createNodeND(0, var_40_node);
-
- __visc__edge(var_39, var_40, 1, 0, 0, 0);
- __visc__edge(var_39, var_40, 1, 1, 1, 0);
- __visc__bindIn(var_40, 50, 2, 0);
- __visc__bindIn(var_40, 51, 3, 0);
-
- void* var_41 = __visc__createNodeND(0, var_41_node);
-
- __visc__edge(var_40, var_41, 1, 0, 0, 0);
- __visc__edge(var_40, var_41, 1, 1, 1, 0);
- __visc__bindIn(var_41, 52, 2, 0);
- __visc__bindIn(var_41, 53, 3, 0);
-
- void* var_42 = __visc__createNodeND(0, var_42_node);
-
- __visc__edge(var_41, var_42, 1, 0, 0, 0);
- __visc__edge(var_41, var_42, 1, 1, 1, 0);
-
- void* var_43 = __visc__createNodeND(0, var_43_node);
-
- __visc__edge(var_42, var_43, 1, 0, 0, 0);
- __visc__edge(var_42, var_43, 1, 1, 1, 0);
-
- void* var_44 = __visc__createNodeND(0, var_44_node);
-
- __visc__edge(var_43, var_44, 1, 0, 0, 0);
- __visc__edge(var_43, var_44, 1, 1, 1, 0);
- __visc__bindIn(var_44, 54, 2, 0);
- __visc__bindIn(var_44, 55, 3, 0);
-
- void* var_45 = __visc__createNodeND(0, var_45_node);
-
- __visc__edge(var_44, var_45, 1, 0, 0, 0);
- __visc__edge(var_44, var_45, 1, 1, 1, 0);
- __visc__bindIn(var_45, 56, 2, 0);
- __visc__bindIn(var_45, 57, 3, 0);
-
- void* var_46 = __visc__createNodeND(0, var_46_node);
-
- __visc__edge(var_45, var_46, 1, 0, 0, 0);
- __visc__edge(var_45, var_46, 1, 1, 1, 0);
-
- void* var_47 = __visc__createNodeND(0, var_47_node);
-
- __visc__edge(var_46, var_47, 1, 0, 0, 0);
- __visc__edge(var_46, var_47, 1, 1, 1, 0);
- __visc__bindIn(var_47, 58, 2, 0);
- __visc__bindIn(var_47, 59, 3, 0);
-
- void* var_48 = __visc__createNodeND(0, var_48_node);
-
- __visc__edge(var_47, var_48, 1, 0, 0, 0);
- __visc__edge(var_47, var_48, 1, 1, 1, 0);
- __visc__bindIn(var_48, 60, 2, 0);
- __visc__bindIn(var_48, 61, 3, 0);
-
- void* var_49 = __visc__createNodeND(0, var_49_node);
-
- __visc__edge(var_48, var_49, 1, 0, 0, 0);
- __visc__edge(var_48, var_49, 1, 1, 1, 0);
-
- __visc__bindOut(var_49, 0, 0, 0);
- __visc__bindOut(var_49, 1, 1, 0);
-
-}
-
-struct ret_t {
- void* tensor;
- size_t bytes;
-};
-
-typedef struct __attribute__((__packed__)) {
- void* input;
- size_t input_bytes;
- void* conv2d_1_w;
- size_t conv2d_1_w_bytes;
- void* conv2d_1_b;
- size_t conv2d_1_b_bytes;
- void* conv2d_2_w;
- size_t conv2d_2_w_bytes;
- void* conv2d_2_b;
- size_t conv2d_2_b_bytes;
- void* conv2d_3_w;
- size_t conv2d_3_w_bytes;
- void* conv2d_3_b;
- size_t conv2d_3_b_bytes;
- void* conv2d_4_w;
- size_t conv2d_4_w_bytes;
- void* conv2d_4_b;
- size_t conv2d_4_b_bytes;
- void* conv2d_5_w;
- size_t conv2d_5_w_bytes;
- void* conv2d_5_b;
- size_t conv2d_5_b_bytes;
- void* conv2d_6_w;
- size_t conv2d_6_w_bytes;
- void* conv2d_6_b;
- size_t conv2d_6_b_bytes;
- void* conv2d_7_w;
- size_t conv2d_7_w_bytes;
- void* conv2d_7_b;
- size_t conv2d_7_b_bytes;
- void* conv2d_8_w;
- size_t conv2d_8_w_bytes;
- void* conv2d_8_b;
- size_t conv2d_8_b_bytes;
- void* conv2d_9_w;
- size_t conv2d_9_w_bytes;
- void* conv2d_9_b;
- size_t conv2d_9_b_bytes;
- void* conv2d_10_w;
- size_t conv2d_10_w_bytes;
- void* conv2d_10_b;
- size_t conv2d_10_b_bytes;
- void* conv2d_11_w;
- size_t conv2d_11_w_bytes;
- void* conv2d_11_b;
- size_t conv2d_11_b_bytes;
- void* conv2d_12_w;
- size_t conv2d_12_w_bytes;
- void* conv2d_12_b;
- size_t conv2d_12_b_bytes;
- void* conv2d_13_w;
- size_t conv2d_13_w_bytes;
- void* conv2d_13_b;
- size_t conv2d_13_b_bytes;
- void* dense_1_w;
- size_t dense_1_w_bytes;
- void* dense_1_b;
- size_t dense_1_b_bytes;
- void* dense_2_w;
- size_t dense_2_w_bytes;
- void* dense_2_b;
- size_t dense_2_b_bytes;
-
- struct ret_t r;
-}
-RootIn;
-
-int main(){
-
-std::string dir_prefix = std::string("vgg16_cifar100_test/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
-void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
-void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
-std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
-void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
-void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
-void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
-void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
-void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
-void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
-std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
-void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,100);
-std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
-void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,100,1,1);
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-
-__visc__init();
-RootIn* args = static_cast<RootIn*>(malloc(sizeof(RootIn)));
-
-args->input = input;
-args->input_bytes = 0;
-args->conv2d_1_w = conv2d_1_w;
-args->conv2d_1_w_bytes = 0;
-args->conv2d_1_b = conv2d_1_b;
-args->conv2d_1_b_bytes = 0;
-args->conv2d_2_w = conv2d_2_w;
-args->conv2d_2_w_bytes = 0;
-args->conv2d_2_b = conv2d_2_b;
-args->conv2d_2_b_bytes = 0;
-args->conv2d_3_w = conv2d_3_w;
-args->conv2d_3_w_bytes = 0;
-args->conv2d_3_b = conv2d_3_b;
-args->conv2d_3_b_bytes = 0;
-args->conv2d_4_w = conv2d_4_w;
-args->conv2d_4_w_bytes = 0;
-args->conv2d_4_b = conv2d_4_b;
-args->conv2d_4_b_bytes = 0;
-args->conv2d_5_w = conv2d_5_w;
-args->conv2d_5_w_bytes = 0;
-args->conv2d_5_b = conv2d_5_b;
-args->conv2d_5_b_bytes = 0;
-args->conv2d_6_w = conv2d_6_w;
-args->conv2d_6_w_bytes = 0;
-args->conv2d_6_b = conv2d_6_b;
-args->conv2d_6_b_bytes = 0;
-args->conv2d_7_w = conv2d_7_w;
-args->conv2d_7_w_bytes = 0;
-args->conv2d_7_b = conv2d_7_b;
-args->conv2d_7_b_bytes = 0;
-args->conv2d_8_w = conv2d_8_w;
-args->conv2d_8_w_bytes = 0;
-args->conv2d_8_b = conv2d_8_b;
-args->conv2d_8_b_bytes = 0;
-args->conv2d_9_w = conv2d_9_w;
-args->conv2d_9_w_bytes = 0;
-args->conv2d_9_b = conv2d_9_b;
-args->conv2d_9_b_bytes = 0;
-args->conv2d_10_w = conv2d_10_w;
-args->conv2d_10_w_bytes = 0;
-args->conv2d_10_b = conv2d_10_b;
-args->conv2d_10_b_bytes = 0;
-args->conv2d_11_w = conv2d_11_w;
-args->conv2d_11_w_bytes = 0;
-args->conv2d_11_b = conv2d_11_b;
-args->conv2d_11_b_bytes = 0;
-args->conv2d_12_w = conv2d_12_w;
-args->conv2d_12_w_bytes = 0;
-args->conv2d_12_b = conv2d_12_b;
-args->conv2d_12_b_bytes = 0;
-args->conv2d_13_w = conv2d_13_w;
-args->conv2d_13_w_bytes = 0;
-args->conv2d_13_b = conv2d_13_b;
-args->conv2d_13_b_bytes = 0;
-args->dense_1_w = dense_1_w;
-args->dense_1_w_bytes = 0;
-args->dense_1_b = dense_1_b;
-args->dense_1_b_bytes = 0;
-args->dense_2_w = dense_2_w;
-args->dense_2_w_bytes = 0;
-args->dense_2_b = dense_2_b;
-args->dense_2_b_bytes = 0;
-
-void* dfg = __visc__launch(0, root, (void*) args);
-
-__visc__wait(dfg);
-
-void *result = static_cast<RootIn*>(args)->input;
-hpvm_request_tensor(result, 0);
-
-__visc__cleanup();
- computeAccuracy2(labels, 10000, result);
-return 0;
-
-}
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+++ /dev/null
@@ -1,2 +0,0 @@
-ãèÏ;ÿCú=×_Q>-î½Z5Ì=&$¿í÷‡>È,…>sv¾è2 ¼.ãÏ<Ã4$>׆0>Û)¿vg§¾Çô¬¿‡·K=Žõ¼=9ó¾Ÿî²¼ée=¬P¡?•¿Ì¾º}Œ=½ ~>–·"¼bKƒ>«î—¾Hu >v$™¾Æj—?cß8>OÏñ=kÇ<>ÙYM>(¿jît>щ>Ä…&¾gH¿·g&=«¿
-k>`M¶¿ ?³0T>ò£>u‹>]ˆÁ½®‚¿m‚¿M—a>ù䑽2> ë>¯ñ
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\ No newline at end of file
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+++ /dev/null
@@ -1,5 +0,0 @@
-Qwü¾ ¶–>ó<
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->.Íô=”í¾
dP¾< >¶¤>?^÷½o) >Ä#?¹”s½ÎM~>‚ ?øáy>FÅT¾4E“=† ¿§B½I€=Û=?öG?¸ÛÑ>N0Ç>=ü?ßÞ¥=lѼ½üÜT>¢îæ>S>M>YЊ=t[N¾ámÄ>Z¶Ò>¯^T>pxõ>‡Ú>¹ÿH¾î÷q>wO$¾
-¿û0/>Ø˹>ót$¿žÁ?º¨®= x½0w>1Z,¾€3ã>Äš×=¥b¸=è6@>ìŒÐ>!4š>ý´ >¸D†>°<Œ=ˆh_=}>ÿ C>‘¿þ½=ã=uj>[W?d¨Ö>Õk±>â(½“Â`¼ML“>·£ ?iC;>p·?èçŠ>G™>²¾yrì>e6ƒ¾ægs>bø?§{(?l¡â»:U¡>\å ?v'é=÷ðœ>+ U>osÖ>õq'>Žçô>Üã&>àI¾[´Î=çE ?-áD½ ‹>ZWy>líœ>û¯>-®¾jA¾`R¿êþL¿1Ôº6¡h»Y„=sµ)?šà—>þÌ?6ܨ=ðîÜ>C>–š>Êÿ>ä¶Ò>n"Ø>6¥Á¾Lƒ½bþ1?8}½b›Ê>ÖÁ> `ú>÷ÂÊ>ß
-G>Ðå>öÑ >ûøq>±Šÿ>UÙ$?Ò?Ïm¼{mb>_I$>'-Ö>+æ+>èd¹>¦ @¿^´†>Z(¯¼Ãͽð¾i>ë h¿ Õ‹=’Ï>›s>,|>þ®>ïºS>Æ–>%>“ ?»Àå>¤¢Á½Û÷Ø>û‚œ;õ)ï=v6̾> >®£”<i°¾¬é>Žà×¼ÜüÏ>0œÌ>˜Lä>ʯ¾–“n>ບ>!û¯>€õ£>G>ÌÍÑ>i1>•â¼§8½ ε>AˆÖ>‰F<A?¯>Ìt>x=wô?ñl ?4®_>t^»>BáÒ>¾fÙ½`:˜½-N@?ñw›¾ð|÷<É'‘>ºJ¾|>⽩ë´>ß0b>áXß½£å'¾fMM>£Ix>¥&Ƚ3úƒ>â'È>þþ>8åÉ>Y|§>û T½9º¾
\ No newline at end of file
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100_front/input.bin b/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100_front/input.bin
deleted file mode 100644
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100_front/labels.bin b/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100_front/labels.bin
deleted file mode 100644
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100_front/labels32.bin b/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100_front/labels32.bin
deleted file mode 100644
index 0e29aa05a086316e36429e20e1a13580e1b0c36a..0000000000000000000000000000000000000000
Binary files a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100_front/labels32.bin and /dev/null differ
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100_front/layer_composition.txt b/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100_front/layer_composition.txt
deleted file mode 100644
index 79818d6f010035c6e19f12881749f4d5b3d3c253..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100_front/layer_composition.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-conv add activation
-conv add activation pool
-conv add activation
-conv add activation pool
-conv add activation
-conv add activation
-conv add activation pool
-conv add activation
-conv add activation
-conv add activation pool
-conv add activation
-conv add activation
-conv add activation pool
-dense add activation
-dense add
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100_front/layers.txt b/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100_front/layers.txt
deleted file mode 100644
index 7eaa520e4a2e451d5ccec5c8737dec8be8458369..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100_front/layers.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-Conv1,10000,3,32,32,64,3,3,3
-Conv2,10000,64,32,32,64,64,3,3
-Conv3,10000,64,16,16,128,64,3,3
-Conv4,10000,128,16,16,128,128,3,3
-Conv5,10000,128,8,8,256,128,3,3
-Conv6,10000,256,8,8,256,256,3,3
-Conv7,10000,256,8,8,256,256,3,3
-Conv8,10000,256,4,4,512,256,3,3
-Conv9,10000,512,4,4,512,512,3,3
-Conv10,10000,512,4,4,512,512,3,3
-Conv11,10000,512,2,2,512,512,3,3
-Conv12,10000,512,2,2,512,512,3,3
-Conv13,10000,512,2,2,512,512,3,3
-FC1,10000,512,512,512
-FC2,10000,512,512,100
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100_front/promise_src.cc b/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100_front/promise_src.cc
deleted file mode 100644
index 0f28f2bfd69d9e8c4895e782bd02173eefcd0993..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100_front/promise_src.cc
+++ /dev/null
@@ -1,138 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-int total_runs = 100;
-for (int i = 0 ; i < total_runs; i++){
-
-
-startMemTracking();
-
-int test_input_size = 10000;
-int batch_size = 10000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-
-
-std::string dir_prefix = std::string("vgg16_cifar100_test/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
-void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
-void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
-std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
-void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
-void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
-void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
-void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
-void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
-void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
-std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
-void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,100);
-std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
-void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,100,1,1);
-
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = ConvLayer_PROMISE(input, -1.7829767, 1.9456929, conv2d_1_w, -0.7450515, 0.71249133, conv2d_1_b, -1.5885142, 0.275554, 1, 1, 1, 1, -1, 0, 1, 0.0, 8.190712, 9);
-void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 8.190712, conv2d_2_w, -0.30790088, 0.43504623, conv2d_2_b, -1.4242363, 1.2602744, 1, 1, 1, 1, 0, 2, 1, 0.0, 19.023172, 9);
-void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 19.023172, conv2d_3_w, -0.29189092, 0.26958522, conv2d_3_b, -1.0527138, 0.9075671, 1, 1, 1, 1, -1, 0, 1, 0.0, 14.428051, 9);
-void* var_3 = ConvLayer_PROMISE(var_2, 0.0, 14.428051, conv2d_4_w, -0.15521508, 0.1829038, conv2d_4_b, -0.845419, 1.9358484, 1, 1, 1, 1, 0, 2, 1, 0.0, 23.065294, 9);
-void* var_4 = ConvLayer_PROMISE(var_3, 0.0, 23.065294, conv2d_5_w, -0.13149762, 0.14811686, conv2d_5_b, -0.7162557, 1.0370971, 1, 1, 1, 1, -1, 0, 1, 0.0, 15.165984, 9);
-void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 15.165984, conv2d_6_w, -0.06236292, 0.08321518, conv2d_6_b, -0.9067523, 0.9922458, 1, 1, 1, 1, -1, 0, 1, 0.0, 13.664733, 9);
-void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 13.664733, conv2d_7_w, -0.06471479, 0.1024472, conv2d_7_b, -0.15943134, 0.7988499, 1, 1, 1, 1, 0, 2, 1, 0.0, 19.025272, 9);
-void* var_7 = ConvLayer_PROMISE(var_6, 0.0, 19.025272, conv2d_8_w, -0.06320205, 0.08291938, conv2d_8_b, -0.32540628, 0.5203079, 1, 1, 1, 1, -1, 0, 1, 0.0, 6.727217, 9);
-void* var_8 = ConvLayer_PROMISE(var_7, 0.0, 6.727217, conv2d_9_w, -0.037707984, 0.051601283, conv2d_9_b, -0.25622904, 0.11251946, 1, 1, 1, 1, -1, 0, 1, 0.0, 3.2003012, 9);
-void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 3.2003012, conv2d_10_w, -0.056007143, 0.09549151, conv2d_10_b, -0.11591503, 0.06267536, 1, 1, 1, 1, 0, 2, 1, 0.0, 4.321189, 9);
-void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 4.321189, conv2d_11_w, -0.060094673, 0.10868926, conv2d_11_b, -0.105962686, 0.09584572, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.936297, 9);
-void* var_11 = ConvLayer_PROMISE(var_10, 0.0, 2.936297, conv2d_12_w, -0.034618977, 0.05792674, conv2d_12_b, -0.4237576, 0.11035452, 1, 1, 1, 1, -1, 0, 1, 0.0, 4.87262, 9);
-void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 4.87262, conv2d_13_w, -0.035480656, 0.058295887, conv2d_13_b, -0.21477045, 0.14263579, 1, 1, 1, 1, 0, 2, 1, 0.0, 10.32133, 9);
-void* var_13 = FCLayer_PROMISE(var_12, 0.0, 10.32133, dense_1_w, -0.08929961, 0.11301676, dense_1_b, -0.20798548, 0.47405547, 1, 0.0, 13.91, 9);
-void* var_14 = FCLayer_PROMISE(var_13, 0.0, 13.91, dense_2_w, -0.6627122, 0.35539475, dense_2_b, -1.0631907, 0.9830786, -1, -70.45701, 87.34367, 9);
-void* var_15 = tensorSoftmax(var_14);
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy2(labels, batch_size, var_15);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-}
-
-dumpExecutionAccuracies();
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100_front/src.cc b/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100_front/src.cc
deleted file mode 100644
index bb792eaf71e851a5bf9791362aa09991dbc8ef68..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar100_front/src.cc
+++ /dev/null
@@ -1,164 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-std::string dir_prefix = std::string("vgg16_cifar100_test/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
-void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
-void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
-std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
-void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
-void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
-void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
-void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
-void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
-void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
-std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
-void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,100);
-std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
-void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,100,1,1);
-
-
-
-startMemTracking();
-
-int test_input_size = 10000;
-int batch_size = 10000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
-void* var_1 = tensorAdd(var_0, conv2d_1_b);
-void* var_2 = tensorRelu(var_1);
-void* var_4 = tensorConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0);
-void* var_5 = tensorAdd(var_4, conv2d_2_b);
-void* var_6 = tensorRelu(var_5);
-void* var_7 = tensorPooling(var_6,0,2,2,0,0,2,2);
-void* var_8 = tensorConvolution(var_7, conv2d_3_w, 1, 1, 1, 1, 1, 0);
-void* var_9 = tensorAdd(var_8, conv2d_3_b);
-void* var_10 = tensorRelu(var_9);
-void* var_12 = tensorConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0);
-void* var_13 = tensorAdd(var_12, conv2d_4_b);
-void* var_14 = tensorRelu(var_13);
-void* var_15 = tensorPooling(var_14,0,2,2,0,0,2,2);
-void* var_16 = tensorConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
-void* var_17 = tensorAdd(var_16, conv2d_5_b);
-void* var_18 = tensorRelu(var_17);
-void* var_20 = tensorConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0);
-void* var_21 = tensorAdd(var_20, conv2d_6_b);
-void* var_22 = tensorRelu(var_21);
-void* var_24 = tensorConvolution(var_22, conv2d_7_w, 1, 1, 1, 1, 1, 0);
-void* var_25 = tensorAdd(var_24, conv2d_7_b);
-void* var_26 = tensorRelu(var_25);
-void* var_27 = tensorPooling(var_26,0,2,2,0,0,2,2);
-void* var_28 = tensorConvolution(var_27, conv2d_8_w, 1, 1, 1, 1, 1, 0);
-void* var_29 = tensorAdd(var_28, conv2d_8_b);
-void* var_30 = tensorRelu(var_29);
-void* var_32 = tensorConvolution(var_30, conv2d_9_w, 1, 1, 1, 1, 1, 0);
-void* var_33 = tensorAdd(var_32, conv2d_9_b);
-void* var_34 = tensorRelu(var_33);
-void* var_36 = tensorConvolution(var_34, conv2d_10_w, 1, 1, 1, 1, 1, 0);
-void* var_37 = tensorAdd(var_36, conv2d_10_b);
-void* var_38 = tensorRelu(var_37);
-void* var_39 = tensorPooling(var_38,0,2,2,0,0,2,2);
-void* var_40 = tensorConvolution(var_39, conv2d_11_w, 1, 1, 1, 1, 1, 0);
-void* var_41 = tensorAdd(var_40, conv2d_11_b);
-void* var_42 = tensorRelu(var_41);
-void* var_44 = tensorConvolution(var_42, conv2d_12_w, 1, 1, 1, 1, 1, 0);
-void* var_45 = tensorAdd(var_44, conv2d_12_b);
-void* var_46 = tensorRelu(var_45);
-void* var_48 = tensorConvolution(var_46, conv2d_13_w, 1, 1, 1, 1, 1, 0);
-void* var_49 = tensorAdd(var_48, conv2d_13_b);
-void* var_50 = tensorRelu(var_49);
-void* var_51 = tensorPooling(var_50,0,2,2,0,0,2,2);
-void* var_54 = tensorGemmGPU(var_51, dense_1_w);
-void* var_55 = tensorAdd(var_54, dense_1_b);
-void* var_56 = tensorRelu(var_55);
-void* var_58 = tensorGemmGPU(var_56, dense_2_w);
-void* var_59 = tensorAdd(var_58, dense_2_b);
-void* var_60 = tensorSoftmax(var_59);
-
-uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy2(labels, batch_size, var_60);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_2/conv2d_10_b.bin b/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_2/conv2d_10_b.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_2/conv2d_2_b.bin b/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_2/conv2d_2_b.bin
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--- a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_2/conv2d_2_b.bin
+++ /dev/null
@@ -1 +0,0 @@
-!Ü=!J¹<«¾j>ÓOä=ŸÐî:²_¾Dr=~>Y«=Ö>³ ½iÛ½ñ3½,3¾ŠÛ>Ç Û=¸ ¾uŒ>ž\<Z7=Z§¬=''6¾o ¨;Ñ»õ¾à¡t¾©¾ òG½àÓ¨¾¥å¼ôÿ]¾,O¨=¶’<»lÏ=¥Ò">¡'Œ½ÃŠ=®¤T¾Z==2éÏ=üjë½ûHF¿õ¾ “œ¼£}=³¬¾ßä¼Ó°”>ÛïQ?VÍ<; =8ñ‰½‹xr¾¢°}<ÿz>îšG¾{“ ½ax6¾¶Î‚»$Ôë¾DpÎ=Š|5¾Á2>¤tY>ä©=
\ No newline at end of file
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_2/dense_2_b.bin b/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_2/dense_2_b.bin
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--- a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_2/dense_2_b.bin
+++ /dev/null
@@ -1 +0,0 @@
-
Sî>:Ä}¾õ…>ºpž?ñ‡¿’ß²>Vâ–¿Q0å<qö>Ò/“<
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_2/src.cc b/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_2/src.cc
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index 44179ee9f39c9547270b45fc84249835350bee5f..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_2/src.cc
+++ /dev/null
@@ -1,141 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-std::string dir_prefix = std::string("vgg16_cifar10/");
-std::string input_path = dir_prefix + std::string("input.bin");
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
-std::string labels_path = dir_prefix + std::string("labels.bin");
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
-void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
-void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
-std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
-void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
-void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
-void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
-void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
-void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
-void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
-std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
-void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,10);
-std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
-void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,10,1,1);
-
-
-void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
-void* var_1 = tensorAdd(var_0, conv2d_1_b);
-void* var_2 = tensorRelu(var_1);
-void* var_4 = tensorConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0);
-void* var_5 = tensorAdd(var_4, conv2d_2_b);
-void* var_6 = tensorRelu(var_5);
-void* var_7 = tensorPooling(var_6,0,2,2,0,0,2,2);
-void* var_8 = tensorConvolution(var_7, conv2d_3_w, 1, 1, 1, 1, 1, 0);
-void* var_9 = tensorAdd(var_8, conv2d_3_b);
-void* var_10 = tensorRelu(var_9);
-void* var_12 = tensorConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0);
-void* var_13 = tensorAdd(var_12, conv2d_4_b);
-void* var_14 = tensorRelu(var_13);
-void* var_15 = tensorPooling(var_14,0,2,2,0,0,2,2);
-void* var_16 = tensorConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
-void* var_17 = tensorAdd(var_16, conv2d_5_b);
-void* var_18 = tensorRelu(var_17);
-void* var_20 = tensorConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0);
-void* var_21 = tensorAdd(var_20, conv2d_6_b);
-void* var_22 = tensorRelu(var_21);
-void* var_24 = tensorConvolution(var_22, conv2d_7_w, 1, 1, 1, 1, 1, 0);
-void* var_25 = tensorAdd(var_24, conv2d_7_b);
-void* var_26 = tensorRelu(var_25);
-void* var_27 = tensorPooling(var_26,0,2,2,0,0,2,2);
-void* var_28 = tensorConvolution(var_27, conv2d_8_w, 1, 1, 1, 1, 1, 0);
-void* var_29 = tensorAdd(var_28, conv2d_8_b);
-void* var_30 = tensorRelu(var_29);
-void* var_32 = tensorConvolution(var_30, conv2d_9_w, 1, 1, 1, 1, 1, 0);
-void* var_33 = tensorAdd(var_32, conv2d_9_b);
-void* var_34 = tensorRelu(var_33);
-void* var_36 = tensorConvolution(var_34, conv2d_10_w, 1, 1, 1, 1, 1, 0);
-void* var_37 = tensorAdd(var_36, conv2d_10_b);
-void* var_38 = tensorRelu(var_37);
-void* var_39 = tensorPooling(var_38,0,2,2,0,0,2,2);
-void* var_40 = tensorConvolution(var_39, conv2d_11_w, 1, 1, 1, 1, 1, 0);
-void* var_41 = tensorAdd(var_40, conv2d_11_b);
-void* var_42 = tensorRelu(var_41);
-void* var_44 = tensorConvolution(var_42, conv2d_12_w, 1, 1, 1, 1, 1, 0);
-void* var_45 = tensorAdd(var_44, conv2d_12_b);
-void* var_46 = tensorRelu(var_45);
-void* var_48 = tensorConvolution(var_46, conv2d_13_w, 1, 1, 1, 1, 1, 0);
-void* var_49 = tensorAdd(var_48, conv2d_13_b);
-void* var_50 = tensorRelu(var_49);
-void* var_51 = tensorPooling(var_50,0,2,2,0,0,2,2);
-void* var_54 = tensorGemmGPU(var_51, dense_1_w);
-void* var_55 = tensorAdd(var_54, dense_1_b);
-void* var_56 = tensorRelu(var_55);
-void* var_58 = tensorGemmGPU(var_56, dense_2_w);
-void* var_59 = tensorAdd(var_58, dense_2_b);
-void* var_60 = tensorSoftmax(var_59);
-
-computeAccuracy2(labels,10000,var_60);
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
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+++ /dev/null
@@ -1 +0,0 @@
- ·z½\y»=ß3Å=¿:â= &@¿Ì¦N¾·¤D=ÿå=†²Œ»n>Å`ݽ²Ñ`½¦¾J¸¾æ±½;¡`6>T=. ¿Ìsä¼¾i}¾‰9p=YÜ÷=ªK=äl¼…ª>Þ«q>¬üy½ >Ä‘(;Ƥ=G>o:ß®=éu
>v‰¾9áé<ûCø=9º:_2Í=ù¾´ <
Œ¾rêœ=ú >UÇ÷=½<Sš=‹½A8¯=" ¾‘vÓ=½|¾&ÓI¾-/í=Ђ¿-ø=sÇ ¾-]´¾pIÌ=_ÕÖ= žŠ=v>S™ö=ë(¾Ö¹=
\ No newline at end of file
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_promise/dense_1_b.bin b/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_promise/dense_1_b.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_promise/dense_1_w.bin b/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_promise/dense_1_w.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_promise/dense_2_b.bin b/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_promise/dense_2_b.bin
deleted file mode 100644
index 026e08646b605bfd004ddc9fb394c03d14e4d00a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_promise/dense_2_b.bin
+++ /dev/null
@@ -1 +0,0 @@
-³·½· ‰¿Ršè>‹ª?˽r> [@>.» >e\=Ü"H¿á×ö¾
\ No newline at end of file
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_promise/input.bin b/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_promise/input.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_promise/labels.bin b/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_promise/labels.bin
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diff --git a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_promise/promise_src.cc b/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_promise/promise_src.cc
deleted file mode 100644
index 193e378bfc3730f48ccdf9ee3c826db14bb6d7ea..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_promise/promise_src.cc
+++ /dev/null
@@ -1,108 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-
-std::string dir_prefix = std::string("vgg16_cifar10_promise/");
-std::string input_path = dir_prefix + std::string("input.bin");
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
-std::string labels_path = dir_prefix + std::string("labels.bin");
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
-void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
-void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
-std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
-void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
-void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
-void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
-void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
-void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
-void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
-std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
-void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,10);
-std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
-void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,10,1,1);
-
-
-void* var_0 = ConvLayer_PROMISE(input, -1.8816367, 2.0934217, conv2d_1_w, -0.5277183, 0.47715914, conv2d_1_b, -0.7505816, 0.23600718, 1, 1, 1, 1, -1, 0, 1, 0.0, 6.4866476, 9);
-void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 6.4866476, conv2d_2_w, -0.26202837, 0.29998416, conv2d_2_b, -0.66326994, 0.8624978, 1, 1, 1, 1, 0, 2, 1, 0.0, 11.21298, 9);
-void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 11.21298, conv2d_3_w, -0.15608788, 0.14187561, conv2d_3_b, -0.3363146, 0.6194402, 1, 1, 1, 1, -1, 0, 1, 0.0, 6.1078873, 9);
-void* var_3 = ConvLayer_PROMISE(var_2, 0.0, 6.1078873, conv2d_4_w, -0.081584536, 0.120576195, conv2d_4_b, -0.49956822, 0.615466, 1, 1, 1, 1, 0, 2, 1, 0.0, 5.005309, 9);
-void* var_4 = ConvLayer_PROMISE(var_3, 0.0, 5.005309, conv2d_5_w, -0.081922, 0.09836473, conv2d_5_b, -0.15489745, 0.24373364, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.5644715, 9);
-void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 1.5644715, conv2d_6_w, -0.056836933, 0.06501525, conv2d_6_b, -0.069405295, 0.0862945, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.7939606, 9);
-void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 0.7939606, conv2d_7_w, -0.05773365, 0.07874803, conv2d_7_b, -0.05009718, 0.055672053, 1, 1, 1, 1, 0, 2, 1, 0.0, 1.1549584, 9);
-void* var_7 = ConvLayer_PROMISE(var_6, 0.0, 1.1549584, conv2d_8_w, -0.036825273, 0.058849376, conv2d_8_b, -0.055235144, 0.024307447, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.6689139, 9);
-void* var_8 = ConvLayer_PROMISE(var_7, 0.0, 0.6689139, conv2d_9_w, -0.016022889, 0.022971416, conv2d_9_b, -0.046823673, 0.027896304, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.92137647, 9);
-void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 0.92137647, conv2d_10_w, -0.027309904, 0.041355837, conv2d_10_b, -0.038741723, 0.029062608, 1, 1, 1, 1, 0, 2, 1, 0.0, 5.9661603, 9);
-void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 5.9661603, conv2d_11_w, -0.03903461, 0.05086205, conv2d_11_b, -0.045523863, 0.055019163, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.780327, 9);
-void* var_11 = ConvLayer_PROMISE(var_10, 0.0, 2.780327, conv2d_12_w, -0.01921058, 0.03577844, conv2d_12_b, -0.05337079, 0.09478004, 1, 1, 1, 1, -1, 0, 1, 0.0, 3.7779117, 9);
-void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 3.7779117, conv2d_13_w, -0.022871122, 0.025569547, conv2d_13_b, -0.15490761, 0.15087973, 1, 1, 1, 1, 0, 2, 1, 0.0, 6.358653, 9);
-void* var_13 = FCLayer_PROMISE(var_12, 0.0, 6.358653, dense_1_w, -0.04849465, 0.048118282, dense_1_b, -0.1256221, 0.39936453, 1, 0.0, 8.22251, 9);
-void* var_14 = FCLayer_PROMISE(var_13, 0.0, 8.22251, dense_2_w, -0.33108112, 0.31025946, dense_2_b, -1.0712193, 1.3323823, -1, -41.86109, 65.92535, 9);
-void* var_15 = tensorSoftmax(var_14);
-
-computeAccuracy2(labels,10000,var_15);
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_promise/src.cc b/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_promise/src.cc
deleted file mode 100644
index 515b7331815f445ee7e2d5e50dbe4dff76b6caae..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/model_params/vgg16_cifar10_promise/src.cc
+++ /dev/null
@@ -1,141 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-std::string dir_prefix = std::string("vgg16_cifar10_promise/");
-std::string input_path = dir_prefix + std::string("input.bin");
-void* input = readTrainedWeights(input_path.c_str(), 0,10000,3,32,32);
-std::string labels_path = dir_prefix + std::string("labels.bin");
-uint8_t* labels = readLabels(labels_path.c_str(),10000);
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
-std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
-void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
-std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
-void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
-void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
-void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
-void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
-std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
-void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
-std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
-void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
-std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
-void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
-void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
-void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
-void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
-void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
-void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
-void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
-void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
-void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
-std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
-void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
-std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
-void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
-std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
-void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,10);
-std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
-void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,10,1,1);
-
-
-void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
-void* var_1 = tensorAdd(var_0, conv2d_1_b);
-void* var_2 = tensorRelu(var_1);
-void* var_4 = tensorConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0);
-void* var_5 = tensorAdd(var_4, conv2d_2_b);
-void* var_6 = tensorRelu(var_5);
-void* var_7 = tensorPooling(var_6,0,2,2,0,0,2,2);
-void* var_8 = tensorConvolution(var_7, conv2d_3_w, 1, 1, 1, 1, 1, 0);
-void* var_9 = tensorAdd(var_8, conv2d_3_b);
-void* var_10 = tensorRelu(var_9);
-void* var_12 = tensorConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0);
-void* var_13 = tensorAdd(var_12, conv2d_4_b);
-void* var_14 = tensorRelu(var_13);
-void* var_15 = tensorPooling(var_14,0,2,2,0,0,2,2);
-void* var_16 = tensorConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
-void* var_17 = tensorAdd(var_16, conv2d_5_b);
-void* var_18 = tensorRelu(var_17);
-void* var_20 = tensorConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0);
-void* var_21 = tensorAdd(var_20, conv2d_6_b);
-void* var_22 = tensorRelu(var_21);
-void* var_24 = tensorConvolution(var_22, conv2d_7_w, 1, 1, 1, 1, 1, 0);
-void* var_25 = tensorAdd(var_24, conv2d_7_b);
-void* var_26 = tensorRelu(var_25);
-void* var_27 = tensorPooling(var_26,0,2,2,0,0,2,2);
-void* var_28 = tensorConvolution(var_27, conv2d_8_w, 1, 1, 1, 1, 1, 0);
-void* var_29 = tensorAdd(var_28, conv2d_8_b);
-void* var_30 = tensorRelu(var_29);
-void* var_32 = tensorConvolution(var_30, conv2d_9_w, 1, 1, 1, 1, 1, 0);
-void* var_33 = tensorAdd(var_32, conv2d_9_b);
-void* var_34 = tensorRelu(var_33);
-void* var_36 = tensorConvolution(var_34, conv2d_10_w, 1, 1, 1, 1, 1, 0);
-void* var_37 = tensorAdd(var_36, conv2d_10_b);
-void* var_38 = tensorRelu(var_37);
-void* var_39 = tensorPooling(var_38,0,2,2,0,0,2,2);
-void* var_40 = tensorConvolution(var_39, conv2d_11_w, 1, 1, 1, 1, 1, 0);
-void* var_41 = tensorAdd(var_40, conv2d_11_b);
-void* var_42 = tensorRelu(var_41);
-void* var_44 = tensorConvolution(var_42, conv2d_12_w, 1, 1, 1, 1, 1, 0);
-void* var_45 = tensorAdd(var_44, conv2d_12_b);
-void* var_46 = tensorRelu(var_45);
-void* var_48 = tensorConvolution(var_46, conv2d_13_w, 1, 1, 1, 1, 1, 0);
-void* var_49 = tensorAdd(var_48, conv2d_13_b);
-void* var_50 = tensorRelu(var_49);
-void* var_51 = tensorPooling(var_50,0,2,2,0,0,2,2);
-void* var_54 = tensorGemmGPU(var_51, dense_1_w);
-void* var_55 = tensorAdd(var_54, dense_1_b);
-void* var_56 = tensorRelu(var_55);
-void* var_58 = tensorGemmGPU(var_56, dense_2_w);
-void* var_59 = tensorAdd(var_58, dense_2_b);
-void* var_60 = tensorSoftmax(var_59);
-
-computeAccuracy2(labels,10000,var_60);
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/.gitignore b/llvm/projects/hpvm-tensor-rt/opentuner/.gitignore
deleted file mode 100644
index 9eb809777b0bcfdb2a7d91f9e671282ca03610a7..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/.gitignore
+++ /dev/null
@@ -1,51 +0,0 @@
-*.py[cod]
-
-# C extensions
-*.so
-
-# Packages
-*.egg
-*.egg-info
-dist
-build
-eggs
-parts
-bin
-var
-sdist
-develop-eggs
-.installed.cfg
-lib
-lib64
-
-# Installer logs
-pip-log.txt
-
-# Unit test / coverage reports
-.coverage
-.tox
-nosetests.xml
-
-# Translations
-*.mo
-
-# Mr Developer
-.mr.developer.cfg
-.project
-.pydevproject
-
-#vim
-*.swp
-
-#virtualenv
-venv
-.ropeproject
-opentuner.log
-.*.swo
-opentuner.db
-.idea
-
-# SMB ROM (for SMB demo)
-smb.nes
-
-MANIFEST
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/AUTHORS.txt b/llvm/projects/hpvm-tensor-rt/opentuner/AUTHORS.txt
deleted file mode 100644
index 620e549e236ad322446694d11dc68a2f39a3ee31..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/AUTHORS.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-Jason Ansel
-Sam Fingeret
-Shoaib Kamil
-Deepak Narayanan
-Jonathan Ragan-Kelley
-Kalyan Veeramachaneni
-Kevin Wu
-Minshu Zhan
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/CHANGES.txt b/llvm/projects/hpvm-tensor-rt/opentuner/CHANGES.txt
deleted file mode 100644
index a0af44222226f64cceb85bb633072a25abb40777..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/CHANGES.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-v0.5.0, 2015-02-10 -- Refactoring and bugfixes.
-v0.4.0, 2014-10-26 -- Add api and bugfixes.
-v0.3.0, 2014-08-11 -- Initial release.
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/LICENSE.txt b/llvm/projects/hpvm-tensor-rt/opentuner/LICENSE.txt
deleted file mode 100644
index 2b602e192b4a2302cae3288e3bd34746ff8475df..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/LICENSE.txt
+++ /dev/null
@@ -1,22 +0,0 @@
-The MIT License (MIT)
-
-Copyright (c) 2014 Jason Ansel
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/MANIFEST.in b/llvm/projects/hpvm-tensor-rt/opentuner/MANIFEST.in
deleted file mode 100644
index 376b77ae8f44a06787d5910191c713c986791a72..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/MANIFEST.in
+++ /dev/null
@@ -1 +0,0 @@
-include *.txt *.md
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/README.md b/llvm/projects/hpvm-tensor-rt/opentuner/README.md
deleted file mode 100644
index 729f35553a0fe22177a38f0545d03a4497bef03c..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/README.md
+++ /dev/null
@@ -1,116 +0,0 @@
-OpenTuner
-=========
-
-Program autotuning has been demonstrated in many domains to achieve better
-or more portable performance. However, autotuners themselves are often not
-very portable between projects because using a domain informed search space
-representation is critical to achieving good results and because no single
-search technique performs best for all problems.
-
-OpenTuner is a new framework for building domain-specific multi-objective
-program autotuners. OpenTuner supports fully customizable configuration
-representations, an extensible technique representation to allow for
-domain-specific techniques, and an easy to use interface for communicating
-with the tuned program. A key capability inside OpenTuner is the use of
-ensembles of disparate search techniques simultaneously, techniques which
-perform well will receive larger testing budgets and techniques which perform
-poorly will be disabled.
-
-System dependencies
--------------------
-
-A list of system dependencies can be found in [debian-packages-deps][]
-which are primarily python 2.6+ (not 3.x) and sqlite3 (or your
-[supported][sqlalchemy-dialects] database backend of choice).
-
-On Ubuntu/Debian there can be installed with:
-
- sudo apt-get install `cat debian-packages-deps | tr '\n' ' '`
-
-[debian-packages-deps]: https://raw.github.com/jansel/opentuner/master/debian-packages-deps
-[sqlalchemy-dialects]: http://docs.sqlalchemy.org/en/rel_0_8/dialects/index.html
-
-
-Installation
--------------------
-OpenTuner (and dependencies) can be installed with
-
- sudo pip install opentuner
-
-or
-
- pip install --user opentuner
-
-This will not install any of the example programs.
-
-
-Development installation
--------------------
-For development (running OpenTuner out of a git checkout), a list of python
-dependencies can be found in [requirements.txt][] these can be installed
-system-wide with `pip`.
-
- sudo apt-get install python-pip
- sudo pip install -r requirements.txt
-
-Or you can use virtual env to create a isolated python environment by running:
-
- python ./venv-bootstrap.py
-
-which will create a ./venv/bin/python (./venv/Scripts/python.exe on windows)
-with all the required packages installed.
-
-[requirements.txt]: https://raw.github.com/jansel/opentuner/master/requirements.txt
-
-
-Checking Installation
----------------------
-
-Quickly checking that a successful installation has been made, may be performed
-by running an example program such as:
-
- ./examples/rosenbrock/rosenbrock.py
-
-
-Tutorials
----------
-
-- [Optimizing Block Matrix Multiplication][gettingstarted]
-- [Creating OpenTuner Techniques][technique-tutorial].
-
-[gettingstarted]: http://opentuner.org/tutorial/gettingstarted/
-[technique-tutorial]: http://opentuner.org/tutorial/techniques/
-
-
-Papers
----------
-
-- [OpenTuner: An Extensible Framework for Program Autotuning][paper1]. <br>
- Jason Ansel, Shoaib Kamil, Kalyan Veeramachaneni, Jonathan Ragan-Kelley,
- Jeffrey Bosboom, Una-May O'Reilly, Saman Amarasinghe. <br>
- International Conference on Parallel Architectures and Compilation
- Techniques. <br>
- Edmonton, Canada. August, 2014. [Slides][slides1]. [Bibtex][bibtex1].
-
-[paper1]: http://groups.csail.mit.edu/commit/papers/2014/ansel-pact14-opentuner.pdf
-[bibtex1]: http://groups.csail.mit.edu/commit/bibtex.cgi?key=ansel:pact:2014
-[slides1]: http://groups.csail.mit.edu/commit/papers/2014/ansel-pact14-opentuner-slides.pdf
-
-
-Contributing Code
------------------
-
-The preferred way to contribute code to OpenTuner is to fork the project
-on github and [submit a pull request][pull-req].
-
-[pull-req]: https://www.openshift.com/wiki/github-workflow-for-submitting-pull-requests
-
-
-Support
--------
-OpenTuner is supported in part by the United States Department of Energy
-[X-Stack][xstack] program as part of [D-TEC][dtec].
-
-[xstack]: http://science.energy.gov/ascr/research/computer-science/ascr-x-stack-portfolio/
-[dtec]: http://www.dtec-xstack.org/
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/__init__.py b/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/accuracy_tuner.py b/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/accuracy_tuner.py
deleted file mode 100644
index 5977fe7ee5b4780139d2c5a865c8231361cf0f2c..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/accuracy_tuner.py
+++ /dev/null
@@ -1,198 +0,0 @@
-#!/usr/bin/env python
-#
-
-import adddeps # fix sys.path
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-
-
-output_dir = ""
-flag_ranges = []
-tuning_flags = []
-binary_name = ""
-accuracy_threshold = 10.0
-opt_confs_index = 9
-evaluated_configs = {}
-
-
-def extractTotalOverhead(file_name):
-
- total_comps = 0.0
- file = open(file_name, "r")
- for x in file:
- words = x.split()
- total_comps += float(words[opt_confs_index])
-
- print total_comps
- return total_comps
-
-
-def getAccuracy(file_name):
-
- file = open(file_name, "r")
- acc_str = file.read()
- accuracy = float(acc_str)
- print accuracy
- return accuracy
-
-
-def createFlagsFile(file_name, cfg):
-
- f = open(file_name, "w+")
- cmd_config = ""
- for flag in tuning_flags:
- flag_value = cfg[flag]
- cmd_config += str(flag_value) + "\n"
-
- f.write(cmd_config)
- f.close()
-
-
-class ClangFlagsTuner(MeasurementInterface):
-
- def __init__(self, args):
- objective = ThresholdAccuracyMinimizeTime(accuracy_threshold)
- input_manager = FixedInputManager(size=num_flags)
- self.configs_list = []
-
- super(ClangFlagsTuner, self).__init__(
- args, program_name=args.binary,
- program_version=self.file_hash(args.binary),
- input_manager=input_manager, objective=objective)
-
-
- def manipulator(self):
- """
- Define the search space by creating a
- ConfigurationManipulator
- """
- manipulator = ConfigurationManipulator()
- for flag in tuning_flags:
- manipulator.add_parameter(
- EnumParameter(flag, flag_ranges
- # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
- )) #default is needed, optimizations don't work without it(tried and tested)
- return manipulator
-
-
- def run(self, desired_result, input, limit):
- """
- Compile and run a given configuration then
- return performance
- """
- cfg = desired_result.configuration.data
-
- # NOTE: creates the file with flags read by the runtime
- createFlagsFile("opentuner_flags", cfg)
-
- run_cmd = binary_name
- print run_cmd
- run_result_call_program = self.call_program(run_cmd)
- #print run_result_call_program
-
- total_comps = extractTotalOverhead("accuracy_summary")
- accuracy = getAccuracy("final_accuracy")
-
- #Result = opentuner.resultsdb.models.Result(time=total_comps)
- Result = opentuner.resultsdb.models.Result()
- Result.time = total_comps
- Result.accuracy = accuracy
-
- if accuracy > accuracy_threshold:
- if accuracy not in evaluated_configs:
- config_tuple = (total_comps, accuracy, cfg)
- self.configs_list.append(config_tuple)
- evaluated_configs[accuracy] = 1
- shutil.copy('accuracy_summary', output_dir + '/' + binary_name + '_' + str(accuracy))
-
-
- return Result
-
-
- def save_final_config(self, configuration):
- """
- called at the end of autotuning with the best resultsdb.models.Configuration
- """
- print "Final configuration", configuration.data
-
- if not os.path.exists(result_dir):
- os.mkdir(result_dir)
-
- createFlagsFile("opentuner_flags", configuration.data)
- run_cmd = binary_name
- run_result_call_program = self.call_program(run_cmd)
-
- accuracy = getAccuracy("final_accuracy")
- shutil.copy('accuracy_summary', result_dir + '/' + binary_name + '_final_' + str(accuracy) )
-
- sorted_list = sorted(self.configs_list, key = lambda tup: tup[0])
- print sorted_list[0:10]
-
- top_elems = 20
- if len(sorted_list) < top_elems:
- top_elems = len(sorted_list)
-
-
- for i in range(top_elems):
- createFlagsFile("opentuner_flags", sorted_list[i][2])
- run_cmd = binary_name
- run_result_call_program = self.call_program(run_cmd)
- accuracy = getAccuracy("final_accuracy")
- shutil.copy('accuracy_summary', result_dir + '/' + binary_name + '_' + str(accuracy) + "_rank_" + str(i) )
-
-
- #os.mkdir(result_dir + "full_results")
-
-
-
-
-if __name__ == '__main__':
-
- argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
- argparser.add_argument('--binary', help='name of binary to run')
- argparser.add_argument('--num-flags', type=int, help='num of flags to tune for')
- argparser.add_argument('--error-range', type=int, help='num of flags to tune for')
- argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
- argparser.add_argument('--result-dir', help='accuracy threshold')
-
-
- args = argparser.parse_args()
- binary_name = str(args.binary)
- print("binary_name = ", binary_name)
- num_flags = int(args.num_flags)
- error_range = int(args.error_range)
- accuracy_threshold = float(args.accuracy)
- print("accuracy = ", accuracy_threshold)
- result_dir = args.result_dir
- if result_dir == "":
- print("Provide --result-dir ")
-
-
- output_dir = result_dir + "/full_results"
- print output_dir
- if not os.path.exists(result_dir):
- os.mkdir(result_dir)
-
- if not os.path.exists(output_dir):
- print("Creating output directory = ", output_dir)
- os.mkdir(output_dir)
-
- for j in range(error_range):
- flag_ranges.append(j)
-
- print("flag_ranges = ", flag_ranges)
-
- for i in range(num_flags):
- tuning_flags.append("flag" + str(i))
-
- ClangFlagsTuner.main(argparser.parse_args())
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/accuracy_tuner_piped.py b/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/accuracy_tuner_piped.py
deleted file mode 100644
index 6d46c5762ead377292337c47d045ee5e58322954..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/accuracy_tuner_piped.py
+++ /dev/null
@@ -1,269 +0,0 @@
-#!/usr/bin/env python
-#
-# Optimize blocksize of apps/mmm_block.cpp
-#
-# This is an extremely simplified version meant only for tutorials
-#
-import adddeps # fix sys.path
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-import subprocess
-import threading
-import psutil
-
-from measure_confidence import dump_high_confidence_files
-from select_top_results import select_top_results
-from time import sleep
-
-
-output_dir = ""
-flag_ranges = []
-tuning_flags = []
-binary_name = ""
-accuracy_threshold = 10.0
-opt_confs_index = 9
-evaluated_configs = {}
-orig_result_dir = ""
-
-
-def extractTotalOverhead(file_name):
-
- total_comps = 0.0
- file = open(file_name, "r")
- for x in file:
- words = x.split()
- total_comps += float(words[opt_confs_index])
-
- print total_comps
- return total_comps
-
-
-def getAccuracy(file_name):
-
- file = open(file_name, "r")
- acc_str = file.read()
- file.close()
-
- accuracy = float(acc_str)
-
- try:
- accuracy = float(acc_str)
- except:
- return 20
-
- print accuracy
- return accuracy
-
-
-
-def kill(proc_pid):
- process = psutil.Process(proc_pid)
- for proc in process.children(recursive=True):
- proc.kill()
- process.kill()
-
-
-
-def createFlagsFile(file_name, cfg):
-
- f = open(file_name, "w+")
- cmd_config = ""
- for flag in tuning_flags:
- flag_value = cfg[flag]
- cmd_config += str(flag_value) + "\n"
-
- f.write(cmd_config)
- f.close()
-
-
-class ClangFlagsTuner(MeasurementInterface):
-
- def __init__(self, args):
- objective = ThresholdAccuracyMinimizeTime(accuracy_threshold)
- input_manager = FixedInputManager(size=num_flags)
- self.configs_list = []
-
- super(ClangFlagsTuner, self).__init__(
- args, program_name=args.binary,
- program_version=self.file_hash(args.binary),
- input_manager=input_manager, objective=objective)
-
-
- FNULL = open(os.devnull, 'wb')
- #run_result_call_program = self.call_program(run_cmd)
- self.start_process = subprocess.Popen([binary_name, "opentuner_run"]) #, stdout=FNULL);
-
- try:
- os.mkfifo("/tmp/myfifo")
- except OSError, e:
- print("FIFO exists")
-
-
-
- def manipulator(self):
- """
- Define the search space by creating a
- ConfigurationManipulator
- """
- manipulator = ConfigurationManipulator()
- for flag in tuning_flags:
- manipulator.add_parameter(
- EnumParameter(flag, flag_ranges
- # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
- )) #default is needed, optimizations don't work without it(tried and tested)
- return manipulator
-
-
- def run(self, desired_result, input, limit):
-
- """
- Run a given configuration then
- return performance
- """
- cfg = desired_result.configuration.data
-
- # NOTE: creates the file with flags read by the runtime
- createFlagsFile("opentuner_flags", cfg)
-
- run_cmd = binary_name
- print run_cmd
- #run_result_call_program = self.call_program(run_cmd)
-
- # Using Named Pipes to signal execution to the DNN outer thread
- fifo = open("/tmp/myfifo", "w")
- fifo.write("start_run")
- fifo.close()
-
- print "Waiting for process to signal back - when done processing one run"
-
- fifo2 = open("/tmp/myfifo", "r")
- fifo2.read()
- fifo2.close()
-
- print "Process Signalled back"
-
- total_comps = extractTotalOverhead("accuracy_summary")
- accuracy = getAccuracy("final_accuracy")
-
-
- #Result = opentuner.resultsdb.models.Result(time=total_comps)
- Result = opentuner.resultsdb.models.Result()
- Result.time = total_comps
- Result.accuracy = accuracy
-
- if accuracy > accuracy_threshold:
- if accuracy not in evaluated_configs:
- config_tuple = (total_comps, accuracy, cfg)
- self.configs_list.append(config_tuple)
- evaluated_configs[accuracy] = 1
- shutil.copy('accuracy_summary', output_dir + '/' + binary_name + '_' + str(accuracy))
-
-
- print "done with one run"
-
- return Result
-
-
- def save_final_config(self, configuration):
-
- print "Dumping High Confidence results"
- sleep(5)
-
- # Only dumping files with 95% confidence
- dump_high_confidence_files(binary_name, orig_result_dir, accuracy_threshold, 95)
- select_top_results(orig_result_dir + "/high_confidence")
-
-
- #self.start_process.kill()
- kill(self.start_process.pid)
-
- """
- called at the end of autotuning with the best resultsdb.models.Configuration
- """
- print "Final configuration", configuration.data
-
- return
-
-
- if not os.path.exists(result_dir):
- os.mkdir(result_dir)
-
- createFlagsFile("opentuner_flags", configuration.data)
- run_cmd = binary_name
- run_result_call_program = self.call_program(run_cmd)
-
- accuracy = getAccuracy("final_accuracy")
- shutil.copy('accuracy_summary', result_dir + '/' + binary_name + '_final_' + str(accuracy) )
-
- sorted_list = sorted(self.configs_list, key = lambda tup: tup[0])
- print sorted_list[0:10]
-
- top_elems = 20
- if len(sorted_list) < top_elems:
- top_elems = len(sorted_list)
-
-
- for i in range(top_elems):
- createFlagsFile("opentuner_flags", sorted_list[i][2])
- run_cmd = binary_name
- run_result_call_program = self.call_program(run_cmd)
- accuracy = getAccuracy("final_accuracy")
- shutil.copy('accuracy_summary', result_dir + '/' + binary_name + '_' + str(accuracy) + "_rank_" + str(i) )
-
-
- #os.mkdir(result_dir + "full_results")
-
-
-
-
-if __name__ == '__main__':
-
- argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
- argparser.add_argument('--binary', help='name of binary to run')
- argparser.add_argument('--num-flags', type=int, help='num of flags to tune for')
- argparser.add_argument('--error-range', type=int, help='num of flags to tune for')
- argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
- argparser.add_argument('--result-dir', help='accuracy threshold')
-
-
- args = argparser.parse_args()
- binary_name = str(args.binary)
- print("binary_name = ", binary_name)
- num_flags = int(args.num_flags)
- error_range = int(args.error_range)
- accuracy_threshold = float(args.accuracy)
- print("accuracy = ", accuracy_threshold)
- result_dir = args.result_dir
- orig_result_dir = result_dir
- if result_dir == "":
- print("Provide --result-dir ")
-
-
- output_dir = result_dir + "/full_results"
- print output_dir
- if not os.path.exists(result_dir):
- os.mkdir(result_dir)
-
- if not os.path.exists(output_dir):
- print("Creating output directory = ", output_dir)
- os.mkdir(output_dir)
-
- for j in range(error_range):
- flag_ranges.append(j)
-
- print("flag_ranges = ", flag_ranges)
-
- for i in range(num_flags):
- tuning_flags.append("flag" + str(i))
-
- ClangFlagsTuner.main(argparser.parse_args())
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/adddeps.py b/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/adddeps.py
deleted file mode 100644
index 61fd4757d6a6045346e5cdcd3dfbfcdc00e236fa..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/adddeps.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# we would prefer a symbolic link, but it does not work on windows
-import os
-target = os.path.join(os.path.dirname(__file__),
- '../opentuner/utils/adddeps.py')
-execfile(target, dict(__file__=target))
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/algo_tuner.py b/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/algo_tuner.py
deleted file mode 100644
index b8145e179893bc0db2631cf1f7ee0f11bcc9be0e..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/algo_tuner.py
+++ /dev/null
@@ -1,318 +0,0 @@
-#!/usr/bin/env python
-#
-# Algorithmic Approximation Tuning
-# Purpose: Tunes for Perforation, Sampling, Numerical Precision (FP16)
-
-
-import adddeps
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-import subprocess
-import threading
-import psutil
-
-from measure_confidence2 import dump_promise_confidence_files3
-from measure_confidence2 import getConfidence, getMinAccuracy
-from select_top_results import select_top_results
-from time import sleep
-from pareto_curve import findParetoConfigs
-
-
-
-
-class TunerData:
- def __init__(self):
- self.binary_path = ""
- self.output_dir = ""
- self.num_layers = 0
- self.knobs_list = []
- self.knobs_speedup = {}
- self.accuracy_threshold = 0
- self.test_id = 0
- self.layer_costs = []
- self.tuning_flags = []
- self.autotuner_runs = 0
-
-
-
-tunerData = TunerData()
-
-
-def readCostFile(file_path):
-
- layer_costs = []
- f = open(file_path)
- for x in f:
- cost = float(x.strip())
- layer_costs.append(cost)
-
- print ("len(layer_costs) = ", layer_costs)
- f.close()
-
- return layer_costs
-
-
-
-def getAccuracy(file_name):
-
- file = open(file_name, "r")
- acc_str = file.read()
- file.close()
- accuracy = float(acc_str)
-
- try:
- accuracy = float(acc_str)
- except:
- return 20
-
- print (accuracy)
- return accuracy
-
-
-
-def createFlagsFile(file_name, cfg):
-
- f = open(file_name, "w+")
- cmd_config = ""
- for i in range(tunerData.num_layers): # flag in tunerData.tuning_flags:
- flag = tunerData.tuning_flags[i]
- flag_value = cfg[flag]
- cmd_config += str(flag_value) + "\n"
-
- f.write(cmd_config)
- f.close()
-
-
-
-def readLayerKnobs(file_path):
-
- f = open(file_path, "r")
- knobs_list = []
- for x in f:
- knobs = []
- vals = x.split(",")
- for val in vals:
- knobs.append(int(val))
-
- knobs_list.append(knobs)
-
- print ("knobs_list = ", knobs_list)
-
- return knobs_list
-
-
-
-def readKnobConfig(file_path):
-
- knobs_speedup = {}
- f = open(file_path, "r")
- for x in f:
- toks = x.split("\t")
- ID = int(toks[0].split(",")[1])
-
- speedup = float(toks[2])
- knobs_speedup[ID] = speedup
-
- print ("knobs_speedup = ", knobs_speedup)
-
- return knobs_speedup
-
-
-
-
-def getConfigCost(cfg):
-
- orig_cost = 0.0
- total_cost = 0.0
- for it in range(tunerData.num_layers):
- flag = tunerData.tuning_flags[it]
- flag_value = cfg[flag]
- op_cost = tunerData.layer_costs[it]
- speedup = tunerData.knobs_speedup[flag_value]
-
- total_cost += (op_cost * 1.0 / speedup * 1.0)
- orig_cost += op_cost
-
- it += 1
-
- speedup = (orig_cost * 1.0) / (total_cost * 1.0)
-
- return total_cost, speedup
-
-
-
-def appendTopLine(f_path, accuracy, total_runs, total_comps, speedup):
-
- f_str = open(f_path, "r").read()
-
- f_out = open(f_path, "w+")
-
- f_out.write("total_runs=" + str(total_runs) + "\tconfidence=100.0" + "\tavg_accuracy=" + str(accuracy) + "\tconfig_cost=" + str(total_comps) + "\tspeedup=" + str(speedup) + "\n" )
- f_out.write(f_str)
-
- f_out.close()
-
-
-
-
-
-class ClangFlagsTuner(MeasurementInterface):
-
- def __init__(self, args):
- objective = ThresholdAccuracyMinimizeTime(tunerData.accuracy_threshold)
- input_manager = FixedInputManager(size=tunerData.num_layers)
- self.configs_list = []
-
- super(ClangFlagsTuner, self).__init__(
- args, program_name=args.binary,
- program_version=self.file_hash(args.binary),
- input_manager=input_manager, objective=objective)
-
-
-
-
- def manipulator(self):
- """
- Define the search space by creating a
- ConfigurationManipulator
- """
- manipulator = ConfigurationManipulator()
-
- for i in range(tunerData.num_layers):
- tunerData.tuning_flags.append("flag" + str(i))
-
-
- #for flag in tunerData.tuning_flags:
- for ind in range(tunerData.num_layers):
- flag = tunerData.tuning_flags[ind]
- manipulator.add_parameter(
- EnumParameter(flag, tunerData.knobs_list[ind]))
-
- print ("ind = ", ind, " len = ", len(tunerData.knobs_list))
- print (tunerData.knobs_list[ind])
- ind += 1
-
- return manipulator
-
-
-
- def run(self, desired_result, input, limit):
-
- """
- Run a given configuration then
- return performance
- """
- global test_id
-
- cfg = desired_result.configuration.data
-
- # NOTE: creates the file with flags read by the runtime
- createFlagsFile("promise_flags", cfg)
-
- run_cmd = tunerData.binary_path
- print "\nbinary_path = ", run_cmd
-
-
- total_runs = 1 # NOTE: Single run sufficient in Algorithmic Approx Tuner
- FNULL = open(os.devnull, 'wb')
- p = subprocess.Popen([run_cmd, str(total_runs)], stdout = FNULL)
- p.wait()
-
-
- accuracy = getAccuracy("final_accuracy")
-
- # getConfigCost returns the cost associated with the selected configuration
- total_comps, speedup = getConfigCost(cfg)
-
-
- Result = opentuner.resultsdb.models.Result()
- Result.time = total_comps
- #Result.accuracy = accuracy
- min_accuracy = getMinAccuracy("run_accuracies.txt")
- print ("min_accuracy = ", min_accuracy)
- Result.accuracy = min_accuracy
-
- if min_accuracy > tunerData.accuracy_threshold:
- config_tuple = (total_comps, accuracy, cfg)
- self.configs_list.append(config_tuple)
- f_path = tunerData.output_dir + '/' + tunerData.binary_path + '_' + str(tunerData.test_id)
- shutil.copy('promise_flags', f_path)
-
- appendTopLine(f_path, accuracy, total_runs, total_comps, speedup)
-
- f_acc = open(tunerData.output_dir + '/' + tunerData.binary_path + '_' + str(tunerData.test_id) + "_accuracy", "w")
- f_acc.write(str(accuracy))
- f_acc.close()
-
-
- tunerData.test_id += 1
-
- return Result
-
-
- def save_final_config(self, configuration):
-
- print "Done with Autotuning Run \n"
- sleep(2)
-
- print "Final configuration", configuration.data
-
- return
-
-
-
-
-if __name__ == '__main__':
-
- argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
- argparser.add_argument('--binary', help='path to target binary')
- argparser.add_argument('--num-layers', type=int, help='num of flags to tune')
- argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
- argparser.add_argument('--result-dir', help='result directory')
- argparser.add_argument('--cost-file', help='layer description')
- argparser.add_argument('--knobs-config', help='knob settings and ID mapping')
- argparser.add_argument('--layer-knobs', help='per-layer Knobs')
-
-
- args = argparser.parse_args()
-
- tunerData.binary_path = str(args.binary)
- tunerData.num_layers = int(args.num_layers)
- tunerData.accuracy_threshold = float(args.accuracy)
-
-
- # NOTE: Reading the cost file (with No of ops) to better guide the Autotuner
- cost_file_path = args.cost_file
- tunerData.layer_costs = readCostFile(cost_file_path)
-
-
- tunerData.knobs_list = readLayerKnobs(args.layer_knobs)
- tunerData.knobs_speedup = readKnobConfig(args.knobs_config)
-
- result_dir = args.result_dir
- if result_dir == "":
- print("Provide --result-dir ")
-
- tunerData.output_dir = result_dir + "/high_confidence/"
- if not os.path.exists(result_dir):
- os.mkdir(result_dir)
-
- if not os.path.exists(tunerData.output_dir):
- print("Creating output directory = ", tunerData.output_dir)
- os.mkdir(tunerData.output_dir)
-
-
-
- ClangFlagsTuner.main(argparser.parse_args())
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/algo_tuner2.py b/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/algo_tuner2.py
deleted file mode 100644
index 4ca0062f93441954d3ee0acc0eabf10352e3a76c..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/algo_tuner2.py
+++ /dev/null
@@ -1,339 +0,0 @@
-#!/usr/bin/env python
-#
-# Algorithmic Approximation Tuning
-# Purpose: Tunes for Perforation, Sampling, Numerical Precision (FP16)
-
-
-import adddeps
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-import subprocess
-import threading
-import psutil
-
-from measure_confidence2 import dump_promise_confidence_files4
-from measure_confidence2 import getConfidence, getMinAccuracy
-from select_top_results import select_top_results
-from time import sleep
-from pareto_curve import findParetoConfigs
-
-
-
-
-class TunerData:
- def __init__(self):
- self.binary_path = ""
- self.output_dir = ""
- self.num_layers = 0
- self.knobs_list = []
- self.knobs_speedup = {}
- self.accuracy_threshold = 0
- self.test_id = 0
- self.layer_costs = []
- self.tuning_flags = []
- self.autotuner_runs = 0
-
-
-
-tunerData = TunerData()
-
-
-orig_result_dir = ""
-
-
-def readCostFile(file_path):
-
- layer_costs = []
- f = open(file_path)
- for x in f:
- cost = float(x.strip())
- layer_costs.append(cost)
-
- print ("len(layer_costs) = ", layer_costs)
- f.close()
-
- return layer_costs
-
-
-
-def getAccuracy(file_name):
-
- file = open(file_name, "r")
- acc_str = file.read()
- file.close()
- accuracy = float(acc_str)
-
- try:
- accuracy = float(acc_str)
- except:
- return 20
-
- print (accuracy)
- return accuracy
-
-
-
-def createFlagsFile(file_name, cfg):
-
- f = open(file_name, "w+")
- cmd_config = ""
- for i in range(tunerData.num_layers): # flag in tunerData.tuning_flags:
- flag = tunerData.tuning_flags[i]
- flag_value = cfg[flag]
- cmd_config += str(flag_value) + "\n"
-
- f.write(cmd_config)
- f.close()
-
-
-
-def readLayerKnobs(file_path):
-
- f = open(file_path, "r")
- knobs_list = []
- for x in f:
- knobs = []
- vals = x.split(",")
- for val in vals:
- knobs.append(int(val))
-
- knobs_list.append(knobs)
-
- print ("knobs_list = ", knobs_list)
-
- return knobs_list
-
-
-
-def readKnobConfig(file_path):
-
- knobs_speedup = {}
- f = open(file_path, "r")
- for x in f:
- toks = x.split("\t")
- ID = int(toks[0].split(",")[1])
-
- speedup = float(toks[2])
- knobs_speedup[ID] = speedup
-
- print ("knobs_speedup = ", knobs_speedup)
-
- return knobs_speedup
-
-
-
-
-def getConfigCost(cfg):
-
- orig_cost = 0.0
- total_cost = 0.0
- for it in range(tunerData.num_layers):
- flag = tunerData.tuning_flags[it]
- flag_value = cfg[flag]
- op_cost = tunerData.layer_costs[it]
- speedup = tunerData.knobs_speedup[flag_value]
-
- total_cost += (op_cost * 1.0 / speedup * 1.0)
- orig_cost += op_cost
-
- it += 1
-
- speedup = (orig_cost * 1.0) / (total_cost * 1.0)
-
- return total_cost, speedup
-
-
-
-def appendTopLine(f_path, accuracy, total_runs, total_comps, speedup):
-
- f_str = open(f_path, "r").read()
-
- f_out = open(f_path, "w+")
-
- f_out.write("total_runs=" + str(total_runs) + "\tconfidence=100.0" + "\tavg_accuracy=" + str(accuracy) + "\tconfig_cost=" + str(total_comps) + "\tspeedup=" + str(speedup) + "\n" )
- f_out.write(f_str)
-
- f_out.close()
-
-
-
-
-
-class ClangFlagsTuner(MeasurementInterface):
-
- def __init__(self, args):
- objective = ThresholdAccuracyMinimizeTime(tunerData.accuracy_threshold)
- input_manager = FixedInputManager(size=tunerData.num_layers)
- self.configs_list = []
-
- super(ClangFlagsTuner, self).__init__(
- args, program_name=args.binary,
- program_version=self.file_hash(args.binary),
- input_manager=input_manager, objective=objective)
-
-
-
-
- def manipulator(self):
- """
- Define the search space by creating a
- ConfigurationManipulator
- """
- manipulator = ConfigurationManipulator()
-
- for i in range(tunerData.num_layers):
- tunerData.tuning_flags.append("flag" + str(i))
-
-
- #for flag in tunerData.tuning_flags:
- for ind in range(tunerData.num_layers):
- flag = tunerData.tuning_flags[ind]
- manipulator.add_parameter(
- EnumParameter(flag, tunerData.knobs_list[ind]))
-
- print ("ind = ", ind, " len = ", len(tunerData.knobs_list))
- print (tunerData.knobs_list[ind])
- ind += 1
-
- return manipulator
-
-
-
- def run(self, desired_result, input, limit):
-
- """
- Run a given configuration then
- return performance
- """
- global test_id
-
- cfg = desired_result.configuration.data
-
- # NOTE: creates the file with flags read by the runtime
- createFlagsFile("promise_flags", cfg)
-
- run_cmd = tunerData.binary_path
- print "\nbinary_path = ", run_cmd
-
-
- input_size = 5000
- offset = 5000
-
- total_runs = 2 # NOTE: Single run sufficient in Algorithmic Approx Tuner
- FNULL = open(os.devnull, 'wb')
- p = subprocess.Popen([run_cmd, str(total_runs), str(tunerData.accuracy_threshold), str(1), str(input_size), str(offset) ], stdout = FNULL)
- p.wait()
-
- #total_runs = 2 # NOTE: Atleast two runs for promise tuner
- #FNULL = open(os.devnull, 'wb')
- #p = subprocess.Popen([run_cmd, str(total_runs)], stdout = FNULL)
- #p.wait()
-
-
- accuracy = getAccuracy("final_accuracy")
-
- # getConfigCost returns the cost associated with the selected configuration
- total_comps, speedup = getConfigCost(cfg)
-
-
- Result = opentuner.resultsdb.models.Result()
- Result.time = total_comps
- #Result.accuracy = accuracy
- min_accuracy = getMinAccuracy("run_accuracies.txt")
- print ("min_accuracy = ", min_accuracy)
- Result.accuracy = min_accuracy
-
- if min_accuracy > tunerData.accuracy_threshold:
- config_tuple = (total_comps, accuracy, cfg)
- self.configs_list.append(config_tuple)
- f_path = tunerData.output_dir + '/' + tunerData.binary_path + '_' + str(tunerData.test_id)
- shutil.copy('promise_flags', f_path)
-
- appendTopLine(f_path, accuracy, total_runs, total_comps, speedup)
-
-
- tunerData.test_id += 1
-
- return Result
-
-
- def save_final_config(self, configuration):
-
- print "Done with Autotuning Run \n"
- sleep(2)
-
-
- #findParetoConfigs(orig_result_dir, layer_costs, accuracy_threshold)
-
- input_dir = orig_result_dir + "/full_results/"
- output_dir = orig_result_dir + "/high_confidence/"
-
- # Only dumping files with 95% confidence
- dump_promise_confidence_files4(tunerData.binary_path, input_dir, output_dir, tunerData.layer_file, tunerData.num_layers, tunerData.accuracy_threshold, tunerData.layer_costs, 95, tunerData.knobs_speedup)
-
-
- print "Final configuration", configuration.data
-
- return
-
-
-
-if __name__ == '__main__':
-
- argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
- argparser.add_argument('--binary', help='path to target binary')
- argparser.add_argument('--num-layers', type=int, help='num of flags to tune')
- argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
- argparser.add_argument('--result-dir', help='result directory')
-
- argparser.add_argument('--layer-file', help='layer file')
-
- argparser.add_argument('--cost-file', help='layer description')
- argparser.add_argument('--knobs-config', help='knob settings and ID mapping')
- argparser.add_argument('--layer-knobs', help='per-layer Knobs')
-
-
- args = argparser.parse_args()
-
- tunerData.binary_path = str(args.binary)
- tunerData.num_layers = int(args.num_layers)
- tunerData.accuracy_threshold = float(args.accuracy)
-
- tunerData.layer_file = args.layer_file
-
- # NOTE: Reading the cost file (with No of ops) to better guide the Autotuner
- cost_file_path = args.cost_file
- tunerData.layer_costs = readCostFile(cost_file_path)
-
-
- tunerData.knobs_list = readLayerKnobs(args.layer_knobs)
- tunerData.knobs_speedup = readKnobConfig(args.knobs_config)
-
- result_dir = args.result_dir
- if result_dir == "":
- print("Provide --result-dir ")
-
- orig_result_dir = result_dir
- tunerData.output_dir = result_dir + "/full_results/"
- if not os.path.exists(result_dir):
- os.mkdir(result_dir)
-
- if not os.path.exists(tunerData.output_dir):
- print("Creating output directory = ", tunerData.output_dir)
- os.mkdir(tunerData.output_dir)
-
-
-
- ClangFlagsTuner.main(argparser.parse_args())
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/approxhpvm_tuner.py b/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/approxhpvm_tuner.py
deleted file mode 100644
index 9ae2266bf481a9dd772fd139b375463b35bcd1b9..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/approxhpvm_tuner.py
+++ /dev/null
@@ -1,262 +0,0 @@
-#!/usr/bin/env python
-#
-# Optimize blocksize of apps/mmm_block.cpp
-#
-# This is an extremely simplified version meant only for tutorials
-#
-import adddeps # fix sys.path
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-import subprocess
-import threading
-import psutil
-
-from measure_confidence2 import dump_high_confidence_files
-from select_top_results import select_top_results
-from time import sleep
-
-
-output_dir = ""
-flag_ranges = []
-tuning_flags = []
-binary_name = ""
-accuracy_threshold = 10.0
-opt_confs_index = 9
-evaluated_configs = {}
-orig_result_dir = ""
-
-
-def copyTunerRuntime():
- tensor_rt_path = os.environ["LLVM_SRC_ROOT"]
- if tensor_rt_path == "":
- print "LLVM_SRC_ROOT NOT SET"
- sys.exit(0)
-
- print "tensor_rt_path = ", tensor_rt_path
-
-
-
-
-def extractTotalOverhead(file_name):
-
- total_comps = 0.0
- file = open(file_name, "r")
- for x in file:
- words = x.split()
- total_comps += float(words[opt_confs_index])
-
- print total_comps
- return total_comps
-
-
-def getAccuracy(file_name):
-
- file = open(file_name, "r")
- acc_str = file.read()
- file.close()
-
- accuracy = float(acc_str)
-
- try:
- accuracy = float(acc_str)
- except:
- return 20
-
- print accuracy
- return accuracy
-
-
-
-def kill(proc_pid):
- process = psutil.Process(proc_pid)
- for proc in process.children(recursive=True):
- proc.kill()
- process.kill()
-
-
-
-def createFlagsFile(file_name, cfg):
-
- f = open(file_name, "w+")
- cmd_config = ""
- for flag in tuning_flags:
- flag_value = cfg[flag]
- cmd_config += str(flag_value) + "\n"
-
- f.write(cmd_config)
- f.close()
-
-
-class ClangFlagsTuner(MeasurementInterface):
-
- def __init__(self, args):
-
- objective = ThresholdAccuracyMinimizeTime(accuracy_threshold)
- input_manager = FixedInputManager(size=num_flags)
- self.configs_list = []
-
- super(ClangFlagsTuner, self).__init__(
- args, program_name=args.binary,
- program_version=self.file_hash(args.binary),
- input_manager=input_manager, objective=objective)
-
-
-
- def manipulator(self):
- """
- Define the search space by creating a
- ConfigurationManipulator
- """
- manipulator = ConfigurationManipulator()
- for flag in tuning_flags:
- manipulator.add_parameter(
- EnumParameter(flag, flag_ranges
- # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
- )) #default is needed, optimizations don't work without it(tried and tested)
- return manipulator
-
-
- def run(self, desired_result, input, limit):
-
- """
- Run a given configuration then
- return performance
- """
- cfg = desired_result.configuration.data
-
- # NOTE: creates the file with flags read by the runtime
- createFlagsFile("opentuner_flags", cfg)
-
- run_cmd = binary_name
- print "binary_name = ", run_cmd
- #run_result_call_program = self.call_program(run_cmd)
- #print "returned \n\n"
-
- FNULL = open(os.devnull, 'wb')
- p = subprocess.Popen(run_cmd, stdout = FNULL)
- p.wait()
-
- total_comps = extractTotalOverhead("accuracy_summary")
- accuracy = getAccuracy("final_accuracy")
-
- #Result = opentuner.resultsdb.models.Result(time=total_comps)
- Result = opentuner.resultsdb.models.Result()
- Result.time = total_comps
- Result.accuracy = accuracy
-
- if accuracy > accuracy_threshold:
- if accuracy not in evaluated_configs:
- config_tuple = (total_comps, accuracy, cfg)
- self.configs_list.append(config_tuple)
- evaluated_configs[accuracy] = 1
- shutil.copy('accuracy_summary', output_dir + '/' + binary_name + '_' + str(accuracy))
-
-
- print "done with one run"
-
- return Result
-
-
- def save_final_config(self, configuration):
-
- print "Dumping High Confidence results"
- sleep(5)
-
- # Only dumping files with 95% confidence
- dump_high_confidence_files(binary_name, orig_result_dir, accuracy_threshold, 95)
- select_top_results(orig_result_dir + "/high_confidence")
-
-
- #self.start_process.kill()
- kill(self.start_process.pid)
-
- """
- called at the end of autotuning with the best resultsdb.models.Configuration
- """
- print "Final configuration", configuration.data
-
- return
-
-
- if not os.path.exists(result_dir):
- os.mkdir(result_dir)
-
- createFlagsFile("opentuner_flags", configuration.data)
- run_cmd = binary_name
- run_result_call_program = self.call_program(run_cmd)
-
- accuracy = getAccuracy("final_accuracy")
- shutil.copy('accuracy_summary', result_dir + '/' + binary_name + '_final_' + str(accuracy) )
-
- sorted_list = sorted(self.configs_list, key = lambda tup: tup[0])
- print sorted_list[0:10]
-
- top_elems = 20
- if len(sorted_list) < top_elems:
- top_elems = len(sorted_list)
-
-
- for i in range(top_elems):
- createFlagsFile("opentuner_flags", sorted_list[i][2])
- run_cmd = binary_name
- run_result_call_program = self.call_program(run_cmd)
- accuracy = getAccuracy("final_accuracy")
- shutil.copy('accuracy_summary', result_dir + '/' + binary_name + '_' + str(accuracy) + "_rank_" + str(i) )
-
-
- #os.mkdir(result_dir + "full_results")
-
-
-
-
-if __name__ == '__main__':
-
- argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
- argparser.add_argument('--binary', help='name of binary to run')
- argparser.add_argument('--num-flags', type=int, help='num of flags to tune for')
- argparser.add_argument('--error-range', type=int, help='num of flags to tune for')
- argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
- argparser.add_argument('--result-dir', help='accuracy threshold')
-
-
- args = argparser.parse_args()
- binary_name = str(args.binary)
- print("binary_name = ", binary_name)
- num_flags = int(args.num_flags)
- error_range = int(args.error_range)
- accuracy_threshold = float(args.accuracy)
- print("accuracy = ", accuracy_threshold)
- result_dir = args.result_dir
- orig_result_dir = result_dir
- if result_dir == "":
- print("Provide --result-dir ")
-
-
- output_dir = result_dir + "/full_results"
- print output_dir
- if not os.path.exists(result_dir):
- os.mkdir(result_dir)
-
- if not os.path.exists(output_dir):
- print("Creating output directory = ", output_dir)
- os.mkdir(output_dir)
-
- for j in range(error_range):
- flag_ranges.append(j)
-
- print("flag_ranges = ", flag_ranges)
-
- for i in range(num_flags):
- tuning_flags.append("flag" + str(i))
-
- ClangFlagsTuner.main(argparser.parse_args())
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/gettingstarted.md b/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/gettingstarted.md
deleted file mode 100644
index 8a442c5f44d6c501f686125d4468ca642f745920..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/gettingstarted.md
+++ /dev/null
@@ -1,215 +0,0 @@
----
-layout: default
-title: OpenTuner - Using OpenTuner
-permalink: /tutorial/gettingstarted/index.html
----
-
-Tutorial: Optimizing Block Matrix Multiplication
-================================================
-
-This tutorial assumes that you have checked out a copy of opentuner. For
-guidelines on how to get opentuner set up, refer [here][setup].
-
-[setup]: http://opentuner.org/tutorial/setup/
-
-Identifying a Program to Autotune
----------------------------------
-
-In order to do autotuning, you first need something to autotune. This will
-normally be your own program that you want to make either fast or better in
-some way. For this tutorial we will use a blocked version of matrix multiply
-as an example. We will use opentuner to find the optimal value of the block
-size parameter.
-
-We will autotune the sample code below(based off of modification of code
-found [here][matrix-multiply-code]), making sure to take the block size as
-a compile time constant to the program.
-
-[matrix-multiply-code]: http://csapp.cs.cmu.edu/public/waside/waside-blocking.pdf
-
-Save the sample code below to examples/tutorials/mmm_block.cpp
-
- #include <stdio.h>
- #include <cstdlib>
-
- #define N 100
-
- int main(int argc, const char** argv)
- {
-
- int n = BLOCK_SIZE * (N/BLOCK_SIZE);
- int a[N][N];
- int b[N][N];
- int c[N][N];
- int sum=0;
- for(int k1=0;k1<n;k1+=BLOCK_SIZE)
- {
- for(int j1=0;j1<n;j1+=BLOCK_SIZE)
- {
- for(int k1=0;k1<n;k1+=BLOCK_SIZE)
- {
- for(int i=0;i<n;i++)
- {
- for(int j=j1;j<j1+BLOCK_SIZE;j++)
- {
- sum = c[i][j];
- for(int k=k1;k<k1+BLOCK_SIZE;k++)
- {
- sum += a[i][k] * b[k][j];
- }
- c[i][j] = sum;
- }
- }
- }
- }
- }
- return 0;
- }
-
-Creating a New Autotuner with Opentuner
-------------------------------------
-Now we need to create a program that uses OpenTuner to optimize the program we just saved.
-
-Save the following code to examples/tutorials/mmm_tuner.py
-
- #!/usr/bin/env python
- #
- # Optimize blocksize of apps/mmm_block.cpp
- #
- # This is an extremely simplified version meant only for tutorials
- #
- import adddeps # fix sys.path
-
- import opentuner
- from opentuner import ConfigurationManipulator
- from opentuner import IntegerParameter
- from opentuner import MeasurementInterface
- from opentuner import Result
-
-
- class GccFlagsTuner(MeasurementInterface):
-
- def manipulator(self):
- """
- Define the search space by creating a
- ConfigurationManipulator
- """
- manipulator = ConfigurationManipulator()
- manipulator.add_parameter(
- IntegerParameter('blockSize', 1, 10))
- return manipulator
-
- def run(self, desired_result, input, limit):
- """
- Compile and run a given configuration then
- return performance
- """
- cfg = desired_result.configuration.data
-
- gcc_cmd = 'g++ mmm_block.cpp '
- gcc_cmd += '-DBLOCK_SIZE='+ cfg['blockSize']
- gcc_cmd += ' -o ./tmp.bin'
-
- compile_result = self.call_program(gcc_cmd)
- assert compile_result['returncode'] == 0
-
- run_cmd = './tmp.bin'
-
- run_result = self.call_program(run_cmd)
- assert run_result['returncode'] == 0
-
- return Result(time=run_result['time'])
-
- def save_final_config(self, configuration):
- """called at the end of tuning"""
- print "Optimal block size written to mmm_final_config.json:", configuration.data
- self.manipulator().save_to_file(configuration.data,
- 'mmm_final_config.json')
-
-
- if __name__ == '__main__':
- argparser = opentuner.default_argparser()
- GccFlagsTuner.main(argparser.parse_args())
-
-
-This file consists of several components, each of which will be discussed in further detail below.
-
-Tuning Programs have a general structure as follows:
-
- from opentuner import MeasurementInterface
- from opentuner import Result
-
-Create an instance of class GccFlagsTuner, which tunes specified parameters using opentuner.
- class GccFlagsTuner(MeasurementInterface):
-
-The manipulator method defines the variable search space by specifying parameters that should be tuned by this instance of GccFlagsTuner
-
- def manipulator(self):
- """
- Define the search space by creating a
- ConfigurationManipulator
- """
- manipulator = ConfigurationManipulator()
- manipulator.add_parameter(
- IntegerParameter('blockSize', 1, 10))
- return manipulator
-
-The run method actually runs opentuner under the given configuration and returns the calculated performance under this configuration. In this example, the blockSize parameter to be tuned is input as a compile-time constant that takes on a value within the specified range each time it is run. However, opentuner also supports other methods of specifying these parameters that may be preferred in different use cases.
-
- def run(self, desired_result, input, limit):
- """
- Compile and run a given configuration then
- return performance
- """
- cfg = desired_result.configuration.data
-
- gcc_cmd = 'g++ mmm_block.cpp '
- gcc_cmd += '-DBLOCK_SIZE='+ cfg['blockSize']
- gcc_cmd += ' -o ./tmp.bin'
-
- compile_result = self.call_program(gcc_cmd)
- assert compile_result['returncode'] == 0
-
- run_cmd = './tmp.bin'
-
- run_result = self.call_program(run_cmd)
- assert run_result['returncode'] == 0
-
- return Result(time=run_result['time'])
-
-We can actually display the result of running opentuner(the optimal block size for our multiplication problem) by creating a method, save_final_config() in our class. This saves a json dictionary of the optimal blockSize parameter found to the file mmm_final_config.json
-
- def save_final_config(self, configuration):
- """called at the end of tuning"""
- print "Optimal block size written to mmm_final_config.json:", configuration.data
- self.manipulator().save_to_file(configuration.data,
- 'mmm_final_config.json')
-
- if __name__ == '__main__':
- argparser = opentuner.default_argparser()
- GccFlagsTuner.main(argparser.parse_args())
-
-Generating and Viewing Results
-------------------------------
-
-Run the following command to autotune our program(The --no-dups flag hides warnings about duplicate results and the --stop-after parameter specifies that we are running opentuner for a maximum of 30 seconds):
-
- python mmm_tuner.py --no-dups --stop-after=30
-
-The results of each run configuration will be displayed as follows(output lines are truncated for readability here):
-
- [ 10s] INFO opentuner.search.plugin.DisplayPlugin: tests=10, best {'BLOCK_SIZE': 4}, cost time=0.0081, found by DifferentialEvolutionAlt[...]
- [ 19s] INFO opentuner.search.metatechniques: AUCBanditMetaTechniqueA: [('DifferentialEvolutionAlt', 477), ('UniformGreedyMutation', 18), ('NormalGreedyMutation', 5), ('RandomNelderMead', 1)]
- [ 20s] INFO opentuner.search.plugin.DisplayPlugin: tests=10, best {'BLOCK_SIZE': 4}, cost time=0.0081, found by DifferentialEvolutionAlt[...]
- [ 30s] INFO opentuner.search.plugin.DisplayPlugin: tests=10, best {'BLOCK_SIZE': 4}, cost time=0.0081, found by DifferentialEvolutionAlt[...]
- [ 30s] INFO opentuner.search.plugin.DisplayPlugin: tests=10, best {'BLOCK_SIZE': 4}, cost time=0.0081, found by DifferentialEvolutionAlt[...]
- Optimal block size written to mmm_final_config.json: {'BLOCK_SIZE': 4}
-
-
-Look up the optimal BlockSize value by inspecting the following created file:
-
- mmm_final_config.json
-
-In this example, the output file content was as follows:
-
- {'BLOCK_SIZE': 4}
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/measure_confidence.py b/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/measure_confidence.py
deleted file mode 100644
index dd7a050ac8428f99872abd25d1aa2f3d794f7e2b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/measure_confidence.py
+++ /dev/null
@@ -1,290 +0,0 @@
-
-import argparse
-import os
-import sys
-from time import sleep
-
-
-def getAccuracy(file_name):
-
- if not os.path.exists(file_name):
- print("final_accuracy file not found ")
- sys.exit(0)
-
- file = open(file_name, "r")
- acc_str = file.read()
- accuracy = float(acc_str)
- print accuracy
- return accuracy
-
-
-total_runs = 40
-skip_lines = 0
-
-
-def test_func():
- print "test_func"
- sys.exit(0)
-
-
-def do_multiple_runs(binary_name, accuracy_threshold, confidence_threshold):
-
- #total_runs = 100.0
- successful_runs = 0.0
- total_acc = 0
-
- for i in range(int(total_runs)):
-
- fifo = open("/tmp/myfifo", "w")
- fifo.write("start_run")
- fifo.close()
-
- print "Waiting for process to signal back - when done processing one run"
-
- fifo2 = open("/tmp/myfifo", "r")
- fifo2.read()
- fifo2.close()
-
- print "Process Signalled back"
-
- accuracy = getAccuracy("final_accuracy")
- total_acc += accuracy
-
- if accuracy > accuracy_threshold:
- successful_runs += 1
-
- confidence = (successful_runs / (total_runs*1.0) ) * 100.0
- print("confidence = ", confidence)
- avg_acc = total_acc / total_runs
- print("average accuracy = ", avg_acc)
-
- return confidence, avg_acc
-
-
-def compute_confidence(binary_name, accuracy, confidence, result_dir, output_dir):
-
- confidence_list = []
-
- if not os.path.exists(result_dir):
- print("Path does not exist")
- sys.exit(0)
-
- file_names = os.listdir(result_dir)
- print file_names
-
- for file_name in file_names:
- # Skip sub-directories
- if os.path.isdir(result_dir + "/" + file_name):
- continue
-
- f = open(result_dir + "/" + file_name)
- tuner_file = open("opentuner_flags", "w+")
-
- index = 0
- results_str = ""
- for x in f:
- if index >= skip_lines:
- error_knob = int(float(x.split()[1]))
- print error_knob
- tuner_file.write(str(error_knob) + "\n")
-
- results_str += x
- index += 1
-
- tuner_file.close()
-
- run_confidence, avg_accuracy = do_multiple_runs(binary_name, accuracy, confidence)
-
- if run_confidence > 90:
- f2 = open(output_dir + "/" + file_name, "w+")
- f2.write("total_runs=" + str(total_runs) + "\t confidence=" + str(run_confidence) + "\t avg_accuracy=" + str(avg_accuracy) + "\n")
- f2.write(results_str)
- f2.close()
-
- conf_result = (run_confidence, avg_accuracy, file_name)
- confidence_list.append(conf_result)
-
- return confidence_list
-
-
-
-
-def compute_promise_confidence(binary_name, accuracy, confidence, result_dir, output_dir):
-
- confidence_list = []
-
- if not os.path.exists(result_dir):
- print("Path does not exist")
- sys.exit(0)
-
- file_names = os.listdir(result_dir)
- print file_names
-
- for file_name in file_names:
- # Skip sub-directories
- if os.path.isdir(result_dir + "/" + file_name):
- continue
-
- f = open(result_dir + "/" + file_name)
- tuner_file = open("opentuner_flags", "w+")
-
- config_str = f.read()
- tuner_file.write(config_str)
- tuner_file.close()
-
- run_confidence, avg_accuracy = do_multiple_runs(binary_name, accuracy, confidence)
-
- if run_confidence > 90:
- f2 = open(output_dir + "/" + file_name, "w+")
- f2.write("total_runs=" + str(total_runs) + "\t confidence=" + str(run_confidence) + "\t avg_accuracy=" + str(avg_accuracy) + "\n")
- f2.write(config_str)
- f2.close()
-
- flags_str = config_str.replace('\n', ',')
- conf_result = (run_confidence, avg_accuracy, file_name, flags_str)
- confidence_list.append(conf_result)
-
- return confidence_list
-
-
-
-
-def dump_high_confidence_files(binary, result_dir, accuracy, confidence):
-
- #result_dir = args.result_dir
- output_dir = result_dir + "/high_confidence"
- result_dir = result_dir + "/full_results"
-
- if not os.path.exists(output_dir):
- os.mkdir(output_dir)
-
-
- confidence_list = compute_confidence(binary, accuracy, confidence, result_dir, output_dir)
- print confidence_list
-
- # descending sort on confidence
- sorted_list = sorted(confidence_list, key = lambda tup: tup[0], reverse=True)
-
- output_file = open(output_dir + "/confidence_summary.txt", "w+")
- for x in sorted_list:
- output_file.write(str(x[0]) + "\t" + str(x[1]) + "\t" + str(x[2]) + "\n")
-
- output_file.close()
- print "Dumped Confidence Summary"
-
-
-def processLayerFile(layer_file_path):
-
- layer_sizes = []
- layer_file = open(layer_file_path, "r")
-
- for layer_size in layer_file:
- try:
- size = int(layer_size)
- layer_sizes.append(size)
- except:
- return layer_sizes
-
- return layer_sizes
-
-
-
-def getLayerConfigStr(config_str, layer_sizes, num_flags):
-
- new_config_str = ""
- config_vals = config_str.split(',')
- it_count = 0
- for val in config_vals:
- if val == "":
- continue
-
- config_val = int(val)
- # For FP32 and FP32 values, each tensor op needs to be annotated
- if config_val == 8 or config_val == 9:
- for i in range(layer_sizes[it_count] - 1):
- new_config_str += val + " "
- new_config_str += val
- if it_count < num_flags - 1:
- new_config_str += ","
- else:
- new_config_str += val
- if it_count < num_flags - 1:
- new_config_str += ","
-
- it_count += 1
-
- return new_config_str
-
-
-def dump_promise_confidence_files(binary, result_dir, layer_file_path, num_flags, accuracy, confidence):
-
- #result_dir = args.result_dir
- output_dir = result_dir + "/high_confidence"
- input_dir = result_dir + "/full_results"
-
- if not os.path.exists(output_dir):
- os.mkdir(output_dir)
-
-
- layer_sizes = processLayerFile(layer_file_path);
- print layer_sizes
- sleep(3)
-
- confidence_list = compute_promise_confidence(binary, accuracy, confidence, input_dir, output_dir)
- print confidence_list
-
- # Ascending sort on accuracy
- sorted_list = sorted(confidence_list, key = lambda tup: tup[1])
-
- promise_file = open(output_dir + "/promise_confs.txt", "w+")
- confidence_file = open(output_dir + "/confidence_summary.txt", "w+")
-
- max_configs = 50
- it_count = 0
- for x in sorted_list:
- if x[1] > accuracy and x[0] > confidence:
- config_str = getLayerConfigStr(x[3], layer_sizes, num_flags)
- promise_file.write(config_str + "\n")
- it_count += 1
- if it_count > max_configs:
- break
-
- confidence_file.write(str(x[0]) + "\t" + str(x[1]) + "\t" + str(x[3]) + "\n")
-
- promise_file.close()
- confidence_file.close()
-
- print "Dumped Confidence Summary"
-
-
-
-
-
-if __name__ == "__main__":
-
- argparser = argparse.ArgumentParser(description='runs best configs to get high confidence on accuracy')
- argparser.add_argument('--result-dir', help='Directory containing OpenTuner configurations')
- argparser.add_argument('--output-dir', help='Directory for storing output directory')
- argparser.add_argument('--binary', help='Binary name to run')
- argparser.add_argument('--accuracy', type=float, help='Accuracy constraint')
- argparser.add_argument('--confidence', type=float, help='Confidence threshold')
-
-
- args = argparser.parse_args()
- result_dir = args.result_dir
- output_dir = args.output_dir
- binary = args.binary
- accuracy = args.accuracy
- confidence = args.confidence
-
- confidence_list = compute_confidence(binary, accuracy, confidence, result_dir, output_dir)
- #print confidence_list
-
- sorted_list = sorted(confidence_list, key = lambda tup: tup[0], reverse=True)
-
- output_file = open(output_dir + "/confidence_summary.txt", "w+")
- for x in sorted_list:
- output_file.write(str(x[0]) + "\t" + str(x[1]) + "\t" + str(x[2]) + "\n")
-
- output_file.close()
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/measure_confidence2.py b/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/measure_confidence2.py
deleted file mode 100644
index f5998ff3c871fe2db625873dc75fcf8fe4452838..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/measure_confidence2.py
+++ /dev/null
@@ -1,664 +0,0 @@
-
-import argparse
-import os
-import sys
-import subprocess
-from time import sleep
-
-
-def getAccuracy(file_name):
-
- if not os.path.exists(file_name):
- print("final_accuracy file not found ")
- sys.exit(0)
-
- file = open(file_name, "r")
- acc_str = file.read()
- accuracy = float(acc_str)
- print accuracy
- return accuracy
-
-
-total_runs = 40.0
-fails_allowed = 3
-skip_lines = 0
-
-
-def test_func():
- print "test_func"
- sys.exit(0)
-
-
-
-def do_multiple_runs(binary_name, accuracy_threshold, confidence_threshold):
-
- successful_runs = 0.0
- unsuccessful_runs = 0.0
- total_acc = 0
-
- for i in range(int(total_runs)):
- FNULL = open(os.devnull, 'wb')
- p = subprocess.Popen(binary_name, stdout = FNULL)
- p.wait()
-
- accuracy = getAccuracy("final_accuracy")
- total_acc += accuracy
-
- if accuracy > accuracy_threshold:
- successful_runs += 1
- else:
- unsuccessful_runs += 1
-
- if unsuccessful_runs > 6:
- break
-
-
- confidence = (successful_runs / total_runs) * 100.0
- print("confidence = ", confidence)
- avg_acc = total_acc / total_runs
- print("average accuracy = ", avg_acc)
-
- return confidence, avg_acc
-
-
-
-def getConfidence(accuracy_outfile, acc_threshold):
-
- f = open(accuracy_outfile, "r")
-
- total_acc = 0.0
- failed = 0
- it = 0
-
- for x in f:
- acc = float(x.strip())
- if acc < acc_threshold:
- failed += 1
-
- total_acc += acc
- it += 1
-
- conf = (it * 1.0 - failed) / it * 100
- avg_acc = total_acc / it
-
- return conf, avg_acc
-
-
-
-def getMinAccuracy(accuracy_outfile):
-
- f = open(accuracy_outfile, "r")
-
- total_acc = 0.0
- failed = 0
- it = 0
-
- acc_list = []
- for x in f:
- acc = float(x.strip())
- acc_list.append(acc)
-
- return min(acc_list)
-
-
-# NOTE: invokes the binary with the number of runs
-def do_multiple_runs2(binary_name, accuracy_threshold, confidence_threshold):
-
- successful_runs = 0.0
- unsuccessful_runs = 0.0
- total_acc = 0
-
- FNULL = open(os.devnull, 'wb')
- p = subprocess.Popen([binary_name, str(int(total_runs)), str(accuracy_threshold), str(fails_allowed)], stdout = FNULL)
- p.wait()
-
- confidence, avg_acc = getConfidence("run_accuracies.txt", accuracy_threshold)
-
- print("confidence = ", confidence)
- print("average accuracy = ", avg_acc)
-
- return confidence, avg_acc
-
-
-
-
-
-def compute_confidence(binary_name, accuracy, confidence, result_dir, output_dir):
-
- confidence_list = []
-
- if not os.path.exists(result_dir):
- print("Path does not exist")
- sys.exit(0)
-
- file_names = os.listdir(result_dir)
- print file_names
-
- for file_name in file_names:
- # Skip sub-directories
- if os.path.isdir(result_dir + "/" + file_name):
- continue
-
- f = open(result_dir + "/" + file_name)
- tuner_file = open("opentuner_flags", "w+")
-
- index = 0
- results_str = ""
- for x in f:
- if index >= skip_lines:
- error_knob = int(float(x.split()[1]))
- print error_knob
- tuner_file.write(str(error_knob) + "\n")
-
- results_str += x
- index += 1
-
- tuner_file.close()
-
- run_confidence, avg_accuracy = do_multiple_runs2(binary_name, accuracy, confidence)
-
- if run_confidence >= 95:
- f2 = open(output_dir + "/" + file_name, "w+")
- f2.write("total_runs=" + str(total_runs) + "\t confidence=" + str(run_confidence) + "\t avg_accuracy=" + str(avg_accuracy) + "\n")
- f2.write(results_str)
- f2.close()
-
- conf_result = (run_confidence, avg_accuracy, file_name)
- confidence_list.append(conf_result)
-
- return confidence_list
-
-
-
-def dump_high_confidence_files(binary, result_dir, accuracy, confidence):
-
- #result_dir = args.result_dir
- output_dir = result_dir + "/high_confidence"
- result_dir = result_dir + "/full_results"
-
- if not os.path.exists(output_dir):
- os.mkdir(output_dir)
-
- confidence_list = compute_confidence(binary, accuracy, confidence, result_dir, output_dir)
- print confidence_list
-
- sorted_list = sorted(confidence_list, key = lambda tup: tup[0], reverse=True)
-
- output_file = open(output_dir + "/confidence_summary.txt", "w+")
- for x in sorted_list:
- output_file.write(str(x[0]) + "\t" + str(x[1]) + "\t" + str(x[2]) + "\n")
-
- output_file.close()
- print "Dumped Confidence Summary"
-
-
-
-
-def processLayerFile(layer_file_path):
-
- layer_sizes = []
- layer_file = open(layer_file_path, "r")
-
- for layer_desc in layer_file:
- try:
- toks = layer_desc.split(",")
- if len(toks) < 2: # Not layer size description
- continue
-
- size = int(toks[1])
- if "NML" in layer_desc:
- size = -1
- layer_sizes.append(size)
- except:
- return layer_sizes
-
- return layer_sizes
-
-
-
-def getLayerConfigStr(config_str, layer_sizes, num_flags):
-
- new_config_str = ""
- config_vals = config_str.split(',')
- it_count = 0
- layer_count = 0
-
- #for layer_size in val in config_vals:
- for layer_depth_size in layer_sizes:
-
- if layer_depth_size == -1:
- new_config_str += "8"
- layer_count += 1
- if layer_count < len(layer_sizes):
- new_config_str += ","
- continue
-
- val = config_vals[it_count]
- if val == "":
- continue
-
- config_val = int(val)
- # For FP32 and FP32 values, each tensor op needs to be annotated
- if config_val == 8 or config_val == 9:
- for i in range(layer_depth_size - 1):
- new_config_str += val + " "
- new_config_str += val
- if layer_count < len(layer_sizes) - 1:
- new_config_str += ","
- else:
- new_config_str += val
- if layer_count < len(layer_sizes) - 1:
- new_config_str += ","
-
- it_count += 1
- layer_count += 1
-
-
- return new_config_str
-
-
-
-def compute_promise_confidence(binary_name, accuracy, confidence, result_dir, output_dir):
-
- confidence_list = []
-
- if not os.path.exists(result_dir):
- print("Path does not exist")
- sys.exit(0)
-
- file_names = os.listdir(result_dir)
- print file_names
-
- for file_name in file_names:
- # Skip sub-directories
- if os.path.isdir(result_dir + "/" + file_name):
- continue
-
- f = open(result_dir + "/" + file_name)
- tuner_file = open("promise_flags", "w+")
-
- config_str = f.read()
- tuner_file.write(config_str)
- tuner_file.close()
-
- run_confidence, avg_accuracy = do_multiple_runs(binary_name, accuracy, confidence)
-
- if run_confidence >= 95:
- f2 = open(output_dir + "/" + file_name, "w+")
- f2.write("total_runs=" + str(total_runs) + "\t confidence=" + str(run_confidence) + "\t avg_accuracy=" + str(avg_accuracy) + "\n")
- f2.write(config_str)
- f2.close()
-
- flags_str = config_str.replace('\n', ',')
- conf_result = (run_confidence, avg_accuracy, file_name, flags_str)
- confidence_list.append(conf_result)
-
- return confidence_list
-
-
-
-def getConfigCost(layer_costs, config_str):
-
- tuning_flags = config_str.split("\n")
-
- it = 0
- orig_cost = 0.0
- total_cost = 0.0
- for flag in tuning_flags:
-
- flag_value = -1
- try:
- flag_value = int(flag)
- except:
- continue
-
- orig_cost += layer_costs[it]
-
- #print ("orig_cost = ", orig_cost, " flag_value = ", flag_value)
-
- if flag_value == 11:
- total_cost += layer_costs[it]
- elif flag_value == 10:
- total_cost += (layer_costs[it] / 1.3)
- elif flag_value == 8 or flag_value == 9:
- total_cost += (layer_costs[it] / 1.6)
- elif flag_value < 8:
- divisor = 5 + (7 - flag_value)
- total_cost += (layer_costs[it] / divisor)
-
- it += 1
-
- speedup = orig_cost * 1.0 / total_cost * 1.0
-
- return total_cost, speedup
-
-
-
-
-
-def getConfigCost2(layer_costs, knobs_speedup, config_flags):
-
- orig_cost = 0.0
- total_cost = 0.0
- for it in range(len(config_flags)):
- flag_value = config_flags[it]
- op_cost = layer_costs[it]
- speedup = knobs_speedup[flag_value]
-
- total_cost += (op_cost * 1.0 / speedup * 1.0)
- orig_cost += op_cost
-
- it += 1
-
- speedup = (orig_cost * 1.0) / (total_cost * 1.0)
-
- return total_cost, speedup
-
-
-
-
-
-def compute_promise_confidence2(binary_name, accuracy, confidence, layer_costs,
- result_dir, output_dir):
-
- confidence_list = []
-
- if not os.path.exists(result_dir):
- print("Path does not exist")
- sys.exit(0)
-
- file_names = os.listdir(result_dir)
- print file_names
-
- for file_name in file_names:
- # Skip sub-directories
- if os.path.isdir(result_dir + "/" + file_name):
- continue
-
- f = open(result_dir + "/" + file_name)
- tuner_file = open("promise_flags", "w+")
-
- config_str = f.read()
- tuner_file.write(config_str)
- tuner_file.close()
-
- #run_confidence, avg_accuracy = do_multiple_runs(binary_name, accuracy, confidence)
- run_confidence, avg_accuracy = do_multiple_runs2(binary_name, accuracy, confidence)
-
- if run_confidence >= 95:
- f2 = open(output_dir + "/" + file_name, "w+")
-
- config_cost, speedup = getConfigCost(layer_costs, config_str)
-
- f2.write("total_runs=" + str(total_runs) + "\t confidence=" + str(run_confidence) + "\t avg_accuracy=" + str(avg_accuracy) + "\t config_cost=" + str(config_cost) + "\t speedup=" + str(speedup) + "\n")
- f2.write(config_str)
- f2.close()
-
- flags_str = config_str.replace('\n', ',')
- conf_result = (run_confidence, avg_accuracy, file_name, flags_str)
- confidence_list.append(conf_result)
-
- return confidence_list
-
-
-
-
-
-def compute_promise_confidence3(binary_name, accuracy, confidence, layer_costs,
- result_dir, output_dir, knobs_speedup):
-
- confidence_list = []
-
- if not os.path.exists(result_dir):
- print("Path does not exist")
- sys.exit(0)
-
- file_names = os.listdir(result_dir)
- print file_names
-
- for file_name in file_names:
- # Skip sub-directories
- if os.path.isdir(result_dir + "/" + file_name):
- continue
-
- f = open(result_dir + "/" + file_name)
- tuner_file = open("promise_flags", "w+")
-
- config_flags = []
- config_str = ""
- it = 0
- for x in f:
-
- if it > 0:
- config_str += x
- config_flags.append(int(x.strip()))
- tuner_file.write(x)
- it += 1
-
- tuner_file.close()
-
-
- #run_confidence, avg_accuracy = do_multiple_runs(binary_name, accuracy, confidence)
- run_confidence, avg_accuracy = do_multiple_runs2(binary_name, accuracy, confidence)
-
- if run_confidence >= 95:
- f2 = open(output_dir + "/" + file_name, "w+")
-
- config_cost, speedup = getConfigCost2(layer_costs, knobs_speedup, config_flags)
-
- f2.write("total_runs=" + str(total_runs) + "\t confidence=" + str(run_confidence) + "\t avg_accuracy=" + str(avg_accuracy) + "\t config_cost=" + str(config_cost) + "\t speedup=" + str(speedup) + "\n")
- f2.write(config_str)
- f2.close()
-
- flags_str = config_str.replace('\n', ',')
- conf_result = (run_confidence, avg_accuracy, file_name, flags_str)
- confidence_list.append(conf_result)
-
- return confidence_list
-
-
-
-
-def dump_promise_confidence_files(binary, result_dir, layer_file_path,
- num_flags, accuracy, confidence):
-
- #result_dir = args.result_dir
- output_dir = result_dir + "/high_confidence"
- input_dir = result_dir + "/full_results"
-
- if not os.path.exists(output_dir):
- os.mkdir(output_dir)
-
- layer_sizes = processLayerFile(layer_file_path);
- print layer_sizes
- sleep(2)
-
- confidence_list = compute_promise_confidence(binary, accuracy, confidence, input_dir, output_dir)
- print confidence_list
-
- # Ascending sort on accuracy
- sorted_list = sorted(confidence_list, key = lambda tup: tup[1])
-
- promise_file = open(output_dir + "/promise_confs.txt", "w+")
- confidence_file = open(output_dir + "/confidence_summary.txt", "w+")
-
- max_configs = 50
- it_count = 0
- for x in sorted_list:
- if x[1] > accuracy and x[0] > confidence:
- config_str = getLayerConfigStr(x[3], layer_sizes, num_flags)
- promise_file.write(config_str + "\n")
- it_count += 1
- if it_count > max_configs:
- break
-
- confidence_file.write(str(x[0]) + "\t" + str(x[1]) + "\t" + str(x[3]) + "\n")
-
- promise_file.close()
- confidence_file.close()
-
- print "Dumped Confidence Summary"
-
-
-
-
-
-def dump_promise_confidence_files2(binary, result_dir, layer_file_path,
- num_flags, accuracy, layer_costs, confidence):
-
-
- #result_dir = args.result_dir
- output_dir = result_dir + "/high_confidence"
- input_dir = result_dir + "/full_results"
-
- if not os.path.exists(output_dir):
- os.mkdir(output_dir)
-
- layer_sizes = processLayerFile(layer_file_path);
- print layer_sizes
- sleep(2)
-
- confidence_list = compute_promise_confidence2(binary, accuracy, confidence, layer_costs, input_dir, output_dir)
- print confidence_list
-
- # Ascending sort on accuracy
- sorted_list = sorted(confidence_list, key = lambda tup: tup[1])
-
- promise_file = open(output_dir + "/promise_confs.txt", "w+")
- confidence_file = open(output_dir + "/confidence_summary.txt", "w+")
-
- max_configs = 50
- it_count = 0
- for x in sorted_list:
- if x[1] > accuracy and x[0] > confidence:
- config_str = getLayerConfigStr(x[3], layer_sizes, num_flags)
- promise_file.write(config_str + "\n")
- it_count += 1
- if it_count > max_configs:
- break
-
- confidence_file.write(str(x[0]) + "\t" + str(x[1]) + "\t" + str(x[3]) + "\n")
-
- promise_file.close()
- confidence_file.close()
-
- print "Dumped Confidence Summary"
-
-
-
-
-def dump_promise_confidence_files3(binary, input_dir, output_dir, layer_file_path,
- num_flags, accuracy, layer_costs, confidence):
-
-
- #result_dir = args.result_dir
- #output_dir = result_dir + "/high_confidence"
- #input_dir = result_dir + "/full_results"
-
- if not os.path.exists(output_dir):
- os.mkdir(output_dir)
-
- layer_sizes = processLayerFile(layer_file_path);
- print layer_sizes
- sleep(2)
-
- confidence_list = compute_promise_confidence2(binary, accuracy, confidence, layer_costs, input_dir, output_dir)
- print confidence_list
-
- # Ascending sort on accuracy
- sorted_list = sorted(confidence_list, key = lambda tup: tup[1])
-
- promise_file = open(output_dir + "/promise_confs.txt", "w+")
- confidence_file = open(output_dir + "/confidence_summary.txt", "w+")
-
- max_configs = 50
- it_count = 0
- for x in sorted_list:
- if x[1] > accuracy and x[0] > confidence:
- config_str = getLayerConfigStr(x[3], layer_sizes, num_flags)
- promise_file.write(config_str + "\n")
- it_count += 1
- if it_count > max_configs:
- break
-
- confidence_file.write(str(x[0]) + "\t" + str(x[1]) + "\t" + str(x[3]) + "\n")
-
- promise_file.close()
- confidence_file.close()
-
- print "Dumped Confidence Summary"
-
-
-
-
-
-def dump_promise_confidence_files4(binary, input_dir, output_dir, layer_file_path,
- num_flags, accuracy, layer_costs, confidence, knobs_speedup):
-
-
- if not os.path.exists(output_dir):
- os.mkdir(output_dir)
-
- layer_sizes = processLayerFile(layer_file_path);
- print layer_sizes
- sleep(2)
-
- confidence_list = compute_promise_confidence3(binary, accuracy, confidence, layer_costs, input_dir, output_dir, knobs_speedup)
- print confidence_list
-
- # Ascending sort on accuracy
- sorted_list = sorted(confidence_list, key = lambda tup: tup[1])
-
- promise_file = open(output_dir + "/promise_confs.txt", "w+")
- confidence_file = open(output_dir + "/confidence_summary.txt", "w+")
-
- max_configs = 50
- it_count = 0
- for x in sorted_list:
- if x[1] > accuracy and x[0] > confidence:
- config_str = getLayerConfigStr(x[3], layer_sizes, num_flags)
- promise_file.write(config_str + "\n")
- it_count += 1
- if it_count > max_configs:
- break
-
- confidence_file.write(str(x[0]) + "\t" + str(x[1]) + "\t" + str(x[3]) + "\n")
-
- promise_file.close()
- confidence_file.close()
-
- print "Dumped Confidence Summary"
-
-
-
-
-
-
-if __name__ == "__main__":
-
- argparser = argparse.ArgumentParser(description='runs best configs to get high confidence on accuracy')
- argparser.add_argument('--result-dir', help='Directory containing OpenTuner configurations')
- argparser.add_argument('--output-dir', help='Directory for storing output directory')
- argparser.add_argument('--binary', help='Binary name to run')
- argparser.add_argument('--accuracy', type=float, help='Accuracy constraint')
- argparser.add_argument('--confidence', type=float, help='Confidence threshold')
-
-
- args = argparser.parse_args()
- result_dir = args.result_dir
- output_dir = args.output_dir
- binary = args.binary
- accuracy = args.accuracy
- confidence = args.confidence
-
- confidence_list = compute_confidence(binary, accuracy, confidence, result_dir, output_dir)
- #print confidence_list
-
- sorted_list = sorted(confidence_list, key = lambda tup: tup[0], reverse=True)
-
- output_file = open(output_dir + "/confidence_summary.txt", "w+")
- for x in sorted_list:
- output_file.write(str(x[0]) + "\t" + str(x[1]) + "\t" + str(x[2]) + "\n")
-
- output_file.close()
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/pareto_curve.py b/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/pareto_curve.py
deleted file mode 100644
index db8233994b855317095c94331fba869d9ad79d16..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/pareto_curve.py
+++ /dev/null
@@ -1,264 +0,0 @@
-
-
-import os
-import shutil
-from measure_confidence2 import getConfigCost
-
-
-AL_THRESHOLD = 0.1
-
-
-class Config:
- def __init__(self):
- self.avg_accuracy = 0
- self.avg_loss = 0
- self.speedup = 1
- self.fname = ""
- self.flags = []
-
-
-
-
-def skipFile(fname):
-
- skip_files = {}
- skip_files["confidence_summary.txt"] = 1
- skip_files["promise_confs.txt"] = 1
-
- if "accuracy" in fname:
- return True
-
- if fname in skip_files:
- return True
- else:
- return False
-
-
-
-
-def loadConfigData(result_dir, layer_costs, baseline_accuracy):
-
- config_arr = []
-
- #result_dir += "/promise_tuner/high_confidence/"
- file_names = os.listdir(result_dir)
-
-
- for fname in file_names:
- if not skipFile(fname):
-
- fpath = result_dir + fname
- config = Config()
- f = open(fpath, "r")
-
- config_str = f.read()
- cost, speedup = getConfigCost(layer_costs, config_str)
-
- config.speedup = speedup
- config.fname = fname
-
- fpath2 = fpath + "_accuracy"
- f2 = open(fpath2, "r")
- acc_str = f2.read().strip()
- accuracy = float(acc_str)
-
- config.avg_accuracy = accuracy
- config.avg_loss = baseline_accuracy - accuracy
-
- config_arr.append(config)
-
-
- return config_arr
-
-
-
-
-class Configuration:
- def __init__(self, name, speedup, energy, accuracy, accuracy_loss):
- self.name = name
- self.speedup = speedup
- self.energy = energy
- self.accuracy = accuracy
- self.accuracy_loss = accuracy_loss
- def __repr__(self):
- return repr((self.name, self.speedup, self.energy, self.accuracy, self.accuracy_loss))
-
-configuration_objects = [
- Configuration('conf1', 1.05, 15, 85, 1.2),
- Configuration('conf2', 2.51, 12, 83, 1.4),
- Configuration('conf3', 2.05, 10, 84, 0.8),
-]
-
-def compute_pareto_points(configurations):
- speedupconfigurations = []
- energyconfigurations = []
- #sort configurations based on speedup
- sorted_configurations = sorted(configurations, key=lambda conf: conf.accuracy_loss)
-
- start_idx = 0
- while start_idx < len(sorted_configurations):
- end_idx = start_idx + 1;
- # find end_idx
- while end_idx < len(sorted_configurations) and (sorted_configurations[end_idx].accuracy_loss - sorted_configurations[start_idx].accuracy_loss < AL_THRESHOLD) :
- end_idx += 1
- # find best speedup end energy in this accuracy loss level
- sp = -1.0
- sp_idx = 0
- en = -1.0
- en_idx = 0
- for i in range(start_idx, end_idx):
- if sorted_configurations[i].speedup > sp:
- sp = sorted_configurations[i].speedup
- sp_idx = i
- if sorted_configurations[i].energy > en:
- en = sorted_configurations[i].energy
- en_idx = i
- sp_not_dominated = True
- # if not empty list of configurations
- if speedupconfigurations:
- if speedupconfigurations[-1].speedup >= sp:
- sp_not_dominated = False
- en_not_dominated = True
- # if not empty list of configurations
- if energyconfigurations:
- if energyconfigurations[-1].energy >= en:
- en_not_dominated = False
- if sp_not_dominated:
- speedupconfigurations.append(sorted_configurations[sp_idx])
- if en_not_dominated:
- energyconfigurations.append(sorted_configurations[en_idx])
- # outer while loop variable increment
- start_idx = end_idx
- return [speedupconfigurations, energyconfigurations]
-
-
-def compute_pareto_points_with_margin(configurations, speedup_band_width, energy_band_width):
- speedupconfigurations = []
- energyconfigurations = []
- #sort configurations based on speedup
- sorted_configurations = sorted(configurations, key=lambda conf: conf.accuracy_loss)
-
- idx_to_sp_conf_dict = {}
- idx_to_en_conf_dict = {}
-
- start_idx = 0
- while start_idx < len(sorted_configurations):
- end_idx = start_idx + 1;
- # find end_idx
- while end_idx < len(sorted_configurations) and (sorted_configurations[end_idx].accuracy_loss - sorted_configurations[start_idx].accuracy_loss < AL_THRESHOLD) :
- end_idx += 1
- # find best speedup end energy in this accuracy loss level
- sp = -1.0
- sp_idx = 0
- en = -1.0
- en_idx = 0
- for i in range(start_idx, end_idx):
- if sorted_configurations[i].speedup > sp:
- sp = sorted_configurations[i].speedup
- sp_idx = i
- if sorted_configurations[i].energy < en:
- en = sorted_configurations[i].energy
- en_idx = i
- sp_not_dominated = True
- # if not empty list of configurations
- if speedupconfigurations:
- if speedupconfigurations[-1].speedup >= sp:
- sp_not_dominated = False
- en_not_dominated = True
- # if not empty list of configurations
- if energyconfigurations:
- if energyconfigurations[-1].energy >= en:
- en_not_dominated = False
- if sp_not_dominated:
- speedupconfigurations.append(sorted_configurations[sp_idx])
- idx_to_sp_conf_dict[start_idx] = len(speedupconfigurations)-1
- if en_not_dominated:
- energyconfigurations.append(sorted_configurations[en_idx])
- idx_to_en_conf_dict[start_idx] = len(energyconfigurations)-1
- # outer while loop variable increment
- start_idx = end_idx
-
- # We want to add configurations in a band of a certain width around the curves
- # not possible to do during contruction, because the quality of the curve would
- # deteriorate quickly
-
- AdjustedSpeedupCurve = []
- AdjustedEnergyCurve = []
-
- start_idx = 0
- while start_idx < len(sorted_configurations):
- end_idx = start_idx + 1;
- # find end_idx
- while end_idx < len(sorted_configurations) and (sorted_configurations[end_idx].accuracy_loss - sorted_configurations[start_idx].accuracy_loss < AL_THRESHOLD) :
- end_idx += 1
- for i in range(start_idx, end_idx):
- if sorted_configurations[i].speedup + speedup_band_width >= speedupconfigurations[idx_to_sp_conf_dict[start_idx]].speedup:
- AdjustedSpeedupCurve.append(sorted_configurations[i])
- if sorted_configurations[i].energy + energy_band_width >= energyconfigurations[idx_to_en_conf_dict[start_idx]].energy:
- AdjustedEnergyCurve.append(sorted_configurations[i])
- # outer while loop variable increment
- start_idx = end_idx
-
- return [AdjustedSpeedupCurve, AdjustedEnergyCurve]
-
-
-
-def findParetoConfigs(base_dir, layer_costs, accuracy):
-
- result_dir = base_dir + "/pareto/"
- try:
- os.mkdir(result_dir)
- except:
- print "could not create dir"
-
- input_dir = base_dir + "/full_results/"
- #result_dir = "../build_tuner/tuner_results/alexnet_cifar10/loss_3/batch15"
- config_arr = loadConfigData(input_dir, layer_costs, accuracy)
-
- config_list = []
-
- it = 0
- for config in config_arr:
- config = Configuration(config.fname , config.speedup, 100, config.avg_accuracy, config.avg_loss)
- config_list.append(config)
-
-
- SPEEDUP_BAND_SIZE = 1.0
- ENERGY_BAND_SIZE = 10
-
- # No Pareto Selection if list is < 50 configurations
- if len(config_list) < 50:
- SPEEDUP_BAND_SIZE = 100 # Include all in Pareto Frontier
-
-
- print ("*SPEEDUP_BAND_SIZE = ", SPEEDUP_BAND_SIZE)
-
- ASC, AEC = compute_pareto_points_with_margin(config_list, SPEEDUP_BAND_SIZE, ENERGY_BAND_SIZE)
-
-
- print ("len(config_list) = ", len(config_list))
- print ("len(ASC) = ", len(ASC))
-
- #print (ASC)
- #print (config_list)
-
- for conf in ASC:
- #dst_path = conf.name.replace("full_results", "pareto")
- src_path = base_dir + "/full_results/" + conf.name
- dst_path = base_dir + "/pareto/" + conf.name
- shutil.copy(src_path, dst_path)
-
-
-
-if __name__ == "__main__":
-
- get_pareto_configs("")
-
- #SC, EC = compute_pareto_points(configuration_objects)
- #ASC, AEC = compute_pareto_points_with_margin(configuration_objects, SPEEDUP_BAND_SIZE, ENERGY_BAND_SIZE)
-
- #print(SC)
- #print(EC)
-
- #print(ASC)
- #print(AEC)
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/promise_tuner2.py b/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/promise_tuner2.py
deleted file mode 100644
index ca96ff16c2d176b3bb91e213005202634916fc41..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/promise_tuner2.py
+++ /dev/null
@@ -1,220 +0,0 @@
-#!/usr/bin/env python
-#
-# Optimize blocksize of apps/mmm_block.cpp
-#
-# This is an extremely simplified version meant only for tutorials
-#
-import adddeps # fix sys.path
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-import subprocess
-import threading
-import psutil
-
-from measure_confidence2 import dump_promise_confidence_files
-from select_top_results import select_top_results
-from time import sleep
-
-
-layer_file = ""
-output_dir = ""
-flag_ranges = []
-tuning_flags = []
-binary_name = ""
-accuracy_threshold = 10.0
-evaluated_configs = {}
-orig_result_dir = ""
-gpu_layers = 0
-
-
-def getAccuracy(file_name):
-
- file = open(file_name, "r")
- acc_str = file.read()
- file.close()
- accuracy = float(acc_str)
-
- try:
- accuracy = float(acc_str)
- except:
- return 20
-
- print accuracy
- return accuracy
-
-
-
-def createFlagsFile(file_name, cfg):
-
- f = open(file_name, "w+")
- cmd_config = ""
- for flag in tuning_flags:
- flag_value = cfg[flag]
- cmd_config += str(flag_value) + "\n"
-
- f.write(cmd_config)
- f.close()
-
-
-class ClangFlagsTuner(MeasurementInterface):
-
- def __init__(self, args):
- objective = ThresholdAccuracyMinimizeTime(accuracy_threshold)
- input_manager = FixedInputManager(size=num_flags)
- self.configs_list = []
-
- super(ClangFlagsTuner, self).__init__(
- args, program_name=args.binary,
- program_version=self.file_hash(args.binary),
- input_manager=input_manager, objective=objective)
-
-
-
-
- def manipulator(self):
- """
- Define the search space by creating a
- ConfigurationManipulator
- """
- manipulator = ConfigurationManipulator()
-
- # NOTE: Skipping first 'gpu_layers' to run on GPU
- for flag in tuning_flags[:gpu_layers]:
- manipulator.add_parameter(
- EnumParameter(flag, [8, 9]))
-
- for flag in tuning_flags[gpu_layers:]:
- manipulator.add_parameter(
- EnumParameter(flag, flag_ranges
- )) #default is needed, optimizations don't work without it(tried and tested)
- return manipulator
-
-
- def run(self, desired_result, input, limit):
-
- """
- Run a given configuration then
- return performance
- """
- cfg = desired_result.configuration.data
-
- # NOTE: creates the file with flags read by the runtime
- createFlagsFile("promise_flags", cfg)
-
- run_cmd = binary_name
- print "binary_name = ", run_cmd
- #run_result_call_program = self.call_program(run_cmd)
- #print "returned \n\n"
-
- FNULL = open(os.devnull, 'wb')
- p = subprocess.Popen(run_cmd, stdout = FNULL)
- p.wait()
-
-
- accuracy = getAccuracy("final_accuracy")
- total_comps = abs(accuracy_threshold - accuracy)
-
-
- Result = opentuner.resultsdb.models.Result()
- Result.time = total_comps
- Result.accuracy = accuracy
-
- if accuracy > accuracy_threshold:
- #if accuracy not in evaluated_configs:
- config_tuple = (total_comps, accuracy, cfg)
- self.configs_list.append(config_tuple)
- evaluated_configs[accuracy] = 1
- shutil.copy('promise_flags', output_dir + '/' + binary_name + '_' + str(accuracy))
-
-
- print "done with one run"
-
- return Result
-
-
- def save_final_config(self, configuration):
-
- print "Dumping High Confidence results \n"
- sleep(20)
-
- # Only dumping files with 95% confidence
- dump_promise_confidence_files(binary_name, orig_result_dir, layer_file, num_flags, accuracy_threshold, 95)
- #select_top_results(orig_result_dir + "/high_confidence")
-
-
-
- """
- called at the end of autotuning with the best resultsdb.models.Configuration
- """
- print "Final configuration", configuration.data
-
- return
-
-
-
-
-
-if __name__ == '__main__':
-
- argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
- argparser.add_argument('--binary', help='path to target binary')
- argparser.add_argument('--num-flags', type=int, help='num of flags to tune')
- argparser.add_argument('--start-range', type=int, help='start range in tuning')
- argparser.add_argument('--error-range', type=int, help='range of error values used in tuning')
- argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
- argparser.add_argument('--result-dir', help='result directory')
- argparser.add_argument('--layer-file', help='layer description')
- argparser.add_argument('--gpu-layers', type=int, help='first N layers to run on GPU')
-
-
- args = argparser.parse_args()
- binary_name = str(args.binary)
- print("binary_name = ", binary_name)
- num_flags = int(args.num_flags)
- start_range = int(args.start_range)
- error_range = int(args.error_range)
- accuracy_threshold = float(args.accuracy)
- print("accuracy = ", accuracy_threshold)
- result_dir = args.result_dir
- orig_result_dir = result_dir
- if result_dir == "":
- print("Provide --result-dir ")
-
- gpu_layers = args.gpu_layers
-
-
- output_dir = result_dir + "/full_results"
- print output_dir
- if not os.path.exists(result_dir):
- os.mkdir(result_dir)
-
- if not os.path.exists(output_dir):
- print("Creating output directory = ", output_dir)
- os.mkdir(output_dir)
-
- #for j in range(error_range):
- # flag_ranges.append(j)
-
- for j in range(start_range, error_range):
- flag_ranges.append(j)
-
-
- print("flag_ranges = ", flag_ranges)
-
- # File with layer description
- layer_file = args.layer_file
-
- for i in range(num_flags):
- tuning_flags.append("flag" + str(i))
-
- ClangFlagsTuner.main(argparser.parse_args())
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/promise_tuner3.py b/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/promise_tuner3.py
deleted file mode 100644
index 04ce0d6158819d5cb014411456e1a985fb17b354..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/promise_tuner3.py
+++ /dev/null
@@ -1,314 +0,0 @@
-#!/usr/bin/env python
-#
-# Optimize blocksize of apps/mmm_block.cpp
-#
-# This is an extremely simplified version meant only for tutorials
-#
-import adddeps # fix sys.path
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-import subprocess
-import threading
-import psutil
-
-from measure_confidence2 import dump_promise_confidence_files3
-from measure_confidence2 import getConfidence, getMinAccuracy
-from select_top_results import select_top_results
-from time import sleep
-from pareto_curve import findParetoConfigs
-
-
-layer_file = ""
-output_dir = ""
-flag_ranges = []
-tuning_flags = []
-binary_name = ""
-accuracy_threshold = 10.0
-evaluated_configs = {}
-orig_result_dir = ""
-gpu_layers = 0
-
-test_id = 0
-
-layer_costs = []
-
-
-def readCostFile(file_path):
-
- f = open(file_path)
- for x in f:
- cost = float(x.strip())
- layer_costs.append(cost)
-
- print ("len(layer_costs) = ", layer_costs)
- f.close()
-
-
-
-def getAccuracy(file_name):
-
- file = open(file_name, "r")
- acc_str = file.read()
- file.close()
- accuracy = float(acc_str)
-
- try:
- accuracy = float(acc_str)
- except:
- return 20
-
- print accuracy
- return accuracy
-
-
-
-def createFlagsFile(file_name, cfg):
-
- f = open(file_name, "w+")
- cmd_config = ""
- for flag in tuning_flags:
- flag_value = cfg[flag]
- cmd_config += str(flag_value) + "\n"
-
- f.write(cmd_config)
- f.close()
-
-
-
-def getConfigCost(cfg):
-
- it = 0
- total_cost = 0.0
- for flag in tuning_flags:
- flag_value = cfg[flag]
- if flag_value == 11:
- total_cost += layer_costs[it]
- elif flag_value == 10:
- total_cost += (layer_costs[it] / 1.3)
- elif flag_value == 8 or flag_value == 9:
- total_cost += (layer_costs[it] / 1.6)
- elif flag_value < 8:
- divisor = 5 + (7 - flag_value)
- total_cost += (layer_costs[it] / divisor)
-
- it += 1
-
- return total_cost
-
-
-
-
-class ClangFlagsTuner(MeasurementInterface):
-
- def __init__(self, args):
- objective = ThresholdAccuracyMinimizeTime(accuracy_threshold)
- input_manager = FixedInputManager(size=num_flags)
- self.configs_list = []
-
- super(ClangFlagsTuner, self).__init__(
- args, program_name=args.binary,
- program_version=self.file_hash(args.binary),
- input_manager=input_manager, objective=objective)
-
-
-
-
- def manipulator(self):
- """
- Define the search space by creating a
- ConfigurationManipulator
- """
- manipulator = ConfigurationManipulator()
-
- flags_arr = []
- for i in range (8, error_range):
- flags_arr.append(i)
-
- # NOTE: Skipping first 'gpu_layers' to run on GPU
- for flag in tuning_flags[:gpu_layers]:
- manipulator.add_parameter(
- EnumParameter(flag, flags_arr))
-
- ind = gpu_layers
- for flag in tuning_flags[gpu_layers:]:
- if ind in skip_layers:
- manipulator.add_parameter(
- EnumParameter(flag, flags_arr))
- print ("8 ..... 11")
- else:
- manipulator.add_parameter(
- EnumParameter(flag, flag_ranges
- )) #default is needed, optimizations don't work without it(tried and tested)
- print ("1 .... 11")
- ind += 1
-
-
- return manipulator
-
-
- def run(self, desired_result, input, limit):
-
- """
- Run a given configuration then
- return performance
- """
- global test_id
-
- cfg = desired_result.configuration.data
-
- # NOTE: creates the file with flags read by the runtime
- createFlagsFile("promise_flags", cfg)
-
- run_cmd = binary_name
- print "\nbinary_name = ", run_cmd
- #run_result_call_program = self.call_program(run_cmd)
-
-
- total_runs = 2
- FNULL = open(os.devnull, 'wb')
- #p = subprocess.Popen(run_cmd, stdout = FNULL)
- p = subprocess.Popen([run_cmd, str(total_runs)], stdout = FNULL)
- p.wait()
-
-
- accuracy = getAccuracy("final_accuracy")
-
- # Get Confidence for multiple runs
- conf, avg_acc = getConfidence("run_accuracies.txt", accuracy_threshold)
-
- # getConfigCost returns the cost associated with the selected configuration
- total_comps = getConfigCost(cfg)
-
-
- Result = opentuner.resultsdb.models.Result()
- Result.time = total_comps
- #Result.accuracy = accuracy
- min_accuracy = getMinAccuracy("run_accuracies.txt")
- print ("min_accuracy = ", min_accuracy)
- Result.accuracy = min_accuracy
-
- # Only pass conf if conf == 100
- if min_accuracy > accuracy_threshold and conf == 100:
- print ("conf = ", conf, " avg_acc = ", avg_acc)
- #if accuracy not in evaluated_configs:
- config_tuple = (total_comps, accuracy, cfg)
- self.configs_list.append(config_tuple)
- evaluated_configs[accuracy] = 1
- shutil.copy('promise_flags', output_dir + '/' + binary_name + '_' + str(test_id))
-
- f_acc = open(output_dir + '/' + binary_name + '_' + str(test_id) + "_accuracy", "w")
- f_acc.write(str(accuracy))
- f_acc.close()
-
-
- test_id += 1
-
- return Result
-
-
- def save_final_config(self, configuration):
-
- print "Dumping High Confidence results \n"
- sleep(2)
-
-
- findParetoConfigs(orig_result_dir, layer_costs, accuracy_threshold)
-
- input_dir = orig_result_dir + "/pareto/"
- output_dir = orig_result_dir + "/high_confidence/"
-
- # Only dumping files with 95% confidence
- dump_promise_confidence_files3(binary_name, input_dir, output_dir, layer_file, num_flags, accuracy_threshold, layer_costs, 95)
- #select_top_results(orig_result_dir + "/high_confidence")
-
-
- """
- called at the end of autotuning with the best resultsdb.models.Configuration
- """
- print "Final configuration", configuration.data
-
- return
-
-
-
-error_range = 11
-
-
-if __name__ == '__main__':
-
- argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
- argparser.add_argument('--binary', help='path to target binary')
- argparser.add_argument('--num-flags', type=int, help='num of flags to tune')
- argparser.add_argument('--start-range', type=int, help='start range in tuning')
- argparser.add_argument('--error-range', type=int, help='range of error values used in tuning')
- argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
- argparser.add_argument('--result-dir', help='result directory')
- argparser.add_argument('--layer-file', help='layer description')
- argparser.add_argument('--cost-file', help='layer description')
- argparser.add_argument('--gpu-layers', type=int, help='first N layers to run on GPU')
- argparser.add_argument('--skip-layers', help='layer IDs to run on GPU')
-
-
- args = argparser.parse_args()
- binary_name = str(args.binary)
- print("binary_name = ", binary_name)
- num_flags = int(args.num_flags)
- start_range = int(args.start_range)
- error_range = int(args.error_range)
- accuracy_threshold = float(args.accuracy)
- print("accuracy = ", accuracy_threshold)
- result_dir = args.result_dir
- orig_result_dir = result_dir
- if result_dir == "":
- print("Provide --result-dir ")
-
- gpu_layers = args.gpu_layers
- skip_layers_str = args.skip_layers
-
- skip_layers = []
- layer_ids = skip_layers_str.split("_")
- for layer_id in layer_ids:
- skip_layers.append(int(layer_id))
-
- print ("skip_layers = ", skip_layers)
-
- # NOTE: Reading the cost file (with No of ops) to better guide the Autotuner
- cost_file_path = args.cost_file
- readCostFile(cost_file_path)
-
-
- output_dir = result_dir + "/full_results"
- print output_dir
- if not os.path.exists(result_dir):
- os.mkdir(result_dir)
-
- if not os.path.exists(output_dir):
- print("Creating output directory = ", output_dir)
- os.mkdir(output_dir)
-
- #for j in range(error_range):
- # flag_ranges.append(j)
-
- for j in range(start_range, error_range):
- flag_ranges.append(j)
-
-
- print("flag_ranges = ", flag_ranges)
-
- # File with layer description
- layer_file = args.layer_file
-
- for i in range(num_flags):
- tuning_flags.append("flag" + str(i))
-
- ClangFlagsTuner.main(argparser.parse_args())
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/promise_tuner_piped.py b/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/promise_tuner_piped.py
deleted file mode 100644
index cf84c503b09b6b74474cd4730d93aabd34b5ee2a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/promise_tuner_piped.py
+++ /dev/null
@@ -1,231 +0,0 @@
-#!/usr/bin/env python
-#
-# Optimize blocksize of apps/mmm_block.cpp
-#
-# This is an extremely simplified version meant only for tutorials
-#
-import adddeps # fix sys.path
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-import subprocess
-import threading
-import psutil
-
-from measure_confidence import dump_promise_confidence_files
-from select_top_results import select_top_results
-from time import sleep
-
-
-layer_file = ""
-output_dir = ""
-flag_ranges = []
-tuning_flags = []
-binary_name = ""
-accuracy_threshold = 10.0
-evaluated_configs = {}
-orig_result_dir = ""
-
-
-def getAccuracy(file_name):
-
- file = open(file_name, "r")
- acc_str = file.read()
- file.close()
- accuracy = float(acc_str)
-
- try:
- accuracy = float(acc_str)
- except:
- return 20
-
- print accuracy
- return accuracy
-
-
-
-def kill(proc_pid):
- process = psutil.Process(proc_pid)
- for proc in process.children(recursive=True):
- proc.kill()
- process.kill()
-
-
-
-def createFlagsFile(file_name, cfg):
-
- f = open(file_name, "w+")
- cmd_config = ""
- for flag in tuning_flags:
- flag_value = cfg[flag]
- cmd_config += str(flag_value) + "\n"
-
- f.write(cmd_config)
- f.close()
-
-
-class ClangFlagsTuner(MeasurementInterface):
-
- def __init__(self, args):
- objective = ThresholdAccuracyMinimizeTime(accuracy_threshold)
- input_manager = FixedInputManager(size=num_flags)
- self.configs_list = []
-
- super(ClangFlagsTuner, self).__init__(
- args, program_name=args.binary,
- program_version=self.file_hash(args.binary),
- input_manager=input_manager, objective=objective)
-
-
- FNULL = open(os.devnull, 'wb')
- #run_result_call_program = self.call_program(run_cmd)
- self.start_process = subprocess.Popen([binary_name, "opentuner_run"] , stdout=FNULL);
-
- try:
- os.mkfifo("/tmp/myfifo")
- except OSError, e:
- print("FIFO exists")
-
-
-
- def manipulator(self):
- """
- Define the search space by creating a
- ConfigurationManipulator
- """
- manipulator = ConfigurationManipulator()
- for flag in tuning_flags:
- manipulator.add_parameter(
- EnumParameter(flag, flag_ranges
- # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
- )) #default is needed, optimizations don't work without it(tried and tested)
- return manipulator
-
-
- def run(self, desired_result, input, limit):
-
- """
- Run a given configuration then
- return performance
- """
- cfg = desired_result.configuration.data
-
- # NOTE: creates the file with flags read by the runtime
- createFlagsFile("opentuner_flags", cfg)
-
- run_cmd = binary_name
- print run_cmd
- #run_result_call_program = self.call_program(run_cmd)
-
- # Using Named Pipes to signal execution to the DNN outer thread
- fifo = open("/tmp/myfifo", "w")
- fifo.write("start_run")
- fifo.close()
-
- print "Waiting for process to signal back - when done processing one run"
-
- fifo2 = open("/tmp/myfifo", "r")
- fifo2.read()
- fifo2.close()
-
- print "Process Signalled back"
-
- accuracy = getAccuracy("final_accuracy")
- total_comps = abs(accuracy_threshold - accuracy)
-
-
- Result = opentuner.resultsdb.models.Result()
- Result.time = total_comps
- Result.accuracy = accuracy
-
- if accuracy > accuracy_threshold:
- #if accuracy not in evaluated_configs:
- config_tuple = (total_comps, accuracy, cfg)
- self.configs_list.append(config_tuple)
- evaluated_configs[accuracy] = 1
- shutil.copy('opentuner_flags', output_dir + '/' + binary_name + '_' + str(accuracy))
-
-
- print "done with one run"
-
- return Result
-
-
- def save_final_config(self, configuration):
-
- print "Dumping High Confidence results \n"
- sleep(20)
-
- # Only dumping files with 95% confidence
- dump_promise_confidence_files(binary_name, orig_result_dir, layer_file, num_flags, accuracy_threshold, 95)
- #select_top_results(orig_result_dir + "/high_confidence")
-
-
- #self.start_process.kill()
- kill(self.start_process.pid)
-
- """
- called at the end of autotuning with the best resultsdb.models.Configuration
- """
- print "Final configuration", configuration.data
-
- return
-
-
-
-
-
-if __name__ == '__main__':
-
- argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
- argparser.add_argument('--binary', help='path to target binary')
- argparser.add_argument('--num-flags', type=int, help='num of flags to tune')
- argparser.add_argument('--error-range', type=int, help='range of error values used in tuning')
- argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
- argparser.add_argument('--result-dir', help='result directory')
- argparser.add_argument('--layer-file', help='layer description')
-
-
- args = argparser.parse_args()
- binary_name = str(args.binary)
- print("binary_name = ", binary_name)
- num_flags = int(args.num_flags)
- error_range = int(args.error_range)
- accuracy_threshold = float(args.accuracy)
- print("accuracy = ", accuracy_threshold)
- result_dir = args.result_dir
- orig_result_dir = result_dir
- if result_dir == "":
- print("Provide --result-dir ")
-
-
- output_dir = result_dir + "/full_results"
- print output_dir
- if not os.path.exists(result_dir):
- os.mkdir(result_dir)
-
- if not os.path.exists(output_dir):
- print("Creating output directory = ", output_dir)
- os.mkdir(output_dir)
-
- for j in range(error_range):
- flag_ranges.append(j)
-
- print("flag_ranges = ", flag_ranges)
-
- # File with layer description
- layer_file = args.layer_file
-
- for i in range(num_flags):
- tuning_flags.append("flag" + str(i))
-
- ClangFlagsTuner.main(argparser.parse_args())
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/psnr_tuner.py b/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/psnr_tuner.py
deleted file mode 100644
index 7d9f601819f985877b6acdd4f1a5ff71f6b74113..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/psnr_tuner.py
+++ /dev/null
@@ -1,323 +0,0 @@
-#!/usr/bin/env python
-#
-# Algorithmic Approximation Tuning
-# Purpose: Tunes for Perforation, Sampling, Numerical Precision (FP16)
-
-
-import adddeps
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-import subprocess
-import threading
-import psutil
-
-from measure_confidence2 import dump_promise_confidence_files3
-from measure_confidence2 import getConfidence, getMinAccuracy
-from select_top_results import select_top_results
-from time import sleep
-from pareto_curve import findParetoConfigs
-
-
-
-
-class TunerData:
- def __init__(self):
- self.binary_path = ""
- self.output_dir = ""
- self.num_layers = 0
- self.knobs_list = []
- self.knobs_speedup = {}
- self.accuracy_threshold = 0
- self.test_id = 0
- self.layer_costs = []
- self.tuning_flags = []
- self.autotuner_runs = 0
-
-
-
-
-tunerData = TunerData()
-
-
-
-
-def readCostFile(file_path):
-
- layer_costs = []
- f = open(file_path)
- for x in f:
- cost = float(x.strip())
- layer_costs.append(cost)
-
- print ("len(layer_costs) = ", layer_costs)
- f.close()
-
- return layer_costs
-
-
-
-def getPSNR_violation(file_name):
-
- file = open(file_name, "r")
- acc_str = file.read()
- file.close()
- accuracy = float(acc_str)
-
- try:
- accuracy = float(acc_str)
- except:
- return 20
-
- print (accuracy)
- return accuracy
-
-
-
-def createFlagsFile(file_name, cfg):
-
- f = open(file_name, "w+")
- cmd_config = ""
- for i in range(tunerData.num_layers): # flag in tunerData.tuning_flags:
- flag = tunerData.tuning_flags[i]
- flag_value = cfg[flag]
- cmd_config += str(flag_value) + "\n"
-
- f.write(cmd_config)
- f.close()
-
-
-
-def readLayerKnobs(file_path):
-
- f = open(file_path, "r")
- knobs_list = []
- for x in f:
- knobs = []
- vals = x.split(",")
- for val in vals:
- knobs.append(int(val))
-
- knobs_list.append(knobs)
-
- print ("knobs_list = ", knobs_list)
-
- return knobs_list
-
-
-
-def readKnobConfig(file_path):
-
- knobs_speedup = {}
- f = open(file_path, "r")
- for x in f:
- toks = x.split("\t")
- ID = int(toks[0].split(",")[1])
-
- speedup = float(toks[2])
- knobs_speedup[ID] = speedup
-
- print ("knobs_speedup = ", knobs_speedup)
-
- return knobs_speedup
-
-
-
-
-def getConfigCost(cfg):
-
- orig_cost = 0.0
- total_cost = 0.0
- for it in range(tunerData.num_layers):
- flag = tunerData.tuning_flags[it]
- flag_value = cfg[flag]
- op_cost = tunerData.layer_costs[it]
- speedup = tunerData.knobs_speedup[flag_value]
-
- total_cost += (op_cost * 1.0 / speedup * 1.0)
- orig_cost += op_cost
-
- it += 1
-
- speedup = (orig_cost * 1.0) / (total_cost * 1.0)
-
- return total_cost, speedup
-
-
-
-def appendTopLine(f_path, accuracy, total_runs, total_comps, speedup):
-
- f_str = open(f_path, "r").read()
-
- f_out = open(f_path, "w+")
- f_out.write("avg_accuracy=" + str(accuracy) + "\tconfig_cost=" + str(total_comps) + "\tspeedup=" + str(speedup) + "\n" )
- f_out.write(f_str)
-
- f_out.close()
-
-
-
-def dumpAccuracyFile(accuracy):
-
- f_acc = open(tunerData.output_dir + '/' + tunerData.binary_path + '_' + str(tunerData.test_id) + "_accuracy", "w")
- f_acc.write(str(accuracy))
- f_acc.close()
-
-
-
-class ClangFlagsTuner(MeasurementInterface):
-
- def __init__(self, args):
- objective = ThresholdAccuracyMinimizeTime(tunerData.accuracy_threshold)
- input_manager = FixedInputManager(size=tunerData.num_layers)
- self.configs_list = []
-
- super(ClangFlagsTuner, self).__init__(
- args, program_name=args.binary,
- program_version=self.file_hash(args.binary),
- input_manager=input_manager, objective=objective)
-
-
-
-
- def manipulator(self):
- """
- Define the search space by creating a
- ConfigurationManipulator
- """
- manipulator = ConfigurationManipulator()
-
- for i in range(tunerData.num_layers):
- tunerData.tuning_flags.append("flag" + str(i))
-
-
- #for flag in tunerData.tuning_flags:
- for ind in range(tunerData.num_layers):
- flag = tunerData.tuning_flags[ind]
-
- manipulator.add_parameter(EnumParameter(flag, tunerData.knobs_list[ind]))
- print ("ind = ", ind, " len = ", len(tunerData.knobs_list))
- print (tunerData.knobs_list[ind])
-
- ind += 1
-
- return manipulator
-
-
-
- def run(self, desired_result, input, limit):
-
- """
- Run a given configuration then
- return performance
- """
- global test_id
-
- cfg = desired_result.configuration.data
-
- # NOTE: creates the file with flags read by the runtime
- createFlagsFile("promise_flags", cfg)
-
- run_cmd = tunerData.binary_path
- print "\nbinary_path = ", run_cmd
-
- input_size = 5000
- offset = 5000
-
- total_runs = 1 # NOTE: Single run sufficient in Algorithmic Approx Tuner
- FNULL = open(os.devnull, 'wb')
- p = subprocess.Popen([run_cmd, str(total_runs), str(tunerData.accuracy_threshold), str(1), str(input_size), str(offset) ], stdout = FNULL)
- p.wait()
-
-
- PSNR_violation = getPSNR_violation("final_accuracy")
-
- # getConfigCost returns the cost associated with the selected configuration
- total_comps, speedup = getConfigCost(cfg)
-
-
- Result = opentuner.resultsdb.models.Result()
- Result.time = total_comps
- #Result.accuracy = accuracy
-
- # NOTE: Fixing Violation Rate at 5% threshold
- if PSNR_violation < 5:
- config_tuple = (total_comps, PSNR_violation, cfg)
- self.configs_list.append(config_tuple)
- f_path = tunerData.output_dir + '/' + tunerData.binary_path + '_' + str(tunerData.test_id)
- shutil.copy('promise_flags', f_path)
-
- appendTopLine(f_path, PSNR_violation, total_runs, total_comps, speedup)
-
- # dumpAccuracyFile(accuracy)
-
-
- tunerData.test_id += 1
-
- return Result
-
-
- def save_final_config(self, configuration):
-
- print "Done with Autotuning Run \n"
- sleep(2)
-
- print "Final configuration", configuration.data
-
- return
-
-
-
-
-if __name__ == '__main__':
-
- argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
- argparser.add_argument('--binary', help='path to target binary')
- argparser.add_argument('--num-layers', type=int, help='num of flags to tune')
- argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
- argparser.add_argument('--result-dir', help='result directory')
- argparser.add_argument('--cost-file', help='layer description')
- argparser.add_argument('--knobs-config', help='knob settings and ID mapping')
- argparser.add_argument('--layer-knobs', help='per-layer Knobs')
-
-
- args = argparser.parse_args()
-
- tunerData.binary_path = str(args.binary)
- tunerData.num_layers = int(args.num_layers)
- tunerData.accuracy_threshold = float(args.accuracy)
-
- # NOTE: Reading the cost file (with No of ops) to better guide the Autotuner
- cost_file_path = args.cost_file
- tunerData.layer_costs = readCostFile(cost_file_path)
-
- tunerData.knobs_list = readLayerKnobs(args.layer_knobs)
- tunerData.knobs_speedup = readKnobConfig(args.knobs_config)
-
-
- result_dir = args.result_dir
- if result_dir == "":
- print("Provide --result-dir ")
-
- tunerData.output_dir = result_dir + "/high_confidence/"
- if not os.path.exists(result_dir):
- os.mkdir(result_dir)
-
- if not os.path.exists(tunerData.output_dir):
- print("Creating output directory = ", tunerData.output_dir)
- os.mkdir(tunerData.output_dir)
-
-
-
- ClangFlagsTuner.main(argparser.parse_args())
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/select_top_results.py b/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/select_top_results.py
deleted file mode 100644
index 7ee878e5f8f84f3f56ea982c1f933b2c1a5b914b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/select_top_results.py
+++ /dev/null
@@ -1,101 +0,0 @@
-
-
-import argparse
-import sys
-import os
-
-
-log_index = 9
-linear_index = 10
-quad_index = 11
-
-top_k = 10
-skip_lines = 1
-
-
-def dump_results(sorted_list, k, result_dir, sub_dir):
-
- ref_dir = result_dir + "/" + sub_dir
- if not os.path.exists(ref_dir):
- os.mkdir(ref_dir)
-
- for i in range(min(k, len(sorted_list)) ):
- file_name = sorted_list[i][1]
- file_name = ref_dir + "/" + file_name + "_rank_" + str(i)
- f = open(file_name, "w+")
- f.write(str(sorted_list[i][2]) + "\t")
- f.write(str(sorted_list[i][3]) + "\t")
- f.write(str(sorted_list[i][4]) + "\n")
- f.write(sorted_list[i][0])
- f.close()
-
-
-
-
-def select_top_results(result_dir):
-
- if not os.path.exists(result_dir):
- print("Path does not exist")
- sys.exit(0)
-
- file_names = os.listdir(result_dir)
- print file_names
-
- results_arr = []
-
- for file_name in file_names:
-
- if file_name == "confidence_summary.txt":
- continue
-
- # Skip sub-directories
- if os.path.isdir(result_dir + "/" + file_name):
- continue
-
- log_result = 0.0
- linear_result = 0.0
- quad_result = 0.0
- file_str = ""
-
- index = 0
- f = open(result_dir + "/" + file_name)
- for x in f:
- if index >= skip_lines:
- words = x.split()
- log_result += float(words[log_index])
- linear_result += float(words[linear_index])
- quad_result += float(words[quad_index])
- file_str += x
-
- index += 1
-
-
- file_result = (file_str, file_name, log_result, linear_result, quad_result)
- results_arr.append(file_result)
-
-
- sorted_list = sorted(results_arr, key = lambda tup: tup[2])
- dump_results(sorted_list, top_k, result_dir, "log")
-
- sorted_list = sorted(results_arr, key = lambda tup: tup[3])
- dump_results(sorted_list, top_k, result_dir, "linear")
-
- sorted_list = sorted(results_arr, key = lambda tup: tup[4])
- dump_results(sorted_list, top_k, result_dir, "quad")
-
-
-#def select_top_configuration(result_dir):
-
-
-if __name__ == "__main__":
-
- argparser = argparse.ArgumentParser(description='runs best configs to get high confidence on accuracy')
- argparser.add_argument('--result-dir', help='Directory containing OpenTuner configurations')
-
- args = argparser.parse_args()
- result_dir = args.result_dir
-
- select_top_results(result_dir)
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/data/alexnet/knobs.txt b/llvm/projects/hpvm-tensor-rt/opentuner/data/alexnet/knobs.txt
deleted file mode 100644
index 050fc6118045090b4a5cc442105181f56d693a77..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/data/alexnet/knobs.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/data/alexnet/op_cost.txt b/llvm/projects/hpvm-tensor-rt/opentuner/data/alexnet/op_cost.txt
deleted file mode 100644
index 04336fca2708d5e5d78849e1c12014f5ddbd1ad7..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/data/alexnet/op_cost.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-11894784.000000
-39321600.000000
-21233664.000000
-28311552.000000
-18874368.000000
-20480.000000
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/data/alexnet2/knobs.txt b/llvm/projects/hpvm-tensor-rt/opentuner/data/alexnet2/knobs.txt
deleted file mode 100644
index c873eeddcdeaa44fe0365bdb5e3292997d0074b6..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/data/alexnet2/knobs.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/data/alexnet2/op_cost.txt b/llvm/projects/hpvm-tensor-rt/opentuner/data/alexnet2/op_cost.txt
deleted file mode 100644
index 5a5722f202dde469dca94c71dd9c5fc1cd7aa32b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/data/alexnet2/op_cost.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-88473.601562
-943718.375000
-471859.187500
-943718.375000
-471859.187500
-943718.375000
-2048.000000
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/data/global_knobs.txt b/llvm/projects/hpvm-tensor-rt/opentuner/data/global_knobs.txt
deleted file mode 100644
index 54a09e08530f13f5435cdb6d4cd48a2b67ef2b38..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/data/global_knobs.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-fp32,11 -1 1.0 tensorConvolution tensorConvolution
-fp16,12 -1 1.5 tensorConvolution tensorHalfConvolution
-perf,21 1,2,0 2.25 tensorConvolution tensorConvPerfCuda
-perf,22 1,2,1 2.25 tensorConvolution tensorConvPerfCuda
-perf,23 1,3,0 1.88 tensorConvolution tensorConvPerfCuda
-perf,24 1,3,1 1.88 tensorConvolution tensorConvPerfCuda
-perf,25 1,3,2 1.88 tensorConvolution tensorConvPerfCuda
-perf,26 2,1,0 2.25 tensorConvolution tensorConvPerfCuda
-perf,27 2,1,1 2.25 tensorConvolution tensorConvPerfCuda
-perf,28 3,1,0 1.88 tensorConvolution tensorConvPerfCuda
-perf,29 3,1,1 1.88 tensorConvolution tensorConvPerfCuda
-perf,30 3,1,2 1.88 tensorConvolution tensorConvPerfCuda
-samp,31 2,0 2.25 tensorConvolution tensorConvSampSim
-samp,32 2,1 2.25 tensorConvolution tensorConvSampSim
-samp,33 4,0 1.8 tensorConvolution tensorConvSampSim
-samp,34 4,1 1.8 tensorConvolution tensorConvSampSim
-samp,35 4,2 1.8 tensorConvolution tensorConvSampSim
-samp,36 4,3 1.8 tensorConvolution tensorConvSampSim
-reduction_samp,41 1 1.5
-reduction_samp,42 1 2.25
-reduction_samp,43 1 1.4
-reduction_samp,44 1 2
-reduction_samp,45 1 1.25
-reduction_samp,46 1 1.8
-swing_level,1 1 12
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-swing_level,7 1 4
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/data/global_knobs_dnn.txt b/llvm/projects/hpvm-tensor-rt/opentuner/data/global_knobs_dnn.txt
deleted file mode 100644
index 2180997527410cfdbf577a116fd39a592e2af05b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/data/global_knobs_dnn.txt
+++ /dev/null
@@ -1,25 +0,0 @@
-fp32,11 -1 1.0 tensorConvolution tensorConvolution
-fp16,12 -1 1.5 tensorConvolution tensorHalfConvolution
-perf,21 1,2,0 2.25 tensorConvolution tensorConvPerfCuda
-perf,22 1,2,1 2.25 tensorConvolution tensorConvPerfCuda
-perf,23 1,3,0 1.88 tensorConvolution tensorConvPerfCuda
-perf,24 1,3,1 1.88 tensorConvolution tensorConvPerfCuda
-perf,25 1,3,2 1.88 tensorConvolution tensorConvPerfCuda
-perf,26 2,1,0 2.25 tensorConvolution tensorConvPerfCuda
-perf,27 2,1,1 2.25 tensorConvolution tensorConvPerfCuda
-perf,28 3,1,0 1.88 tensorConvolution tensorConvPerfCuda
-perf,29 3,1,1 1.88 tensorConvolution tensorConvPerfCuda
-perf,30 3,1,2 1.88 tensorConvolution tensorConvPerfCuda
-samp,31 2,0 2.25 tensorConvolution tensorConvSampSim
-samp,32 2,1 2.25 tensorConvolution tensorConvSampSim
-samp,33 4,0 1.8 tensorConvolution tensorConvSampSim
-samp,34 4,1 1.8 tensorConvolution tensorConvSampSim
-samp,35 4,2 1.8 tensorConvolution tensorConvSampSim
-samp,36 4,3 1.8 tensorConvolution tensorConvSampSim
-swing_level,1 1 12
-swing_level,2 1 10
-swing_level,3 1 9
-swing_level,4 1 8
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-swing_level,6 1 5
-swing_level,7 1 4
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/data/lenet/knobs.txt b/llvm/projects/hpvm-tensor-rt/opentuner/data/lenet/knobs.txt
deleted file mode 100644
index 8973c89f7a89f9c62c12f8371d16eebad7264b31..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/data/lenet/knobs.txt
+++ /dev/null
@@ -1,4 +0,0 @@
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-12
-12
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/data/lenet/op_cost.txt b/llvm/projects/hpvm-tensor-rt/opentuner/data/lenet/op_cost.txt
deleted file mode 100644
index 74b1b668e2f27f3ddb77dcac7fff9890c70a6f02..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/data/lenet/op_cost.txt
+++ /dev/null
@@ -1,4 +0,0 @@
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diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/data/mobilenet/knobs.txt b/llvm/projects/hpvm-tensor-rt/opentuner/data/mobilenet/knobs.txt
deleted file mode 100644
index 900ad3944d5203d4552a75140358388c99bea181..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/data/mobilenet/knobs.txt
+++ /dev/null
@@ -1,15 +0,0 @@
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diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/data/mobilenet/op_cost.txt b/llvm/projects/hpvm-tensor-rt/opentuner/data/mobilenet/op_cost.txt
deleted file mode 100644
index 673e704b7e37e19c090e98799189a4411bad9f7c..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/data/mobilenet/op_cost.txt
+++ /dev/null
@@ -1,28 +0,0 @@
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diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/data/mobilenet_shallow/knobs.txt b/llvm/projects/hpvm-tensor-rt/opentuner/data/mobilenet_shallow/knobs.txt
deleted file mode 100644
index c7273f3fc6e487ada58eaed7bc036f707c3ce541..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/data/mobilenet_shallow/knobs.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
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-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/data/mobilenet_shallow/op_cost.txt b/llvm/projects/hpvm-tensor-rt/opentuner/data/mobilenet_shallow/op_cost.txt
deleted file mode 100644
index 7266441905a08c1ef1796dec8ee6c05660998378..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/data/mobilenet_shallow/op_cost.txt
+++ /dev/null
@@ -1,8 +0,0 @@
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diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/data/resnet/knobs.txt b/llvm/projects/hpvm-tensor-rt/opentuner/data/resnet/knobs.txt
deleted file mode 100644
index eadcb5ebff73feb75b9f7533f7703252ab895afc..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/data/resnet/knobs.txt
+++ /dev/null
@@ -1,22 +0,0 @@
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diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/data/resnet/op_cost.txt b/llvm/projects/hpvm-tensor-rt/opentuner/data/resnet/op_cost.txt
deleted file mode 100644
index fdba070cfc5eac559c8384306993fb52a1eb2e04..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/data/resnet/op_cost.txt
+++ /dev/null
@@ -1,22 +0,0 @@
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diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/data/vgg16_cifar10/knobs.txt b/llvm/projects/hpvm-tensor-rt/opentuner/data/vgg16_cifar10/knobs.txt
deleted file mode 100644
index d238fa1036729f79cc66bdaa14667dcf16c60a9a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/data/vgg16_cifar10/knobs.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
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diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/data/vgg16_cifar10/op_cost.txt b/llvm/projects/hpvm-tensor-rt/opentuner/data/vgg16_cifar10/op_cost.txt
deleted file mode 100644
index 5f58ebcc043915d28cf874a1f67e5b2637db1dfc..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/data/vgg16_cifar10/op_cost.txt
+++ /dev/null
@@ -1,15 +0,0 @@
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diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/data/vgg16_cifar100/knobs.txt b/llvm/projects/hpvm-tensor-rt/opentuner/data/vgg16_cifar100/knobs.txt
deleted file mode 100644
index d238fa1036729f79cc66bdaa14667dcf16c60a9a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/data/vgg16_cifar100/knobs.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
-12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36
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diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/data/vgg16_cifar100/op_cost.txt b/llvm/projects/hpvm-tensor-rt/opentuner/data/vgg16_cifar100/op_cost.txt
deleted file mode 100644
index 8c6daad2e2902e3ac821d99ebbe12e21b6428cc7..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/data/vgg16_cifar100/op_cost.txt
+++ /dev/null
@@ -1,15 +0,0 @@
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diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/debian-packages-deps b/llvm/projects/hpvm-tensor-rt/opentuner/debian-packages-deps
deleted file mode 100644
index ea49289a875cfe80df1de02307e03f7791c00adf..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/debian-packages-deps
+++ /dev/null
@@ -1,9 +0,0 @@
-build-essential
-git
-gnuplot
-libfreetype6-dev
-libpng-dev
-libsqlite3-dev
-python-dev
-python-pip
-sqlite3
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/docs/Makefile b/llvm/projects/hpvm-tensor-rt/opentuner/docs/Makefile
deleted file mode 100644
index 1c028b3a91e5750dc927f6a865923ca2e9ac141a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/docs/Makefile
+++ /dev/null
@@ -1,177 +0,0 @@
-# Makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line.
-SPHINXOPTS =
-SPHINXBUILD = sphinx-build
-PAPER =
-BUILDDIR = build
-
-# User-friendly check for sphinx-build
-ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)
-$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/)
-endif
-
-# Internal variables.
-PAPEROPT_a4 = -D latex_paper_size=a4
-PAPEROPT_letter = -D latex_paper_size=letter
-ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source
-# the i18n builder cannot share the environment and doctrees with the others
-I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source
-
-.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext
-
-help:
- @echo "Please use \`make <target>' where <target> is one of"
- @echo " html to make standalone HTML files"
- @echo " dirhtml to make HTML files named index.html in directories"
- @echo " singlehtml to make a single large HTML file"
- @echo " pickle to make pickle files"
- @echo " json to make JSON files"
- @echo " htmlhelp to make HTML files and a HTML help project"
- @echo " qthelp to make HTML files and a qthelp project"
- @echo " devhelp to make HTML files and a Devhelp project"
- @echo " epub to make an epub"
- @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
- @echo " latexpdf to make LaTeX files and run them through pdflatex"
- @echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx"
- @echo " text to make text files"
- @echo " man to make manual pages"
- @echo " texinfo to make Texinfo files"
- @echo " info to make Texinfo files and run them through makeinfo"
- @echo " gettext to make PO message catalogs"
- @echo " changes to make an overview of all changed/added/deprecated items"
- @echo " xml to make Docutils-native XML files"
- @echo " pseudoxml to make pseudoxml-XML files for display purposes"
- @echo " linkcheck to check all external links for integrity"
- @echo " doctest to run all doctests embedded in the documentation (if enabled)"
-
-clean:
- rm -rf $(BUILDDIR)/*
-
-html:
- $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
- @echo
- @echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
-
-dirhtml:
- $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
- @echo
- @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
-
-singlehtml:
- $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
- @echo
- @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
-
-pickle:
- $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
- @echo
- @echo "Build finished; now you can process the pickle files."
-
-json:
- $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
- @echo
- @echo "Build finished; now you can process the JSON files."
-
-htmlhelp:
- $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
- @echo
- @echo "Build finished; now you can run HTML Help Workshop with the" \
- ".hhp project file in $(BUILDDIR)/htmlhelp."
-
-qthelp:
- $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
- @echo
- @echo "Build finished; now you can run "qcollectiongenerator" with the" \
- ".qhcp project file in $(BUILDDIR)/qthelp, like this:"
- @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/OpenTuner.qhcp"
- @echo "To view the help file:"
- @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/OpenTuner.qhc"
-
-devhelp:
- $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
- @echo
- @echo "Build finished."
- @echo "To view the help file:"
- @echo "# mkdir -p $$HOME/.local/share/devhelp/OpenTuner"
- @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/OpenTuner"
- @echo "# devhelp"
-
-epub:
- $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
- @echo
- @echo "Build finished. The epub file is in $(BUILDDIR)/epub."
-
-latex:
- $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
- @echo
- @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
- @echo "Run \`make' in that directory to run these through (pdf)latex" \
- "(use \`make latexpdf' here to do that automatically)."
-
-latexpdf:
- $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
- @echo "Running LaTeX files through pdflatex..."
- $(MAKE) -C $(BUILDDIR)/latex all-pdf
- @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
-
-latexpdfja:
- $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
- @echo "Running LaTeX files through platex and dvipdfmx..."
- $(MAKE) -C $(BUILDDIR)/latex all-pdf-ja
- @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
-
-text:
- $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
- @echo
- @echo "Build finished. The text files are in $(BUILDDIR)/text."
-
-man:
- $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
- @echo
- @echo "Build finished. The manual pages are in $(BUILDDIR)/man."
-
-texinfo:
- $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
- @echo
- @echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo."
- @echo "Run \`make' in that directory to run these through makeinfo" \
- "(use \`make info' here to do that automatically)."
-
-info:
- $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
- @echo "Running Texinfo files through makeinfo..."
- make -C $(BUILDDIR)/texinfo info
- @echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo."
-
-gettext:
- $(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale
- @echo
- @echo "Build finished. The message catalogs are in $(BUILDDIR)/locale."
-
-changes:
- $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes
- @echo
- @echo "The overview file is in $(BUILDDIR)/changes."
-
-linkcheck:
- $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
- @echo
- @echo "Link check complete; look for any errors in the above output " \
- "or in $(BUILDDIR)/linkcheck/output.txt."
-
-doctest:
- $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
- @echo "Testing of doctests in the sources finished, look at the " \
- "results in $(BUILDDIR)/doctest/output.txt."
-
-xml:
- $(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml
- @echo
- @echo "Build finished. The XML files are in $(BUILDDIR)/xml."
-
-pseudoxml:
- $(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml
- @echo
- @echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml."
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/docs/rtd-requirements.txt b/llvm/projects/hpvm-tensor-rt/opentuner/docs/rtd-requirements.txt
deleted file mode 100644
index e30d149ed5e3356ff54d915b556dfaed0dfb6148..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/docs/rtd-requirements.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-argparse>=1.2.1
-django==1.6.1
-fn>=0.2.12
-SQLAlchemy>=0.8.2
-virtualenv==1.9.1
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/docs/source/conf.py b/llvm/projects/hpvm-tensor-rt/opentuner/docs/source/conf.py
deleted file mode 100644
index a27fabf403e0e7f6081d906819167e94eb236b61..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/docs/source/conf.py
+++ /dev/null
@@ -1,261 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# OpenTuner documentation build configuration file, created by
-# sphinx-quickstart on Sat Jan 3 04:13:12 2015.
-#
-# This file is execfile()d with the current directory set to its
-# containing dir.
-#
-# Note that not all possible configuration values are present in this
-# autogenerated file.
-#
-# All configuration values have a default; values that are commented out
-# serve to show the default.
-
-import sys
-import os
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-sys.path.insert(0, os.path.abspath('../..'))
-
-# -- General configuration ------------------------------------------------
-
-# If your documentation needs a minimal Sphinx version, state it here.
-#needs_sphinx = '1.0'
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = [
- 'sphinx.ext.autodoc',
- 'sphinx.ext.pngmath',
-]
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
-
-# The suffix of source filenames.
-source_suffix = '.rst'
-
-# The encoding of source files.
-#source_encoding = 'utf-8-sig'
-
-# The master toctree document.
-master_doc = 'index'
-
-# General information about the project.
-project = u'OpenTuner'
-copyright = u'2015, Jason Ansel'
-
-# The version info for the project you're documenting, acts as replacement for
-# |version| and |release|, also used in various other places throughout the
-# built documents.
-#
-# The short X.Y version.
-version = '0.0'
-# The full version, including alpha/beta/rc tags.
-release = '0.0'
-
-# The language for content autogenerated by Sphinx. Refer to documentation
-# for a list of supported languages.
-#language = None
-
-# There are two options for replacing |today|: either, you set today to some
-# non-false value, then it is used:
-#today = ''
-# Else, today_fmt is used as the format for a strftime call.
-#today_fmt = '%B %d, %Y'
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-exclude_patterns = []
-
-# The reST default role (used for this markup: `text`) to use for all
-# documents.
-#default_role = None
-
-# If true, '()' will be appended to :func: etc. cross-reference text.
-#add_function_parentheses = True
-
-# If true, the current module name will be prepended to all description
-# unit titles (such as .. function::).
-#add_module_names = True
-
-# If true, sectionauthor and moduleauthor directives will be shown in the
-# output. They are ignored by default.
-#show_authors = False
-
-# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = 'sphinx'
-
-# A list of ignored prefixes for module index sorting.
-#modindex_common_prefix = []
-
-# If true, keep warnings as "system message" paragraphs in the built documents.
-#keep_warnings = False
-
-
-# -- Options for HTML output ----------------------------------------------
-
-# The theme to use for HTML and HTML Help pages. See the documentation for
-# a list of builtin themes.
-html_theme = 'default'
-
-# Theme options are theme-specific and customize the look and feel of a theme
-# further. For a list of options available for each theme, see the
-# documentation.
-#html_theme_options = {}
-
-# Add any paths that contain custom themes here, relative to this directory.
-#html_theme_path = []
-
-# The name for this set of Sphinx documents. If None, it defaults to
-# "<project> v<release> documentation".
-#html_title = None
-
-# A shorter title for the navigation bar. Default is the same as html_title.
-#html_short_title = None
-
-# The name of an image file (relative to this directory) to place at the top
-# of the sidebar.
-#html_logo = None
-
-# The name of an image file (within the static path) to use as favicon of the
-# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
-# pixels large.
-#html_favicon = None
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
-
-# Add any extra paths that contain custom files (such as robots.txt or
-# .htaccess) here, relative to this directory. These files are copied
-# directly to the root of the documentation.
-#html_extra_path = []
-
-# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
-# using the given strftime format.
-#html_last_updated_fmt = '%b %d, %Y'
-
-# If true, SmartyPants will be used to convert quotes and dashes to
-# typographically correct entities.
-#html_use_smartypants = True
-
-# Custom sidebar templates, maps document names to template names.
-#html_sidebars = {}
-
-# Additional templates that should be rendered to pages, maps page names to
-# template names.
-#html_additional_pages = {}
-
-# If false, no module index is generated.
-#html_domain_indices = True
-
-# If false, no index is generated.
-#html_use_index = True
-
-# If true, the index is split into individual pages for each letter.
-#html_split_index = False
-
-# If true, links to the reST sources are added to the pages.
-#html_show_sourcelink = True
-
-# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
-#html_show_sphinx = True
-
-# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
-#html_show_copyright = True
-
-# If true, an OpenSearch description file will be output, and all pages will
-# contain a <link> tag referring to it. The value of this option must be the
-# base URL from which the finished HTML is served.
-#html_use_opensearch = ''
-
-# This is the file name suffix for HTML files (e.g. ".xhtml").
-#html_file_suffix = None
-
-# Output file base name for HTML help builder.
-htmlhelp_basename = 'OpenTunerdoc'
-
-
-# -- Options for LaTeX output ---------------------------------------------
-
-latex_elements = {
-# The paper size ('letterpaper' or 'a4paper').
-#'papersize': 'letterpaper',
-
-# The font size ('10pt', '11pt' or '12pt').
-#'pointsize': '10pt',
-
-# Additional stuff for the LaTeX preamble.
-#'preamble': '',
-}
-
-# Grouping the document tree into LaTeX files. List of tuples
-# (source start file, target name, title,
-# author, documentclass [howto, manual, or own class]).
-latex_documents = [
- ('index', 'OpenTuner.tex', u'OpenTuner Documentation',
- u'Jason Ansel', 'manual'),
-]
-
-# The name of an image file (relative to this directory) to place at the top of
-# the title page.
-#latex_logo = None
-
-# For "manual" documents, if this is true, then toplevel headings are parts,
-# not chapters.
-#latex_use_parts = False
-
-# If true, show page references after internal links.
-#latex_show_pagerefs = False
-
-# If true, show URL addresses after external links.
-#latex_show_urls = False
-
-# Documents to append as an appendix to all manuals.
-#latex_appendices = []
-
-# If false, no module index is generated.
-#latex_domain_indices = True
-
-
-# -- Options for manual page output ---------------------------------------
-
-# One entry per manual page. List of tuples
-# (source start file, name, description, authors, manual section).
-man_pages = [
- ('index', 'opentuner', u'OpenTuner Documentation',
- [u'Jason Ansel'], 1)
-]
-
-# If true, show URL addresses after external links.
-#man_show_urls = False
-
-
-# -- Options for Texinfo output -------------------------------------------
-
-# Grouping the document tree into Texinfo files. List of tuples
-# (source start file, target name, title, author,
-# dir menu entry, description, category)
-texinfo_documents = [
- ('index', 'OpenTuner', u'OpenTuner Documentation',
- u'Jason Ansel', 'OpenTuner', 'One line description of project.',
- 'Miscellaneous'),
-]
-
-# Documents to append as an appendix to all manuals.
-#texinfo_appendices = []
-
-# If false, no module index is generated.
-#texinfo_domain_indices = True
-
-# How to display URL addresses: 'footnote', 'no', or 'inline'.
-#texinfo_show_urls = 'footnote'
-
-# If true, do not generate a @detailmenu in the "Top" node's menu.
-#texinfo_no_detailmenu = False
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/docs/source/index.rst b/llvm/projects/hpvm-tensor-rt/opentuner/docs/source/index.rst
deleted file mode 100644
index 48f7468982f559d60ef98736539567fa0a320ec3..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/docs/source/index.rst
+++ /dev/null
@@ -1,27 +0,0 @@
-.. OpenTuner documentation master file, created by
- sphinx-quickstart on Sat Jan 3 04:13:12 2015.
- You can adapt this file completely to your liking, but it should at least
- contain the root `toctree` directive.
-
-Welcome to OpenTuner's documentation!
-=====================================
-This is still under construction
-
-
-Contents:
-
-.. toctree::
- :maxdepth: 2
-
- params
- techniques
-
-
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/docs/source/params.rst b/llvm/projects/hpvm-tensor-rt/opentuner/docs/source/params.rst
deleted file mode 100644
index b8d08cd300d466c5be43dabfa0e4db1abd12182b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/docs/source/params.rst
+++ /dev/null
@@ -1,339 +0,0 @@
-.. currentmodule:: opentuner.search.manipulator
-
-****************
-Parameters
-****************
-
-This will be an overview of parameters in OpenTuner.
-
-Each Parameter instance is created with a name. Most methods in parameters operate on configurations, dict-like objects spawned by the ConfigurationManipulator. Configurations contain values corresponding to a collection of instances of named parameters.
-
-A Parameter’s methods may mutate the value in a configuration corresponding to the name of the particular parameter instance. These methods are called operators.
-
-==============================
-Parameter Types and Operators
-==============================
-
-Each parameter has a set of operators. These operators take in a set of parent configurations and mutate the corresponding parameter value in the first configuration according to the parent values. Operators form the set of available transformations for search techniques to generate new configurations to test.
-
-Operator methods can be identified by the prefix 'op#_', where # is the number of required input configurations. The prefix 'opn\_' specifies an arbitrary number of input configurations, as a list. The first argument into an operator is always the configuration that will be mutated. This is followed by the required parent configurations, then any required arguments, and finally optional arguments.
-
-Any operators defined for a Parameter are inherited by its subclasses.
-
------------------
-Parameter
------------------
-This is an abstract base interface for parameters.
-
-.. autoclass:: Parameter
-
- .. automethod:: op1_randomize
-
- .. automethod:: op3_swarm
-
- .. automethod:: op4_set_linear
-
- .. automethod:: opn_stochastic_mix
-
-
--------------------------
-Primitive Parameter
--------------------------
-.. autoclass:: PrimitiveParameter
- :show-inheritance:
-
- *Inherited Operators:*
-
- :meth:`Parameter.op1_randomize`,
- :meth:`Parameter.op3_swarm`,
- :meth:`Parameter.opn_stochastic_mix`
-
- .. automethod:: op1_normal_mutation
-
- **This paragraph can have examples for the above operator**
-
- .. automethod:: op4_set_linear
-
-
-------------------------
-Numeric Parameter
-------------------------
-.. autoclass:: NumericParameter
- :show-inheritance:
-
- *Inherited Operators:*
-
- :meth:`PrimitiveParameter.op1_normal_mutation`,
- :meth:`Parameter.op3_swarm`,
- :meth:`PrimitiveParameter.op4_set_linear`,
- :meth:`Parameter.opn_stochastic_mix`
-
- .. automethod:: op1_randomize
-
- .. automethod:: op1_scale
-
- .. automethod:: op3_difference
-
- .. automethod:: opn_sum
-
-
-------------------------
-Integer Parameter
-------------------------
-.. autoclass:: IntegerParameter
- :show-inheritance:
-
- *Inherited Operators:*
-
- :meth:`PrimitiveParameter.op1_normal_mutation`,
- :meth:`NumericParameter.op1_randomize`,
- :meth:`NumericParameter.op1_scale`,
- :meth:`NumericParameter.op3_difference`,
- :meth:`PrimitiveParameter.op4_set_linear`,
- :meth:`Parameter.opn_stochastic_mix`,
- :meth:`NumericParameter.opn_sum`
-
- .. automethod:: op3_swarm
-
-
-------------------------
-Float Parameter
-------------------------
-.. autoclass:: FloatParameter
- :show-inheritance:
-
- *Inherited Operators:*
-
- :meth:`PrimitiveParameter.op1_normal_mutation`,
- :meth:`NumericParameter.op1_randomize`,
- :meth:`NumericParameter.op1_scale`,
- :meth:`NumericParameter.op3_difference`,
- :meth:`PrimitiveParameter.op4_set_linear`,
- :meth:`Parameter.opn_stochastic_mix`,
- :meth:`NumericParameter.opn_sum`
-
- .. automethod:: op3_swarm
-
-
-------------------------
-ScaledNumericParameter
-------------------------
-.. autoclass:: ScaledNumericParameter
- :show-inheritance:
-
- *Inherited Operators:*
-
- :meth:`PrimitiveParameter.op1_normal_mutation`,
- :meth:`NumericParameter.op1_randomize`,
- :meth:`NumericParameter.op1_scale`,
- :meth:`NumericParameter.op3_difference`,
- :meth:`Parameter.op3_swarm`,
- :meth:`PrimitiveParameter.op4_set_linear`,
- :meth:`Parameter.opn_stochastic_mix`,
- :meth:`NumericParameter.opn_sum`
-
-
-------------------------
-LogIntegerParameter
-------------------------
-.. autoclass:: LogIntegerParameter
- :show-inheritance:
-
- *Inherited Operators:*
-
- :meth:`PrimitiveParameter.op1_normal_mutation`,
- :meth:`NumericParameter.op1_randomize`,
- :meth:`NumericParameter.op1_scale`,
- :meth:`NumericParameter.op3_difference`,
- :meth:`FloatParameter.op3_swarm`,
- :meth:`PrimitiveParameter.op4_set_linear`,
- :meth:`Parameter.opn_stochastic_mix`,
- :meth:`NumericParameter.opn_sum`
-
-
-------------------------
-LogFloatParameter
-------------------------
-.. autoclass:: LogFloatParameter
- :show-inheritance:
-
- *Inherited Operators:*
-
- :meth:`PrimitiveParameter.op1_normal_mutation`,
- :meth:`NumericParameter.op1_randomize`,
- :meth:`NumericParameter.op1_scale`,
- :meth:`NumericParameter.op3_difference`,
- :meth:`FloatParameter.op3_swarm`,
- :meth:`PrimitiveParameter.op4_set_linear`,
- :meth:`Parameter.opn_stochastic_mix`,
- :meth:`NumericParameter.opn_sum`
-
-
-------------------------
-PowerOfTwoParameter
-------------------------
-.. autoclass:: LogFloatParameter
- :show-inheritance:
-
- *Inherited Operators:*
-
- :meth:`PrimitiveParameter.op1_normal_mutation`,
- :meth:`NumericParameter.op1_randomize`,
- :meth:`NumericParameter.op1_scale`,
- :meth:`NumericParameter.op3_difference`,
- :meth:`IntegerParameter.op3_swarm`,
- :meth:`PrimitiveParameter.op4_set_linear`,
- :meth:`Parameter.opn_stochastic_mix`,
- :meth:`NumericParameter.opn_sum`
-
-
-------------------------
-Complex Parameter
-------------------------
-.. autoclass:: ComplexParameter
- :show-inheritance:
-
- *Inherited Operators:*
-
- :meth:`Parameter.op3_swarm`,
- :meth:`Parameter.opn_stochastic_mix`
-
- .. automethod:: op1_randomize
-
- .. automethod:: op4_set_linear
-
-
-------------------------
-Boolean Parameter
-------------------------
-.. autoclass:: BooleanParameter
- :show-inheritance:
-
- *Inherited Operators:*
-
- :meth:`Parameter.op3_swarm`,
- :meth:`ComplexParameter.op4_set_linear`,
- :meth:`Parameter.opn_stochastic_mix`
-
- .. automethod:: op1_flip
-
- .. automethod:: op1_randomize
-
- .. automethod:: op3_swarm
-
---------------------------
-Switch Parameter
---------------------------
-.. autoclass:: SwitchParameter
- :show-inheritance:
-
- *Inherited Operators:*
-
- :meth:`Parameter.op3_swarm`,
- :meth:`ComplexParameter.op4_set_linear`,
- :meth:`Parameter.opn_stochastic_mix`
-
- .. automethod:: op1_randomize
-
---------------------------
-Enum Parameter
---------------------------
-.. autoclass:: EnumParameter
- :show-inheritance:
-
- *Inherited Operators:*
-
- :meth:`Parameter.op3_swarm`,
- :meth:`ComplexParameter.op4_set_linear`,
- :meth:`Parameter.opn_stochastic_mix`
-
- .. automethod:: op1_randomize
-
-
---------------------------
-Permutation Parameter
---------------------------
-.. autoclass:: PermutationParameter
- :show-inheritance:
-
- *Inherited Operators:*
-
- :meth:`ComplexParameter.op4_set_linear`,
- :meth:`Parameter.opn_stochastic_mix`
-
- .. automethod:: op1_randomize
-
- .. automethod:: op1_small_random_change
-
- .. automethod:: op2_random_swap
-
- .. automethod:: op2_random_invert
-
- .. automethod:: op3_cross
-
- .. automethod:: op3_cross_PX
-
- .. automethod:: op3_cross_PMX
-
- .. automethod:: op3_cross_CX
-
- .. automethod:: op3_cross_OX1
-
- .. automethod:: op3_cross_OX3
-
- .. automethod:: op3_swarm
-
---------------------------
-Array
---------------------------
-.. autoclass:: Array
- :show-inheritance:
-
- *Inherited Operators:*
-
- :meth:`ComplexParameter.op1_randomize`,
- :meth:`ComplexParameter.op4_set_linear`,
- :meth:`Parameter.opn_stochastic_mix`
-
- .. automethod:: op3_cross
-
- .. automethod:: op3_swarm
-
-
---------------------------
-BooleanArray
---------------------------
-.. autoclass:: BooleanArray
- :show-inheritance:
-
- *Inherited Operators:*
-
- :meth:`Array.op3_cross`,
- :meth:`Array.op3_swarm`,
- :meth:`ComplexParameter.op4_set_linear`,
- :meth:`Parameter.opn_stochastic_mix`
-
- .. automethod:: op1_randomize
-
- .. automethod:: op3_swarm_parallel
-
-
---------------------------
-FloatArray
---------------------------
-.. autoclass:: FloatArray
- :show-inheritance:
-
- *Inherited Operators:*
-
- :meth:`Array.op3_cross`,
- :meth:`Array.op3_swarm`,
- :meth:`ComplexParameter.op4_set_linear`,
- :meth:`Parameter.opn_stochastic_mix`
-
- .. automethod:: op1_randomize
-
- .. automethod:: op3_swarm_parallel
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/docs/source/techniques.rst b/llvm/projects/hpvm-tensor-rt/opentuner/docs/source/techniques.rst
deleted file mode 100644
index 3bbebddedbd9c4b999fa8a4f58244ee482ffe673..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/docs/source/techniques.rst
+++ /dev/null
@@ -1,51 +0,0 @@
-.. currentmodule:: opentuner.search.composableevolutionarytechniques
-
-********************
-Current Techniques
-********************
-
-OpenTuner has a library of existing search techniques.
-
-=================================
-Composable Search Techniques
-=================================
-
-A ComposableEvolutionaryTechnique allows for composition between the search technique and any operators. Creating a ComposableEvolutionaryTechnique requires implementing 3 methods:
-
- * :meth:`minimum_number_of_parents <ComposableEvolutionaryTechnique.minimum_number_of_parents>`
- * :meth:`get_parents <ComposableEvolutionaryTechnique.get_parents>`
- * :meth:`update_population <ComposableEvolutionaryTechnique.update_population>`
-
-Additionally, the following methods may be overridden for further customization
-
- * :meth:`make_population_member <ComposableEvolutionaryTechnique.make_population_member>`
- * :meth:`select_parameters <ComposableEvolutionaryTechnique.select_parameters>`
- * :meth:`get_default_operator <ComposableEvolutionaryTechnique.get_default_operator>`
-
-The following methods are useful when choosing parents or updating the population:
-
- * :meth:`lt <ComposableEvolutionaryTechnique.lt>`
- * :meth:`lte <ComposableEvolutionaryTechnique.lte>`
- * :meth:`get_global_best_configuration <ComposableEvolutionaryTechnique.get_global_best_configuration>`
-
-A ComposableEvolutionaryTechnique will yields configurations generated by successive iterations of applying operators on the configurations returned by :meth:`get_parents <ComposableEvolutionaryTechnique.get_parents>` and updating the population with the new configuration through :meth:`update_population <ComposableEvolutionaryTechnique.update_population>`
-
-.. autoclass:: ComposableEvolutionaryTechnique
-
- .. automethod:: minimum_number_of_parents
-
- .. automethod:: get_parents
-
- .. automethod:: update_population
-
- .. automethod:: make_population_member
-
- .. automethod:: select_parameters
-
- .. automethod:: get_default_operator
-
- .. automethod:: lt
-
- .. automethod:: lte
-
- .. automethod:: get_global_best_configuration
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/.gitignore b/llvm/projects/hpvm-tensor-rt/opentuner/examples/.gitignore
deleted file mode 100644
index f525a6259ba8a55dbb66c2eb9b3489e9784ae523..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/.gitignore
+++ /dev/null
@@ -1,4 +0,0 @@
-*-journal
-stats
-opentuner.log
-opentuner.db
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/.gitignore b/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/.gitignore
deleted file mode 100644
index fab2c2b13c5afd35380ae5cf8f4317d2acc58a06..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/.gitignore
+++ /dev/null
@@ -1,5 +0,0 @@
-tmp.bin
-cc_flags.json
-gccflags_final_config.cmd
-gccflags_final_config.json
-cc_params.json
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/adddeps.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/adddeps.py
deleted file mode 100644
index ede22a8fcdb2a94db7915ff3beb90894b2cb8592..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/adddeps.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# we would prefer a symbolic link, but it does not work on windows
-import os
-target = os.path.join(os.path.dirname(__file__),
- '../../opentuner/utils/adddeps.py')
-execfile(target, dict(__file__=target))
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/apps/.gitignore b/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/apps/.gitignore
deleted file mode 100644
index f06d3e01a2bedbfadb4c05ad181eb8745ac2f608..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/apps/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-fft.c
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/apps/matrixmultiply.cpp b/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/apps/matrixmultiply.cpp
deleted file mode 100644
index 9989ffbf4a2ff1f6dffcfbdcab1b7e3f3116c7a1..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/apps/matrixmultiply.cpp
+++ /dev/null
@@ -1,80 +0,0 @@
-// based on: http://blogs.msdn.com/b/xiangfan/archive/2009/04/28/optimize-your-code-matrix-multiplication.aspx
-// by Xiang Fan
-
-#include <algorithm>
-#include <iostream>
-
-#define N 512
-
-
-template<class T>
-T** make_test_matrix() {
- T** data = new T*[N];
- for (int i = 0; i < N; i++) {
- data[i] = new T[N];
- }
- for(int i = 0; i < N; i++) {
- for(int j = 0; j < N; j++) {
- data[i][j] = (int) i * j;
- }
- }
- return data;
-}
-
-
-
-template<typename T>
-void Transpose(int size, T** __restrict__ m)
-{
- for (int i = 0; i < size; i++) {
- for (int j = i + 1; j < size; j++) {
- std::swap(m[i][j], m[j][i]);
- }
- }
-}
-template<typename T>
-void SeqMatrixMult3(int size, T** __restrict__ m1, T** __restrict__ m2,
- T** __restrict__ result) {
- Transpose(size, m2);
- for (int i = 0; i < size; i++) {
- for (int j = 0; j < size; j++) {
- T c = 0;
- for (int k = 0; k < size; k++) {
- c += m1[i][k] * m2[j][k];
- }
- result[i][j] = c;
- }
- }
- Transpose(size, m2);
-}
-
-
-template<typename T>
-void test() {
- T** a = make_test_matrix<T>();
- T** b = make_test_matrix<T>();
- T** c = make_test_matrix<T>();
- SeqMatrixMult3(N, a, b, c);
-
-
- T avg = 0;
- for(int i = 0; i < N; i++) {
- for(int j = 0; j < N; j++) {
- avg += c[i][j] / (T)(N*N);
- }
- }
- // print out average so caller can check answer
- std::cout << avg << std::endl;
-}
-
-
-int main(int argc, const char** argv) {
- test<float>();
- return 0;
-}
-
-
-
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/apps/raytracer.cpp b/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/apps/raytracer.cpp
deleted file mode 100644
index 3cb1192c6a0d9cbd3502186dc391efa71d5cde18..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/apps/raytracer.cpp
+++ /dev/null
@@ -1,277 +0,0 @@
-/*
- A very basic raytracer example.
- Copyright (C) 2012 www.scratchapixel.com
-
- This program is free software: you can redistribute it and/or modify
- it under the terms of the GNU General Public License as published by
- the Free Software Foundation, either version 3 of the License, or
- (at your option) any later version.
-
- This program is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- GNU General Public License for more details.
-
- You should have received a copy of the GNU General Public License
- along with this program. If not, see <http://www.gnu.org/licenses/>.
-
- - changes 02/04/13: fixed flag in ofstream causing a bug under Windows,
- added default values for M_PI and INFINITY
- - changes 24/05/13: small change to way we compute the refraction direction
- vector (eta=ior if we are inside and 1/ior if we are outside the sphere)
-
- Compile with the following command: c++ -o raytracer -O3 -Wall raytracer.cpp
-
-*/
-
-#include <cstdlib>
-#include <cstdio>
-#include <cmath>
-#include <fstream>
-#include <vector>
-#include <iostream>
-#include <cassert>
-
-#if defined(__linux__) || defined(__APPLE__)
- // "Compiled for Linux
-#else
- // Windows doesn't define these values by default, Linux does
- #define M_PI 3.141592653589793
- #define INFINITY 1e8
-#endif
-
-template<typename T>
-class Vec3
-{
-public:
- T x, y, z;
- Vec3() : x(T(0)), y(T(0)), z(T(0)) {}
- Vec3(T xx) : x(xx), y(xx), z(xx) {}
- Vec3(T xx, T yy, T zz) : x(xx), y(yy), z(zz) {}
- Vec3& normalize()
- {
- T nor2 = length2();
- if (nor2 > 0) {
- T invNor = 1 / sqrt(nor2);
- x *= invNor, y *= invNor, z *= invNor;
- }
- return *this;
- }
- Vec3<T> operator * (const T &f) const { return Vec3<T>(x * f, y * f, z * f); }
- Vec3<T> operator * (const Vec3<T> &v) const { return Vec3<T>(x * v.x, y * v.y, z * v.z); }
- T dot(const Vec3<T> &v) const { return x * v.x + y * v.y + z * v.z; }
- Vec3<T> operator - (const Vec3<T> &v) const { return Vec3<T>(x - v.x, y - v.y, z - v.z); }
- Vec3<T> operator + (const Vec3<T> &v) const { return Vec3<T>(x + v.x, y + v.y, z + v.z); }
- Vec3<T>& operator += (const Vec3<T> &v) { x += v.x, y += v.y, z += v.z; return *this; }
- Vec3<T>& operator *= (const Vec3<T> &v) { x *= v.x, y *= v.y, z *= v.z; return *this; }
- Vec3<T> operator - () const { return Vec3<T>(-x, -y, -z); }
- T length2() const { return x * x + y * y + z * z; }
- T length() const { return sqrt(length2()); }
- friend std::ostream & operator << (std::ostream &os, const Vec3<T> &v)
- {
- os << "[" << v.x << " " << v.y << " " << v.z << "]";
- return os;
- }
-};
-
-template<typename T>
-class Sphere
-{
-public:
- Vec3<T> center; /// position of the sphere
- T radius, radius2; /// sphere radius and radius^2
- Vec3<T> surfaceColor, emissionColor; /// surface color and emission (light)
- T transparency, reflection; /// surface transparency and reflectivity
- Sphere(const Vec3<T> &c, const T &r, const Vec3<T> &sc,
- const T &refl = 0, const T &transp = 0, const Vec3<T> &ec = 0) :
- center(c), radius(r), radius2(r * r), surfaceColor(sc), emissionColor(ec),
- transparency(transp), reflection(refl)
- {}
- // compute a ray-sphere intersection using the geometric solution
- bool intersect(const Vec3<T> &rayorig, const Vec3<T> &raydir, T *t0 = NULL, T *t1 = NULL) const
- {
- Vec3<T> l = center - rayorig;
- T tca = l.dot(raydir);
- if (tca < 0) return false;
- T d2 = l.dot(l) - tca * tca;
- if (d2 > radius2) return false;
- T thc = sqrt(radius2 - d2);
- if (t0 != NULL && t1 != NULL) {
- *t0 = tca - thc;
- *t1 = tca + thc;
- }
-
- return true;
- }
-};
-
-#define MAX_RAY_DEPTH 5
-
-template<typename T>
-T mix(const T &a, const T &b, const T &mix)
-{
- return b * mix + a * (T(1) - mix);
-}
-
-// This is the main trace function. It takes a ray as argument (defined by its origin
-// and direction). We test if this ray intersects any of the geometry in the scene.
-// If the ray intersects an object, we compute the intersection point, the normal
-// at the intersection point, and shade this point using this information.
-// Shading depends on the surface property (is it transparent, reflective, diffuse).
-// The function returns a color for the ray. If the ray intersects an object that
-// is the color of the object at the intersection point, otherwise it returns
-// the background color.
-template<typename T>
-Vec3<T> trace(const Vec3<T> &rayorig, const Vec3<T> &raydir,
- const std::vector<Sphere<T> *> &spheres, const int &depth)
-{
- //if (raydir.length() != 1) std::cerr << "Error " << raydir << std::endl;
- T tnear = INFINITY;
- const Sphere<T> *sphere = NULL;
- // find intersection of this ray with the sphere in the scene
- for (unsigned i = 0; i < spheres.size(); ++i) {
- T t0 = INFINITY, t1 = INFINITY;
- if (spheres[i]->intersect(rayorig, raydir, &t0, &t1)) {
- if (t0 < 0) t0 = t1;
- if (t0 < tnear) {
- tnear = t0;
- sphere = spheres[i];
- }
- }
- }
- // if there's no intersection return black or background color
- if (!sphere) return Vec3<T>(2);
- Vec3<T> surfaceColor = 0; // color of the ray/surfaceof the object intersected by the ray
- Vec3<T> phit = rayorig + raydir * tnear; // point of intersection
- Vec3<T> nhit = phit - sphere->center; // normal at the intersection point
- nhit.normalize(); // normalize normal direction
- // If the normal and the view direction are not opposite to each other
- // reverse the normal direction. That also means we are inside the sphere so set
- // the inside bool to true. Finally reverse the sign of IdotN which we want
- // positive.
- T bias = 1e-4; // add some bias to the point from which we will be tracing
- bool inside = false;
- if (raydir.dot(nhit) > 0) nhit = -nhit, inside = true;
- if ((sphere->transparency > 0 || sphere->reflection > 0) && depth < MAX_RAY_DEPTH) {
- T facingratio = -raydir.dot(nhit);
- // change the mix value to tweak the effect
- T fresneleffect = mix<T>(pow(1 - facingratio, 3), 1, 0.1);
- // compute reflection direction (not need to normalize because all vectors
- // are already normalized)
- Vec3<T> refldir = raydir - nhit * 2 * raydir.dot(nhit);
- refldir.normalize();
- Vec3<T> reflection = trace(phit + nhit * bias, refldir, spheres, depth + 1);
- Vec3<T> refraction = 0;
- // if the sphere is also transparent compute refraction ray (transmission)
- if (sphere->transparency) {
- T ior = 1.1, eta = (inside) ? ior : 1 / ior; // are we inside or outside the surface?
- T cosi = -nhit.dot(raydir);
- T k = 1 - eta * eta * (1 - cosi * cosi);
- Vec3<T> refrdir = raydir * eta + nhit * (eta * cosi - sqrt(k));
- refrdir.normalize();
- refraction = trace(phit - nhit * bias, refrdir, spheres, depth + 1);
- }
- // the result is a mix of reflection and refraction (if the sphere is transparent)
- surfaceColor = (reflection * fresneleffect +
- refraction * (1 - fresneleffect) * sphere->transparency) * sphere->surfaceColor;
- }
- else {
- // it's a diffuse object, no need to raytrace any further
- for (unsigned i = 0; i < spheres.size(); ++i) {
- if (spheres[i]->emissionColor.x > 0) {
- // this is a light
- Vec3<T> transmission = 1;
- Vec3<T> lightDirection = spheres[i]->center - phit;
- lightDirection.normalize();
- for (unsigned j = 0; j < spheres.size(); ++j) {
- if (i != j) {
- T t0, t1;
- if (spheres[j]->intersect(phit + nhit * bias, lightDirection, &t0, &t1)) {
- transmission = 0;
- break;
- }
- }
- }
- surfaceColor += sphere->surfaceColor * transmission *
- std::max(T(0), nhit.dot(lightDirection)) * spheres[i]->emissionColor;
- }
- }
- }
-
- return surfaceColor + sphere->emissionColor;
-}
-
-// Main rendering function. We compute a camera ray for each pixel of the image
-// trace it and return a color. If the ray hits a sphere, we return the color of the
-// sphere at the intersection point, else we return the background color.
-template<typename T>
-unsigned int render(const std::vector<Sphere<T> *> &spheres)
-{
- unsigned width = 640, height = 480;
- Vec3<T> *image = new Vec3<T>[width * height], *pixel = image;
- T invWidth = 1 / T(width), invHeight = 1 / T(height);
- T fov = 30, aspectratio = width / T(height);
- T angle = tan(M_PI * 0.5 * fov / T(180));
- // Trace rays
- for (unsigned y = 0; y < height; ++y) {
- for (unsigned x = 0; x < width; ++x, ++pixel) {
- T xx = (2 * ((x + 0.5) * invWidth) - 1) * angle * aspectratio;
- T yy = (1 - 2 * ((y + 0.5) * invHeight)) * angle;
- Vec3<T> raydir(xx, yy, -1);
- raydir.normalize();
- *pixel = trace(Vec3<T>(0), raydir, spheres, 0);
- }
- }
-#if 0
- // Save result to a PPM image (keep these flags if you compile under Windows)
- std::ofstream ofs("./untitled.ppm", std::ios::out | std::ios::binary);
- ofs << "P6\n" << width << " " << height << "\n255\n";
- for (unsigned i = 0; i < width * height; ++i) {
- ofs << (unsigned char)(std::min(T(1), image[i].x) * 255) <<
- (unsigned char)(std::min(T(1), image[i].y) * 255) <<
- (unsigned char)(std::min(T(1), image[i].z) * 255);
- }
- ofs.close();
-#endif
-
- unsigned int bad_hash = 0;
- for (unsigned i = 0; i < width * height; ++i) {
- bad_hash = bad_hash*31 + (unsigned int)(std::min(T(1), image[i].x) * 255);
- bad_hash = bad_hash*31 + (unsigned int)(std::min(T(1), image[i].y) * 255);
- bad_hash = bad_hash*31 + (unsigned int)(std::min(T(1), image[i].z) * 255);
- }
- delete [] image;
-
- return bad_hash;
-}
-
-volatile unsigned int dont_optimize_me;
-
-int main(int argc, char **argv) {
- srand48(13);
- std::vector<Sphere<float> *> spheres;
- // position, radius, surface color, reflectivity, transparency, emission color
- spheres.push_back(new Sphere<float>(Vec3<float>(0, -10004, -20), 10000, Vec3<float>(0.2), 0, 0.0));
- spheres.push_back(new Sphere<float>(Vec3<float>(0, 0, -20), 4, Vec3<float>(1.00, 0.32, 0.36), 1, 0.5));
- spheres.push_back(new Sphere<float>(Vec3<float>(5, -1, -15), 2, Vec3<float>(0.90, 0.76, 0.46), 1, 0.0));
- spheres.push_back(new Sphere<float>(Vec3<float>(5, 0, -25), 3, Vec3<float>(0.65, 0.77, 0.97), 1, 0.0));
- spheres.push_back(new Sphere<float>(Vec3<float>(-5.5, 0, -15), 3, Vec3<float>(0.90, 0.90, 0.90), 1, 0.0));
- // light
- spheres.push_back(new Sphere<float>(Vec3<float>(0, 20, -30), 3, Vec3<float>(0), 0, 0, Vec3<float>(3)));
-
- dont_optimize_me = render<float>(spheres);
- __asm__ __volatile__ ("" ::: "memory"); // memory barrier
- if(dont_optimize_me == 0x4bd7c0e0) {
- //printf("CORRECT\n");
- } else {
- printf("ERROR: WRONG ANSWER\n");
- }
-
- while (!spheres.empty()) {
- Sphere<float> *sph = spheres.back();
- spheres.pop_back();
- delete sph;
- }
-
- return 0;
-}
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/apps/tsp_ga.cpp b/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/apps/tsp_ga.cpp
deleted file mode 100644
index 0e8f232cb099d37facffa0440b41dfd57efd4b2e..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/apps/tsp_ga.cpp
+++ /dev/null
@@ -1,548 +0,0 @@
-//
-// based on: https://bitbucket.org/knordkvist/tsp-ga/overview
-// by Kristoffer Nordkvist
-//
-#include <algorithm>
-#include <assert.h>
-#include <iostream>
-#include <limits>
-#include <math.h>
-#include <sstream>
-#include <stdio.h>
-#include <stdio.h>
-#include <stdlib.h>
-#include <stdlib.h>
-#include <string.h>
-#include <string>
-#include <time.h>
-
-class TSP
-{
- public:
- TSP(const double crossoverProbability, const double mutationProbability);
-
- /* The constants used in this project */
- static const unsigned int chromosones = 30, cities = 20, xMin = 0, xMax = 1000, yMin = 0, yMax = 500;
-
- /* Generate a random population of chromosones */
- void randomPopulation();
-
- /* Create a new population using crossover and mutation */
- void nextPopulation();
-
- /* Returns the fitness of the best chromosone */
- double getBestFitness() const;
-
- /* Returns a string representation of the best path */
- std::string getBestPathString() const;
-
- /* Returns the total distance of the best chromosone path */
- double getLowestTotalDistance() const;
-
- /* Returns the populations average length */
- double getAverageDistance() const;
- private:
- const double crossoverProbability, mutationProbability;
-
- /* Gets the total distance of the supplied path */
- double totalDistance(int const * const chromosone) const;
-
- /* The coordinates for each city, (x,y) for the first city is found in (citiesX[0], citiesY[0]) */
- double citiesX[cities], citiesY[cities];
-
- /* The chromosone containing the shortest path */
- int *bestChromosone;
-
- /* Contains the current population of chromosones */
- int (* solutions)[cities],
- /* The two chromosones with the best fitness functions */
- //bestChromosone1[cities], bestChromosone2[cities],
- /* Used to store the new chromosones when creating a new population */
- (* newPopulation)[cities];
-
- /* Returns a random double r, 0 <= r <= max */
- static double randomInclusive(const double max);
-
- /* Returns a random double r, 0 <= r < max */
- static double randomExclusive(const double max);
-
- /* True if the two chromosones represent the same path */
- static bool areChromosonesEqual(int const * const chromosoneA, int const * const chromosoneB);
-
- /* Evaluate the fitness the supplied chromosone */
- double evaluateFitness(const int * const chromosone) const;
-
- /* Selects a chromosone from the current population using Roulette Wheel Selection.
- * Using the algorithm described in http://www.obitko.com/tutorials/genetic-algorithms/selection.php.
- */
- int * rouletteSelection(double const * const fitness) const;
-
- /* Replace the element at offspringIndex with the first element found in other that does not exist in offspringToRepair */
- void repairOffspring(int * const offspringToRepair, int missingIndex, const int * const other);
-
- /* Might swap one gene with another, depending on the mutation probability */
- void mutate(int * const chromosone);
-
- /* Cross over the parents to form new offspring using Multi-Point Crossover, collisions are handled as shown in lecture 5.
- * The chromosones might be a copy of their parents, depending on the crossover probability.
- */
- void crossover(const int * const parentA, const int * const parentB, int * const offspringA, int * const offspringB);
-
- /* Checks if the supplied chromosone is in newPopulation */
- bool hasDuplicate(const int * const chromosone, size_t populationCount);
-
- /* Copies the supplied chromosone to the new population */
- void copyToNewPopulation(const int * const chromosone, size_t index);
-
- /* Make the chromosone represent a path, which is chosen by random */
- static void setRandomPath(int * const chromosone);
-};
-
-using namespace std;
-
-TSP::TSP(double crossoverProbability, double mutationProbability) : crossoverProbability(crossoverProbability),
- mutationProbability(mutationProbability), solutions(new int[chromosones][cities]), newPopulation(new int[chromosones][cities])
-{
- /* Seed the random number generator */
- //srand((unsigned int)time(NULL));
- srand(17);
- /* Use the same number to generate a specific sequence */
- //srand(0);
- /* Set random coordinates */
- for(size_t coordinateIndex = 0; coordinateIndex < cities; ++coordinateIndex)
- {
- /* 0 <= x <= xMax */
- citiesX[coordinateIndex] = randomInclusive(xMax);
- /* 0 <= y <= yMax */
- citiesY[coordinateIndex] = randomInclusive(yMax);
- }
-
- /* Generate random population */
- randomPopulation();
-}
-
-void TSP::randomPopulation()
-{
- /* Iterate throught each chromosone... */
- for(size_t chromosoneIndex = 0; chromosoneIndex < chromosones; ++chromosoneIndex)
- {
- /* ... and give it a random path */
- setRandomPath(solutions[chromosoneIndex]);
- }
-}
-
-double TSP::getBestFitness() const
-{
- return evaluateFitness(bestChromosone);
-}
-
-double TSP::getAverageDistance() const
-{
- double distance = 0;
- for(size_t chromosoneIndex = 0; chromosoneIndex < chromosones; ++chromosoneIndex)
- {
- distance += totalDistance(solutions[chromosoneIndex]);
- }
- return distance/chromosones;
-}
-
-string TSP::getBestPathString() const
-{
- stringstream path;
- for(size_t gene = 0; gene < cities; ++gene)
- {
- if(gene != 0)
- {
- path << ",";
- }
- path << bestChromosone[gene];
- }
- return path.str();
-}
-
-double TSP::getLowestTotalDistance() const
-{
- return totalDistance(bestChromosone);
-}
-
-void TSP::nextPopulation()
-{
- double fitness[chromosones];
- /* Fill an array with a fitness score for each chromosone,
- * the index of a score corresponds with the chromosone's index in solutions[index]
- */
- for(size_t chromosoneIndex = 0; chromosoneIndex < chromosones; ++chromosoneIndex)
- {
- fitness[chromosoneIndex] = evaluateFitness(solutions[chromosoneIndex]);
- }
-
- /* Use elitism, find and copy over the two best chromosones to the new population */
- int eliteIndex1 = 0, eliteIndex2 = 0;
- /* find the best solution */
- eliteIndex1 = max_element(fitness, fitness + chromosones) - fitness;
- this->bestChromosone = solutions[eliteIndex1];
-
- double highestFitness = 0;
- /* Find the second best solution */
- for(size_t chromosoneIndex = 0; chromosoneIndex < chromosones; ++chromosoneIndex)
- {
- if(chromosoneIndex != eliteIndex1 && fitness[chromosoneIndex] > highestFitness)
- {
- highestFitness = fitness[chromosoneIndex];
- eliteIndex2 = chromosoneIndex;
- }
- }
-
- /* Keep track of how many chromosones exists in the new population */
- size_t offspringCount = 0;
- /* Copy over the two best solutions to the new population */
- copyToNewPopulation(solutions[eliteIndex1], offspringCount);
- ++offspringCount;
- copyToNewPopulation(solutions[eliteIndex2], offspringCount);
- ++offspringCount;
-
- /* Create the rest of the new population, break this loop when the new population is complete */
- while(true)
- {
- int * parentA;
- int * parentB;
- parentA = rouletteSelection(fitness);
- parentB = rouletteSelection(fitness);
- while (parentB == parentA)
- {
- parentB = rouletteSelection(fitness);
- }
- int offspringA[cities];
- int offspringB[cities];
- crossover(parentA, parentB, offspringA, offspringB);
- mutate(offspringA);
- mutate(offspringB);
-
- /* Add to new population if an equal chromosone doesn't exist already */
- if(!hasDuplicate(offspringA, offspringCount))
- {
- copyToNewPopulation(offspringA, offspringCount);
- ++offspringCount;
- }
- /* We need to check if the new population is filled */
- if(offspringCount == chromosones)
- {
- break;
- }
- if(!hasDuplicate(offspringB, offspringCount))
- {
- copyToNewPopulation(offspringB, offspringCount);
- ++offspringCount;
- }
- /* Check again so that we don't accidentaly write all over the heap and have to spend an evening wondering why the heap is corrupt... :) */
- if(offspringCount == chromosones)
- {
- break;
- }
- }
-
- /*
- * We now have a new population,
- * now it needs to replace the current population
- * so that we don't go through the same population every time we run this function
- */
- for(size_t chromosoneIndex = 0; chromosoneIndex < chromosones; ++chromosoneIndex)
- {
- memcpy(solutions[chromosoneIndex], newPopulation[chromosoneIndex], sizeof(int) * cities);
- }
-}
-
-bool TSP::hasDuplicate(const int * const chromosone, size_t populationCount)
-{
- /* Iterate throught each chromosone in newPopulation and compare them gene by gene */
- for(size_t chromosoneIndex = 0; chromosoneIndex < populationCount; ++chromosoneIndex)
- {
- int genesCompared = 0;
- for(size_t gene = 0; gene < cities; ++gene)
- {
- if(chromosone[gene] != newPopulation[chromosoneIndex][gene])
- {
- /* These chromosones are not equal! */
- break;
- }
- ++genesCompared;
- }
-
- if(genesCompared == cities)
- {
- return true;
- }
- }
-
- return false;
-}
-
-void TSP::mutate(int * const chromosone)
-{
- /* 0.0 <= random <= 1 */
- {
- double random = randomInclusive(1);
- /* Nope, didn't happen */
- if(random > mutationProbability)
- {
- return;
- }
- }
-
- int tmp;
- int random1 = (int)randomExclusive(cities);
- int random2 = (int)randomExclusive(cities);
- while(random1 == random2)
- {
- random2 = (int)randomExclusive(cities);
- }
-
- tmp = chromosone[random1];
- chromosone[random1] = chromosone[random2];
- chromosone[random2] = tmp;
-
-}
-
-void TSP::crossover(int const * const parentA, const int * const parentB, int * offspringA, int * offspringB)
-{
- {
- /* There is a chance we don't perform a crossover,
- * in that case the offspring is a copy of the parents
- */
- /* 0.0 <= random <= 1 */
- double random = randomInclusive(1);
- /* The offspring is a copy of their parents */
- if(random > crossoverProbability)
- {
- memcpy(offspringA, parentA, sizeof(int) * cities);
- memcpy(offspringB, parentB, sizeof(int) * cities);
- return;
- }
- }
- /* Perform multi-point crossover to generate offspring */
-
- /* 0 <= cuttOffIndex <= cities */
- int cuttOffIndex1 = (int)randomInclusive(cities);
- int cuttOffIndex2 = (int)randomInclusive(cities);
- while(cuttOffIndex2 == cuttOffIndex1)
- {
- cuttOffIndex2 = (int)randomExclusive(cities);
- }
-
- unsigned int start;
- unsigned int end;
- if(cuttOffIndex1 < cuttOffIndex2)
- {
- start = cuttOffIndex1;
- end = cuttOffIndex2;
- }
- else
- {
- start = cuttOffIndex2;
- end = cuttOffIndex1;
- }
- /* Offspring A is initially copy of parent A */
- memcpy(offspringA, parentA, sizeof(int) * cities);
- /* Offspring B is initially copy of parent B */
- memcpy(offspringB, parentB, sizeof(int) * cities);
-
- /* Put a sequence of parent B in offspring A */
- memcpy(offspringA + start, parentB + start, sizeof(int) * (end - start));
- /* Put a sequence of parent A in offspring B */
- memcpy(offspringB + start, parentA + start, sizeof(int) * (end - start));
-
- /* Mark collisions in offspring with -1*/
- for(size_t cityIndex = 0; cityIndex < cities; ++cityIndex)
- {
- /* Index is part of the parent sequence */
- if((cityIndex >= start && cityIndex < end)) {
- /* Do nothing, we want to keep this sequence intact */
- }
- else
- {
- /* Check if the item at cityIndex also occurs somewhere in the copied substring */
- for(size_t substringIndex = start; substringIndex < end; ++substringIndex)
- {
- /* A duplicate, mark it */
- if(offspringA[cityIndex] == offspringA[substringIndex])
- {
- offspringA[cityIndex] = -1;
- }
- if(offspringB[cityIndex] == offspringB[substringIndex])
- {
- offspringB[cityIndex] = -1;
- }
- }
- }
-
- }
-
- /*
- * Go through the offspring,
- * if an element is marked we fill the hole with an element from the other offspring
- */
- for(size_t offspringIndex = 0; offspringIndex < cities; ++offspringIndex)
- {
- /* There is a hole here */
- if(offspringA[offspringIndex] == -1)
- {
- repairOffspring(offspringA, offspringIndex, offspringB);
- }
- if(offspringB[offspringIndex] == -1)
- {
- repairOffspring(offspringB, offspringIndex, offspringA);
- }
- }
-}
-
-void TSP::repairOffspring(int * const offspringToRepair, int missingIndex, const int * const other)
-{
- /* Iterate through the other offspring until we find an element which doesn't exist in the offspring we are repairing */
- for(size_t patchIndex = 0; patchIndex < cities; ++patchIndex)
- {
- /* Look for other[patchIndex] in offspringToRepair */
- int *missing = find(offspringToRepair, offspringToRepair + cities, other[patchIndex]);
-
- /* The element at other[patchIndex] is missing from offspringToRepair */
- if(missing == (offspringToRepair + cities))
- {
- //cout << "1:" << offspringToRepair[missingIndex] << endl;
- offspringToRepair[missingIndex] = other[patchIndex];
- //cout << "2:" << offspringToRepair[missingIndex] << endl;
- break;
- }
- }
-}
-
-void TSP::copyToNewPopulation(int const * const chromosone, size_t index)
-{
- assert(index < chromosones && "Index out of bounds");
- for(size_t i = 0; i < cities; ++i)
- {
- newPopulation[index][i] = chromosone[i];
- }
-
-}
-
-int * TSP::rouletteSelection(double const * const fitness) const
-{
- double sum = 0;
- /* Calculate sum of all chromosome fitnesses in population */
- for(size_t i = 0; i < chromosones; ++i)
- {
- sum += fitness[i];
- }
-
- /* 0.0 <= random <= sum */
- double random = randomInclusive(sum);
-
- sum = 0;
- /* Go through the population and sum fitnesses from 0 to sum s. When the sum s is greater or equal to r; stop and return the chromosome where you are */
- for(size_t i = 0; i < chromosones; ++i)
- {
- sum += fitness[i];
- if(sum >= random)
- {
- return solutions[i];
- }
- }
- assert(false && "A chromosone should have been picked by now");
- return(NULL);
-}
-
-void TSP::setRandomPath(int * chromosone)
-{
- for(size_t i = 0; i < cities; ++i)
- {
- chromosone[i] = i;
- }
-
- /*
- * Shuffle the chromosone using the Fisher–Yates shuffle.
- */
- for(size_t i = cities-1; i > 0; --i)
- {
- /* 0 <= random <= i */
- int random = (int)randomInclusive(i);
- int temp = chromosone[i];
- chromosone[i] = chromosone[random];
- chromosone[random] = temp;
- }
-}
-
-double TSP::evaluateFitness(int const * const chromosone) const
-{
- return 1/totalDistance(chromosone);
-}
-
-double TSP::totalDistance(int const * const chromosone) const
-{
- double distance = 0;
- /* Calculate the total distance between all cities */
- for(size_t i = 0; i < cities-1; ++i)
- {
- double dx = citiesX[chromosone[i]] - citiesX[chromosone[i+1]];
- double dy = citiesY[chromosone[i]] - citiesY[chromosone[i+1]];
-
- /* The distance between two points is the square root of (dx^2+dy^2) */
- distance += sqrt((pow(dx, 2.0) + pow(dy, 2.0)));
- }
- /* We complete the tour by adding the distance between the last and the first city */
- double dx = citiesX[chromosone[cities-1]] - citiesX[chromosone[0]];
- double dy = citiesY[chromosone[cities-1]] - citiesY[chromosone[0]];
- distance += sqrt((pow(dx, 2.0) + pow(dy, 2.0)));
-
- return distance;
-}
-
-double TSP::randomInclusive(double max)
-{
- /* Generate random number r, 0.0 <= r <= max */
- //return ((double)rand() / (double)RAND_MAX * max);
- return ((double)rand() * max) / (double)RAND_MAX;
-}
-
-double TSP::randomExclusive(double max)
-{
- /* Generate random number r, 0.0 <= r < max */
- //return ((double)rand() / ((double)RAND_MAX + 1) * max);
- return ((double)rand() * max) / ((double)RAND_MAX + 1);
-}
-
-int main(int argc, const char *argv[])
-{
- /* 90% mutation probability, 2% mutation probability */
- TSP *tsp = new TSP(0.9, 0.02);
- size_t generations = 0, generationsWithoutImprovement = 0;
- double bestFitness = -1;
- double initialAverage = tsp->getAverageDistance();
- /* We'll stop when we've gone 10k generations without improvement */
- while(generations < 10000)
- {
- tsp->nextPopulation();
- ++generations;
- double newFitness = tsp->getBestFitness();
- /* The new fitness is higher, the chromosone is better */
- if(newFitness > bestFitness)
- {
- bestFitness = newFitness;
- generationsWithoutImprovement = 0;
- //cout << "Best goal function: " << tsp->getBestFitness() << endl;
- }
- else
- {
- ++generationsWithoutImprovement;
- }
- }
- //cout << "DONE!" << endl;
- cout << "Number of generations: " << generations << endl;
- cout << "Best chromosone info: " << endl;
- cout << "\t-Path: " << tsp->getBestPathString() << endl;
- cout << "\t-Goal function: " << tsp->getBestFitness() << endl;
- cout << "\t-Distance: " << tsp->getLowestTotalDistance() << endl;
- cout << "Average distance: " << tsp->getAverageDistance() << endl;
- cout << "Initial average: " << initialAverage << endl;
- delete tsp;
- return 0;
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/cc_param_defaults.json b/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/cc_param_defaults.json
deleted file mode 100644
index 067a26573a08ea6956e407833fa9ca17faafa4bf..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/cc_param_defaults.json
+++ /dev/null
@@ -1 +0,0 @@
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"max": 0, "min": 0}, "l1-cache-line-size": {"default": 32, "max": 0, "min": 0}, "case-values-threshold": {"default": 0, "max": 0, "min": 0}, "max-pending-list-length": {"default": 32, "max": 0, "min": 0}, "sccvn-max-alias-queries-per-access": {"default": 1000, "max": 0, "min": 0}, "max-vartrack-expr-depth": {"default": 12, "max": 0, "min": 0}, "loop-block-tile-size": {"default": 51, "max": 0, "min": 0}, "sms-loop-average-count-threshold": {"default": 0, "max": 0, "min": 0}, "vect-max-peeling-for-alignment": {"default": -1, "max": 64, "min": -1}, "selsched-max-lookahead": {"default": 50, "max": 0, "min": 0}, "omega-max-keys": {"default": 500, "max": 0, "min": 0}, "sccvn-max-scc-size": {"default": 10000, "max": 0, "min": 10}, "predictable-branch-outcome": {"default": 2, "max": 50, "min": 0}, "ssp-buffer-size": {"default": 8, "max": 0, "min": 1}, "max-delay-slot-insn-search": {"default": 100, "max": 0, "min": 0}, "sms-min-sc": {"default": 2, "max": 1, "min": 1}, "lra-max-considered-reload-pseudos": {"default": 500, "max": 0, "min": 0}, "tracer-dynamic-coverage-feedback": {"default": 95, "max": 100, "min": 0}, "omega-max-eqs": {"default": 128, "max": 0, "min": 0}, "max-fields-for-field-sensitive": {"default": 0, "max": 0, "min": 0}, "max-sched-region-insns": {"default": 100, "max": 0, "min": 0}, "large-stack-frame-growth": {"default": 1000, "max": 0, "min": 0}, "omega-max-wild-cards": {"default": 18, "max": 0, "min": 0}, "max-sched-extend-regions-iters": {"default": 0, "max": 0, "min": 0}, "max-unswitch-insns": {"default": 50, "max": 0, "min": 0}, "ipcp-unit-growth": {"default": 10, "max": 0, "min": 0}, "max-unswitch-level": {"default": 3, "max": 0, "min": 0}, "l1-cache-size": {"default": 64, "max": 0, "min": 0}, "max-grow-copy-bb-insns": {"default": 8, "max": 0, "min": 0}, "max-iterations-computation-cost": {"default": 10, "max": 0, "min": 0}, "ipa-cp-array-index-hint-bonus": {"default": 48, "max": 0, "min": 0}, "ggc-min-heapsize": {"default": 4096, "max": 0, "min": 0}, "align-threshold": {"default": 100, "max": 0, "min": 1}, "graphite-max-bbs-per-function": {"default": 100, "max": 0, "min": 0}, "max-vartrack-reverse-op-size": {"default": 50, "max": 0, "min": 0}, "ipa-sra-ptr-growth-factor": {"default": 2, "max": 0, "min": 0}, "max-completely-peeled-insns": {"default": 100, "max": 0, "min": 0}, "ipa-cp-eval-threshold": {"default": 500, "max": 0, "min": 0}, "large-stack-frame": {"default": 256, "max": 0, "min": 0}, "max-modulo-backtrack-attempts": {"default": 40, "max": 0, "min": 0}, "omega-hash-table-size": {"default": 550, "max": 0, "min": 0}, "max-goto-duplication-insns": {"default": 8, "max": 0, "min": 0}, "max-sched-ready-insns": {"default": 100, "max": 0, "min": 0}, "max-iterations-to-track": {"default": 1000, "max": 0, "min": 0}, "scev-max-expr-complexity": {"default": 10, "max": 0, "min": 0}, "cxx-max-namespaces-for-diagnostic-help": {"default": 1000, "max": 0, "min": 0}, "max-reload-search-insns": {"default": 100, "max": 0, "min": 0}, "use-canonical-types": {"default": 1, "max": 1, "min": 0}, "gcse-after-reload-critical-fraction": {"default": 10, "max": 0, "min": 0}, "sched-state-edge-prob-cutoff": {"default": 10, "max": 100, "min": 0}, "sched-spec-prob-cutoff": {"default": 40, "max": 100, "min": 0}, "unlikely-bb-count-fraction": {"default": 20, "max": 10000, "min": 1}, "slp-max-insns-in-bb": {"default": 1000, "max": 0, "min": 0}, "max-peel-branches": {"default": 32, "max": 0, "min": 0}, "large-unit-insns": {"default": 10000, "max": 0, "min": 0}, "iv-always-prune-cand-set-bound": {"default": 10, "max": 0, "min": 0}, "vect-max-version-for-alias-checks": {"default": 10, "max": 0, "min": 0}, "max-predicted-iterations": {"default": 100, "max": 0, "min": 0}, "allow-packed-load-data-races": {"default": 1, "max": 1, "min": 0}}
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/gccflags.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/gccflags.py
deleted file mode 100755
index 1edc5bb8a3ce886e968a5f7d7d2e4f362ff8940b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/gccflags.py
+++ /dev/null
@@ -1,412 +0,0 @@
-#!/usr/bin/env python
-import adddeps # fix sys.path
-
-import math
-import argparse
-import ast
-import collections
-import json
-import logging
-import opentuner
-import os
-import random
-import re
-import shutil
-import subprocess
-import sys
-
-from opentuner.resultsdb.models import Result, TuningRun
-from opentuner.search import manipulator
-
-FLAGS_WORKING_CACHE_FILE = 'cc_flags.json'
-PARAMS_DEFAULTS_CACHE_FILE = 'cc_param_defaults.json'
-PARAMS_DEF_PATH = '~/gcc-4.9.0/gcc/params.def'
-PARAMS_WORKING_CACHE_FILE = 'cc_params.json'
-
-log = logging.getLogger('gccflags')
-
-argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
-argparser.add_argument('source', help='source file to compile')
-argparser.add_argument('--compile-template',
- default='{cc} {source} -o {output} -lpthread {flags}',
- help='command to compile {source} into {output} with'
- ' {flags}')
-argparser.add_argument('--compile-limit', type=float, default=30,
- help='kill gcc if it runs more than {default} sec')
-argparser.add_argument('--scaler', type=int, default=4,
- help='by what factor to try increasing parameters')
-argparser.add_argument('--cc', default='g++', help='g++ or gcc')
-argparser.add_argument('--output', default='./tmp.bin',
- help='temporary file for compiler to write to')
-argparser.add_argument('--debug', action='store_true',
- help='on gcc errors try to find minimal set '
- 'of args to reproduce error')
-argparser.add_argument('--force-killall', action='store_true',
- help='killall cc1plus before each collection')
-argparser.add_argument('--memory-limit', default=1024 ** 3, type=int,
- help='memory limit for child process')
-argparser.add_argument('--no-cached-flags', action='store_true',
- help='regenerate the lists of legal flags each time')
-argparser.add_argument('--flags-histogram', action='store_true',
- help='print out a histogram of flags')
-argparser.add_argument('--flag-importance',
- help='Test the importance of different flags from a '
- 'given json file.')
-
-
-class GccFlagsTuner(opentuner.measurement.MeasurementInterface):
- def __init__(self, *pargs, **kwargs):
- super(GccFlagsTuner, self).__init__(program_name=args.source, *pargs,
- **kwargs)
- self.gcc_version = self.extract_gcc_version()
- self.cc_flags = self.extract_working_flags()
- self.cc_param_defaults = self.extract_param_defaults()
- self.cc_params = self.extract_working_params()
-
- # these bugs are hardcoded for now
- # sets of options which causes gcc to barf
- if True:
- # These bugs were for gcc 4.7 on ubuntu
- self.cc_bugs = (['-fipa-matrix-reorg', '-fwhole-program'],
- ['-fno-tree-coalesce-inlined-vars'],
- ['-fno-inline-atomics'],
- ['-ftoplevel-reorder', '-fno-unit-at-a-time'])
- else:
- # Bugs for gcc 4.9 (work in progress, incomplete list)
- self.cc_bugs = (['-ftoplevel-reorder', '-fno-unit-at-a-time'], )
-
- self.result_list = {}
- self.parallel_compile = True
- try:
- os.stat('./tmp')
- except OSError:
- os.mkdir('./tmp')
- self.run_baselines()
-
- def run_baselines(self):
- log.info("baseline perfs -O0=%.4f -O1=%.4f -O2=%.4f -O3=%.4f",
- *[self.run_with_flags(['-O%d' % i], None).time
- for i in range(4)])
-
- def extract_gcc_version(self):
- m = re.search(r'([0-9]+)[.]([0-9]+)[.]([0-9]+)', subprocess.check_output([
- self.args.cc, '--version']))
- if m:
- gcc_version = tuple(map(int, m.group(1, 2, 3)))
- else:
- gcc_version = None
- log.debug('gcc version %s', gcc_version)
- return gcc_version
-
- def extract_working_flags(self):
- """
- Figure out which gcc flags work (don't cause gcc to barf) by running
- each one.
- """
- if os.path.isfile(FLAGS_WORKING_CACHE_FILE) and not args.no_cached_flags:
- # use cached version
- found_cc_flags = json.load(open(FLAGS_WORKING_CACHE_FILE))
- else:
- # extract flags from --help=optimizers
- optimizers, err = subprocess.Popen([self.args.cc, '--help=optimizers'],
- stdout=subprocess.PIPE).communicate()
- found_cc_flags = re.findall(r'^ (-f[a-z0-9-]+) ', optimizers,
- re.MULTILINE)
- log.info('Determining which of %s possible gcc flags work',
- len(found_cc_flags))
- found_cc_flags = filter(self.check_if_flag_works, found_cc_flags)
- json.dump(found_cc_flags, open(FLAGS_WORKING_CACHE_FILE, 'w'))
- return found_cc_flags
-
- def extract_param_defaults(self):
- """
- Get the default, minimum, and maximum for each gcc parameter.
- Requires source code for gcc to be in your home directory.
- This example ships with a cached version so it does not require source.
- """
- if os.path.isfile(PARAMS_DEFAULTS_CACHE_FILE) and not args.no_cached_flags:
- # use cached version
- param_defaults = json.load(open(PARAMS_DEFAULTS_CACHE_FILE))
- else:
- # default values of params need to be extracted from source code,
- # since they are not in --help
- param_defaults = dict()
- params_def = open(os.path.expanduser(PARAMS_DEF_PATH)).read()
- for m in re.finditer(r'DEFPARAM *\((([^")]|"[^"]*")*)\)', params_def):
- param_def_str = (m.group(1)
- # Hacks!!!
- .replace('GGC_MIN_EXPAND_DEFAULT', '30')
- .replace('GGC_MIN_HEAPSIZE_DEFAULT', '4096')
- .replace('50 * 1024 * 1024', '52428800'))
- try:
- name, desc, default, param_min, param_max = ast.literal_eval(
- '[' + param_def_str.split(',', 1)[1] + ']')
- param_defaults[name] = {'default': default,
- 'min': param_min,
- 'max': param_max}
- except:
- log.exception("error with %s", param_def_str)
- json.dump(param_defaults, open(PARAMS_DEFAULTS_CACHE_FILE, 'w'))
- return param_defaults
-
- def extract_working_params(self):
- """
- Figure out which gcc params work (don't cause gcc to barf) by running
- each one to test.
- """
- params, err = subprocess.Popen(
- [self.args.cc, '--help=params'], stdout=subprocess.PIPE).communicate()
- all_params = re.findall(r'^ ([a-z0-9-]+) ', params, re.MULTILINE)
- all_params = sorted(set(all_params) &
- set(self.cc_param_defaults.keys()))
- if os.path.isfile(PARAMS_WORKING_CACHE_FILE) and not args.no_cached_flags:
- # use cached version
- return json.load(open(PARAMS_WORKING_CACHE_FILE))
- else:
- log.info('Determining which of %s possible gcc params work',
- len(all_params))
- working_params = []
- for param in all_params:
- if self.check_if_flag_works('--param={}={}'.format(
- param, self.cc_param_defaults[param]['default'])):
- working_params.append(param)
- json.dump(working_params, open(PARAMS_WORKING_CACHE_FILE, 'w'))
- return working_params
-
- def check_if_flag_works(self, flag, try_inverted=True):
- cmd = args.compile_template.format(source=args.source, output=args.output,
- flags=flag, cc=args.cc)
- compile_result = self.call_program(cmd, limit=args.compile_limit)
- if compile_result['returncode'] != 0:
- log.warning("removing flag %s because it results in compile error", flag)
- return False
- if 'warning: this target' in compile_result['stderr']:
- log.warning("removing flag %s because not supported by target", flag)
- return False
- if 'has been renamed' in compile_result['stderr']:
- log.warning("removing flag %s because renamed", flag)
- return False
- if try_inverted and flag[:2] == '-f':
- if not self.check_if_flag_works(invert_gcc_flag(flag),
- try_inverted=False):
- log.warning("Odd... %s works but %s does not", flag,
- invert_gcc_flag(flag))
- return False
- return True
-
- def manipulator(self):
- m = manipulator.ConfigurationManipulator()
- m.add_parameter(manipulator.IntegerParameter('-O', 0, 3))
- for flag in self.cc_flags:
- m.add_parameter(manipulator.EnumParameter(flag, ['on', 'off', 'default']))
- for param in self.cc_params:
- defaults = self.cc_param_defaults[param]
- if defaults['max'] <= defaults['min']:
- defaults['max'] = float('inf')
- defaults['max'] = min(defaults['max'],
- max(1, defaults['default']) * args.scaler)
- defaults['min'] = max(defaults['min'],
- max(1, defaults['default']) / args.scaler)
-
- if param == 'l1-cache-line-size':
- # gcc requires this to be a power of two or it internal errors
- m.add_parameter(manipulator.PowerOfTwoParameter(param, 4, 256))
- elif defaults['max'] > 128:
- m.add_parameter(manipulator.LogIntegerParameter(
- param, defaults['min'], defaults['max']))
- else:
- m.add_parameter(manipulator.IntegerParameter(
- param, defaults['min'], defaults['max']))
-
- return m
-
- def cfg_to_flags(self, cfg):
- flags = ['-O%d' % cfg['-O']]
- for flag in self.cc_flags:
- if cfg[flag] == 'on':
- flags.append(flag)
- elif cfg[flag] == 'off':
- flags.append(invert_gcc_flag(flag))
-
- for param in self.cc_params:
- flags.append('--param=%s=%d' % (param, cfg[param]))
-
- # workaround sets of flags that trigger compiler crashes/hangs
- for bugset in self.cc_bugs:
- if len(set(bugset) & set(flags)) == len(bugset):
- flags.remove(bugset[-1])
- return flags
-
- def make_command(self, cfg):
- return args.compile_template.format(source=args.source, output=args.output,
- flags=' '.join(self.cfg_to_flags(cfg)),
- cc=args.cc)
-
- def get_tmpdir(self, result_id):
- return './tmp/%d' % result_id
-
- def cleanup(self, result_id):
- tmp_dir = self.get_tmpdir(result_id)
- shutil.rmtree(tmp_dir)
-
- def compile_and_run(self, desired_result, input, limit):
- cfg = desired_result.configuration.data
- compile_result = self.compile(cfg, 0)
- return self.run_precompiled(desired_result, input, limit, compile_result, 0)
-
- compile_results = {'ok': 0, 'timeout': 1, 'error': 2}
-
- def run_precompiled(self, desired_result, input, limit, compile_result,
- result_id):
- if self.args.force_killall:
- os.system('killall -9 cc1plus 2>/dev/null')
- # Make sure compile was successful
- if compile_result == self.compile_results['timeout']:
- return Result(state='TIMEOUT', time=float('inf'))
- elif compile_result == self.compile_results['error']:
- return Result(state='ERROR', time=float('inf'))
-
- tmp_dir = self.get_tmpdir(result_id)
- output_dir = '%s/%s' % (tmp_dir, args.output)
- try:
- run_result = self.call_program([output_dir], limit=limit,
- memory_limit=args.memory_limit)
- except OSError:
- return Result(state='ERROR', time=float('inf'))
-
- if run_result['returncode'] != 0:
- if run_result['timeout']:
- return Result(state='TIMEOUT', time=float('inf'))
- else:
- log.error('program error')
- return Result(state='ERROR', time=float('inf'))
-
- return Result(time=run_result['time'])
-
- def debug_gcc_error(self, flags):
- def fails(subflags):
- cmd = args.compile_template.format(source=args.source, output=args.output,
- flags=' '.join(subflags),
- cc=args.cc)
- compile_result = self.call_program(cmd, limit=args.compile_limit)
- return compile_result['returncode'] != 0
-
- if self.args.debug:
- while len(flags) > 8:
- log.error("compile error with %d flags, diagnosing...", len(flags))
- tmpflags = filter(lambda x: random.choice((True, False)), flags)
- if fails(tmpflags):
- flags = tmpflags
-
- # linear scan
- minimal_flags = []
- for i in xrange(len(flags)):
- tmpflags = minimal_flags + flags[i + 1:]
- if not fails(tmpflags):
- minimal_flags.append(flags[i])
- log.error("compiler crashes/hangs with flags: %s", minimal_flags)
-
- def compile(self, config_data, result_id):
- flags = self.cfg_to_flags(config_data)
- return self.compile_with_flags(flags, result_id)
-
- def compile_with_flags(self, flags, result_id):
- tmp_dir = self.get_tmpdir(result_id)
- try:
- os.stat(tmp_dir)
- except OSError:
- os.mkdir(tmp_dir)
- output_dir = '%s/%s' % (tmp_dir, args.output)
- cmd = args.compile_template.format(source=args.source, output=output_dir,
- flags=' '.join(flags),
- cc=args.cc)
-
- compile_result = self.call_program(cmd, limit=args.compile_limit,
- memory_limit=args.memory_limit)
- if compile_result['returncode'] != 0:
- if compile_result['timeout']:
- log.warning("gcc timeout")
- return self.compile_results['timeout']
- else:
- log.warning("gcc error %s", compile_result['stderr'])
- self.debug_gcc_error(flags)
- return self.compile_results['error']
- return self.compile_results['ok']
-
- def run_with_flags(self, flags, limit):
- return self.run_precompiled(None, None, limit,
- self.compile_with_flags(flags, 0), 0)
-
- def save_final_config(self, configuration):
- """called at the end of tuning"""
- print "Best flags written to gccflags_final_config.{json,cmd}"
- self.manipulator().save_to_file(configuration.data,
- 'gccflags_final_config.json')
- with open('gccflags_final_config.cmd', 'w') as fd:
- fd.write(self.make_command(configuration.data))
-
- def flags_histogram(self, session):
- counter = collections.Counter()
- q = session.query(TuningRun).filter_by(state='COMPLETE')
- total = q.count()
- for tr in q:
- print tr.program.name
- for flag in self.cfg_to_flags(tr.final_config.data):
- counter[flag] += 1.0 / total
- print counter.most_common(20)
-
- def flag_importance(self):
- """
- Test the importance of each flag by measuring the performance with that
- flag removed. Print out a table for paper
- """
- with open(self.args.flag_importance) as fd:
- best_cfg = json.load(fd)
- flags = self.cfg_to_flags(best_cfg)
- counter = collections.Counter()
- baseline_time = self.flags_mean_time(flags)
- for flag in flags[1:]:
- delta_flags = [f for f in flags if f != flag]
- flag_time = self.flags_mean_time(delta_flags)
- impact = max(0.0, flag_time - baseline_time)
- if math.isinf(impact):
- impact = 0.0
- counter[flag] = impact
- print flag, '{:.4f}'.format(impact)
- total_impact = sum(counter.values())
- remaining_impact = total_impact
- print r'\bf Flag & \bf Importance \\\hline'
- for flag, impact in counter.most_common(20):
- print r'{} & {:.1f}\% \\\hline'.format(flag, 100.0 * impact / total_impact)
- remaining_impact -= impact
- print r'{} other flags & {:.1f}% \\\hline'.format(
- len(flags) - 20, 100.0 * remaining_impact / total_impact)
-
- def flags_mean_time(self, flags, trials=10):
- precompiled = self.compile_with_flags(flags, 0)
- total = 0.0
- for _ in xrange(trials):
- total += self.run_precompiled(None, None, None, precompiled, 0).time
- return total / trials
-
- def prefix_hook(self, session):
- if self.args.flags_histogram:
- self.flags_histogram(session)
- sys.exit(0)
- if self.args.flag_importance:
- self.flag_importance()
- sys.exit(0)
-
-
-
-def invert_gcc_flag(flag):
- assert flag[:2] == '-f'
- if flag[2:5] != 'no-':
- return '-fno-' + flag[2:]
- return '-f' + flag[5:]
-
-
-if __name__ == '__main__':
- opentuner.init_logging()
- args = argparser.parse_args()
- GccFlagsTuner.main(args)
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/gccflags_minimal.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/gccflags_minimal.py
deleted file mode 100755
index 0363b984a8c67064102e9025ce57d388c2585514..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/gccflags/gccflags_minimal.py
+++ /dev/null
@@ -1,92 +0,0 @@
-#!/usr/bin/env python
-#
-# Autotune flags to g++ to optimize the performance of apps/raytracer.cpp
-#
-# This is an extremely simplified version meant only for tutorials
-#
-import adddeps # fix sys.path
-
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import EnumParameter
-from opentuner import IntegerParameter
-from opentuner import MeasurementInterface
-from opentuner import Result
-
-GCC_FLAGS = [
- 'align-functions', 'align-jumps', 'align-labels',
- 'align-loops', 'asynchronous-unwind-tables',
- 'branch-count-reg', 'branch-probabilities',
- # ... (176 total)
-]
-
-# (name, min, max)
-GCC_PARAMS = [
- ('early-inlining-insns', 0, 1000),
- ('gcse-cost-distance-ratio', 0, 100),
- ('iv-max-considered-uses', 0, 1000),
- # ... (145 total)
-]
-
-
-class GccFlagsTuner(MeasurementInterface):
-
- def manipulator(self):
- """
- Define the search space by creating a
- ConfigurationManipulator
- """
- manipulator = ConfigurationManipulator()
- manipulator.add_parameter(
- IntegerParameter('opt_level', 0, 3))
- for flag in GCC_FLAGS:
- manipulator.add_parameter(
- EnumParameter(flag,
- ['on', 'off', 'default']))
- for param, min, max in GCC_PARAMS:
- manipulator.add_parameter(
- IntegerParameter(param, min, max))
- return manipulator
-
- def compile(self, cfg, id):
- """
- Compile a given configuration in parallel
- """
- gcc_cmd = 'g++ apps/raytracer.cpp -o ./tmp{0}.bin'.format(id)
- gcc_cmd += ' -O{0}'.format(cfg['opt_level'])
- for flag in GCC_FLAGS:
- if cfg[flag] == 'on':
- gcc_cmd += ' -f{0}'.format(flag)
- elif cfg[flag] == 'off':
- gcc_cmd += ' -fno-{0}'.format(flag)
- for param, min, max in GCC_PARAMS:
- gcc_cmd += ' --param {0}={1}'.format(
- param, cfg[param])
- return self.call_program(gcc_cmd)
-
- def run_precompiled(self, desired_result, input, limit, compile_result, id):
- """
- Run a compile_result from compile() sequentially and return performance
- """
- assert compile_result['returncode'] == 0
-
- try:
- run_result = self.call_program('./tmp{0}.bin'.format(id))
- assert run_result['returncode'] == 0
- finally:
- self.call_program('rm ./tmp{0}.bin'.format(id))
-
- return Result(time=run_result['time'])
-
- def compile_and_run(self, desired_result, input, limit):
- """
- Compile and run a given configuration then
- return performance
- """
- cfg = desired_result.configuration.data
- compile_result = self.compile(cfg, 0)
- return self.run_precompiled(desired_result, input, limit, compile_result, 0)
-
-if __name__ == '__main__':
- argparser = opentuner.default_argparser()
- GccFlagsTuner.main(argparser.parse_args())
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/.gitignore b/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/.gitignore
deleted file mode 100644
index ebdc2a395f4c8b509233d88992512c4cf4ae3364..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/.gitignore
+++ /dev/null
@@ -1,2 +0,0 @@
-dump-call-graph
-*.callgraph
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/adddeps.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/adddeps.py
deleted file mode 100644
index ede22a8fcdb2a94db7915ff3beb90894b2cb8592..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/adddeps.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# we would prefer a symbolic link, but it does not work on windows
-import os
-target = os.path.join(os.path.dirname(__file__),
- '../../opentuner/utils/adddeps.py')
-execfile(target, dict(__file__=target))
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/bilateral_grid.cpp b/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/bilateral_grid.cpp
deleted file mode 100644
index 6f1c97ffb85967223bf4e2ebc16d8ae0c2bcd02b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/bilateral_grid.cpp
+++ /dev/null
@@ -1,93 +0,0 @@
-#include "Halide.h"
-#include <stdio.h>
-
-using namespace Halide;
-
-int main(int argc, char **argv) {
- // if (argc < 2) {
- // printf("Usage: bilateral_grid <s_sigma>\n");
- // // printf("Spatial sigma is a compile-time parameter, please provide it as an argument.\n"
- // // "(llvm's ptx backend doesn't handle integer mods by non-consts yet)\n");
- // return 0;
- // }
-
- ImageParam input(Float(32), 2);
- float r_sigma = 0.1;
- // int s_sigma = atoi(argv[1]);
- int s_sigma = 4;
- Var x("x"), y("y"), z("z"), c("c");
-
- // Add a boundary condition
- Func clamped("clamped");
- clamped(x, y) = input(clamp(x, 0, input.width()-1),
- clamp(y, 0, input.height()-1));
-
- // Construct the bilateral grid
- RDom r(0, s_sigma, 0, s_sigma);
- Expr val = clamped(x * s_sigma + r.x - s_sigma/2, y * s_sigma + r.y - s_sigma/2);
- val = clamp(val, 0.0f, 1.0f);
- Expr zi = cast<int>(val * (1.0f/r_sigma) + 0.5f);
- Func grid("grid"), histogram("histogram");
- histogram(x, y, zi, c) += select(c == 0, val, 1.0f);
-
- // Introduce a dummy function, so we can schedule the histogram within it
- grid(x, y, z, c) = histogram(x, y, z, c);
-
- // Blur the grid using a five-tap filter
- Func blurx("blurx"), blury("blury"), blurz("blurz");
- blurx(x, y, z, _) = grid(x-2, y, z, _) + grid(x-1, y, z, _)*4 + grid(x, y, z, _)*6 + grid(x+1, y, z, _)*4 + grid(x+2, y, z, _);
- blury(x, y, z, _) = blurx(x, y-2, z, _) + blurx(x, y-1, z, _)*4 + blurx(x, y, z, _)*6 + blurx(x, y+1, z, _)*4 + blurx(x, y+2, z, _);
- blurz(x, y, z, _) = blury(x, y, z-2, _) + blury(x, y, z-1, _)*4 + blury(x, y, z, _)*6 + blury(x, y, z+1, _)*4 + blury(x, y, z+2, _);
-
- // Take trilinear samples to compute the output
- val = clamp(clamped(x, y), 0.0f, 1.0f);
- Expr zv = val * (1.0f/r_sigma);
- zi = cast<int>(zv);
- Expr zf = zv - zi;
- Expr xf = cast<float>(x % s_sigma) / s_sigma;
- Expr yf = cast<float>(y % s_sigma) / s_sigma;
- Expr xi = x/s_sigma;
- Expr yi = y/s_sigma;
- Func interpolated("interpolated");
- interpolated(x, y, _) =
- lerp(lerp(lerp(blurz(xi, yi, zi, _), blurz(xi+1, yi, zi, _), xf),
- lerp(blurz(xi, yi+1, zi, _), blurz(xi+1, yi+1, zi, _), xf), yf),
- lerp(lerp(blurz(xi, yi, zi+1, _), blurz(xi+1, yi, zi+1, _), xf),
- lerp(blurz(xi, yi+1, zi+1, _), blurz(xi+1, yi+1, zi+1, _), xf), yf), zf);
-
- // Normalize
- Func bilateral_grid("bilateral_grid");
- bilateral_grid(x, y) = interpolated(x, y, 0)/interpolated(x, y, 1);
-
- AUTOTUNE_HOOK(bilateral_grid);
-
- char *target = getenv("HL_TARGET");
- if (target && std::string(target) == "ptx") {
-
- // GPU schedule
- grid.compute_root().reorder(z, c, x, y).cuda_tile(x, y, 8, 8);
-
- // Compute the histogram into shared memory before spilling it to global memory
- histogram.store_at(grid, Var("blockidx")).compute_at(grid, Var("threadidx"));
-
- blurx.compute_root().cuda_tile(x, y, z, 16, 16, 1);
- blury.compute_root().cuda_tile(x, y, z, 16, 16, 1);
- blurz.compute_root().cuda_tile(x, y, z, 8, 8, 4);
- bilateral_grid.compute_root().cuda_tile(x, y, s_sigma, s_sigma);
- } else {
-
- // CPU schedule
- grid.compute_root().reorder(c, z, x, y).parallel(y);
- histogram.compute_at(grid, x).unroll(c);
- blurx.compute_root().parallel(z).vectorize(x, 4);
- blury.compute_root().parallel(z).vectorize(x, 4);
- blurz.compute_root().parallel(z).vectorize(x, 4);
- bilateral_grid.compute_root().parallel(y).vectorize(x, 4);
- }
-
- BASELINE_HOOK(bilateral_grid);
-
- //bilateral_grid.compile_to_file("bilateral_grid", r_sigma, input);
-
- return 0;
-}
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/bilateral_grid.settings b/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/bilateral_grid.settings
deleted file mode 100644
index 7b829b779a9f7d05ef7b677ea307430728e29f16..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/bilateral_grid.settings
+++ /dev/null
@@ -1,10 +0,0 @@
-{"input_size": "2048, 2048",
- "functions": [
- {"name": "clamped", "vars": ["x", "y"], "calls": []},
- {"name": "histogram", "vars": ["x", "y", "c"], "calls": ["clamped"]},
- {"name": "grid", "vars": ["x", "y", "z", "c"], "calls": ["histogram"]},
- {"name": "blurx", "vars": ["x", "y", "z"], "calls": ["grid"]},
- {"name": "blury", "vars": ["x", "y", "z"], "calls": ["blurx"]},
- {"name": "blurz", "vars": ["x", "y", "z"], "calls": ["blury"]},
- {"name": "interpolated", "vars": ["x", "y"], "calls": ["blurz", "clamped"]},
- {"name": "bilateral_grid", "vars": ["x", "y"], "calls": ["interpolated"]}]}
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/halide_blur.cpp b/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/halide_blur.cpp
deleted file mode 100644
index 7a38dd45fd8bf48ecc1d7489efe991dd320c0b63..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/halide_blur.cpp
+++ /dev/null
@@ -1,32 +0,0 @@
-#include <Halide.h>
-using namespace Halide;
-
-#define AUTOTUNE_HOOK(x)
-#define BASELINE_HOOK(x)
-
-int main(int argc, char **argv) {
-
- ImageParam in_img(UInt(16), 2);
- Func blur_x("blur_x"), blur_y("blur_y");
- Var x("x"), y("y"), xi("xi"), yi("yi");
-
- Func input;
- input(x,y) = in_img(clamp(x, 1, in_img.width()-1),
- clamp(y, 1, in_img.height())-1);
-
- // The algorithm
- blur_x(x, y) = (input(x, y) + input(x+1, y) + input(x+2, y))/3;
- blur_y(x, y) = (blur_x(x, y) + blur_x(x, y+1) + blur_x(x, y+2))/3;
-
- AUTOTUNE_HOOK(blur_y);
-
- // How to schedule it
- blur_y.split(y, y, yi, 8).parallel(y).vectorize(x, 8);
- blur_x.store_at(blur_y, y).compute_at(blur_y, yi).vectorize(x, 8);
-
- BASELINE_HOOK(blur_y);
-
- blur_y.compile_to_file("halide_blur", in_img);
-
- return 0;
-}
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/halide_blur.settings b/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/halide_blur.settings
deleted file mode 100644
index af0deeac34966616ff0f0af7a008c0c6f74ef2cb..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/halide_blur.settings
+++ /dev/null
@@ -1,4 +0,0 @@
-{"input_size": "4096, 4096",
- "functions": [
- {"name": "blur_x", "vars": ["x", "y"], "calls": []},
- {"name": "blur_y", "vars": ["x", "y"], "calls": ["blur_x"]}]}
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/interpolate-simple.cpp b/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/interpolate-simple.cpp
deleted file mode 100644
index 74d141721db7e22667505f35d1c894d081ae064d..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/interpolate-simple.cpp
+++ /dev/null
@@ -1,208 +0,0 @@
-#include "Halide.h"
-
-#define AUTOTUNE_HOOK(x)
-#define BASELINE_HOOK(x)
-
-using namespace Halide;
-
-#include <iostream>
-#include <limits>
-
-#include <sys/time.h>
-
-using std::vector;
-
-double now() {
- struct timeval tv;
- gettimeofday(&tv, NULL);
- static bool first_call = true;
- static time_t first_sec = 0;
- if (first_call) {
- first_call = false;
- first_sec = tv.tv_sec;
- }
- assert(tv.tv_sec >= first_sec);
- return (tv.tv_sec - first_sec) + (tv.tv_usec / 1000000.0);
-}
-
-int main(int argc, char **argv) {
- ImageParam input(Float(32), 3, "input");
-
- const unsigned int levels = 3;
-
- Func downsampled[levels];
- Func downx[levels];
- Func interpolated[levels];
- Func upsampled[levels];
- Func upsampledx[levels];
- Var x("x"), y("y"), c("c");
-
- downsampled[0] = Func("downsampled");
- downx[0] = Func("downx");
- interpolated[0] = Func("interpolated");
- upsampled[0] = Func("upsampled");
- upsampledx[0] = Func("upsampledx");
-
- Func clamped("clamped");
- clamped(x, y, c) = input(clamp(x, 0, input.width()-1), clamp(y, 0, input.height()-1), c);
-
- // This triggers a bug in llvm 3.3 (3.2 and trunk are fine), so we
- // rewrite it in a way that doesn't trigger the bug. The rewritten
- // form assumes the input alpha is zero or one.
- // downsampled[0](x, y, c) = select(c < 3, clamped(x, y, c) * clamped(x, y, 3), clamped(x, y, 3));
- downsampled[0](x, y, c) = clamped(x, y, c) * clamped(x, y, 3);
-
- for (unsigned int l = 1; l < levels; ++l) {
- downx[l] = Func("downx");
- downsampled[l] = Func("downsampled");
- downx[l](x, y, c) = (downsampled[l-1](x*2-1, y, c) +
- 2.0f * downsampled[l-1](x*2, y, c) +
- downsampled[l-1](x*2+1, y, c)) * 0.25f;
- downsampled[l](x, y, c) = (downx[l](x, y*2-1, c) +
- 2.0f * downx[l](x, y*2, c) +
- downx[l](x, y*2+1, c)) * 0.25f;
- }
- interpolated[levels-1] = Func("interpolated");
- interpolated[levels-1](x, y, c) = downsampled[levels-1](x, y, c);
- for (unsigned int l = levels-2; l < levels; --l) {
- upsampledx[l] = Func("upsampledx");
- upsampled[l] = Func("upsampled");
- interpolated[l] = Func("interpolated");
- upsampledx[l](x, y, c) = select((x % 2) == 0,
- interpolated[l+1](x/2, y, c),
- 0.5f * (interpolated[l+1](x/2, y, c) +
- interpolated[l+1](x/2+1, y, c)));
- upsampled[l](x, y, c) = select((y % 2) == 0,
- upsampledx[l](x, y/2, c),
- 0.5f * (upsampledx[l](x, y/2, c) +
- upsampledx[l](x, y/2+1, c)));
- interpolated[l](x, y, c) = downsampled[l](x, y, c) + (1.0f - downsampled[l](x, y, 3)) * upsampled[l](x, y, c);
- }
-
- Func normalize("normalize");
- normalize(x, y, c) = interpolated[0](x, y, c) / interpolated[0](x, y, 3);
-
- Func final("final");
- final(x, y, c) = normalize(x, y, c);
-
- AUTOTUNE_HOOK(final);
-
- int sched;
- char *target = getenv("HL_TARGET");
- if (target && std::string(target) == "ptx") {
- sched = 4;
- } else {
- sched = 2;
- }
-
- switch (sched) {
- case 0:
- {
- //std::cout << "Flat schedule." << std::endl;
- for (unsigned int l = 0; l < levels; ++l) {
- downsampled[l].compute_root();
- interpolated[l].compute_root();
- }
- final.compute_root();
- break;
- }
- case 1:
- {
- //std::cout << "Flat schedule with vectorization." << std::endl;
- for (unsigned int l = 0; l < levels; ++l) {
- downsampled[l].compute_root().vectorize(x,4);
- interpolated[l].compute_root().vectorize(x,4);
- }
- final.compute_root();
- break;
- }
- case 2:
- {
- Var xi, yi;
- //std::cout << "Flat schedule with parallelization + vectorization." << std::endl;
- clamped.compute_root().parallel(y).reorder(c, x, y).reorder_storage(c, x, y).vectorize(c, 4);
- for (unsigned int l = 1; l < levels-1; ++l) {
- if (l > 0) downsampled[l].compute_root().parallel(y).reorder(c, x, y).reorder_storage(c, x, y).vectorize(c, 4);
- interpolated[l].compute_root().parallel(y).reorder(c, x, y).reorder_storage(c, x, y).vectorize(c, 4);
- interpolated[l].unroll(x, 2).unroll(y, 2);
- }
- final.reorder(c, x, y).bound(c, 0, 3).parallel(y);
- final.tile(x, y, xi, yi, 2, 2).unroll(xi).unroll(yi);
- break;
- }
- case 3:
- {
- //std::cout << "Flat schedule with vectorization sometimes." << std::endl;
- for (unsigned int l = 0; l < levels; ++l) {
- if (l + 4 < levels) {
- Var yo,yi;
- downsampled[l].compute_root().vectorize(x,4);
- interpolated[l].compute_root().vectorize(x,4);
- } else {
- downsampled[l].compute_root();
- interpolated[l].compute_root();
- }
- }
- final.compute_root();
- break;
- }
- case 4:
- {
- //std::cout << "GPU schedule." << std::endl;
-
- // Some gpus don't have enough memory to process the entire
- // image, so we process the image in tiles.
- Var yo, yi, xo, xi;
- final.reorder(c, x, y).bound(c, 0, 3).vectorize(x, 4);
- final.tile(x, y, xo, yo, xi, yi, input.width()/4, input.height()/4);
- normalize.compute_at(final, xo).reorder(c, x, y).cuda_tile(x, y, 16, 16).unroll(c);
-
- // Start from level 1 to save memory - level zero will be computed on demand
- for (unsigned int l = 1; l < levels; ++l) {
- int tile_size = 32 >> l;
- if (tile_size < 1) tile_size = 1;
- if (tile_size > 16) tile_size = 16;
- downsampled[l].compute_root().cuda_tile(x, y, c, tile_size, tile_size, 4);
- interpolated[l].compute_at(final, xo).cuda_tile(x, y, c, tile_size, tile_size, 4);
- }
-
- break;
- }
- default:
- assert(0 && "No schedule with this number.");
- }
-
- BASELINE_HOOK(final);
-
-#if 0
- // JIT compile the pipeline eagerly, so we don't interfere with timing
- final.compile_jit();
-
- // Image<float> in_png = load<float>(argv[1]);
- Image<float> out(2048, 2048, 3);
- // assert(in_png.channels() == 4);
- // input.set(in_png);
- final.infer_input_bounds(out);
-
- std::cout << "Running... " << std::endl;
- double min = std::numeric_limits<double>::infinity();
- const unsigned int iters = 20;
-
- for (unsigned int x = 0; x < iters; ++x) {
- double before = now();
- final.realize(out);
- double after = now();
- double amt = after - before;
-
- std::cout << " " << amt * 1000 << std::endl;
- if (amt < min) min = amt;
-
- }
- std::cout << " took " << min * 1000 << " msec." << std::endl;
-
- // vector<Argument> args;
- // args.push_back(input);
- // final.compile_to_assembly("test.s", args);
- // save(out, argv[2]);
-#endif
-}
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/interpolate-simple.settings b/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/interpolate-simple.settings
deleted file mode 100644
index cffd184ed6bc406bb88fb52d1e67ac2349df9532..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/interpolate-simple.settings
+++ /dev/null
@@ -1,185 +0,0 @@
-{
- "functions": [
- {
- "calls": [],
- "name": "clamped",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "clamped"
- ],
- "name": "downsampled",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downx$2"
- ],
- "name": "downsampled$2",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downx$3"
- ],
- "name": "downsampled$3",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled"
- ],
- "name": "downx$2",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled$2"
- ],
- "name": "downx$3",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled$3"
- ],
- "name": "interpolated$2",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled$2",
- "upsampled$2"
- ],
- "name": "interpolated$3",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled",
- "upsampled$3"
- ],
- "name": "interpolated$4",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "interpolated$4"
- ],
- "name": "normalize",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "upsampledx$2"
- ],
- "name": "upsampled$2",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "upsampledx$3"
- ],
- "name": "upsampled$3",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "interpolated$2"
- ],
- "name": "upsampledx$2",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "interpolated$3"
- ],
- "name": "upsampledx$3",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "normalize"
- ],
- "name": "final",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- }
- ],
- "input_size": "2048, 2048, 3"
-}
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/interpolate-simplest.cpp b/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/interpolate-simplest.cpp
deleted file mode 100644
index cf570558360236d16b21379901e40b3ee8481fd4..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/interpolate-simplest.cpp
+++ /dev/null
@@ -1,208 +0,0 @@
-#include "Halide.h"
-
-#define AUTOTUNE_HOOK(x)
-#define BASELINE_HOOK(x)
-
-using namespace Halide;
-
-#include <iostream>
-#include <limits>
-
-#include <sys/time.h>
-
-using std::vector;
-
-double now() {
- struct timeval tv;
- gettimeofday(&tv, NULL);
- static bool first_call = true;
- static time_t first_sec = 0;
- if (first_call) {
- first_call = false;
- first_sec = tv.tv_sec;
- }
- assert(tv.tv_sec >= first_sec);
- return (tv.tv_sec - first_sec) + (tv.tv_usec / 1000000.0);
-}
-
-int main(int argc, char **argv) {
- ImageParam input(Float(32), 3, "input");
-
- const unsigned int levels = 2;
-
- Func downsampled[levels];
- Func downx[levels];
- Func interpolated[levels];
- Func upsampled[levels];
- Func upsampledx[levels];
- Var x("x"), y("y"), c("c");
-
- downsampled[0] = Func("downsampled");
- downx[0] = Func("downx");
- interpolated[0] = Func("interpolated");
- upsampled[0] = Func("upsampled");
- upsampledx[0] = Func("upsampledx");
-
- Func clamped("clamped");
- clamped(x, y, c) = input(clamp(x, 0, input.width()-1), clamp(y, 0, input.height()-1), c);
-
- // This triggers a bug in llvm 3.3 (3.2 and trunk are fine), so we
- // rewrite it in a way that doesn't trigger the bug. The rewritten
- // form assumes the input alpha is zero or one.
- // downsampled[0](x, y, c) = select(c < 3, clamped(x, y, c) * clamped(x, y, 3), clamped(x, y, 3));
- downsampled[0](x, y, c) = clamped(x, y, c) * clamped(x, y, 3);
-
- for (unsigned int l = 1; l < levels; ++l) {
- downx[l] = Func("downx");
- downsampled[l] = Func("downsampled");
- downx[l](x, y, c) = (downsampled[l-1](x*2-1, y, c) +
- 2.0f * downsampled[l-1](x*2, y, c) +
- downsampled[l-1](x*2+1, y, c)) * 0.25f;
- downsampled[l](x, y, c) = (downx[l](x, y*2-1, c) +
- 2.0f * downx[l](x, y*2, c) +
- downx[l](x, y*2+1, c)) * 0.25f;
- }
- interpolated[levels-1] = Func("interpolated");
- interpolated[levels-1](x, y, c) = downsampled[levels-1](x, y, c);
- for (unsigned int l = levels-2; l < levels; --l) {
- upsampledx[l] = Func("upsampledx");
- upsampled[l] = Func("upsampled");
- interpolated[l] = Func("interpolated");
- upsampledx[l](x, y, c) = select((x % 2) == 0,
- interpolated[l+1](x/2, y, c),
- 0.5f * (interpolated[l+1](x/2, y, c) +
- interpolated[l+1](x/2+1, y, c)));
- upsampled[l](x, y, c) = select((y % 2) == 0,
- upsampledx[l](x, y/2, c),
- 0.5f * (upsampledx[l](x, y/2, c) +
- upsampledx[l](x, y/2+1, c)));
- interpolated[l](x, y, c) = downsampled[l](x, y, c) + (1.0f - downsampled[l](x, y, 3)) * upsampled[l](x, y, c);
- }
-
- Func normalize("normalize");
- normalize(x, y, c) = interpolated[0](x, y, c) / interpolated[0](x, y, 3);
-
- Func final("final");
- final(x, y, c) = normalize(x, y, c);
-
- AUTOTUNE_HOOK(final);
-
- int sched;
- char *target = getenv("HL_TARGET");
- if (target && std::string(target) == "ptx") {
- sched = 4;
- } else {
- sched = 2;
- }
-
- switch (sched) {
- case 0:
- {
- //std::cout << "Flat schedule." << std::endl;
- for (unsigned int l = 0; l < levels; ++l) {
- downsampled[l].compute_root();
- interpolated[l].compute_root();
- }
- final.compute_root();
- break;
- }
- case 1:
- {
- //std::cout << "Flat schedule with vectorization." << std::endl;
- for (unsigned int l = 0; l < levels; ++l) {
- downsampled[l].compute_root().vectorize(x,4);
- interpolated[l].compute_root().vectorize(x,4);
- }
- final.compute_root();
- break;
- }
- case 2:
- {
- Var xi, yi;
- //std::cout << "Flat schedule with parallelization + vectorization." << std::endl;
- clamped.compute_root().parallel(y).reorder(c, x, y).reorder_storage(c, x, y).vectorize(c, 4);
- for (unsigned int l = 1; l < levels-1; ++l) {
- if (l > 0) downsampled[l].compute_root().parallel(y).reorder(c, x, y).reorder_storage(c, x, y).vectorize(c, 4);
- interpolated[l].compute_root().parallel(y).reorder(c, x, y).reorder_storage(c, x, y).vectorize(c, 4);
- interpolated[l].unroll(x, 2).unroll(y, 2);
- }
- final.reorder(c, x, y).bound(c, 0, 3).parallel(y);
- final.tile(x, y, xi, yi, 2, 2).unroll(xi).unroll(yi);
- break;
- }
- case 3:
- {
- //std::cout << "Flat schedule with vectorization sometimes." << std::endl;
- for (unsigned int l = 0; l < levels; ++l) {
- if (l + 4 < levels) {
- Var yo,yi;
- downsampled[l].compute_root().vectorize(x,4);
- interpolated[l].compute_root().vectorize(x,4);
- } else {
- downsampled[l].compute_root();
- interpolated[l].compute_root();
- }
- }
- final.compute_root();
- break;
- }
- case 4:
- {
- //std::cout << "GPU schedule." << std::endl;
-
- // Some gpus don't have enough memory to process the entire
- // image, so we process the image in tiles.
- Var yo, yi, xo, xi;
- final.reorder(c, x, y).bound(c, 0, 3).vectorize(x, 4);
- final.tile(x, y, xo, yo, xi, yi, input.width()/4, input.height()/4);
- normalize.compute_at(final, xo).reorder(c, x, y).cuda_tile(x, y, 16, 16).unroll(c);
-
- // Start from level 1 to save memory - level zero will be computed on demand
- for (unsigned int l = 1; l < levels; ++l) {
- int tile_size = 32 >> l;
- if (tile_size < 1) tile_size = 1;
- if (tile_size > 16) tile_size = 16;
- downsampled[l].compute_root().cuda_tile(x, y, c, tile_size, tile_size, 4);
- interpolated[l].compute_at(final, xo).cuda_tile(x, y, c, tile_size, tile_size, 4);
- }
-
- break;
- }
- default:
- assert(0 && "No schedule with this number.");
- }
-
- BASELINE_HOOK(final);
-
-#if 0
- // JIT compile the pipeline eagerly, so we don't interfere with timing
- final.compile_jit();
-
- // Image<float> in_png = load<float>(argv[1]);
- Image<float> out(2048, 2048, 3);
- // assert(in_png.channels() == 4);
- // input.set(in_png);
- final.infer_input_bounds(out);
-
- std::cout << "Running... " << std::endl;
- double min = std::numeric_limits<double>::infinity();
- const unsigned int iters = 20;
-
- for (unsigned int x = 0; x < iters; ++x) {
- double before = now();
- final.realize(out);
- double after = now();
- double amt = after - before;
-
- std::cout << " " << amt * 1000 << std::endl;
- if (amt < min) min = amt;
-
- }
- std::cout << " took " << min * 1000 << " msec." << std::endl;
-
- // vector<Argument> args;
- // args.push_back(input);
- // final.compile_to_assembly("test.s", args);
- // save(out, argv[2]);
-#endif
-}
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/interpolate-simplest.settings b/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/interpolate-simplest.settings
deleted file mode 100644
index 5f22d20f5f9cf355bfd07ca408264f73031a14d0..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/interpolate-simplest.settings
+++ /dev/null
@@ -1,124 +0,0 @@
-{
- "functions": [
- {
- "calls": [],
- "name": "clamped",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "clamped"
- ],
- "name": "downsampled",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downx$2"
- ],
- "name": "downsampled$2",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled"
- ],
- "name": "downx$2",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled$2"
- ],
- "name": "interpolated$2",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled",
- "upsampled$2"
- ],
- "name": "interpolated$3",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "interpolated$3"
- ],
- "name": "normalize",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "upsampledx$2"
- ],
- "name": "upsampled$2",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "interpolated$2"
- ],
- "name": "upsampledx$2",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "normalize"
- ],
- "name": "final",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- }
- ],
- "input_size": "2048, 2048, 3"
-}
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/interpolate.cpp b/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/interpolate.cpp
deleted file mode 100644
index 1ca4ae5bd352fa524ed6aeafbe5795c913e1f6b8..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/interpolate.cpp
+++ /dev/null
@@ -1,208 +0,0 @@
-#include "Halide.h"
-
-#define AUTOTUNE_HOOK(x)
-#define BASELINE_HOOK(x)
-
-using namespace Halide;
-
-#include <iostream>
-#include <limits>
-
-#include <sys/time.h>
-
-using std::vector;
-
-double now() {
- struct timeval tv;
- gettimeofday(&tv, NULL);
- static bool first_call = true;
- static time_t first_sec = 0;
- if (first_call) {
- first_call = false;
- first_sec = tv.tv_sec;
- }
- assert(tv.tv_sec >= first_sec);
- return (tv.tv_sec - first_sec) + (tv.tv_usec / 1000000.0);
-}
-
-int main(int argc, char **argv) {
- ImageParam input(Float(32), 3, "input");
-
- const unsigned int levels = 10;
-
- Func downsampled[levels];
- Func downx[levels];
- Func interpolated[levels];
- Func upsampled[levels];
- Func upsampledx[levels];
- Var x("x"), y("y"), c("c");
-
- downsampled[0] = Func("downsampled");
- downx[0] = Func("downx");
- interpolated[0] = Func("interpolated");
- upsampled[0] = Func("upsampled");
- upsampledx[0] = Func("upsampledx");
-
- Func clamped("clamped");
- clamped(x, y, c) = input(clamp(x, 0, input.width()-1), clamp(y, 0, input.height()-1), c);
-
- // This triggers a bug in llvm 3.3 (3.2 and trunk are fine), so we
- // rewrite it in a way that doesn't trigger the bug. The rewritten
- // form assumes the input alpha is zero or one.
- // downsampled[0](x, y, c) = select(c < 3, clamped(x, y, c) * clamped(x, y, 3), clamped(x, y, 3));
- downsampled[0](x, y, c) = clamped(x, y, c) * clamped(x, y, 3);
-
- for (unsigned int l = 1; l < levels; ++l) {
- downx[l] = Func("downx");
- downsampled[l] = Func("downsampled");
- downx[l](x, y, c) = (downsampled[l-1](x*2-1, y, c) +
- 2.0f * downsampled[l-1](x*2, y, c) +
- downsampled[l-1](x*2+1, y, c)) * 0.25f;
- downsampled[l](x, y, c) = (downx[l](x, y*2-1, c) +
- 2.0f * downx[l](x, y*2, c) +
- downx[l](x, y*2+1, c)) * 0.25f;
- }
- interpolated[levels-1] = Func("interpolated");
- interpolated[levels-1](x, y, c) = downsampled[levels-1](x, y, c);
- for (unsigned int l = levels-2; l < levels; --l) {
- upsampledx[l] = Func("upsampledx");
- upsampled[l] = Func("upsampled");
- interpolated[l] = Func("interpolated");
- upsampledx[l](x, y, c) = select((x % 2) == 0,
- interpolated[l+1](x/2, y, c),
- 0.5f * (interpolated[l+1](x/2, y, c) +
- interpolated[l+1](x/2+1, y, c)));
- upsampled[l](x, y, c) = select((y % 2) == 0,
- upsampledx[l](x, y/2, c),
- 0.5f * (upsampledx[l](x, y/2, c) +
- upsampledx[l](x, y/2+1, c)));
- interpolated[l](x, y, c) = downsampled[l](x, y, c) + (1.0f - downsampled[l](x, y, 3)) * upsampled[l](x, y, c);
- }
-
- Func normalize("normalize");
- normalize(x, y, c) = interpolated[0](x, y, c) / interpolated[0](x, y, 3);
-
- Func final("final");
- final(x, y, c) = normalize(x, y, c);
-
- AUTOTUNE_HOOK(final);
-
- int sched;
- char *target = getenv("HL_TARGET");
- if (target && std::string(target) == "ptx") {
- sched = 4;
- } else {
- sched = 2;
- }
-
- switch (sched) {
- case 0:
- {
- //std::cout << "Flat schedule." << std::endl;
- for (unsigned int l = 0; l < levels; ++l) {
- downsampled[l].compute_root();
- interpolated[l].compute_root();
- }
- final.compute_root();
- break;
- }
- case 1:
- {
- //std::cout << "Flat schedule with vectorization." << std::endl;
- for (unsigned int l = 0; l < levels; ++l) {
- downsampled[l].compute_root().vectorize(x,4);
- interpolated[l].compute_root().vectorize(x,4);
- }
- final.compute_root();
- break;
- }
- case 2:
- {
- Var xi, yi;
- //std::cout << "Flat schedule with parallelization + vectorization." << std::endl;
- clamped.compute_root().parallel(y).reorder(c, x, y).reorder_storage(c, x, y).vectorize(c, 4);
- for (unsigned int l = 1; l < levels-1; ++l) {
- if (l > 0) downsampled[l].compute_root().parallel(y).reorder(c, x, y).reorder_storage(c, x, y).vectorize(c, 4);
- interpolated[l].compute_root().parallel(y).reorder(c, x, y).reorder_storage(c, x, y).vectorize(c, 4);
- interpolated[l].unroll(x, 2).unroll(y, 2);
- }
- final.reorder(c, x, y).bound(c, 0, 3).parallel(y);
- final.tile(x, y, xi, yi, 2, 2).unroll(xi).unroll(yi);
- break;
- }
- case 3:
- {
- //std::cout << "Flat schedule with vectorization sometimes." << std::endl;
- for (unsigned int l = 0; l < levels; ++l) {
- if (l + 4 < levels) {
- Var yo,yi;
- downsampled[l].compute_root().vectorize(x,4);
- interpolated[l].compute_root().vectorize(x,4);
- } else {
- downsampled[l].compute_root();
- interpolated[l].compute_root();
- }
- }
- final.compute_root();
- break;
- }
- case 4:
- {
- //std::cout << "GPU schedule." << std::endl;
-
- // Some gpus don't have enough memory to process the entire
- // image, so we process the image in tiles.
- Var yo, yi, xo, xi;
- final.reorder(c, x, y).bound(c, 0, 3).vectorize(x, 4);
- final.tile(x, y, xo, yo, xi, yi, input.width()/4, input.height()/4);
- normalize.compute_at(final, xo).reorder(c, x, y).cuda_tile(x, y, 16, 16).unroll(c);
-
- // Start from level 1 to save memory - level zero will be computed on demand
- for (unsigned int l = 1; l < levels; ++l) {
- int tile_size = 32 >> l;
- if (tile_size < 1) tile_size = 1;
- if (tile_size > 16) tile_size = 16;
- downsampled[l].compute_root().cuda_tile(x, y, c, tile_size, tile_size, 4);
- interpolated[l].compute_at(final, xo).cuda_tile(x, y, c, tile_size, tile_size, 4);
- }
-
- break;
- }
- default:
- assert(0 && "No schedule with this number.");
- }
-
- BASELINE_HOOK(final);
-
-#if 0
- // JIT compile the pipeline eagerly, so we don't interfere with timing
- final.compile_jit();
-
- // Image<float> in_png = load<float>(argv[1]);
- Image<float> out(2048, 2048, 3);
- // assert(in_png.channels() == 4);
- // input.set(in_png);
- final.infer_input_bounds(out);
-
- std::cout << "Running... " << std::endl;
- double min = std::numeric_limits<double>::infinity();
- const unsigned int iters = 20;
-
- for (unsigned int x = 0; x < iters; ++x) {
- double before = now();
- final.realize(out);
- double after = now();
- double amt = after - before;
-
- std::cout << " " << amt * 1000 << std::endl;
- if (amt < min) min = amt;
-
- }
- std::cout << " took " << min * 1000 << " msec." << std::endl;
-
- // vector<Argument> args;
- // args.push_back(input);
- // final.compile_to_assembly("test.s", args);
- // save(out, argv[2]);
-#endif
-}
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/interpolate.settings b/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/interpolate.settings
deleted file mode 100644
index 3a51d8062674581182fb204ddb943f85cd3b4de4..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/interpolate.settings
+++ /dev/null
@@ -1,612 +0,0 @@
-{
- "functions": [
- {
- "calls": [],
- "name": "clamped",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "clamped"
- ],
- "name": "downsampled",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downx$10"
- ],
- "name": "downsampled$10",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downx$2"
- ],
- "name": "downsampled$2",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downx$3"
- ],
- "name": "downsampled$3",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downx$4"
- ],
- "name": "downsampled$4",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downx$5"
- ],
- "name": "downsampled$5",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downx$6"
- ],
- "name": "downsampled$6",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downx$7"
- ],
- "name": "downsampled$7",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downx$8"
- ],
- "name": "downsampled$8",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downx$9"
- ],
- "name": "downsampled$9",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled$9"
- ],
- "name": "downx$10",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled"
- ],
- "name": "downx$2",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled$2"
- ],
- "name": "downx$3",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled$3"
- ],
- "name": "downx$4",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled$4"
- ],
- "name": "downx$5",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled$5"
- ],
- "name": "downx$6",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled$6"
- ],
- "name": "downx$7",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled$7"
- ],
- "name": "downx$8",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled$8"
- ],
- "name": "downx$9",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled$2",
- "upsampled$9"
- ],
- "name": "interpolated$10",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled",
- "upsampled$10"
- ],
- "name": "interpolated$11",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled$10"
- ],
- "name": "interpolated$2",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled$9",
- "upsampled$2"
- ],
- "name": "interpolated$3",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled$8",
- "upsampled$3"
- ],
- "name": "interpolated$4",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled$7",
- "upsampled$4"
- ],
- "name": "interpolated$5",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled$6",
- "upsampled$5"
- ],
- "name": "interpolated$6",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled$5",
- "upsampled$6"
- ],
- "name": "interpolated$7",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled$4",
- "upsampled$7"
- ],
- "name": "interpolated$8",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "downsampled$3",
- "upsampled$8"
- ],
- "name": "interpolated$9",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "interpolated$11"
- ],
- "name": "normalize",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "upsampledx$10"
- ],
- "name": "upsampled$10",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "upsampledx$2"
- ],
- "name": "upsampled$2",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "upsampledx$3"
- ],
- "name": "upsampled$3",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "upsampledx$4"
- ],
- "name": "upsampled$4",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "upsampledx$5"
- ],
- "name": "upsampled$5",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "upsampledx$6"
- ],
- "name": "upsampled$6",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "upsampledx$7"
- ],
- "name": "upsampled$7",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "upsampledx$8"
- ],
- "name": "upsampled$8",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "upsampledx$9"
- ],
- "name": "upsampled$9",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "interpolated$10"
- ],
- "name": "upsampledx$10",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "interpolated$2"
- ],
- "name": "upsampledx$2",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "interpolated$3"
- ],
- "name": "upsampledx$3",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "interpolated$4"
- ],
- "name": "upsampledx$4",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "interpolated$5"
- ],
- "name": "upsampledx$5",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "interpolated$6"
- ],
- "name": "upsampledx$6",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "interpolated$7"
- ],
- "name": "upsampledx$7",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "interpolated$8"
- ],
- "name": "upsampledx$8",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "interpolated$9"
- ],
- "name": "upsampledx$9",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- },
- {
- "calls": [
- "normalize"
- ],
- "name": "final",
- "update_calls": [],
- "vars": [
- "x",
- "y",
- "c"
- ]
- }
- ],
- "input_size": "1024, 1024, 3"
-}
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/wavelet.cpp b/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/wavelet.cpp
deleted file mode 100644
index e2cb008790ac0161a2365388744cb9b482a6d7b8..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/wavelet.cpp
+++ /dev/null
@@ -1,93 +0,0 @@
-#include "Halide.h"
-
-#define AUTOTUNE_HOOK(x)
-#define BASELINE_HOOK(x)
-
-using namespace Halide;
-
-Var x("x"), y("y"), c("c");
-
-Func haar_x(Func in) {
- Func out;
- out(x, y, c) = select(c == 0,
- (in(2*x, y) + in(2*x+1, y)),
- (in(2*x, y) - in(2*x+1, y)))/2;
- out.unroll(c, 2);
- return out;
-}
-
-Func inverse_haar_x(Func in) {
- Func out;
- out(x, y) = select(x%2 == 0,
- in(x/2, y, 0) + in(x/2, y, 1),
- in(x/2, y, 0) - in(x/2, y, 1));
- out.unroll(x, 2);
- return out;
-}
-
-
-const float D0 = 0.4829629131445341f;
-const float D1 = 0.83651630373780772f;
-const float D2 = 0.22414386804201339f;
-const float D3 = -0.12940952255126034f;
-
-/*
-const float D0 = 0.34150635f;
-const float D1 = 0.59150635f;
-const float D2 = 0.15849365f;
-const float D3 = -0.1830127f;
-*/
-
-Func daubechies_x(Func in) {
- Func out;
- out(x, y, c) = select(c == 0,
- D0*in(2*x-1, y) + D1*in(2*x, y) + D2*in(2*x+1, y) + D3*in(2*x+2, y),
- D3*in(2*x-1, y) - D2*in(2*x, y) + D1*in(2*x+1, y) - D0*in(2*x+2, y));
- //out.unroll(c, 2);
- return out;
-}
-
-Func inverse_daubechies_x(Func in) {
- Func out("inv_daub_x");
- out(x, y) = select(x%2 == 0,
- D2*in(x/2, y, 0) + D1*in(x/2, y, 1) + D0*in(x/2+1, y, 0) + D3*in(x/2+1, y, 1),
- D3*in(x/2, y, 0) - D0*in(x/2, y, 1) + D1*in(x/2+1, y, 0) - D2*in(x/2+1, y, 1));
- //out.unroll(x, 2);
- return out;
-}
-
-int main(int argc, char **argv) {
-
- ImageParam image(Float(32), 2);
- ImageParam wavelet(Float(32), 3);
-
- // Add a boundary condition for daubechies
- Func clamped;
- clamped(x, y) = image(clamp(x, 0, image.width()-1),
- clamp(y, 0, image.height()-1));
- Func wavelet_clamped("wavelet_clamped");
- wavelet_clamped(x, y, c) = wavelet(clamp(x, 0, wavelet.width()-1),
- clamp(y, 0, wavelet.height()-1), c);
-
-
- // Func inv_haar_x = inverse_haar_x(wavelet_clamped);
- // inv_haar_x.compile_to_file("inverse_haar_x", wavelet);
-
- // Func for_haar_x = haar_x(clamped);
- // for_haar_x.compile_to_file("haar_x", image);
-
- Func inv_daub_x = inverse_daubechies_x(wavelet_clamped);
- //inv_daub_x.compile_to_file("inverse_daubechies_x", wavelet);
-
- AUTOTUNE_HOOK(inv_daub_x);
- inv_daub_x.unroll(x, 2).vectorize(x, 8).parallel(y);
- BASELINE_HOOK(inv_daub_x);
-
- // Func for_daub_x = daubechies_x(clamped);
- //for_daub_x.compile_to_file("daubechies_x", image);
-
- return 0;
-}
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/wavelet.settings b/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/wavelet.settings
deleted file mode 100644
index 6fbb5c4006dd77fee03f8635de2b22079eb0a908..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/apps/wavelet.settings
+++ /dev/null
@@ -1,4 +0,0 @@
-{"input_size": "2048, 2048",
- "functions": [
- {"name": "wavelet_clamped", "vars": ["x", "y", "c"], "calls": []},
- {"name": "inv_daub_x", "vars": ["x", "y"], "calls": ["wavelet_clamped"]}]}
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/halidetuner.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/halidetuner.py
deleted file mode 100755
index 08e6732575557e41736fb71a321ee7190e181e7e..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/halidetuner.py
+++ /dev/null
@@ -1,682 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-#
-# Example of synthesizing Halide schedules using OpenTuner. This program
-# expects a compiled version of Halide to exist at ~/Halide or at the location
-# specified by --halide-dir.
-#
-# Halide programs must be modified by:
-# 1) Inserting AUTOTUNE_HOOK(Func) directly after the algorithm definition
-# in main()
-# 2) Creating a settings file that describes the functions and variables
-# (see apps/halide_blur.settings for an example)
-#
-# Halide can be found here: https://github.com/halide/Halide
-#
-
-import adddeps # fix sys.path
-
-import argparse
-import collections
-import hashlib
-import json
-import logging
-import math
-import os
-import re
-import subprocess
-import tempfile
-import textwrap
-from cStringIO import StringIO
-from fn import _
-from pprint import pprint
-
-import opentuner
-from opentuner.search.manipulator import ConfigurationManipulator
-from opentuner.search.manipulator import PowerOfTwoParameter
-from opentuner.search.manipulator import PermutationParameter
-from opentuner.search.manipulator import BooleanParameter
-from opentuner.search.manipulator import ScheduleParameter
-
-
-COMPILE_CMD = (
- '{args.cxx} "{cpp}" -o "{bin}" -I "{args.halide_dir}/include" '
- '"{args.halide_dir}/bin/$BUILD_PREFIX/libHalide.a" -ldl -lcurses -lpthread {args.cxxflags} '
- '-DAUTOTUNE_N="{args.input_size}" -DAUTOTUNE_TRIALS={args.trials} '
- '-DAUTOTUNE_LIMIT={limit} -fno-rtti')
-
-log = logging.getLogger('halide')
-
-parser = argparse.ArgumentParser(parents=opentuner.argparsers())
-parser.add_argument('source', help='Halide source file annotated with '
- 'AUTOTUNE_HOOK')
-parser.add_argument('--halide-dir', default=os.path.expanduser('~/Halide'),
- help='Installation directory for Halide')
-parser.add_argument('--input-size',
- help='Input size to test with')
-parser.add_argument('--trials', default=3, type=int,
- help='Number of times to test each schedule')
-parser.add_argument('--nesting', default=2, type=int,
- help='Maximum depth for generated loops')
-parser.add_argument('--max-split-factor', default=8, type=int,
- help='The largest value a single split() can add')
-parser.add_argument('--compile-command', default=COMPILE_CMD,
- help='How to compile generated C++ code')
-parser.add_argument('--cxx', default='c++',
- help='C++ compiler to use (e.g., g++ or clang++)')
-parser.add_argument('--cxxflags', default='',
- help='Extra flags to the C++ compiler')
-parser.add_argument('--tmp-dir',
- default=('/run/shm' if os.access('/run/shm', os.W_OK)
- else '/tmp'),
- help='Where to store generated tests')
-parser.add_argument('--settings-file',
- help='Override location of json encoded settings')
-parser.add_argument('--debug-error',
- help='Stop on errors matching a given string')
-parser.add_argument('--limit', type=float, default=30,
- help='Kill compile + runs taking too long (seconds)')
-parser.add_argument('--memory-limit', type=int, default=1024 ** 3,
- help='Set memory ulimit on unix based systems')
-parser.add_argument('--enable-unroll', action='store_true',
- help='Enable .unroll(...) generation')
-parser.add_argument('--enable-store-at', action='store_true',
- help='Never generate .store_at(...)')
-parser.add_argument('--gated-store-reorder', action='store_true',
- help='Only reorder storage if a special parameter is given')
-group = parser.add_mutually_exclusive_group()
-group.add_argument('--random-test', action='store_true',
- help='Generate a random configuration and run it')
-group.add_argument('--random-source', action='store_true',
- help='Generate a random configuration and print source ')
-group.add_argument('--make-settings-file', action='store_true',
- help='Create a skeleton settings file from call graph')
-
-
-# class HalideRandomConfig(opentuner.search.technique.SearchTechnique):
-# def desired_configuration(self):
-# '''
-# inject random configs with no compute_at() calls to kickstart the search process
-# '''
-# cfg = self.manipulator.random()
-# for k in cfg.keys():
-# if re.match('.*_compute_level', k):
-# cfg[k] = LoopLevel.INLINE
-# return cfg
-#
-# technique.register(bandittechniques.AUCBanditMetaTechnique([
-# HalideRandomConfig(),
-# differentialevolution.DifferentialEvolutionAlt(),
-# evolutionarytechniques.UniformGreedyMutation(),
-# evolutionarytechniques.NormalGreedyMutation(mutation_rate=0.3),
-# ], name = "HalideMetaTechnique"))
-
-
-class HalideTuner(opentuner.measurement.MeasurementInterface):
- def __init__(self, args):
- # args.technique = ['HalideMetaTechnique']
- super(HalideTuner, self).__init__(args, program_name=args.source)
- timing_prefix = open(os.path.join(os.path.dirname(__file__),
- 'timing_prefix.h')).read()
- self.template = timing_prefix + open(args.source).read()
- self.min_collection_cost = float('inf')
- if not args.settings_file:
- args.settings_file = os.path.splitext(args.source)[0] + '.settings'
- if not args.make_settings_file:
- with open(args.settings_file) as fd:
- self.settings = json.load(fd)
- self.post_dominators = post_dominators(self.settings)
- if not args.input_size:
- args.input_size = self.settings['input_size']
- else:
- self.settings = None
- self.post_dominators = None
- args.input_size = '1, 1'
- # set "program_version" based on hash of halidetuner.py, program source
- h = hashlib.md5()
- #with open(__file__) as src:
- # h.update(src.read())
- with open(args.source) as src:
- h.update(src.read())
- self._version = h.hexdigest()
-
- def compute_order_parameter(self, func):
- name = func['name']
- schedule_vars = []
- schedule_deps = dict()
- for var in func['vars']:
- schedule_vars.append((var, 0))
- for i in xrange(1, self.args.nesting):
- schedule_vars.append((var, i))
- schedule_deps[(var, i - 1)] = [(var, i)]
- return ScheduleParameter('{0}_compute_order'.format(name), schedule_vars,
- schedule_deps)
-
- def manipulator(self):
- """
- The definition of the manipulator is meant to mimic the Halide::Schedule
- data structure and defines the configuration space to search
- """
- manipulator = HalideConfigurationManipulator(self)
- manipulator.add_parameter(HalideComputeAtScheduleParameter(
- 'schedule', self.args, self.settings['functions'],
- self.post_dominators))
- for func in self.settings['functions']:
- name = func['name']
- manipulator.add_parameter(PermutationParameter(
- '{0}_store_order'.format(name), func['vars']))
- manipulator.add_parameter(
- BooleanParameter('{0}_store_order_enabled'.format(name)))
- manipulator.add_parameter(self.compute_order_parameter(func))
- for var in func['vars']:
- manipulator.add_parameter(PowerOfTwoParameter(
- '{0}_vectorize'.format(name), 1, self.args.max_split_factor))
- manipulator.add_parameter(PowerOfTwoParameter(
- '{0}_unroll'.format(name), 1, self.args.max_split_factor))
- manipulator.add_parameter(BooleanParameter(
- '{0}_parallel'.format(name)))
- for nesting in xrange(1, self.args.nesting):
- manipulator.add_parameter(PowerOfTwoParameter(
- '{0}_splitfactor_{1}_{2}'.format(name, nesting, var),
- 1, self.args.max_split_factor))
-
- return manipulator
-
- def cfg_to_schedule(self, cfg):
- """
- Produce a Halide schedule from a configuration dictionary
- """
- o = StringIO()
- cnt = 0
- temp_vars = list()
- schedule = ComputeAtStoreAtParser(cfg['schedule'], self.post_dominators)
- compute_at = schedule.compute_at
- store_at = schedule.store_at
-
- # build list of all used variable names
- var_names = dict()
- var_name_order = dict()
- for func in self.settings['functions']:
- name = func['name']
- compute_order = cfg['{0}_compute_order'.format(name)]
- for var in func['vars']:
- var_names[(name, var, 0)] = var
- for nesting in xrange(1, self.args.nesting):
- split_factor = cfg.get('{0}_splitfactor_{1}_{2}'.format(
- name, nesting, var), 0)
- if split_factor > 1 and (name, var, nesting - 1) in var_names:
- var_names[(name, var, nesting)] = '_{var}{cnt}'.format(
- func=name, var=var, nesting=nesting, cnt=cnt)
- temp_vars.append(var_names[(name, var, nesting)])
- cnt += 1
- var_name_order[name] = [var_names[(name, v, n)] for v, n in compute_order
- if (name, v, n) in var_names]
-
- # set a schedule for each function
- for func in self.settings['functions']:
- name = func['name']
- inner_var_name = var_name_order[name][-1] # innermost variable in the reordered list for this func
- vectorize = cfg['{0}_vectorize'.format(name)]
- if self.args.enable_unroll:
- unroll = cfg['{0}_unroll'.format(name)]
- else:
- unroll = 1
-
- print >> o, 'Halide::Func(funcs["%s"])' % name
-
- for var in func['vars']:
- # handle all splits
- for nesting in xrange(1, self.args.nesting):
- split_factor = cfg.get('{0}_splitfactor_{1}_{2}'.format(
- name, nesting, var), 0)
- if split_factor <= 1:
- break
-
- for nesting2 in xrange(nesting + 1, self.args.nesting):
- split_factor2 = cfg.get('{0}_splitfactor_{1}_{2}'.format(
- name, nesting2, var), 0)
- if split_factor2 <= 1:
- break
- split_factor *= split_factor2
- var_name = var_names[(name, var, nesting)]
- last_var_name = var_names[(name, var, nesting - 1)]
-
- # apply unroll, vectorize factors to all surrounding splits iff we're the innermost var
- if var_name == inner_var_name:
- split_factor *= unroll
- split_factor *= vectorize
-
- print >> o, '.split({0}, {0}, {1}, {2})'.format(
- last_var_name, var_name, split_factor)
-
- # drop unused variables and truncate (Halide supports only 10 reorders)
- if len(var_name_order[name]) > 1:
- print >> o, '.reorder({0})'.format(
- ', '.join(reversed(var_name_order[name][:10])))
-
- # reorder_storage
- store_order_enabled = cfg['{0}_store_order_enabled'.format(name)]
- if store_order_enabled or not self.args.gated_store_reorder:
- store_order = cfg['{0}_store_order'.format(name)]
- if len(store_order) > 1:
- print >> o, '.reorder_storage({0})'.format(', '.join(store_order))
-
- if unroll > 1:
- # apply unrolling to innermost var
- print >> o, '.unroll({0}, {1})'.format(
- var_name_order[name][-1], unroll * vectorize)
-
- if vectorize > 1:
- # apply vectorization to innermost var
- print >> o, '.vectorize({0}, {1})'.format(
- var_name_order[name][-1], vectorize)
-
- # compute_at(not root)
- if (compute_at[name] is not None and
- len(var_name_order[compute_at[name][0]]) >= compute_at[name][1]):
- at_func, at_idx = compute_at[name]
- try:
- at_var = var_name_order[at_func][-at_idx]
- print >> o, '.compute_at(Halide::Func(funcs["{0}"]), {1})'.format(at_func, at_var)
- if not self.args.enable_store_at:
- pass # disabled
- elif store_at[name] is None:
- print >> o, '.store_root()'
- elif store_at[name] != compute_at[name]:
- at_func, at_idx = store_at[name]
- at_var = var_name_order[at_func][-at_idx]
- print >> o, '.store_at(Halide::Func(funcs["{0}"]), {1})'.format(at_func, at_var)
- except IndexError:
- # this is expected when at_idx is too large
- # TODO: implement a cleaner fix
- pass
- # compute_root
- else:
- parallel = cfg['{0}_parallel'.format(name)]
- if parallel:
- # only apply parallelism to outermost var of root funcs
- print >> o, '.parallel({0})'.format(var_name_order[name][0])
- print >> o, '.compute_root()'
-
- print >> o, ';'
-
- if temp_vars:
- return 'Halide::Var {0};\n{1}'.format(
- ', '.join(temp_vars), o.getvalue())
- else:
- return o.getvalue()
-
- def schedule_to_source(self, schedule):
- """
- Generate a temporary Halide cpp file with schedule inserted
- """
-
- def repl_autotune_hook(match):
- tmpl = '''
- {
- std::map<std::string, Halide::Internal::Function> funcs = Halide::Internal::find_transitive_calls((%(func)s).function());
-
- %(sched)s
-
- _autotune_timing_stub(%(func)s);
- }'''
- return tmpl % {"sched": schedule.replace('\n', '\n '), "func": match.group(1)}
-
- source = re.sub(r'\n\s*AUTOTUNE_HOOK\(\s*([a-zA-Z0-9_]+)\s*\)',
- repl_autotune_hook, self.template)
- return source
-
- def run_schedule(self, schedule, limit):
- """
- Generate a temporary Halide cpp file with schedule inserted and run it
- with our timing harness found in timing_prefix.h.
- """
- return self.run_source(self.schedule_to_source(schedule), limit)
-
- def run_baseline(self):
- """
- Generate a temporary Halide cpp file with schedule inserted and run it
- with our timing harness found in timing_prefix.h.
- """
-
- def repl_autotune_hook(match):
- return '\n\n_autotune_timing_stub(%s);' % match.group(1)
-
- source = re.sub(r'\n\s*BASELINE_HOOK\(\s*([a-zA-Z0-9_]+)\s*\)',
- repl_autotune_hook, self.template)
- return self.run_source(source)
-
- def run_source(self, source, limit=0, extra_args=''):
- cmd = ''
- with tempfile.NamedTemporaryFile(suffix='.cpp', prefix='halide',
- dir=self.args.tmp_dir) as cppfile:
- cppfile.write(source)
- cppfile.flush()
- # binfile = os.path.splitext(cppfile.name)[0] + '.bin'
- # binfile = '/tmp/halide.bin'
- binfile = ''
- with tempfile.NamedTemporaryFile(suffix='.bin', prefix='halide',
- dir=self.args.tmp_dir, delete=False) as binfiletmp:
-
- binfile = binfiletmp.name # unique temp file to allow multiple concurrent tuner runs
- assert(binfile)
- cmd = self.args.compile_command.format(
- cpp=cppfile.name, bin=binfile, args=self.args,
- limit=math.ceil(limit) if limit < float('inf') else 0)
- cmd += ' ' + extra_args
- compile_result = self.call_program(cmd, limit=self.args.limit,
- memory_limit=self.args.memory_limit)
- if compile_result['returncode'] != 0:
- log.error('compile failed: %s', compile_result)
- return None
-
- try:
- result = self.call_program(binfile,
- limit=self.args.limit,
- memory_limit=self.args.memory_limit)
- stdout = result['stdout']
- stderr = result['stderr']
- returncode = result['returncode']
-
- if result['timeout']:
- log.info('compiler timeout %d (%.2f+%.0f cost)', self.args.limit,
- compile_result['time'], self.args.limit)
- return float('inf')
- elif returncode == 142 or returncode == -14:
- log.info('program timeout %d (%.2f+%.2f cost)', math.ceil(limit),
- compile_result['time'], result['time'])
- return None
- elif returncode != 0:
- log.error('invalid schedule (returncode=%d): %s', returncode,
- stderr.strip())
- with tempfile.NamedTemporaryFile(suffix='.cpp', prefix='halide-error',
- dir=self.args.tmp_dir, delete=False) as errfile:
- errfile.write(source)
- log.error('failed schedule logged to %s.\ncompile as `%s`.', errfile.name, cmd)
- if self.args.debug_error is not None and (
- self.args.debug_error in stderr
- or self.args.debug_error == ""):
- self.debug_schedule('/tmp/halideerror.cpp', source)
- return None
- else:
- try:
- time = json.loads(stdout)['time']
- except:
- log.exception('error parsing output: %s', result)
- return None
- log.info('success: %.4f (collection cost %.2f + %.2f)',
- time, compile_result['time'], result['time'])
- self.min_collection_cost = min(
- self.min_collection_cost, result['time'])
- return time
- finally:
- os.unlink(binfile)
-
- def run_cfg(self, cfg, limit=0):
- try:
- schedule = self.cfg_to_schedule(cfg)
- except:
- log.exception('error generating schedule')
- return None
- return self.run_schedule(schedule, limit)
-
- def run(self, desired_result, input, limit):
- time = self.run_cfg(desired_result.configuration.data, limit)
- if time is not None:
- return opentuner.resultsdb.models.Result(time=time)
- else:
- return opentuner.resultsdb.models.Result(state='ERROR',
- time=float('inf'))
-
- def save_final_config(self, configuration):
- """called at the end of tuning"""
- print 'Final Configuration:'
- print self.cfg_to_schedule(configuration.data)
-
- def debug_log_schedule(self, filename, source):
- open(filename, 'w').write(source)
- print 'offending schedule written to {0}'.format(filename)
-
- def debug_schedule(self, filename, source):
- self.debug_log_schedule(filename, source)
- raw_input('press ENTER to continue')
-
- def make_settings_file(self):
- dump_call_graph_dir = os.path.join(os.path.dirname(__file__),
- 'dump-call-graph')
- if not os.path.isdir(dump_call_graph_dir):
- subprocess.check_call(['git', 'clone',
- 'http://github.com/halide/dump-call-graph.git'])
- assert os.path.isdir(dump_call_graph_dir)
-
- dump_call_graph_cpp = os.path.join(dump_call_graph_dir, 'DumpCallGraph.cpp')
- callgraph_file = self.args.settings_file + '.callgraph'
-
- def repl_autotune_hook(match):
- return r'''dump_call_graph("%s", %s);
- printf("{\"time\": 0}\n");
- exit(0);''' % (callgraph_file, match.group(1))
-
- source = re.sub(r'\n\s*AUTOTUNE_HOOK\(\s*([a-zA-Z0-9_]+)\s*\)',
- repl_autotune_hook, self.template)
- # TODO: BUG! - this only works correctly if given an absolute path to the
- # program (or explicit settings file). Otherwise it generates the callgraph
- # in a tmp dir somewhere and fails to find it in a local path here.
- source = open(dump_call_graph_cpp).read() + source
- self.run_source(source, extra_args='-I{0}'.format(dump_call_graph_dir))
- callgraph = json.load(open(callgraph_file))
- settings = {'input_size': '1024, 1024', 'functions': callgraph}
- json.dump(settings, open(self.args.settings_file, 'w'), sort_keys=True,
- indent=2)
- print textwrap.dedent('''
-
- {0} has been generated based on call graph of program.
-
- This file likely needs some manual tweaks in order to work correctly.
- The input size should be changed to have the right number of dimensions.
- Any naming differences between variable names and function names must
- be applied manually. Some temporary variables not in the source code
- need to be manually removed.
-
- '''.format(self.args.settings_file))
-
-
-class ComputeAtStoreAtParser(object):
- """
- A recursive descent parser to force proper loop nesting, and enforce post
- dominator scheduling constraints
-
- For each function input will have tokens like:
- ('foo', 's') = store_at location for foo
- ('foo', '2'), ('foo', '1') = opening the loop nests for foo,
- the inner 2 variables
- ('foo', 'c') = the computation of foo, and closing all loop nests
-
- The order of these tokens define a loop nest tree which we reconstruct
- """
-
- def __init__(self, tokens, post_dominators):
- self.tokens = list(tokens) # input, processed back to front
- self.post_dominators = post_dominators
- self.compute_at = dict()
- self.store_at = dict()
- self.process_root()
-
- def process_root(self):
- old_len = len(self.tokens)
- out = []
- while self.tokens:
- if self.tokens[-1][1] == 's':
- # store at root
- self.store_at[self.tokens[-1][0]] = None
- out.append(self.tokens.pop())
- else:
- self.process_loopnest(out, [])
- self.tokens = list(reversed(out))
- assert old_len == len(self.tokens)
-
- def process_loopnest(self, out, stack):
- func, idx = self.tokens[-1]
- out.append(self.tokens.pop())
- if idx != 'c':
- raise Exception('Invalid schedule')
-
- self.compute_at[func] = None
- for targ_func, targ_idx in reversed(stack):
- if targ_func in self.post_dominators[func]:
- self.compute_at[func] = (targ_func, targ_idx)
- break
-
- close_tokens = [(f, i) for f, i in self.tokens if f == func and i != 's']
- while close_tokens:
- if self.tokens[-1] == close_tokens[-1]:
- # proper nesting
- close_tokens.pop()
- out.append(self.tokens.pop())
- elif self.tokens[-1][1] == 'c':
- self.process_loopnest(out, stack + close_tokens[-1:])
- elif self.tokens[-1][1] == 's':
- # self.tokens[-1] is computed at this level
- if func in self.post_dominators[self.tokens[-1][0]]:
- self.store_at[self.tokens[-1][0]] = close_tokens[-1]
- else:
- self.store_at[self.tokens[-1][0]] = None
- out.append(self.tokens.pop())
- else:
- # improper nesting, just close the loop and search/delete close_tokens
- out.extend(reversed(close_tokens))
- self.tokens = [x for x in self.tokens if x not in close_tokens]
- break
-
-
-class HalideConfigurationManipulator(ConfigurationManipulator):
- def __init__(self, halide_tuner):
- super(HalideConfigurationManipulator, self).__init__()
- self.halide_tuner = halide_tuner
-
- def hash_config(self, config):
- """
- Multiple configs can lead to the same schedule, so we provide a custom
- hash function that hashes the resulting schedule instead of the raw config.
- This will lead to fewer duplicate tests.
- """
- self.normalize(config)
- try:
- schedule = self.halide_tuner.cfg_to_schedule(config)
- return hashlib.sha256(schedule).hexdigest()
- except:
- log.warning('error hashing config', exc_info=True)
- return super(HalideConfigurationManipulator, self).hash_config(config)
-
-
-class HalideComputeAtScheduleParameter(ScheduleParameter):
- def __init__(self, name, args, functions, post_dominators):
- """
- Custom ScheduleParameter that normalizes using ComputeAtStoreAtParser
- """
- super(HalideComputeAtScheduleParameter, self).__init__(
- name, *self.gen_nodes_deps(args, functions))
- self.post_dominators = post_dominators
-
- def gen_nodes_deps(self, args, functions):
- """
- Compute the list of nodes and point-to-point deps to provide to base class
- """
- nodes = list()
- deps = collections.defaultdict(list)
- for func in functions:
- last = None
- for idx in reversed(['c'] + # 'c' = compute location (and close loops)
- range(1, len(func['vars']) * args.nesting + 1) +
- ['s']): # 's' = storage location
- name = (func['name'], idx)
- if last is not None:
- # variables must go in order
- deps[last].append(name)
- last = name
- nodes.append(name)
- if idx == 'c':
- # computes must follow call graph order
- for callee in func['calls']:
- deps[(callee, 'c')].append(name)
- return nodes, deps
-
- def normalize(self, cfg):
- """
- First enforce basic point-to-point deps (in base class), then call
- ComputeAtStoreAtParser to normalize schedule.
- """
- super(HalideComputeAtScheduleParameter, self).normalize(cfg)
- cfg[self.name] = ComputeAtStoreAtParser(cfg[self.name],
- self.post_dominators).tokens
-
-
-def post_dominators(settings):
- """
- Compute post dominator tree using textbook iterative algorithm for the
- call graph defined in settings
- """
- functions = [f['name'] for f in settings['functions']]
- calls = dict([(f['name'], set(f['calls'])) for f in settings['functions']])
- inverse_calls = collections.defaultdict(set)
- for k, callees in calls.items():
- for v in callees:
- inverse_calls[v].add(k)
- dom = {functions[-1]: set([functions[-1]])}
- for f in functions[:-1]:
- dom[f] = set(functions)
- change = True
- while change:
- change = False
- for f in functions[:-1]:
- old = dom[f]
- dom[f] = set([f]) | reduce(
- _ & _, [dom[c] for c in inverse_calls[f]], set(functions))
- if old != dom[f]:
- change = True
- return dom
-
-
-def random_test(args):
- """
- Generate and run a random schedule
- """
-
- opentuner.tuningrunmain.init_logging()
- m = HalideTuner(args)
- cfg = m.manipulator().random()
- pprint(cfg)
- print
- schedule = m.cfg_to_schedule(cfg)
- print schedule
- print
- print 'Schedule', m.run_schedule(schedule, 30)
- print 'Baseline', m.run_baseline()
-
-
-def random_source(args):
- """
- Dump the source code of a random schedule
- """
- opentuner.tuningrunmain.init_logging()
- m = HalideTuner(args)
- cfg = m.manipulator().random()
- schedule = m.cfg_to_schedule(cfg)
- source = m.schedule_to_source(schedule)
- print source
-
-
-def main(args):
- if args.random_test:
- random_test(args)
- elif args.random_source:
- random_source(args)
- elif args.make_settings_file:
- opentuner.tuningrunmain.init_logging()
- HalideTuner(args).make_settings_file()
- else:
- HalideTuner.main(args)
-
-
-if __name__ == '__main__':
- main(parser.parse_args())
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/timing_prefix.h b/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/timing_prefix.h
deleted file mode 100644
index d8bbc5f57b6177f3a88a28d57fef2d72bf8c3050..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/halide/timing_prefix.h
+++ /dev/null
@@ -1,100 +0,0 @@
-#include <Halide.h>
-#include <stdio.h>
-#include <sys/time.h>
-#include <unistd.h>
-
-#include <map>
-#include <string>
-
-// How many times to run (and take min)
-// #define AUTOTUNE_TRIALS 3
-
-// Limit in seconds to try running for (0 = no limit)
-// #define AUTOTUNE_LIMIT 0
-
-// Size to run with
-// #define AUTOTUNE_N 1024, 1024
-
-inline void _autotune_timing_stub(Halide::Func& func) {
- func.compile_jit();
-
- // TODO: this assumes scalar/non-Tuple outputs - should generalize to a Realization
- std::vector<Halide::Type> out_types = func.output_types();
- std::vector<buffer_t> out_raw_bufs;
- std::vector<Halide::Buffer> out_bufs;
-
- for (int i = 0; i < out_types.size(); i++) {
- // Use the Buffer constructor as a helper to set up the buffer_t,
- // but then throw away its allocation which we don't really want.
- Halide::Buffer bufinit(out_types[i], AUTOTUNE_N);
- out_raw_bufs.push_back(*bufinit.raw_buffer());
- out_raw_bufs[i].host = NULL;
- // TODO: free the host pointer?!
- out_bufs.push_back(Halide::Buffer(out_types[i], &out_raw_bufs[i]));
- assert(out_bufs[i].host_ptr() == NULL); // make sure we don't have an allocation
- }
- Halide::Realization output(out_bufs);
- func.infer_input_bounds(output);
- // assert(output[0].host_ptr()); // for now, the API doesn't seem to allocate outputs
-
- // TODO: this should go into Func::infer_input_bounds(Realization)
- for (int i = 0; i < output.size(); i++) {
- assert(!output[i].host_ptr()); // for now, the API doesn't seem to allocate outputs
- buffer_t buf = *output[i].raw_buffer();
-
- // Figure out how much memory to allocate for this buffer
- size_t min_idx = 0, max_idx = 0;
- for (int d = 0; d < 4; d++) {
- if (buf.stride[d] > 0) {
- min_idx += buf.min[d] * buf.stride[d];
- max_idx += (buf.min[d] + buf.extent[d] - 1) * buf.stride[d];
- } else {
- max_idx += buf.min[d] * buf.stride[d];
- min_idx += (buf.min[d] + buf.extent[d] - 1) * buf.stride[d];
- }
- }
- size_t total_size = (max_idx - min_idx);
- while (total_size & 0x1f) total_size++;
-
- // Allocate enough memory with the right dimensionality.
- Halide::Buffer buffer(output[i].type(), total_size,
- buf.extent[1] > 0 ? 1 : 0,
- buf.extent[2] > 0 ? 1 : 0,
- buf.extent[3] > 0 ? 1 : 0);
-
- // Rewrite the buffer fields to match the ones returned
- for (int d = 0; d < 4; d++) {
- buffer.raw_buffer()->min[d] = buf.min[d];
- buffer.raw_buffer()->stride[d] = buf.stride[d];
- buffer.raw_buffer()->extent[d] = buf.extent[d];
- }
-
- output[i] = buffer;
- }
-
- timeval t1, t2;
- double rv = 0;
- const unsigned int timeout = AUTOTUNE_LIMIT;
- alarm(timeout);
- for (int i = 0; i < AUTOTUNE_TRIALS; i++) {
- gettimeofday(&t1, NULL);
- func.realize(output);
- gettimeofday(&t2, NULL);
- alarm(0); // disable alarm
- double t = (t2.tv_sec - t1.tv_sec) + (t2.tv_usec - t1.tv_usec)/1000000.0;
- if(i == 0 || t < rv)
- rv = t;
- }
- printf("{\"time\": %.10f}\n", rv);
- exit(0);
-}
-
-
-#ifndef AUTOTUNE_HOOK
-#define AUTOTUNE_HOOK(x)
-#endif
-
-#ifndef BASELINE_HOOK
-#define BASELINE_HOOK(x)
-#endif
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/hpl/HPL.dat.mako b/llvm/projects/hpvm-tensor-rt/opentuner/examples/hpl/HPL.dat.mako
deleted file mode 100644
index 93354a2292a3bb3ddec1e2278e49b039b72d6bb1..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/hpl/HPL.dat.mako
+++ /dev/null
@@ -1,31 +0,0 @@
-HPLinpack benchmark input file
-Innovative Computing Laboratory, University of Tennessee
-HPL.out output file name (if any)
-0 device out (6=stdout,7=stderr,file)
-1 # of problems sizes (N)
-${size} Ns
-1 # of NBs
-${blocksize} NBs
-${row_or_colmajor_pmapping} PMAP process mapping (0=Row-,1=Column-major)
-1 # of process grids (P x Q)
-2 Ps PxQ must equal nprocs
-2 Qs
-16.0 threshold
-1 # of panel fact
-${pfact} PFACTs (0=left, 1=Crout, 2=Right)
-1 # of recursive stopping criterium
-${nbmin} NBMINs (>= 1)
-1 # of panels in recursion
-${ndiv} NDIVs
-1 # of recursive panel fact.
-${rfact} RFACTs (0=left, 1=Crout, 2=Right)
-1 # of broadcast
-${bcast} BCASTs (0=1rg,1=1rM,2=2rg,3=2rM,4=Lng,5=LnM)
-1 # of lookahead depth
-${depth} DEPTHs (>=0)
-${swap} SWAP (0=bin-exch,1=long,2=mix)
-${swapping_threshold} swapping threshold (default had 64)
-${L1_transposed} L1 in (0=transposed,1=no-transposed) form
-${U_transposed} U in (0=transposed,1=no-transposed) form
-1 Equilibration (0=no,1=yes)
-${mem_alignment} memory alignment in double (> 0) (4,8,16)
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/hpl/adddeps.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/hpl/adddeps.py
deleted file mode 100644
index ede22a8fcdb2a94db7915ff3beb90894b2cb8592..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/hpl/adddeps.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# we would prefer a symbolic link, but it does not work on windows
-import os
-target = os.path.join(os.path.dirname(__file__),
- '../../opentuner/utils/adddeps.py')
-execfile(target, dict(__file__=target))
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/hpl/hpl.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/hpl/hpl.py
deleted file mode 100644
index 4cbbe798249b61eae23b5142337a056ef58e83bd..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/hpl/hpl.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import adddeps #fix sys.path
-
-import argparse
-import logging
-
-import opentuner
-from opentuner.search.manipulator import (ConfigurationManipulator,
- IntegerParameter,
- FloatParameter)
-from opentuner.search.objective import MinimizeTime
-from opentuner.measurement import MeasurementInterface
-from opentuner.measurement.inputmanager import FixedInputManager
-from opentuner.tuningrunmain import TuningRunMain
-
-log = logging.getLogger(__name__)
-
-parser = argparse.ArgumentParser(parents=opentuner.argparsers())
-
-parser.add_argument('--size', type=int, default=800,
- help='dimensions for the HPL matrix')
-parser.add_argument('--nprocs', type=int, default=4,
- help='number of processors for each HPL run (minimum=4)')
-parser.add_argument('--xhpl', type=str, default="hpl-2.1/bin/OSX/xhpl",
- help='location of xhpl binary')
-
-class HPLinpack(MeasurementInterface):
- def run(self, desired_result, input, limit):
- self.output_hpl_datfile(desired_result.configuration.data)
- import subprocess, os
- binary = self.args.xhpl
- subprocess.call(["mpirun", "-np", str(self.args.nprocs), binary])
-
- val = self.get_time_from_hpl_output()
-
- return opentuner.resultsdb.models.Result(time=val)
-
- def manipulator(self):
- #FIXME: should some of these be expressed as booleans or switch parameters?
- #FIXME: how to express P and Q, given PxQ=nprocs, with nprocs being fixed?
- #FIXME: how to express logscaled parameter with a particular base?
- manipulator = ConfigurationManipulator()
- manipulator.add_parameter(IntegerParameter("blocksize", 1, 64))
- manipulator.add_parameter(IntegerParameter("row_or_colmajor_pmapping", 0, 1))
- manipulator.add_parameter(IntegerParameter("pfact", 0, 2))
- manipulator.add_parameter(IntegerParameter("nbmin", 1, 4))
- manipulator.add_parameter(IntegerParameter("ndiv", 2, 2))
- manipulator.add_parameter(IntegerParameter("rfact", 0, 4))
- manipulator.add_parameter(IntegerParameter("bcast", 0, 5))
- manipulator.add_parameter(IntegerParameter("depth", 0, 4))
- manipulator.add_parameter(IntegerParameter("swap", 0, 2))
- manipulator.add_parameter(IntegerParameter("swapping_threshold", 64, 128))
- manipulator.add_parameter(IntegerParameter("L1_transposed", 0, 1))
- manipulator.add_parameter(IntegerParameter("U_transposed", 0, 1))
- manipulator.add_parameter(IntegerParameter("mem_alignment", 4, 16))
-
- return manipulator
-
- def output_hpl_datfile(self, params):
- """HPL uses an input file to express the parameters, and this uses mako to render it."""
- params["size"] = self.args.size
- from mako.template import Template
- template = Template(filename="HPL.dat.mako")
- with open("HPL.dat", "w") as f:
- f.write(template.render(**params))
-
- def get_time_from_hpl_output(self, fname="HPL.out"):
- """Returns the elapsed time only, from the HPL output file"""
- #FIXME: clean up with REs
- elapsed = 0.0
- with open(fname) as f:
- line = f.readline()
- while (line[0:3] != "T/V"):
- line = f.readline()
- line = f.readline()
- while (line[0:3] != "T/V"):
- line = f.readline()
- f.readline() # line of dashes
- splitted = f.readline().split()
- elapsed = float(splitted[5])
-
- return elapsed
-
-
- def program_name(self):
- return "HPL"
-
- def program_version(self):
- return "size=%d,nprocs=%d" % (self.args.size, self.args.nprocs)
-
- def save_final_config(self, configuration):
- '''
- called at the end of autotuning with the best resultsdb.models.Configuration
- '''
- print "Final configuration", configuration.data
-
-if __name__ == '__main__':
- args = parser.parse_args()
- HPLinpack.main(args)
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/mario/README.md b/llvm/projects/hpvm-tensor-rt/opentuner/examples/mario/README.md
deleted file mode 100644
index f094e987f5e48d72aef426b39ef268e86f47e3c0..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/mario/README.md
+++ /dev/null
@@ -1,33 +0,0 @@
-This is an OpenTuner-based tuner that learns a series of button presses that complete the first level of Super Mario Bros. for the original Nintendo Entertainment System.
-
-## Dependencies
-
-- FCEUX, a NES emulator
-- `xvfb-run`, to run the emulator headless (optional, but speeds up tuning)
-- Super Mario Bros., assumed to be named `smb.nes`, which we can't help you get for legal reasons
-
-## Running
-
-Run the tuner with `./mario.py --technique=PSO_GA_Bandit`; it will launch FCEUX to run trials. You can experiment with other techniques or `--parallelism` (the number of trials to run in parallel) too.
-
-You can implement your own configuration representation by subclassing Representation and passing `--representation=YourRepresentation`. Your Representation class needs to provide a ConfigurationManipulator populated with parameters and a method to translate these parameters to button presses. There are already a few representations implemented to use as examples.
-
-You can implement your own fitness function by subclassing FitnessFunction and passing `--fitness-function=YourFunction`. Your function receives a win/loss boolean, the number of pixels moved to the right when the trial ended, and the number of frames that elapsed during the trial. Lower fitness scores are better. There are a few existing fitness functions; in particular, `ProgressTimesAverageSpeed` also tries to optimize speed.
-
-If you want to watch the trials (or don't have `xvfb-run` available), pass `--headful`.
-
-## Playing the results
-
-When a tuning run completes, the best configuration (as judged by the fitness function) is written to `<hostname>-<tuningrun>.fm2`. This file can be played back in FCEUX to watch the best configuration.
-
-You can also use the `--tuning-run=` option (passing the tuning run number in the best configuration `.fm2`) to generate a new-bests `.fm2`, which will contain each tuning trial that was the best configuration found so far during the tuning run, concatenated back-to-back. You also need to pass `--database` pointing to the database containing that tuning run, and if you passed `--representation` or `--fitness-function` during the tuning run, you need to pass the same values for those parameters. So your final command might look like `./mario.py --tuning-run=42 --database=opentuner.db/hostname.db --representation=NaiveRepresentation --fitness-function=ProgressTimesAverageSpeed > new-bests-42.fm2`.
-
-## TODO
-
-- use the [fm2 format](http://www.fceux.com/web/help/fceux.html?fm2.html)'s subtitle support in new-bests movies to show run number and fitness score
-
-## Links
-
-- [Videos showing OpenTuner playing Super Mario Bros](https://www.youtube.com/playlist?list=PLngnz1zPEA08FWy8wF9JbGqjlm-elHmlb)
-- [Slides describing representation and results](http://groups.csail.mit.edu/commit/papers/2014/ansel-pact14-opentuner-slides.pdf) (see slide 16)
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/mario/adddeps.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/mario/adddeps.py
deleted file mode 100644
index ede22a8fcdb2a94db7915ff3beb90894b2cb8592..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/mario/adddeps.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# we would prefer a symbolic link, but it does not work on windows
-import os
-target = os.path.join(os.path.dirname(__file__),
- '../../opentuner/utils/adddeps.py')
-execfile(target, dict(__file__=target))
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/mario/fceux-hook.lua b/llvm/projects/hpvm-tensor-rt/opentuner/examples/mario/fceux-hook.lua
deleted file mode 100644
index ce00288149936154f5dc64f94cdbcbcba04d4758..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/mario/fceux-hook.lua
+++ /dev/null
@@ -1,25 +0,0 @@
-player_state_addr = 0x000E;
-player_state_dying = 6;
-player_float_addr = 0x001D;
-player_float_flagpole = 3;
-player_page_addr = 0x006D;
-player_horizpos_addr = 0x0086;
-minimum_frames = 197;
-
-emu.speedmode("maximum");
-while true do
- if (emu.framecount() > minimum_frames) then
- --dead?
- local dead = memory.readbyte(player_state_addr) == player_state_dying;
- --flagpole?
- local won = memory.readbyte(player_float_addr) == player_float_flagpole;
- if (dead or won) then
- local str = (dead and "died" or "won");
- local x_pos = math.floor(memory.readbyteunsigned(player_page_addr)*256 + memory.readbyteunsigned(player_horizpos_addr));
- local framecount = emu.framecount();
- io.write(str, " ", x_pos, " ", framecount, "\n");
- os.exit(0);
- end;
- end;
- emu.frameadvance();
-end
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/mario/mario.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/mario/mario.py
deleted file mode 100755
index d388321e1f3b4f70a5d1262f43b89702ea924c79..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/mario/mario.py
+++ /dev/null
@@ -1,341 +0,0 @@
-#!/usr/bin/env python2
-
-"""OpenTuner plays Super Mario Bros. for NES
-
-We write a movie file and ask the emulator to play it back while running
-fceux-hook.lua, which checks for death/flagpole and prints the fitness to
-stdout where OpenTuner, as the parent process, can read it.
-"""
-
-import adddeps #fix sys.path
-import argparse
-import base64
-import pickle
-import tempfile
-import subprocess
-import re
-import zlib
-import abc
-import sys
-import os
-import traceback
-import collections
-import socket
-
-import opentuner
-from opentuner.search.manipulator import ConfigurationManipulator, IntegerParameter, EnumParameter, BooleanParameter
-from opentuner.measurement import MeasurementInterface
-from opentuner.measurement.inputmanager import FixedInputManager
-from opentuner.tuningrunmain import TuningRunMain
-from opentuner.search.objective import MinimizeTime
-
-def instantiate(class_name):
- return getattr(sys.modules[__name__], class_name)()
-
-argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
-argparser.add_argument('--tuning-run', type=int, help='concatenate new bests from given tuning run into single movie')
-argparser.add_argument('--headful', action='store_true', help='run headful (not headless) for debugging or live demo')
-argparser.add_argument('--xvfb-delay', type=int, default=0, help='delay between launching xvfb and fceux')
-argparser.add_argument('--representation', default='DurationRepresentation', type=instantiate, help='name of representation class')
-argparser.add_argument('--fitness-function', default='Progress', type=instantiate, help='name of fitness function class')
-
-def call_or_die(command, failmsg=None):
- try:
- p = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
- stdout, stderr = p.communicate()
- return stdout, stderr, p.returncode
- except:
- print "Failed to execute", command
- traceback.print_exc()
- print "Child traceback:"
- print sys.exc_info()[1].child_traceback
- if failmsg:
- print failmsg
- sys.exit(1)
-
-# Functions for building FCEUX movie files (.fm2 files)
-
-def fm2_line(up, down, left, right, a, b, start, select, reset=False):
- """formats one frame of input with the given button presses"""
- return ''.join(('|1|' if reset else '|0|') +
- ('R' if right else '.') +
- ('L' if left else '.') +
- ('D' if down else '.') +
- ('U' if up else '.') +
- ('T' if start else '.') +
- ('D' if select else '.') +
- ('B' if b else '.') +
- ('A' if a else '.') +
- '|........||')
-
-def maxd(iterable, default):
- try:
- return max(iterable)
- except ValueError:
- return default
-
-def fm2_lines(up, down, left, right, a, b, start, select, reset=set(), minFrame=None, maxFrame=None):
- """formats many frames using the given button-press sets"""
- if minFrame is None:
- minFrame = 0
- if maxFrame is None:
- maxFrame = max(maxd(up, 0), maxd(down, 0), maxd(left, 0), maxd(right, 0), maxd(a, 0), maxd(b, 0), maxd(start, 0), maxd(select, 0), maxd(reset, 0)) + 1
- lines = list()
- for i in xrange(minFrame, maxFrame):
- lines.append(fm2_line(i in up, i in down, i in left, i in right, i in a, i in b, i in start, i in select, i in reset))
- return lines
-
-def fm2_smb_header():
- return ["version 3",
- "emuVersion 9828",
- "romFilename smb.nes",
- "romChecksum base64:jjYwGG411HcjG/j9UOVM3Q==",
- "guid 51473540-E9D7-11E3-ADFC-46CE3219C4E0",
- "fourscore 0",
- "port0 1",
- "port1 1",
- "port2 0"]
-
-def fm2_smb(left, right, down, b, a, header=True, padding=True, minFrame=None, maxFrame=None):
- reset = set()
- start = set()
- if padding:
- left = set([x+196 for x in left])
- right = set([x+196 for x in right])
- down = set([x+196 for x in down])
- b = set([x+196 for x in b])
- a = set([x+196 for x in a])
- reset.add(0)
- start.add(33)
- lines = fm2_lines(set(), down, left, right, a, b, start, set(), reset, minFrame, maxFrame)
- if header:
- return "\n".join(fm2_smb_header() + lines)
- else:
- return "\n".join(lines)
-
-display_numbers = collections.deque()
-
-def run_movie(fm2, args):
- with tempfile.NamedTemporaryFile(suffix=".fm2", delete=True) as f:
- f.write(fm2)
- f.flush()
- cmd = []
- if not args.headful:
- display = display_numbers.pop()
- cmd += ["xvfb-run", "-n", display, "-w", str(args.xvfb_delay), "-e", "/dev/stderr"]
- cmd += ["fceux", "--playmov", f.name, "--loadlua",
- "fceux-hook.lua", "--nogui", "--volume", "0", "--no-config", "1",
- "smb.nes"]
- stdout, stderr, returncode = call_or_die(cmd)
- if not args.headful:
- display_numbers.append(display)
- match = re.search(r"^(won|died) (\d+) (\d+)$", stdout, re.MULTILINE)
- if not match:
- print stderr
- print stdout
- raise ValueError
- wl = match.group(1)
- x_pos = int(match.group(2))
- framecount = int(match.group(3))
- return (wl, x_pos, framecount)
-
-class Representation(object):
- """Interface for pluggable tuning representations."""
- __metaclass__ = abc.ABCMeta
-
- @abc.abstractmethod
- def manipulator():
- """Return a ConfigurationManipulator for this representation."""
- pass
-
- @abc.abstractmethod
- def interpret(cfg):
- """Unpack this representation into button-press sets (L, R, D, B, A)."""
- pass
-
-class NaiveRepresentation(Representation):
- """Uses a parameter per (button, frame) pair."""
- def manipulator(self):
- m = ConfigurationManipulator()
- for i in xrange(0, 12000):
- m.add_parameter(BooleanParameter('L{}'.format(i)))
- m.add_parameter(BooleanParameter('R{}'.format(i)))
- m.add_parameter(BooleanParameter('D{}'.format(i)))
- m.add_parameter(BooleanParameter('B{}'.format(i)))
- m.add_parameter(BooleanParameter('A{}'.format(i)))
- return m
-
- def interpret(self, cfg):
- left = set()
- right = set()
- down = set()
- running = set()
- jumping = set()
- for i in xrange(0, 12000):
- if cfg['L{}'.format(i)]:
- left.add(i)
- if cfg['R{}'.format(i)]:
- right.add(i)
- if cfg['D{}'.format(i)]:
- down.add(i)
- if cfg['B{}'.format(i)]:
- running.add(i)
- if cfg['A{}'.format(i)]:
- jumping.add(i)
- return left, right, down, running, jumping
-
-class DurationRepresentation(Representation):
- def manipulator(self):
- m = ConfigurationManipulator()
- for i in xrange(0, 1000):
- #bias 3:1 in favor of moving right
- m.add_parameter(EnumParameter('move{}'.format(i), ["R", "L", "RB", "LB", "N", "LR", "LRB", "R2", "RB2", "R3", "RB3"]))
- m.add_parameter(IntegerParameter('move_duration{}'.format(i), 1, 60))
- #m.add_parameter(BooleanParameter("D"+str(i)))
- for i in xrange(0, 1000):
- m.add_parameter(IntegerParameter('jump_frame{}'.format(i), 0, 24000))
- m.add_parameter(IntegerParameter('jump_duration{}'.format(i), 1, 32))
- return m
-
- def interpret(self, cfg):
- left = set()
- right = set()
- down = set()
- running = set()
- start = 0
- for i in xrange(0, 1000):
- move = cfg['move{}'.format(i)]
- move_duration = cfg['move_duration{}'.format(i)]
- if "R" in move:
- right.update(xrange(start, start + move_duration))
- if "L" in move:
- left.update(xrange(start, start + move_duration))
- if "B" in move:
- running.update(xrange(start, start + move_duration))
- start += move_duration
- jumping = set()
- for i in xrange(0, 1000):
- jump_frame = cfg['jump_frame{}'.format(i)]
- jump_duration = cfg['jump_duration{}'.format(i)]
- jumping.update(xrange(jump_frame, jump_frame + jump_duration))
- return left, right, down, running, jumping
-
-class AlphabetRepresentation(Representation):
- def manipulator(self):
- m = ConfigurationManipulator()
- for i in xrange(0, 400*60):
- m.add_parameter(EnumParameter('{}'.format(i), xrange(0, 16)))
- return m
-
- def interpret(self, cfg):
- left = set()
- right = set()
- down = set()
- running = set()
- jumping = set()
- for i in xrange(0, 400*60):
- bits = cfg[str(i)]
- if bits & 1:
- left.add(i)
- if bits & 2:
- right.add(i)
- if bits & 4:
- running.add(i)
- if bits & 8:
- jumping.add(i)
- #if bits & 16:
- # down.add(i)
- return left, right, down, running, jumping
-
-class FitnessFunction(object):
- """Interface for pluggable fitness functions."""
- __metaclass__ = abc.ABCMeta
-
- @abc.abstractmethod
- def __call__(won, x_pos, elapsed_frames):
- """Return the fitness (float, lower is better)."""
- pass
-
-class Progress(FitnessFunction):
- def __call__(self, won, x_pos, elapsed_frames):
- return -float(x_pos)
-
-class ProgressPlusTimeRemaining(FitnessFunction):
- def __call__(self, won, x_pos, elapsed_frames):
- """x_pos plus 1 for each frame remaining on the timer on a win. This results in a large discontinuity at wins. This was the fitness function used for the OpenTuner paper, though the paper only discussed time-to-first-win."""
- return -float(x_pos + 400*60 - elapsed_frames) if won else -float(x_pos)
-
-class ProgressTimesAverageSpeed(FitnessFunction):
- def __call__(self, won, x_pos, elapsed_frames):
- return -x_pos * (float(x_pos)/elapsed_frames)
-
-class SMBMI(MeasurementInterface):
- def __init__(self, args):
- super(SMBMI, self).__init__(args)
- self.parallel_compile = True
- self.args = args
-
- def manipulator(self):
- return self.args.representation.manipulator()
-
- def compile(self, cfg, id):
- left, right, down, running, jumping = self.args.representation.interpret(cfg)
- fm2 = fm2_smb(left, right, down, running, jumping)
- try:
- wl, x_pos, framecount = run_movie(fm2, self.args)
- except ValueError:
- return opentuner.resultsdb.models.Result(state='ERROR', time=float('inf'))
- print wl, x_pos, framecount
- return opentuner.resultsdb.models.Result(state='OK', time=self.args.fitness_function("won" in wl, x_pos, framecount))
-
- def run_precompiled(self, desired_result, input, limit, compile_result, id):
- return compile_result
-
- def run(self, desired_result, input, limit):
- pass
-
- def save_final_config(self, cfg):
- left, right, down, running, jumping = args.representation.interpret(cfg.data)
- fm2 = fm2_smb(left, right, down, running, jumping)
- _, _, framecount = run_movie(fm2, self.args)
- filename = '{}-{}.fm2'.format(socket.gethostname(), self.driver.tuning_run.id)
- with open(filename, 'w') as f:
- f.write(fm2_smb(left, right, down, running, jumping, maxFrame=framecount))
-
-def new_bests_movie(args):
- stdout, stderr, returncode = call_or_die(["sqlite3", args.database, "select configuration_id from result where tuning_run_id = %d and was_new_best = 1 order by collection_date;" % args.tuning_run])
- if returncode:
- print "Error retrieving new-best configurations:", stderr
- sys.exit(1)
- cids = stdout.split()
- print '\n'.join(fm2_smb_header())
- for cid in cids:
- stdout, stderr, returncode = call_or_die(["sqlite3", args.database, "select quote(data) from configuration where id = %d;" % int(cid)])
- if returncode:
- print "Error retriving configuration data:", cid, stderr
- sys.exit(1)
- cfg = pickle.loads(zlib.decompress(base64.b16decode(stdout.strip()[2:-1])))
- left, right, down, running, jumping = args.representation.interpret(cfg)
- fm2 = fm2_smb(left, right, down, running, jumping)
- _, _, framecount = run_movie(fm2, args)
- print fm2_smb(left, right, down, running, jumping, header=False, maxFrame=framecount)
-
-if __name__ == '__main__':
- args = argparser.parse_args()
- call_or_die(["fceux", "--help"], failmsg="Is fceux on your PATH?")
- if not args.headful:
- call_or_die(["xvfb-run", "--help"], failmsg="Is xvfb-run on your PATH? (or, pass --headful)")
- for n in xrange(99, 99 + args.parallelism):
- display_numbers.append(str(n))
- if args.tuning_run:
- call_or_die(["sqlite3", "-version"], failmsg="Is sqlite3 on your PATH?")
- if args.database is not None:
- new_bests_movie(args)
- else:
- print "must specify --database"
- else:
- if os.path.isfile('smb.nes'):
- SMBMI.main(args)
- else:
- print "smb.nes not found"
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/petabricks/.gitignore b/llvm/projects/hpvm-tensor-rt/opentuner/examples/petabricks/.gitignore
deleted file mode 100644
index a6e67132d61e0bd837b953376dc866031d5f742a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/petabricks/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-linux_x86_64
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/petabricks/README.md b/llvm/projects/hpvm-tensor-rt/opentuner/examples/petabricks/README.md
deleted file mode 100644
index e4b446468658463cab275862143c95add38d0eb5..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/petabricks/README.md
+++ /dev/null
@@ -1,24 +0,0 @@
-Source for PetaBricks binaries can be found at:
- - https://github.com/petabricks/petabricks/
- - https://code.google.com/p/petabricks/
-
-
-Basic usage for running the raw programs is:
-```
-./Prog --config=CONFIG -n=N --time --accuracy --max-sec=TIMEOUT --trials=1
-
---config=<STRING>
- filename of the program configuration (see example in .cfg.default file)
---n=<INTEGER>
- generate a random input of the given size and run it
---time
- print timing results in xml format
---accuracy
- print out accuracy of answer
---max-sec=<NUMBER> (default: 1.79769e+308)
- terminate measurement if it exceeds the given number of seconds
-
-many more options are given by running ./Prog --help
-```
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/petabricks/adddeps.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/petabricks/adddeps.py
deleted file mode 100644
index ede22a8fcdb2a94db7915ff3beb90894b2cb8592..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/petabricks/adddeps.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# we would prefer a symbolic link, but it does not work on windows
-import os
-target = os.path.join(os.path.dirname(__file__),
- '../../opentuner/utils/adddeps.py')
-execfile(target, dict(__file__=target))
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/petabricks/deps.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/petabricks/deps.py
deleted file mode 100644
index c03a106a85827c1c4faed505b78d4f18e168c7e9..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/petabricks/deps.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import os
-import sys
-
-project_root = os.path.normpath(os.path.join(
- os.path.dirname(os.path.abspath(__file__)), '../..'))
-sys.path.insert(0, project_root)
-
-
-try:
- from lxml import etree
-except ImportError:
- try:
- # Python 2.5
- import xml.etree.cElementTree as etree
- except ImportError:
- import xml.etree.ElementTree as etree
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/petabricks/download_benchmarks.sh b/llvm/projects/hpvm-tensor-rt/opentuner/examples/petabricks/download_benchmarks.sh
deleted file mode 100755
index aaf333b455a0414575b338625e45b58db8188c5b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/petabricks/download_benchmarks.sh
+++ /dev/null
@@ -1,8 +0,0 @@
-#!/bin/sh
-if test -e linux_x86_64
-then
- echo "benchmarks already downloaded"
-else
- wget -O- http://people.csail.mit.edu/jansel/petabricks_benchmarks_linux_x86_64.tar.bz2 | tar jxv
-fi
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/petabricks/import_old_result.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/petabricks/import_old_result.py
deleted file mode 100755
index 9add4ed035c50e2f7f21dc79f6af89571137b257..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/petabricks/import_old_result.py
+++ /dev/null
@@ -1,116 +0,0 @@
-#!/usr/bin/env python
-
-import adddeps # fix sys.path
-
-import argparse
-import json
-import logging
-import os
-import re
-import sys
-import uuid
-import subprocess
-
-try:
- from lxml import etree
-except ImportError:
- import xml.etree.ElementTree as etree
-
-import opentuner
-from opentuner import resultsdb
-from datetime import datetime
-from datetime import timedelta
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-
-log = logging.getLogger(__name__)
-
-argparser = argparse.ArgumentParser()
-argparser.add_argument('--database', default='opentuner.db/import.db')
-argparser.add_argument('--limit', type=float, default=10)
-argparser.add_argument('program')
-argparser.add_argument('candidatelog')
-
-
-def run(args, cfg):
- limit = args.limit
- cmd = [args.program,
- '--time',
- '--accuracy',
- '--config=' + cfg,
- '--max-sec=%.10f' % args.limit,
- '-n=%d' % args.n]
- p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
- out, err = p.communicate()
-
- result = opentuner.resultsdb.models.Result()
- try:
- root = etree.XML(out)
- result.time = float(root.find('stats/timing').get('average'))
- result.accuracy = float(root.find('stats/accuracy').get('average'))
- if result.time < limit + 3600:
- result.state = 'OK'
- else:
- # time will be 2**31 if timeout
- result.state = 'TIMEOUT'
- except:
- log.exception('run error')
- log.warning('program crash, out = %s / err = %s', out, err)
- result.state = 'ERROR'
- result.time = float('inf')
- result.accuracy = float('-inf')
- return result
-
-
-def main(args):
- if '://' not in args.database:
- args.database = 'sqlite:///' + args.database
- engine, Session = opentuner.resultsdb.connect(args.database)
- session = Session()
-
- program_settings = json.load(open(args.program + '.settings'))
- args.n = program_settings['n']
- args.technique = ['Imported']
- objective = ThresholdAccuracyMinimizeTime(program_settings['accuracy'])
-
- tuningrun = resultsdb.models.TuningRun(
- uuid=uuid.uuid4().hex,
- name='import',
- args=args,
- start_date=datetime.now(),
- objective=objective,
- program_version=resultsdb.models.ProgramVersion.get(
- session, 'PetaBricksInterface', args.program, 'imported'),
- state='COMPLETE',
- )
- session.add(tuningrun)
-
- for gen, line in enumerate(open(args.candidatelog)):
- if line[0] != '#':
- line = re.split('\t', line)
- date = tuningrun.start_date + timedelta(seconds=float(line[0]))
- cfg = os.path.normpath(
- os.path.join(os.path.dirname(args.candidatelog), '..', line[5]))
- result = run(args, cfg)
- result.was_new_best = True
- result.tuning_run = tuningrun
- result.collection_date = date
- session.add(result)
- desired_result = resultsdb.models.DesiredResult(
- limit=args.limit,
- tuning_run=tuningrun,
- generation=gen,
- requestor='Imported',
- request_date=date,
- start_date=date,
- result=result,
- state='COMPLETE')
- session.add(desired_result)
- tuningrun.end_date = date
- print gen, date, result.time
-
- session.commit()
-
-
-if __name__ == '__main__':
- opentuner.tuningrunmain.init_logging()
- sys.exit(main(argparser.parse_args()))
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/petabricks/pbtuner.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/petabricks/pbtuner.py
deleted file mode 100755
index 7163294494eec42b73a232b6ee00fb7164d2c691..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/petabricks/pbtuner.py
+++ /dev/null
@@ -1,188 +0,0 @@
-#!/usr/bin/env python
-
-import adddeps # fix sys.path
-
-import re
-import argparse
-import logging
-import subprocess
-import tempfile
-import json
-from pprint import pprint
-
-import opentuner
-from opentuner.search.manipulator import (ConfigurationManipulator,
- IntegerParameter,
- LogIntegerParameter,
- FloatParameter,
- LogFloatParameter,
- SelectorParameter,
- SwitchParameter,
- PermutationParameter, )
-
-try:
- from lxml import etree
-except ImportError:
- import xml.etree.ElementTree as etree
-
-from opentuner.measurement import MeasurementInterface
-from opentuner.measurement.inputmanager import FixedInputManager
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-
-log = logging.getLogger("pbtuner")
-
-parser = argparse.ArgumentParser(parents=opentuner.argparsers())
-parser.add_argument('program',
- help='PetaBricks binary program to autotune')
-parser.add_argument('--program-cfg-default',
- help="override default program config exemplar location")
-parser.add_argument('--program-cfg-output',
- help="location final autotuned configuration is written")
-parser.add_argument('--program-settings',
- help="override default program settings file location")
-parser.add_argument('--program-input',
- help="use only a given input for autotuning")
-parser.add_argument('--upper-limit', type=float, default=30,
- help="time limit to apply to initial test")
-parser.add_argument('--test-config', action='store_true')
-
-
-class PetaBricksInterface(MeasurementInterface):
- def __init__(self, args):
- self.program_settings = json.load(open(args.program_settings))
- input_manager = FixedInputManager(size=self.program_settings['n'])
- objective = ThresholdAccuracyMinimizeTime(self.program_settings['accuracy'])
-
- # pass many settings to parent constructor
- super(PetaBricksInterface, self).__init__(
- args, program_name=args.program,
- program_version=self.file_hash(args.program),
- input_manager=input_manager, objective=objective)
-
- def build_config(self, cfg):
- r = dict()
-
- # direct copy
- for k, v in cfg.iteritems():
- if k[0] != '.':
- r[k] = v
-
- for name, choices in self.choice_sites.items():
- param = self.manipulator.parameters_dict(cfg)['.' + name]
- lvl = 0
- for cutoff, choice in param.selector_iter(cfg):
- lvl += 1
- r['%s_lvl%d_rule' % (name, lvl)] = choice
- if lvl > 1:
- r['%s_lvl%d_cutoff' % (name, lvl)] = cutoff
-
- return r
-
- def run(self, desired_result, input, limit):
- limit = min(limit, self.args.upper_limit)
- with tempfile.NamedTemporaryFile(suffix='.petabricks.cfg') as cfgtmp:
- for k, v in self.build_config(desired_result.configuration.data).items():
- print >> cfgtmp, k, '=', v
- cfgtmp.flush()
- if args.program_input:
- input_opts = ['--iogen-run=' + args.program_input,
- '--iogen-n=%d' % input.input_class.size]
- else:
- input_opts = ['-n=%d' % input.input_class.size]
-
- cmd = [args.program,
- '--time',
- '--accuracy',
- '--max-sec=%.8f' % limit,
- '--config=' + cfgtmp.name] + input_opts
- log.debug("cmd: %s", ' '.join(cmd))
- p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
- out, err = p.communicate()
-
- result = opentuner.resultsdb.models.Result()
- try:
- root = etree.XML(out)
- result.time = float(root.find('stats/timing').get('average'))
- result.accuracy = float(root.find('stats/accuracy').get('average'))
- if result.time < limit + 3600:
- result.state = 'OK'
- else:
- #time will be 2**31 if timeout
- result.state = 'TIMEOUT'
- except:
- log.warning("program crash, out = %s / err = %s", out, err)
- result.state = 'ERROR'
- result.time = float('inf')
- result.accuracy = float('-inf')
- return result
-
- def save_final_config(self, configuration):
- """
- called at the end of autotuning with the best
- resultsdb.models.Configuration
- """
- with open(args.program_cfg_output, 'w') as fd:
- cfg = self.build_config(configuration.data)
- for k, v in sorted(cfg.items()):
- print >> fd, k, '=', v
- log.info("final configuration written to %s", args.program_cfg_output)
-
- def manipulator(self):
- """create the configuration manipulator, from example config"""
- upper_limit = self.program_settings['n'] + 1
- cfg = open(self.args.program_cfg_default).read()
- manipulator = ConfigurationManipulator()
-
- self.choice_sites = dict()
-
- for m in re.finditer(r" *([a-zA-Z0-9_-]+)[ =]+([0-9e.+-]+) *"
- r"[#] *([a-z]+).* ([0-9]+) to ([0-9]+)", cfg):
- k, v, valtype, minval, maxval = m.group(1, 2, 3, 4, 5)
- minval = float(minval)
- maxval = float(maxval)
- if upper_limit:
- maxval = min(maxval, upper_limit)
- assert valtype == 'int'
- #log.debug("param %s %f %f", k, minval, maxval)
-
- m1 = re.match(r'(.*)_lvl[0-9]+_rule', k)
- m2 = re.match(r'(.*)_lvl[0-9]+_cutoff', k)
- if m1:
- self.choice_sites[m1.group(1)] = int(maxval)
- elif m2:
- pass
- elif k == 'worker_threads':
- manipulator.add_parameter(IntegerParameter(k, 1, 16))
- elif k == 'distributedcutoff':
- pass
- elif minval == 0 and maxval < 64:
- manipulator.add_parameter(SwitchParameter(k, maxval))
- else:
- manipulator.add_parameter(LogIntegerParameter(k, minval, maxval))
-
- for name, choices in self.choice_sites.items():
- manipulator.add_parameter(
- SelectorParameter('.' + name, range(choices + 1),
- upper_limit / choices))
-
- self.manipulator = manipulator
- return manipulator
-
- def test_config(self):
- pprint(self.manipulator().random())
-
-
-if __name__ == '__main__':
- args = parser.parse_args()
- if not args.program_cfg_default:
- args.program_cfg_default = args.program + '.cfg.default'
- if not args.program_cfg_output:
- args.program_cfg_output = args.program + '.cfg'
- if not args.program_settings:
- args.program_settings = args.program + '.settings'
- if args.test_config:
- PetaBricksInterface(args).test_config()
- else:
- PetaBricksInterface.main(args)
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/petabricks/testwrapper.sh b/llvm/projects/hpvm-tensor-rt/opentuner/examples/petabricks/testwrapper.sh
deleted file mode 100755
index 2b6a94e57a6b4205638dd0560da79482494ac20b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/petabricks/testwrapper.sh
+++ /dev/null
@@ -1,11 +0,0 @@
-#!/bin/bash
-COUNT=50
-for Z in `seq $COUNT`
-do
- for T in `./pbtuner.py --list-techniques $@`;
- do
- echo $Z/$COUNT $T
- ./pbtuner.py --technique=$T $@
- done
-done
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/py_api/adddeps.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/py_api/adddeps.py
deleted file mode 100644
index ede22a8fcdb2a94db7915ff3beb90894b2cb8592..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/py_api/adddeps.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# we would prefer a symbolic link, but it does not work on windows
-import os
-target = os.path.join(os.path.dirname(__file__),
- '../../opentuner/utils/adddeps.py')
-execfile(target, dict(__file__=target))
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/py_api/api_example.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/py_api/api_example.py
deleted file mode 100755
index e87a8fffe1544714247b4435a3b5ed7d3f92eb03..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/py_api/api_example.py
+++ /dev/null
@@ -1,58 +0,0 @@
-#!/usr/bin/python
-"""
-Examples usage of a Python API interface to opentuner.
-
-Unlike the other examples, this code lets the user control the main() of
-the program and calls into opentuner to get new configurations to test.
-"""
-
-import adddeps # add opentuner to path in dev mode
-
-import opentuner
-from opentuner.api import TuningRunManager
-from opentuner.measurement.interface import DefaultMeasurementInterface
-from opentuner.resultsdb.models import Result
-from opentuner.search.manipulator import ConfigurationManipulator
-from opentuner.search.manipulator import IntegerParameter
-import logging
-import argparse
-
-log = logging.getLogger(__name__)
-
-
-def test_func(cfg):
- x = cfg['x']
- y = (x - 10) * (x - 10)
- log.debug("f({}) -> {}".format(x, y))
- return y
-
-
-def main():
- parser = argparse.ArgumentParser(parents=opentuner.argparsers())
- args = parser.parse_args()
- manipulator = ConfigurationManipulator()
- manipulator.add_parameter(IntegerParameter('x', -200, 200))
- interface = DefaultMeasurementInterface(args=args,
- manipulator=manipulator,
- project_name='examples',
- program_name='api_test',
- program_version='0.1')
- api = TuningRunManager(interface, args)
- for x in xrange(500):
- desired_result = api.get_next_desired_result()
- if desired_result is None:
- # The search space for this example is very small, so sometimes
- # the techniques have trouble finding a config that hasn't already
- # been tested. Change this to a continue to make it try again.
- break
- cfg = desired_result.configuration.data
- result = Result(time=test_func(cfg))
- api.report_result(desired_result, result)
-
- best_cfg = api.get_best_configuration()
- api.finish()
- print 'best x found was', best_cfg['x']
-
-if __name__ == '__main__':
- main()
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/py_api/multiple_tuning_runs.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/py_api/multiple_tuning_runs.py
deleted file mode 100755
index 5e0918e3afe49ce7a819f36312d770cdb73a5003..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/py_api/multiple_tuning_runs.py
+++ /dev/null
@@ -1,83 +0,0 @@
-#!/usr/bin/python
-"""
-Examples usage of a Python API interface to opentuner.
-
-Unlike the other examples, this code lets the user control the main() of
-the program and calls into opentuner to get new configurations to test.
-
-This version runs multiple tuning runs at once in a single process.
-"""
-
-import adddeps # add opentuner to path in dev mode
-
-import opentuner
-from opentuner.api import TuningRunManager
-from opentuner.measurement.interface import DefaultMeasurementInterface
-from opentuner.resultsdb.models import Result
-from opentuner.search.manipulator import ConfigurationManipulator
-from opentuner.search.manipulator import IntegerParameter
-import logging
-import argparse
-
-log = logging.getLogger(__name__)
-
-
-def test_func1(cfg):
- x = cfg['x']
- y = (x - 10) * (x - 10)
- log.debug("f({}) -> {}".format(x, y))
- return y
-
-
-def test_func2(cfg):
- x = cfg['x']
- y = (x + 10) * (x + 10)
- log.debug("f({}) -> {}".format(x, y))
- return y
-
-
-def test_func3(cfg):
- x = cfg['x']
- y = (x + 20) * (x + 20)
- log.debug("f({}) -> {}".format(x, y))
- return y
-
-
-def create_test_tuning_run(db):
- parser = argparse.ArgumentParser(parents=opentuner.argparsers())
- args = parser.parse_args()
- args.database = db
- manipulator = ConfigurationManipulator()
- manipulator.add_parameter(IntegerParameter('x', -200, 200))
- interface = DefaultMeasurementInterface(args=args,
- manipulator=manipulator,
- project_name='examples',
- program_name='api_test',
- program_version='0.1')
- api = TuningRunManager(interface, args)
- return api
-
-
-def main():
- apis = [create_test_tuning_run('sqlite:////tmp/a.db'),
- create_test_tuning_run('sqlite:////tmp/b.db'),
- create_test_tuning_run('sqlite:////tmp/c.db')]
- test_funcs = [test_func1, test_func2, test_func3]
- for x in xrange(100):
- for api, test_func in zip(apis, test_funcs):
- desired_result = api.get_next_desired_result()
- if desired_result is None:
- continue
- cfg = desired_result.configuration.data
- result = Result(time=test_func(cfg))
- api.report_result(desired_result, result)
-
- best_cfgs = [api.get_best_configuration() for api in apis]
- for api in apis:
- api.finish()
-
- print('best x configs: {}'.format(best_cfgs))
-
-if __name__ == '__main__':
- main()
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/rosenbrock/.gitignore b/llvm/projects/hpvm-tensor-rt/opentuner/examples/rosenbrock/.gitignore
deleted file mode 100644
index aa0571caf15bdf4665fee72a1d87051d12718127..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/rosenbrock/.gitignore
+++ /dev/null
@@ -1,3 +0,0 @@
-rosenbrock.db
-*.db
-opentuner.log
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/rosenbrock/adddeps.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/rosenbrock/adddeps.py
deleted file mode 100644
index ede22a8fcdb2a94db7915ff3beb90894b2cb8592..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/rosenbrock/adddeps.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# we would prefer a symbolic link, but it does not work on windows
-import os
-target = os.path.join(os.path.dirname(__file__),
- '../../opentuner/utils/adddeps.py')
-execfile(target, dict(__file__=target))
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/rosenbrock/rosenbrock.makefile b/llvm/projects/hpvm-tensor-rt/opentuner/examples/rosenbrock/rosenbrock.makefile
deleted file mode 100755
index 7b9be87a9a8ba698083ad4ac2c228ed3f11ed8df..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/rosenbrock/rosenbrock.makefile
+++ /dev/null
@@ -1,24 +0,0 @@
-#!/usr/bin/make -f
-# use -j4 to run in parallel
-
-FN := rosenbrock
-DIMS := 4
-TECHNIQUES := $(shell ./rosenbrock.py --list-techniques)
-define test_loop
-DB="sqlite:///opentuner.db/$$RUN.db"; \
-for TEQ in $(TECHNIQUES); do \
- ./rosenbrock.py --function=$(FN) \
- --technique=$$TEQ \
- --dimensions=$(DIMS) \
- --database=$$DB; \
-done;
-endef
-
-default: run.1 run.2 run.3 run.4 run.5 run.6 run.7 run.8 run.9 run.10 run.11 \
-run.12 run.13 run.14 run.15 run.16 run.17 run.18 run.19 run.20 run.21 run.22 \
-run.23 run.24 run.25 run.26 run.27 run.28 run.29 run.30
-
-run.%:
- RUN=$* $(test_loop)
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/rosenbrock/rosenbrock.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/rosenbrock/rosenbrock.py
deleted file mode 100755
index da426f239bdcdd945eca8630db0c96a8a60544d6..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/rosenbrock/rosenbrock.py
+++ /dev/null
@@ -1,85 +0,0 @@
-#!/usr/bin/env python
-#
-# This is a simple testcase purely for testing the autotuner
-#
-# http://en.wikipedia.org/wiki/Rosenbrock_function
-#
-# Also supports some other test functions taken from:
-# http://en.wikipedia.org/wiki/Test_functions_for_optimization
-#
-
-import adddeps # fix sys.path
-
-import argparse
-import logging
-
-import opentuner
-from opentuner.measurement import MeasurementInterface
-from opentuner.search.manipulator import ConfigurationManipulator
-from opentuner.search.manipulator import FloatParameter
-
-log = logging.getLogger(__name__)
-
-parser = argparse.ArgumentParser(parents=opentuner.argparsers())
-parser.add_argument('--dimensions', type=int, default=2,
- help='dimensions for the Rosenbrock function')
-parser.add_argument('--domain', type=float, default=1000,
- help='bound for variables in each dimension')
-parser.add_argument('--function', default='rosenbrock',
- choices=('rosenbrock', 'sphere', 'beale'),
- help='function to use')
-
-
-class Rosenbrock(MeasurementInterface):
- def run(self, desired_result, input, limit):
- cfg = desired_result.configuration.data
- val = 0.0
- if self.args.function == 'rosenbrock':
- # the actual rosenbrock function:
- for d in xrange(self.args.dimensions - 1):
- x0 = cfg[d]
- x1 = cfg[d + 1]
- val += 100.0 * (x1 - x0 ** 2) ** 2 + (x0 - 1) ** 2
- elif self.args.function == 'sphere':
- for d in xrange(self.args.dimensions):
- xi = cfg[d]
- val += xi ** 2
- elif self.args.function == 'beale':
- assert self.args.dimensions == 2
- assert self.args.domain == 4.5
- x = cfg[0]
- y = cfg[1]
- val = ((1.5 - x + x * y) ** 2 +
- (2.25 - x + x * y ** 2) ** 2 +
- (2.625 - x + x * y ** 3) ** 2)
- return opentuner.resultsdb.models.Result(time=val)
-
- def manipulator(self):
- manipulator = ConfigurationManipulator()
- for d in xrange(self.args.dimensions):
- manipulator.add_parameter(FloatParameter(d,
- -self.args.domain,
- self.args.domain))
- return manipulator
-
- def program_name(self):
- return self.args.function
-
- def program_version(self):
- return "%dx%d" % (self.args.dimensions, self.args.domain)
-
- def save_final_config(self, configuration):
- """
- called at the end of autotuning with the best resultsdb.models.Configuration
- """
- print "Final configuration", configuration.data
-
-
-if __name__ == '__main__':
- args = parser.parse_args()
- if args.function == 'beale':
- # fixed for this function
- args.domain = 4.5
- args.dimensions = 2
- Rosenbrock.main(args)
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tsp/adddeps.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/tsp/adddeps.py
deleted file mode 100644
index ede22a8fcdb2a94db7915ff3beb90894b2cb8592..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tsp/adddeps.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# we would prefer a symbolic link, but it does not work on windows
-import os
-target = os.path.join(os.path.dirname(__file__),
- '../../opentuner/utils/adddeps.py')
-execfile(target, dict(__file__=target))
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tsp/att48_d.txt b/llvm/projects/hpvm-tensor-rt/opentuner/examples/tsp/att48_d.txt
deleted file mode 100644
index b93e36ccfa194c574fd9473921fcee2d6820015c..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tsp/att48_d.txt
+++ /dev/null
@@ -1,48 +0,0 @@
- 0 4727 1205 6363 3657 3130 2414 563 463 5654 1713 1604 2368 2201 1290 1004 3833 2258 3419 2267 2957 720 1700 5279 2578 6076 3465 2654 3625 3115 1574 3951 1748 2142 6755 2383 3306 1029 3530 825 2188 4820 3489 1947 6835 1542 2379 3744
- 4727 0 3588 2012 1842 6977 6501 5187 5028 2327 4148 4723 3635 3125 4907 3930 7463 6338 7243 5105 4043 4022 3677 2863 3106 1850 7173 6630 1204 6814 6001 3447 5253 2656 3123 6274 7183 5622 3085 4564 2756 1591 7027 6186 3472 5461 4390 2088
- 1205 3588 0 5163 2458 3678 3071 1742 1444 4462 1184 1520 1498 1103 1501 951 4298 2903 3967 2169 2209 652 828 4136 1518 4873 3954 3254 2446 3581 2441 2960 1966 950 5564 2916 3878 2035 2482 1027 1395 3617 3891 2686 5661 2023 1867 2560
- 6363 2012 5163 0 2799 8064 7727 6878 6581 1402 5366 5946 4679 4378 6225 5709 8417 7578 8296 6135 4802 5707 4982 2322 4178 320 8186 7800 2778 7859 7408 3763 6461 4223 1427 7451 8263 7131 3669 6011 4638 1681 7987 7502 1877 6758 5360 2844
- 3657 1842 2458 2799 0 5330 4946 4200 3824 2012 2573 3157 1924 1580 3427 3179 5749 4793 5577 3409 2223 3066 2185 1860 1401 2491 5486 5035 894 5141 4611 1669 3677 1590 3113 4682 5533 4352 1252 3227 2426 1169 5313 4706 3241 3962 2651 304
- 3130 6977 3678 8064 5330 0 743 3209 2670 6929 2831 2266 3407 3854 2178 4076 727 881 293 1930 3310 3672 3315 6199 3932 7745 365 482 5774 261 1659 4513 1746 4431 7910 769 207 2225 4435 2681 5053 6384 550 1224 7805 1670 2704 5230
- 2414 6501 3071 7727 4946 743 0 2468 1952 6673 2380 1795 3051 3405 1604 3382 1469 168 1020 1681 3110 2993 2827 6009 3552 7412 1104 267 5300 821 916 4348 1270 3890 7698 332 900 1484 4185 2049 4415 6051 1219 482 7635 1054 2432 4884
- 563 5187 1742 6878 4200 3209 2468 0 718 6203 2241 2051 2920 2762 1687 1304 3932 2331 3487 2669 3487 1175 2260 5840 3141 6596 3563 2728 4120 3240 1559 4507 2082 2658 7304 2512 3364 985 4091 1319 2544 5358 3632 1987 7391 1785 2879 4296
- 463 5028 1444 6581 3824 2670 1952 718 0 5789 1602 1343 2330 2291 970 1451 3376 1796 2959 1951 2835 1112 1725 5346 2628 6285 3007 2193 3889 2661 1122 3920 1372 2391 6883 1927 2845 611 3543 676 2590 4993 3039 1486 6934 1112 2196 3876
- 5654 2327 4462 1402 2012 6929 6673 6203 5789 0 4392 4947 3648 3501 5274 5183 7216 6535 7140 5022 3621 5077 4090 922 3207 1131 7014 6714 2437 6707 6477 2476 5432 3599 1102 6376 7121 6284 2497 5160 4318 937 6795 6507 1268 5773 4249 1914
- 1713 4148 1184 5366 2573 2831 2380 2241 1602 4392 0 586 766 1029 883 2040 3353 2224 3100 1049 1246 1625 503 3841 1196 5054 3042 2488 2945 2676 2087 2331 1114 1650 5459 2132 3037 1958 1997 931 2513 3701 2923 2137 5459 1394 711 2534
- 1604 4723 1520 5946 3157 2266 1795 2051 1343 4947 586 0 1299 1612 406 2208 2824 1639 2542 694 1586 1767 1050 4357 1770 5633 2498 1907 3520 2128 1558 2778 531 2171 6003 1552 2472 1538 2506 791 2912 4277 2403 1564 5983 827 892 3109
- 2368 3635 1498 4679 1924 3407 3051 2920 2330 3648 766 1299 0 646 1642 2446 3840 2905 3655 1488 730 2096 697 3076 533 4363 3567 3122 2453 3219 2842 1592 1791 1480 4706 2772 3610 2721 1232 1656 2550 3001 3403 2860 4697 2126 756 1836
- 2201 3125 1103 4378 1580 3854 3405 2762 2291 3501 1029 1612 646 0 1853 2026 4349 3247 4119 1997 1341 1753 606 3078 419 4070 4052 3517 1923 3690 3032 1866 2142 838 4593 3161 4060 2788 1380 1663 1932 2736 3915 3138 4647 2395 1351 1592
- 1290 4907 1501 6225 3427 2178 1604 1687 970 5274 883 406 1642 1853 0 2029 2803 1438 2466 986 1987 1593 1253 4716 2072 5915 2454 1764 3710 2082 1204 3164 497 2287 6342 1419 2379 1134 2867 554 2885 4569 2405 1289 6338 555 1297 3406
- 1004 3930 951 5709 3179 4076 3382 1304 1451 5183 2040 2208 2446 2026 2029 0 4759 3220 4368 2900 3151 442 1765 4960 2444 5443 4396 3610 2932 4034 2572 3891 2525 1590 6278 3313 4261 2033 3398 1476 1241 4287 4390 2928 6419 2428 2749 3337
- 3833 7463 4298 8417 5749 727 1469 3932 3376 7216 3353 2824 3840 4349 2803 4759 0 1601 477 2359 3617 4345 3851 6433 4372 8098 370 1206 6267 726 2384 4754 2335 4991 8148 1452 609 2949 4752 3331 5687 6746 437 1948 8005 2334 3098 5618
- 2258 6338 2903 7578 4793 881 168 2331 1796 6535 2224 1639 2905 3247 1438 3220 1601 0 1165 1563 2988 2829 2666 5882 3401 7263 1233 399 5138 923 794 4227 1117 3724 7565 286 1049 1348 4051 1881 4248 5903 1322 355 7508 887 2302 4736
- 3419 7243 3967 8296 5577 293 1020 3487 2959 7140 3100 2542 3655 4119 2466 4368 477 1165 0 2170 3520 3965 3588 6393 4183 7977 202 767 6041 438 1932 4706 2027 4711 8107 1061 132 2503 4652 2972 5344 6617 486 1501 7989 1962 2939 5469
- 2267 5105 2169 6135 3409 1930 1681 2669 1951 5022 1049 694 1488 1997 986 2900 2359 1563 2170 0 1430 2460 1547 4333 2019 5817 2079 1694 3910 1733 1813 2668 654 2694 6029 1366 2130 1991 2525 1474 3542 4455 1923 1641 5957 1071 777 3302
- 2957 4043 2209 4802 2223 3310 3110 3487 2835 3621 1246 1586 730 1341 1987 3151 3617 2988 3520 1430 0 2779 1387 2905 1062 4482 3398 3119 2922 3087 3115 1240 1953 2175 4607 2796 3501 3119 1136 2173 3268 3136 3189 3029 4527 2355 711 2042
- 720 4022 652 5707 3066 3672 2993 1175 1112 5077 1625 1767 2096 1753 1593 442 4345 2829 3965 2460 2779 0 1401 4781 2166 5427 3984 3212 2946 3620 2224 3603 2089 1496 6178 2906 3861 1719 3132 1040 1479 4211 3969 2553 6290 2012 2336 3189
- 1700 3677 828 4982 2185 3315 2827 2260 1725 4090 503 1050 697 606 1253 1765 3851 2666 3588 1547 1387 1401 0 3621 903 4675 3537 2954 2475 3169 2427 2254 1578 1148 5177 2598 3521 2194 1833 1074 2054 3340 3423 2541 5213 1801 1077 2190
- 5279 2863 4136 2322 1860 6199 6009 5840 5346 922 3841 4357 3076 3078 4716 4960 6433 5882 6393 4333 2905 4781 3621 0 2718 2042 6254 6024 2569 5966 5913 1687 4807 3384 1716 5699 6384 5787 1852 4687 4285 1272 6022 5892 1629 5178 3581 1639
- 2578 3106 1518 4178 1401 3932 3552 3141 2628 3207 1196 1770 533 419 2072 2444 4372 3401 4183 2019 1062 2166 903 2718 0 3864 4097 3635 1932 3748 3274 1448 2284 1164 4286 3283 4136 3086 967 1973 2285 2507 3935 3331 4312 2589 1284 1340
- 6076 1850 4873 320 2491 7745 7412 6596 6285 1131 5054 5633 4363 4070 5915 5443 8098 7263 7977 5817 4482 5427 4675 2042 3864 0 7866 7483 2515 7539 7101 3449 6146 3938 1375 7134 7944 6831 3349 5709 4397 1363 7667 7190 1798 6446 5041 2528
- 3465 7173 3954 8186 5486 365 1104 3563 3007 7014 3042 2498 3567 4052 2454 4396 370 1233 202 2079 3398 3984 3537 6254 4097 7866 0 839 5973 374 2019 4569 1996 4669 7970 1085 305 2581 4532 2976 5339 6509 287 1581 7844 1974 2838 5369
- 2654 6630 3254 7800 5035 482 267 2728 2193 6714 2488 1907 3122 3517 1764 3610 1206 399 767 1694 3119 3212 2954 6024 3635 7483 839 0 5427 558 1181 4349 1377 4044 7723 356 653 1744 4218 2241 4614 6121 955 743 7644 1231 2465 4957
- 3625 1204 2446 2778 894 5774 5300 4120 3889 2437 2945 3520 2453 1923 3710 2932 6267 5138 6041 3910 2922 2946 2475 2569 1932 2515 5973 5427 0 5612 4824 2550 4050 1498 3476 5071 5980 4470 2096 3388 1911 1501 5831 4994 3704 4264 3209 1196
- 3115 6814 3581 7859 5141 261 821 3240 2661 6707 2676 2128 3219 3690 2082 4034 726 923 438 1733 3087 3620 3169 5966 3748 7539 374 558 5612 0 1716 4280 1624 4298 7679 735 420 2263 4216 2606 4967 6179 400 1277 7567 1609 2501 5032
- 1574 6001 2441 7408 4611 1659 916 1559 1122 6477 2087 1558 2842 3032 1204 2572 2384 794 1932 1813 3115 2224 2427 5913 3274 7101 2019 1181 4824 1716 0 4330 1180 3346 7545 1023 1808 578 4062 1438 3693 5763 2115 440 7537 763 2404 4603
- 3951 3447 2960 3763 1669 4513 4348 4507 3920 2476 2331 2778 1592 1866 3164 3891 4754 4227 4706 2668 1240 3603 2254 1687 1448 3449 4569 4349 2550 4280 4330 0 3184 2510 3402 4031 4698 4281 533 3245 3612 2187 4339 4265 3296 3576 1941 1381
- 1748 5253 1966 6461 3677 1746 1270 2082 1372 5432 1114 531 1791 2142 497 2525 2335 1117 2027 654 1953 2089 1578 4807 2284 6146 1996 1377 4050 1624 1180 3184 0 2685 6475 1022 1952 1341 2963 1050 3358 4787 1926 1086 6436 422 1244 3619
- 2142 2656 950 4223 1590 4431 3890 2658 2391 3599 1650 2171 1480 838 2287 1590 4991 3724 4711 2694 2175 1496 1148 3384 1164 3938 4669 4044 1498 4298 3346 2510 2685 0 4697 3693 4636 2975 1981 1909 1124 2718 4565 3548 4830 2839 2140 1751
- 6755 3123 5564 1427 3113 7910 7698 7304 6883 1102 5459 6003 4706 4593 6342 6278 8148 7565 8107 6029 4607 6178 5177 1716 4286 1375 7970 7723 3476 7679 7545 3402 6475 4697 0 7393 8097 7370 3515 6249 5379 2001 7738 7556 461 6829 5267 3013
- 2383 6274 2916 7451 4682 769 332 2512 1927 6376 2132 1552 2772 3161 1419 3313 1452 286 1061 1366 2796 2906 2598 5699 3283 7134 1085 356 5071 735 1023 4031 1022 3693 7393 0 965 1542 3883 1913 4286 5772 1121 600 7322 902 2128 4608
- 3306 7183 3878 8263 5533 207 900 3364 2845 7121 3037 2472 3610 4060 2379 4261 609 1049 132 2130 3501 3861 3521 6384 4136 7944 305 653 5980 420 1808 4698 1952 4636 8097 965 0 2380 4629 2877 5250 6583 570 1380 7986 1866 2904 5432
- 1029 5622 2035 7131 4352 2225 1484 985 611 6284 1958 1538 2721 2788 1134 2033 2949 1348 2503 1991 3119 1719 2194 5787 3086 6831 2581 1744 4470 2263 578 4281 1341 2975 7370 1542 2380 0 3952 1127 3197 5518 2658 1002 7395 951 2429 4380
- 3530 3085 2482 3669 1252 4435 4185 4091 3543 2497 1997 2506 1232 1380 2867 3398 4752 4051 4652 2525 1136 3132 1833 1852 967 3349 4532 4218 2096 4216 4062 533 2963 1981 3515 3883 4629 3952 0 2873 3080 2012 4324 4046 3478 3328 1755 1000
- 825 4564 1027 6011 3227 2681 2049 1319 676 5160 931 791 1656 1663 554 1476 3331 1881 2972 1474 2173 1040 1074 4687 1973 5709 2976 2241 3388 2606 1438 3245 1050 1909 6249 1913 2877 1127 2873 0 2374 4392 2943 1659 6285 1012 1563 3254
- 2188 2756 1395 4638 2426 5053 4415 2544 2590 4318 2513 2912 2550 1932 2885 1241 5687 4248 5344 3542 3268 1479 2054 4285 2285 4397 5339 4614 1911 4967 3693 3612 3358 1124 5379 4286 5250 3197 3080 2374 0 3386 5284 3997 5585 3386 3125 2664
- 4820 1591 3617 1681 1169 6384 6051 5358 4993 937 3701 4277 3001 2736 4569 4287 6746 5903 6617 4455 3136 4211 3340 1272 2507 1363 6509 6121 1501 6179 5763 2187 4787 2718 2001 5772 6583 5518 2012 4392 3386 0 6314 5837 2205 5095 3680 1169
- 3489 7027 3891 7987 5313 550 1219 3632 3039 6795 2923 2403 3403 3915 2405 4390 437 1322 486 1923 3189 3969 3423 6022 3935 7667 287 955 5831 400 2115 4339 1926 4565 7738 1121 570 2658 4324 2943 5284 6314 0 1676 7603 1964 2662 5184
- 1947 6186 2686 7502 4706 1224 482 1987 1486 6507 2137 1564 2860 3138 1289 2928 1948 355 1501 1641 3029 2553 2541 5892 3331 7190 1581 743 4994 1277 440 4265 1086 3548 7556 600 1380 1002 4046 1659 3997 5837 1676 0 7521 744 2325 4670
- 6835 3472 5661 1877 3241 7805 7635 7391 6934 1268 5459 5983 4697 4647 6338 6419 8005 7508 7989 5957 4527 6290 5213 1629 4312 1798 7844 7644 3704 7567 7537 3296 6436 4830 461 7322 7986 7395 3478 6285 5585 2205 7603 7521 0 6805 5208 3102
- 1542 5461 2023 6758 3962 1670 1054 1785 1112 5773 1394 827 2126 2395 555 2428 2334 887 1962 1071 2355 2012 1801 5178 2589 6446 1974 1231 4264 1609 763 3576 422 2839 6829 902 1866 951 3328 1012 3386 5095 1964 744 6805 0 1644 3928
- 2379 4390 1867 5360 2651 2704 2432 2879 2196 4249 711 892 756 1351 1297 2749 3098 2302 2939 777 711 2336 1077 3581 1284 5041 2838 2465 3209 2501 2404 1941 1244 2140 5267 2128 2904 2429 1755 1563 3125 3680 2662 2325 5208 1644 0 2532
- 3744 2088 2560 2844 304 5230 4884 4296 3876 1914 2534 3109 1836 1592 3406 3337 5618 4736 5469 3302 2042 3189 2190 1639 1340 2528 5369 4957 1196 5032 4603 1381 3619 1751 3013 4608 5432 4380 1000 3254 2664 1169 5184 4670 3102 3928 2532 0
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tsp/p01_d.txt b/llvm/projects/hpvm-tensor-rt/opentuner/examples/tsp/p01_d.txt
deleted file mode 100644
index 0464ad3143b4dff3176414a0b343f762ae5379b7..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tsp/p01_d.txt
+++ /dev/null
@@ -1,15 +0,0 @@
- 0 29 82 46 68 52 72 42 51 55 29 74 23 72 46
- 29 0 55 46 42 43 43 23 23 31 41 51 11 52 21
- 82 55 0 68 46 55 23 43 41 29 79 21 64 31 51
- 46 46 68 0 82 15 72 31 62 42 21 51 51 43 64
- 68 42 46 82 0 74 23 52 21 46 82 58 46 65 23
- 52 43 55 15 74 0 61 23 55 31 33 37 51 29 59
- 72 43 23 72 23 61 0 42 23 31 77 37 51 46 33
- 42 23 43 31 52 23 42 0 33 15 37 33 33 31 37
- 51 23 41 62 21 55 23 33 0 29 62 46 29 51 11
- 55 31 29 42 46 31 31 15 29 0 51 21 41 23 37
- 29 41 79 21 82 33 77 37 62 51 0 65 42 59 61
- 74 51 21 51 58 37 37 33 46 21 65 0 61 11 55
- 23 11 64 51 46 51 51 33 29 41 42 61 0 62 23
- 72 52 31 43 65 29 46 31 51 23 59 11 62 0 59
- 46 21 51 64 23 59 33 37 11 37 61 55 23 59 0
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tsp/p01_s.txt b/llvm/projects/hpvm-tensor-rt/opentuner/examples/tsp/p01_s.txt
deleted file mode 100644
index 38afab553d2a9c23c1abda12a95f6367d5d093e2..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tsp/p01_s.txt
+++ /dev/null
@@ -1,16 +0,0 @@
- 1
-13
- 2
-15
- 9
- 5
- 7
- 3
-12
-14
-10
- 8
- 6
- 4
-11
- 1
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tsp/tsp.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/tsp/tsp.py
deleted file mode 100755
index 0ddff5156497331daffef9b7385a20d63423bbd0..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tsp/tsp.py
+++ /dev/null
@@ -1,59 +0,0 @@
-#!/usr/bin/env python
-#
-# This is a simple testcase purely for testing the autotuner on permutations
-#
-# http://en.wikipedia.org/wiki/Travelling_salesman_problem
-#
-
-import adddeps #fix sys.path
-
-import argparse
-import logging
-
-import opentuner
-from opentuner.search.manipulator import (ConfigurationManipulator,
- PermutationParameter)
-from opentuner.search.objective import MinimizeTime
-from opentuner.measurement import MeasurementInterface
-from opentuner.measurement.inputmanager import FixedInputManager
-from opentuner.tuningrunmain import TuningRunMain
-
-
-parser = argparse.ArgumentParser(parents=opentuner.argparsers())
-parser.add_argument('data', help='distance matrix file')
-
-class TSP(MeasurementInterface):
- def __init__(self, args):
- super(TSP, self).__init__(args)
- data = args.data
- m = open(data).readlines()
- self.distance = [[int(i) for i in l.split()] for l in m]
-
- def run(self, desired_result, input, limit):
- cfg = desired_result.configuration.data
- p = cfg[0] # cheating: should use manipulator function
- t = self.eval_path(p)
- return opentuner.resultsdb.models.Result(time=t)
-
- def eval_path(self, p):
- """ Given permutation of cities as a list of indices,
- return total path length """
- out = sum(self.distance[p[i]][p[i+1]] for i in range(len(p)-1))
-## print out, p
- return out
-
- def manipulator(self):
- manipulator = ConfigurationManipulator()
- manipulator.add_parameter(PermutationParameter(0, range(len(self.distance))))
- return manipulator
-
- def solution(self):
- p = [1,13,2,15,9,5,7,3,12,14,10,8,6,4,11]
- return self.eval_path(p)
-
-
-
-if __name__ == '__main__':
- args = parser.parse_args()
- TSP.main(args)
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/#accuracy_tuner.py# b/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/#accuracy_tuner.py#
deleted file mode 100644
index 2110d0d692831e37f30023af05b92a0d91d9623c..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/#accuracy_tuner.py#
+++ /dev/null
@@ -1,203 +0,0 @@
-#!/usr/bin/env python
-#
-# Optimize blocksize of apps/mmm_block.cpp
-#
-# This is an extremely simplified version meant only for tutorials
-#
-import adddeps # fix sys.path
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-
-
-output_dir = ""
-flag_ranges = []
-tuning_flags = []
-binary_name = ""
-accuracy_threshold = 10.0
-opt_confs_index = 9
-evaluated_configs = {}
-
-
-def extractTotalOverhead(file_name):
-
- total_comps = 0.0
- file = open(file_name, "r")
- for x in file:
- words = x.split()
- total_comps += float(words[opt_confs_index])
-
- print total_comps
- return total_comps
-
-
-def getAccuracy(file_name):
-
- file = open(file_name, "r")
- acc_str = file.read()
- accuracy = float(acc_str)
- print accuracy
- return accuracy
-
-
-def createFlagsFile(file_name, cfg):
-
- f = open(file_name, "w+")
- cmd_config = ""
- for flag in tuning_flags:
- flag_value = cfg[flag]
- cmd_config += str(flag_value) + "\n"
-
- f.write(cmd_config)
- f.close()
-
-
-class ClangFlagsTuner(MeasurementInterface):
-
- def __init__(self, args):
- objective = ThresholdAccuracyMinimizeTime(accuracy_threshold)
- input_manager = FixedInputManager(size=num_flags)
- self.configs_list = []
-
- super(ClangFlagsTuner, self).__init__(
- args, program_name=args.binary,
- program_version=self.file_hash(args.binary),
- input_manager=input_manager, objective=objective)
-
-
-
-
- def manipulator(self):
- """
- Define the search space by creating a
- ConfigurationManipulator
- """
- manipulator = ConfigurationManipulator()
- for flag in tuning_flags:
- manipulator.add_parameter(
- EnumParameter(flag, flag_ranges
- # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
- )) #default is needed, optimizations don't work without it(tried and tested)
- return manipulator
-
-
- def run(self, desired_result, input, limit):
- """
- Compile and run a given configuration then
- return performance
- """
- cfg = desired_result.configuration.data
-
- # NOTE: creates the file with flags read by the runtime
- createFlagsFile("opentuner_flags", cfg)
-
- run_cmd = binary_name
- print run_cmd
- run_result_call_program = self.call_program(run_cmd)
- #print run_result_call_program
-
- total_comps = extractTotalOverhead("accuracy_summary")
- accuracy = getAccuracy("final_accuracy")
-
- #Result = opentuner.resultsdb.models.Result(time=total_comps)
- Result = opentuner.resultsdb.models.Result()
- Result.time = total_comps
- Result.accuracy = accuracy
-
- if accuracy > accuracy_threshold:
- if accuracy not in evaluated_configs:
- config_tuple = (total_comps, accuracy, cfg)
- self.configs_list.append(config_tuple)
- evaluated_configs[accuracy] = 1
- shutil.copy('accuracy_summary', output_dir + '/' + binary_name + '_' + str(accuracy))
-
-
- return Result
-
-
- def save_final_config(self, configuration):
- """
- called at the end of autotuning with the best resultsdb.models.Configuration
- """
- print "Final configuration", configuration.data
-
- if not os.path.exists(result_dir):
- os.mkdir(result_dir)
-
- createFlagsFile("opentuner_flags", configuration.data)
- run_cmd = binary_name
- run_result_call_program = self.call_program(run_cmd)
-
- accuracy = getAccuracy("final_accuracy")
- shutil.copy('accuracy_summary', result_dir + '/' + binary_name + '_final_' + str(accuracy) )
-
- sorted_list = sorted(self.configs_list, key = lambda tup: tup[0])
- print sorted_list[0:10]
-
- top_elems = 20
- if len(sorted_list) < top_elems:
- top_elems = len(sorted_list)
-
-
- for i in range(top_elems):
- createFlagsFile("opentuner_flags", sorted_list[i][2])
- run_cmd = binary_name
- run_result_call_program = self.call_program(run_cmd)
- accuracy = getAccuracy("final_accuracy")
- shutil.copy('accuracy_summary', result_dir + '/' + binary_name + '_' + str(accuracy) + "_rank_" + str(i) )
-
-
- #os.mkdir(result_dir + "full_results")
-
-
-
-
-if __name__ == '__main__':
-
- argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
- argparser.add_argument('--binary', help='name of binary to run')
- argparser.add_argument('--num-flags', type=int, help='num of flags to tune for')
- argparser.add_argument('--error-range', type=int, help='num of flags to tune for')
- argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
- argparser.add_argument('--result-dir', help='accuracy threshold')
-
-
- args = argparser.parse_args()
- binary_name = str(args.binary)
- print("binary_name = ", binary_name)
- num_flags = int(args.num_flags)
- error_range = int(args.error_range)
- accuracy_threshold = float(args.accuracy)
- print("accuracy = ", accuracy_threshold)
- result_dir = args.result_dir
- if result_dir == "":
- print("Provide --result-dir ")
-
-
- output_dir = result_dir + "/full_results"
- print output_dir
- if not os.path.exists(result_dir):
- os.mkdir(result_dir)
-
- if not os.path.exists(output_dir):
- print("Creating output directory = ", output_dir)
- os.mkdir(output_dir)
-
- for j in range(error_range):
- flag_ranges.append(j)
-
- print("flag_ranges = ", flag_ranges)
-
- for i in range(num_flags):
- tuning_flags.append("flag" + str(i))
-
- ClangFlagsTuner.main(argparser.parse_args())
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/__init__.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/accuracy_tuner.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/accuracy_tuner.py
deleted file mode 100644
index 5977fe7ee5b4780139d2c5a865c8231361cf0f2c..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/accuracy_tuner.py
+++ /dev/null
@@ -1,198 +0,0 @@
-#!/usr/bin/env python
-#
-
-import adddeps # fix sys.path
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-
-
-output_dir = ""
-flag_ranges = []
-tuning_flags = []
-binary_name = ""
-accuracy_threshold = 10.0
-opt_confs_index = 9
-evaluated_configs = {}
-
-
-def extractTotalOverhead(file_name):
-
- total_comps = 0.0
- file = open(file_name, "r")
- for x in file:
- words = x.split()
- total_comps += float(words[opt_confs_index])
-
- print total_comps
- return total_comps
-
-
-def getAccuracy(file_name):
-
- file = open(file_name, "r")
- acc_str = file.read()
- accuracy = float(acc_str)
- print accuracy
- return accuracy
-
-
-def createFlagsFile(file_name, cfg):
-
- f = open(file_name, "w+")
- cmd_config = ""
- for flag in tuning_flags:
- flag_value = cfg[flag]
- cmd_config += str(flag_value) + "\n"
-
- f.write(cmd_config)
- f.close()
-
-
-class ClangFlagsTuner(MeasurementInterface):
-
- def __init__(self, args):
- objective = ThresholdAccuracyMinimizeTime(accuracy_threshold)
- input_manager = FixedInputManager(size=num_flags)
- self.configs_list = []
-
- super(ClangFlagsTuner, self).__init__(
- args, program_name=args.binary,
- program_version=self.file_hash(args.binary),
- input_manager=input_manager, objective=objective)
-
-
- def manipulator(self):
- """
- Define the search space by creating a
- ConfigurationManipulator
- """
- manipulator = ConfigurationManipulator()
- for flag in tuning_flags:
- manipulator.add_parameter(
- EnumParameter(flag, flag_ranges
- # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
- )) #default is needed, optimizations don't work without it(tried and tested)
- return manipulator
-
-
- def run(self, desired_result, input, limit):
- """
- Compile and run a given configuration then
- return performance
- """
- cfg = desired_result.configuration.data
-
- # NOTE: creates the file with flags read by the runtime
- createFlagsFile("opentuner_flags", cfg)
-
- run_cmd = binary_name
- print run_cmd
- run_result_call_program = self.call_program(run_cmd)
- #print run_result_call_program
-
- total_comps = extractTotalOverhead("accuracy_summary")
- accuracy = getAccuracy("final_accuracy")
-
- #Result = opentuner.resultsdb.models.Result(time=total_comps)
- Result = opentuner.resultsdb.models.Result()
- Result.time = total_comps
- Result.accuracy = accuracy
-
- if accuracy > accuracy_threshold:
- if accuracy not in evaluated_configs:
- config_tuple = (total_comps, accuracy, cfg)
- self.configs_list.append(config_tuple)
- evaluated_configs[accuracy] = 1
- shutil.copy('accuracy_summary', output_dir + '/' + binary_name + '_' + str(accuracy))
-
-
- return Result
-
-
- def save_final_config(self, configuration):
- """
- called at the end of autotuning with the best resultsdb.models.Configuration
- """
- print "Final configuration", configuration.data
-
- if not os.path.exists(result_dir):
- os.mkdir(result_dir)
-
- createFlagsFile("opentuner_flags", configuration.data)
- run_cmd = binary_name
- run_result_call_program = self.call_program(run_cmd)
-
- accuracy = getAccuracy("final_accuracy")
- shutil.copy('accuracy_summary', result_dir + '/' + binary_name + '_final_' + str(accuracy) )
-
- sorted_list = sorted(self.configs_list, key = lambda tup: tup[0])
- print sorted_list[0:10]
-
- top_elems = 20
- if len(sorted_list) < top_elems:
- top_elems = len(sorted_list)
-
-
- for i in range(top_elems):
- createFlagsFile("opentuner_flags", sorted_list[i][2])
- run_cmd = binary_name
- run_result_call_program = self.call_program(run_cmd)
- accuracy = getAccuracy("final_accuracy")
- shutil.copy('accuracy_summary', result_dir + '/' + binary_name + '_' + str(accuracy) + "_rank_" + str(i) )
-
-
- #os.mkdir(result_dir + "full_results")
-
-
-
-
-if __name__ == '__main__':
-
- argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
- argparser.add_argument('--binary', help='name of binary to run')
- argparser.add_argument('--num-flags', type=int, help='num of flags to tune for')
- argparser.add_argument('--error-range', type=int, help='num of flags to tune for')
- argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
- argparser.add_argument('--result-dir', help='accuracy threshold')
-
-
- args = argparser.parse_args()
- binary_name = str(args.binary)
- print("binary_name = ", binary_name)
- num_flags = int(args.num_flags)
- error_range = int(args.error_range)
- accuracy_threshold = float(args.accuracy)
- print("accuracy = ", accuracy_threshold)
- result_dir = args.result_dir
- if result_dir == "":
- print("Provide --result-dir ")
-
-
- output_dir = result_dir + "/full_results"
- print output_dir
- if not os.path.exists(result_dir):
- os.mkdir(result_dir)
-
- if not os.path.exists(output_dir):
- print("Creating output directory = ", output_dir)
- os.mkdir(output_dir)
-
- for j in range(error_range):
- flag_ranges.append(j)
-
- print("flag_ranges = ", flag_ranges)
-
- for i in range(num_flags):
- tuning_flags.append("flag" + str(i))
-
- ClangFlagsTuner.main(argparser.parse_args())
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/accuracy_tuner_piped.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/accuracy_tuner_piped.py
deleted file mode 100644
index 6d46c5762ead377292337c47d045ee5e58322954..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/accuracy_tuner_piped.py
+++ /dev/null
@@ -1,269 +0,0 @@
-#!/usr/bin/env python
-#
-# Optimize blocksize of apps/mmm_block.cpp
-#
-# This is an extremely simplified version meant only for tutorials
-#
-import adddeps # fix sys.path
-
-import argparse
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-from opentuner.search.objective import ThresholdAccuracyMinimizeTime
-from opentuner.measurement.inputmanager import FixedInputManager
-import shutil
-import os
-import sys
-import subprocess
-import threading
-import psutil
-
-from measure_confidence import dump_high_confidence_files
-from select_top_results import select_top_results
-from time import sleep
-
-
-output_dir = ""
-flag_ranges = []
-tuning_flags = []
-binary_name = ""
-accuracy_threshold = 10.0
-opt_confs_index = 9
-evaluated_configs = {}
-orig_result_dir = ""
-
-
-def extractTotalOverhead(file_name):
-
- total_comps = 0.0
- file = open(file_name, "r")
- for x in file:
- words = x.split()
- total_comps += float(words[opt_confs_index])
-
- print total_comps
- return total_comps
-
-
-def getAccuracy(file_name):
-
- file = open(file_name, "r")
- acc_str = file.read()
- file.close()
-
- accuracy = float(acc_str)
-
- try:
- accuracy = float(acc_str)
- except:
- return 20
-
- print accuracy
- return accuracy
-
-
-
-def kill(proc_pid):
- process = psutil.Process(proc_pid)
- for proc in process.children(recursive=True):
- proc.kill()
- process.kill()
-
-
-
-def createFlagsFile(file_name, cfg):
-
- f = open(file_name, "w+")
- cmd_config = ""
- for flag in tuning_flags:
- flag_value = cfg[flag]
- cmd_config += str(flag_value) + "\n"
-
- f.write(cmd_config)
- f.close()
-
-
-class ClangFlagsTuner(MeasurementInterface):
-
- def __init__(self, args):
- objective = ThresholdAccuracyMinimizeTime(accuracy_threshold)
- input_manager = FixedInputManager(size=num_flags)
- self.configs_list = []
-
- super(ClangFlagsTuner, self).__init__(
- args, program_name=args.binary,
- program_version=self.file_hash(args.binary),
- input_manager=input_manager, objective=objective)
-
-
- FNULL = open(os.devnull, 'wb')
- #run_result_call_program = self.call_program(run_cmd)
- self.start_process = subprocess.Popen([binary_name, "opentuner_run"]) #, stdout=FNULL);
-
- try:
- os.mkfifo("/tmp/myfifo")
- except OSError, e:
- print("FIFO exists")
-
-
-
- def manipulator(self):
- """
- Define the search space by creating a
- ConfigurationManipulator
- """
- manipulator = ConfigurationManipulator()
- for flag in tuning_flags:
- manipulator.add_parameter(
- EnumParameter(flag, flag_ranges
- # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
- )) #default is needed, optimizations don't work without it(tried and tested)
- return manipulator
-
-
- def run(self, desired_result, input, limit):
-
- """
- Run a given configuration then
- return performance
- """
- cfg = desired_result.configuration.data
-
- # NOTE: creates the file with flags read by the runtime
- createFlagsFile("opentuner_flags", cfg)
-
- run_cmd = binary_name
- print run_cmd
- #run_result_call_program = self.call_program(run_cmd)
-
- # Using Named Pipes to signal execution to the DNN outer thread
- fifo = open("/tmp/myfifo", "w")
- fifo.write("start_run")
- fifo.close()
-
- print "Waiting for process to signal back - when done processing one run"
-
- fifo2 = open("/tmp/myfifo", "r")
- fifo2.read()
- fifo2.close()
-
- print "Process Signalled back"
-
- total_comps = extractTotalOverhead("accuracy_summary")
- accuracy = getAccuracy("final_accuracy")
-
-
- #Result = opentuner.resultsdb.models.Result(time=total_comps)
- Result = opentuner.resultsdb.models.Result()
- Result.time = total_comps
- Result.accuracy = accuracy
-
- if accuracy > accuracy_threshold:
- if accuracy not in evaluated_configs:
- config_tuple = (total_comps, accuracy, cfg)
- self.configs_list.append(config_tuple)
- evaluated_configs[accuracy] = 1
- shutil.copy('accuracy_summary', output_dir + '/' + binary_name + '_' + str(accuracy))
-
-
- print "done with one run"
-
- return Result
-
-
- def save_final_config(self, configuration):
-
- print "Dumping High Confidence results"
- sleep(5)
-
- # Only dumping files with 95% confidence
- dump_high_confidence_files(binary_name, orig_result_dir, accuracy_threshold, 95)
- select_top_results(orig_result_dir + "/high_confidence")
-
-
- #self.start_process.kill()
- kill(self.start_process.pid)
-
- """
- called at the end of autotuning with the best resultsdb.models.Configuration
- """
- print "Final configuration", configuration.data
-
- return
-
-
- if not os.path.exists(result_dir):
- os.mkdir(result_dir)
-
- createFlagsFile("opentuner_flags", configuration.data)
- run_cmd = binary_name
- run_result_call_program = self.call_program(run_cmd)
-
- accuracy = getAccuracy("final_accuracy")
- shutil.copy('accuracy_summary', result_dir + '/' + binary_name + '_final_' + str(accuracy) )
-
- sorted_list = sorted(self.configs_list, key = lambda tup: tup[0])
- print sorted_list[0:10]
-
- top_elems = 20
- if len(sorted_list) < top_elems:
- top_elems = len(sorted_list)
-
-
- for i in range(top_elems):
- createFlagsFile("opentuner_flags", sorted_list[i][2])
- run_cmd = binary_name
- run_result_call_program = self.call_program(run_cmd)
- accuracy = getAccuracy("final_accuracy")
- shutil.copy('accuracy_summary', result_dir + '/' + binary_name + '_' + str(accuracy) + "_rank_" + str(i) )
-
-
- #os.mkdir(result_dir + "full_results")
-
-
-
-
-if __name__ == '__main__':
-
- argparser = argparse.ArgumentParser(parents=opentuner.argparsers())
- argparser.add_argument('--binary', help='name of binary to run')
- argparser.add_argument('--num-flags', type=int, help='num of flags to tune for')
- argparser.add_argument('--error-range', type=int, help='num of flags to tune for')
- argparser.add_argument('--accuracy', type=float, help='accuracy threshold')
- argparser.add_argument('--result-dir', help='accuracy threshold')
-
-
- args = argparser.parse_args()
- binary_name = str(args.binary)
- print("binary_name = ", binary_name)
- num_flags = int(args.num_flags)
- error_range = int(args.error_range)
- accuracy_threshold = float(args.accuracy)
- print("accuracy = ", accuracy_threshold)
- result_dir = args.result_dir
- orig_result_dir = result_dir
- if result_dir == "":
- print("Provide --result-dir ")
-
-
- output_dir = result_dir + "/full_results"
- print output_dir
- if not os.path.exists(result_dir):
- os.mkdir(result_dir)
-
- if not os.path.exists(output_dir):
- print("Creating output directory = ", output_dir)
- os.mkdir(output_dir)
-
- for j in range(error_range):
- flag_ranges.append(j)
-
- print("flag_ranges = ", flag_ranges)
-
- for i in range(num_flags):
- tuning_flags.append("flag" + str(i))
-
- ClangFlagsTuner.main(argparser.parse_args())
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/adddeps.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/adddeps.py
deleted file mode 100644
index 72de04cf55e138a5ee5d0fdaf11da4b692045706..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/adddeps.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# we would prefer a symbolic link, but it does not work on windows
-import os
-target = os.path.join(os.path.dirname(__file__),
- '../../opentuner/utils/adddeps.py')
-execfile(target, dict(__file__=target))
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/gettingstarted.md b/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/gettingstarted.md
deleted file mode 100644
index 8a442c5f44d6c501f686125d4468ca642f745920..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/gettingstarted.md
+++ /dev/null
@@ -1,215 +0,0 @@
----
-layout: default
-title: OpenTuner - Using OpenTuner
-permalink: /tutorial/gettingstarted/index.html
----
-
-Tutorial: Optimizing Block Matrix Multiplication
-================================================
-
-This tutorial assumes that you have checked out a copy of opentuner. For
-guidelines on how to get opentuner set up, refer [here][setup].
-
-[setup]: http://opentuner.org/tutorial/setup/
-
-Identifying a Program to Autotune
----------------------------------
-
-In order to do autotuning, you first need something to autotune. This will
-normally be your own program that you want to make either fast or better in
-some way. For this tutorial we will use a blocked version of matrix multiply
-as an example. We will use opentuner to find the optimal value of the block
-size parameter.
-
-We will autotune the sample code below(based off of modification of code
-found [here][matrix-multiply-code]), making sure to take the block size as
-a compile time constant to the program.
-
-[matrix-multiply-code]: http://csapp.cs.cmu.edu/public/waside/waside-blocking.pdf
-
-Save the sample code below to examples/tutorials/mmm_block.cpp
-
- #include <stdio.h>
- #include <cstdlib>
-
- #define N 100
-
- int main(int argc, const char** argv)
- {
-
- int n = BLOCK_SIZE * (N/BLOCK_SIZE);
- int a[N][N];
- int b[N][N];
- int c[N][N];
- int sum=0;
- for(int k1=0;k1<n;k1+=BLOCK_SIZE)
- {
- for(int j1=0;j1<n;j1+=BLOCK_SIZE)
- {
- for(int k1=0;k1<n;k1+=BLOCK_SIZE)
- {
- for(int i=0;i<n;i++)
- {
- for(int j=j1;j<j1+BLOCK_SIZE;j++)
- {
- sum = c[i][j];
- for(int k=k1;k<k1+BLOCK_SIZE;k++)
- {
- sum += a[i][k] * b[k][j];
- }
- c[i][j] = sum;
- }
- }
- }
- }
- }
- return 0;
- }
-
-Creating a New Autotuner with Opentuner
-------------------------------------
-Now we need to create a program that uses OpenTuner to optimize the program we just saved.
-
-Save the following code to examples/tutorials/mmm_tuner.py
-
- #!/usr/bin/env python
- #
- # Optimize blocksize of apps/mmm_block.cpp
- #
- # This is an extremely simplified version meant only for tutorials
- #
- import adddeps # fix sys.path
-
- import opentuner
- from opentuner import ConfigurationManipulator
- from opentuner import IntegerParameter
- from opentuner import MeasurementInterface
- from opentuner import Result
-
-
- class GccFlagsTuner(MeasurementInterface):
-
- def manipulator(self):
- """
- Define the search space by creating a
- ConfigurationManipulator
- """
- manipulator = ConfigurationManipulator()
- manipulator.add_parameter(
- IntegerParameter('blockSize', 1, 10))
- return manipulator
-
- def run(self, desired_result, input, limit):
- """
- Compile and run a given configuration then
- return performance
- """
- cfg = desired_result.configuration.data
-
- gcc_cmd = 'g++ mmm_block.cpp '
- gcc_cmd += '-DBLOCK_SIZE='+ cfg['blockSize']
- gcc_cmd += ' -o ./tmp.bin'
-
- compile_result = self.call_program(gcc_cmd)
- assert compile_result['returncode'] == 0
-
- run_cmd = './tmp.bin'
-
- run_result = self.call_program(run_cmd)
- assert run_result['returncode'] == 0
-
- return Result(time=run_result['time'])
-
- def save_final_config(self, configuration):
- """called at the end of tuning"""
- print "Optimal block size written to mmm_final_config.json:", configuration.data
- self.manipulator().save_to_file(configuration.data,
- 'mmm_final_config.json')
-
-
- if __name__ == '__main__':
- argparser = opentuner.default_argparser()
- GccFlagsTuner.main(argparser.parse_args())
-
-
-This file consists of several components, each of which will be discussed in further detail below.
-
-Tuning Programs have a general structure as follows:
-
- from opentuner import MeasurementInterface
- from opentuner import Result
-
-Create an instance of class GccFlagsTuner, which tunes specified parameters using opentuner.
- class GccFlagsTuner(MeasurementInterface):
-
-The manipulator method defines the variable search space by specifying parameters that should be tuned by this instance of GccFlagsTuner
-
- def manipulator(self):
- """
- Define the search space by creating a
- ConfigurationManipulator
- """
- manipulator = ConfigurationManipulator()
- manipulator.add_parameter(
- IntegerParameter('blockSize', 1, 10))
- return manipulator
-
-The run method actually runs opentuner under the given configuration and returns the calculated performance under this configuration. In this example, the blockSize parameter to be tuned is input as a compile-time constant that takes on a value within the specified range each time it is run. However, opentuner also supports other methods of specifying these parameters that may be preferred in different use cases.
-
- def run(self, desired_result, input, limit):
- """
- Compile and run a given configuration then
- return performance
- """
- cfg = desired_result.configuration.data
-
- gcc_cmd = 'g++ mmm_block.cpp '
- gcc_cmd += '-DBLOCK_SIZE='+ cfg['blockSize']
- gcc_cmd += ' -o ./tmp.bin'
-
- compile_result = self.call_program(gcc_cmd)
- assert compile_result['returncode'] == 0
-
- run_cmd = './tmp.bin'
-
- run_result = self.call_program(run_cmd)
- assert run_result['returncode'] == 0
-
- return Result(time=run_result['time'])
-
-We can actually display the result of running opentuner(the optimal block size for our multiplication problem) by creating a method, save_final_config() in our class. This saves a json dictionary of the optimal blockSize parameter found to the file mmm_final_config.json
-
- def save_final_config(self, configuration):
- """called at the end of tuning"""
- print "Optimal block size written to mmm_final_config.json:", configuration.data
- self.manipulator().save_to_file(configuration.data,
- 'mmm_final_config.json')
-
- if __name__ == '__main__':
- argparser = opentuner.default_argparser()
- GccFlagsTuner.main(argparser.parse_args())
-
-Generating and Viewing Results
-------------------------------
-
-Run the following command to autotune our program(The --no-dups flag hides warnings about duplicate results and the --stop-after parameter specifies that we are running opentuner for a maximum of 30 seconds):
-
- python mmm_tuner.py --no-dups --stop-after=30
-
-The results of each run configuration will be displayed as follows(output lines are truncated for readability here):
-
- [ 10s] INFO opentuner.search.plugin.DisplayPlugin: tests=10, best {'BLOCK_SIZE': 4}, cost time=0.0081, found by DifferentialEvolutionAlt[...]
- [ 19s] INFO opentuner.search.metatechniques: AUCBanditMetaTechniqueA: [('DifferentialEvolutionAlt', 477), ('UniformGreedyMutation', 18), ('NormalGreedyMutation', 5), ('RandomNelderMead', 1)]
- [ 20s] INFO opentuner.search.plugin.DisplayPlugin: tests=10, best {'BLOCK_SIZE': 4}, cost time=0.0081, found by DifferentialEvolutionAlt[...]
- [ 30s] INFO opentuner.search.plugin.DisplayPlugin: tests=10, best {'BLOCK_SIZE': 4}, cost time=0.0081, found by DifferentialEvolutionAlt[...]
- [ 30s] INFO opentuner.search.plugin.DisplayPlugin: tests=10, best {'BLOCK_SIZE': 4}, cost time=0.0081, found by DifferentialEvolutionAlt[...]
- Optimal block size written to mmm_final_config.json: {'BLOCK_SIZE': 4}
-
-
-Look up the optimal BlockSize value by inspecting the following created file:
-
- mmm_final_config.json
-
-In this example, the output file content was as follows:
-
- {'BLOCK_SIZE': 4}
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/measure_confidence.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/measure_confidence.py
deleted file mode 100644
index 655bdb024f72f0fd47807b5aa2696f9fb89b40e6..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/measure_confidence.py
+++ /dev/null
@@ -1,164 +0,0 @@
-
-import argparse
-import os
-import sys
-
-
-def getAccuracy(file_name):
-
- if not os.path.exists(file_name):
- print("final_accuracy file not found ")
- sys.exit(0)
-
- file = open(file_name, "r")
- acc_str = file.read()
- accuracy = float(acc_str)
- print accuracy
- return accuracy
-
-
-total_runs = 100.0
-skip_lines = 0
-
-
-def test_func():
- print "test_func"
- sys.exit(0)
-
-
-def do_multiple_runs(binary_name, accuracy_threshold, confidence_threshold):
-
- #total_runs = 100.0
- successful_runs = 0.0
- total_acc = 0
-
- for i in range(int(total_runs)):
-
- fifo = open("/tmp/myfifo", "w")
- fifo.write("start_run")
- fifo.close()
-
- print "Waiting for process to signal back - when done processing one run"
-
- fifo2 = open("/tmp/myfifo", "r")
- fifo2.read()
- fifo2.close()
-
- print "Process Signalled back"
-
- accuracy = getAccuracy("final_accuracy")
- total_acc += accuracy
-
- if accuracy > accuracy_threshold:
- successful_runs += 1
-
- confidence = (successful_runs / total_runs) * 100.0
- print("confidence = ", confidence)
- avg_acc = total_acc / total_runs
- print("average accuracy = ", avg_acc)
-
- return confidence, avg_acc
-
-
-def compute_confidence(binary_name, accuracy, confidence, result_dir, output_dir):
-
- confidence_list = []
-
- if not os.path.exists(result_dir):
- print("Path does not exist")
- sys.exit(0)
-
- file_names = os.listdir(result_dir)
- print file_names
-
- for file_name in file_names:
- # Skip sub-directories
- if os.path.isdir(result_dir + "/" + file_name):
- continue
-
- f = open(result_dir + "/" + file_name)
- tuner_file = open("opentuner_flags", "w+")
-
- index = 0
- results_str = ""
- for x in f:
- if index >= skip_lines:
- error_knob = int(float(x.split()[1]))
- print error_knob
- tuner_file.write(str(error_knob) + "\n")
-
- results_str += x
- index += 1
-
- tuner_file.close()
-
- run_confidence, avg_accuracy = do_multiple_runs(binary_name, accuracy, confidence)
-
- if run_confidence > 90:
- f2 = open(output_dir + "/" + file_name, "w+")
- f2.write("total_runs=" + str(total_runs) + "\t confidence=" + str(run_confidence) + "\t avg_accuracy=" + str(avg_accuracy) + "\n")
- f2.write(results_str)
- f2.close()
-
- conf_result = (run_confidence, avg_accuracy, file_name)
- confidence_list.append(conf_result)
-
- return confidence_list
-
-
-
-def dump_high_confidence_files(binary, result_dir, accuracy, confidence):
-
- #result_dir = args.result_dir
- output_dir = result_dir + "/high_confidence"
- result_dir = result_dir + "/full_results"
-
- if not os.path.exists(output_dir):
- os.mkdir(output_dir)
-
-
- confidence_list = compute_confidence(binary, accuracy, confidence, result_dir, output_dir)
- print confidence_list
-
- sorted_list = sorted(confidence_list, key = lambda tup: tup[0], reverse=True)
-
- output_file = open(output_dir + "/confidence_summary.txt", "w+")
- for x in sorted_list:
- output_file.write(str(x[0]) + "\t" + str(x[1]) + "\t" + str(x[2]) + "\n")
-
- output_file.close()
- print "Dumped Confidence Summary"
-
-
-
-
-
-
-if __name__ == "__main__":
-
- argparser = argparse.ArgumentParser(description='runs best configs to get high confidence on accuracy')
- argparser.add_argument('--result-dir', help='Directory containing OpenTuner configurations')
- argparser.add_argument('--output-dir', help='Directory for storing output directory')
- argparser.add_argument('--binary', help='Binary name to run')
- argparser.add_argument('--accuracy', type=float, help='Accuracy constraint')
- argparser.add_argument('--confidence', type=float, help='Confidence threshold')
-
-
- args = argparser.parse_args()
- result_dir = args.result_dir
- output_dir = args.output_dir
- binary = args.binary
- accuracy = args.accuracy
- confidence = args.confidence
-
- confidence_list = compute_confidence(binary, accuracy, confidence, result_dir, output_dir)
- #print confidence_list
-
- sorted_list = sorted(confidence_list, key = lambda tup: tup[0], reverse=True)
-
- output_file = open(output_dir + "/confidence_summary.txt", "w+")
- for x in sorted_list:
- output_file.write(str(x[0]) + "\t" + str(x[1]) + "\t" + str(x[2]) + "\n")
-
- output_file.close()
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/mmm_block.cpp b/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/mmm_block.cpp
deleted file mode 100755
index 0bb76845f8d6653d1c90a0a5b387e75c46e18233..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/mmm_block.cpp
+++ /dev/null
@@ -1,36 +0,0 @@
-#include <stdio.h>
-#include <cstdlib>
-
-#define N 100
-
-int main(int argc, const char** argv)
-{
-
- int n = BLOCK_SIZE * (N/BLOCK_SIZE);
- int a[N][N];
- int b[N][N];
- int c[N][N];
- int sum=0;
- for(int k1=0;k1<n;k1+=BLOCK_SIZE)
- {
- for(int j1=0;j1<n;j1+=BLOCK_SIZE)
- {
- for(int k1=0;k1<n;k1+=BLOCK_SIZE)
- {
- for(int i=0;i<n;i++)
- {
- for(int j=j1;j<j1+BLOCK_SIZE;j++)
- {
- sum = c[i][j];
- for(int k=k1;k<k1+BLOCK_SIZE;k++)
- {
- sum += a[i][k] * b[k][j];
- }
- c[i][j] = sum;
- }
- }
- }
- }
- }
- return 0;
-}
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/mmm_tuner.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/mmm_tuner.py
deleted file mode 100644
index f92c4c3bfc9640514e4879b1e46480613015c207..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/mmm_tuner.py
+++ /dev/null
@@ -1,116 +0,0 @@
-#!/usr/bin/env python
-#
-# Optimize blocksize of apps/mmm_block.cpp
-#
-# This is an extremely simplified version meant only for tutorials
-#
-import adddeps # fix sys.path
-
-import opentuner
-from opentuner import ConfigurationManipulator
-from opentuner import MeasurementInterface
-from opentuner import Result
-from opentuner import EnumParameter
-import os
-import sys
-
-
-tuning_flags = []
-
-binary_name = ""
-
-
-class ClangFlagsTuner(MeasurementInterface):
- def manipulator(self):
- """
- Define the search space by creating a
- ConfigurationManipulator
- """
- manipulator = ConfigurationManipulator()
- for flag in tuning_flags:
- manipulator.add_parameter(
- EnumParameter(flag,
- [0, 1, 2, 3, 4, 5, 6])) #default is needed, optimizations don't work without it(tried and tested)
- return manipulator
-
- def compile(self, cfg, id):
- """
- Compile a given configuration in parallel
- """
- cmd_config = ""
- for flag in tuning_flags:
- flag_value = cfg[flag]
- cmd_config += " " + flag_value
-
- run_cmd = binary_name + cmd_config
- return self.call_program(run_cmd)
-
- def run_precompiled(self, desired_result, input, limit, compile_result, id):
- """
- Run a compile_result from compile() sequentially and return performance
- """
- run_result_call_program = self.call_program(binary_filename.format(id))
- run_result_getFileSize = self.getFileSize(output_filename)
- self.store_size_list(run_result_getFileSize)
- return Result(size=run_result_getFileSize['binary_size'],time=run_result_call_program['time'])
-
- def run(self, desired_result, input, limit):
- """
- Compile and run a given configuration then
- return performance
- """
- cfg = desired_result.configuration.data
- self.store_config_list(cfg)
- compile_result = self.compile(cfg, 0)
- return self.run_precompiled(desired_result, input, limit, compile_result, 0)
-
- list_size = [] # list of file sizes
- list_config = [] #list of configurations
- list_size_config = [] #list of file size with corresponding optimization
- list_N_size_config=[]
-
- def store_size_list(self, binary_size):
- """stores file size in a list"""
- self.list_size.append(binary_size)
-
- def store_config_list(self,cfg):
- """stores configurations in a list"""
- self.list_config.append(cfg)
-
- counter = 0
- def save_final_config(self,configuration):
- """saves list of file size with corresponding optimization in a file"""
- for list in self.list_size:
- dict_size_config = {self.list_size[self.counter]['binary_size']: self.list_config[self.counter]}
- self.list_size_config.append(dict_size_config)
- self.list_size_config.sort()
- self.counter += 1
- self.extract_topN_resuls(10)
- print "ALL file sizes along with corresponding configurations writtent to size_config.json"
- self.manipulator().save_to_file(self.list_size_config,
- 'size_config.json')
-
- def extract_topN_resuls(self,N):
- """extracts top N results w.r.t size,N currently set to 10"""
- counter=0
- for list in self.list_size_config:
- if counter < N:
- self.list_N_size_config.append(list)
- print "Top "+str(N)+" file sizes along with corresponding configurations writtent to TopN_size_config.json"
- self.manipulator().save_to_file(self.list_size_config,
- 'TopN_size_config.json')
-
-
-
-if __name__ == '__main__':
-
- binary_name = sys.argv[1]
- num_flags = int(sys.argv[2])
-
- for i in range(num_flags):
- tuning_flags.append("flag" + str(i))
-
- print tuning_flags
-
- argparser = opentuner.default_argparser()
- ClangFlagsTuner.main(argparser.parse_args())
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/select_top_results.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/select_top_results.py
deleted file mode 100644
index 7ee878e5f8f84f3f56ea982c1f933b2c1a5b914b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/tutorials/select_top_results.py
+++ /dev/null
@@ -1,101 +0,0 @@
-
-
-import argparse
-import sys
-import os
-
-
-log_index = 9
-linear_index = 10
-quad_index = 11
-
-top_k = 10
-skip_lines = 1
-
-
-def dump_results(sorted_list, k, result_dir, sub_dir):
-
- ref_dir = result_dir + "/" + sub_dir
- if not os.path.exists(ref_dir):
- os.mkdir(ref_dir)
-
- for i in range(min(k, len(sorted_list)) ):
- file_name = sorted_list[i][1]
- file_name = ref_dir + "/" + file_name + "_rank_" + str(i)
- f = open(file_name, "w+")
- f.write(str(sorted_list[i][2]) + "\t")
- f.write(str(sorted_list[i][3]) + "\t")
- f.write(str(sorted_list[i][4]) + "\n")
- f.write(sorted_list[i][0])
- f.close()
-
-
-
-
-def select_top_results(result_dir):
-
- if not os.path.exists(result_dir):
- print("Path does not exist")
- sys.exit(0)
-
- file_names = os.listdir(result_dir)
- print file_names
-
- results_arr = []
-
- for file_name in file_names:
-
- if file_name == "confidence_summary.txt":
- continue
-
- # Skip sub-directories
- if os.path.isdir(result_dir + "/" + file_name):
- continue
-
- log_result = 0.0
- linear_result = 0.0
- quad_result = 0.0
- file_str = ""
-
- index = 0
- f = open(result_dir + "/" + file_name)
- for x in f:
- if index >= skip_lines:
- words = x.split()
- log_result += float(words[log_index])
- linear_result += float(words[linear_index])
- quad_result += float(words[quad_index])
- file_str += x
-
- index += 1
-
-
- file_result = (file_str, file_name, log_result, linear_result, quad_result)
- results_arr.append(file_result)
-
-
- sorted_list = sorted(results_arr, key = lambda tup: tup[2])
- dump_results(sorted_list, top_k, result_dir, "log")
-
- sorted_list = sorted(results_arr, key = lambda tup: tup[3])
- dump_results(sorted_list, top_k, result_dir, "linear")
-
- sorted_list = sorted(results_arr, key = lambda tup: tup[4])
- dump_results(sorted_list, top_k, result_dir, "quad")
-
-
-#def select_top_configuration(result_dir):
-
-
-if __name__ == "__main__":
-
- argparser = argparse.ArgumentParser(description='runs best configs to get high confidence on accuracy')
- argparser.add_argument('--result-dir', help='Directory containing OpenTuner configurations')
-
- args = argparser.parse_args()
- result_dir = args.result_dir
-
- select_top_results(result_dir)
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/unitary/adddeps.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/unitary/adddeps.py
deleted file mode 100644
index ede22a8fcdb2a94db7915ff3beb90894b2cb8592..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/unitary/adddeps.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# we would prefer a symbolic link, but it does not work on windows
-import os
-target = os.path.join(os.path.dirname(__file__),
- '../../opentuner/utils/adddeps.py')
-execfile(target, dict(__file__=target))
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/unitary/cla_func.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/unitary/cla_func.py
deleted file mode 100644
index f4787a2f23f175457ee527f8569dca39bf450605..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/unitary/cla_func.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import numpy as np
-import math
-
-
-class Op:
- def __init__(self):
- self.M = []
- self.name = [];
- self.mutation_partners = [];
- self.anti_operator = [];
-
- self.mutation_partners_no = []
- self.anti_operator_no = []
-
- # create all operators
- self.create_operators()
-
- # check unitarity of all operators
- self.check_unitarity()
-
- # determine the indices of the mutation partners
- self.determine_index_of_mutation_partners()
-
- # determine the indices of the anti-operators
- self.determine_index_of_anti_operators()
-
-
- def create_operators(self):
-
- # example with +Z
- #self.M.append(np.matrix([[1.0, 2.0], [2.0+2.0j, 3.0]]))
- # watch out: python needs 1.0 instead of just 1 to assume float variables
- #self.name.append('asd');
- #self.mutation_partners.append(['+z','+w']);
- #self.anti_operator.append('+w');
-
- # Operators
- alpha = math.pi / 3.0;
- da = math.pi / 10.0;
-
- # operator 1 +z
- self.M.append(np.matrix(
- [[math.cos(da / 2.0) - 1j * math.sin(da / 2.0), 0.0],
- [0.0, math.cos(da / 2.0) + 1j * math.sin(da / 2.0)]]))
- self.name.append('+z');
- self.mutation_partners.append(['-z', '+w', '-w']);
- self.anti_operator.append('-z');
-
- # operator 2 -z
- self.M.append(np.matrix(
- [[math.cos(-da / 2.0) - 1j * math.sin(-da / 2.0), 0.0],
- [0.0, math.cos(-da / 2.0) + 1j * math.sin(-da / 2.0)]]))
- self.name.append('-z');
- self.mutation_partners.append(['+z', '+w', '-w']);
- self.anti_operator.append('+z');
-
- # operator 3 +w
- self.M.append(np.matrix([
- [math.cos(da / 2.0) - 1j * math.cos(alpha) * math.sin(da / 2.0),
- -math.sin(alpha) * math.sin(da / 2.0)],
- [math.sin(alpha) * math.sin(da / 2.0),
- math.cos(da / 2.0) + 1j * math.cos(alpha) * math.sin(da / 2.0)]]))
- self.name.append('+w');
- self.mutation_partners.append(['+z', '-z', '-w']);
- self.anti_operator.append('-w');
-
- # operator 4 -w
- self.M.append(np.matrix([
- [math.cos(-da / 2.0) - 1j * math.cos(alpha) * math.sin(-da / 2.0),
- -math.sin(alpha) * math.sin(-da / 2.0)],
- [math.sin(alpha) * math.sin(-da / 2.0),
- math.cos(-da / 2.0) + 1j * math.cos(alpha) * math.sin(-da / 2.0)]]))
- self.name.append('-w');
- self.mutation_partners.append(['+z', '-z', '+w']);
- self.anti_operator.append('+w');
-
-
- def check_unitarity(self):
- # this function checks if all defined operators are unitary
- # in case one isn't unitary the program stops
- for k in range(len(self.M)):
- if (np.trace(self.M[k] * self.M[k].getH()) - 2 != 0):
- print "Operator " + self.name[k] + " (no. " + str(
- k) + ") isn't unitary!"
- exit()
-
- def determine_index_of_mutation_partners(self):
- # create a field for each operator with an array of possible other gates for the mutation step
- for k in range(len(self.M)):
- hlp = []
- for m in range(len(self.mutation_partners[k])):
- # go through all possible partners and find them among the operators
- for n in range(len(self.M)):
- if self.mutation_partners[k][m] is self.name[n]:
- hlp.append(n)
- self.mutation_partners_no.append(hlp)
-
- def determine_index_of_anti_operators(self):
- # determine the Anti operator index
- for k in range(len(self.M)):
- found_operator = False
- for n in range(len(self.M)):
- # go through all possible partners and find them among the operators
- if self.anti_operator[k] is self.name[n]:
- self.anti_operator_no.append(n);
- found_operator = True
-
- if found_operator == False:
- print "Couldn't find the anti-operator for operator " + self.name[
- k] + " (no " + str(k) + ")"
-
- def __str__(self):
- # just a test to play around
- hlpstr = ''
- for k in range(len(self.M)):
- hlpstr = hlpstr + self.name[k] + " " + str(
- self.anti_operator_no[k]) + "\n"
-
- return "Operator Class:\n" + hlpstr
-
-
-def calc_fidelity(sequence, Op, Ugoal):
- # Op will be function that return operator matrix
- # Ugoal 2x2 unitary matrix
- # sequence = [1 2 3 4];
- # return = fidelity
-
- # example:
- # sequence = [1 4 2 4 5];
- # Uapprox = Op(1) * Op(4) * Op(2) * Op(4) * Op(5);
-
- # create identity matrix
- Uapprox = np.eye(len(Ugoal))
-
- for k in range(len(sequence)):
- Uapprox = Op.M[sequence[k]] * Uapprox
-
- # M.getH() returns the complex conjugate of self
- result = (1.0 / len(Ugoal)) * abs(np.trace(Ugoal * Uapprox.getH()))
-
- return result
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/unitary/input_generator.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/unitary/input_generator.py
deleted file mode 100644
index 009af836f435d013050ff877c4cd66d86019edfc..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/unitary/input_generator.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import numpy as np
-import math
-import random
-
-
-def generate_random_Ugoal_HARD(N, **kwargs):
- # N is the length of random matrix multiplication yielding Ugoal
- # N ~ 100 should be enough
- # This method is hard because it creates Ugoal over the whole space
- # Ugoal 2x2 unitary matrix
-
- # create identity matrix
- Ugoal = np.eye(2)
-
- # create all N random angles in 2*pi*[0,1)
- seq_angle = 2.0 * math.pi * np.random.rand(1, N)
-
- # determine random operator
- help2 = np.random.randint(3, size=(1, N))
-
- for k in range(N):
- hlp = seq_angle[0][k];
- if help2[0][k] == 0:
- Ugoal = X_Mat(hlp) * Ugoal
- elif help2[0][k] == 1:
- Ugoal = Y_Mat(hlp) * Ugoal
- else:
- Ugoal = Z_Mat(hlp) * Ugoal
-
- return Ugoal
-
-
-def generate_random_Ugoal_EASY(N, alpha):
- # N is the length of random matrix multiplication yielding Ugoal
- # N ~ 100 should be enough
- # alpha is the used angle between rotation axes
- # This method is easy because it creates Ugoal over the whole space
- # Ugoal 2x2 unitary matrix
-
- # create identity matrix
- Ugoal = np.eye(2)
-
- # create all N random angles in 2*pi*[0,1)
- seq_angle = 2.0 * math.pi * np.random.rand(1, N)
-
- # determine random operator
- help2 = np.random.randint(2, size=(1, N))
-
- for k in range(N):
- hlp = seq_angle[0][k];
- if help2[0][k] == 0:
- Ugoal = Z_Mat(hlp) * Ugoal
- else:
- Ugoal = W_Mat(hlp, alpha) * Ugoal
-
- return Ugoal
-
-
-def generate_random_Ugoal_RANDOM(**kwargs):
- # Random guess with the following parametrization for U
- # U = @(q1, q2, q3) [
- # [ cos(q1)*exp( i*q2 ), sin(q1)*exp( i*q3 )];
- # [-sin(q1)*exp(-i*q3 ), cos(q1)*exp(-i*q2 )]
- # ];
-
- # create random angles
- q1 = random.uniform(0.0, 0.5 * math.pi)
- q2 = random.uniform(0.0, 2.0 * math.pi)
- q3 = random.uniform(0.0, 2.0 * math.pi)
-
- return np.matrix([
- [math.cos(q1) * my_cexp(q2), math.sin(q1) * my_cexp(q3)],
- [-math.sin(q1) * my_cexp(-q3), math.cos(q1) * my_cexp(-q2)]])
-
-
-def my_cexp(x):
- return math.cos(x) + 1j * math.sin(x)
-
-
-def X_Mat(a):
- return np.matrix([[math.cos(a / 2.0), -1j * math.sin(a / 2.0)],
- [-1j * math.sin(a / 2.0), math.cos(a / 2.0)]])
-
-
-def Y_Mat(a):
- return np.matrix([[math.cos(a / 2.0), -math.sin(a / 2.0)],
- [math.sin(a / 2.0), math.cos(a / 2.0)]])
-
-
-def Z_Mat(a):
- return np.matrix([[math.cos(-a / 2.0) + 1j * math.sin(-a / 2.0), 0],
- [0, math.cos(a / 2.0) + 1j * math.sin(a / 2.0)]])
-
-
-def W_Mat(a, alpha):
- return np.matrix([[math.cos(a / 2) - 1j * math.cos(alpha) * math.sin(a / 2.0),
- -math.sin(a / 2.0) * math.sin(alpha)],
- [math.sin(a / 2.0) * math.sin(alpha),
- math.cos(a / 2.0) + 1j * math.cos(alpha) * math.sin(
- a / 2.0)]])
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/unitary/problem_description.pdf b/llvm/projects/hpvm-tensor-rt/opentuner/examples/unitary/problem_description.pdf
deleted file mode 100644
index e8d09de95a8a6416bf88f10a4d6e4a0fca92670d..0000000000000000000000000000000000000000
Binary files a/llvm/projects/hpvm-tensor-rt/opentuner/examples/unitary/problem_description.pdf and /dev/null differ
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/unitary/testwrapper.sh b/llvm/projects/hpvm-tensor-rt/opentuner/examples/unitary/testwrapper.sh
deleted file mode 100755
index e08593a8764b81a8e68380f9d46753c7a73859c0..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/unitary/testwrapper.sh
+++ /dev/null
@@ -1,11 +0,0 @@
-#!/bin/bash
-COUNT=50
-for Z in `seq $COUNT`
-do
- for T in `./unitary.py --list-techniques $@`;
- do
- echo $Z/$COUNT $T
- ./unitary.py --technique=$T $@
- done
-done
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/examples/unitary/unitary.py b/llvm/projects/hpvm-tensor-rt/opentuner/examples/unitary/unitary.py
deleted file mode 100755
index cfa5fe114155f9a7efbd25d191d520846e3d4017..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/examples/unitary/unitary.py
+++ /dev/null
@@ -1,136 +0,0 @@
-#!/usr/bin/env python
-#
-# This is a quantum control example motivated by the experimental need
-# to synthesize unitary matrices in SU(2) in optimal time, given an
-# explicit and finite control set generating the whole space, and an
-# admissible error.
-#
-# See problem_description.pdf for additional details.
-#
-# Contributed by Clarice D. Aiello <clarice@mit.edu>
-#
-
-import adddeps # fix sys.path
-
-import argparse
-import logging
-import math
-import random
-import sys
-
-try:
- import numpy as np
-except:
- print >> sys.stderr, '''
-
-ERROR: import numpy failed, please install numpy
-
-Possible things to try:
- ../../venv/bin/pip install numpy
- ../../venv/bin/easy_install numpy
- sudo apt-get install python-numpy
-
-'''
- raise
-
-import opentuner
-
-from math import sqrt
-import cla_func
-from input_generator import (generate_random_Ugoal_HARD,
- generate_random_Ugoal_EASY,
- generate_random_Ugoal_RANDOM)
-
-from opentuner.search.manipulator import (ConfigurationManipulator,
- SwitchParameter,
- IntegerParameter,
- FloatParameter)
-
-
-def generate_random_Ugoal_FIXED(**kwargs):
- Ag = -1 / sqrt(10);
- Bg = sqrt(2) / sqrt(10);
- Cg = -sqrt(3) / sqrt(10);
- Dg = -sqrt(4) / sqrt(10);
- return cla_func.np.matrix(
- [[Ag + Cg * 1j, Bg + Dg * 1j], [-Bg + Dg * 1j, Ag - Cg * 1j]])
-
-
-log = logging.getLogger(__name__)
-
-generators = {
- 'hard': generate_random_Ugoal_HARD,
- 'easy': generate_random_Ugoal_EASY,
- 'random': generate_random_Ugoal_RANDOM,
- 'fixed': generate_random_Ugoal_FIXED,
-}
-
-parser = argparse.ArgumentParser(parents=opentuner.argparsers())
-parser.add_argument('--seq-len', type=int, default=10,
- help='maximum length for generated sequence')
-parser.add_argument('--goal-type', choices=generators.keys(), default='hard',
- help='method used to generate goal')
-parser.add_argument('--goal-n', type=int, default=100,
- help='argument to ugoal generator')
-parser.add_argument('--goal-alpha', type=float,
- default=random.random() * math.pi,
- help='argument to ugoal generator')
-
-
-class Unitary(opentuner.measurement.MeasurementInterface):
- def __init__(self, *pargs, **kwargs):
- super(Unitary, self).__init__(*pargs, **kwargs)
-
- self.op = cla_func.Op()
- self.num_operators = len(self.op.M)
- self.Ugoal = generators[args.goal_type](N=args.goal_n,
- alpha=args.goal_alpha)
-
-
- def run(self, desired_result, input, limit):
- cfg = desired_result.configuration.data
-
- sequence = [cfg[i] for i in xrange(self.args.seq_len)
- if cfg[i] < self.num_operators]
- # sequence can be shorter than self.args.seq_len with null operator
-
- if len(sequence) > 0:
- accuracy = cla_func.calc_fidelity(sequence, self.op, self.Ugoal)
- # ~.99 is acceptable
- else:
- accuracy = 0.0
-
- return opentuner.resultsdb.models.Result(time=0.0,
- accuracy=accuracy,
- size=len(sequence))
-
- def manipulator(self):
- manipulator = ConfigurationManipulator()
- for d in xrange(self.args.seq_len):
- # we add 1 to num_operators allow a ignored 'null' operator
- manipulator.add_parameter(SwitchParameter(d, self.num_operators + 1))
- return manipulator
-
- def save_final_config(self, configuration):
- '''
- called at the end of autotuning with the best resultsdb.models.Configuration
- '''
- cfg = configuration.data
- sequence = [cfg[i] for i in xrange(self.args.seq_len)
- if cfg[i] < self.num_operators]
- print "Final sequence", sequence
-
- def objective(self):
- # we could have also chosen to store 1.0 - accuracy in the time field
- # and use the default MinimizeTime() objective
- return opentuner.search.objective.MaximizeAccuracyMinimizeSize()
-
-
-if __name__ == '__main__':
- args = parser.parse_args()
- Unitary.main(args)
-
-
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/gen-venv-bootstrap.py b/llvm/projects/hpvm-tensor-rt/opentuner/gen-venv-bootstrap.py
deleted file mode 100755
index ff159bb1080e7f3f0979e4b60f4d41eea5c9d1e9..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/gen-venv-bootstrap.py
+++ /dev/null
@@ -1,39 +0,0 @@
-#!./venv/bin/python
-
-extra = '''
-
-default_target_dir = 'venv'
-
-pip_install_packages = filter(len, open('requirements.txt').readlines())
-
-import os
-import subprocess
-import sys
-
-def adjust_options(options, args):
- if len(args)==0:
- os.chdir(os.path.dirname(__file__))
- args.append(default_target_dir)
-
-def after_install(options, home_dir):
- from os.path import join
- pip = join(home_dir, 'bin/pip')
- if not os.path.exists(pip):
- # on windows
- pip = join(home_dir, 'Scripts/pip.exe')
- if not os.path.exists(pip):
- print "error", pip, "is missing"
- if sys.version_info < (2, 7):
- subprocess.call([pip, 'install', 'importlib'])
- for prog in pip_install_packages:
- subprocess.call([pip, 'install', prog])
-
-'''
-
-import os
-import virtualenv
-
-os.chdir(os.path.dirname(__file__))
-output = virtualenv.create_bootstrap_script(extra)
-f = open('venv-bootstrap.py', 'w').write(output)
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/install_reqs.sh b/llvm/projects/hpvm-tensor-rt/opentuner/install_reqs.sh
deleted file mode 100644
index e671a5f2a1619f7960fa7471774aa94cab3e0bd6..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/install_reqs.sh
+++ /dev/null
@@ -1,3 +0,0 @@
-pip2 install sqlalchemy
-pip2 install psutil
-pip2 install opentuner
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/misc/livedisplay.gnuplot b/llvm/projects/hpvm-tensor-rt/opentuner/misc/livedisplay.gnuplot
deleted file mode 100644
index 1d4f13021303b0df3c2821eac3935524f494e18f..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/misc/livedisplay.gnuplot
+++ /dev/null
@@ -1,10 +0,0 @@
-
-set terminal x11
-set xlabel "Autotuning Seconds"
-set ylabel "Runtime Seconds"
-set xrange [0:600]
-
-plot "/tmp/livedisplay.dat" u 1:2 w lp lw 3 title "Best Execution Time", \
- "/tmp/livedisplaydetails.dat" w p lw 2 title "Tests (excluding timeouts)"
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/misc/livedisplay.py b/llvm/projects/hpvm-tensor-rt/opentuner/misc/livedisplay.py
deleted file mode 100755
index 5aa3d552d8e5506236d9e004c1f66370b7f19a23..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/misc/livedisplay.py
+++ /dev/null
@@ -1,57 +0,0 @@
-#!/usr/bin/env python
-import os
-import argparse
-import subprocess
-import time
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--gnuplot-filename', default='livedisplay.gnuplot')
-parser.add_argument('--data', default='/tmp/livedisplay.dat')
-parser.add_argument('--details', default='/tmp/livedisplaydetails.dat')
-parser.add_argument('--xrange', type=float, default=300)
-parser.add_argument('--yrange', type=float, default=.05)
-parser.add_argument('--yrange2', type=float, default=1.0)
-parser.add_argument('--remote')
-args = parser.parse_args()
-
-if args.remote:
- if os.path.exists(args.data):
- os.unlink(args.data)
- if os.path.exists(args.details):
- os.unlink(args.details)
- syncproc = subprocess.Popen(
- ["ssh", args.remote, "tail -f -n10000 " + args.data],
- stdout=open(args.data, "w"))
- syncproc2 = subprocess.Popen(
- ["ssh", args.remote, "tail -f -n10000 " + args.details],
- stdout=open(args.details, "w"))
-
-while '\n' not in open(args.data).read():
- time.sleep(1)
-while '\n' not in open(args.details).read():
- time.sleep(1)
-
-p1 = subprocess.Popen(["gnuplot"], stdin=subprocess.PIPE)
-p1.stdin.write(open(args.gnuplot_filename).read())
-print >> p1.stdin, 'set title "Zoomed out"'
-print >> p1.stdin, "set xrange [0:%f]" % args.xrange
-print >> p1.stdin, "set yrange [0:%f]" % args.yrange2
-p1.stdin.flush()
-
-time.sleep(1)
-
-p2 = subprocess.Popen(["gnuplot"], stdin=subprocess.PIPE)
-p2.stdin.write(open(args.gnuplot_filename).read())
-print >> p2.stdin, 'set title "Zoomed in"'
-print >> p2.stdin, "set xrange [0:%f]" % args.xrange
-print >> p2.stdin, "set yrange [0:%f]" % args.yrange
-p2.stdin.flush()
-
-procs = [p1, p2]
-
-while True:
- time.sleep(1)
- for p in procs:
- print >> p.stdin, "replot"
- p.stdin.flush()
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/__init__.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/__init__.py
deleted file mode 100644
index 09a5dead02d214f4dce641069d7be66c124f278a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/__init__.py
+++ /dev/null
@@ -1,41 +0,0 @@
-
-import measurement
-import resultsdb
-import search
-import tuningrunmain
-from opentuner.measurement import MeasurementInterface
-from opentuner.resultsdb.models import Configuration
-from opentuner.resultsdb.models import DesiredResult
-from opentuner.resultsdb.models import Result
-from opentuner.resultsdb.models import TuningRun
-from opentuner.search.manipulator import ConfigurationManipulator
-from opentuner.search.manipulator import EnumParameter
-from opentuner.search.manipulator import FloatParameter
-from opentuner.search.manipulator import IntegerParameter
-from opentuner.search.manipulator import LogFloatParameter
-from opentuner.search.manipulator import LogIntegerParameter
-from opentuner.search.manipulator import PermutationParameter
-from opentuner.search.manipulator import ScheduleParameter
-from opentuner.search.manipulator import SwitchParameter
-from opentuner.tuningrunmain import init_logging
-
-
-def argparsers():
- """
- return a list of ArguementParser to be used as parents to the user's
- """
- return [
- measurement.driver.argparser,
- measurement.interface.argparser,
- search.driver.argparser,
- search.plugin.argparser,
- search.technique.argparser,
- #stats.argparser,
- tuningrunmain.argparser,
- ]
-
-
-def default_argparser():
- import argparse
- return argparse.ArgumentParser(parents=argparsers())
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/api.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/api.py
deleted file mode 100644
index 19a2f60935d7a700771778f0a1304f5ff5cbea6d..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/api.py
+++ /dev/null
@@ -1,87 +0,0 @@
-from datetime import datetime
-from opentuner import tuningrunmain
-
-
-class TuningRunManager(tuningrunmain.TuningRunMain):
- """
- This class manages a tuning run in a "slave" configuration, where main()
- is controlled by an another program.
- """
- def __init__(self, measurement_interface, args, **kwargs):
- super(TuningRunManager, self).__init__(measurement_interface, args, **kwargs)
- self.init()
- self.tuning_run.state = 'RUNNING'
- self.commit(force=True)
- self.search_driver.external_main_begin()
-
- def get_next_desired_result(self):
- """
- Returns a opentuner.resultsdb.DesiredResult that should be tested next.
- """
- dr = self.measurement_driver.query_pending_desired_results().first()
- if dr is None:
- self.search_driver.external_main_generation()
- dr = self.measurement_driver.query_pending_desired_results().first()
- if dr is None:
- return None
- self.measurement_driver.claim_desired_result(dr)
- dr.limit = self.measurement_driver.run_time_limit(dr)
- return dr
-
- def get_desired_results(self):
- """
- Returns a list of all opentuner.resultsdb.DesiredResult that should be tested next.
- """
- drs = self.measurement_driver.query_pending_desired_results().all()
- if len(drs) == 0:
- self.search_driver.external_main_generation()
- drs = self.measurement_driver.query_pending_desired_results().all()
- if len(drs) == 0:
- return []
- for dr in drs:
- self.measurement_driver.claim_desired_result(dr)
- dr.limit = self.measurement_driver.run_time_limit(dr)
-
- return drs
-
- def report_result(self, desired_result, result, result_input=None):
- """
- Report a measured result. desired_result should have been returned by
- get_next_desired_result().
- """
- self.measurement_driver.report_result(desired_result, result, result_input)
-
- def get_best_configuration(self):
- """
- The best configuration found so far. From the current tuning run only.
- """
- try:
- return self.search_driver.best_result.configuration.data
- except AttributeError:
- return None
-
- def get_best_result(self):
- """
- The best result found so far. From the current tuning run only.
- """
- try:
- return self.search_driver.best_result
- except AttributeError:
- return None
-
- def finish(self):
- """
- Called at the end of the tuning process to call hooks and close database
- connections.
- """
- self.search_driver.external_main_end()
- self.measurement_interface.save_final_config(
- self.search_driver.best_result.configuration)
- self.tuning_run.final_config = self.search_driver.best_result.configuration
- self.tuning_run.state = 'COMPLETE'
- self.tuning_run.end_date = datetime.now()
- self.commit(force=True)
- self.session.close()
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/driverbase.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/driverbase.py
deleted file mode 100644
index 5486889c0dcedd4342a9cb463aa0d5047f3c0932..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/driverbase.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from opentuner.resultsdb.models import *
-
-
-class DriverBase(object):
- """
- shared base class between MeasurementDriver and SearchDriver
- """
-
- def __init__(self,
- session,
- tuning_run,
- objective,
- tuning_run_main,
- args,
- **kwargs):
- self.args = args
- self.objective = objective
- self.session = session
- self.tuning_run_main = tuning_run_main
- self.tuning_run = tuning_run
- self.program = tuning_run.program
-
- def results_query(self,
- generation=None,
- objective_ordered=False,
- config=None):
- q = self.session.query(Result)
- q = q.filter_by(tuning_run=self.tuning_run)
-
- if config:
- q = q.filter_by(configuration=config)
-
- if generation is not None:
- subq = (self.session.query(DesiredResult.result_id)
- .filter_by(tuning_run=self.tuning_run,
- generation=generation))
- q = q.filter(Result.id.in_(subq.subquery()))
-
- if objective_ordered:
- q = self.objective.result_order_by(q)
-
- return q
-
- def requests_query(self):
- q = self.session.query(DesiredResult).filter_by(tuning_run=self.tuning_run)
- return q
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/measurement/#interface.py# b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/measurement/#interface.py#
deleted file mode 100644
index 4fe23da5d904183fa4d3c340a74e89918052823e..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/measurement/#interface.py#
+++ /dev/null
@@ -1,359 +0,0 @@
-
-import abc
-import argparse
-import errno
-import hashlib
-import logging
-import os
-import re
-import signal
-import subprocess
-import threading
-import time
-from multiprocessing.pool import ThreadPool
-
-try:
- import resource
-except ImportError:
- resource = None
-
-try:
- import fcntl
-except ImportError:
- fcntl = None
-
-import opentuner
-from opentuner import resultsdb
-from opentuner.resultsdb.models import *
-
-log = logging.getLogger(__name__)
-
-argparser = argparse.ArgumentParser(add_help=False)
-argparser.add_argument('--parallel-compile', action='store_true',
- default=False,
- help="present if compiling can be done in parallel")
-
-the_io_thread_pool = None
-
-
-class MeasurementInterface(object):
- """
- abstract base class for compile and measurement
- """
- __metaclass__ = abc.ABCMeta
-
- def __init__(self,
- args=None,
- project_name=None,
- program_name='unknown',
- program_version='unknown',
- manipulator=None,
- objective=None,
- input_manager=None):
- self.args = args
- self._project = project_name
- self._program = program_name
- self._version = program_version
- self._objective = objective
- self._manipulator = manipulator
- self._input_manager = input_manager
-
- self.pids = []
- self.pid_lock = threading.Lock()
- self.parallel_compile = args.parallel_compile
- # If parallel_compile is False then compile_and_run() will be invoked
- # sequentially otherwise the driver first invokes compile() in parallel
- # followed by run_precompiled() sequentially
-
- def compile(self, config_data, id):
- """
- Compile in PARALLEL according to the configuration in config_data
- (obtained from desired_result.configuration) Should use id parameter
- to determine output location of executable Return value will be passed
- to run_precompiled as compile_result, useful for storing error/timeout
- information
- """
- if self.parallel_compile:
- raise RuntimeError('MeasurementInterface.compile() not implemented for',
- 'parallel compilation')
- pass
-
- def run_precompiled(self, desired_result, input, limit, compile_result, id):
- """
- Run the given desired_result SEQUENTIALLY on input and produce a Result()
- Abort early if limit (in seconds) is reached Assume that the executable
- to be measured has already been compiled to an executable corresponding to
- identifier id by compile() The compile_result is the return result of compile(),
- and it will be None if compile() was not called
- """
- if self.parallel_compile:
- raise RuntimeError('MeasurementInterface.run_precompiled() not implemented',
- 'for parallel compilation')
- pass
-
- def cleanup(self, id):
- """
- Clean up any temporary files associated with the executable
- """
- pass
-
- def pre_process(self):
- """
- The process before each iteration This method will be called
- once per iteration before all threads are launched
- """
- pass
-
- def post_process(self):
- """
- The process after each iteration This method will be called
- once per iteration after all threads are committed
- """
- pass
-
- def extra_convergence_criteria(self, result):
- """
- The extra convergence criteria which returns True if the
- current result is acceptable by the user
- """
- return False
-
- #@abc.abstractmethod
- def compile_and_run(self, desired_result, input, limit):
- """
- Compile and run the given desired_result on input and produce a
- Result(), abort early if limit (in seconds) is reached This function
- is only used for sequential execution flow
-
- FIXME: Shoud uncomment @abc.abstractmethod Now comment out for
- compatiability
- """
- return self.run(desired_result, input, limit)
-
- def run(self, desired_result, input, limit):
- """
- run the given desired_result on input and produce a Result(),
- abort early if limit (in seconds) is reached
- """
- return opentuner.resultdb.models.Result()
-
- def save_final_config(self, config):
- """
- called at the end of autotuning with the best resultsdb.models.Configuration
- """
- try:
- config_str = repr(config.data)
- if len(config_str) > 256:
- config_str = config_str[:256] + '...'
- log.info('final configuration: %s', config_str)
- log.info('you may want to implement save_final_config(), to store this')
- except:
- log.error('error printing configuration', exc_info=True)
-
- def db_program_version(self, session):
- """return a version identifier for the program being tuned"""
- return resultsdb.models.ProgramVersion.get(
- session=session,
- project=self.project_name(),
- name=self.program_name(),
- version=self.program_version(),
- parameter_info=self.manipulator().parameters_to_json(),
- )
-
- def set_driver(self, measurement_driver):
- self.driver = measurement_driver
-
- def project_name(self):
- if self._project is not None:
- return self._project
- autoname = re.sub('(Measurement?)Interface$', '', self.__class__.__name__)
- if autoname:
- return autoname
- else:
- return 'unknown'
-
- def program_name(self):
- return self._program
-
- def program_version(self):
- return self._version
-
- def file_hash(self, filename):
- """helper used to generate program versions"""
- return hashlib.sha256(open(filename).read()).hexdigest()
-
- def manipulator(self):
- """
- called once to create the search.manipulator.ConfigurationManipulator
- """
- if self._manipulator is None:
- msg = ('MeasurementInterface.manipulator() must be implemented or a '
- '"manipulator=..." must be provided to the constructor')
- log.error(msg)
- raise Exception(msg)
- return self._manipulator
-
- def objective(self):
- """
- called once to create the search.objective.SearchObjective
- """
- if self._objective is None:
- from ..search.objective import MinimizeTime
-
- return MinimizeTime()
- return self._objective
-
- def input_manager(self):
- """
- called once to create the measurement.inputmanager.InputManager
- """
- if self._objective is None:
- from .inputmanager import FixedInputManager
-
- return FixedInputManager()
- return self._input_manager
-
- def seed_configurations(self):
- """
- Extra seed configuration objects to add to those given on the command line.
- Configuration objects (typically dictionaries) not database objects.
- """
- return []
-
- def kill_all(self):
- self.pid_lock.acquire()
- for pid in self.pids:
- goodkillpg(pid)
- self.pids = []
- self.pid_lock.release()
-
- def call_program(self, cmd, limit=None, memory_limit=None, **kwargs):
- """
- call cmd and kill it if it runs for longer than limit
-
- returns dictionary like
- {'returncode': 0,
- 'stdout': '', 'stderr': '',
- 'timeout': False, 'time': 1.89}
- """
- the_io_thread_pool_init(self.args.parallelism)
- if limit is float('inf'):
- limit = None
- if type(cmd) in (str, unicode):
- kwargs['shell'] = True
- killed = False
- t0 = time.time()
- p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
- preexec_fn=preexec_setpgid_setrlimit(memory_limit),
- **kwargs)
- # Add p.pid to list of processes to kill in case of keyboardinterrupt
- self.pid_lock.acquire()
- self.pids.append(p.pid)
- self.pid_lock.release()
-
- try:
- stdout_result = the_io_thread_pool.apply_async(p.stdout.read)
- stderr_result = the_io_thread_pool.apply_async(p.stderr.read)
- while p.returncode is None:
- if limit is None:
- goodwait(p)
- elif limit and time.time() > t0 + limit:
- killed = True
- goodkillpg(p.pid)
- goodwait(p)
- else:
- # still waiting...
- sleep_for = limit - (time.time() - t0)
- if not stdout_result.ready():
- stdout_result.wait(sleep_for)
- elif not stderr_result.ready():
- stderr_result.wait(sleep_for)
- else:
- #TODO(jansel): replace this with a portable waitpid
- time.sleep(0.001)
- p.poll()
- except:
- if p.returncode is None:
- goodkillpg(p.pid)
- raise
- finally:
- # No longer need to kill p
- self.pid_lock.acquire()
- if p.pid in self.pids:
- self.pids.remove(p.pid)
- self.pid_lock.release()
-
- t1 = time.time()
- return {'time': float('inf') if killed else (t1 - t0),
- 'timeout': killed,
- 'returncode': p.returncode,
- 'stdout': stdout_result.get(),
- 'stderr': stderr_result.get()}
-
- def prefix_hook(self, session):
- pass
-
- @classmethod
- def main(cls, args, *pargs, **kwargs):
- from opentuner.tuningrunmain import TuningRunMain
-
- return TuningRunMain(cls(args, *pargs, **kwargs), args).main()
-
-
-class DefaultMeasurementInterface(MeasurementInterface):
- def run(self, desired_result, input, limit):
- raise RuntimeError('MeasurementInterface.run() not implemented')
-
-
-def preexec_setpgid_setrlimit(memory_limit):
- if resource is not None:
- def _preexec():
- os.setpgid(0, 0)
- try:
- resource.setrlimit(resource.RLIMIT_CORE, (0, 0))
- except ValueError:
- pass # No permission
- if memory_limit:
- try:
- (soft, hard) = resource.getrlimit(resource.RLIMIT_AS)
- resource.setrlimit(resource.RLIMIT_AS, (min(soft, memory_limit),
- min(hard, memory_limit)))
- except ValueError:
- pass # No permission
- return _preexec
-
-
-def the_io_thread_pool_init(parallelism=1):
- global the_io_thread_pool
- if the_io_thread_pool is None:
- the_io_thread_pool = ThreadPool(2 * parallelism)
- # make sure the threads are started up
- the_io_thread_pool.map(int, range(2 * parallelism))
-
-
-def goodkillpg(pid):
- """
- wrapper around kill to catch errors
- """
- log.debug("killing pid %d", pid)
- try:
- if hasattr(os, 'killpg'):
- os.killpg(pid, signal.SIGKILL)
- else:
- os.kill(pid, signal.SIGKILL)
- except:
- log.error('error killing process %s', pid, exc_info=True)
-
-
-def goodwait(p):
- """
- python doesn't check if its system calls return EINTR, retry if it does
- """
- while True:
- try:
- rv = p.wait()
- return rv
- except OSError, e:
- if e.errno != errno.EINTR:
- raise
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/measurement/.#interface.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/measurement/.#interface.py
deleted file mode 120000
index 68c682013089268d9e8f3e50ca41da1228c544c5..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/measurement/.#interface.py
+++ /dev/null
@@ -1 +0,0 @@
-hashim@hashim-VirtualBox.2708:1511328915
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/measurement/__init__.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/measurement/__init__.py
deleted file mode 100644
index c289e8d6f5081d846ef431f36649b6e976df1a82..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/measurement/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-
-import driver
-import interface
-from interface import MeasurementInterface
-from driver import MeasurementDriver
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/measurement/driver.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/measurement/driver.py
deleted file mode 100644
index d00886920a95e2b7c61ca41b6aea0a89247ab8c9..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/measurement/driver.py
+++ /dev/null
@@ -1,271 +0,0 @@
-import argparse
-import logging
-import time
-import socket
-import os
-from multiprocessing.pool import ThreadPool
-from datetime import datetime
-
-from sqlalchemy.exc import SQLAlchemyError
-from sqlalchemy.orm.exc import NoResultFound
-
-from opentuner.driverbase import DriverBase
-from opentuner.resultsdb.models import *
-
-log = logging.getLogger(__name__)
-
-argparser = argparse.ArgumentParser(add_help=False)
-argparser.add_argument('--machine-class',
- help="name of the machine class being run on")
-
-
-class MeasurementDriver(DriverBase):
- """
- manages the measurement process, reading DesiredResults and creating Results
- """
-
- def __init__(self,
- measurement_interface,
- input_manager,
- **kwargs):
- super(MeasurementDriver, self).__init__(**kwargs)
-
- if not self.args.machine_class:
- self.args.machine_class = 'default'
-
- self.interface = measurement_interface
- self.input_manager = input_manager
- self.commit = self.tuning_run_main.commit
- self.upper_limit_multiplier = 10.0
- self.default_limit_multiplier = 2.0
-
- self.laptime = time.time()
- self.machine = self.get_machine()
-
- def get_machine(self):
- """
- get (or create) the machine we are currently running on
- """
- hostname = socket.gethostname()
- try:
- self.session.flush()
- return self.session.query(Machine).filter_by(name=hostname).one()
- except sqlalchemy.orm.exc.NoResultFound:
- m = Machine(name=hostname,
- cpu=_cputype(),
- cores=_cpucount(),
- memory_gb=_memorysize() / (
- 1024.0 ** 3) if _memorysize() else 0,
- machine_class=self.get_machine_class())
- self.session.add(m)
- return m
-
- def get_machine_class(self):
- """
- get (or create) the machine class we are currently running on
- """
- return MachineClass.get(self.session, name=self.args.machine_class)
-
- def run_time_limit(self, desired_result, default=3600.0 * 24 * 365 * 10):
- """return a time limit to apply to a test run (in seconds)"""
- best = self.results_query(objective_ordered=True).first()
- if best is None:
- if desired_result.limit:
- return desired_result.limit
- else:
- return default
-
- if desired_result.limit:
- return min(desired_result.limit, self.upper_limit_multiplier * best.time)
- else:
- return self.default_limit_multiplier * best.time
-
- def report_result(self, desired_result, result, input=None):
- result.configuration = desired_result.configuration
- result.input = input
- result.machine = self.machine
- result.tuning_run = self.tuning_run
- result.collection_date = datetime.now()
- self.session.add(result)
- desired_result.result = result
- desired_result.state = 'COMPLETE'
- self.input_manager.after_run(desired_result, input)
- result.collection_cost = self.lap_timer()
- self.session.flush() # populate result.id
- log.debug(
- 'Result(id=%d, cfg=%d, time=%.4f, accuracy=%.2f, collection_cost=%.2f)',
- result.id,
- result.configuration.id,
- result.time,
- result.accuracy if result.accuracy is not None else float('NaN'),
- result.collection_cost)
- self.commit()
-
- def run_desired_result(self, desired_result, compile_result=None,
- exec_id=None):
- """
- create a new Result using input manager and measurment interface
- Optional compile_result paramater can be passed to run_precompiled as
- the return value of compile()
- Optional exec_id paramater can be passed to run_precompiled in case of
- locating a specific executable
- """
- desired_result.limit = self.run_time_limit(desired_result)
-
- input = self.input_manager.select_input(desired_result)
- self.session.add(input)
- self.session.flush()
-
- log.debug('running desired result %s on input %s', desired_result.id,
- input.id)
-
- self.input_manager.before_run(desired_result, input)
-
- if self.interface.parallel_compile:
- result = self.interface.run_precompiled(desired_result, input,
- desired_result.limit,
- compile_result, exec_id)
- else:
- result = self.interface.compile_and_run(desired_result, input,
- desired_result.limit)
-
- self.report_result(desired_result, result, input)
-
- def lap_timer(self):
- """return the time elapsed since the last call to lap_timer"""
- t = time.time()
- r = t - self.laptime
- self.laptime = t
- return r
-
- def claim_desired_result(self, desired_result):
- """
- claim a desired result by changing its state to running
- return True if the result was claimed for this process
- """
- self.commit()
- try:
- self.session.refresh(desired_result)
- if desired_result.state == 'REQUESTED':
- desired_result.state = 'RUNNING'
- desired_result.start_date = datetime.now()
- self.commit()
- return True
- except SQLAlchemyError:
- self.session.rollback()
- return False
-
- def query_pending_desired_results(self):
- q = (self.session.query(DesiredResult)
- .filter_by(tuning_run=self.tuning_run,
- state='REQUESTED')
- .order_by(DesiredResult.generation,
- DesiredResult.priority.desc()))
- return q
-
- def process_all(self):
- """
- process all desired_results in the database
- """
- self.lap_timer() # reset timer
- q = self.query_pending_desired_results()
-
- if self.interface.parallel_compile:
- desired_results = []
- thread_args = []
-
- def compile_result(args):
- interface, data, result_id = args
- return interface.compile(data, result_id)
-
- for dr in q.all():
- if self.claim_desired_result(dr):
- desired_results.append(dr)
- thread_args.append((self.interface, dr.configuration.data, dr.id))
- if len(desired_results) == 0:
- return
- thread_pool = ThreadPool(len(desired_results))
- # print 'Compiling %d results' % len(thread_args)
- try:
- # Use map_async instead of map because of bug where keyboardinterrupts are ignored
- # See http://stackoverflow.com/questions/1408356/keyboard-interrupts-with-pythons-multiprocessing-pool
- compile_results = thread_pool.map_async(compile_result,
- thread_args).get(9999999)
- except Exception:
- # Need to kill other processes because only one thread receives
- # exception
- self.interface.kill_all()
- raise
- # print 'Running %d results' % len(thread_args)
- for dr, compile_result in zip(desired_results, compile_results):
- # Make sure compile was successful
- self.run_desired_result(dr, compile_result, dr.id)
- try:
- self.interface.cleanup(dr.id)
- except RuntimeError, e:
- print e
- # print 'Done!'
- thread_pool.close()
- else:
- for dr in q.all():
- if self.claim_desired_result(dr):
- self.run_desired_result(dr)
-
-
-def _cputype():
- try:
- return re.search(r"model name\s*:\s*([^\n]*)",
- open("/proc/cpuinfo").read()).group(1)
- except:
- pass
- try:
- # for OS X
- import subprocess
-
- return subprocess.Popen(["sysctl", "-n", "machdep.cpu.brand_string"],
- stdout=subprocess.PIPE).communicate()[0].strip()
- except:
- log.warning("failed to get cpu type")
- return "unknown"
-
-
-def _cpucount():
- try:
- return int(os.sysconf("SC_NPROCESSORS_ONLN"))
- except:
- pass
- try:
- return int(os.sysconf("_SC_NPROCESSORS_ONLN"))
- except:
- pass
- try:
- return int(os.environ["NUMBER_OF_PROCESSORS"])
- except:
- pass
- try:
- return int(os.environ["NUM_PROCESSORS"])
- except:
- log.warning("failed to get the number of processors")
- return 1
-
-
-def _memorysize():
- try:
- return int(os.sysconf("SC_PHYS_PAGES") * os.sysconf("SC_PAGE_SIZE"))
- except:
- pass
- try:
- return int(os.sysconf("_SC_PHYS_PAGES") * os.sysconf("_SC_PAGE_SIZE"))
- except:
- pass
- try:
- # for OS X
- import subprocess
-
- return int(subprocess.Popen(["sysctl", "-n", "hw.memsize"],
- stdout=subprocess.PIPE)
- .communicate()[0].strip())
- except:
- log.warning("failed to get total memory")
- return 1024 ** 3
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/measurement/inputmanager.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/measurement/inputmanager.py
deleted file mode 100644
index 7acaeaa0cfa178c7e62716a29cca2e9497f255d1..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/measurement/inputmanager.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import abc
-import opentuner
-from opentuner.resultsdb.models import *
-
-
-class InputManager(object):
- """
- abstract base class for compile and measurement
- """
- __metaclass__ = abc.ABCMeta
-
- def set_driver(self, measurement_driver):
- self.driver = measurement_driver
- self.session = measurement_driver.session
- self.program = measurement_driver.tuning_run.program
-
- @abc.abstractmethod
- def select_input(self, desired_result):
- """
- select the input to be used to test desired_result
- """
- return opentuner.resultsdb.models.Input()
-
-
- def before_run(self, desired_result, input):
- """hook called before an input is used"""
- pass
-
- def after_run(self, desired_result, input):
- """hook called after an input is used"""
- pass
-
- def get_input_class(self):
- return None
-
-
-class FixedInputManager(InputManager):
- """
- an input manage that produces a single input for all tests
- """
-
- def __init__(self,
- input_class_name='fixed',
- size=-1,
- path=None,
- extra=None):
- self.input_class_name = input_class_name
- self.size = size
- self.path = path
- self.extra = extra
- self.the_input = None
- super(FixedInputManager, self).__init__()
-
-
- def get_input_class(self):
- return InputClass.get(self.session,
- program=self.program,
- name=self.input_class_name,
- size=self.size)
-
- def create_input(self, desired_result):
- """create the fixed input database object, result will be cached"""
- return Input(input_class=self.get_input_class(),
- path=self.path,
- extra=self.extra)
-
- def select_input(self, desired_result):
- if self.the_input is None:
- self.the_input = self.create_input(desired_result)
- return self.the_input
-
-
-
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/measurement/interface.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/measurement/interface.py
deleted file mode 100644
index 174902488289fe4ef038a9dd3553ea13acc68f2b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/measurement/interface.py
+++ /dev/null
@@ -1,366 +0,0 @@
-import abc
-import argparse
-import errno
-import hashlib
-import logging
-import os
-import re
-import signal
-import subprocess
-import threading
-import time
-from multiprocessing.pool import ThreadPool
-
-try:
- import resource
-except ImportError:
- resource = None
-
-try:
- import fcntl
-except ImportError:
- fcntl = None
-
-import opentuner
-from opentuner import resultsdb
-from opentuner.resultsdb.models import *
-
-log = logging.getLogger(__name__)
-
-argparser = argparse.ArgumentParser(add_help=False)
-argparser.add_argument('--parallel-compile', action='store_true',
- default=False,
- help="present if compiling can be done in parallel")
-
-the_io_thread_pool = None
-
-
-class MeasurementInterface(object):
- """
- abstract base class for compile and measurement
- """
- __metaclass__ = abc.ABCMeta
-
- def __init__(self,
- args=None,
- project_name=None,
- program_name='unknown',
- program_version='unknown',
- manipulator=None,
- objective=None,
- input_manager=None):
- self.args = args
- self._project = project_name
- self._program = program_name
- self._version = program_version
- self._objective = objective
- self._manipulator = manipulator
- self._input_manager = input_manager
-
- self.pids = []
- self.pid_lock = threading.Lock()
- self.parallel_compile = args.parallel_compile
- # If parallel_compile is False then compile_and_run() will be invoked
- # sequentially otherwise the driver first invokes compile() in parallel
- # followed by run_precompiled() sequentially
-
- def compile(self, config_data, id):
- """
- Compile in PARALLEL according to the configuration in config_data
- (obtained from desired_result.configuration) Should use id parameter
- to determine output location of executable Return value will be passed
- to run_precompiled as compile_result, useful for storing error/timeout
- information
- """
- if self.parallel_compile:
- raise RuntimeError('MeasurementInterface.compile() not implemented for',
- 'parallel compilation')
- pass
-
- def run_precompiled(self, desired_result, input, limit, compile_result, id):
- """
- Run the given desired_result SEQUENTIALLY on input and produce a Result()
- Abort early if limit (in seconds) is reached Assume that the executable
- to be measured has already been compiled to an executable corresponding to
- identifier id by compile() The compile_result is the return result of compile(),
- and it will be None if compile() was not called
- """
- if self.parallel_compile:
- raise RuntimeError('MeasurementInterface.run_precompiled() not implemented',
- 'for parallel compilation')
- pass
-
- def cleanup(self, id):
- """
- Clean up any temporary files associated with the executable
- """
- pass
-
- def pre_process(self):
- """
- The process before each iteration This method will be called
- once per iteration before all threads are launched
- """
- pass
-
- def post_process(self):
- """
- The process after each iteration This method will be called
- once per iteration after all threads are committed
- """
- pass
-
- def extra_convergence_criteria(self, result):
- """
- The extra convergence criteria which returns True if the
- current result is acceptable by the user
- """
- return False
-
- #@abc.abstractmethod
- def compile_and_run(self, desired_result, input, limit):
- """
- Compile and run the given desired_result on input and produce a
- Result(), abort early if limit (in seconds) is reached This function
- is only used for sequential execution flow
-
- FIXME: Shoud uncomment @abc.abstractmethod Now comment out for
- compatiability
- """
- return self.run(desired_result, input, limit)
-
- def run(self, desired_result, input, limit):
- """
- run the given desired_result on input and produce a Result(),
- abort early if limit (in seconds) is reached
- """
- return opentuner.resultdb.models.Result()
-
- def save_final_config(self, config):
- """
- called at the end of autotuning with the best resultsdb.models.Configuration
- """
- try:
- config_str = repr(config.data)
- if len(config_str) > 256:
- config_str = config_str[:256] + '...'
- log.info('final configuration: %s', config_str)
- log.info('you may want to implement save_final_config(), to store this')
- except:
- log.error('error printing configuration', exc_info=True)
-
- def db_program_version(self, session):
- """return a version identifier for the program being tuned"""
- return resultsdb.models.ProgramVersion.get(
- session=session,
- project=self.project_name(),
- name=self.program_name(),
- version=self.program_version(),
- parameter_info=self.manipulator().parameters_to_json(),
- )
-
- def set_driver(self, measurement_driver):
- self.driver = measurement_driver
-
- def project_name(self):
- if self._project is not None:
- return self._project
- autoname = re.sub('(Measurement?)Interface$', '', self.__class__.__name__)
- if autoname:
- return autoname
- else:
- return 'unknown'
-
- def program_name(self):
- return self._program
-
- def program_version(self):
- return self._version
-
- def file_hash(self, filename):
- """helper used to generate program versions"""
- return hashlib.sha256(open(filename).read()).hexdigest()
-
- def manipulator(self):
- """
- called once to create the search.manipulator.ConfigurationManipulator
- """
- if self._manipulator is None:
- msg = ('MeasurementInterface.manipulator() must be implemented or a '
- '"manipulator=..." must be provided to the constructor')
- log.error(msg)
- raise Exception(msg)
- return self._manipulator
-
- def objective(self):
- """
- called once to create the search.objective.SearchObjective
- """
- if self._objective is None:
- from ..search.objective import MinimizeSize
-
- return MinimizeSize()
- return self._objective
-
- def input_manager(self):
- """
- called once to create the measurement.inputmanager.InputManager
- """
- if self._objective is None:
- from .inputmanager import FixedInputManager
-
- return FixedInputManager()
- return self._input_manager
-
- def seed_configurations(self):
- """
- Extra seed configuration objects to add to those given on the command line.
- Configuration objects (typically dictionaries) not database objects.
- """
- return []
-
- def kill_all(self):
- self.pid_lock.acquire()
- for pid in self.pids:
- goodkillpg(pid)
- self.pids = []
- self.pid_lock.release()
-
- def call_program(self, cmd, limit=None, memory_limit=None, **kwargs):
- """
- call cmd and kill it if it runs for longer than limit
-
- returns dictionary like
- {'returncode': 0,
- 'stdout': '', 'stderr': '',
- 'timeout': False, 'time': 1.89}
- """
- the_io_thread_pool_init(self.args.parallelism)
- if limit is float('inf'):
- limit = None
- if type(cmd) in (str, unicode):
- kwargs['shell'] = True
- killed = False
- t0 = time.time()
- p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
- preexec_fn=preexec_setpgid_setrlimit(memory_limit),
- **kwargs)
- # Add p.pid to list of processes to kill in case of keyboardinterrupt
- self.pid_lock.acquire()
- self.pids.append(p.pid)
- self.pid_lock.release()
-
- try:
- stdout_result = the_io_thread_pool.apply_async(p.stdout.read)
- stderr_result = the_io_thread_pool.apply_async(p.stderr.read)
- while p.returncode is None:
- if limit is None:
- goodwait(p)
- elif limit and time.time() > t0 + limit:
- killed = True
- goodkillpg(p.pid)
- goodwait(p)
- else:
- # still waiting...
- sleep_for = limit - (time.time() - t0)
- if not stdout_result.ready():
- stdout_result.wait(sleep_for)
- elif not stderr_result.ready():
- stderr_result.wait(sleep_for)
- else:
- #TODO(jansel): replace this with a portable waitpid
- time.sleep(0.001)
- p.poll()
- except:
- if p.returncode is None:
- goodkillpg(p.pid)
- raise
- finally:
- # No longer need to kill p
- self.pid_lock.acquire()
- if p.pid in self.pids:
- self.pids.remove(p.pid)
- self.pid_lock.release()
-
- # TODO-autotune: Extract the file size and use it
- # FIXIT: Appropriately update the file size
- t1 = time.time()
- return {'time': float('inf') if killed else (t1 - t0),
- 'timeout': killed,
- 'returncode': p.returncode,
- 'stdout': stdout_result.get(),
- 'stderr': stderr_result.get(),
- }
-
- def getFileSize(self,filename):
- fileinfo=os.stat(filename)
- file_size=fileinfo.st_size
- return {'binary_size': file_size}
-
- def prefix_hook(self, session):
- pass
-
- @classmethod
- def main(cls, args, *pargs, **kwargs):
- from opentuner.tuningrunmain import TuningRunMain
-
- return TuningRunMain(cls(args, *pargs, **kwargs), args).main()
-
-
-class DefaultMeasurementInterface(MeasurementInterface):
- def run(self, desired_result, input, limit):
- raise RuntimeError('MeasurementInterface.run() not implemented')
-
-
-def preexec_setpgid_setrlimit(memory_limit):
- if resource is not None:
- def _preexec():
- os.setpgid(0, 0)
- try:
- resource.setrlimit(resource.RLIMIT_CORE, (0, 0))
- except ValueError:
- pass # No permission
- if memory_limit:
- try:
- (soft, hard) = resource.getrlimit(resource.RLIMIT_AS)
- resource.setrlimit(resource.RLIMIT_AS, (min(soft, memory_limit),
- min(hard, memory_limit)))
- except ValueError:
- pass # No permission
- return _preexec
-
-
-def the_io_thread_pool_init(parallelism=1):
- global the_io_thread_pool
- if the_io_thread_pool is None:
- the_io_thread_pool = ThreadPool(2 * parallelism)
- # make sure the threads are started up
- the_io_thread_pool.map(int, range(2 * parallelism))
-
-
-def goodkillpg(pid):
- """
- wrapper around kill to catch errors
- """
- log.debug("killing pid %d", pid)
- try:
- if hasattr(os, 'killpg'):
- os.killpg(pid, signal.SIGKILL)
- else:
- os.kill(pid, signal.SIGKILL)
- except:
- log.error('error killing process %s', pid, exc_info=True)
-
-
-def goodwait(p):
- """
- python doesn't check if its system calls return EINTR, retry if it does
- """
- while True:
- try:
- rv = p.wait()
- return rv
- except OSError, e:
- if e.errno != errno.EINTR:
- raise
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/resultsdb/__init__.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/resultsdb/__init__.py
deleted file mode 100644
index a0150a1577e22cdfd50e490bb4a0c6b735bfcac8..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/resultsdb/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-
-from connect import connect
-
-import models
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/resultsdb/connect.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/resultsdb/connect.py
deleted file mode 100644
index 1a04d05447a3b62d241a4f2402c22cac15b98b3b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/resultsdb/connect.py
+++ /dev/null
@@ -1,66 +0,0 @@
-from sqlalchemy import create_engine
-from sqlalchemy.orm import scoped_session, sessionmaker
-from models import Base, _Meta
-import logging
-import time
-from pprint import pprint
-
-log = logging.getLogger(__name__)
-
-DB_VERSION = "0.0"
-
-if False: # profiling of queries
- import atexit
- from sqlalchemy import event
- from collections import Counter
- from sqlalchemy.engine import Engine
- the_query_totals = Counter()
-
- @event.listens_for(Engine, "before_cursor_execute")
- def before_cursor_execute(conn, cursor, statement,
- parameters, context, executemany):
- context._query_start_time = time.time()
-
- @event.listens_for(Engine, "after_cursor_execute")
- def after_cursor_execute(conn, cursor, statement,
- parameters, context, executemany):
- total = time.time() - context._query_start_time
- the_query_totals[statement] += total
-
- @atexit.register
- def report():
- pprint(the_query_totals.most_common(10))
-
-
-def connect(dbstr):
- engine = create_engine(dbstr, echo = False)
- connection = engine.connect()
-
- #handle case that the db was initialized before a version table existed yet
- if engine.dialect.has_table(connection, "program"):
- # if there are existing tables
- if not engine.dialect.has_table(connection, "_meta"):
- # if no version table, assume outdated db version and error
- connection.close()
- raise Exception("Your opentuner database is currently out of date. Save a back up and reinitialize")
-
- # else if we have the table already, make sure version matches
- if engine.dialect.has_table(connection, "_meta"):
- Session = scoped_session(sessionmaker(autocommit=False,
- autoflush=False,
- bind=engine))
- version = _Meta.get_version(Session)
- if not DB_VERSION == version:
- raise Exception('Your opentuner database version {} is out of date with the current version {}'.format(version, DB_VERSION))
-
- Base.metadata.create_all(engine)
-
- Session = scoped_session(sessionmaker(autocommit=False,
- autoflush=False,
- bind=engine))
- # mark database with current version
- _Meta.add_version(Session, DB_VERSION)
- Session.commit()
-
- return engine, Session
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/resultsdb/models.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/resultsdb/models.py
deleted file mode 100644
index dd88ae8e51c0d94db2364cbc444b9a11d2667116..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/resultsdb/models.py
+++ /dev/null
@@ -1,319 +0,0 @@
-from sqlalchemy.ext.declarative import declarative_base
-from sqlalchemy.ext.declarative import declared_attr
-from sqlalchemy import create_engine
-from sqlalchemy.orm import relationship, backref
-from sqlalchemy import (
- Column, Integer, String, DateTime, Boolean, Enum,
- Float, PickleType, ForeignKey, Text, func, Index)
-import sqlalchemy
-import re
-
-from cPickle import dumps, loads
-from gzip import zlib
-class CompressedPickler(object):
- @classmethod
- def dumps(cls, obj, protocol=2):
- s = dumps(obj, protocol)
- sz = zlib.compress(s, 9)
- if len(sz) < len(s):
- return sz
- else:
- return s
-
- @classmethod
- def loads(cls, string):
- try:
- s = zlib.decompress(string)
- except:
- s = string
- return loads(s)
-
-class Base(object):
- @declared_attr
- def __tablename__(cls):
- """convert camel case to underscores"""
- return re.sub(r'([a-z])([A-Z])', r'\1_\2', cls.__name__).lower()
-
- id = Column(Integer, primary_key=True, index=True)
-
-
-Base = declarative_base(cls=Base)
-
-class _Meta(Base):
- """ meta table to track current version """
- db_version = Column(String(128))
-
- @classmethod
- def has_version(cls, session, version):
- try:
- session.flush()
- session.query(_Meta).filter_by(db_version=version).one()
- return True
- except sqlalchemy.orm.exc.NoResultFound:
- return False
-
- @classmethod
- def get_version(cls, session):
- try:
- session.flush()
- x = session.query(_Meta).one()
- return x.db_version
- except sqlalchemy.orm.exc.NoResultFound:
- return None
-
- @classmethod
- def add_version(cls, session, version):
- if not cls.has_version(session, version):
- session.add(_Meta(db_version=version))
-
-
-class Program(Base):
- project = Column(String(128))
- name = Column(String(128))
-
- @classmethod
- def get(cls, session, project, name):
- try:
- session.flush()
- return session.query(Program).filter_by(project=project, name=name).one()
- except sqlalchemy.orm.exc.NoResultFound:
- t = Program(project=project, name=name)
- session.add(t)
- return t
-
-
-class ProgramVersion(Base):
- program_id = Column(ForeignKey(Program.id))
- program = relationship(Program, backref='versions')
- version = Column(String(128))
- parameter_info = Column(Text)
-
- @property
- def name(self):
- return self.program.name
-
- @property
- def project(self):
- return self.program.project
-
- @classmethod
- def get(cls, session, project, name, version, parameter_info=None):
- program = Program.get(session, project, name)
- try:
- session.flush()
- if parameter_info is None:
- return session.query(ProgramVersion).filter_by(program=program,
- version=version).one()
- else:
- return session.query(ProgramVersion).filter_by(program=program,
- version=version,
- parameter_info=parameter_info).one()
- except sqlalchemy.orm.exc.NoResultFound:
- t = ProgramVersion(program=program, version=version, parameter_info=parameter_info)
- session.add(t)
- return t
-
-
-class Configuration(Base):
- program_id = Column(ForeignKey(Program.id))
- program = relationship(Program)
- hash = Column(String(64))
- data = Column(PickleType(pickler=CompressedPickler))
-
- @classmethod
- def get(cls, session, program, hashv, datav):
- try:
- session.flush()
- return (session.query(Configuration)
- .filter_by(program=program, hash=hashv).one())
- except sqlalchemy.orm.exc.NoResultFound:
- t = Configuration(program=program, hash=hashv, data=datav)
- session.add(t)
- return t
-
-
-Index('ix_configuration_custom1', Configuration.program_id, Configuration.hash)
-
-
-class MachineClass(Base):
- name = Column(String(128))
-
- @classmethod
- def get(cls, session, name):
- try:
- session.flush()
- return session.query(MachineClass).filter_by(name=name).one()
- except sqlalchemy.orm.exc.NoResultFound:
- t = MachineClass(name=name)
- session.add(t)
- return t
-
-
-class Machine(Base):
- name = Column(String(128))
-
- cpu = Column(String(128))
- cores = Column(Integer)
- memory_gb = Column(Float)
-
- machine_class_id = Column(ForeignKey(MachineClass.id))
- machine_class = relationship(MachineClass, backref='machines')
-
-
-class InputClass(Base):
- program_id = Column(ForeignKey(Program.id))
- program = relationship(Program, backref='inputs')
-
- name = Column(String(128))
- size = Column(Integer)
-
- @classmethod
- def get(cls, session, program, name='default', size=-1):
- try:
- session.flush()
- return session.query(InputClass).filter_by(program=program,
- name=name,
- size=size).one()
- except sqlalchemy.orm.exc.NoResultFound:
- t = InputClass(program=program, name=name, size=size)
- session.add(t)
- return t
-
-
-class Input(Base):
- #state = Column(Enum('ANY_MACHINE', 'SINGLE_MACHINE', 'DELETED'),
- # default='ANY_MACHINE', name='t_input_state')
-
- input_class_id = Column(ForeignKey(InputClass.id))
- input_class = relationship(InputClass, backref='inputs')
-
- #optional, set only for state='SINGLE_MACHINE'
- #machine_id = Column(ForeignKey(MachineClass.id))
- #machine = relationship(MachineClass, backref='inputs')
-
- #optional, for use by InputManager
- path = Column(Text)
- extra = Column(PickleType(pickler=CompressedPickler))
-
-
-class TuningRun(Base):
- uuid = Column(String(32), index=True, unique=True)
-
- program_version_id = Column(ForeignKey(ProgramVersion.id))
- program_version = relationship(ProgramVersion, backref='tuning_runs')
-
- machine_class_id = Column(ForeignKey(MachineClass.id))
- machine_class = relationship(MachineClass, backref='tuning_runs')
-
- input_class_id = Column(ForeignKey(InputClass.id))
- input_class = relationship(InputClass, backref='tuning_runs')
-
- name = Column(String(128), default='unnamed')
- args = Column(PickleType(pickler=CompressedPickler))
- objective = Column(PickleType(pickler=CompressedPickler))
-
- state = Column(Enum('QUEUED', 'RUNNING', 'COMPLETE', 'ABORTED',
- name='t_tr_state'),
- default='QUEUED')
- start_date = Column(DateTime, default=func.now())
- end_date = Column(DateTime)
-
- final_config_id = Column(ForeignKey(Configuration.id))
- final_config = relationship(Configuration)
-
- #__mapper_args__ = {'primary_key': uuid}
-
- @property
- def program(self):
- return self.program_version.program
-
-
-class Result(Base):
- #set by MeasurementDriver:
- configuration_id = Column(ForeignKey(Configuration.id))
- configuration = relationship(Configuration)
-
- machine_id = Column(ForeignKey(Machine.id))
- machine = relationship(Machine, backref='results')
-
- input_id = Column(ForeignKey(Input.id))
- input = relationship(Input, backref='results')
-
- tuning_run_id = Column(ForeignKey(TuningRun.id), index=True)
- tuning_run = relationship(TuningRun, backref='results')
-
- collection_date = Column(DateTime, default=func.now())
- collection_cost = Column(Float)
-
- #set by MeasurementInterface:
- state = Column(Enum('OK', 'TIMEOUT', 'ERROR',
- name='t_result_state'),
- default='OK')
- time = Column(Float)
- accuracy = Column(Float)
- energy = Column(Float)
- size = Column(Float)
- confidence = Column(Float)
- #extra = Column(PickleType)
-
- #set by SearchDriver
- was_new_best = Column(Boolean)
-
-
-Index('ix_result_custom1', Result.tuning_run_id, Result.was_new_best)
-
-
-class DesiredResult(Base):
- #set by the technique:
- configuration_id = Column(ForeignKey(Configuration.id))
- configuration = relationship(Configuration)
- limit = Column(Float)
-
- #set by the search driver
- priority = Column(Float)
- tuning_run_id = Column(ForeignKey(TuningRun.id))
- tuning_run = relationship(TuningRun, backref='desired_results')
- generation = Column(Integer)
- requestor = Column(String(128))
- request_date = Column(DateTime, default=func.now())
-
- #set by the measurement driver
- state = Column(Enum('UNKNOWN', 'REQUESTED', 'RUNNING',
- 'COMPLETE', 'ABORTED',
- name="t_dr_state"),
- default='UNKNOWN')
- result_id = Column(ForeignKey(Result.id), index=True)
- result = relationship(Result, backref='desired_results')
- start_date = Column(DateTime)
-
- #input_id = Column(ForeignKey(Input.id))
- #input = relationship(Input, backref='desired_results')
-
-
-Index('ix_desired_result_custom1', DesiredResult.tuning_run_id,
- DesiredResult.generation)
-
-Index('ix_desired_result_custom2', DesiredResult.tuning_run_id,
- DesiredResult.configuration_id)
-
-
-# track bandit meta-technique information if a bandit meta-technique is used for a tuning run.
-class BanditInfo(Base):
- tuning_run_id = Column(ForeignKey(TuningRun.id))
- tuning_run = relationship(TuningRun, backref='bandit_info')
- # the bandit exploration/exploitation tradeoff
- c = Column(Float)
- # the bandit window
- window = Column(Integer)
-
-class BanditSubTechnique(Base):
- bandit_info_id = Column(ForeignKey(BanditInfo.id))
- bandit_info = relationship(BanditInfo, backref='subtechniques')
- name = Column(String(128))
-
-
-if __name__ == '__main__':
- #test:
- engine = create_engine('sqlite:///:memory:', echo=True)
- Base.metadata.create_all(engine)
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/__init__.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/__init__.py
deleted file mode 100644
index bb4ce57bb2d1760bd9fb6ebe196f39072a43ab4a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-
-import driver
-import objective
-import plugin
-import technique
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/bandittechniques.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/bandittechniques.py
deleted file mode 100644
index 29816c03de1c52b4b6318991faafb488952e4019..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/bandittechniques.py
+++ /dev/null
@@ -1,316 +0,0 @@
-import abc
-import copy
-import logging
-import math
-import random
-from collections import deque
-
-from .metatechniques import MetaSearchTechnique
-from .technique import register, SearchTechnique, all_techniques, get_random_generator_technique
-
-log = logging.getLogger(__name__)
-
-
-class BanditQueue(object):
- def __init__(self, keys, C=0.05, window=500, **kwargs):
- """
- C is exploration/exploitation tradeoff
- window is how long to remember past results
- """
- super(BanditQueue, self).__init__(**kwargs)
- self.C = C
- self.history = deque()
- self.keys = keys
- self.use_counts = dict(((k, 0) for k in keys))
- self.window = window
- self.request_count = 0
-
- @abc.abstractmethod
- def exploitation_term(self, key):
- """
- value 0 to 1.0 to represent quality of technique
- """
- return 0.0
-
- def exploration_term(self, key):
- """
- value represent how unsure we are (optimal bandit solution)
- """
- if self.use_counts[key] > 0:
- return math.sqrt((2.0 * math.log(len(self.history), 2.0))
- / self.use_counts[key])
- else:
- return float('inf')
-
- def bandit_score(self, key):
- return (self.exploitation_term(key) +
- self.C * self.exploration_term(key))
-
- def ordered_keys(self):
- """select the next technique to use"""
-
- keys = list(self.keys)
- random.shuffle(keys) # break ties randomly
- keys.sort(key=self.bandit_score)
-
- self.request_count += 1
- if log.isEnabledFor(logging.DEBUG) and (self.request_count % 1000) == 0:
- log.debug(str([
- (t, self.exploitation_term(t), self.C * self.exploration_term(t))
- for t in keys]))
-
- return reversed(keys)
-
- def on_result(self, key, value):
- self.history.append((key, value))
- self.on_push_history(key, value)
- if len(self.history) > self.window:
- self.on_pop_history(*self.history.popleft())
-
- def on_push_history(self, key, value):
- self.use_counts[key] += 1
-
- def on_pop_history(self, key, value):
- self.use_counts[key] -= 1
-
-
-class AUCBanditQueue(BanditQueue):
- """
- Area Under the Receiving Operator Curve (AUC) credit assignment
-
- See:
- Comparison-based adaptive strategy selection with bandits in differential
- evolution. Fialho et al.
- """
-
- def __init__(self, *args, **kwargs):
- super(AUCBanditQueue, self).__init__(*args, **kwargs)
- self.debug = kwargs.get('debug', False)
- self.auc_sum = dict(((t, 0) for t in self.keys))
- self.auc_decay = dict(((t, 0) for t in self.keys))
-
- def exploitation_term_slow(self, key):
- """
- value 0 to 1.0 to represent quality of key
-
- computes the area under the curve where finding a new
- global best results in adding 1 to a cumulative total
- """
- score = 0.0
- pos = 0
- for t, value in self.history:
- if t is key:
- pos += 1
- if value:
- score += pos
- if pos:
- return score * 2.0 / (pos * (pos + 1.0))
- else:
- return 0.0
-
- def exploitation_term_fast(self, key):
- """
- value 0 to 1.0 to represent quality of key
-
- optimized O(1) implementation exploitation_term_slow()
- """
- score = self.auc_sum[key]
- pos = self.use_counts[key]
- if pos:
- return score * 2.0 / (pos * (pos + 1.0))
- else:
- return 0.0
-
- def exploitation_term(self, key):
- v1 = self.exploitation_term_fast(key)
- if self.debug:
- v2 = self.exploitation_term_slow(key)
- assert v1 == v2
- return v1
-
- def on_push_history(self, key, value):
- super(AUCBanditQueue, self).on_push_history(key, value)
- if value:
- self.auc_sum[key] += self.use_counts[key]
- self.auc_decay[key] += 1
-
- def on_pop_history(self, key, value):
- super(AUCBanditQueue, self).on_pop_history(key, value)
- self.auc_sum[key] -= self.auc_decay[key]
- if value:
- self.auc_decay[key] -= 1
-
-
-
-class AUCBanditMetaTechnique(MetaSearchTechnique):
- def __init__(self, techniques, bandit_kwargs=dict(), **kwargs):
- super(AUCBanditMetaTechnique, self).__init__(techniques, **kwargs)
- self.bandit = AUCBanditQueue([t.name for t in techniques], **bandit_kwargs)
- self.name_to_technique = dict(((t.name, t) for t in self.techniques))
-
- def select_technique_order(self):
- """select the next technique to use"""
- return (self.name_to_technique[k] for k in self.bandit.ordered_keys())
-
- def on_technique_result(self, technique, result):
- self.bandit.on_result(technique.name, result.was_new_best)
-
- def on_technique_no_desired_result(self, technique):
- """treat not providing a configuration as not a best"""
- self.bandit.on_result(technique.name, 0)
-
- @classmethod
- def generate_technique(cls, manipulator=None, num_techniques=5, retry_count=3, generator_weight=10, *args, **kwargs):
- """
- Generate a bandit by randomly selecting existing techniques or composable techniques.
- If a composable technique is selected, the operators are then chosen
-
- :param manipulator: a ConfigurationManipulator used to enumerate parameters
- :param num_techniques: max number of subtechniques in the bandit
- :param retry_count: number of times to try getting a new technique before giving up
- :param generator_weight: weight to increase probability of choosing to generate a technique
- """
- techniques, generators = all_techniques()
-
- # get set of parameters to consider
- paramset = set()
- for p in manipulator.params:
- paramset.add(type(p))
-
- # filter techniques to get rid of metatechniques
- basetechniques = [t for t in techniques if not isinstance(t, MetaSearchTechnique)]
- bandit_techniques = []
- for i in range(num_techniques):
- for j in range(retry_count):
- # pick a technique or generate a composable
- if random.random() < float(len(basetechniques)) / (len(basetechniques) + generator_weight*len(generators)):
- candidate = copy.deepcopy(random.choice(basetechniques))
- else:
- # pick a random generator
- candidate = get_random_generator_technique(generators, manipulator=manipulator)
- if not (candidate.name in [t.name for t in bandit_techniques]):
- bandit_techniques.append(candidate)
- break
-
- # make a bandit of the output list
- return cls(bandit_techniques, name="GeneratedBandit", *args, **kwargs)
-
-
-class AUCBanditMutationTechnique(SearchTechnique):
- def __init__(self, bandit_kwargs=dict(), **kwargs):
- super(AUCBanditMutationTechnique, self).__init__(**kwargs)
- self.bandit = None
- self.bandit_kwargs = bandit_kwargs
- self.pending_results = []
-
- def handle_requested_result(self, result):
- for i in xrange(len(self.pending_results)):
- cfg, name, index = self.pending_results[i]
- if result.configuration == cfg:
- self.bandit.on_result((name, index), result.was_new_best)
- del self.pending_results[i]
- return
- log.warning("unexpected result")
-
- def desired_configuration(self):
- """
- use bandit to pick a single manipulator and apply it
- """
- seed = self.get_seed()
- if self.bandit is None:
- self.init_bandit(seed)
-
- cfg = self.manipulator.copy(seed)
- hash1 = self.manipulator.hash_config(cfg)
- params = self.manipulator.parameters_dict(cfg)
- for name, index in self.bandit.ordered_keys():
- if name in params:
- param = params[name]
- fns = param.manipulators(cfg)
- fn = fns[index % len(fns)]
- fn(cfg)
- hash2 = self.manipulator.hash_config(cfg)
- if hash1 != hash2:
- cfg = self.driver.get_configuration(cfg)
- self.pending_results.append((cfg, name, index))
- log.debug("applied %s[%s] manipulator function", name, index)
- return cfg
-
- return None
-
-
- def init_bandit(self, cfg):
- options = []
- for param in self.manipulator.parameters(cfg):
- for i in xrange(len(param.manipulators(cfg))):
- options.append((param.name, i))
- # TODO(jansel): remove assumption that set of parameters are fixed
- self.bandit = AUCBanditQueue(options, **self.bandit_kwargs)
-
- def get_seed(self):
- """seed mutation with global best"""
- if (self.driver.best_result is not None and
- self.driver.best_result.state == 'OK'):
- return self.driver.best_result.configuration.data
- else:
- return self.manipulator.random()
-
-
-import evolutionarytechniques
-import differentialevolution
-import simplextechniques
-import patternsearch
-import simulatedannealing
-from pso import PSO, HybridParticle
-import globalGA
-register(AUCBanditMutationTechnique())
-
-register(AUCBanditMetaTechnique([
- differentialevolution.DifferentialEvolutionAlt(),
- evolutionarytechniques.UniformGreedyMutation(),
- evolutionarytechniques.NormalGreedyMutation(mutation_rate=0.3),
- simplextechniques.RandomNelderMead(),
- ], name = "AUCBanditMetaTechniqueA"))
-register(AUCBanditMetaTechnique([
- differentialevolution.DifferentialEvolutionAlt(),
- evolutionarytechniques.UniformGreedyMutation(),
- ], name = "AUCBanditMetaTechniqueB"))
-register(AUCBanditMetaTechnique([
- differentialevolution.DifferentialEvolutionAlt(),
- patternsearch.PatternSearch(),
- ], name = "AUCBanditMetaTechniqueC"))
-register(AUCBanditMetaTechnique([
- PSO(crossover = 'op3_cross_OX3'),
- PSO(crossover = 'op3_cross_OX1'),
- PSO(crossover = 'op3_cross_CX'),
- PSO(crossover = 'op3_cross_PMX'),
- PSO(crossover = 'op3_cross_PX'),
- evolutionarytechniques.GA(crossover = 'op3_cross_OX3', mutation_rate=0.01, crossover_rate=0.8),
- evolutionarytechniques.GA(crossover = 'op3_cross_OX1', mutation_rate=0.01, crossover_rate=0.8),
- evolutionarytechniques.GA(crossover = 'op3_cross_CX', mutation_rate=0.01, crossover_rate=0.8),
- evolutionarytechniques.GA(crossover = 'op3_cross_PX', mutation_rate=0.01, crossover_rate=0.8),
- evolutionarytechniques.GA(crossover = 'op3_cross_PMX', mutation_rate=0.01, crossover_rate=0.8),
- evolutionarytechniques.UniformGreedyMutation(name='ga-base', mutation_rate=0.01)
- ], name = "PSO_GA_Bandit"))
-register(AUCBanditMetaTechnique([
- differentialevolution.DifferentialEvolutionAlt(),
- simulatedannealing.PseudoAnnealingSearch()
- ], name = "test"))
-register(AUCBanditMetaTechnique([
- differentialevolution.DifferentialEvolutionAlt(),
- evolutionarytechniques.UniformGreedyMutation(),
- evolutionarytechniques.NormalGreedyMutation(mutation_rate=0.3),
- simplextechniques.RandomNelderMead(),
- simulatedannealing.PseudoAnnealingSearch()
- ], name = "test2"))
-register(AUCBanditMetaTechnique([
- PSO(crossover='op3_cross_OX1'),
- PSO(crossover='op3_cross_PMX'),
- PSO(crossover='op3_cross_PX'),
- evolutionarytechniques.GA(crossover='op3_cross_OX1', crossover_rate=0.5),
- evolutionarytechniques.GA(crossover='op3_cross_PMX', crossover_rate=0.5),
- evolutionarytechniques.GA(crossover='op3_cross_PX', crossover_rate=0.5),
- differentialevolution.DifferentialEvolutionAlt(),
- globalGA.NormalGreedyMutation( crossover_rate=0.5, crossover_strength=0.2, name='GGA')
- ], name='PSO_GA_DE'))
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/composableevolutionarytechniques.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/composableevolutionarytechniques.py
deleted file mode 100644
index e511744f30b8a7d271539e4ed26e247b5574c2b5..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/composableevolutionarytechniques.py
+++ /dev/null
@@ -1,512 +0,0 @@
-import random
-import time
-import sys
-import json
-from fn import _
-from technique import all_techniques
-from technique import register
-from technique import register_generator
-from technique import SequentialSearchTechnique
-from manipulator import *
-from opentuner.search.manipulator import Parameter
-
-
-class PopulationMember(object):
- """
- An extendable object representing a population member for ComposableEvolutionaryTechniques.
- Must have the field "config" which is a configuration
- """
- def __init__(self, config):
- self.config = config
- self.timestamp = time.time()
-
- def touch(self):
- """
- Update the timestamp on a PopulationMember
- """
- self.timestamp = time.time()
-
-
-class ComposableEvolutionaryTechnique(SequentialSearchTechnique):
- """
- An abstract base class for a technique that is composable with operators
- """
- __metaclass__ = abc.ABCMeta
-
- # operator_map - from param-type to dict with operator name + list of arguments TODO
- # min_parent - minimum number of parents returned. Limits which operators can be used
- def __init__(self,
- operator_map = {},
- population_size = 50,
- initial_configs = None,
- *pargs,
- **kwargs):
- """
-
- :param operator_map:
- :param population_size:
- :param initial_configs:
- :param pargs:
- :param kwargs:
- :return:
- """
- super(ComposableEvolutionaryTechnique, self).__init__(*pargs, **kwargs)
- # generate a name based on operators if no name
-
- self.initial_configurations = initial_configs
- self.population_size = population_size
- self.operator_map = operator_map # map from parameter type to an operator function
-
- def set_operator_map(self, operator_map):
- self.operator_map = operator_map
-
- @classmethod
- def get_hyper_parameters(cls):
- return ['population_size']
-
- def default_generated_name(self):
- """
- Gets the default name for this technique based on its operator map
-
- Name is in the format
- classname paramname;opname;[arg1,arg2,[[kwarg1,v1][kwarg2,v2]]] paramname2;opname2;...
- """
- # TODO - include technique hyperparameters
- parts = [self.base_name()]
- for param in sorted(self.operator_map, cmp=lambda x,y: cmp(x.__name__, y.__name__)):
- subparts = [param.__name__]
- operator_info = self.operator_map[param]
- subparts.append(operator_info['op_name'])
- args = list(operator_info['args'])
- kwargs = operator_info['kwargs']
- args.append([(k,kwargs[k]) for k in sorted(kwargs)])
- subparts.append(json.dumps(args, separators=(',', ':')))
- parts.append(';'.join(subparts))
- return ' '.join(parts)
-
-
- def make_population_member(self, config):
- """
- Given a configuration, returns an object representing a single member of the
- population with the given configuration. Meta-data about the configuration,
- such as last selection time as a parent, can be attached to the object.
-
- This can be overriden to return a custom population member for use in
- :py:meth:`get_parents` and :py:meth:`update_population`
-
- :param config: the configuration that this population member will represent
- :return: a population member reresenting the input configuration.
- """
- return PopulationMember(config)
-
- def select_parameters(self, params):
- """
- Given all the available parameters, return a subset of parameters to operate
- on when generating a new configuration.
-
- Can override this to operate on only a subset of parameters.
-
- :param params: a list of all the available parameters
- :return: a subset of params
- """
- return params
-
- @abc.abstractmethod
- def minimum_number_of_parents(self):
- """
- Return the minimum number of parents ever returned by :py:meth:`get_parents`.
- This limits which operators can be composed with the technique. Operators
- requiring more input configurations than the minimum number of parents will
- result in an error.
-
- :return: the minimum number of parents ever generated.
- """
- return 1
-
- @abc.abstractmethod
- def get_parents(self, population):
- """
- Given the current population, return a list of configurations that will be
- used to generate a new configuration via operators. Returning less parents
- than guaranteed by :py:meth:`minimum_number_of_parents` results in an error.
-
- The parents will be passed to operators in order. If there are more parents
- than required by an operator, extra parents will be discarded.
-
- Note that operators mutate the first configuration passed in.
-
- :param population: the current population in the technique
- :return: a list of parent configurations to generate a new configuration from
- """
- pass
-
- @abc.abstractmethod
- def update_population(self, config, population):
- """
- Update the population given the newest configuration and current population
- in the technique. should return the new population
-
- :param config: the newest generated configuration
- :param population: the current population in this iteration of the technique
- :return: the updated population
- """
- pass
-
- def get_initial_population(self):
- """
- Returns an initial population by passing initial configurations into
- :py:meth:`make_population_member`
-
- :return: an initial list of objects returned by :py:meth:`make_population_member`.
- """
- init_configs = self.initial_configurations
- if not init_configs:
- init_configs = [self.manipulator.random() for i in range(self.population_size)]
- return [PopulationMember(config) for config in init_configs]
-
- def lt(self, cfg_a, cfg_b):
- """
- Return whether cfg_a has a better objective function score than cfg_b
-
- :param cfg_a: first configuration
- :param cfg_b: second configuration
- :return: True if cfg_a is better than cfg_b
- """
- def config(cfg):
- return self.driver.get_configuration(cfg)
- return self.objective.lt(config(cfg_a), config(cfg_b))
-
- def lte(self, cfg_a, cfg_b):
- """
- Return whether cfg_a's objective function score is at least as good as cfg_b's
- score
-
- :param cfg_a: first configuration
- :param cfg_b: second configuration
- :return: True if cfg_a is at least as good as cfg_b
- """
- def config(cfg):
- return self.driver.get_configuration(cfg)
- return self.objective.lte(config(cfg_a), config(cfg_b))
-
- def get_global_best_configuration(self):
- """
- Return the current global best configuration in the search
-
- :return: the current global best configuration
- """
- if (self.driver.best_result is not None and
- self.driver.best_result.state == 'OK'):
- return self.manipulator.copy(self.driver.best_result.configuration.data)
- else:
- return self.manipulator.random()
-
- def get_default_operator(self, param_type):
- """
- Given a parameter type, return a dictionary with information about the
- operator to be used for the parameter. The returned dictionary must contain
- the following 3 key, value pairs
-
- 1. 'op_name' - the string name of the operator
- 2. 'args' - an iterable of the non-configuration arguments in order
- 3. 'kwargs' - a dictionary from any optional arguments to their values
-
- :return: a dictionary containing information about the operator to apply for the input parameter type
- """
- return {'op_name': 'op1_nop', 'args': [], 'kwargs': {}}
-
- # HELPER METHODS FOR BUILDING OPERATOR MAP
- @classmethod
- def add_to_map(cls, operator_map, param_type, operator_name, *args, **kwargs):
- """
- A helper method for adding parameter to operator mappings into the operator
- map.
-
- :param operator_map: the operator map to add to
- :param param_type: the parameter type to use the this operator on
- :param operator_name: the string name of the operator method
- :param *args: any non-configuration arguments to the operator
- :param **kwargs: any keyword arguemnts for the operator
- """
- if(isinstance(param_type, Parameter)):
- ptype = type(param_type)
- elif (type(param_type) == str):
- ptype = reduce(getattr, param_type.split("."), sys.modules[__name__])
- else:
- ptype = param_type;
-
- operator_map[ptype] = {'op_name': operator_name, 'args':args, 'kwargs':kwargs}
-
-
- def main_generator(self):
- """
- The primary body of the search technique.
- Initializes an initial population and then yields configurations by applying
- operators to get_parents.
- """
- min_parents = self.minimum_number_of_parents();
- # convert a manipulator configuration to a db.models.Configuration
- def get_driver_configuration(cfg):
- return self.driver.get_configuration(cfg)
-
- # initialize the population
- population = self.get_initial_population()
-
- # measure initial population
- for p in population:
- yield get_driver_configuration(p.config)
-
- while True:
- # get parents
- parents = self.get_parents(population)
- if len(parents) < min_parents:
- log.error("%s: Number of parents returned %d is less than the guaranteed"
- + " minimum returned by minimum_number_of_parents() %d. ",
- self.name, len(parents), min_parents)
- # fail and let other techniques work forever
- while True:
- yield None
-
- params = self.select_parameters(self.manipulator.params)
- config = self.get_new_config(parents, params)
- yield get_driver_configuration(config)
-
- population = self.update_population(config, population)
-
- # safety check that population has all been tested
- for p in population:
- if not self.driver.has_results(get_driver_configuration(p.config)):
- yield get_driver_configuration(p.config)
-
- def get_new_config(self, parents, params):
- """
- Return a new configuration to test, given a list of parent configurations
- This mutates the first parent
-
- :param parents: A list of parent configurations
- :params: A list of parameters to operate on
- :return: The mutated configuration (first parent)
- """
- for param in params:
- self.apply_operator(param, parents) #TODO
- return parents[0]
-
- def apply_operator(self, param, parents):
- """
- Apply the appropriate operator for param to parents.
- If an operator takes less input configurations than the number of parents,
- only the first parents are passed in. If operator takes more input configs
- than minimum_number_of_parents, logs an error and doesn't do anything
- """
- x = self.get_operator(type(param))
-
- operator_name = x['op_name']
- if not self.is_valid_operator(type(param), operator_name):
- # do nothing
- return
-
- # operator is already in valid form and starts with op1, op2, op3, op4, or opn
- num_parents_required = operator_name[2]
- if num_parents_required == 'n':
- args = parents[0] + [parents[1:]]
- else:
- num_parents_required = int(num_parents_required)
- args = parents[:num_parents_required]
- args.extend(x['args'])
-
- kwargs = x['kwargs']
-
- getattr(param, operator_name)(*args, **kwargs)
-
- def get_operator(self, param_type):
- if param_type in self.operator_map:
- return self.operator_map[param_type]
- return self.get_default_operator(param_type)
-
- def is_valid_operator(self, param_type, operator_name):
- if not hasattr(param_type, operator_name):
- log.error("%s: %s is not a valid operator for Parameter type %s",
- self.name, operator_name, param_type.__name__)
- return False
-
- if operator_name[:3] not in ['op1','op2','op3','op4','opn']:
- log.error("%s: %s is not a valid operator for Parameter type %s",
- self.name, operator_name, param_type.__name__)
- return False
-
- num_parents_required = operator_name[2]
- if num_parents_required == 'n':
- return True
-
- num_parents_required = int(num_parents_required)
- minimum_number_of_parents = self.minimum_number_of_parents()
-
- if num_parents_required > minimum_number_of_parents:
- log.error("%s: %s for Parameter type %s requires more input configs "
- + "than minimum number of parents, %d, produced by this technique",
- self.name, operator_name, param_type.__name__, minimum_number_of_parents)
- return False
-
- return True
-
- @classmethod
- def generate_technique(cls, manipulator=None, *args, **kwargs):
- """
- generate a composable technique with random operators
- """
- from manipulator import composable_operators
- # randomly select a composable technique to generate
- t = cls(*args, **kwargs)
- if manipulator is None:
- return t
-
- paramset = set()
- for p in manipulator.params:
- paramset.add(type(p))
-
- # add some random operator for each param
- operator_map = {}
- for param in paramset:
- operators = composable_operators(param, t.minimum_number_of_parents())
- # TODO - sometimes use "default" operator (don't choose an operator?
- # TODO - lower chance of picking op1_nop?
- ComposableEvolutionaryTechnique.add_to_map(operator_map, param, random.choice(operators))
-
- t.set_operator_map(operator_map)
- t.use_default_generated_name()
- return t
-
-
-class RandomThreeParentsComposableTechnique(ComposableEvolutionaryTechnique):
- """
- based on DifferentialEvolution
- """
-
- def __init__(self, cr = 0.9, must_mutate_count=1, information_sharing=1, *pargs, **kwargs):
- super(RandomThreeParentsComposableTechnique, self).__init__(*pargs, **kwargs)
- self.cr = cr
- self.must_mutate_count = must_mutate_count
- self.information_sharing = information_sharing
-
- @classmethod
- def get_hyper_parameters(cls):
- return ['population_size', 'cr', 'must_mutate_count', 'information_sharing']
-
- def minimum_number_of_parents(self):
- return 4
-
- def get_parents(self, population):
- self.use_f = random.random()
- population.sort(key=_.timestamp) # sort population by timestamp
-
- # copy oldest
- cfg = self.manipulator.copy(population[0].config)
-
- shuffled_population = map(_.config, population[1:])
- # mix in the global best configuration
- shuffled_population += ([self.get_global_best_configuration()]
- * self.information_sharing)
- random.shuffle(shuffled_population)
-
- # return oldest configuration +_3 other configurations
- return [cfg] + shuffled_population[0:3]
-
- def update_population(self, config, population):
- # replace the oldest configuration if the new one is better.
- population.sort(key=_.timestamp)
- if self.lt(config, population[0].config):
- population[0].config = config
-
- # mark that oldest configuration is updated
- population[0].touch()
-
- return population
-
- def select_parameters(self, params):
- """
- randomly select a subset of parameters to operate on
- """
- random.shuffle(params)
- ret_list = params[:self.must_mutate_count]
- for param in params[self.must_mutate_count:]:
- if random.random() < self.cr:
- ret_list.append(param)
- return ret_list
-
- def get_default_operator(self, param_type):
- return {'op_name': 'op4_set_linear', 'args': [1.0, self.use_f, -self.use_f], 'kwargs': {}}
-
-class GreedyComposableTechnique(ComposableEvolutionaryTechnique):
- """
- Always mixes in global best as parents
- """
- def __init__(self,
- mutation_rate = 0.1,
- must_mutate_count = 1,
- population_size = 10,
- *pargs, **kwargs):
- super(GreedyComposableTechnique, self).__init__(*pargs, **kwargs)
- self.mutation_rate = mutation_rate
- self.must_mutate_count = must_mutate_count
- self.population_size = population_size
-
-
- @classmethod
- def get_hyper_parameters(cls):
- return ['mutation_rate', 'must_mutate_count']
-
- def minimum_number_of_parents(self):
- # specify that we will return at least 4 cfgs from get_parents
- # this maximizes # of operators we can use
- return 4
-
- def get_parents(self, population):
- population.sort(key=_.timestamp) # sort population by timestamp
-
- # get a 50-50 mix of base and best cfgs as many operators do nothing given identical input cfgs
- cfg = self.manipulator.copy(population[0].config)
- # duplicate to get a total of 4 configurations to fulfill the promise in minimum_number_of_parents
- cfgs = [self.get_global_best_configuration(), cfg]*2
- # return a random 50-50 mix of the current configuration and global best to pass into operators
- random.shuffle(cfgs)
- return cfgs
-
- def update_population(self, config, population):
- # replace the oldest configuration if the new one is better.
- population.sort(key=_.timestamp)
- if self.lt(config, population[0].config):
- population[0].config = config
-
- # mark that oldest configuration is updated
- population[0].touch()
-
- return population
-
- def select_parameters(self, params):
- random.shuffle(params)
- ret_list = params[:self.must_mutate_count]
- for param in params[self.must_mutate_count:]:
- if random.random() < self.mutation_rate:
- ret_list.append(param)
- return ret_list
-
- def get_default_operator(self, param_type):
- return {'op_name': 'op1_randomize', 'args': [], 'kwargs':{}}
-
-
-register(RandomThreeParentsComposableTechnique(name='ComposableDiffEvolution',
- population_size=30))
-register_generator(RandomThreeParentsComposableTechnique)
-register_generator(GreedyComposableTechnique)
-
-
-op_map = {}
-ComposableEvolutionaryTechnique.add_to_map(op_map,
- PermutationParameter,
- "op3_cross", xchoice='op3_cross_CX')
-ComposableEvolutionaryTechnique.add_to_map(op_map,
- "FloatArray",
- "op3_cross", strength=0.4)
-register(RandomThreeParentsComposableTechnique(name='ComposableDiffEvolutionCX',
- operator_map=op_map,
- population_size=30))
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/differentialevolution.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/differentialevolution.py
deleted file mode 100644
index cecffc460c5cdbef184fc244a70f9a6af251bddd..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/differentialevolution.py
+++ /dev/null
@@ -1,148 +0,0 @@
-import random
-import time
-import logging
-from fn import _
-from technique import register
-from technique import SearchTechnique
-
-log = logging.getLogger(__name__)
-log.setLevel(logging.WARNING)
-
-
-class PopulationMember(object):
- def __init__(self, config, submitted=True):
- self.config = config
- self.submitted = submitted
- self.timestamp = time.time()
- self.candidate_replacement = None
-
- def touch(self):
- self.timestamp = time.time()
-
-
-class DifferentialEvolution(SearchTechnique):
- """
- based on http://cci.lbl.gov/cctbx_sources/scitbx/differential_evolution.py
- """
-
- def __init__(self,
- population_size=30,
- cr=0.9, # crossover rate
- n_cross=1, # force at least 1 to crossover
- information_sharing=1, # number token sharing pop members
- duplicate_retries=5, # how many times to retry on duplicate
- *pargs, **kwargs):
-
- self.population_size = population_size
- self.cr = cr
- self.n_cross = n_cross
- self.information_sharing = information_sharing
- self.population = None
- self.duplicate_retries = duplicate_retries
- self.limit = None
- super(DifferentialEvolution, self).__init__(*pargs, **kwargs)
-
- @classmethod
- def get_hyper_parameters(cls):
- return ['population_size', 'cr', 'n_cross', 'information_sharing']
-
- def initial_population(self):
- self.population = [PopulationMember(
- self.driver.get_configuration(
- self.manipulator.random()), submitted=False)
- for z in xrange(self.population_size)]
-
- def oldest_pop_member(self):
- # since tests are run in parallel, exclude things with a replacement pending
- pop_without_replacements = filter(lambda x: x.candidate_replacement is None,
- self.population)
- if not pop_without_replacements:
- # everything has a pending replacement
- return None
- pop_without_replacements.sort(key=_.timestamp)
- return pop_without_replacements[0]
-
- def desired_configuration(self):
- """
- return a cfg that we should test,
- """
- if not self.population:
- # first time called
- self.initial_population()
-
- # make sure initial population is completely submitted
- for p in self.population:
- if not p.submitted:
- p.submitted = True
- if p is self.population[-1]:
- log.info('initial population testing done')
- return p.config
-
- # pp is member of population to be replaced
- oldest_pop_member = self.oldest_pop_member()
- if not oldest_pop_member:
- return None
-
- config = None
- for retry in xrange(self.duplicate_retries):
- config = self.driver.get_configuration(
- self.create_new_configuration(oldest_pop_member))
- if not self.driver.has_results(config):
- break
- # new configuration would have been a duplicate, try again
-
- oldest_pop_member.touch() # move to back of the line for next replacement
- oldest_pop_member.candidate_replacement = config
- self.limit = self.driver.objective.limit_from_config(
- oldest_pop_member.config)
- return oldest_pop_member.candidate_replacement
-
- def create_new_configuration(self, parent_pop_member):
- cfg = self.manipulator.copy(parent_pop_member.config.data)
- cfg_params = self.manipulator.proxy(cfg)
-
- # pick 3 random parents, not pp
- shuffled_pop = list(set(self.population) - set([parent_pop_member]))
-
- # share information with other techniques
- if self.driver.best_result:
- shuffled_pop += ([PopulationMember(self.driver.best_result.configuration)]
- * self.information_sharing)
-
- random.shuffle(shuffled_pop)
- x1, x2, x3 = map(_.config.data, shuffled_pop[0:3])
-
- use_f = random.random() / 2.0 + 0.5
-
- params = self.manipulator.param_names(cfg, x1, x2, x3)
- random.shuffle(params)
- for i, k in enumerate(params):
- if i < self.n_cross or random.random() < self.cr:
- # cfg = x1 + use_f*(x2 - x3)
- cfg_params[k].op4_set_linear(x1, x2, x3, 1.0, use_f, -use_f)
-
- return cfg
-
- def handle_requested_result(self, result):
- """called when new results are added"""
- for p in self.population:
- if p.candidate_replacement == result.configuration:
- if self.objective.lt(p.candidate_replacement, p.config):
- # candidate replacement was better, replace it!
- p.config = p.candidate_replacement
- log.info('better point')
- p.candidate_replacement = None
-
-
-class DifferentialEvolutionAlt(DifferentialEvolution):
- def __init__(self, cr=0.2, **kwargs):
- kwargs['cr'] = cr
- super(DifferentialEvolutionAlt, self).__init__(**kwargs)
-
-
-register(DifferentialEvolution())
-register(DifferentialEvolutionAlt())
-register(DifferentialEvolution(population_size=100, cr=0.2,
- name='DifferentialEvolution_20_100'))
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/driver.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/driver.py
deleted file mode 100644
index 7924e36e6fbc772e375ce344e8de919f66e8c6b4..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/driver.py
+++ /dev/null
@@ -1,301 +0,0 @@
-import argparse
-import copy
-import logging
-import os
-import sys
-
-from datetime import datetime
-from fn import _
-from opentuner.driverbase import DriverBase
-from opentuner.resultsdb.models import Configuration
-from opentuner.resultsdb.models import DesiredResult
-from opentuner.resultsdb.models import Result
-from opentuner.resultsdb.models import BanditInfo
-from opentuner.resultsdb.models import BanditSubTechnique
-from opentuner.search import plugin
-from opentuner.search import technique
-from opentuner.search.bandittechniques import AUCBanditMetaTechnique
-
-log = logging.getLogger(__name__)
-
-argparser = argparse.ArgumentParser(add_help=False)
-argparser.add_argument('--test-limit', type=int, default=5000,
- help='stop tuning after given tests count')
-argparser.add_argument('--stop-after', type=float,
- help='stop tuning after given seconds')
-argparser.add_argument('--parallelism', type=int, default=8,
- help='how many tests to support at once')
-argparser.add_argument('--pipelining', type=int, default=0,
- help='how long a delay (in generations) before results are available')
-argparser.add_argument('--bail-threshold', type=int, default=5000,
- help='abort if no requests have been made in X generations')
-argparser.add_argument('--no-dups', action='store_true',
- help='don\'t print out warnings for duplicate requests')
-argparser.add_argument('--seed-configuration', action='append', default=[],
- metavar='FILENAME', help="""
- Start search at a given configuration. Can be
- specified multiple times. Configurations are loaded
- with ConfigurationManipulator.load_from_file()
- and file format is detected from extension.""")
-
-
-class SearchDriver(DriverBase):
- """
- controls the search process managing root_technique and creating
- DesiredResults
- """
-
- def __init__(self, manipulator, extra_seeds=None, extra_criteria=None, **kwargs):
- super(SearchDriver, self).__init__(**kwargs)
- if extra_seeds is None:
- extra_seeds = []
- self.manipulator = manipulator
- self.wait_for_results = self.tuning_run_main.results_wait
- self.commit = self.tuning_run_main.commit
- self.extra_criteria = extra_criteria
-
- self.generation = 0
- self.test_count = 0
- self.plugins = plugin.get_enabled(self.args)
- self.pending_result_callbacks = list() # (DesiredResult, function) tuples
- # deepcopy is required to have multiple tuning runs in a single process
- if self.args.list_techniques:
- techniques, generators = technique.all_techniques()
- for t in techniques:
- print t.name
- sys.exit(0)
-
- if self.args.generate_bandit_technique:
- # generate a bandit
- self.root_technique = AUCBanditMetaTechnique.generate_technique(manipulator)
- else:
- self.root_technique = copy.deepcopy(technique.get_root(self.args))
-
- if isinstance(self.root_technique, AUCBanditMetaTechnique):
- self.session.flush()
- info = BanditInfo(tuning_run=self.tuning_run,
- c=self.root_technique.bandit.C,
- window=self.root_technique.bandit.window,)
- self.session.add(info)
- for t in self.root_technique.techniques:
- subtechnique = BanditSubTechnique(bandit_info=info,
- name=t.name)
- self.session.add(subtechnique)
-
-
- self.objective.set_driver(self)
- self.pending_config_ids = set()
- self.best_result = None
- self.new_results = []
-
- for t in self.plugins:
- t.set_driver(self)
- self.root_technique.set_driver(self)
- self.seed_cfgs = list(extra_seeds)
- for cfg_filename in reversed(self.args.seed_configuration):
- if os.path.exists(cfg_filename):
- self.seed_cfgs.append(manipulator.load_from_file(cfg_filename))
- else:
- log.error('no such file for --seed-configuration %s', cfg_filename)
-
- self.plugins.sort(key=_.priority)
-
- def add_plugin(self, p):
- if p in self.plugins:
- return
- self.plugins.append(p)
- self.plugins.sort(key=_.priority)
- p.set_driver(self)
-
- def convergence_criteria(self):
- """returns true if the tuning process should stop"""
- if self.args.stop_after:
- elapsed = (datetime.now() - self.tuning_run.start_date)
- try:
- elapsed = elapsed.total_seconds()
- except: # python 2.6
- elapsed = elapsed.days * 86400 + elapsed.seconds
- if elapsed > self.args.stop_after:
- return True
- if self.test_count > self.args.test_limit:
- return True
- if self.extra_criteria:
- if self.extra_criteria(self.new_results):
- return True
- return False
-
- def register_result_callback(self, desired_result, callback):
- if desired_result.result is not None:
- callback(desired_result.result)
- else:
- self.pending_result_callbacks.append((desired_result, callback))
-
- def result_callbacks(self):
- pending = self.pending_result_callbacks
- self.pending_result_callbacks = list()
- for dr, callback in pending:
- if dr.result is not None:
- callback(dr.result)
- continue
- elif self.generation - dr.generation > self.args.pipelining:
- # see if we can find a result
- results = self.results_query(config=dr.configuration).all()
- log.warning("Result callback %d (requestor=%s) pending for "
- "%d generations %d results available",
- dr.id, dr.requestor, self.generation - dr.generation,
- len(results))
- if len(results):
- dr.result = results[0]
- callback(dr.result)
- continue
- # try again later
- self.pending_result_callbacks.append((dr, callback))
-
- def has_results(self, config):
- return self.results_query(config=config).count() > 0
-
- def run_generation_techniques(self):
- tests_this_generation = 0
- self.plugin_proxy.before_techniques()
- for z in xrange(self.args.parallelism):
-
- if self.seed_cfgs:
- config = self.get_configuration(self.seed_cfgs.pop())
- dr = DesiredResult(configuration=config,
- requestor='seed',
- generation=self.generation,
- request_date=datetime.now(),
- tuning_run=self.tuning_run)
- else:
- dr = self.root_technique.desired_result()
- if dr is None or dr is False:
- log.debug("no desired result, skipping to testing phase")
- break
- self.session.flush() # populate configuration_id
- duplicates = (self.session.query(DesiredResult)
- .filter_by(tuning_run=self.tuning_run,
- configuration_id=dr.configuration_id)
- .filter(DesiredResult.id != dr.id)
- .order_by(DesiredResult.request_date)
- .limit(1).all())
- self.session.add(dr)
- if len(duplicates):
- if not self.args.no_dups:
- log.warning("duplicate configuration request #%d %s/%s %s",
- self.test_count,
- dr.requestor,
- duplicates[0].requestor,
- 'OLD' if duplicates[0].result else 'PENDING')
- self.session.flush()
- desired_result_id = dr.id
-
- def callback(result):
- dr = self.session.query(DesiredResult).get(desired_result_id)
- dr.result = result
- dr.state = 'COMPLETE'
- dr.start_date = datetime.now()
-
- self.register_result_callback(duplicates[0], callback)
- else:
- log.debug("desired result id=%d, cfg=%d", dr.id, dr.configuration_id)
- dr.state = 'REQUESTED'
- self.test_count += 1
- tests_this_generation += 1
- self.plugin_proxy.after_techniques()
- return tests_this_generation
-
- def process_new_results(self):
- self.new_results = []
- for result in (self.results_query()
- .filter_by(was_new_best=None)
- .order_by(Result.collection_date)):
- self.plugin_proxy.on_result(result)
- self.new_results.append(result)
- if self.best_result is None:
- self.best_result = result
- result.was_new_best = True
- elif self.objective.lt(result, self.best_result):
- self.best_result = result
- result.was_new_best = True
- self.plugin_proxy.on_new_best_result(result)
- else:
- result.was_new_best = False
- self.result_callbacks()
-
- def run_generation_results(self, offset=0):
- self.commit()
- self.plugin_proxy.before_results_wait()
- self.wait_for_results(self.generation + offset)
- self.plugin_proxy.after_results_wait()
- self.process_new_results()
-
- @property
- def plugin_proxy(self):
- """
- forward any method calls on the returned object to all plugins
- """
- plugins = self.plugins
-
- class PluginProxy(object):
- def __getattr__(self, method_name):
- def plugin_method_proxy(*args, **kwargs):
- rv = []
- for plugin in plugins:
- rv.append(getattr(plugin, method_name)(*args, **kwargs))
- return filter(lambda x: x is not None, rv)
-
- return plugin_method_proxy
-
- return PluginProxy()
-
- def get_configuration(self, cfg):
- """called by SearchTechniques to create Configuration objects"""
- self.manipulator.normalize(cfg)
- hashv = self.manipulator.hash_config(cfg)
- config = Configuration.get(self.session,self.program, hashv, cfg)
- return config
-
- def main(self):
- self.plugin_proxy.set_driver(self)
- self.plugin_proxy.before_main()
-
- no_tests_generations = 0
-
- # prime pipeline with tests
- for z in xrange(self.args.pipelining):
- self.run_generation_techniques()
- self.generation += 1
-
- while not self.convergence_criteria():
- if self.run_generation_techniques() > 0:
- no_tests_generations = 0
- elif no_tests_generations <= self.args.bail_threshold:
- no_tests_generations += 1
- else:
- break
- self.run_generation_results(offset=-self.args.pipelining)
- self.generation += 1
-
- self.plugin_proxy.after_main()
-
- def external_main_begin(self):
- self.plugin_proxy.set_driver(self)
- self.plugin_proxy.before_main()
-
- def external_main_generation(self):
- if self.generation > 0:
- self.plugin_proxy.after_results_wait()
- self.process_new_results()
- self.run_generation_techniques()
- self.commit()
- self.plugin_proxy.before_results_wait()
-
- def external_main_end(self):
- self.plugin_proxy.after_main()
-
-
-
-
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/evolutionarytechniques.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/evolutionarytechniques.py
deleted file mode 100644
index e663ac1345cfbd0823df2231fc3e8040298059f9..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/evolutionarytechniques.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import abc
-import copy
-import random
-from technique import SearchTechnique
-from opentuner.search import technique
-
-class EvolutionaryTechnique(SearchTechnique):
- def __init__(self,
- mutation_rate = 0.1,
- crossover_rate = 0.0,
- must_mutate_count = 1,
- *pargs, **kwargs):
- super(EvolutionaryTechnique, self).__init__(*pargs, **kwargs)
- self.mutation_rate = mutation_rate
- self.crossover_rate = crossover_rate
- self.must_mutate_count = must_mutate_count
-
- @classmethod
- def get_hyper_parameters(cls):
- return ['mutation_rate', 'crossover_rate', 'must_mutate_count']
-
-
- def desired_configuration(self):
- """
- return a (cfg, priority) that we should test,
- through random mutation and crossover
- """
- #TODO: set limit value
-
- parents = self.selection()
- parents = map(copy.deepcopy, parents)
- parent_hashes = map(self.manipulator.hash_config, parents)
-
- if len(parents) > 1:
- cfg = self.crossover(parents)
- else:
- cfg = parents[0]
-
- for z in xrange(10): #retries
- self.mutation(cfg)
- if self.manipulator.hash_config(cfg) in parent_hashes:
- continue # try again
- return cfg
-
- def mutation(self, cfg):
- """
- mutate cfg in place
- """
- params = self.manipulator.parameters(cfg)
- random.shuffle(params)
- for param in params[:self.must_mutate_count]:
- self.mutate_param(cfg, param)
- for param in params[self.must_mutate_count:]:
- if random.random() < self.mutation_rate:
- self.mutate_param(cfg, param)
-
- def mutate_param(self, cfg, param):
- """
- mutate single parameter of cfg in place
- """
- param.op1_randomize(cfg)
-
- def crossover(self):
- raise Exception('Not implemented')
-
- def selection(self):
- """return a list of parent configurations to use"""
- if random.random() < self.crossover_rate:
- return [self.select(),
- self.select()]
- else:
- return [self.select()]
-
- @abc.abstractmethod
- def select(self):
- """return a single random parent configuration"""
- return None
-
-class GreedySelectionMixin(object):
- """
- EvolutionaryTechnique mixin for greedily selecting the best known
- configuration
- """
- def select(self):
- """return a single random parent configuration"""
- if (self.driver.best_result is not None and
- self.driver.best_result.state == 'OK'):
- return self.driver.best_result.configuration.data
- else:
- return self.manipulator.random()
-
-class NormalMutationMixin(object):
- """
- Mutate primitive parameters according to normal distribution
- """
-
- def __init__(self, sigma = 0.1, *pargs, **kwargs):
- super(NormalMutationMixin, self).__init__(*pargs, **kwargs)
- self.sigma = sigma
-
- def mutate_param(self, cfg, param):
- """
- mutate single parameter of cfg in place
- """
- if param.is_primitive():
- param.op1_normal_mutation(cfg, self.sigma)
- else:
- random.choice(param.manipulators(cfg))(cfg)
-
-
-class CrossoverMixin(object):
- def __init__(self, crossover, *pargs, **kwargs):
- super(CrossoverMixin, self).__init__(*pargs, **kwargs)
- self.crossover_op = crossover
- self.name = 'ga-'+crossover.replace("op3_cross_","")
-
- def crossover(self, cfgs):
- """
- Crossover the first permtation parameter, if found, of two parents and
- return one offspring cfg
- """
- cfg1, cfg2, = cfgs
- new = self.manipulator.copy(cfg1)
- params = self.manipulator.parameters(cfg1)
- for param in params:
- if param.is_permutation() and param.size>6:
- getattr(param, self.crossover_op)(new, cfg1, cfg2, d=param.size/3)
- return new
-
-
-class UniformGreedyMutation(GreedySelectionMixin, EvolutionaryTechnique):
- pass
-
-class NormalGreedyMutation(NormalMutationMixin, GreedySelectionMixin, EvolutionaryTechnique):
- pass
-
-class GA(CrossoverMixin, UniformGreedyMutation):
- pass
-
-technique.register(GA(crossover = 'op3_cross_OX3', mutation_rate=0.10, crossover_rate=0.8))
-technique.register(GA(crossover = 'op3_cross_OX1', mutation_rate=0.10,crossover_rate=0.8))
-technique.register(GA(crossover = 'op3_cross_PX', mutation_rate=0.10, crossover_rate=0.8))
-technique.register(GA(crossover = 'op3_cross_CX', mutation_rate=0.10, crossover_rate=0.8))
-technique.register(GA(crossover = 'op3_cross_PMX', mutation_rate=0.10, crossover_rate=0.8))
-technique.register(UniformGreedyMutation(name='ga-base', mutation_rate=0.10))
-
-technique.register(UniformGreedyMutation(name='UniformGreedyMutation05', mutation_rate=0.05))
-technique.register(UniformGreedyMutation(name='UniformGreedyMutation10', mutation_rate=0.10))
-technique.register(UniformGreedyMutation(name='UniformGreedyMutation20', mutation_rate=0.20))
-technique.register(NormalGreedyMutation(name='NormalGreedyMutation05', mutation_rate=0.05))
-technique.register(NormalGreedyMutation(name='NormalGreedyMutation10', mutation_rate=0.10))
-technique.register(NormalGreedyMutation(name='NormalGreedyMutation20', mutation_rate=0.20))
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/globalGA.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/globalGA.py
deleted file mode 100644
index e9b1f711746bbd42d0fb6e7ca3972d467c703e66..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/globalGA.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import abc
-import copy
-import random
-from technique import SearchTechnique
-from opentuner.search import technique
-
-class GlobalEvolutionaryTechnique(SearchTechnique):
- def __init__(self,
- mutation_rate = 0.1,
- crossover_rate = 0.0,
- must_mutate_count = 1,
- crossover_strength = 0.1,
- *pargs, **kwargs):
- super(GlobalEvolutionaryTechnique, self).__init__(*pargs, **kwargs)
- self.mutation_rate = mutation_rate
- self.crossover_rate = crossover_rate
- self.must_mutate_count = must_mutate_count
- self.crossover_strength = crossover_strength
-
- @classmethod
- def get_hyper_parameters(cls):
- return ['mutation_rate', 'crossover_rate', 'must_mutate_count', 'crossover_strength']
-
- def desired_configuration(self):
- """
- return a (cfg, priority) that we should test,
- through random mutation and crossover
- """
- #TODO: set limit value
-
- parents = self.selection()
- parents = map(copy.deepcopy, parents)
- parent_hashes = map(self.manipulator.hash_config, parents)
-
- if len(parents) > 1:
- cfg = self.crossover(parents)
- else:
- cfg = parents[0]
-
- for z in xrange(10): #retries
- self.mutation(cfg)
- if self.manipulator.hash_config(cfg) in parent_hashes:
- continue # try again
- return cfg
-
- def mutation(self, cfg):
- """
- mutate cfg in place
- """
- params = self.manipulator.parameters(cfg)
- random.shuffle(params)
- for param in params[:self.must_mutate_count]:
- self.mutate_param(cfg, param)
- for param in params[self.must_mutate_count:]:
- if random.random() < self.mutation_rate:
- self.mutate_param(cfg, param)
-
- def mutate_param(self, cfg, param):
- """
- mutate single parameter of cfg in place
- """
- param.op1_randomize(cfg)
-
- def crossover(self, cfgs):
- cfg1, cfg2, = cfgs
- new = self.manipulator.copy(cfg1)
- params = self.manipulator.parameters(cfg1)
- random.shuffle(params)
- d = int(self.crossover_strength*len(params))
- for param in params[:d]:
- param.set_value(new, param.get_value(cfg2))
- return new
-
- def selection(self):
- """return a list of parent configurations to use"""
- if random.random() < self.crossover_rate:
- return [self.select(),
- self.select()]
- else:
- return [self.select()]
-
- @abc.abstractmethod
- def select(self):
- """return a single random parent configuration"""
- return None
-
-class GreedySelectionMixin(object):
- """
- EvolutionaryTechnique mixin for greedily selecting the best known
- configuration
- """
- def select(self):
- """return a single random parent configuration"""
- if (self.driver.best_result is not None and
- self.driver.best_result.state == 'OK'):
- return self.driver.best_result.configuration.data
- else:
- return self.manipulator.random()
-
-class NormalMutationMixin(object):
- """
- Mutate primitive parameters according to normal distribution
- """
-
- def __init__(self, sigma = 0.1, *pargs, **kwargs):
- super(NormalMutationMixin, self).__init__(*pargs, **kwargs)
- self.sigma = sigma
-
- def mutate_param(self, cfg, param):
- """
- mutate single parameter of cfg in place
- """
- if param.is_primitive():
- param.op1_normal_mutation(cfg, self.sigma)
- else:
- random.choice(param.manipulators(cfg))(cfg)
-
-
-class UniformGreedyMutation(GreedySelectionMixin, GlobalEvolutionaryTechnique):
- pass
-
-class NormalGreedyMutation(NormalMutationMixin, GreedySelectionMixin, GlobalEvolutionaryTechnique):
- pass
-
-technique.register(NormalGreedyMutation( crossover_rate=0.5, crossover_strength=0.2, name='GGA'))
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/manipulator.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/manipulator.py
deleted file mode 100755
index decd476bf37ec2c12d2578b9b8266e5f8c705b12..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/manipulator.py
+++ /dev/null
@@ -1,1853 +0,0 @@
-# vim: tabstop=2 shiftwidth=2 softtabstop=2 expandtab autoindent smarttab
-import abc
-import collections
-import copy
-import hashlib
-import json
-import logging
-import math
-import os
-import pickle
-import random
-from fn import _
-import argparse
-from datetime import datetime
-import numpy
-import inspect
-import sys
-
-log = logging.getLogger(__name__)
-argparser = argparse.ArgumentParser(add_help=False)
-argparser.add_argument('--list-params', '-lp',
- help='list available parameter classes')
-
-
-class ConfigurationManipulatorBase(object):
- """
- abstract interface for objects used by search techniques to mutate
- configurations
- """
- __metaclass__ = abc.ABCMeta
-
- # List of file formats, which can be extended by subclasses. Used in
- # write_to_file() and load_from_file(). Objects in list must define
- # load(fd) and dump(cfg, fd).
- FILE_FORMATS = {'default': json, 'json': json,
- 'pickle': pickle, 'pk': pickle}
-
- def validate(self, config):
- """is the given config valid???"""
- return all(map(_.validate(config), self.parameters(config)))
-
- def normalize(self, config):
- """mutate config into canonical form"""
- for param in self.parameters(config):
- param.normalize(config)
-
- def set_search_driver(self, search_driver):
- """called exactly once during setup"""
- pass
-
- def copy(self, config):
- """produce copy of config"""
- return copy.deepcopy(config)
-
- def parameters_dict(self, config):
- """convert self.parameters() to a dictionary by name"""
- return dict([(p.name, p) for p in self.parameters(config)])
-
- def param_names(self, *args):
- """return union of parameter names in args"""
- return sorted(reduce(set.union,
- [set(map(_.name, self.parameters(cfg)))
- for cfg in args]))
-
- def linear_config(self, a, cfg_a, b, cfg_b, c, cfg_c):
- """return a configuration that is a linear combination of 3 other configs"""
- dst = self.copy(cfg_a)
- dst_params = self.proxy(dst)
- for k in self.param_names(dst, cfg_a, cfg_b, cfg_c):
- dst_params[k].op4_set_linear(cfg_a, cfg_b, cfg_c, a, b, c)
- return dst
-
- def _get_serializer(self, filename, format=None):
- """
- Extract the correct file format serializer from self.FILE_FORMATS.
- Guess the format by extension if one is not given.
- """
- if format is None:
- format = os.path.splitext(filename)[1].lower().replace('.', '')
- if format not in self.FILE_FORMATS:
- serializer = self.FILE_FORMATS['default']
- if len(self.FILE_FORMATS) > 1:
- log.warning('Unknown file format "%s", using "%s" instead', format,
- serializer.__name__)
- else:
- serializer = self.FILE_FORMATS[format]
- return serializer
-
- def save_to_file(self, cfg, filename, format=None):
- """
- Write cfg to filename. Guess the format by extension if one is not given.
- """
- with open(filename, 'a+') as fd:
- self._get_serializer(filename, format).dump(cfg, fd)
-
- def load_from_file(self, filename, format=None):
- """
- Read cfg from filename. Guess the format by extension if one is not given.
- """
- with open(filename, 'rb') as fd:
- return self._get_serializer(filename, format).load(fd)
-
- def proxy(self, cfg):
- return ManipulatorProxy(self, cfg)
-
- @abc.abstractmethod
- def random(self):
- """produce a random initial configuration"""
- return
-
- @abc.abstractmethod
- def parameters(self, config):
- """return a list of of Parameter objects"""
- return list()
-
- @abc.abstractmethod
- def hash_config(self, config):
- """produce unique hash value for the given config"""
- return
-
-
-class ConfigurationManipulator(ConfigurationManipulatorBase):
- """
- a configuration manipulator using a fixed set of parameters and storing
- configs in a dict-like object
- """
-
- def __init__(self, params=None, config_type=dict, seed_config=None, **kwargs):
- if params is None:
- params = []
- self.params = list(params)
- self.config_type = config_type
- self.search_driver = None
- self._seed_config = seed_config
- super(ConfigurationManipulator, self).__init__(**kwargs)
- for p in self.params:
- p.parent = self
-
- def add_parameter(self, p):
- p.set_parent(self)
- self.params.append(p)
-
- #TODO sub parameters should be recursed on
- # not currently an issue since no doubly-nested sub-parameters
- sub_params = p.sub_parameters()
- for sp in sub_params:
- sp.set_parent(p)
- self.params.extend(sub_params)
-
- def set_search_driver(self, search_driver):
- self.search_driver = search_driver
-
- def seed_config(self):
- """produce a fixed seed configuration"""
- if self._seed_config:
- cfg = copy.deepcopy(self._seed_config)
- else:
- cfg = self.config_type()
- for p in self.params:
- if not isinstance(p.name, str) or '/' not in p.name:
- cfg[p.name] = p.seed_value()
- return cfg
-
- def random(self):
- """produce a random configuration"""
- cfg = self.seed_config()
- for p in self.parameters(cfg):
- p.op1_randomize(cfg)
- return cfg
-
- def parameters(self, config):
- """return a list of Parameter objects"""
- if type(config) is not self.config_type:
- log.error("wrong type, expected %s got %s",
- str(self.config_type),
- str(type(config)))
- raise TypeError()
- return self.params
-
- def parameters_to_json(self):
- """
- output information about the parameters in this manipulator in json format:
- [ConfigurationManipulator,{pinfo:count,pinfo:count ...}]
- where pinfo has a similar form to describe the parameter's sub-parameters:
- [param_name,{pinfo:count,pinfo:count ...}]
- """
- def param_info_to_json(param, sub_parameters):
- """
- recursively output information about a parameter and its subparameters in a json format:
-
- [parameter_name, {subparam_info:count,subparam_info:count,...}]
- or if no subparams
- [parameter_name,{}]
-
- where subparam_info are sorted alphabetically. Note we can't directly use json since
- sets/dictionaries aren't always ordered by key
- """
- sub_parameter_counts = {}
- # build the string
- if isinstance(param, str):
- param_name = param
- else:
- param_name = param.__class__.__name__
- out = ['[', param_name, ',{']
-
- if len(sub_parameters) > 0:
- # count sub params
- for sp in sub_parameters:
- spout = param_info_to_json(sp, sp.sub_parameters())
- sub_parameter_counts[spout] = sub_parameter_counts.get(spout, 0) + 1
- # add the count map in sorted order
- for sp in sorted(sub_parameter_counts):
- out.append(sp)
- out.append(':')
- out.append(str(sub_parameter_counts[sp]))
- out.append(',')
- out.pop() # remove trailing comma
-
- out.append('}]')
- return ''.join(out)
-
- # filter out subparameters to avoid double counting
- params = [p for p in self.params if p.parent is self]
- return param_info_to_json(self, params)
-
- def hash_config(self, config):
- """produce unique hash value for the given config"""
- m = hashlib.sha256()
- params = list(self.parameters(config))
- params.sort(key=_.name)
- for i, p in enumerate(params):
- m.update(str(p.name))
- m.update(p.hash_value(config))
- m.update(str(i))
- m.update("|")
- return m.hexdigest()
-
- def search_space_size(self):
- """estimate the size of the search space, not precise"""
- return reduce(_ * _, [x.search_space_size() for x in self.params])
-
- def difference(self, cfg1, cfg2):
- cfg = self.copy(cfg1)
- for param in self.parameters(cfg1):
- if param.is_primitive(cfg1):
- # TODO: check range
- param.set_value(cfg, param.get_value(cfg1) - param.get_value(cfg2))
- else:
- pass
- return cfg
-
- def applySVs(self, cfg, sv_map, args, kwargs):
- """
- Apply operators to each parameter according to given map. Updates cfg.
- Parameters with no operators specified are not updated.
- cfg: configuration data
- sv_map: python dict that maps string parameter name to class method name
- arg_map: python dict that maps string parameter name to class method
- arguments
- """
- # TODO: check consistency between sv_map and cfg
- param_dict = self.parameters_dict(cfg)
- for pname in self.param_names(cfg):
- param = param_dict[pname]
- getattr(param, sv_map[pname])(cfg, *args[pname], **kwargs[pname])
-
-
-class Parameter(object):
- """
- abstract base class for parameters in a ConfigurationManipulator
- """
- __metaclass__ = abc.ABCMeta
-
- def __init__(self, name):
- self.name = name
- self.parent = None
- super(Parameter, self).__init__()
-
- def _to_storage_type(self, val):
- """hook to support transformation applied while stored"""
- return val
-
- def _from_storage_type(self, sval):
- """hook to support transformation applied while stored"""
- return sval
-
- def _read_node(self, config):
- """hook to support different storage structures"""
- node = config
- if not isinstance(self.name, str):
- return node, self.name
- name_parts = self.name.split('/')
- for part in name_parts[:-1]:
- if isinstance(node, list):
- part = int(part)
- node = node[part]
- part = name_parts[-1]
- if isinstance(node, list):
- part = int(part)
- return node, part
-
- def _get(self, config):
- """hook to support different storage structures"""
- node, part = self._read_node(config)
- return self._from_storage_type(node[part])
-
- def _set(self, config, v):
- """hook to support different storage structures"""
- node, part = self._read_node(config)
- node[part] = self._to_storage_type(v)
-
- def set_parent(self, manipulator):
- self.parent = manipulator
-
- def validate(self, config):
- """is the given config valid???"""
- return True
-
- def is_primitive(self, ignored=None):
- return isinstance(self, PrimitiveParameter)
-
- def is_permutation(self, ignored=None):
- return isinstance(self, PermutationParameter)
-
- def manipulators(self, config):
- """
- a list of manipulator functions to change this value in the config
- manipulators must be functions that take a config and change it in place
-
- default implementation just has op1_randomize as only operation
- """
- return [self.op1_randomize]
-
- def normalize(self, config):
- """
- mutate this parameter into a canonical form
- """
- pass
-
- def sub_parameters(self):
- """
- additional parameters added with this parameter
- """
- return []
-
- @abc.abstractmethod
- def op1_randomize(self, cfg):
- """
- Set this parameter's value in a configuration to a random value
-
- :param config: the configuration to be changed
- """
- pass
-
- @abc.abstractmethod
- def seed_value(self):
- """some legal value of this parameter (for creating initial configs)"""
- return
-
- @abc.abstractmethod
- def copy_value(self, src, dst):
- """copy the value of this parameter from src to dst config"""
- pass
-
- @abc.abstractmethod
- def same_value(self, cfg1, cfg2):
- """test if cfg1 and cfg2 have the same value of this parameter"""
- return
-
- @abc.abstractmethod
- def hash_value(self, config):
- """produce unique hash for this value in the config"""
- return
-
- @abc.abstractmethod
- def op4_set_linear(self, cfg, cfg_a, cfg_b, cfg_c, a, b, c):
- """
- Sets the parameter value in a configuration to a linear combination of 3
- other configurations: :math:`a*cfg_a + b*cfg_b + c*cfg_c`
-
- :param cfg: the configuration to be changed
- :param cfg_a: a parent configuration
- :param cfg_b: a parent configuration
- :param cfg_c: a parent configuration
- :param a: weight for cfg_a
- :param b: weight for cfg_b
- :param c: weight for cfg_c
- """
- pass
-
- def search_space_size(self):
- return 1
-
- def op1_nop(self, cfg):
- """
- The 'null' operator. Does nothing.
-
- :param cfg: the configuration to be changed
- """
- pass
-
- # Stochastic variators
- def op3_swarm(self, cfg, cfg1, cfg2, c, c1, c2, *args, **kwargs):
- """
- Stochastically 'move' the parameter value in a configuration towards those
- in two parent configurations. This is done by calling :py:meth:`opn_stochastic_mix`
-
- :param cfg: the configuration to be changed
- :param cfg1: a parent configuration
- :param cfg2: a parent configuration
- :param c: weight of original configuration
- :param c1: weight for cfg1
- :param c2: weight for cfg2
- """
- # default to probabilistic treatment
- self.opn_stochastic_mix(cfg, [cfg, cfg1, cfg2], [c, c1, c2])
-
- def opn_stochastic_mix(self, cfg, cfgs, ratio, *args, **kwargs):
- """
- Stochastically recombine a list of parent values into a single result.
-
- This randomly copies a value from a list of parents configurations according
- to a list of weights.
-
- :param cfg: the configuration to be changed
- :param cfgs: a list of parent configurations
- :param ratio: a list of floats representing the weight of each configuration
- in cfgs
-
- """
- assert len(cfgs) == len(ratio)
- r = random.random()
- c = numpy.array(ratio, dtype=float) / sum(ratio)
- for i in range(len(c)):
- if r < sum(c[:i + 1]):
- self.copy_value(cfg, cfgs[i])
- break
-
-
-class PrimitiveParameter(Parameter):
- """
- An abstract interface implemented by parameters that represent a single
- dimension in a cartesian space in a legal range
- """
- __metaclass__ = abc.ABCMeta
-
- def __init__(self, name, value_type=float, **kwargs):
- self.value_type = value_type
- super(PrimitiveParameter, self).__init__(name, **kwargs)
-
- def hash_value(self, config):
- """produce unique hash for this value in the config"""
- self.normalize(config)
- return hashlib.sha256(repr(self.get_value(config))).hexdigest()
-
- def copy_value(self, src, dst):
- """copy the value of this parameter from src to dst config"""
- self.set_value(dst, self.get_value(src))
-
- def same_value(self, cfg1, cfg2):
- """test if cfg1 and cfg2 have the same value of this parameter"""
- return self.get_value(cfg1) == self.get_value(cfg2)
-
- def is_integer_type(self):
- """true if self.value_type can only represent integers"""
- return self.value_type(0) == self.value_type(0.1)
-
- def get_unit_value(self, config):
- """get_value scaled such that range is between 0.0 and 1.0"""
- low, high = self.legal_range(config)
- if self.is_integer_type():
- # account for rounding
- low -= 0.4999
- high += 0.4999
- val = self.get_value(config)
- if low < high:
- return float(val - low) / float(high - low)
- else:
- if low > high:
- log.warning('invalid range for parameter %s, %s to %s',
- self.name, low, high)
- # only a single legal value!
- return 0.0
-
- def set_unit_value(self, config, unit_value):
- """set_value scaled such that range is between 0.0 and 1.0"""
- assert 0.0 <= unit_value <= 1.0
- low, high = self.legal_range(config)
- if self.is_integer_type():
- # account for rounding
- low -= 0.4999
- high += 0.4999
- if low < high:
- val = unit_value * float(high - low) + low
- if self.is_integer_type():
- val = round(val)
- val = max(low, min(val, high))
- self.set_value(config, self.value_type(val))
-
- def op1_normal_mutation(self, cfg, sigma=0.1, *args, **kwargs):
- """
- apply normally distributed noise to this parameter's value in a
- configuration
-
- :param cfg: The configuration to be changed
- :param sigma: the std. deviation of the normally distributed noise on a unit
- scale
- """
- v = self.get_unit_value(cfg)
- v += random.normalvariate(0.0, sigma)
- # handle boundary cases by reflecting off the edge
- if v < 0.0:
- v *= -1.0
- if v > 1.0:
- v = 1.0 - (v % 1)
- self.set_unit_value(cfg, v)
-
- def op4_set_linear(self, cfg, cfg_a, cfg_b, cfg_c, a, b, c):
- """
- set the parameter value in a configuration to a linear combination of 3
- other configurations: :math:`a*cfg_a + b*cfg_b + c*cfg_c`
-
- :param cfg: The configuration to be changed
- :param cfg_a: a parent configuration
- :param cfg_b: a parent configuration
- :param cfg_c: a parent configuration
- :param a: weight for cfg_a
- :param b: weight for cfg_b
- :param c: weight for cfg_c
- """
- va = self.get_unit_value(cfg_a)
- vb = self.get_unit_value(cfg_b)
- vc = self.get_unit_value(cfg_c)
- v = a * va + b * vb + c * vc
- v = max(0.0, min(v, 1.0))
-
- self.set_unit_value(cfg, v)
-
- def manipulators(self, config):
- """
- a list of manipulator functions to change this value in the config
- manipulators must be functions that take a config and change it in place
-
- for primitive params default implementation is uniform random and normal
- """
- return [self.op1_randomize, self.op1_normal_mutation]
-
- @abc.abstractmethod
- def set_value(self, config, value):
- """assign this value in the given configuration"""
- pass
-
- @abc.abstractmethod
- def get_value(self, config):
- """retrieve this value from the given configuration"""
- return 0
-
- @abc.abstractmethod
- def legal_range(self, config):
- """return the legal range for this parameter, inclusive"""
- return 0, 1
-
-
-class NumericParameter(PrimitiveParameter):
- """
- A parameter representing a number with a minimum and maximum value
- """
- def __init__(self, name, min_value, max_value, **kwargs):
- """min/max are inclusive"""
- assert min_value <= max_value
- super(NumericParameter, self).__init__(name, **kwargs)
- # after super call so self.value_type is initialized
- self.min_value = self.value_type(min_value)
- self.max_value = self.value_type(max_value)
-
- def seed_value(self):
- """some legal value of this parameter (for creating initial configs)"""
- return self.min_value
-
- def set_value(self, config, value):
- assert value >= self.min_value
- assert value <= self.max_value
- self._set(config, value)
-
- def get_value(self, config):
- return self._get(config)
-
- def legal_range(self, config):
- return self.min_value, self.max_value
-
- def op1_randomize(self, config):
- """
- Set this parameter's value in a configuration to a random value in its legal
- range
-
- :param config: the configuration to be changed
- """
- if self.is_integer_type():
- self.set_value(config, random.randint(*self.legal_range(config)))
- else:
- self.set_value(config, random.uniform(*self.legal_range(config)))
-
- def op1_scale(self, cfg, k):
- """
- Scale this parameter's value in a configuration by a constant factor
-
- :param cfg: the configuration to be changed
- :param k: the constant factor to scale the parameter value by
- """
- v = self.get_value(cfg) * k
- v = max(self.min_value, min(self.max_value, v))
- self.set_value(cfg, v)
-
- def op3_difference(self, cfg, cfg1, cfg2):
- """
- Set this parameter's value in a configuration to the difference between this
- parameter's values in 2 other configs (cfg2 - cfg1)
-
- :param cfg: the configuration to be changed
- :param cfg1: The configuration whose parameter value is being subtracted
- :param cfg2: The configuration whose parameter value is subtracted from
- """
- v = self.get_value(cfg2) - self.get_value(cfg1)
- v = max(self.min_value, min(self.max_value, v))
- self.set_value(cfg, v)
-
- def opn_sum(self, cfg, *cfgs):
- """
- Set this parameter's value in a configuration to the sum of it's values in a
- list of configurations
-
- :param cfg: the configuration to be changed
- :param cfgs: a list of configurations to sum
- """
- v = sum([self.get_value(c) for c in cfgs])
- v = max(self.min_value, min(self.max_value, v))
- self.set_value(cfg, v)
-
- def search_space_size(self):
- if self.value_type is float:
- return 2 ** 32
- else:
- return self.max_value - self.min_value + 1 # inclusive range
-
-
-class IntegerParameter(NumericParameter):
- """
- A parameter representing an integer value in a legal range
- """
- def __init__(self, name, min_value, max_value, **kwargs):
- """min/max are inclusive"""
- kwargs['value_type'] = int
- super(IntegerParameter, self).__init__(name, min_value, max_value, **kwargs)
-
- def op3_swarm(self, cfg, cfg1, cfg2, c=1, c1=0.5,
- c2=0.5, velocity=0, sigma=0.2, *args, **kwargs):
- """
- Simulates a single update step in particle swarm optimization by updating
- the current position and returning a new velocity.
-
- The new velocity is given by
-
- .. math:: c*velocity + r1*c1*(cfg1-cfg) + r2*c2*(cfg2-cfg)
-
- where r1 and r2 are random values between 0 and 1.
-
- The new current position is the new velocity with gaussian noise added.
-
- :param cfg: the configuration to be changed. Represents the current position
- :param cfg1: a configuration to shift towards. Should be the local best
- position
- :param cfg2: a configuration to shift towards. Should be the global best
- position
- :param c: the weight of the current velocity
- :param c1: weight of cfg1
- :param c2: weight of cfg2
- :param velocity: the old velocity
- :param sigma: standard deviation of the gaussian noise, on a unit-scale
- :return: the new velocity, a float
-
- """
- vmin, vmax = self.legal_range(cfg)
- k = vmax - vmin
- # calculate the new velocity
- v = velocity * c + (self.get_value(cfg1) - self.get_value(
- cfg)) * c1 * random.random() + (self.get_value(
- cfg2) - self.get_value(cfg)) * c2 * random.random()
- # Map velocity to continuous space with sigmoid
- s = k / (1 + numpy.exp(-v)) + vmin
- # Add Gaussian noise
- p = random.gauss(s, sigma * k)
- # Discretize and bound
- p = int(min(vmax, max(round(p), vmin)))
- self.set_value(cfg, p)
- return v
-
-
-class FloatParameter(NumericParameter):
- def __init__(self, name, min_value, max_value, **kwargs):
- """min/max are inclusive"""
- kwargs['value_type'] = float
- super(FloatParameter, self).__init__(name, min_value, max_value, **kwargs)
-
- def op3_swarm(self, cfg, cfg1, cfg2, c=1, c1=0.5,
- c2=0.5, velocity=0, *args, **kwargs):
- """
-
- Simulates a single update step in particle swarm optimization by updating
- the current position and returning a new velocity.
-
- The new velocity is given by
-
- .. math:: c*velocity + r1*c1*(cfg1-cfg) + r2*c2*(cfg2-cfg)
-
- where r1 and r2 are random values between 0 and 1
-
- The new current position is the old current position offset by the new
- velocity:
-
- :param cfg: the configuration to be changed. Represents the current position
- :param cfg1: a configuration to shift towards. Should be the local best
- position
- :param cfg2: a configuration to shift towards. Should be the global best
- position
- :param c: the weight of the current velocity
- :param c1: weight of cfg1
- :param c2: weight of cfg2
- :param velocity: the old velocity
- :return: the new velocity, a float
-
- """
- vmin, vmax = self.legal_range(cfg)
- v = velocity * c + (self.get_value(cfg1) - self.get_value(
- cfg)) * c1 * random.random() + (self.get_value(
- cfg2) - self.get_value(cfg)) * c2 * random.random()
- p = self.get_value(cfg) + v
- p = min(vmax, max(p, vmin))
- self.set_value(cfg, p)
- return v
-
-
-class ScaledNumericParameter(NumericParameter):
- """
- A Parameter that is stored in configurations normally, but has a scaled
- value when accessed using 'get_value'.
- Because search techniques interact with Parameters through get_value, these
- parameters are searched on a different scale (e.g. log scale).
- """
-
- @abc.abstractmethod
- def _scale(self, v):
- """
- called on a value when getting it from it's configuration. Transforms the
- actual value to the scale it is searched on
- """
- return v
-
- @abc.abstractmethod
- def _unscale(self, v):
- """
- called on a value when storing it. Transforms a value from it's search scale
- to it's actual value
- """
- return v
-
- def set_value(self, config, value):
- NumericParameter.set_value(self, config, self._unscale(value))
-
- def get_value(self, config):
- return self._scale(NumericParameter.get_value(self, config))
-
- def legal_range(self, config):
- return map(self._scale, NumericParameter.legal_range(self, config))
-
-
-class LogIntegerParameter(ScaledNumericParameter, FloatParameter):
- """
- an integer value that is searched on a log scale, but stored without scaling
- """
-
- def _scale(self, v):
- return math.log(v + 1.0 - self.min_value, 2.0)
-
- def _unscale(self, v):
- v = 2.0 ** v - 1.0 + self.min_value
- v = int(round(v))
- return v
-
- def legal_range(self, config):
- low, high = NumericParameter.legal_range(self, config)
- # increase the bounds account for rounding
- return self._scale(low - 0.4999), self._scale(high + 0.4999)
-
-
-class LogFloatParameter(ScaledNumericParameter, FloatParameter):
- """
- a float parameter that is searched on a log scale, but stored without scaling
- """
-
- def _scale(self, v):
- return math.log(v + 1.0 - self.min_value, 2.0)
-
- def _unscale(self, v):
- v = 2.0 ** v - 1.0 + self.min_value
- return v
-
-
-class PowerOfTwoParameter(ScaledNumericParameter, IntegerParameter):
- """
- An integer power of two, with a min and max value. Searched by the exponent
- """
-
- def __init__(self, name, min_value, max_value, **kwargs):
- kwargs['value_type'] = int
- assert min_value >= 1
- assert math.log(min_value, 2) % 1 == 0 # must be power of 2
- assert math.log(max_value, 2) % 1 == 0 # must be power of 2
- super(PowerOfTwoParameter, self).__init__(name, min_value, max_value,
- **kwargs)
-
- def _scale(self, v):
- return int(math.log(v, 2))
-
- def _unscale(self, v):
- return 2 ** int(v)
-
- def legal_range(self, config):
- return int(math.log(self.min_value, 2)), int(math.log(self.max_value, 2))
-
- def search_space_size(self):
- return int(math.log(super(PowerOfTwoParameter, self).search_space_size(), 2))
-
-
-##################
-
-class ComplexParameter(Parameter):
- """
- A non-cartesian parameter that can't be manipulated directly, but has a set
- of user defined manipulation functions
- """
-
- def copy_value(self, src, dst):
- """copy the value of this parameter from src to dst config"""
- self._set(dst, copy.deepcopy(self._get(src)))
-
- def same_value(self, cfg1, cfg2):
- """test if cfg1 and cfg2 have the same value of this parameter"""
- return self._get(cfg1) == self._get(cfg2)
-
- def hash_value(self, config):
- """produce unique hash for this value in the config"""
- self.normalize(config)
- return hashlib.sha256(repr(self._get(config))).hexdigest()
-
- def get_value(self, config):
- return self._get(config)
-
- def set_value(self, config, value):
- self._set(config, value)
-
- def op4_set_linear(self, cfg, cfg_a, cfg_b, cfg_c, a, b, c):
- """
- set this value to :math:`a*cfg_a + b*cfg_b + c*cfg_c`
-
- this operation is not possible in general with complex parameters but
- we make an attempt to "fake" it for common use cases
-
- basically a call to randomize unless after normalization,
- a = 1.0, b == -c, and cfg_b == cfg_c, in which case nothing is done
-
- :param cfg: the configuration to be changed
- :param cfg_a: a parent configuration
- :param cfg_b: a parent configuration
- :param cfg_c: a parent configuration
- :param a: weight for cfg_a
- :param b: weight for cfg_b
- :param c: weight for cfg_c
- """
- # attempt to normalize order, we prefer a==1.0
- if a != 1.0 and b == 1.0: # swap a and b
- a, cfg_a, b, cfg_b = b, cfg_b, a, cfg_a
- if a != 1.0 and c == 1.0: # swap a and c
- a, cfg_a, c, cfg_c = c, cfg_c, a, cfg_a
-
- # attempt to normalize order, we prefer b==-c
- if b < c: # swap b and c
- b, cfg_b, c, cfg_c = c, cfg_c, b, cfg_b
- if b != -c and a == -c: # swap a and c
- a, cfg_a, c, cfg_c = c, cfg_c, a, cfg_a
-
- if a == 1.0 and b == -c:
- self.copy_value(cfg_a, cfg)
- self.add_difference(cfg, b, cfg_b, cfg_c) # TODO inline this logic?
- else:
- # TODO: should handle more cases
- self.op1_randomize(cfg)
-
- def add_difference(self, cfg_dst, scale, cfg_b, cfg_c):
- """
- add the difference cfg_b-cfg_c to cfg_dst
-
- this is the key operation used in differential evolution
- and some simplex techniques
-
- this operation is not possible in general with complex parameters but
- we make an attempt to "fake" it
- """
- if not self.same_value(cfg_b, cfg_c):
- self.op1_randomize(cfg_dst)
-
- @abc.abstractmethod
- def op1_randomize(self, config):
- """
- randomize this value without taking into account the current position
- :param config: the configuration to be changed
- """
- pass
-
- @abc.abstractmethod
- def seed_value(self):
- """some legal value of this parameter (for creating initial configs)"""
- return
-
-
-class BooleanParameter(ComplexParameter):
- def manipulators(self, config):
- return [self.op1_flip]
-
- def get_value(self, config):
- return self._get(config)
-
- def set_value(self, config, value):
- self._set(config, value)
-
- def op1_randomize(self, config):
- """
- Set this parameter's value in a configuration randomly
-
- :param config: the configuration to be changed
- """
- self._set(config, self.seed_value())
-
- def seed_value(self):
- return random.choice((True, False))
-
- def op1_flip(self, config):
- """
- Flip this parameter's value in a configuration
-
- :param config: the configuration to be changed
- """
- self._set(config, not self._get(config))
-
- def search_space_size(self):
- return 2
-
- def op3_swarm(self, cfg, cfg1, cfg2, c=1, c1=0.5,
- c2=0.5, velocity=0, *args, **kwargs):
- """
- Simulates a single update step in particle swarm optimization by updating
- the current position and returning a new velocity.
-
- The new velocity is given by
-
- .. math:: c*velocity + r1*c1*(cfg1-cfg) + r2*c2*(cfg2-cfg)
-
- where r1 and r2 are random values between 0 and 1
-
- The new current position is randomly chosen based on the new velocity
-
- :param cfg: the configuration to be changed. Represents the current position
- :param cfg1: a configuration to shift towards. Should be the local best position
- :param cfg2: a configuration to shift towards. Should be the global best position
- :param c: the weight of the current velocity
- :param c1: weight of cfg1
- :param c2: weight of cfg2
- :param velocity: the old velocity
- :param args:
- :param kwargs:
- :return: the new velocity, a float
-
- """
- v = velocity * c + (self.get_value(cfg1) - self.get_value(
- cfg)) * c1 * random.random() + (self.get_value(
- cfg2) - self.get_value(cfg)) * c2 * random.random()
- # Map velocity to continuous space with sigmoid
- s = 1 / (1 + numpy.exp(-v))
- # Decide position randomly
- p = (s - random.random()) > 0
- self.set_value(cfg, p)
- return v
-
-
-class SwitchParameter(ComplexParameter):
- """
- A parameter representing an unordered collection of options with no implied
- correlation between the choices. The choices are range(option_count)
- """
-
- def __init__(self, name, option_count):
- self.option_count = option_count
- super(SwitchParameter, self).__init__(name)
-
- def op1_randomize(self, config):
- """
- Set this parameter's value in a configuration to a random value
-
- :param config: the configuration to be changed
- """
- self._set(config, random.randrange(self.option_count))
-
- def seed_value(self):
- return random.randrange(self.option_count)
-
- def search_space_size(self):
- return max(1, self.option_count)
-
-
-class EnumParameter(ComplexParameter):
- """
- same as a SwitchParameter but choices are taken from an arbitrarily typed list
- """
-
- def __init__(self, name, options):
- super(EnumParameter, self).__init__(name)
- self.options = list(options)
-
- def op1_randomize(self, config):
- """
- Set this parameter's value in a configuration to a random value
-
- :param config: the configuration to be changed
- """
- self._set(config, random.choice(self.options))
-
- def seed_value(self):
- return random.choice(self.options)
-
- def search_space_size(self):
- return max(1, len(self.options))
-
-
-class PermutationParameter(ComplexParameter):
- """
- A parameter representing a permutation (or ordering) as a list of items
- """
- def __init__(self, name, items):
- super(PermutationParameter, self).__init__(name)
- self._items = list(items)
- self.size = len(items)
-
- def op1_randomize(self, config):
- """
- Set this parameter's value in a configuration to a random value
-
- :param config: the configuration to be changed
- """
- random.shuffle(self._get(config))
- self.normalize(config)
-
- def op1_small_random_change(self, config, p=0.25):
- """
- Iterates through the list and probabilistically swaps each element with the
- next element
-
- :param p: probability of swapping an element with the next element
- :param config: the configuration to be changed
- """
- cfg_item = self._get(config)
- for i in xrange(1, len(cfg_item)):
- if random.random() < p:
- # swap
- cfg_item[i - 1], cfg_item[i] = cfg_item[i], cfg_item[i - 1]
- self.normalize(config)
-
- def seed_value(self):
- return list(self._items) # copy
-
- def manipulators(self, config):
- return [self.op1_randomize, self.op1_small_random_change]
-
- def get_value(self, config):
- return self._get(config)
-
- def set_value(self, config, value):
- self._set(config, value)
-
- def search_space_size(self):
- return math.factorial(max(1, len(self._items)))
-
- def op3_cross(self, cfg, cfg1, cfg2, xchoice='op3_cross_OX1', strength=0.3,
- *args, **kwargs):
- """
- Calls the crossover operator specified by xchoice
- Passes argument d = strength*(size of the permutation)
-
- :param cfg: the configuration to be changed
- :param cfg1: a parent configuration
- :param cfg2: a parent configuration
- :param xchoice: string specifying which crossover operator to use (should start with op3_cross prefix)
- :param strength: the strength of the crossover
- """
- dd = int(round(self.size * strength))
- if dd < 1:
- log.warning('Crossover length too small. Cannot create new solution.')
- if dd >= self.size:
- log.warning('Crossover length too big. Cannot create new solution.')
- getattr(self, xchoice)(cfg, cfg1, cfg2, d=dd, *args, **kwargs)
-
- def op3_swarm(self, cfg, cfg1, cfg2, xchoice='op3_cross_OX1', c=0.5,
- c1=0.5, c2=0.5, strength=0.3, velocity=0, *args, **kwargs):
- """
- Replacement for particle swarm optimization iterative step for permutations.
- Given a target cfg and 2 parent cfgs, probabilistically performs an
- op3_cross with one of the 2 parents.
-
- :param cfg: the configuration to be changed. Represents the current position
- :param cfg1: a configuration to shift towards. Should be the local best
- position
- :param cfg2: a configuration to shift towards. Should be the global best
- position
- :param xchoice: which crossover operator should be used
- :param c: the probability of not performing a crossover
- :param c1: the probability of performing a crossover with cfg1 (if a
- crossover is performed)
- :param c2: unused
- :param strength: the strength of the crossover
- :param velocity: the old velocity - unused
- """
- if random.uniform(0, 1) > c:
- if random.uniform(0, 1) < c1:
- # Select crossover operator
- self.op3_cross(cfg, cfg, cfg1, xchoice, strength)
- else:
- self.op3_cross(cfg, cfg, cfg2, xchoice, strength)
-
- # swap-based operators
- def op2_random_swap(self, cfg, cfg1, *args, **kwargs):
- """
- Swap a random pair of items in cfg1 and save the result into cfg
-
- :param cfg: the configuration to be changed
- :param cfg1: the configuration whose PermutationParameter's elements are
- swapped and copied into cfg
- """
- p = self.get_value(cfg1)[:]
- r = random.randint(0, len(p) - 1)
- s = random.randint(0, len(p) - 1)
- v1 = p[r]
- v2 = p[s]
- p[r] = v2
- p[s] = v1
- self.set_value(cfg, p)
-
- def op2_random_invert(self, cfg, cfg1, strength=0.3, *args, **kwargs):
- """
- Reverse the ordering of a random subsection of size d in cfg1 and save the
- result in cfg where d = strength*total-size
-
- :param cfg: the configuration to be changed
- :param cfg1: the configuration whose PermutationParameter is inverted
- :param strength: the size of the reversed subsection as a fraction of the
- total size
- """
- p = self.get_value(cfg1)[:]
- d = int(round(len(p) * strength))
- r = random.randint(0, len(p) - d)
- subpath = p[r:r + d][:]
- subpath.reverse()
- p[r:r + d] = subpath
- self.set_value(cfg, p)
-
- # Crossover operators
- def op3_cross_PX(self, cfg, cfg1, cfg2, d=0):
- """
- Partition crossover (Whitley 2009?)
-
- Chooses a random cut point and reorders elements in cfg1 up to the cut point
- according to their order in cfg2.
-
- Saves the result in cfg
-
- :param cfg: the configuration to be changed
- :param cfg1: the first parent configuration. The "base" configuration
- :param cfg2: the second parent configuration. Is "crossed into" cfg1
- :param d: unused
- """
- p1 = self.get_value(cfg1)
- p2 = self.get_value(cfg2)
- c1 = random.randint(2, len(p1))
- self.set_value(cfg, sorted(p1[:c1], key=lambda x: p2.index(x)) + p1[c1:])
-
- def op3_cross_PMX(self, cfg, cfg1, cfg2, d=0):
- """
- Partially-mapped crossover Goldberg & Lingle (1985)
-
- Replaces a random section of cfg1 with the corresponding section in cfg2.
- Displaced elements in cfg1 are moved to the old position of the elements
- displacing them
-
- :param cfg: the configuration to be changed
- :param cfg1: the first parent configuration. The "base" configuration
- :param cfg2: the second parent configuration. Is "crossed into" cfg1
- :param d: the size of the crossover
- """
- if d == 0:
- d = max(1, int(round(self.size * 0.3))) # default to 1/3 of permutation size
- p1 = self.get_value(cfg1)[:]
- p2 = self.get_value(cfg2)[:]
-
- r = random.randint(0, len(p1) - d)
-
- c1 = p1[r:r + d]
- c2 = p2[r:r + d]
-
- # get new permutation by crossing over a section of p2 onto p1
- pnew = self.get_value(cfg1)[:]
- pnew[r:r + d] = c2
- # fix conflicts by taking displaced elements in crossed over section
- # displaced = (elements x in c1 where x does not have corresponding value in c2)
- # and putting them where the value that displaced them was
-
- #candidates for displacement
- candidate_indices = set(range(r) + range(r+d, len(p1)))
- # Check through displaced elements to find values to swap conflicts to
- while c1 != []:
- n = c1[0]
- #try to match up a value in c1 to the equivalent value in c2
- while c2[0] in c1:
- if n == c2[0]:
- # already match up
- break
- # find position idx of c2[0] in c1
- link_idx = c1.index(c2[0])
- # get value of c2 at idx
- link = c2[link_idx]
- # remove c2[idx] and c1[idx] since they match up when we swap c2[0] with c2[idx] (this avoids an infinite loop)
- del c2[link_idx]
- del c1[link_idx]
- # swap new value into c2[0]
- c2[0] = link
-
- if n != c2[0]:
- # first check if we can swap in the crossed over section still
- if n in c2:
- c2[c2.index(n)] = c2[0]
- else:
- # assign first instance of c2[0] outside of the crossed over section in pnew to c1[0]
- for idx in candidate_indices:
- if pnew[idx] == c2[0]:
- pnew[idx] = c1[0]
- candidate_indices.remove(idx) # make sure we don't override this value now
- break
- # remove first elements
- del c1[0]
- del c2[0]
- self.set_value(cfg, pnew)
-
- def op3_cross_CX(self, cfg, cfg1, cfg2, d=0):
- """
- Implementation of a cyclic crossover.
-
- Repeatedly replaces elements of cfg1 with the element at the same index in
- cfg2. This is done until a cycle is reached and cfg1 is valid again. The
- initial replacement is random.
-
- Saves the result in cfg.
-
- :param cfg: the configuration to be changed
- :param cfg1: the first parent configuration. The "base" configuration
- :param cfg2: the second parent configuration. Is "crossed into" cfg1
- :param d: unused
- """
- p1 = self.get_value(cfg1)
- p2 = self.get_value(cfg2)
- p = p1[:]
-
- s = random.randint(0, len(p1) - 1)
- i = s
- indices = set()
-
- while len(indices) < len(p1): # should never exceed this
- indices.add(i)
- val = p1[i]
- i = p2.index(val)
- # deal with duplicate values
- while i in indices:
- if i == s:
- break
- i = p2[i+1:].index(val) + i + 1
- if i == s:
- break
-
- for j in indices:
- p[j] = p2[j]
-
- self.set_value(cfg, p)
-
- def op3_cross_OX1(self, cfg, cfg1, cfg2, d=0):
- """
- Ordered Crossover (Davis 1985)
-
- Exchanges a subpath from cfg2 into cfg1 while maintaining the order of the
- remaining elements in cfg1.
-
- Saves the result in cfg.
-
- :param cfg: the configuration to be changed
- :param cfg1: the first parent configuration. The "base" configuration
- :param cfg2: the second parent configuration. Is "crossed into" cfg1
- :param d: size of the exchanged subpath
- """
- if d == 0:
- d = max(1, int(round(self.size * 0.3))) # default to 1/3 of permutation size
- p1 = self.get_value(cfg1)
- p2 = self.get_value(cfg2)
- c1 = p1[:]
- c2 = p2[:]
- # Randomly find cut points
- r = random.randint(0, len(
- p1) - d) # Todo: treat path as circle i.e. allow cross-boundary cuts
- [c1.remove(i) for i in p2[r:int(r + d)]]
- self.set_value(cfg, c1[:r] + p2[r:r + d] + c1[r:])
-
- def op3_cross_OX3(self, cfg, cfg1, cfg2, d=0):
- """
- Ordered crossover variation 3 (Deep 2010)
-
- Same as op3_cross_OX1, except the parents have different cut points for
- their subpaths
-
- :param cfg: the configuration to be changed
- :param cfg1: the first parent configuration. The "base" configuration
- :param cfg2: the second parent configuration. Is "crossed into" cfg1
- :param d: size of the exchanged subpath
- """
- if d == 0:
- d = max(1, int(round(self.size * 0.3))) # default to 1/3 of permutation size
- p1 = self.get_value(cfg1)
- p2 = self.get_value(cfg2)
- c1 = p1[:]
- c2 = p2[:]
- # Randomly find cut points
- # Todo: treat path as circle i.e. allow cross-boundary cuts
- r1 = random.randint(0, len(p1) - d)
- r2 = random.randint(0, len(p1) - d)
- [c1.remove(i) for i in p2[r2:r2 + d]]
- self.set_value(cfg, c1[:r1] + p2[r2:r2 + d] + c1[r1:])
-
- def search_space_size(self):
- return math.factorial(max(1, len(self._items)))
-
-
-class ScheduleParameter(PermutationParameter):
- def __init__(self, name, items, deps):
- super(ScheduleParameter, self).__init__(name, items)
- self.deps = dict((k, set(v)) for k, v in deps.items())
- log.debug("ScheduleParameter(%s, %s, %s)", repr(name), repr(items),
- repr(deps))
- self._expand_deps()
-
- def _expand_deps(self):
- """expand self.deps to include recursive dependencies"""
- fixed_point = False
- while not fixed_point:
- fixed_point = True
- for k in self.deps.keys():
- oldlen = len(self.deps[k])
- for dep in list(self.deps[k]):
- if dep in self.deps:
- self.deps[k].update(self.deps[dep])
- if oldlen != len(self.deps[k]):
- fixed_point = False
-
- # verify schedule is valid
- items = set(self._items)
- for k, v in self.deps.items():
- if k in v:
- raise Exception("ScheduleParameter('%s') cycle: %s depends on itself" %
- (self.name, k))
-
- if v - items:
- raise Exception("ScheduleParameter('%s'): %s is unknown" %
- (self.name, v - items))
-
- if set(self.deps.keys()) - items:
- raise Exception("ScheduleParameter('%s'): %s is unknown" %
- (self.name, set(self.deps.keys()) - items))
-
- def is_topologically_sorted(self, values):
- used = set()
- for v in values:
- if v in self.deps and self.deps[v].union(used):
- return False
- used.add(v)
- return True
-
- def topologically_sorted_depth_first(self, values):
- """faster but not stable enough"""
- if self.is_topologically_sorted(values):
- return values
- sorted_values = []
- used = set()
- deps = dict((k, sorted(v, key=values.index, reverse=True))
- for k, v in self.deps.items())
-
- def visit(v):
- if v in used:
- return
- if v in deps:
- for dv in deps[v]:
- visit(dv)
- used.add(v)
- sorted_values.append(v)
-
- for v in reversed(values):
- visit(v)
- return list(reversed(sorted_values))
-
- def topologically_sorted(self, values):
- if self.is_topologically_sorted(values):
- return values
- deps = copy.deepcopy(self.deps)
- queue = collections.deque(reversed(values))
- sorted_values = []
- while queue:
- v = queue.popleft()
- if v in deps and deps[v]:
- queue.append(v)
- else:
- for k, d in deps.items():
- d.discard(v)
- if not d:
- del deps[k]
- sorted_values.append(v)
-
- return list(reversed(sorted_values))
-
- def normalize(self, cfg):
- self._set(cfg, self.topologically_sorted(self._get(cfg)))
-
-
-class SelectorParameter(ComplexParameter):
- def __init__(self, name, choices, max_cutoff,
- order_class=PermutationParameter,
- offset_class=LogIntegerParameter):
- super(SelectorParameter, self).__init__(name)
- self.choices = choices
- self.max_cutoff = max_cutoff
- self.order_param = order_class('{0}/order'.format(name), choices)
- self.offset_params = [
- offset_class('{0}/offsets/{1}'.format(name, i), 0, max_cutoff)
- for i in xrange(len(choices) - 1)]
-
- def sub_parameters(self):
- return [self.order_param] + self.offset_params
-
- def seed_value(self):
- return {'order': self.order_param.seed_value(),
- 'offsets': [co.seed_value() for co in self.offset_params]}
-
- def op1_randomize(self, config):
- random.choice(self.sub_parameters()).op1_randomize(config)
-
- def selector_iter(self, config):
- """
- yield (cutoff, choice) pairs
- cutoff will be None on the first value
- """
- order = config[self.name]['order']
- yield (None, order[0])
- cutoff = 0
- for n, offset in enumerate(config[self.name]['offsets']):
- if offset > 0:
- cutoff += offset
- yield cutoff, order[n + 1]
-
-
-class ParameterArray(ComplexParameter):
- """
- Represents an array of Parameters
- """
- def __init__(self, name, count, element_type, *args, **kwargs):
- super(ParameterArray, self).__init__(name)
- self.count = count
-
- self.sub_params = [
- element_type('{0}/{1}'.format(name, i), *args[i], **kwargs[i])
- for i in xrange(count)]
-
- def sub_parameters(self):
- return self.sub_params
-
- def seed_value(self):
- return [p.seed_value() for p in self.sub_params]
-
- def op1_randomize(self, config):
- """
- randomly selects a sub-parameter and randomizes it
-
- :param config: the configuration to be changed
- """
- random.choice(self.sub_parameters()).op1_randomize(config)
-
-
-class BooleanParameterArray(ParameterArray):
- """
- Represents an array of BooleanParameters - currently unimplimented
- """
- def __init__(self, name, count):
- super(BooleanParameterArray, self).__init__(name, count, BooleanParameter)
-
- def op3_swarm(self, cfg, cfg1, cfg2, *args, **kwargs):
- # TODO
- pass
-
- def op3_cross(self, cfg, cfg1, cfg2, *args, **kwargs):
- # TODO
- pass
-
-
-class IntegerParameterArray(ParameterArray):
- """
- Represents an array of IntegerParameters - currently unimplemented
- """
- def __init__(self, name, min_values, max_values):
- assert len(min_values) == len(max_values)
- super(IntegerParameterArray, self).__init__(name, len(min_values),
- IntegerParameter,
- min_value=min_values,
- max_value=max_values)
-
- def op3_swarm(self, cfg, cfg1, cfg2, *args, **kwargs):
- # TODO
- pass
-
- def op3_cross(self, cfg, cfg1, cfg2, *args, **kwargs):
- # TODO
- pass
-
-
-class Array(ComplexParameter):
- """
- An interface for parameters representing an array of values.
- """
- # TODO: constraints? (upper & lower bound etc)
- def __init__(self, name, size):
- super(Array, self).__init__(name)
- self.size = size
-
- def op3_cross(self, cfg, cfg1, cfg2, strength=0.3, *args, **kwargs):
- """
- Crosses two arrays by replacing a random subsection of cfg1 with the
- corresponding subsection of cfg2.The size of the chunk is a fixed fraction
- of the total length, given by the strength
-
- Behaves like a specialized 2-point crossover, where the first cut point is
- random and the second cut is a set distance after.
-
- :param cfg: the configuration to be changed
- :param cfg1: the configuration being inserted into
- :param cfg2: the configuration being inserted
- :param strength: the size of the crossover, as a fraction of total array
- length
- """
- d = int(round(self.size * strength))
- if d < 1:
- log.debug('Crossover length too small. Cannot create new solution.')
- if d >= self.size:
- log.debug('Crossover length too big. Cannot create new solution.')
- p1 = self.get_value(cfg1)
- p2 = self.get_value(cfg2)
- r = random.randint(0, len(
- p1) - d) # Todo: treat path as circle i.e. allow cross-boundary cuts
- p = numpy.concatenate([p1[:r], p2[r:r + d], p1[r + d:]])
- self.set_value(cfg, p)
-
- def op3_swarm(self, cfg, cfg1, cfg2, c=1, c1=0.5,
- c2=0.5, velocity=0, strength=0.3, *args, **kwargs):
- """
- Replacement for a particle swarm optimization iterative step for arrays.
- Given a target cfg and 2 parent cfgs, probabilistically performs an
- :py:meth:`op3_cross` with one of the 2 parents.
-
- :param cfg: the configuration to be changed. Represents the cfg position
- :param cfg1: a configuration to shift towards. Should be the local best
- position
- :param cfg2: a configuration to shift towards. Should be the global best
- position
- :param c: the probability of not performing a crossover
- :param c1: the probability of performing a crossover with cfg1 (if a
- crossover is performed)
- :param c2: unused
- :param velocity: the old velocity - unused
- :param strength: the strength of the crossover
- """
- if random.uniform(0, 1) > c:
- if random.uniform(0, 1) < c1:
- # Select crossover operator
- self.op3_cross(cfg, cfg, cfg1, strength)
- else:
- self.op3_cross(cfg, cfg, cfg2, strength)
-
- def get_value(self, config):
- return self._get(config)
-
- def set_value(self, config, value):
- self._set(config, value)
-
-
-class BooleanArray(Array):
- """
- Represents an array of boolean values which are either 0 or 1
- """
- def op3_swarm_parallel(self, cfg, cfg1, cfg2, c=1,
- c1=0.5, c2=0.5, velocities=0):
- """
- Simulates a single particle swarm optimization step for each element in the
- array by updating each position and returning an array of new velocities.
-
- The new velocities are given by
-
- .. math:: c*velocity + r1*c1*(cfg1-cfg) + r2*c2*(cfg2-cfg)
-
- where r1 and r2 are random values between 0 and 1. In each iteration, r1 and
- r2 are constant across array elements
-
- The new cfg positions are randomly chosen based on the new velocities
-
- :param cfg: the configuration to be changed. This represents the current
- position
- :param cfg1: a configuration to shift towards. Should be the local best
- position
- :param cfg2: a configuration to shift towards. Should be the global best
- position
- :param c: the weight of the current velocities
- :param c1: weight of cfg1
- :param c2: weight of cfg2
- :param velocities: the current velocities
- :return: a numpy array of new velocities
- """
- vs = velocities * c + (self.get_value(cfg1) - self.get_value(
- cfg)) * c1 * random.random() + (self.get_value(
- cfg2) - self.get_value(cfg)) * c2 * random.random()
- # Map velocity to continuous space with sigmoid
- ss = 1 / (1 + numpy.exp(-vs))
- # Decide position randomly
- ps = (ss - numpy.random.rand(1, self.size)) > 0
- self.set_value(cfg, ps)
- return vs
-
- def op1_randomize(self, config):
- """
- Set this parameter's value in a configuration randomly
-
- :param config: the configuration to be changed
- """
- value = numpy.random.rand(1, self.size) > 0.5
- self._set(config, value)
-
- def seed_value(self):
- return numpy.random.rand(1, self.size) > 0.5
-
-
-class FloatArray(Array):
- """
- Represents an array of float values
- """
- def __init__(self, name, size, fmax, fmin):
- super(FloatArray, self).__init__(name, size)
- self.fmax = fmax
- self.fmin = fmin
-
- def op1_randomize(self, config):
- """
- Set this parameter's value in a configuration randomly
-
- :param config: the configuration to be changed
- """
- value = numpy.random.rand(1, self.size) * (
- self.fmax - self.fmin) + self.fmin
- self._set(config, value)
-
- def seed_value(self):
- value = numpy.random.rand(1, self.size) * (
- self.fmax - self.fmin) + self.fmin
- return value
-
- def op3_swarm_parallel(self, cfg, cfg1, cfg2, c=1,
- c1=0.5, c2=0.5, velocities=0):
- """
- Simulates a single particle swarm optimization step for each element in the
- array by updating the each position and returning an array of new velocities
-
- The new velocity is given by
-
- .. math:: c*velocity + r1*c1*(cfg1-cfg) + r2*c2*(cfg2-cfg)
-
- where r1 and r2 are random values between 0 and 1. In each iteration, r1 and
- r2 are constant across array elements
-
- The new cfg positions are randomly chosen based on the new velocities
-
- :param cfg: the configuration to be changed. This represents the current
- position
- :param cfg1: a configuration to shift towards. Should be the local best
- position
- :param cfg2: a configuration to shift towards. Should be the global best
- position
- :param c: the weight of the cfg velocities
- :param c1: weight of cfg1
- :param c2: weight of cfg2
- :param velocities: the cfg velocities
- :return: a numpy array of new velocities
- """
- vs = velocities * c + (self.get_value(cfg1) - self.get_value(
- cfg)) * c1 * random.random() + (self.get_value(
- cfg2) - self.get_value(cfg)) * c2 * random.random()
- p = self.get_value(cfg) + vs
- p[p > self.fmax] = self.fmax
- p[p < self.fmin] = self.fmin
- self.set_value(cfg, p)
- return vs
-
-
-##################
-
-class ManipulatorProxy(object):
- """
- wrapper around configuration manipulator and config pair
- """
-
- def __init__(self, manipulator, cfg):
- self.cfg = cfg
- self.manipulator = manipulator
- self.params = manipulator.parameters_dict(self.cfg)
-
- def keys(self):
- return self.params.keys()
-
- def __getitem__(self, k):
- return ParameterProxy(self.params[k], self.cfg)
-
-
-class ParameterProxy(object):
- """
- wrapper aint parameter and config pair, adds config
- as first argument to all method calls to parameter
- """
-
- def __init__(self, param, cfg):
- self.cfg = cfg
- self.param = param
-
- def __getattr__(self, key):
- """equivalent of self.param.key(self.cfg, ...)"""
- member = getattr(self.param, key)
-
- def param_method_proxy(*args, **kwargs):
- return member(self.cfg, *args, **kwargs)
-
- if callable(member):
- return param_method_proxy
- else:
- # we should only hit this for key == 'name'
- return member
-
-
-# Inspection Methods
-def operators(param, num_parents):
- """
- Return a list of operators for the given parameter that take the specified
- number of input configurations
-
- :param param: a Parameter class
- :param num_parents: a String specifying number of inputs required by the operator.
- should be one of '1', '2', '3', '4', or 'n'
- """
- ops = []
- methods = inspect.getmembers(param, inspect.ismethod)
- for m in methods:
- name, obj = m
- if is_operator(name, num_parents):
- ops.append(name)
- return ops
-
-def composable_operators(param, min_num_parents):
- """
- Return a list of operators for the given parameter that can be programatically composed
- with a composable technique generating min_num_parents.
-
- Programatically composable operators have no non-cfg arguments
-
- :param param: a Parameter class
- :param min_num_parents: the minimum number of parents passed to the operator
- """
- if min_num_parents < 1:
- return []
-
- allowed_num_parents = ['n']
- for i in range(1,5):
- if i > min_num_parents:
- break
- allowed_num_parents.append(str(i))
-
- ops = []
- methods = inspect.getmembers(param, inspect.ismethod)
- for m in methods:
- name, obj = m
- argspec = inspect.getargspec(obj)
- numargs = len(argspec.args) - (len(argspec.defaults) if argspec.defaults else 0)
- for num_parents in allowed_num_parents:
- if is_operator(name, num_parents):
- if num_parents == 'n':
- if numargs == 3: # self, cfg, cfgs
- ops.append(name)
- else:
- if numargs == (1 + int(num_parents)):
- ops.append(name)
- break
- return ops
-
-
-def is_operator(name, num_parents):
- """
- Tells whether a method is an operator taking in the specified number of inputs
- from the method name
-
- :param name: the method name
- :param num_parents: a String specifying number of inputs required by the operator.
- should be one of '1', '2', '3', '4', or 'n'
- """
- return ('op' + num_parents + '_') == name[:4]
-
-def all_operators():
- """
- Return a dictionary mapping from parameter names to lists of operator function
- names
- """
- ops = {}
- for p in all_params():
- name, obj = p
- all_ops = []
- for num in ['1', '2', '3', '4', 'n']:
- all_ops += operators(obj, num)
- ops[name] = all_ops
- return ops
-
-def all_params():
- params = inspect.getmembers(sys.modules[__name__], lambda x: inspect.isclass(
- x) and x.__module__ == __name__ and issubclass(x, Parameter))
- return params
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/metatechniques.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/metatechniques.py
deleted file mode 100644
index 2e33e7961ab2d7f9b16ea48cb680dd751af32d7a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/metatechniques.py
+++ /dev/null
@@ -1,186 +0,0 @@
-import abc
-import logging
-from collections import deque, defaultdict
-from fn import _
-
-from .technique import SearchTechniqueBase
-
-log = logging.getLogger(__name__)
-
-class MetaSearchTechnique(SearchTechniqueBase):
- """
- a technique made up of a collection of other techniques
- """
- def __init__(self, techniques, log_freq = 500, *pargs, **kwargs):
- super(MetaSearchTechnique, self).__init__(*pargs, **kwargs)
- self.techniques = techniques
- self.request_count = 0
- self.log_freq = log_freq
- self.logging_use_counters = defaultdict(int)
- self.unique_names()
-
- def unique_names(self):
- names = set()
- for t in self.techniques:
- while t.name in names:
- t.name += '~'
- t.name = intern(t.name)
- names.add(t.name)
-
- def set_driver(self, driver):
- super(MetaSearchTechnique, self).set_driver(driver)
- for t in self.techniques:
- t.set_driver(driver)
- self.driver = driver
-
- def desired_result(self):
- techniques = self.select_technique_order()
- for technique in techniques:
- dr = technique.desired_result()
- if dr is not None:
- if dr is False:
- # technique is waiting for results
- continue
- self.driver.register_result_callback(dr,
- lambda result: self.on_technique_result(technique, result))
- if self.log_freq:
- self.logging_use_counters[technique.name] += 1
- self.debug_log()
- self.request_count += 1
- return dr
- else:
- self.on_technique_no_desired_result(technique)
- return None
-
- def on_technique_no_desired_result(self, technique):
- """called if a sub-technique returns None"""
- pass
-
- def on_technique_result(self, technique, result):
- """callback for results of sub-techniques"""
- pass
-
- @abc.abstractmethod
- def select_technique_order(self):
- """select the order of next techniques to try"""
- return []
-
- def debug_log(self):
- if self.log_freq and sum(self.logging_use_counters.values())>self.log_freq:
- log.info("%s: %s", self.name,
- str(sorted(self.logging_use_counters.items(), key = _[1]*-1)))
- self.logging_use_counters = defaultdict(int)
-
-class RoundRobinMetaSearchTechnique(MetaSearchTechnique):
- """evenly switch between all source techniques"""
- def __init__(self, techniques, **kwargs):
- techniques = deque(techniques)
- super(RoundRobinMetaSearchTechnique, self).__init__(techniques, **kwargs)
-
- def select_technique_order(self):
- rv = list(self.techniques)
- self.techniques.rotate(1)
- return rv
-
-class RecyclingMetaTechnique(MetaSearchTechnique):
- """
- periodically restart techniques that are not performing well compared to
- global best
- """
- def __init__(self,
- techniques_generators,
- window = 100,
- factor = 5.0,
- **kwargs):
- if 'log_freq' not in kwargs:
- kwargs['log_freq'] = None
- techniques = deque((g(seed_cfg = None) for g in techniques_generators))
- self.rename_i = 0
- for t in techniques:
- self.rename_technique(t)
- super(RecyclingMetaTechnique, self).__init__(techniques, **kwargs)
- self.best_results = defaultdict(lambda: None)
- self.factor = factor
- self.last_check = 0
- self.old_best_results = defaultdict(lambda: None)
- self.technique_generators = deque(techniques_generators)
- self.window = window
-
- def rename_technique(self, technique):
- technique.name += ".R%d" % self.rename_i
- self.rename_i += 1
-
- def on_technique_result(self, technique, result):
- """callback for results of sub-techniques"""
- if (self.best_results[technique] is None or
- self.driver.objective.lt(result, self.best_results[technique])):
- self.best_results[technique] = result
-
- def technique_cmp(self, a, b):
- # a1 = self.old_best_results[a]
- # a2 = self.best_results[a]
- # b1 = self.old_best_results[b]
- # b2 = self.best_results[b]
- # if a1 is None and b1 is None:
- # return 0
- # if a1 is None:
- # return -1
- # if b1 is None:
- # return 1
- # return self.driver.objective.project_compare(a1, a2, b1, b2, self.factor)
-
- # not ready techniques go to the back
- if not a.is_ready() or not b.is_ready():
- return cmp(b.is_ready(), a.is_ready())
-
- a = self.best_results[a]
- b = self.best_results[b]
- if a is None and b is None:
- return 0
- if a is None:
- return -1
- if b is None:
- return 1
- return self.driver.objective.compare(a, b)
-
- def recycle_techniques(self):
- techniques = list(self.techniques)
- techniques.sort(cmp=self.technique_cmp)
- worst = techniques[-1]
-
- if (not worst.is_ready()
- or (self.old_best_results[worst] is not None
- and self.driver.objective.lt(self.driver.best_result,
- self.best_results[worst]))):
- techniques_new = deque()
- tn = None
- for t, gen in zip(self.techniques, self.technique_generators):
- if t is worst:
- tn = gen(seed_cfg=self.driver.best_result.configuration.data)
- self.rename_technique(tn)
- tn.set_driver(self.driver)
- log.info("%s replacing %s with %s", self.name, t.name, tn.name)
- techniques_new.append(tn)
- else:
- techniques_new.append(t)
- self.techniques = techniques_new
- else:
- log.debug("%s: not replacing techniques", self.name)
-
- self.old_best_results = self.best_results
- self.best_results = defaultdict(lambda: None)
- for t in self.techniques:
- self.best_results[t] = self.old_best_results[t]
-
- def select_technique_order(self):
- """
- round robin between techniques
- """
- if self.last_check + self.window < self.request_count:
- self.last_check = self.request_count
- self.recycle_techniques()
- rv = list(self.techniques)
- self.techniques.rotate(1)
- self.technique_generators.rotate(1)
- return rv
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/objective.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/objective.py
deleted file mode 100644
index b46a2f54b2f0922f774548c1c2d009ffa581512e..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/objective.py
+++ /dev/null
@@ -1,338 +0,0 @@
-import abc
-import logging
-
-from fn import _
-
-import opentuner
-from opentuner.resultsdb.models import *
-
-log = logging.getLogger(__name__)
-
-
-class SearchObjective(object):
- """
- delegates the comparison of results and configurations
- """
- __metaclass__ = abc.ABCMeta
-
- @abc.abstractmethod
- def result_order_by_terms(self):
- """return database columns required to order by the objective"""
- return []
-
- @abc.abstractmethod
- def result_compare(self, result1, result2):
- """cmp() compatible comparison of resultsdb.models.Result"""
- return
-
- def config_compare(self, config1, config2):
- """cmp() compatible comparison of resultsdb.models.Configuration"""
- return self.result_compare(self.driver.results_query(config=config1).one(),
- self.driver.results_query(config=config2).one())
-
- @abc.abstractmethod
- def result_relative(self, result1, result2):
- """return None, or a relative goodness of resultsdb.models.Result"""
- return
-
- def config_relative(self, config1, config2):
- """return None, or a relative goodness of resultsdb.models.Configuration"""
- return self.result_relative(self.driver.results_query(config=config1).one(),
- self.driver.results_query(config=config2).one())
-
-
- def __init__(self):
- self.driver = None
-
- def set_driver(self, driver):
- self.driver = driver
-
- def result_order_by(self, q):
- return q.order_by(*self.result_order_by_terms())
-
- def compare(self, a, b):
- """cmp() compatible compare"""
- if isinstance(a, Configuration):
- return self.config_compare(a, b)
- if isinstance(a, Result):
- return self.result_compare(a, b)
- assert False
-
- def relative(self, a, b):
- if isinstance(a, Configuration):
- return self.config_relative(a, b)
- if isinstance(a, Result):
- return self.result_relative(a, b)
- assert None
-
- def lt(self, a, b):
- return self.compare(a, b) < 0
-
- def lte(self, a, b):
- return self.compare(a, b) <= 0
-
- def gt(self, a, b):
- return self.compare(a, b) > 0
-
- def gte(self, a, b):
- return self.compare(a, b) >= 0
-
- def min(self, *l):
- if len(l) == 1:
- l = l[0]
- rv = l[0]
- for i in l[1:]:
- if self.lt(i, rv):
- rv = i
- return rv
-
- def max(self, *l):
- if len(l) == 1:
- l = l[0]
- rv = l[0]
- for i in l[1:]:
- if self.gt(i, rv):
- rv = i
- return rv
-
- def limit_from_config(self, config):
- """
- a time limit to kill a result after such that it can be compared to config
- """
- results = self.driver.results_query(config=config)
- if results.count() == 0:
- return None
- else:
- return max(map(_.time, self.driver.results_query(config=config)))
-
-
- def project_compare(self, a1, a2, b1, b2, factor=1.0):
- """
- linearly project both a and b forward to see how they will compare in the
- future
- """
- a3 = Result()
- b3 = Result()
- a3.time = _project(a1.time, a2.time, factor)
- a3.accuracy = _project(a1.accuracy, a2.accuracy, factor)
- a3.energy = _project(a1.energy, a2.energy, factor)
- a3.confidence = _project(a1.confidence, a2.confidence, factor)
- return self.result_compare(a3, b3)
-
- def display(self, result):
- """
- produce a string version of a resultsdb.models.Result()
- """
- rv = []
- for k in ('time', 'accuracy', 'energy', 'size', 'confidence'):
- v = getattr(result, k)
- if v is not None:
- rv.append('%s=%.4f' % (k, float(v)))
- return ', '.join(rv)
-
- def filter_acceptable(self, query):
- """Return a Result() query that only returns acceptable results"""
- return query
-
- def is_acceptable(self, result):
- """Test if a Result() meets thresholds"""
- return True
-
- def stats_quality_score(self, result, worst_result, best_result):
- """return a score for statistics"""
- if not self.is_acceptable(result):
- return worst_result.time
- else:
- return result.time
-
-
-def _project(a1, a2, factor):
- if a1 is None or a2 is None:
- return None
- return a2 + factor * (a2 - a1)
-
-
-class MinimizeTime(SearchObjective):
- """
- minimize Result().time
- """
-
- def result_order_by_terms(self):
- """return database columns required to order by the objective"""
- return [Result.time]
-
- def result_compare(self, result1, result2):
- """cmp() compatible comparison of resultsdb.models.Result"""
- return cmp(result1.time, result2.time)
-
- def config_compare(self, config1, config2):
- """cmp() compatible comparison of resultsdb.models.Configuration"""
- return cmp(min(map(_.time, self.driver.results_query(config=config1))),
- min(map(_.time, self.driver.results_query(config=config2))))
-
- def result_relative(self, result1, result2):
- """return None, or a relative goodness of resultsdb.models.Result"""
- if result2.time == 0:
- return float('inf') * result1.time
- return result1.time / result2.time
-
-class MinimizeSize(SearchObjective):
-
- def result_order_by_terms(self):
- """return database columns required to order by the objective"""
- return [Result.size]
-
- def result_compare(self, result1, result2):
- """cmp() compatible comparison of resultsdb.models.Result"""
- return cmp(result1.size, result2.size)
-
- def result_relative(self, result1, result2):
- """return None, or a relative goodness of resultsdb.models.Result"""
- if result2.size == 0:
- return float('inf') * result1.size
- return result1.size / result2.size
-
-
-class MinimizeSizeMinimizeTime(SearchObjective):
- """
- minimize Result.size() and Result.time()
- """
- def result_order_by_terms(self):
- """return database columns required to order by the objective"""
- return [Result.time, Result.size]
-
-
- def result_compare(self, result1, result2):
- """cmp() compatible comparison of resultsdb.models.Result"""
- return cmp((result1.time, result1.size),(result2.time,result2.size))
-
- def result_relative(self, result1, result2):
- """return None, or a relative goodness of resultsdb.models.Result"""
- log.warning('result_relative() not yet implemented for %s',
- self.__class__.__name__)
-
-class MaximizeAccuracy(SearchObjective):
- """
- maximize Result().accuracy
- """
-
- def result_order_by_terms(self):
- """return database columns required to order by the objective"""
- return [-Result.accuracy]
-
- def result_compare(self, result1, result2):
- """cmp() compatible comparison of resultsdb.models.Result"""
- # note opposite order
- return cmp(result2.accuracy, result1.accuracy)
-
- def result_relative(self, result1, result2):
- """return None, or a relative goodness of resultsdb.models.Result"""
- # note opposite order
- if result1.accuracy == 0:
- return float('inf') * result2.accuracy
- return result2.accuracy / result1.accuracy
-
- def stats_quality_score(self, result, worst_result, best_result):
- """return a score for statistics"""
- if not self.is_acceptable(result):
- return worst_result.time
- else:
- return result.time
-
- def stats_raw_score(self, result):
- return result.accuracy
-
-
-class MaximizeAccuracyMinimizeSize(MaximizeAccuracy):
- """
- maximize Result().accuracy, break ties with Result().size
- """
-
- def result_order_by_terms(self):
- """return database columns required to order by the objective"""
- return [-Result.accuracy, Result.size]
-
- def result_compare(self, result1, result2):
- """cmp() compatible comparison of resultsdb.models.Result"""
- return cmp((-result1.accuracy, result1.size),
- (-result2.accuracy, result2.size))
-
- def display(self, result):
- """
- produce a string version of a resultsdb.models.Result()
- """
- return "accuracy=%.8f, size=%.1f" % (result.accuracy, result.size)
-
- def result_relative(self, result1, result2):
- """return None, or a relative goodness of resultsdb.models.Result"""
- # unimplemented for now
- log.warning('result_relative() not yet implemented for %s',
- self.__class__.__name__)
- return None
-
-
-class ThresholdAccuracyMinimizeTime(SearchObjective):
- """
- if accuracy >= target:
- minimize time
- else:
- maximize accuracy
- """
-
- def __init__(self, accuracy_target, low_accuracy_limit_multiplier=10.0):
- self.accuracy_target = accuracy_target
- self.low_accuracy_limit_multiplier = low_accuracy_limit_multiplier
- super(ThresholdAccuracyMinimizeTime, self).__init__()
-
- def result_order_by_terms(self):
- """return database columns required to order by the objective"""
-
- return ["min(accuracy, %f) desc" % self.accuracy_target,
- opentuner.resultsdb.models.Result.time]
-
- def result_compare(self, result1, result2):
- """cmp() compatible comparison of resultsdb.models.Result"""
- return cmp((-min(self.accuracy_target, result1.accuracy),
- result1.time),
- (-min(self.accuracy_target, result2.accuracy), result2.time))
-
- def config_compare(self, config1, config2):
- """cmp() compatible comparison of resultsdb.models.Configuration"""
- return self.result_compare(
- self.driver.results_query(config=config1, objective_ordered=True)[0],
- self.driver.results_query(config=config2, objective_ordered=True)[0])
-
- def limit_from_config(self, config):
- """
- a time limit to kill a result after such that it can be compared to config
- """
- results = self.driver.results_query(config=config)
- if results.count() == 0:
- return None
- if self.accuracy_target > min(map(_.accuracy, results)):
- m = self.low_accuracy_limit_multiplier
- else:
- m = 1.0
- return m * max(map(_.time, results))
-
-
- def filter_acceptable(self, query):
- """Return a Result() query that only returns acceptable results"""
- return query.filter(opentuner.resultsdb.models.Result.accuracy
- >= self.accuracy_target)
-
- def is_acceptable(self, result):
- """Test if a Result() meets thresholds"""
- return result.accuracy >= self.accuracy_target
-
- def result_relative(self, result1, result2):
- """return None, or a relative goodness of resultsdb.models.Result"""
- # unimplemented for now
- log.warning('result_relative() not yet implemented for %s',
- self.__class__.__name__)
- return None
-
-
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/patternsearch.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/patternsearch.py
deleted file mode 100644
index 7b526e7897f2c673552899ae3a115d6e2e06737b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/patternsearch.py
+++ /dev/null
@@ -1,72 +0,0 @@
-
-
-from opentuner.search import technique
-
-class PatternSearch(technique.SequentialSearchTechnique):
- def main_generator(self):
-
- objective = self.objective
- driver = self.driver
- manipulator = self.manipulator
-
- # start at a random position
- center = driver.get_configuration(manipulator.random())
- self.yield_nonblocking(center)
-
- # initial step size is arbitrary
- step_size = 0.1
-
- while True:
- points = list()
- for param in manipulator.parameters(center.data):
- if param.is_primitive():
- # get current value of param, scaled to be in range [0.0, 1.0]
- unit_value = param.get_unit_value(center.data)
-
- if unit_value > 0.0:
- # produce new config with param set step_size lower
- down_cfg = manipulator.copy(center.data)
- param.set_unit_value(down_cfg, max(0.0, unit_value - step_size))
- down_cfg = driver.get_configuration(down_cfg)
- self.yield_nonblocking(down_cfg)
- points.append(down_cfg)
-
- if unit_value < 1.0:
- # produce new config with param set step_size higher
- up_cfg = manipulator.copy(center.data)
- param.set_unit_value(up_cfg, min(1.0, unit_value + step_size))
- up_cfg = driver.get_configuration(up_cfg)
- self.yield_nonblocking(up_cfg)
- points.append(up_cfg)
-
- else: # ComplexParameter
- for mutate_function in param.manipulators(center.data):
- cfg = manipulator.copy(center.data)
- mutate_function(cfg)
- cfg = driver.get_configuration(cfg)
- self.yield_nonblocking(cfg)
- points.append(cfg)
-
-
- yield None # wait for all results
-
- #sort points by quality, best point will be points[0], worst is points[-1]
- points.sort(cmp=objective.compare)
-
- if (objective.lt(driver.best_result.configuration, center)
- and driver.best_result.configuration != points[0]):
- # another technique found a new global best, switch to that
- center = driver.best_result.configuration
- elif objective.lt(points[0], center):
- # we found a better point, move there
- center = points[0]
- else:
- # no better point, shrink the pattern
- step_size /= 2.0
-
-# register our new technique in global list
-technique.register(PatternSearch())
-
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/plugin.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/plugin.py
deleted file mode 100644
index ad8481837cbee62ba8c3f1c94a27529261953bb0..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/plugin.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import abc
-import argparse
-import logging
-import time
-
-from datetime import datetime
-from fn import _
-
-log = logging.getLogger(__name__)
-display_log = logging.getLogger(__name__ + ".DisplayPlugin")
-
-argparser = argparse.ArgumentParser(add_help=False)
-argparser.add_argument('--results-log',
- help="file to store log of the best configuration times")
-argparser.add_argument('--results-log-details',
- help="file to store log of the non-best configuration times")
-argparser.add_argument('--quiet', action='store_true',
- help="print less information")
-argparser.add_argument('--display-frequency', default=10, type=int,
- help="how often for DisplayPlugin to print")
-
-class SearchPlugin(object):
- @property
- def priority(self):
- """control order the plugin hooks gets run in, lower runs first"""
- return 0
-
- def set_driver(self, driver):
- """called before all other methods"""
- self.driver = driver
-
- def before_main(self): pass
- def after_main(self): pass
-
- def before_techniques(self): pass
- def after_techniques(self): pass
-
- def before_results_wait(self): pass
- def after_results_wait(self): pass
-
- def on_result(self, result):
- """
- called once for every new result
- """
- pass
-
- def on_result_for_technique(self, result, technique):
- """
- called right before a result is given to a technique
- (result may be requested by multiple techniques)
- """
- pass
-
- def on_new_best_result(self, result):
- """
- called whenever the global best result changes
- """
- pass
-
-class DisplayPlugin(SearchPlugin):
- __metaclass__ = abc.ABCMeta
- def __init__(self, display_period=5):
- super(DisplayPlugin, self).__init__()
- self.last = time.time()
- self.start = time.time()
- self.display_period = display_period
-
- def after_results_wait(self):
- t = time.time()
- if t - self.display_period > self.last:
- # call display every 5 seconds
- self.last = t
- self.display(t)
-
- def after_main(self):
- self.display()
-
- @abc.abstractmethod
- def display(self, t=None):
- pass
-
-
-class LogDisplayPlugin(DisplayPlugin):
- def display(self, t=None):
- if not t:
- t = time.time()
- count = self.driver.results_query().count()
- best = self.driver.results_query(objective_ordered = True).first()
- if best is None:
- log.warning("no results yet")
- return
- requestor = ','.join(map(_.requestor, best.desired_results))
- display_log.info("tests=%d, best %s, cost %s, found by %s",
- count,
- cfg_repr(best.configuration),
- self.driver.objective.display(best),
- requestor,
- )
-
-class FileDisplayPlugin(SearchPlugin):
- def __init__(self, out, details, *args, **kwargs):
- super(FileDisplayPlugin, self).__init__(*args, **kwargs)
- self.last_best = float('inf')
- self.start_date = datetime.now()
- if out:
- self.out = open(out, "w")
- else:
- self.out = None
- if out == details:
- self.details = self.out
- self.out = None
- elif details:
- self.details = open(details, "w")
- else:
- self.details = None
-
- def on_result(self, result):
- if self.out and result.time < self.last_best:
- self.last_best = result.time
- print >>self.out, \
- (result.collection_date - self.start_date).total_seconds(), \
- result.time
- self.out.flush()
- if self.details:
- print >>self.details, \
- (result.collection_date - self.start_date).total_seconds(), \
- result.time
- self.details.flush()
-
-def get_enabled(args):
- plugins = []
- if not args.quiet:
- plugins.append(LogDisplayPlugin(args.display_frequency))
- if args.results_log or args.results_log_details:
- plugins.append(FileDisplayPlugin(args.results_log,
- args.results_log_details))
- return plugins
-
-def cfg_repr(cfg):
- try:
- s = repr(cfg.data)
- if len(s) < 100:
- return s
- except:
- pass
- return "#{0}".format(cfg.id)
-
-
-
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/pso.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/pso.py
deleted file mode 100644
index 3b8c37a7787b900a70f80ffab00d5c90b46c7541..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/pso.py
+++ /dev/null
@@ -1,81 +0,0 @@
-# -*- coding: utf-8 -*-
-# vim: tabstop=2 shiftwidth=2 softtabstop=2 expandtab autoindent smarttab
-from manipulator import *
-from opentuner.search import technique
-import random
-import math
-
-class PSO(technique.SequentialSearchTechnique ):
- """ Particle Swarm Optimization """
- def __init__(self, crossover, N = 30, init_pop=None, *pargs, **kwargs):
- """
- crossover: name of crossover operator function
- """
- super(PSO, self).__init__(*pargs, **kwargs)
- self.crossover = crossover
- self.name = 'pso-'+crossover.replace("op3_cross_","")
- self.init_pop = init_pop
- self.N = N
-
- def main_generator(self):
-
- objective = self.objective
- driver = self.driver
- m = self.manipulator
- def config(cfg):
- return driver.get_configuration(cfg)
-
- population = self.init_pop
- if not population:
- population = [HybridParticle(m, self.crossover) for i in range(self.N)]
-
- for p in population:
- yield driver.get_configuration(p.position)
-
- while True:
- for particle in population:
- g = driver.best_result.configuration.data
- old=m.copy(particle.position)
- particle.move(g)
- yield config(particle.position)
- # update individual best
- if objective.lt(config(particle.position), config(particle.best)):
- particle.best = particle.position
-
-class HybridParticle(object):
- def __init__(self, m, crossover_choice, omega=0.5, phi_l=0.5, phi_g=0.5):
-
- """
- m: a configuraiton manipulator
- omega: influence of the particle's last velocity, a float in range [0,1] ; omega=1 means even speed
- phi_l: influence of the particle's distance to its historial best position, a float in range [0,1]
- phi_g: influence of the particle's distance to the global best position, a float in range [0,1]
- """
-
- self.manipulator = m
- self.position = self.manipulator.random()
- self.best = self.position
- self.omega = omega
- self.phi_l = phi_l
- self.phi_g = phi_g
- self.crossover_choice = crossover_choice
- self.velocity = {}
- for p in self.manipulator.params:
- # Velocity as a continous value
- self.velocity[p.name]=0
-
- def move(self, global_best):
- """
- Update parameter values using corresponding operators.
- TODO: introduce operator choice map
- """
- m = self.manipulator
- for p in m.params:
- self.velocity[p.name] = p.op3_swarm(self.position, global_best, self.best, c=self.omega, c1=self.phi_g, c2=self.phi_l, xchoice=self.crossover_choice, velocity=self.velocity[p.name])
-
-
-technique.register(PSO(crossover = 'op3_cross_OX3'))
-technique.register(PSO(crossover = 'op3_cross_OX1'))
-technique.register(PSO(crossover = 'op3_cross_PMX'))
-technique.register(PSO(crossover = 'op3_cross_PX'))
-technique.register(PSO(crossover = 'op3_cross_CX'))
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/simplextechniques.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/simplextechniques.py
deleted file mode 100644
index 3cfec0eebb25cf3c7ff2cc2bc69d558454660e32..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/simplextechniques.py
+++ /dev/null
@@ -1,457 +0,0 @@
-import abc
-import logging
-import math
-from collections import defaultdict
-from fn import _
-from fn.iters import map, filter
-from .manipulator import Parameter
-from .metatechniques import RecyclingMetaTechnique
-from .technique import SequentialSearchTechnique, register
-
-log = logging.getLogger(__name__)
-
-
-class SimplexTechnique(SequentialSearchTechnique):
- """
- Base class with utility functions common
- to simplex type methods
- """
-
- def __init__(self, seed_cfg=None, *args, **kwargs):
- super(SimplexTechnique, self).__init__(*args, **kwargs)
- self.centroid = None
- self.last_simplex_points = None
- self.seed_cfg = seed_cfg
- self.simplex_points = []
-
- def calculate_centroid(self):
- """
- average of all the PrimitiveParameters in self.simplex_points
- ComplexParameters are copied from self.simplex_points[0]
- """
- sums = defaultdict(float)
- counts = defaultdict(int)
-
- for config in self.simplex_points:
- cfg = config.data
- for param in self.manipulator.parameters(cfg):
- if param.is_primitive():
- sums[param.name] += param.get_unit_value(cfg)
- counts[param.name] += 1
-
- centroid = self.manipulator.copy(self.simplex_points[0].data)
- for param in self.manipulator.parameters(centroid):
- if param.is_primitive():
- param.set_unit_value(centroid,
- sums[param.name] / float(counts[param.name]))
-
- return centroid
-
- def cfg_to_str(self, cfg):
- params = list(filter(Parameter.is_primitive,
- self.manipulator.parameters(cfg)))
- params.sort(key=_.name)
- return str(tuple(map(lambda x: x.get_unit_value(cfg), params)))
-
- def debug_log(self):
- for i, config in enumerate(self.simplex_points):
- log.debug("simplex_points[%d] = %s", i, self.cfg_to_str(config.data))
- if self.centroid:
- log.debug("centroid = %s", self.cfg_to_str(self.centroid))
-
- def linear_point(self, p1, p2, scale):
- """
- return a point on the line passing between p1 and p2 at position scale
- such that p1 + scale*(p1 - p2)
- """
- return self.manipulator.linear_config(1.0, p1, scale, p1, -scale, p2)
-
- def convergence_criterea(self):
- """True will cause the simplex method to stop"""
- if self.rounds_since_novel_request > 3 * len(self.simplex_points) + 1:
- return True
- if self.last_simplex_points == self.simplex_points:
- return True
- self.last_simplex_points = list(self.simplex_points)
- return False
-
- def initial_simplex_seed(self):
- """
- return a point to base the initial simplex on
- """
- if self.seed_cfg is not None:
- return self.seed_cfg
- return self.manipulator.random()
-
- @abc.abstractmethod
- def initial_simplex(self):
- """
- return a initial list of configurations
- """
- return []
-
-
-class RandomInitialMixin(object):
- """
- start with random initial simplex
- """
-
- def initial_simplex(self):
- # we implicitly assume number of parameters is fixed here, however
- # it will work if it isn't (simplex size is undefined)
- cfg0 = self.initial_simplex_seed()
- params = self.manipulator.parameters(cfg0)
- return [cfg0] + [self.manipulator.random()
- for p in params
- if p.is_primitive()]
-
-
-class RightInitialMixin(object):
- """
- start with random initial right triangle like simplex
- """
-
- def __init__(self, initial_unit_edge_length=0.1, *args, **kwargs):
- assert initial_unit_edge_length <= 0.5
- self.initial_unit_edge_length = initial_unit_edge_length
- super(RightInitialMixin, self).__init__(*args, **kwargs)
-
- def initial_simplex(self):
- cfg0 = self.initial_simplex_seed()
- simplex = [cfg0]
- params = self.manipulator.parameters(cfg0)
- params = filter(lambda x: x.is_primitive(), params)
- for p in params:
- simplex.append(self.manipulator.copy(cfg0))
- v = p.get_unit_value(simplex[-1])
- if v <= 0.5:
- v += self.initial_unit_edge_length
- else:
- v -= self.initial_unit_edge_length
- p.set_unit_value(simplex[-1], v)
- return simplex
-
-
-class RegularInitialMixin(object):
- """
- start with random initial regular simplex (all edges equal length)
- """
-
- def __init__(self, initial_unit_edge_length=0.1, *args, **kwargs):
- assert initial_unit_edge_length <= 0.5
- self.initial_unit_edge_length = initial_unit_edge_length
- super(RegularInitialMixin, self).__init__(*args, **kwargs)
-
- def initial_simplex(self):
- cfg0 = self.initial_simplex_seed()
- simplex = [cfg0]
- params = self.manipulator.parameters(cfg0)
- params = list(filter(lambda x: x.is_primitive(), params))
- if len(params) == 0:
- return simplex
-
- q = (((math.sqrt(len(params) + 1.0) - 1.0) / (len(params) * math.sqrt(2.0)))
- * self.initial_unit_edge_length)
- p = q + ((1.0 / math.sqrt(2.0)) * self.initial_unit_edge_length)
-
- base = [x.get_unit_value(cfg0) for x in params]
- for j in xrange(len(base)):
- if max(p, q) + base[j] > 1.0:
- #flip this dimension as we would overflow our [0,1] bounds
- base[j] *= -1.0
-
- for i in xrange(len(params)):
- simplex.append(self.manipulator.copy(cfg0))
- params[i].set_unit_value(simplex[-1], abs(base[i] + p))
- for j in xrange(i + 1, len(params)):
- params[j].set_unit_value(simplex[-1], abs(base[i] + q))
-
- return simplex
-
-
-class NelderMead(SimplexTechnique):
- """
- Nelder-Mead downhill simplex method.
-
- Based on description of method on page 82 of
- 'Noisy Optimization With Evolution Strategies' by Dirk V. Arnold.
-
- We set alpha=2.0 by default instead of the often recommended alpha=1.0 to
- avoid a common degenerate case, where the volume of the simplex becomes zero.
- This is easiest to see with a single parameter. Let the simplex points
- be x0,x1. Let the centroid be c=(x0+x1)/2.0 and the reflection point be:
- reflection = c + alpha*(c-x1) = (x0+x1)*(1+alpha)/2 - x1
- The problem is, if we set alpha = 1.0, then the x1's cancel out and the
- reflection point becomes just reflection=x0, which also happens to be the
- second best point, meaning we will use it. So in a single step of the
- algorithm the simplex becomes singular.
- """
-
- def __init__(self,
- alpha=2.0,
- gamma=2.0,
- beta=0.5,
- sigma=0.5,
- *args, **kwargs):
- self.alpha = alpha
- self.gamma = gamma
- self.beta = beta
- self.sigma = sigma
- super(NelderMead, self).__init__(*args, **kwargs)
-
- @classmethod
- def get_hyper_parameters(cls):
- return ['alpha', 'gamma', 'beta', 'sigma']
-
-
- def main_generator(self):
- objective = self.objective
- driver = self.driver
-
- # test the entire initial simplex
- self.simplex_points = list(map(driver.get_configuration,
- self.initial_simplex()))
-
- if len(self.simplex_points) <= 1:
- log.warning("only 1 point in simplex, will not use %s", self.name)
- return
-
- log.debug("initial points")
- for p in self.simplex_points:
- self.yield_nonblocking(p)
- yield None # wait until results are ready
-
- while not self.convergence_criterea():
- # next steps assume this ordering
- self.simplex_points.sort(cmp=objective.compare)
- # set limit from worst point
- self.limit = objective.limit_from_config(self.simplex_points[-1])
- self.centroid = self.calculate_centroid()
- if log.isEnabledFor(logging.DEBUG):
- self.debug_log()
-
- reflection = self.reflection_point()
- yield reflection
-
- if objective.lt(reflection, self.simplex_points[0]):
- #expansion case
- expansion = self.expansion_point(reflection)
- yield expansion
-
- if objective.lt(expansion, reflection):
- log.debug("using expansion point")
- self.simplex_points[-1] = expansion
- else:
- log.debug("using reflection point (considered expansion)")
- self.simplex_points[-1] = reflection
-
- elif objective.lt(reflection, self.simplex_points[1]):
- #reflection case
- log.debug("using reflection point")
- self.simplex_points[-1] = reflection
- else:
- # contraction case
- if objective.lte(reflection, self.simplex_points[-1]):
- # outside contraction
- contract_base = reflection
- else:
- # inside contraction
- contract_base = self.simplex_points[-1]
-
- contraction = self.contraction_point(contract_base)
- yield contraction
-
- if objective.lte(contraction, contract_base):
- log.debug("using contraction point")
- self.simplex_points[-1] = contraction
- else:
- #reduction case
- log.debug("performing shrink reduction")
- self.perform_shrink_reduction()
- for p in self.simplex_points:
- self.yield_nonblocking(p)
- yield None # wait until results are ready
-
- def reflection_point(self):
- """
- reflect worst point across centroid
- """
- return self.driver.get_configuration(
- self.linear_point(self.centroid,
- self.simplex_points[-1].data,
- self.alpha))
-
- def expansion_point(self, reflection):
- """
- reflect worst point across centroid more (by default 2x as much)
- """
- return self.driver.get_configuration(
- self.linear_point(self.centroid,
- reflection.data,
- -self.gamma))
-
- def contraction_point(self, contract_base):
- """
- reflect worst point across centroid less
- """
- return self.driver.get_configuration(
- self.linear_point(self.centroid,
- contract_base.data,
- -self.beta))
-
- def perform_shrink_reduction(self):
- """
- shrink the simplex in size by sigma=1/2 (default), moving it closer to the
- best point
- """
- for i in xrange(1, len(self.simplex_points)):
- self.simplex_points[i] = self.driver.get_configuration(
- self.linear_point(self.simplex_points[0].data,
- self.simplex_points[i].data,
- -self.sigma))
-
-
-class Torczon(SimplexTechnique):
- """
- Torczon multi-directional search algorithm.
-
- Based on description of method on page 85 of
- 'Noisy Optimization With Evolution Strategies' by Dirk V. Arnold.
- """
-
- def __init__(self,
- alpha=1.0,
- gamma=2.0,
- beta=0.5,
- *args, **kwargs):
- self.alpha = alpha
- self.gamma = gamma
- self.beta = beta
- super(Torczon, self).__init__(*args, **kwargs)
-
- @classmethod
- def get_hyper_parameters(cls):
- return ['alpha', 'gamma', 'beta']
-
-
- def main_generator(self):
- objective = self.objective
- driver = self.driver
-
- # test the entire initial simplex
- self.simplex_points = list(map(driver.get_configuration,
- self.initial_simplex()))
- if len(self.simplex_points) <= 1:
- log.warning("only 1 point in simplex, will not use %s", self.name)
- return
-
- log.debug("initial points")
- for p in self.simplex_points:
- self.yield_nonblocking(p)
- yield None # wait until results are ready
- self.simplex_points.sort(cmp=objective.compare)
-
- while not self.convergence_criterea():
- # set limit from worst point
- self.limit = objective.limit_from_config(self.simplex_points[-1])
-
- if log.isEnabledFor(logging.DEBUG):
- self.debug_log()
-
- reflected = self.reflected_simplex()
- yield None # wait until results are ready
- reflected.sort(cmp=objective.compare)
-
- # this next condition implies reflected[0] < simplex_points[0] since
- # reflected is sorted and contains simplex_points[0] (saves a db query)
- if reflected[0] is not self.simplex_points[0]:
- expanded = self.expanded_simplex()
- yield None # wait until results are ready
- expanded.sort(cmp=objective.compare)
-
- if objective.lt(expanded[0], reflected[0]):
- log.debug("expansion performed")
- self.simplex_points = expanded
- else:
- log.debug("reflection performed")
- self.simplex_points = reflected
- else:
- contracted = self.contracted_simplex()
- yield None # wait until results are ready
- contracted.sort(cmp=objective.compare)
-
- log.debug("contraction performed")
- self.simplex_points = contracted
-
- def scaled_simplex(self, scale):
- """
- assumes self.simplex_points[0] is best point and returns a new simplex
- reflected across self.simplex_points[0] by scale
- """
- simplex = list(self.simplex_points) # shallow copy
- for i in xrange(1, len(simplex)):
- simplex[i] = self.driver.get_configuration(
- self.linear_point(simplex[0].data, simplex[i].data, scale))
- self.yield_nonblocking(simplex[i])
- return simplex
-
- def reflected_simplex(self):
- return self.scaled_simplex(self.alpha)
-
- def expanded_simplex(self):
- return self.scaled_simplex(self.gamma)
-
- def contracted_simplex(self):
- return self.scaled_simplex(-self.beta)
-
-
-class RandomNelderMead(RandomInitialMixin, NelderMead):
- pass
-
-
-class RightNelderMead(RightInitialMixin, NelderMead):
- pass
-
-
-class RegularNelderMead(RegularInitialMixin, NelderMead):
- pass
-
-
-class RandomTorczon(RandomInitialMixin, Torczon):
- pass
-
-
-class RightTorczon(RightInitialMixin, Torczon):
- pass
-
-
-class RegularTorczon(RegularInitialMixin, Torczon):
- pass
-
-
-class MultiNelderMead(RecyclingMetaTechnique):
- def __init__(self):
- super(MultiNelderMead, self).__init__([RightNelderMead, RandomNelderMead,
- RegularNelderMead])
-
-
-class MultiTorczon(RecyclingMetaTechnique):
- def __init__(self):
- super(MultiTorczon, self).__init__([RightTorczon, RandomTorczon,
- RegularTorczon])
-
-
-register(RandomNelderMead())
-register(RegularNelderMead())
-register(RightNelderMead())
-register(MultiNelderMead())
-register(RandomTorczon())
-register(RegularTorczon())
-register(RightTorczon())
-register(MultiTorczon())
-
-
-
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/simulatedannealing.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/simulatedannealing.py
deleted file mode 100644
index 45b315f2e6bbceda2822ae72623e8c0032afe66b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/simulatedannealing.py
+++ /dev/null
@@ -1,133 +0,0 @@
-from opentuner.search import technique
-import math
-import random
-#Default interval steps for cooling schedules
-DEFAULT_INTERVAL = 100
-
-#Pseudo-annealing - no relative energy input into acceptance function
-class PseudoAnnealingSearch(technique.SequentialSearchTechnique):
- def __init__(self,
- temps = [30,0], #temperature schedule
- intervals = [], #duration schedule
- loop = True, #Do we loop the schedule if we reach the end?
- *pargs, **kwargs):
- #fill intervals sufficiently
- ext_intervals = list(intervals)
- for i in range(len(temps)-len(intervals)-1):
- ext_intervals.append(DEFAULT_INTERVAL)
-
- #create temperature schedule (list of temps)
- cool_schedule = [temps[0]]
- for i in range(len(temps)-1):
- step = (float(temps[i+1]) - temps[i])/ext_intervals[i]
- for j in range(ext_intervals[i]):
- cool_schedule.append(max(cool_schedule[-1] + step,0))
-
- self.cool_schedule = cool_schedule
- self.loop = loop
- self.scaling = 50 #scaling of acceptance function
-
- super(PseudoAnnealingSearch,self).__init__(*pargs,**kwargs)
-
-
- def main_generator(self):
- objective = self.objective
- driver = self.driver
- manipulator = self.manipulator
-
- #Start in a random spot
- state = driver.get_configuration(manipulator.random())
- yield state
- #schedule counter
- counter = 0
- max_time = len(self.cool_schedule)-1
- #Check whether relative objective implemented
- has_rel = objective.relative(state,state) is not None
- has_rel=False
-
- while True:
- #Determine temperature
- temp = self.cool_schedule[min(counter,max_time)]
- #scale stepsize with temp and time (arbitrary)
- step_size = math.exp(-(20 + counter/100)/(temp+ 1))
-
- #get candidate neighbors using manipulator
- points = list()
- points.append(state)
- for param in manipulator.parameters(state.data):
- if param.is_primitive():
- # get current value of param, scaled to be in range [0.0, 1.0]
- unit_value = param.get_unit_value(state.data)
- if unit_value > 0.0:
- # produce new config with param set step_size lower
- down_cfg = manipulator.copy(state.data)
- param.set_unit_value(down_cfg, max(0.0, unit_value - step_size*random.random()))
- down_cfg = driver.get_configuration(down_cfg)
- self.yield_nonblocking(down_cfg)
- points.append(down_cfg)
-
- if unit_value < 1.0:
- # produce new config with param set step_size higher
- up_cfg = manipulator.copy(state.data)
- param.set_unit_value(up_cfg, min(1.0, unit_value + step_size*random.random()))
- up_cfg = driver.get_configuration(up_cfg)
- self.yield_nonblocking(up_cfg)
- points.append(up_cfg)
- else: # ComplexParameter
- for mutate_function in param.manipulators(state.data):
- cfg = manipulator.copy(state.data)
- mutate_function(cfg)
- cfg = driver.get_configuration(cfg)
- self.yield_nonblocking(cfg)
- points.append(cfg)
- yield None # wait for all results
-
- #Relative comparison implemented
- if has_rel:
- while True:
- if len(points) == 0:
- state = driver.best_result.configuration
- break
- candidate = points.pop(random.randint(0,len(points)-1))
- #compare to global best
- if random.random() < AcceptanceFunction(1, objective.relative(candidate,driver.best_result.configuration), temp, self.scaling):
- state = candidate
- break
- #No relative compare
- else:
- #sort points by "energy" (quality)
- points.sort(cmp=objective.compare)
-
- #Make decision about changing state
- #probability picking next-best state is exp^(-1/temp)
- #repeat and cycle to get state p-dist resembling this
- sel = 0
- while AcceptanceFunction(0,1,temp,1)>random.random():
- sel += 1
- state = points[sel%len(points)]
-
- #switch to the global best if temperature is low (i.e. we aren't moving much)
- if AcceptanceFunction(0,1,temp,1)< .0001 and objective.lt(driver.best_result.configuration, state):
- state = driver.best_result.configuration
-
- #update counter
- counter +=1
- if counter>max_time and self.loop:
- counter=counter-max_time
-
-
-#Acceptance probability function for annealing
-def AcceptanceFunction(e,e_new,temp,scaling):
- #Standard acceptance probability function using relative "goodness"
- if e>=e_new:
- return 1
- if temp == 0:
- return 0
- if scaling*(e_new-e)/temp > 10:
- #for practical purposes, probability is too low.
- return 0
- return math.exp(scaling*(e-e_new)/temp)
-
-
-#register technique
-technique.register(PseudoAnnealingSearch())
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/technique.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/technique.py
deleted file mode 100644
index 849391df9bb37454301c90a520fbbe6b5025c683..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/search/technique.py
+++ /dev/null
@@ -1,358 +0,0 @@
-import abc
-import argparse
-import logging
-import os
-import random
-import sys
-
-from importlib import import_module
-from datetime import datetime
-from fn import _
-
-from opentuner.resultsdb.models import *
-from plugin import SearchPlugin
-
-log = logging.getLogger(__name__)
-#log.setLevel(logging.DEBUG)
-
-argparser = argparse.ArgumentParser(add_help=False)
-argparser.add_argument('--technique','-t', action='append',
- help="which technique to use")
-argparser.add_argument('--list-techniques','-lt', action='store_true',
- help="list techniques available and exit")
-argparser.add_argument('--generate-bandit-technique','-gbt', action='store_true',
- help="randomly generate a bandit to use")
-
-class SearchTechniqueBase(object):
- """
- abstract base class for search techniques, with minimal interface
- """
- __metaclass__ = abc.ABCMeta
-
- def __init__(self, name = None):
- super(SearchTechniqueBase, self).__init__()
- if name:
- self.name = name
- else:
- self.name = self.default_name()
-
- def is_ready(self):
- """test if enough data has been gathered to use this technique"""
- return True
-
- def default_name(self):
- """name of this SearchTechnique uses for display/accounting"""
- return self.__class__.__name__
-
- def handle_requested_result(self, result):
- """called for each new Result(), requested by this technique"""
- pass
-
- @abc.abstractmethod
- def set_driver(self, driver):
- """called at start of tuning process"""
- return
-
- @abc.abstractmethod
- def desired_result(self):
- """
- return at most count resultsdb.models.DesiredResult objects based on past
- performance
- """
- return
-
-class SearchTechnique(SearchPlugin, SearchTechniqueBase):
- """
- a search search technique with basic utility functions
- """
-
- def __init__(self, *pargs, **kwargs):
- super(SearchTechnique, self).__init__(*pargs, **kwargs)
- self.driver = None
- self.manipulator = None
- self.objective = None
- self.request_count = 0
-
- def set_driver(self, driver):
- super(SearchTechnique, self).set_driver(driver)
- self.manipulator = driver.manipulator
- self.objective = driver.objective
- driver.add_plugin(self)
-
- def desired_result(self):
- """
- create and return a resultsdb.models.DesiredResult
- returns None if no desired results and False if waiting for results
- """
- cfg = self.desired_configuration()
- if cfg is None:
- return None
- if cfg is False:
- return False
- if type(cfg) is Configuration:
- config = cfg
- else:
- config = self.driver.get_configuration(cfg)
- desired = DesiredResult(configuration=config,
- requestor=self.name,
- generation=self.driver.generation,
- request_date=datetime.now(),
- tuning_run=self.driver.tuning_run)
- if hasattr(self, 'limit'):
- desired.limit = self.limit
- self.driver.register_result_callback(desired, self.handle_requested_result)
- self.request_count += 1
- return desired
-
- @abc.abstractmethod
- def desired_configuration(self):
- """
- return a cfg that we should test
- given a ConfigurationManipulator and SearchDriver
- return None if there are no configurations to test
- return False if waiting for results
- """
- return dict()
-
- def handle_requested_result(self, result):
- """called for each new Result(), regardless of who requested it"""
- pass
-
- def default_generated_name(self):
- """ The default generated name for this technique """
- return self.base_name()
-
- def use_default_generated_name(self):
- """ set the name of this technique to the default generated name """
- self.name = self.default_generated_name()
-
- def base_name(self):
- """
- Return the base name of this technique with form
- classname;hyperparam1,v1;hyperparam2,v2 ...
- where hyperparams are taken in order from get_hyper_parameters()
-
- Should only be called after this technique has finished initializing.
- """
- out = [self.__class__.__name__]
- for hyper_parameter in self.get_hyper_parameters():
- # get hyperparam,v as a string and append
- try:
- out.append(hyper_parameter + ',' + str(getattr(self, hyper_parameter)))
- except AttributeError:
- log.error("Uninitialized hyper-parameter %s for technique %s.",
- hyper_parameter, self.__class__.__name__)
-
- return ';'.join(out)
-
- @classmethod
- def get_hyper_parameters(cls):
- """
- return a list of hyper-parameters names for this technique
-
- Name strings must match the corresponding attribute with the hyper-parameter
- value on technique instances. Names should also match the key word argument
- used when initializing an instance. Hyperparameters should only take literal
- values.
-
- For example, given hyper parameter "mutation_rate", then the __init__ method
- should have 'mutation_rate' as a key word argument and later have the line
- self.mutation_rate = mutation_rate
- """
- return []
-
- @classmethod
- def generate_technique(cls, manipulator=None, *args, **kwargs):
- """ return a new technique based off this instance """
- t = cls(*args, **kwargs)
- t.use_default_generated_name()
- return t
-
-class PureRandom(SearchTechnique):
- """
- request configurations completely randomly
- """
- def desired_configuration(self):
- return self.manipulator.random()
-
-class AsyncProceduralSearchTechnique(SearchTechnique):
- def __init__(self, *pargs, **kwargs):
- super(AsyncProceduralSearchTechnique, self).__init__(*pargs, **kwargs)
- self.gen = None
- self.done = False
- self.latest_results = []
-
- def call_main_generator(self):
- """passthrough (used in subclasses)"""
- return self.main_generator()
-
- def desired_configuration(self):
- if self.gen is None:
- log.debug("%s: creating generator", self.name)
- self.gen = self.call_main_generator()
- if not self.done:
- try:
- return self.gen.next()
- except StopIteration:
- log.debug("%s: generator finished", self.name)
- self.done = True
- return None
-
- @abc.abstractmethod
- def main_generator(self):
- """
- custom generator to conduct this search, should:
- yield config
- to request tests and call driver.get_results() to read the results
-
- in AsyncProceduralSearchTechnique results are ready at an undefined
- time (`yield False` to stall and wait for them)
-
- in SequentialSearchTechnique results are ready after the yield
- """
- pass
-
- def is_ready(self):
- return not self.done
-
-class SequentialSearchTechnique(AsyncProceduralSearchTechnique):
- def __init__(self, novelty_threshold=50, reset_threshold=500, *pargs, **kwargs):
- super(SequentialSearchTechnique, self).__init__(*pargs, **kwargs)
- self.pending_tests = []
- self.novelty_threshold = novelty_threshold
- self.rounds_since_novel_request = 0
- self.reset_threshold = reset_threshold
-
- def yield_nonblocking(self, cfg):
- """
- within self.main_generator() act like `yield cfg`, but don't wait for the
- results until the following yield (spawn/sync style)
- """
- if cfg:
- self.pending_tests.append(cfg)
-
- def call_main_generator(self):
- """insert waits for results after every yielded item"""
- subgen = self.main_generator()
- self.rounds_since_novel_request = 0
- while True:
- self.rounds_since_novel_request += 1
- if (self.rounds_since_novel_request % self.novelty_threshold) == 0:
- log.warning("%s has not requested a new result for %d rounds",
- self.name, self.rounds_since_novel_request)
- if (self.rounds_since_novel_request > self.reset_threshold):
- log.warning("%s is being reset", self.name)
- subgen = self.main_generator()
- self.rounds_since_novel_request = 0
- yield None # give other techniques a shot
- try:
- p = subgen.next()
- if p:
- self.pending_tests.append(p)
- except StopIteration:
- return
- finally:
- for p in self.pending_tests:
- if not self.driver.has_results(p):
- self.rounds_since_novel_request = 0
- yield p
-
- # wait for all pending_tests to have results
- c = 0
- while self.pending_tests:
- log.debug("%s: waiting for %d pending tests",
- self.name, len(self.pending_tests))
- c += 1
- if (c % 100) == 0:
- log.error("%s: still waiting for %d pending tests (c=%d)",
- self.name, len(self.pending_tests), c)
-
- self.pending_tests = filter(lambda x: not self.driver.has_results(x),
- self.pending_tests)
- if self.pending_tests:
- self.rounds_since_novel_request = 0
- yield False # wait
-
-#list of all techniques
-the_registry = list()
-
-#list of technique generators
-the_generator_registry = list()
-
-def register(t):
- the_registry.append(t)
-
-def register_generator(cls, generator_weight=1.0, *args, **kwargs):
- """
- register a technique generator - a tuple of (technique class, args, kwargs)
- where args and kwargs will be passed into the generate_technique classmethod -
- with specified probability weight when randomly choosing a generator
-
- :param cls: a technique class to use as a generator
- :param generator_weight: probability weighting when randomly choosing a generator
- :param args: arguments to pass into generate_technique class method
- :param kwargs: arguments to pass into generate_technique class method
- """
- the_generator_registry.append(((cls, args, kwargs), generator_weight))
-
-register(PureRandom())
-
-def get_random_generator_technique(generators=None, manipulator=None):
- """
- Takes in a sequence of ((generator, args, kwargs), weight) tuples.
- Returns a random generated technique info tuple
-
- :param generators: optional argument to avoid repeated getting of generators
- :param manipulator: manipulator to pass to generate_technique class method.
- """
- if generators is None:
- techniques, generators = all_techniques()
- g, args, kwargs = weighted_choice(generators)
- return g.generate_technique(manipulator, *args, **kwargs)
-
-
-def weighted_choice(choices):
- """ takes in a sequence of (choice, weight) tuples and randomly returns one """
- total = sum(w for c, w in choices)
- r = random.uniform(0, total)
- upto = 0
- for c, w in choices:
- upto += w
- if upto > r:
- return c
- return random.choice([c for c, w in choices])
-
-
-def all_techniques():
- #import all modules in search to ensure techniques are Registered
- for f in sorted(os.listdir(os.path.dirname(__file__))):
- m = re.match(r'^(.*)[.]py$', f)
- if m:
- import_module('opentuner.search.'+m.group(1))
-
- return the_registry, the_generator_registry
-
-def get_enabled(args):
- techniques, generators = all_techniques()
- if args.list_techniques:
- for t in techniques:
- print t.name
- sys.exit(0)
-
- if not args.technique:
- # no techniques specified, default technique
- args.technique = ['AUCBanditMetaTechniqueA']
-
- for unknown in set(args.technique) - set(map(_.name, techniques)):
- log.error('unknown technique %s', unknown)
- raise Exception('Unknown technique: --technique={}'.format(unknown))
-
- return [t for t in techniques if t.name in args.technique]
-
-def get_root(args):
- from metatechniques import RoundRobinMetaSearchTechnique
- enabled = get_enabled(args)
- if len(enabled) == 1:
- return enabled[0]
- return RoundRobinMetaSearchTechnique(get_enabled(args))
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/tuningrunmain.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/tuningrunmain.py
deleted file mode 100644
index 9bcf1b5270286ee405373822d3919ae8854a24c3..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/tuningrunmain.py
+++ /dev/null
@@ -1,224 +0,0 @@
-# vim: tabstop=2 shiftwidth=2 softtabstop=2 expandtab autoindent smarttab
-import argparse
-import copy
-import inspect
-import logging
-import math
-import os
-import socket
-import sys
-import time
-import uuid
-from datetime import datetime
-
-from opentuner import resultsdb
-from opentuner.search.driver import SearchDriver
-from opentuner.measurement.driver import MeasurementDriver
-
-log = logging.getLogger(__name__)
-
-argparser = argparse.ArgumentParser(add_help=False)
-argparser.add_argument('--label',
- help="name for the TuningRun")
-argparser.add_argument('--print-search-space-size', action='store_true',
- help="Print out the estimated size of the search space and exit")
-argparser.add_argument('--database',
- help=("database to store tuning results in, see: "
- "http://docs.sqlalchemy.org/en/rel_0_8/core/engines.html#database-urls"))
-argparser.add_argument('--print-params','-pp',action='store_true',
- help='show parameters of the configuration being tuned')
-
-
-class CleanStop(Exception):
- pass
-
-
-class LogFormatter(logging.Formatter):
- def format(self, record):
- record.relativeCreated /= 1000.0
- try:
- # python 2.7
- return super(LogFormatter, self).format(record)
- except:
- # python 2.6
- return _OldFormatter.format(self, record)
-
-
-_OldFormatter = logging.Formatter
-logging.Formatter = LogFormatter
-
-try:
- # python 2.7
- from logging.config import dictConfig
-except:
- # python 2.6
- from .utils.dictconfig import dictConfig
-
-the_logging_config = {
- 'version': 1,
- 'disable_existing_loggers': False,
- 'formatters': {'console': {'format': '[%(relativeCreated)6.0fs] '
- '%(levelname)7s %(name)s: '
- '%(message)s'},
- 'file': {'format': '[%(asctime)-15s] '
- '%(levelname)7s %(name)s: '
- '%(message)s '
- '@%(filename)s:%(lineno)d'}},
- 'handlers': {'console': {'class': 'logging.StreamHandler',
- 'formatter': 'console',
- 'level': 'INFO'},
- 'file': {'class': 'logging.FileHandler',
- 'filename': 'opentuner.log',
- 'formatter': 'file',
- 'level': 'WARNING'}},
- 'loggers': {'': {'handlers': ['console', 'file'],
- 'level': 'INFO',
- 'propagate': True}}}
-
-
-def init_logging():
- dictConfig(the_logging_config)
- global init_logging
- init_logging = lambda: None
-
-
-class TuningRunMain(object):
- def __init__(self,
- measurement_interface,
- args,
- search_driver=SearchDriver,
- measurement_driver=MeasurementDriver):
- init_logging()
-
- manipulator = measurement_interface.manipulator()
- if args.print_search_space_size:
- print "10^{%.2f}" % math.log(manipulator.search_space_size(), 10)
- sys.exit(0)
- # show internal parameter representation
- if args.print_params:
- cfg = manipulator.seed_config()
- d = manipulator.parameters_dict(cfg)
- params_dict ={}
- for k in d:
- cls = d[k].__class__.__name__
- p = (k, d[k].search_space_size())
- if cls in params_dict:
- params_dict[cls].append(p)
- else:
- params_dict[cls] = [p]
- for k in params_dict:
- print k, params_dict[k]
- print
- sys.exit(0)
-
- input_manager = measurement_interface.input_manager()
- objective = measurement_interface.objective()
-
- if not args.database:
- #args.database = 'sqlite://' #in memory
- if not os.path.isdir('opentuner.db'):
- os.mkdir('opentuner.db')
- args.database = 'sqlite:///' + os.path.join('opentuner.db',
- socket.gethostname() + '.db')
-
- if '://' not in args.database:
- args.database = 'sqlite:///' + args.database
-
- if not args.label:
- args.label = 'unnamed'
-
- #self.fake_commit = ('sqlite' in args.database)
- self.fake_commit = True
-
- self.args = args
-
- self.engine, self.Session = resultsdb.connect(args.database)
- self.session = self.Session()
- self.tuning_run = None
- self.search_driver_cls = search_driver
- self.measurement_driver_cls = measurement_driver
- self.measurement_interface = measurement_interface
- self.input_manager = input_manager
- self.manipulator = manipulator
- self.objective = objective
- self.objective_copy = copy.copy(objective)
- self.last_commit_time = time.time()
-
- def init(self):
- if self.tuning_run is None:
- program_version = (self.measurement_interface
- .db_program_version(self.session))
- self.session.flush()
- self.measurement_interface.prefix_hook(self.session)
- self.tuning_run = (
- resultsdb.models.TuningRun(
- uuid=uuid.uuid4().hex,
- name=self.args.label,
- args=self.args,
- start_date=datetime.now(),
- program_version=program_version,
- objective=self.objective_copy,
- ))
- self.session.add(self.tuning_run)
-
- driver_kwargs = {
- 'args': self.args,
- 'input_manager': self.input_manager,
- 'manipulator': self.manipulator,
- 'measurement_interface': self.measurement_interface,
- 'objective': self.objective,
- 'session': self.session,
- 'tuning_run_main': self,
- 'tuning_run': self.tuning_run,
- 'extra_seeds': self.measurement_interface.seed_configurations(),
- 'extra_criteria': self.measurement_interface.extra_convergence_criteria
- }
-
- self.search_driver = self.search_driver_cls(**driver_kwargs)
-
- self.measurement_driver = self.measurement_driver_cls(**driver_kwargs)
- self.measurement_interface.set_driver(self.measurement_driver)
- self.input_manager.set_driver(self.measurement_driver)
-
- self.tuning_run.machine_class = self.measurement_driver.get_machine_class()
- self.tuning_run.input_class = self.input_manager.get_input_class()
-
- def commit(self, force=False):
- if (force or not self.fake_commit or
- time.time() - self.last_commit_time > 30):
- self.session.commit()
- self.last_commit_time = time.time()
- else:
- self.session.flush()
-
- def main(self):
- self.init()
- try:
- self.tuning_run.state = 'RUNNING'
- self.commit(force=True)
- self.search_driver.main()
- if self.search_driver.best_result:
- self.measurement_interface.save_final_config(
- self.search_driver.best_result.configuration)
- self.tuning_run.final_config = self.search_driver.best_result.configuration
- self.tuning_run.state = 'COMPLETE'
- except:
- self.tuning_run.state = 'ABORTED'
- raise
- finally:
- self.tuning_run.end_date = datetime.now()
- self.commit(force=True)
- self.session.close()
-
- def results_wait(self, generation):
- """called by search_driver to wait for results"""
- #single process version:
- self.measurement_interface.pre_process()
- self.measurement_driver.process_all()
- self.measurement_interface.post_process()
-
-def main(interface, args, *pargs, **kwargs):
- if inspect.isclass(interface):
- interface = interface(args=args, *pargs, **kwargs)
- return TuningRunMain(interface, args).main()
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/utils/__init__.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/utils/adddeps.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/utils/adddeps.py
deleted file mode 100644
index e2fc74064b605e92367907a7641442df0cf97cd9..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/utils/adddeps.py
+++ /dev/null
@@ -1,13 +0,0 @@
-
-import sys
-from os.path import normpath, realpath, dirname, join, isfile
-
-project_root = normpath(join(dirname(realpath(__file__)), '../..'))
-
-if 'venv' not in ','.join(sys.path):
- venv_activate = join(project_root, 'venv/bin/activate_this.py')
- if isfile(venv_activate):
- execfile(venv_activate, dict(__file__=venv_activate))
-
-sys.path.insert(0, project_root)
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/utils/compactdb.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/utils/compactdb.py
deleted file mode 100755
index 25a70d2d3b2658e877aa51a1462d5a9366635057..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/utils/compactdb.py
+++ /dev/null
@@ -1,54 +0,0 @@
-#!/usr/bin/env python
-
-if __name__ == '__main__':
- import adddeps
-
-import argparse
-import logging
-import sys
-
-import opentuner
-from opentuner.resultsdb.models import *
-
-log = logging.getLogger('opentuner.utils.compactdb')
-
-argparser = argparse.ArgumentParser()
-argparser.add_argument('database')
-argparser.add_argument('--level', type=int, default=2)
-
-
-def main(args):
- if '://' not in args.database:
- args.database = "sqlite:///" + args.database
- engine, Session = opentuner.resultsdb.connect(args.database)
- session = Session()
-
- config_count = session.query(Configuration).count()
- # result_count = session.query(Result).count()
- # desired_result_count = session.query(DesiredResult).count()
-
- if args.level >= 1:
- q = (session.query(Configuration)
- .filter(~Configuration.id.in_(session.query(Result.configuration_id)
- .filter_by(was_new_best=True)
- .subquery()))
- .filter(Configuration.data != None))
-
- log.info("%s: compacted %d of %d Configurations",
- args.database,
- q.update({'data': None}, False),
- config_count)
- session.commit()
-
- if args.level >= 2:
- session.execute('VACUUM;')
- session.commit()
-
- log.info('done')
-
-
-if __name__ == '__main__':
- opentuner.tuningrunmain.init_logging()
- sys.exit(main(argparser.parse_args()))
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/utils/dictconfig.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/utils/dictconfig.py
deleted file mode 100644
index 7b835a41084d1c24f40002e93940c574b60bb696..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/utils/dictconfig.py
+++ /dev/null
@@ -1,544 +0,0 @@
-# This is a copy of the Python logging.config.dictconfig module,
-# reproduced with permission. It is provided here for backwards
-# compatibility for Python versions prior to 2.7.
-#
-# Copyright 2009-2010 by Vinay Sajip. All Rights Reserved.
-#
-# Permission to use, copy, modify, and distribute this software and its
-# documentation for any purpose and without fee is hereby granted,
-# provided that the above copyright notice appear in all copies and that
-# both that copyright notice and this permission notice appear in
-# supporting documentation, and that the name of Vinay Sajip
-# not be used in advertising or publicity pertaining to distribution
-# of the software without specific, written prior permission.
-# VINAY SAJIP DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING
-# ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL
-# VINAY SAJIP BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR
-# ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER
-# IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT
-# OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
-
-import logging.handlers
-import re
-import sys
-import types
-
-
-IDENTIFIER = re.compile('^[a-z_][a-z0-9_]*$', re.I)
-
-def valid_ident(s):
- m = IDENTIFIER.match(s)
- if not m:
- raise ValueError('Not a valid Python identifier: %r' % s)
- return True
-
-#
-# This function is defined in logging only in recent versions of Python
-#
-try:
- from logging import _checkLevel
-except ImportError:
- def _checkLevel(level):
- if isinstance(level, int):
- rv = level
- elif str(level) == level:
- if level not in logging._levelNames:
- raise ValueError('Unknown level: %r' % level)
- rv = logging._levelNames[level]
- else:
- raise TypeError('Level not an integer or a '
- 'valid string: %r' % level)
- return rv
-
-# The ConvertingXXX classes are wrappers around standard Python containers,
-# and they serve to convert any suitable values in the container. The
-# conversion converts base dicts, lists and tuples to their wrapped
-# equivalents, whereas strings which match a conversion format are converted
-# appropriately.
-#
-# Each wrapper should have a configurator attribute holding the actual
-# configurator to use for conversion.
-
-class ConvertingDict(dict):
- """A converting dictionary wrapper."""
-
- def __getitem__(self, key):
- value = dict.__getitem__(self, key)
- result = self.configurator.convert(value)
- #If the converted value is different, save for next time
- if value is not result:
- self[key] = result
- if type(result) in (ConvertingDict, ConvertingList,
- ConvertingTuple):
- result.parent = self
- result.key = key
- return result
-
- def get(self, key, default=None):
- value = dict.get(self, key, default)
- result = self.configurator.convert(value)
- #If the converted value is different, save for next time
- if value is not result:
- self[key] = result
- if type(result) in (ConvertingDict, ConvertingList,
- ConvertingTuple):
- result.parent = self
- result.key = key
- return result
-
- def pop(self, key, default=None):
- value = dict.pop(self, key, default)
- result = self.configurator.convert(value)
- if value is not result:
- if type(result) in (ConvertingDict, ConvertingList,
- ConvertingTuple):
- result.parent = self
- result.key = key
- return result
-
-class ConvertingList(list):
- """A converting list wrapper."""
- def __getitem__(self, key):
- value = list.__getitem__(self, key)
- result = self.configurator.convert(value)
- #If the converted value is different, save for next time
- if value is not result:
- self[key] = result
- if type(result) in (ConvertingDict, ConvertingList,
- ConvertingTuple):
- result.parent = self
- result.key = key
- return result
-
- def pop(self, idx=-1):
- value = list.pop(self, idx)
- result = self.configurator.convert(value)
- if value is not result:
- if type(result) in (ConvertingDict, ConvertingList,
- ConvertingTuple):
- result.parent = self
- return result
-
-class ConvertingTuple(tuple):
- """A converting tuple wrapper."""
- def __getitem__(self, key):
- value = tuple.__getitem__(self, key)
- result = self.configurator.convert(value)
- if value is not result:
- if type(result) in (ConvertingDict, ConvertingList,
- ConvertingTuple):
- result.parent = self
- result.key = key
- return result
-
-class BaseConfigurator(object):
- """
- The configurator base class which defines some useful defaults.
- """
-
- CONVERT_PATTERN = re.compile(r'^(?P<prefix>[a-z]+)://(?P<suffix>.*)$')
-
- WORD_PATTERN = re.compile(r'^\s*(\w+)\s*')
- DOT_PATTERN = re.compile(r'^\.\s*(\w+)\s*')
- INDEX_PATTERN = re.compile(r'^\[\s*(\w+)\s*\]\s*')
- DIGIT_PATTERN = re.compile(r'^\d+$')
-
- value_converters = {
- 'ext' : 'ext_convert',
- 'cfg' : 'cfg_convert',
- }
-
- # We might want to use a different one, e.g. importlib
- importer = __import__
-
- def __init__(self, config):
- self.config = ConvertingDict(config)
- self.config.configurator = self
-
- def resolve(self, s):
- """
- Resolve strings to objects using standard import and attribute
- syntax.
- """
- name = s.split('.')
- used = name.pop(0)
- try:
- found = self.importer(used)
- for frag in name:
- used += '.' + frag
- try:
- found = getattr(found, frag)
- except AttributeError:
- self.importer(used)
- found = getattr(found, frag)
- return found
- except ImportError:
- e, tb = sys.exc_info()[1:]
- v = ValueError('Cannot resolve %r: %s' % (s, e))
- v.__cause__, v.__traceback__ = e, tb
- raise v
-
- def ext_convert(self, value):
- """Default converter for the ext:// protocol."""
- return self.resolve(value)
-
- def cfg_convert(self, value):
- """Default converter for the cfg:// protocol."""
- rest = value
- m = self.WORD_PATTERN.match(rest)
- if m is None:
- raise ValueError("Unable to convert %r" % value)
- else:
- rest = rest[m.end():]
- d = self.config[m.groups()[0]]
- #print d, rest
- while rest:
- m = self.DOT_PATTERN.match(rest)
- if m:
- d = d[m.groups()[0]]
- else:
- m = self.INDEX_PATTERN.match(rest)
- if m:
- idx = m.groups()[0]
- if not self.DIGIT_PATTERN.match(idx):
- d = d[idx]
- else:
- try:
- n = int(idx) # try as number first (most likely)
- d = d[n]
- except TypeError:
- d = d[idx]
- if m:
- rest = rest[m.end():]
- else:
- raise ValueError('Unable to convert '
- '%r at %r' % (value, rest))
- #rest should be empty
- return d
-
- def convert(self, value):
- """
- Convert values to an appropriate type. dicts, lists and tuples are
- replaced by their converting alternatives. Strings are checked to
- see if they have a conversion format and are converted if they do.
- """
- if not isinstance(value, ConvertingDict) and isinstance(value, dict):
- value = ConvertingDict(value)
- value.configurator = self
- elif not isinstance(value, ConvertingList) and isinstance(value, list):
- value = ConvertingList(value)
- value.configurator = self
- elif not isinstance(value, ConvertingTuple) and\
- isinstance(value, tuple):
- value = ConvertingTuple(value)
- value.configurator = self
- return value
-
- def configure_custom(self, config):
- """Configure an object with a user-supplied factory."""
- c = config.pop('()')
- if not hasattr(c, '__call__') and hasattr(types, 'ClassType') and type(c) != types.ClassType:
- c = self.resolve(c)
- props = config.pop('.', None)
- # Check for valid identifiers
- kwargs = dict([(k, config[k]) for k in config if valid_ident(k)])
- result = c(**kwargs)
- if props:
- for name, value in props.items():
- setattr(result, name, value)
- return result
-
- def as_tuple(self, value):
- """Utility function which converts lists to tuples."""
- if isinstance(value, list):
- value = tuple(value)
- return value
-
-class DictConfigurator(BaseConfigurator):
- """
- Configure logging using a dictionary-like object to describe the
- configuration.
- """
-
- def configure(self):
- """Do the configuration."""
-
- config = self.config
- if 'version' not in config:
- raise ValueError("dictionary doesn't specify a version")
- if config['version'] != 1:
- raise ValueError("Unsupported version: %s" % config['version'])
- incremental = config.pop('incremental', False)
- EMPTY_DICT = {}
- logging._acquireLock()
- try:
- if incremental:
- handlers = config.get('handlers', EMPTY_DICT)
- # incremental handler config only if handler name
- # ties in to logging._handlers (Python 2.7)
- if sys.version_info[:2] == (2, 7):
- for name in handlers:
- if name not in logging._handlers:
- raise ValueError('No handler found with '
- 'name %r' % name)
- else:
- try:
- handler = logging._handlers[name]
- handler_config = handlers[name]
- level = handler_config.get('level', None)
- if level:
- handler.setLevel(_checkLevel(level))
- except StandardError as e:
- raise ValueError('Unable to configure handler '
- '%r: %s' % (name, e))
- loggers = config.get('loggers', EMPTY_DICT)
- for name in loggers:
- try:
- self.configure_logger(name, loggers[name], True)
- except StandardError as e:
- raise ValueError('Unable to configure logger '
- '%r: %s' % (name, e))
- root = config.get('root', None)
- if root:
- try:
- self.configure_root(root, True)
- except StandardError as e:
- raise ValueError('Unable to configure root '
- 'logger: %s' % e)
- else:
- disable_existing = config.pop('disable_existing_loggers', True)
-
- logging._handlers.clear()
- del logging._handlerList[:]
-
- # Do formatters first - they don't refer to anything else
- formatters = config.get('formatters', EMPTY_DICT)
- for name in formatters:
- try:
- formatters[name] = self.configure_formatter(
- formatters[name])
- except StandardError as e:
- raise ValueError('Unable to configure '
- 'formatter %r: %s' % (name, e))
- # Next, do filters - they don't refer to anything else, either
- filters = config.get('filters', EMPTY_DICT)
- for name in filters:
- try:
- filters[name] = self.configure_filter(filters[name])
- except StandardError as e:
- raise ValueError('Unable to configure '
- 'filter %r: %s' % (name, e))
-
- # Next, do handlers - they refer to formatters and filters
- # As handlers can refer to other handlers, sort the keys
- # to allow a deterministic order of configuration
- handlers = config.get('handlers', EMPTY_DICT)
- for name in sorted(handlers):
- try:
- handler = self.configure_handler(handlers[name])
- handler.name = name
- handlers[name] = handler
- except StandardError as e:
- raise ValueError('Unable to configure handler '
- '%r: %s' % (name, e))
- # Next, do loggers - they refer to handlers and filters
-
- #we don't want to lose the existing loggers,
- #since other threads may have pointers to them.
- #existing is set to contain all existing loggers,
- #and as we go through the new configuration we
- #remove any which are configured. At the end,
- #what's left in existing is the set of loggers
- #which were in the previous configuration but
- #which are not in the new configuration.
- root = logging.root
- existing = list(root.manager.loggerDict)
- #The list needs to be sorted so that we can
- #avoid disabling child loggers of explicitly
- #named loggers. With a sorted list it is easier
- #to find the child loggers.
- existing.sort()
- #We'll keep the list of existing loggers
- #which are children of named loggers here...
- child_loggers = []
- #now set up the new ones...
- loggers = config.get('loggers', EMPTY_DICT)
- for name in loggers:
- if name in existing:
- i = existing.index(name)
- prefixed = name + "."
- pflen = len(prefixed)
- num_existing = len(existing)
- i = i + 1 # look at the entry after name
- while (i < num_existing) and\
- (existing[i][:pflen] == prefixed):
- child_loggers.append(existing[i])
- i = i + 1
- existing.remove(name)
- try:
- self.configure_logger(name, loggers[name])
- except StandardError as e:
- raise ValueError('Unable to configure logger '
- '%r: %s' % (name, e))
-
- #Disable any old loggers. There's no point deleting
- #them as other threads may continue to hold references
- #and by disabling them, you stop them doing any logging.
- #However, don't disable children of named loggers, as that's
- #probably not what was intended by the user.
- for log in existing:
- logger = root.manager.loggerDict[log]
- if log in child_loggers:
- logger.level = logging.NOTSET
- logger.handlers = []
- logger.propagate = True
- elif disable_existing:
- logger.disabled = True
-
- # And finally, do the root logger
- root = config.get('root', None)
- if root:
- try:
- self.configure_root(root)
- except StandardError as e:
- raise ValueError('Unable to configure root '
- 'logger: %s' % e)
- finally:
- logging._releaseLock()
-
- def configure_formatter(self, config):
- """Configure a formatter from a dictionary."""
- if '()' in config:
- factory = config['()'] # for use in exception handler
- try:
- result = self.configure_custom(config)
- except TypeError as te:
- if "'format'" not in str(te):
- raise
- #Name of parameter changed from fmt to format.
- #Retry with old name.
- #This is so that code can be used with older Python versions
- #(e.g. by Django)
- config['fmt'] = config.pop('format')
- config['()'] = factory
- result = self.configure_custom(config)
- else:
- fmt = config.get('format', None)
- dfmt = config.get('datefmt', None)
- result = logging.Formatter(fmt, dfmt)
- return result
-
- def configure_filter(self, config):
- """Configure a filter from a dictionary."""
- if '()' in config:
- result = self.configure_custom(config)
- else:
- name = config.get('name', '')
- result = logging.Filter(name)
- return result
-
- def add_filters(self, filterer, filters):
- """Add filters to a filterer from a list of names."""
- for f in filters:
- try:
- filterer.addFilter(self.config['filters'][f])
- except StandardError as e:
- raise ValueError('Unable to add filter %r: %s' % (f, e))
-
- def configure_handler(self, config):
- """Configure a handler from a dictionary."""
- formatter = config.pop('formatter', None)
- if formatter:
- try:
- formatter = self.config['formatters'][formatter]
- except StandardError as e:
- raise ValueError('Unable to set formatter '
- '%r: %s' % (formatter, e))
- level = config.pop('level', None)
- filters = config.pop('filters', None)
- if '()' in config:
- c = config.pop('()')
- if not hasattr(c, '__call__') and hasattr(types, 'ClassType') and type(c) != types.ClassType:
- c = self.resolve(c)
- factory = c
- else:
- klass = self.resolve(config.pop('class'))
- #Special case for handler which refers to another handler
- if issubclass(klass, logging.handlers.MemoryHandler) and\
- 'target' in config:
- try:
- config['target'] = self.config['handlers'][config['target']]
- except StandardError as e:
- raise ValueError('Unable to set target handler '
- '%r: %s' % (config['target'], e))
- elif issubclass(klass, logging.handlers.SMTPHandler) and\
- 'mailhost' in config:
- config['mailhost'] = self.as_tuple(config['mailhost'])
- elif issubclass(klass, logging.handlers.SysLogHandler) and\
- 'address' in config:
- config['address'] = self.as_tuple(config['address'])
- factory = klass
- kwargs = dict([(k, config[k]) for k in config if valid_ident(k)])
- try:
- result = factory(**kwargs)
- except TypeError as te:
- if "'stream'" not in str(te):
- raise
- #The argument name changed from strm to stream
- #Retry with old name.
- #This is so that code can be used with older Python versions
- #(e.g. by Django)
- kwargs['strm'] = kwargs.pop('stream')
- result = factory(**kwargs)
- if formatter:
- result.setFormatter(formatter)
- if level is not None:
- result.setLevel(_checkLevel(level))
- if filters:
- self.add_filters(result, filters)
- return result
-
- def add_handlers(self, logger, handlers):
- """Add handlers to a logger from a list of names."""
- for h in handlers:
- try:
- logger.addHandler(self.config['handlers'][h])
- except StandardError as e:
- raise ValueError('Unable to add handler %r: %s' % (h, e))
-
- def common_logger_config(self, logger, config, incremental=False):
- """
- Perform configuration which is common to root and non-root loggers.
- """
- level = config.get('level', None)
- if level is not None:
- logger.setLevel(_checkLevel(level))
- if not incremental:
- #Remove any existing handlers
- for h in logger.handlers[:]:
- logger.removeHandler(h)
- handlers = config.get('handlers', None)
- if handlers:
- self.add_handlers(logger, handlers)
- filters = config.get('filters', None)
- if filters:
- self.add_filters(logger, filters)
-
- def configure_logger(self, name, config, incremental=False):
- """Configure a non-root logger from a dictionary."""
- logger = logging.getLogger(name)
- self.common_logger_config(logger, config, incremental)
- propagate = config.get('propagate', None)
- if propagate is not None:
- logger.propagate = propagate
-
- def configure_root(self, config, incremental=False):
- """Configure a root logger from a dictionary."""
- root = logging.getLogger()
- self.common_logger_config(root, config, incremental)
-
-dictConfigClass = DictConfigurator
-
-def dictConfig(config):
- """Configure logging using a dictionary."""
- dictConfigClass(config).configure()
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/utils/stats.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/utils/stats.py
deleted file mode 100755
index 99449c8a900a3f8ad53c6c12fbbc4d2197b1cb45..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/utils/stats.py
+++ /dev/null
@@ -1,468 +0,0 @@
-#!/usr/bin/env python
-
-if __name__ == '__main__':
- import adddeps
-
-import argparse
-import csv
-import hashlib
-import itertools
-import logging
-import math
-import os
-import sqlalchemy.orm.exc
-import subprocess
-import sys
-
-from collections import defaultdict
-from fn import _
-from fn import Stream
-from fn.iters import repeat
-from pprint import pprint
-
-import opentuner
-from opentuner import resultsdb
-from opentuner.resultsdb.models import *
-
-log = logging.getLogger('opentuner.utils.stats')
-
-argparser = argparse.ArgumentParser()
-argparser.add_argument('--label')
-argparser.add_argument('--stats', action='store_true',
- help="run in stats mode")
-argparser.add_argument('--by-request-count', action='store_true',
- help='report stats by request count')
-argparser.add_argument('--stats-quanta', type=float, default=10,
- help="step size in seconds for binning with --stats")
-argparser.add_argument('--stats-dir', default='stats',
- help="directory to output --stats to")
-argparser.add_argument('--stats-input', default="opentuner.db")
-argparser.add_argument('--min-runs', type=int, default=1,
- help="ignore series with less then N runs")
-
-PCTSTEPS = map(_/20.0, xrange(21))
-
-def mean(vals):
- n = 0.0
- d = 0.0
- for v in vals:
- if v is not None:
- n += v
- d += 1.0
- if d == 0.0:
- return None
- return n/d
-
-def median(vals):
- vals = sorted(vals)
- a = (len(vals)-1)/2
- b = (len(vals))/2
- return (vals[a]+vals[b])/2.0
-
-def percentile(vals, pct):
- vals = sorted(vals)
- pos = (len(vals)-1) * pct
- a = int(math.floor(pos))
- b = min(len(vals) - 1, a + 1)
- return (1.0-(pos-a))*vals[a] + (pos-a)*vals[b]
-
-def variance(vals):
- vals = filter(lambda x: x is not None, vals)
- avg = mean(vals)
- if avg is None:
- return None
- if avg in (float('inf'), float('-inf')):
- return avg
- return mean(map((_ - avg) ** 2, vals))
-
-def stddev(vals):
- var = variance(vals)
- if var is None:
- return None
- return math.sqrt(var)
-
-def hash_args(x):
- d = dict(vars(x))
- for k in ('database', 'results_log', 'results_log_details'):
- d[k] = None
- return hashlib.sha256(str(sorted(d.items()))).hexdigest()[:20]
-
-def run_label(tr, short = False):
- techniques = ','.join(tr.args.technique)
- if not tr.name or tr.name=='unnamed':
- if short:
- return techniques
- else:
- return "%s_%s" % (techniques, hash_args(tr.args)[:6])
- else:
- return tr.name
-
-def run_dir(base, tr):
- return os.path.join(base,
- tr.program.project,
- tr.program.name.split('/')[-1],
- tr.program_version.version[:16])
-
-class StatsMain(object):
- def __init__(self, args):
- self.args = args
- path = args.stats_input
- self.dbs = list()
- for f in os.listdir(path):
- if 'journal' in f:
- continue
- try:
- e, sm = resultsdb.connect('sqlite:///'+os.path.join(path, f))
- self.dbs.append(sm())
- except:
- log.error('failed to load database: %s',
- os.path.join(path, f),
- exc_info=True)
-
- def main(self):
- dir_label_runs = defaultdict(lambda: defaultdict(list))
- for session in self.dbs:
- q = (session.query(resultsdb.models.TuningRun)
- .filter_by(state='COMPLETE')
- .order_by('name'))
-
- if self.args.label:
- q = q.filter(TuningRun.name.in_(
- map(str.strip,self.args.label.split(','))))
-
- for tr in q:
- d = run_dir(self.args.stats_dir, tr)
- d = os.path.normpath(d)
- dir_label_runs[d][run_label(tr)].append((tr, session))
-
- summary_report = defaultdict(lambda: defaultdict(list))
- for d, label_runs in dir_label_runs.iteritems():
- if not os.path.isdir(d):
- os.makedirs(d)
- session = label_runs.values()[0][0][1]
- objective = label_runs.values()[0][0][0].objective
- all_run_ids = map(_[0].id, itertools.chain(*label_runs.values()))
- q = (session.query(Result)
- .filter(Result.tuning_run_id.in_(all_run_ids))
- .filter(Result.time < float('inf'))
- .filter_by(was_new_best=True, state='OK'))
- total = q.count()
- if total == 0:
- continue
- q = objective.filter_acceptable(q)
- acceptable = q.count()
- q = q.order_by(*objective.result_order_by_terms())
- best = q.limit(1).one()
- worst = q.offset(acceptable-1).limit(1).one()
-
- map(len, label_runs.values())
-
- log.info("%s -- best %.4f / worst %.f4 "
- "-- %d of %d acceptable -- %d techniques with %d to %d runs",
- d,
- best.time,
- worst.time,
- acceptable,
- total,
- len(label_runs.values()),
- min(map(len, label_runs.values())),
- max(map(len, label_runs.values())))
-
- for label, runs in sorted(label_runs.items()):
- if len(runs) < self.args.min_runs:
- print len(runs) ,self.args.min_runs
- continue
- log.debug('%s/%s has %d runs %s',d, label, len(runs), runs[0][0].args.technique)
- self.combined_stats_over_time(d, label, runs, objective, worst, best)
-
- final_scores = list()
- for run, session in runs:
- try:
- final = (session.query(Result)
- .filter_by(tuning_run=run,
- configuration=run.final_config)
- .limit(1)
- .one())
- except sqlalchemy.orm.exc.NoResultFound:
- continue
- final_scores.append(objective.stats_quality_score(final, worst, best))
- final_scores.sort()
- if final_scores:
- norm = objective.stats_quality_score(best, worst, best)
- if norm > 0.00001:
- summary_report[d][run_label(run, short=True)] = (
- percentile(final_scores, 0.5) / norm,
- percentile(final_scores, 0.1) / norm,
- percentile(final_scores, 0.9) / norm,
- )
- else:
- summary_report[d][run_label(run, short=True)] = (
- percentile(final_scores, 0.5) + norm + 1.0,
- percentile(final_scores, 0.1) + norm + 1.0,
- percentile(final_scores, 0.9) + norm + 1.0,
- )
-
-
- with open(self.args.stats_dir+ "/summary.dat", 'w') as o:
- # make summary report
- keys = sorted(reduce(set.union,
- [set(x.keys()) for x in summary_report.values()],
- set()))
- print >>o, '#####',
- for k in keys:
- print >>o, k,
- print >>o
- for d, label_vals in sorted(summary_report.items()):
- print >>o, d.split('/')[-2],
- for k in keys:
- if k in label_vals:
- print >>o, '-', label_vals[k][0], label_vals[k][1], label_vals[k][2],
- else:
- print >>o, '-', '-', '-', '-',
- print >>o
-
- if keys:
- plotcmd = ["""1 w lines lt 1 lc rgb "black" notitle""",
- """'summary.dat' using 3:4:5:xtic(1) ti "%s" """ % keys[0]]
- for n, k in enumerate(keys[1:]):
- plotcmd.append("""'' using %d:%d:%d ti "%s" """ % (
- 4*n + 7,
- 4*n + 8,
- 4*n + 9,
- k))
- self.gnuplot_summary_file(self.args.stats_dir, 'summary', plotcmd)
-
-
-
- for d, label_runs in dir_label_runs.iteritems():
- labels = [k for k,v in label_runs.iteritems()
- if len(v)>=self.args.min_runs]
- self.gnuplot_file(d,
- "medianperfe",
- ['"%s_percentiles.dat" using 1:12:4:18 with errorbars title "%s"' % (l,l) for l in labels])
- self.gnuplot_file(d,
- "meanperfe",
- ['"%s_percentiles.dat" using 1:21:4:18 with errorbars title "%s"' % (l,l) for l in labels])
- self.gnuplot_file(d,
- "medianperfl",
- ['"%s_percentiles.dat" using 1:12 with lines title "%s"' % (l,l) for l in labels])
- self.gnuplot_file(d,
- "meanperfl",
- ['"%s_percentiles.dat" using 1:21 with lines title "%s"' % (l,l) for l in labels])
-
- # print
- # print "10% Scores", d
- # pprint(self.technique_scores(d, labels, '0.1'))
- # print
- # print "90% Scores", d
- # pprint(self.technique_scores(d, labels, '0.9'))
- # print
- # print "Mean Scores", d
- # pprint(self.technique_scores(d, labels, 'mean'))
- print
- print "Median Scores", d
- pprint(self.technique_scores(d, labels, '0.5'))
-
-
- def technique_scores(self, directory, labels, ykey, xkey='#sec', factor=10.0):
- max_duration = None
- min_value = float('inf')
- for label in labels:
- try:
- dr = csv.DictReader(open(os.path.join(directory,label+"_percentiles.dat")), delimiter=' ', lineterminator='\n')
- lastrow = list(dr)[-1]
- max_duration = max(max_duration, float(lastrow[xkey]))
- min_value = min(min_value, float(lastrow[ykey]))
- except:
- log.exception("failed computing score")
-
- scores = list()
-
- for label in labels:
- try:
- dr = csv.DictReader(open(os.path.join(directory,label+"_percentiles.dat")), delimiter=' ', lineterminator='\n')
- score = 0.0
- lastsec = 0.0
- value = float('inf')
- for row in dr:
- duration = float(row[xkey]) - lastsec
- lastsec = float(row[xkey])
- value = float(row[ykey])
- score += duration * (value - min_value)
- score += (factor*max_duration - lastsec) * (value - min_value)
- scores.append((score, label))
- except:
- log.exception("failed computing score")
-
- return sorted(scores)
-
-
- def combined_stats_over_time(self,
- output_dir,
- label,
- runs,
- objective,
- worst,
- best,
- ):
- """
- combine stats_over_time() vectors for multiple runs
- """
-
- #extract_fn = lambda dr: objective.stats_quality_score(dr.result, worst, best)
- extract_fn = _.result.time
- combine_fn = min
- no_data = 999
-
- log.debug("writing stats for %s to %s", label, output_dir)
- by_run = [self.stats_over_time(session, run, extract_fn, combine_fn, no_data)
- for run, session in runs]
- max_len = max(map(len, by_run))
-
- by_run_streams = [Stream() << x << repeat(x[-1], max_len-len(x))
- for x in by_run]
- by_quanta = zip(*by_run_streams[:])
-
- def data_file(suffix, headers, value_function):
- with open(os.path.join(output_dir, label+suffix), 'w') as fd:
- out = csv.writer(fd, delimiter=' ', lineterminator='\n')
- out.writerow(['#sec'] + headers)
- for quanta, values in enumerate(by_quanta):
- sec = quanta*self.args.stats_quanta
- out.writerow([sec] + value_function(values))
-
- #data_file('_details.dat',
- # map(lambda x: 'run%d'%x, xrange(max_len)),
- # list)
- #self.gnuplot_file(output_dir,
- # label+'_details',
- # [('"'+label+'_details.dat"'
- # ' using 1:%d'%i +
- # ' with lines'
- # ' title "Run %d"'%i)
- # for i in xrange(max_len)])
-
- data_file('_mean.dat',
- ['#sec', 'mean', 'stddev'],
- lambda values: [mean(values), stddev(values)])
- self.gnuplot_file(output_dir,
- label+'_mean',
- ['"'+label+'_mean.dat" using 1:2 with lines title "Mean"'])
-
- def extract_percentiles(values):
- values = sorted(values)
- return ([values[int(round(p*(len(values)-1)))] for p in PCTSTEPS]
- + [mean(values)])
- data_file("_percentiles.dat", PCTSTEPS + ['mean'], extract_percentiles)
- self.gnuplot_file(output_dir,
- label+'_percentiles',
- reversed([
- '"'+label+'_percentiles.dat" using 1:2 with lines title "0%"',
- # '"" using 1:3 with lines title "5%"',
- '"" using 1:4 with lines title "10%"',
- # '"" using 1:5 with lines title "25%"',
- '"" using 1:6 with lines title "20%"',
- # '"" using 1:7 with lines title "35%"',
- '"" using 1:8 with lines title "30%"',
- # '"" using 1:9 with lines title "45%"',
- '"" using 1:10 with lines title "40%"',
- # '"" using 1:11 with lines title "55%"',
- '"" using 1:12 with lines title "50%"',
- # '"" using 1:13 with lines title "65%"',
- '"" using 1:14 with lines title "70%"',
- # '"" using 1:15 with lines title "75%"',
- '"" using 1:16 with lines title "80%"',
- # '"" using 1:17 with lines title "85%"',
- '"" using 1:18 with lines title "90%"',
- # '"" using 1:19 with lines title "95%"',
- '"'+label+'_percentiles.dat" using 1:20 with lines title "100%"',
- ]))
-
- def gnuplot_file(self, output_dir, prefix, plotcmd):
- with open(os.path.join(output_dir, prefix+'.gnuplot'), 'w') as fd:
- print >>fd, 'set terminal postscript eps enhanced color'
- print >>fd, 'set output "%s"' % (prefix+'.eps')
- print >>fd, 'set ylabel "Execution Time (seconds)"'
- print >>fd, 'set xlabel "Autotuning Time (seconds)"'
- print >>fd, 'plot', ',\\\n'.join(plotcmd)
-
- try:
- subprocess.call(['gnuplot', prefix+'.gnuplot'], cwd=output_dir, stdin=None)
- except OSError:
- log.error("command gnuplot not found")
-
- def gnuplot_summary_file(self, output_dir, prefix, plotcmd):
- with open(os.path.join(output_dir, prefix+'.gnuplot'), 'w') as fd:
- print >>fd, 'set terminal postscript eps enhanced color'
- print >>fd, 'set output "%s"' % (prefix+'.eps')
- print >>fd, '''
-set boxwidth 0.9
-set style fill solid 1.00 border 0
-set style histogram errorbars gap 2 lw 1
-set style data histograms
-set xtics rotate by -45
-set bars 0.5
-set yrange [0:20]
-
-set yrange [0:10]
-set key out vert top left
-set size 1.5,1
-set ytics 1
-
-'''
- print >>fd, 'plot', ',\\\n'.join(plotcmd)
- subprocess.call(['gnuplot', prefix+'.gnuplot'], cwd=output_dir, stdin=None)
-
-
- def stats_over_time(self,
- session,
- run,
- extract_fn,
- combine_fn,
- no_data = None):
- """
- return reduce(combine_fn, map(extract_fn, data)) for each quanta of the
- tuning run
- """
- value_by_quanta = [ no_data ]
- start_date = run.start_date
-
- subq = (session.query(Result.id)
- .filter_by(tuning_run = run, was_new_best = True, state='OK'))
-
- q = (session.query(DesiredResult)
- .join(Result)
- .filter(DesiredResult.state=='COMPLETE',
- DesiredResult.tuning_run == run,
- DesiredResult.result_id.in_(subq.subquery()))
- .order_by(DesiredResult.request_date))
-
- first_id = None
- for dr in q:
- if first_id is None:
- first_id = dr.id
- td = (dr.request_date - start_date)
- duration = td.seconds + (td.days * 24 * 3600.0)
- if self.args.by_request_count:
- quanta = dr.id - first_id
- else:
- quanta = int(duration / self.args.stats_quanta)
- while len(value_by_quanta) <= quanta:
- value_by_quanta.append(value_by_quanta[-1])
-
- if value_by_quanta[-1] is no_data:
- value_by_quanta[-1] = extract_fn(dr)
- else:
- value_by_quanta[-1] = combine_fn(value_by_quanta[-1], extract_fn(dr))
-
- return value_by_quanta
-
-
-
-
-
-if __name__ == '__main__':
- opentuner.tuningrunmain.init_logging()
- sys.exit(StatsMain(argparser.parse_args()).main())
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/utils/stats_matplotlib.py b/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/utils/stats_matplotlib.py
deleted file mode 100644
index 54e9132a662fa68089ce3d0ba00cb6502bd2c712..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/opentuner/utils/stats_matplotlib.py
+++ /dev/null
@@ -1,290 +0,0 @@
-#!usr/bin/python
-
-if __name__ == '__main__':
- import adddeps
-
-import itertools
-import math
-import matplotlib.pyplot as plt
-import numpy
-import os
-import sqlalchemy
-import sqlalchemy.orm.exc
-
-from collections import defaultdict
-from fn import _
-from fn import Stream
-from fn.iters import repeat
-from opentuner import resultsdb
-
-PCTSTEPS = map(_/20.0, xrange(21))
-
-
-def mean(vals):
- """
- Arguments,
- vals: List of floating point numbers
- Returns,
- The mean of the numbers in the input list
- None if all values in the list are None
- """
- filtered_values = [float(x) for x in vals if x is not None]
- if len(filtered_values) == 0:
- return None
- return numpy.mean(numpy.array(filtered_values))
-
-
-def stddev(vals):
- """
- Arguments,
- vals: List of floating point numbers
- Returns,
- The standard deviation of numbers in the input list
- None if all values in the list are None
- """
- filtered_values = [float(x) for x in vals if x is not None]
- if len(filtered_values) == 0:
- return None
- return math.sqrt(numpy.var(numpy.array(filtered_values)))
-
-
-def get_dbs(path, db_type='sqlite:///'):
- """
- Arguments,
- path: Path of directory containing .db files
- Returns,
- A list of (engine, session) pairs to the dbs pointed to by
- the db files
- """
- dbs = list()
- for f in os.listdir(path):
- if 'journal' in f:
- continue
- try:
- db_path = os.path.join(path, f)
- e, sm = resultsdb.connect(db_type + db_path)
- dbs.append(sm())
- except Exception as e:
- print e
- print "Error encountered while connecting to db"
- return dbs
-
-
-def matplotlibplot_file(labels, xlim = None, ylim = None, disp_types=['median']):
- """
- Arguments,
- labels: List of labels that need to be included in the plot
- xlim: Integer denoting the maximum X-coordinate in the plot
- ylim: Integer denoting the maximum Y-coordinate in the plot
- disp_types: List of measures that are to be displayed in the plot
- Returns,
- A figure object representing the required plot
- """
-
- figure = plt.figure()
- values = get_values(labels)
- for label in values:
- (mean_values, percentile_values) = values[label]
- for disp_type in disp_types:
- cols = None
- data = percentile_values
-
- if disp_type == 'median':
- cols = [11]
- elif disp_type == 'mean':
- cols = [1]
- data = mean_values
- elif disp_type == 'all_percentiles':
- cols = range(1,22)
-
- plotted_data = [[] for x in xrange(len(cols))]
-
- x_indices = []
- for data_point in data[1:]:
- x_indices.append(int(data_point[0]))
- for i in range(0, len(cols)):
- plotted_data[i].append(float(data_point[cols[i]]))
- args = []
- for to_plot in plotted_data:
- args.append(x_indices)
- args.append(to_plot)
-
- plt.plot(*args, label='%s(%s)' % (label, disp_type))
-
- if xlim is not None:
- plt.xlim(xlim)
- if ylim is not None:
- plt.ylim(ylim)
-
- plt.xlabel('Autotuning Time (seconds)')
- plt.ylabel('Execution Time (seconds)')
- plt.legend(loc='upper right')
- return figure
-
-
-def run_label(tr):
- techniques = ','.join(tr.args.technique)
- if not tr.name or tr.name == 'unnamed':
- return techniques
- return tr.name
-
-
-def combined_stats_over_time(label,
- runs,
- objective,
- worst,
- best,
- ):
- """
- combine stats_over_time() vectors for multiple runs
- """
-
- extract_fn = _.result.time
- combine_fn = min
- no_data = 999
-
- by_run = [stats_over_time(session, run, extract_fn, combine_fn, no_data)
- for run, session in runs]
- max_len = max(map(len, by_run))
-
- by_run_streams = [Stream() << x << repeat(x[-1], max_len-len(x))
- for x in by_run]
- by_quanta = zip(*by_run_streams[:])
-
- # TODO: Fix this, this variable should be configurable
- stats_quanta = 10
- def get_data(value_function):
- final_values = []
- for quanta, values in enumerate(by_quanta):
- sec = quanta*stats_quanta
- final_values.append([sec] + value_function(values))
- return final_values
-
- mean_values = get_data(lambda values: [mean(values), stddev(values)])
-
- def extract_percentiles(values):
- values = sorted(values)
- return ([values[int(round(p*(len(values)-1)))] for p in PCTSTEPS]
- + [mean(values)])
- percentile_values = get_data(extract_percentiles)
- return mean_values, percentile_values
-
-
-def stats_over_time(session,
- run,
- extract_fn,
- combine_fn,
- no_data = None):
- """
- return reduce(combine_fn, map(extract_fn, data)) for each quanta of the
- tuning run
- """
- value_by_quanta = [ no_data ]
- start_date = run.start_date
-
- subq = (session.query(resultsdb.models.Result.id)
- .filter_by(tuning_run = run, was_new_best = True, state='OK'))
-
- q = (session.query(resultsdb.models.DesiredResult)
- .join(resultsdb.models.Result)
- .filter(resultsdb.models.DesiredResult.state=='COMPLETE',
- resultsdb.models.DesiredResult.tuning_run == run,
- resultsdb.models.DesiredResult.result_id.in_(subq.subquery()))
- .order_by(resultsdb.models.DesiredResult.request_date))
-
- first_id = None
- for dr in q:
- if first_id is None:
- first_id = dr.id
- td = (dr.request_date - start_date)
- duration = td.seconds + (td.days * 24 * 3600.0)
- # TODO: Make this variable configurable
- by_request_count = True
- stats_quanta = 10
- if by_request_count:
- quanta = dr.id - first_id
- else:
- quanta = int(duration / stats_quanta)
- while len(value_by_quanta) <= quanta:
- value_by_quanta.append(value_by_quanta[-1])
-
- if value_by_quanta[-1] is no_data:
- value_by_quanta[-1] = extract_fn(dr)
- else:
- value_by_quanta[-1] = combine_fn(value_by_quanta[-1], extract_fn(dr))
-
- return value_by_quanta
-
-
-def get_all_labels():
- """
- Returns,
- List of labels that are in the complete state
- """
- dbs = get_dbs(os.getcwd())
- all_labels = list()
- for db in dbs:
- all_labels.extend(db.query(resultsdb.models.TuningRun.name)
- .filter_by(state='COMPLETE')
- .distinct()
- .all())
- all_labels = [str(element[0]) for element in all_labels]
- return all_labels
-
-
-def get_values(labels):
- """
- Arguments,
- labels: List of labels whose values are of interest
- Returns,
- A list of (mean, percentile) tuples, corresponding to the
- provided list of labels
- """
- dbs = get_dbs(os.getcwd())
- dir_label_runs = defaultdict(lambda: defaultdict(list))
- for db in dbs:
- q = (db.query(resultsdb.models.TuningRun)
- .filter_by(state='COMPLETE')
- .order_by('name'))
- if labels:
- q = q.filter(resultsdb.models.TuningRun.name.in_(labels))
- for tr in q:
- dir_label_runs[run_label(tr)][run_label(tr)].append((tr, db))
- all_run_ids = list()
- returned_values = {}
- for d, label_runs in dir_label_runs.iteritems():
- all_run_ids = map(_[0].id, itertools.chain(*label_runs.values()))
- session = label_runs.values()[0][0][1]
- objective = label_runs.values()[0][0][0].objective
-
- q = (session.query(resultsdb.models.Result)
- .filter(resultsdb.models.Result.tuning_run_id.in_(all_run_ids))
- .filter(resultsdb.models.Result.time < float('inf'))
- .filter_by(was_new_best=True, state='OK'))
- total = q.count()
- q = objective.filter_acceptable(q)
- acceptable = q.count()
- q = q.order_by(*objective.result_order_by_terms())
- best = q.limit(1).one()
- worst = q.offset(acceptable - 1).limit(1).one()
-
- for label, runs in sorted(label_runs.items()):
- (mean_values, percentile_values) = combined_stats_over_time(label, runs, objective, worst, best)
- returned_values[label] = (mean_values, percentile_values)
- final_scores = list()
- for run, session in runs:
- try:
- final = (session.query(resultsdb.models.Result)
- .filter_by(tuning_run = run,
- configuration = run.final_config)
- .limit(1).one())
- except sqlalchemy.orm.exc.NoResultFound:
- continue
- final_scores.append(objective.stats_quality_score(final, worst, best))
- final_scores.sort()
- return returned_values
-
-if __name__ == '__main__':
- labels = [u'timeouts', u'always_reorder', u'add_store_at', u'all_options']
- get_values(labels)
- print get_all_labels()
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/optional-requirements.txt b/llvm/projects/hpvm-tensor-rt/opentuner/optional-requirements.txt
deleted file mode 100644
index 9848f674cb6e5ca1faba757abd98eb5066e4688d..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/optional-requirements.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-django==1.6.1
-matplotlib==1.1.1
-virtualenv==1.9.1
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/requirements.txt b/llvm/projects/hpvm-tensor-rt/opentuner/requirements.txt
deleted file mode 100644
index fa9cfeca2ede04002798fea0db669de3c87879d4..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/requirements.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-argparse>=1.2.1
-fn>=0.2.12
-numpy>=1.8.0
-pysqlite>=2.6.3
-SQLAlchemy>=0.8.2
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/setup.py b/llvm/projects/hpvm-tensor-rt/opentuner/setup.py
deleted file mode 100755
index 633d4359d9e9655b5241521208fecc37bc4ab65f..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/setup.py
+++ /dev/null
@@ -1,33 +0,0 @@
-#!/usr/bin/python
-try:
- from setuptools import setup
-except ImportError:
- try:
- from setuptools.core import setup
- except ImportError:
- from distutils.core import setup
-
-try:
- from pypandoc import convert
- read_md = lambda f: convert(f, 'rest')
-except ImportError:
- print("warning: pypandoc module not found, could not convert Markdown to RST")
- read_md = lambda f: open(f, 'r').read()
-
-required = open('requirements.txt').read().splitlines()
-required = [l.strip() for l in required
- if l.strip() and not l.strip().startswith('#')]
-
-setup(
- name='opentuner',
- version='0.8.0',
- url='http://opentuner.org/',
- license='MIT',
- author='Jason Ansel',
- author_email='jansel@jansel.net',
- description='An extensible framework for program autotuning',
- long_description=read_md('README.md'),
- packages=['opentuner', 'opentuner.resultsdb', 'opentuner.utils',
- 'opentuner.measurement', 'opentuner.search'],
- install_requires=required,
-)
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/manage.py b/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/manage.py
deleted file mode 100644
index f27b5b8db13b490f7599856364f59c6fedcbfe6e..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/manage.py
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/usr/bin/env python
-import os
-import sys
-
-if __name__ == "__main__":
- os.environ.setdefault("DJANGO_SETTINGS_MODULE", "stats_app.settings")
-
- from django.core.management import execute_from_command_line
-
- execute_from_command_line(sys.argv)
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/__init__.py b/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/settings.py b/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/settings.py
deleted file mode 100644
index 09505be03e5621e4df952e878b52973da9588ffc..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/settings.py
+++ /dev/null
@@ -1,162 +0,0 @@
-# Django settings for stats_app project.
-import os
-
-DEBUG = True
-TEMPLATE_DEBUG = DEBUG
-
-ADMINS = (
- # ('Your Name', 'your_email@example.com'),
-)
-
-MANAGERS = ADMINS
-DIRECTORY_NAME = os.path.dirname(os.path.realpath(__file__))
-
-DATABASES = {
- 'default': {
- 'ENGINE': 'django.db.backends.sqlite3', # Add 'postgresql_psycopg2', 'mysql', 'sqlite3' or 'oracle'.
- 'NAME': DIRECTORY_NAME + '/db', # Or path to database file if using sqlite3.
- # The following settings are not used with sqlite3:
- 'USER': '',
- 'PASSWORD': '',
- 'HOST': '', # Empty for localhost through domain sockets or '127.0.0.1' for localhost through TCP.
- 'PORT': '', # Set to empty string for default.
- }
-}
-
-# Hosts/domain names that are valid for this site; required if DEBUG is False
-# See https://docs.djangoproject.com/en/1.5/ref/settings/#allowed-hosts
-ALLOWED_HOSTS = []
-
-# Local time zone for this installation. Choices can be found here:
-# http://en.wikipedia.org/wiki/List_of_tz_zones_by_name
-# although not all choices may be available on all operating systems.
-# In a Windows environment this must be set to your system time zone.
-TIME_ZONE = 'America/Chicago'
-
-# Language code for this installation. All choices can be found here:
-# http://www.i18nguy.com/unicode/language-identifiers.html
-LANGUAGE_CODE = 'en-us'
-
-SITE_ID = 1
-
-# If you set this to False, Django will make some optimizations so as not
-# to load the internationalization machinery.
-USE_I18N = True
-
-# If you set this to False, Django will not format dates, numbers and
-# calendars according to the current locale.
-USE_L10N = True
-
-# If you set this to False, Django will not use timezone-aware datetimes.
-USE_TZ = True
-
-# Absolute filesystem path to the directory that will hold user-uploaded files.
-# Example: "/var/www/example.com/media/"
-MEDIA_ROOT = ''
-
-# URL that handles the media served from MEDIA_ROOT. Make sure to use a
-# trailing slash.
-# Examples: "http://example.com/media/", "http://media.example.com/"
-MEDIA_URL = ''
-
-# Absolute path to the directory static files should be collected to.
-# Don't put anything in this directory yourself; store your static files
-# in apps' "static/" subdirectories and in STATICFILES_DIRS.
-# Example: "/var/www/example.com/static/"
-STATIC_ROOT = ''
-
-# URL prefix for static files.
-# Example: "http://example.com/static/", "http://static.example.com/"
-STATIC_URL = '/static/'
-
-# Additional locations of static files
-STATICFILES_DIRS = (
- # Put strings here, like "/home/html/static" or "C:/www/django/static".
- # Always use forward slashes, even on Windows.
- # Don't forget to use absolute paths, not relative paths.
- DIRECTORY_NAME + '/static',
-)
-
-# List of finder classes that know how to find static files in
-# various locations.
-STATICFILES_FINDERS = (
- 'django.contrib.staticfiles.finders.FileSystemFinder',
- 'django.contrib.staticfiles.finders.AppDirectoriesFinder',
-# 'django.contrib.staticfiles.finders.DefaultStorageFinder',
-)
-
-# Make this unique, and don't share it with anybody.
-SECRET_KEY = 't!!j*1gt0(5n%6nj-lirzja-9uj6s86s#0@kp2@8v&x#+c2+c-'
-
-# List of callables that know how to import templates from various sources.
-TEMPLATE_LOADERS = (
- 'django.template.loaders.filesystem.Loader',
- 'django.template.loaders.app_directories.Loader',
-# 'django.template.loaders.eggs.Loader',
-)
-
-MIDDLEWARE_CLASSES = (
- 'django.middleware.common.CommonMiddleware',
- 'django.contrib.sessions.middleware.SessionMiddleware',
- 'django.middleware.csrf.CsrfViewMiddleware',
- 'django.contrib.auth.middleware.AuthenticationMiddleware',
- 'django.contrib.messages.middleware.MessageMiddleware',
- # Uncomment the next line for simple clickjacking protection:
- # 'django.middleware.clickjacking.XFrameOptionsMiddleware',
-)
-
-ROOT_URLCONF = 'stats_app.urls'
-
-# Python dotted path to the WSGI application used by Django's runserver.
-WSGI_APPLICATION = 'stats_app.wsgi.application'
-
-TEMPLATE_DIRS = (
- # Put strings here, like "/home/html/django_templates" or "C:/www/django/templates".
- # Always use forward slashes, even on Windows.
- # Don't forget to use absolute paths, not relative paths.
- DIRECTORY_NAME + '/templates',
-)
-
-INSTALLED_APPS = (
- 'django.contrib.auth',
- 'django.contrib.contenttypes',
- 'django.contrib.sessions',
- 'django.contrib.sites',
- 'django.contrib.messages',
- 'django.contrib.staticfiles',
- # Uncomment the next line to enable the admin:
- 'django.contrib.admin',
- # Uncomment the next line to enable admin documentation:
- 'django.contrib.admindocs',
-)
-
-SESSION_SERIALIZER = 'django.contrib.sessions.serializers.JSONSerializer'
-
-# A sample logging configuration. The only tangible logging
-# performed by this configuration is to send an email to
-# the site admins on every HTTP 500 error when DEBUG=False.
-# See http://docs.djangoproject.com/en/dev/topics/logging for
-# more details on how to customize your logging configuration.
-LOGGING = {
- 'version': 1,
- 'disable_existing_loggers': False,
- 'filters': {
- 'require_debug_false': {
- '()': 'django.utils.log.RequireDebugFalse'
- }
- },
- 'handlers': {
- 'mail_admins': {
- 'level': 'ERROR',
- 'filters': ['require_debug_false'],
- 'class': 'django.utils.log.AdminEmailHandler'
- }
- },
- 'loggers': {
- 'django.request': {
- 'handlers': ['mail_admins'],
- 'level': 'ERROR',
- 'propagate': True,
- },
- }
-}
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/static/charts.css b/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/static/charts.css
deleted file mode 100644
index e32e2832aeac39540f2fb2c39e3817b6ab85cf3b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/static/charts.css
+++ /dev/null
@@ -1,11 +0,0 @@
-img.center {
-display: block;
-margin-left: auto;
-margin-right: auto;
-
-padding: 8px;
-border: solid;
-border-color: #dddddd #aaaaaa #aaaaaa #dddddd;
-border-width: 1px 2px 2px 1px;
-background-color: white;
-}
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/templates/charts.html b/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/templates/charts.html
deleted file mode 100644
index d38bb4c0c6c7c31d9cfce0bbc74d57f7601b3c83..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/templates/charts.html
+++ /dev/null
@@ -1,41 +0,0 @@
-<!DOCTYPE html>
-<head>
-<link rel="stylesheet" type="text/css" href="{{ STATIC_URL }}charts.css" media="screen" />
-<html lang="en">
- <title>{% block title %}Graph{% endblock %}</title>
-</head>
-
-<body>
- <div id="graphForm">
- <script src="http://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js"> </script>
- <script>
- function callback() {{
- var values = $('#graphForm form').serialize();
- $('#graphForm img').attr("src", "graph.png?" + values);
- }}
- </script>
- <p style="text-align:center">
- <img src="graph.png" id="graph" />
- </p>
- <form method = "GET" action="" style="text-align:center">
- <h3>X Limits:</h3>
- <input type="range" name="xlim" min="0" max="10000">
- <br><h3>Y Limits:</h3>
- <input type="range" name="ylim" min="0" max="20">
- <br>
- <h3>Labels:</h3>
- {0}
- <br>
- <h3>Measure:</h3>
- <b>Mean:</b>
- <input type="checkbox" name="disp_type" value="mean">
- <b>Median:</b>
- <input type="checkbox" name="disp_type" value="median">
- <b>All percentiles:</b>
- <input type="checkbox" name="disp_type" value="all_percentiles">
- <br>
- <input type="button" value="Graph!" onclick="callback()">
- </form>
- </div>
-</body>
-</html>
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/urls.py b/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/urls.py
deleted file mode 100644
index 15743290ec27ae4f2a4e633e80483d972190c870..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/urls.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from django.conf.urls import patterns, include, url
-
-# Uncomment the next two lines to enable the admin:
-from django.contrib import admin
-import views.charts
-admin.autodiscover()
-
-urlpatterns = patterns('',
- # Examples:
- # url(r'^$', 'stats_app.views.home', name='home'),
- # url(r'^stats_app/', include('stats_app.foo.urls')),
-
- # Uncomment the admin/doc line below to enable admin documentation:
- # url(r'^admin/doc/', include('django.contrib.admindocs.urls')),
-
- # Uncomment the next line to enable the admin:
- url(r'^admin/', include(admin.site.urls)),
- url(r'^graph.png$', views.charts.display_graph, name='graph'),
- url(r'^$', views.charts.display_full_page, name='graph_page'),
-)
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/views.py b/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/views.py
deleted file mode 100644
index 7cb32b3655aa032c745e97a088ad80365dd9c551..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/views.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from django.http import HttpResponse
-
-
-def index(request):
- return HttpResponse("Hello, world. You're at the stats application index.")
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/views/__init__.py b/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/views/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/views/charts.py b/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/views/charts.py
deleted file mode 100644
index c3a2ebff32281967f11640ef16a39353ca501d1a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/views/charts.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import datetime
-import django
-from django.shortcuts import render
-from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
-from matplotlib.dates import DateFormatter
-from matplotlib.figure import Figure
-import random
-
-from opentuner.utils import stats_matplotlib as stats
-
-
-def display_graph(request):
- """
- Handles request to display graph with provided parameters
- """
- request_dict = dict(request.GET.iterlists())
-
- xlim = request_dict.get('xlim', None)
- if xlim:
- xlim = int(xlim[0])
- else:
- xlim = 5000
- xlim = [0, xlim]
-
- ylim = request_dict.get('ylim', None)
- if ylim:
- ylim = int(ylim[0])
- else:
- ylim = 10
- ylim = [0, ylim]
-
- labels = request_dict.get('labels', None)
-
- disp_types = request_dict.get('disp_type', None)
- if not disp_types:
- disp_types = ['median']
-
- fig = stats.matplotlibplot_file(labels, xlim=xlim, ylim=ylim, disp_types=disp_types)
- canvas = FigureCanvas(fig)
- response = django.http.HttpResponse(content_type='image/png')
- canvas.print_png(response)
- return response
-
-
-def display_full_page(request):
- """
- Handles request to display the full page
- """
- all_labels = stats.get_all_labels()
- label_list = get_label_list(all_labels)
- html = render(request, 'charts.html')
- content = html.content
- content = content.format(label_list)
- html.content = content
- return html
-
-
-def get_label_list(all_labels):
- """
- Returns list of html form inputs corresponding to the different
- labels in the provided db file
- """
- label_list = ''
- for label in all_labels:
- label_list += '<b>%s</b>:<input type="checkbox" name="labels" value="%s">' % (label, label)
- return label_list
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/wsgi.py b/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/wsgi.py
deleted file mode 100644
index 90f54d8e3dd53cadeeb3eafa33e1abd734485cd0..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/stats_app/stats_app/wsgi.py
+++ /dev/null
@@ -1,32 +0,0 @@
-"""
-WSGI config for stats_app project.
-
-This module contains the WSGI application used by Django's development server
-and any production WSGI deployments. It should expose a module-level variable
-named ``application``. Django's ``runserver`` and ``runfcgi`` commands discover
-this application via the ``WSGI_APPLICATION`` setting.
-
-Usually you will have the standard Django WSGI application here, but it also
-might make sense to replace the whole Django WSGI application with a custom one
-that later delegates to the Django one. For example, you could introduce WSGI
-middleware here, or combine a Django application with an application of another
-framework.
-
-"""
-import os
-
-# We defer to a DJANGO_SETTINGS_MODULE already in the environment. This breaks
-# if running multiple sites in the same mod_wsgi process. To fix this, use
-# mod_wsgi daemon mode with each site in its own daemon process, or use
-# os.environ["DJANGO_SETTINGS_MODULE"] = "stats_app.settings"
-os.environ.setdefault("DJANGO_SETTINGS_MODULE", "stats_app.settings")
-
-# This application object is used by any WSGI server configured to use this
-# file. This includes Django's development server, if the WSGI_APPLICATION
-# setting points here.
-from django.core.wsgi import get_wsgi_application
-application = get_wsgi_application()
-
-# Apply WSGI middleware here.
-# from helloworld.wsgi import HelloWorldApplication
-# application = HelloWorldApplication(application)
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/tests/test_manipulator.py b/llvm/projects/hpvm-tensor-rt/opentuner/tests/test_manipulator.py
deleted file mode 100644
index c6548510fdc40036978728a1c2ad06ff05a6b9e9..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/tests/test_manipulator.py
+++ /dev/null
@@ -1,270 +0,0 @@
-import unittest
-import opentuner
-import mock
-import random
-import numpy
-from opentuner.search import manipulator
-
-def faked_random(nums):
- f = fake_random(nums)
- def inner(*args, **kwargs):
- return f.next()
- return inner
-
-def fake_random(nums):
- i = 0
- while True:
- yield nums[i]
- i = (i+1) % len(nums)
-
-
-class PermutationOperatorTests(unittest.TestCase):
-
- def setUp(self):
- """
- Set up a few configurations. The values of the PermutationParameter are:
- config1 - 0 1 2 3 4 5 6 7 8 9
- config2 - 4 3 2 1 0 9 8 7 6 5
- config3 - 1 0 4 2 7 9 5 3 6 8
-
- """
- self.manipulator = manipulator.ConfigurationManipulator()
- self.param1 = manipulator.PermutationParameter("param1", [0,1,2,3,4,5,6,7,8,9])
- self.manipulator.add_parameter(self.param1)
-
- self.cfg = self.manipulator.seed_config()
- self.config1 = self.manipulator.seed_config()
- self.config2 = self.manipulator.seed_config()
- self.config3 = self.manipulator.seed_config()
-
- # repeating values
- self.config4 = self.manipulator.seed_config()
- self.config5 = self.manipulator.seed_config()
-
-
- self.param1.set_value(self.config1, [0,1,2,3,4,5,6,7,8,9])
- self.param1.set_value(self.config2, [4,3,2,1,0,9,8,7,6,5])
- self.param1.set_value(self.config3, [1,0,4,2,7,9,5,3,6,8])
-
- # repeating values
- self.param1.set_value(self.config4, [1,2,3,4,2,3,4,3,4,4])
- self.param1.set_value(self.config5, [4,2,4,3,3,1,3,4,2,4])
-
- @mock.patch('random.randint', side_effect=faked_random([1,6]))
- def test_op2_random_swap_1_6(self, randint_func):
- # operator shouuld swap the indices at 1 and 6
- self.param1.op2_random_swap(self.cfg, self.config1)
-
- self.assertEqual(self.param1.get_value(self.cfg),[0,6,2,3,4,5,1,7,8,9])
- self.assertEqual(self.param1.get_value(self.config1),[0,1,2,3,4,5,6,7,8,9])
-
-
- @mock.patch('random.randint', side_effect=faked_random([7,2]))
- def test_op2_random_invert(self, randint_func):
- #should reverse a section of length 3 starting at index given by randint
- self.param1.op2_random_invert(self.cfg, self.config1)
- self.assertEqual(self.param1.get_value(self.cfg),[0,1,2,3,4,5,6,9,8,7])
-
- self.param1.op2_random_invert(self.cfg, self.config1)
- self.assertEqual(self.param1.get_value(self.cfg),[0,1,4,3,2,5,6,7,8,9])
-
-
- @mock.patch('random.randint', side_effect=faked_random([0]))
- def test_op3_cross_PMX_str5(self, randint_func):
- # should perform PMX with a cut at 0 and crossover size 5
- self.param1.op3_cross(self.cfg, self.config1, self.config3,
- xchoice='op3_cross_PMX', strength=0.5)
- self.assertEqual(self.param1.get_value(self.cfg),[1,0,4,2,7,5,6,3,8,9])
-
- @mock.patch('random.randint', side_effect=faked_random([5]))
- @mock.patch('random.uniform', side_effect=faked_random([0.4]))
- def test_op3_swarm_CX_no_cross(self, uniform_func, randint_func):
- # should perform no cross
- self.param1.op3_swarm(self.config1, self.config2, self.config3,
- xchoice='op3_cross_CX', c=0.8)
- self.assertEqual(self.param1.get_value(self.config1),[0,1,2,3,4,5,6,7,8,9])
-
-
- @mock.patch('random.randint', side_effect=faked_random([5]))
- @mock.patch('random.uniform', side_effect=faked_random([0.4]))
- def test_op3_swarm_CX_cross_p1(self, uniform_func, randint_func):
- # should cross the first parent
- self.param1.op3_swarm(self.config1, self.config2, self.config3,
- xchoice='op3_cross_CX', c=0.3, c1=0.5, c2="unused")
- self.assertEqual(self.param1.get_value(self.config1),[0,1,2,3,4,9,6,7,8,5])
-
- @mock.patch('random.randint', side_effect=faked_random([5]))
- @mock.patch('random.uniform', side_effect=faked_random([0.4]))
- def test_op3_swarm_CX_cross_p2(self, uniform_func, randint_func):
- # should cross the second parent
- self.param1.op3_swarm(self.config1, self.config2, self.config3,
- xchoice='op3_cross_CX', c=0.3, c1=0.3, c2="unused")
- self.assertEqual(self.param1.get_value(self.config1),[0,1,2,3,4,9,5,7,6,8])
-
-
- @mock.patch('random.randint', side_effect=faked_random([5]))
- def test_op3_cross_PX_5(self, randint_func):
- # Random cut point = 5 (index = 4)
- self.param1.op3_cross_PX(self.cfg, self.config1, self.config3, 2)
- self.assertEqual(self.param1.get_value(self.cfg),[1,0,4,2,3,5,6,7,8,9])
-
- @mock.patch('random.randint', side_effect=faked_random([2]))
- def test_op3_cross_PMX_0_d4(self, randint_func):
- # cut = 2, d = 4
- self.param1.op3_cross_PMX(self.cfg, self.config2, self.config3, 4)
- self.assertEqual(self.param1.get_value(self.cfg),[1,3,4,2,7,9,8,0,6,5])
-
-
- @mock.patch('random.randint', side_effect=faked_random([0]))
- def test_op3_cross_PMX_0_d5(self, randint_func):
- # cut = 0, d = 5
- self.param1.op3_cross_PMX(self.cfg, self.config1, self.config3, 5)
- self.assertEqual(self.param1.get_value(self.cfg),[1,0,4,2,7,5,6,3,8,9])
-
- @mock.patch('random.randint', side_effect=faked_random([4]))
- def test_op3_cross_PMX_dups(self, randint_func):
- # cut = 4, d = 5
- self.param1.op3_cross_PMX(self.cfg, self.config5, self.config4, 5)
-
- # [4,2,4,3,3,1,3,4,2,4]
- # [1,2,3,4,2,3,4,3,4,4]
- # expected:
- # [1,2,4,3,2,3,4,3,4,4]
-
- self.assertEqual(self.param1.get_value(self.cfg), [1,2,4,3,2,3,4,3,4,4])
-
-
- @mock.patch('random.randint', side_effect=faked_random([5]))
- def test_op3_cross_CX_5(self, randint_func):
- # initial replacement at index 5
- self.param1.op3_cross_CX(self.cfg, self.config1, self.config2, "unused")
- self.assertEqual(self.param1.get_value(self.cfg),[0,1,2,3,4,9,6,7,8,5])
- self.param1.op3_cross_CX(self.cfg, self.config1, self.config3, "unused")
- self.assertEqual(self.param1.get_value(self.cfg),[0,1,2,3,4,9,5,7,6,8])
-
- @mock.patch('random.randint', side_effect=faked_random([0]))
- def test_op3_cross_CX_dups(self, randint_func):
- # initial replacement at index 4
- self.param1.op3_cross_CX(self.cfg, self.config5, self.config4, "unused")
-
- # [4,2,4,3,3,1,3,4,2,4]
- # [1,2,3,4,2,3,4,3,4,4]
- # expected:
- # [1,2,3,4,3,3,4,4,2,4]
-
- self.assertEqual(self.param1.get_value(self.cfg), [1,2,3,4,3,3,4,4,2,4])
-
-
- @mock.patch('random.randint', side_effect=faked_random([3]))
- def test_op3_cross_OX1_3_d4(self, randint_func):
- # cut at 3
- # d = 4
- self.param1.op3_cross_OX1(self.cfg, self.config1, self.config2, 4)
- self.assertEqual(self.param1.get_value(self.cfg),[2,3,4,1,0,9,8,5,6,7])
- self.param1.op3_cross_OX1(self.cfg, self.config1, self.config3, 4)
- self.assertEqual(self.param1.get_value(self.cfg),[0,1,3,2,7,9,5,4,6,8])
-
- @mock.patch('random.randint', side_effect=faked_random([4,2]))
- def test_op3_cross_OX3_2_5_d4(self, randint_func):
- # cuts at 4,2
- # d = 4
- self.param1.op3_cross_OX3(self.cfg, self.config1, self.config2, 4)
- self.assertEqual(self.param1.get_value(self.cfg),[3,4,5,6,2,1,0,9,7,8])
- self.param1.op3_cross_OX3(self.cfg, self.config1, self.config3, 4)
- self.assertEqual(self.param1.get_value(self.cfg),[0,1,3,5,4,2,7,9,6,8])
-
-
-class FloatArrayOperatorTests(unittest.TestCase):
- """
- also tests the operators for Array (since Array is abstract)
- """
-
- def setUp(self):
- """
- Set up a few configurations. The values of the FloatArray are:
- config1 - 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9
- config2 - 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9
- config3 - 3.0 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9
- """
- self.manipulator = manipulator.ConfigurationManipulator()
- self.param1 = manipulator.FloatArray("param1", 10, 4, 0)
- self.manipulator.add_parameter(self.param1)
-
- self.cfg = self.manipulator.seed_config()
- self.config1 = self.manipulator.seed_config()
- self.config2 = self.manipulator.seed_config()
- self.config3 = self.manipulator.seed_config()
-
- self.param1.set_value(self.config1, numpy.array([1.0,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9]))
- self.param1.set_value(self.config2, numpy.array([2.0,2.1,2.2,2.3,2.4,2.5,2.6,2.7,2.8,2.9]))
- self.param1.set_value(self.config3, numpy.array([3.0,3.1,3.2,3.3,3.4,3.5,3.6,3.7,3.8,3.8]))
-
-
- @mock.patch('random.randint', side_effect=faked_random([3]))
- def test_op3_cross_3_str4(self, randint_func):
- self.param1.op3_cross(self.cfg, self.config1, self.config2, strength=0.4)
-
- val = self.param1.get_value(self.cfg)
- expected = [1.0,1.1,1.2,2.3,2.4,2.5,2.6,1.7,1.8,1.9]
- for i in range(len(val)):
- self.assertAlmostEqual(val[i], expected[i])
-
- @mock.patch('random.randint', side_effect=faked_random([3]))
- @mock.patch('random.uniform', side_effect=faked_random([0.4]))
- def test_op3_swarm_no_cross(self, uniform_func, randint_func):
- #should perform no cross
- self.param1.op3_swarm(self.config1, self.config2, self.config3,
- xchoice='op3_cross_CX', c=0.8)
- val = self.param1.get_value(self.config1)
- expected = [1.0,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9]
- for i in range(len(val)):
- self.assertAlmostEqual(val[i], expected[i])
-
-
- @mock.patch('random.randint', side_effect=faked_random([3]))
- @mock.patch('random.uniform', side_effect=faked_random([0.4]))
- def test_op3_swarm_cross_p1(self, uniform_func, randint_func):
- #should cross the first parent
- self.param1.op3_swarm(self.config1, self.config2, self.config3,
- xchoice='op3_cross_CX', c=0.3, c1=0.5, c2="unused")
- val = self.param1.get_value(self.config1)
- expected = [1.0,1.1,1.2,2.3,2.4,2.5,1.6,1.7,1.8,1.9]
- for i in range(len(val)):
- self.assertAlmostEqual(val[i], expected[i])
-
-
- @mock.patch('random.randint', side_effect=faked_random([3]))
- @mock.patch('random.uniform', side_effect=faked_random([0.4]))
- def test_op3_swarm_cross_p2(self, uniform_func, randint_func):
- #should cross the second parent
- self.param1.op3_swarm(self.config1, self.config2, self.config3,
- xchoice='op3_cross_CX', c=0.3, c1=0.3, c2="unused")
- val = self.param1.get_value(self.config1)
- expected = [1.0,1.1,1.2,3.3,3.4,3.5,1.6,1.7,1.8,1.9]
- self.assertEqual(len(val),len(expected))
- for i in range(len(val)):
- self.assertAlmostEqual(val[i], expected[i])
-
- @mock.patch('random.random', side_effect=faked_random([0.2, 0.4]))
- def test_op3_swarm_parallel(self, random_func):
- # r1 = 0.2, r2 = 0.4, velocities = [-2,0,0,0,0,0,1,1.5,2,3]
- # max and min are 4, 0
- velocities = numpy.array([-2.0,0.0,0,0,0,0,1.0,1.5,2,3.0])
-
- vs = self.param1.op3_swarm_parallel(self.config1, self.config2, self.config3, velocities=velocities)
- vs_expected = [-1.5,.5,.5,.5,.5,.5,1.5,2.0,2.5,3.48]
-
- self.assertEqual(len(vs),len(vs_expected))
-
- for i in range(len(vs)):
- self.assertAlmostEqual(vs[i], vs_expected[i])
-
-
- val = self.param1.get_value(self.config1)
- expected = [0,1.6,1.7,1.8,1.9,2.0,3.1,3.7,4,4]
- self.assertEqual(len(val),len(expected))
- for i in range(len(val)):
- self.assertAlmostEqual(val[i], expected[i])
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/tests/test_technique.py b/llvm/projects/hpvm-tensor-rt/opentuner/tests/test_technique.py
deleted file mode 100644
index c6107bace942a5ac85533878131fb953439ea3f7..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/tests/test_technique.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import unittest
-import opentuner
-import mock
-from opentuner.search.composableevolutionarytechniques import ComposableEvolutionaryTechnique
-from opentuner.search import manipulator
-
-def faked_random(nums):
- f = fake_random(nums)
- def inner(*args, **kwargs):
- return f.next()
- return inner
-
-def fake_random(nums):
- i = 0
- while True:
- yield nums[i]
- i = (i+1) % len(nums)
-
-class EmptyComposableEvolutionaryTechnique(ComposableEvolutionaryTechnique):
- def __init__(self, *pargs, **kwargs):
- super(EmptyComposableEvolutionaryTechnique, self).__init__(*pargs, **kwargs)
-
- def minimum_number_of_parents(self):
- return 4
-
- def get_parents(self, population):
- cfg = self.manipulator.copy(population[0].config)
-
- return [cfg]
-
- def update_population(self, config, population):
- # replace the oldest configuration if the new one is better.
- population[0].config = config
-
- return population
-
-class ComposableSearchTechniqueTests(unittest.TestCase):
-
- def setUp(self):
- self.operator_map = {}
- ComposableEvolutionaryTechnique.add_to_map(self.operator_map,
- manipulator.PermutationParameter,
- "op3_cross", xchoice='op3_cross_CX')
- ComposableEvolutionaryTechnique.add_to_map(self.operator_map,
- "FloatArray",
- "op3_cross", strength=0.4)
- self.technique = EmptyComposableEvolutionaryTechnique(operator_map = self.operator_map)
-
- def test_add_to_map(self):
- op_map = {}
- op_map[manipulator.PermutationParameter] = {'op_name': 'op3_cross',
- 'args': (),
- 'kwargs': {'xchoice': 'op3_cross_CX'}}
- op_map[manipulator.FloatArray] = {'op_name': 'op3_cross',
- 'args': (),
- 'kwargs': {'strength': 0.4}}
- self.assertDictEqual(self.operator_map, op_map)
-
- def test_get_default_oeprator(self):
- default = self.technique.get_default_operator(manipulator.PermutationParameter)
- self.assertDictEqual(default, {'op_name': 'op1_nop', 'args': [], 'kwargs': {}})
-
-
- def test_get_operator(self):
- default = self.technique.get_operator(manipulator.IntegerParameter)
- self.assertDictEqual(default, {'op_name': 'op1_nop', 'args': [], 'kwargs': {}})
-
- default = self.technique.get_operator(manipulator.PermutationParameter)
- self.assertDictEqual(default, {'op_name': 'op3_cross','args': (),'kwargs': {'xchoice': 'op3_cross_CX'}})
-
- @mock.patch('opentuner.search.manipulator.PermutationParameter.op3_cross')
- def test_apply_operator(self, op3_cross_func):
- param_instance = manipulator.PermutationParameter('temp', [1,2,3,4,5])
- self.technique.apply_operator(param_instance, ['p1', 'p2', 'p3', 'p4'])
- op3_cross_func.assert_called_once_with('p1', 'p2', 'p3', xchoice='op3_cross_CX')
-
-#TODO tests for RandomThreeParentsComposableTechnique
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/venv-bootstrap.py b/llvm/projects/hpvm-tensor-rt/opentuner/venv-bootstrap.py
deleted file mode 100755
index 6d6ad0113b72ffe5610b28ac2717442cba6eff8c..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/opentuner/venv-bootstrap.py
+++ /dev/null
@@ -1,2611 +0,0 @@
-#!/usr/bin/env python
-## WARNING: This file is generated
-#!/usr/bin/env python
-"""Create a "virtual" Python installation
-"""
-
-# If you change the version here, change it in setup.py
-# and docs/conf.py as well.
-__version__ = "1.9.1" # following best practices
-virtualenv_version = __version__ # legacy, again
-
-import base64
-import sys
-import os
-import codecs
-import optparse
-import re
-import shutil
-import logging
-import tempfile
-import zlib
-import errno
-import glob
-import distutils.sysconfig
-from distutils.util import strtobool
-import struct
-import subprocess
-
-if sys.version_info < (2, 6):
- print('ERROR: %s' % sys.exc_info()[1])
- print('ERROR: this script requires Python 2.6 or greater.')
- sys.exit(101)
-
-try:
- set
-except NameError:
- from sets import Set as set
-try:
- basestring
-except NameError:
- basestring = str
-
-try:
- import ConfigParser
-except ImportError:
- import configparser as ConfigParser
-
-join = os.path.join
-py_version = 'python%s.%s' % (sys.version_info[0], sys.version_info[1])
-
-is_jython = sys.platform.startswith('java')
-is_pypy = hasattr(sys, 'pypy_version_info')
-is_win = (sys.platform == 'win32')
-is_cygwin = (sys.platform == 'cygwin')
-is_darwin = (sys.platform == 'darwin')
-abiflags = getattr(sys, 'abiflags', '')
-
-user_dir = os.path.expanduser('~')
-if is_win:
- default_storage_dir = os.path.join(user_dir, 'virtualenv')
-else:
- default_storage_dir = os.path.join(user_dir, '.virtualenv')
-default_config_file = os.path.join(default_storage_dir, 'virtualenv.ini')
-
-if is_pypy:
- expected_exe = 'pypy'
-elif is_jython:
- expected_exe = 'jython'
-else:
- expected_exe = 'python'
-
-
-REQUIRED_MODULES = ['os', 'posix', 'posixpath', 'nt', 'ntpath', 'genericpath',
- 'fnmatch', 'locale', 'encodings', 'codecs',
- 'stat', 'UserDict', 'readline', 'copy_reg', 'types',
- 're', 'sre', 'sre_parse', 'sre_constants', 'sre_compile',
- 'zlib']
-
-REQUIRED_FILES = ['lib-dynload', 'config']
-
-majver, minver = sys.version_info[:2]
-if majver == 2:
- if minver >= 6:
- REQUIRED_MODULES.extend(['warnings', 'linecache', '_abcoll', 'abc'])
- if minver >= 7:
- REQUIRED_MODULES.extend(['_weakrefset'])
- if minver <= 3:
- REQUIRED_MODULES.extend(['sets', '__future__'])
-elif majver == 3:
- # Some extra modules are needed for Python 3, but different ones
- # for different versions.
- REQUIRED_MODULES.extend(['_abcoll', 'warnings', 'linecache', 'abc', 'io',
- '_weakrefset', 'copyreg', 'tempfile', 'random',
- '__future__', 'collections', 'keyword', 'tarfile',
- 'shutil', 'struct', 'copy', 'tokenize', 'token',
- 'functools', 'heapq', 'bisect', 'weakref',
- 'reprlib'])
- if minver >= 2:
- REQUIRED_FILES[-1] = 'config-%s' % majver
- if minver == 3:
- import sysconfig
- platdir = sysconfig.get_config_var('PLATDIR')
- REQUIRED_FILES.append(platdir)
- # The whole list of 3.3 modules is reproduced below - the current
- # uncommented ones are required for 3.3 as of now, but more may be
- # added as 3.3 development continues.
- REQUIRED_MODULES.extend([
- #"aifc",
- #"antigravity",
- #"argparse",
- #"ast",
- #"asynchat",
- #"asyncore",
- "base64",
- #"bdb",
- #"binhex",
- #"bisect",
- #"calendar",
- #"cgi",
- #"cgitb",
- #"chunk",
- #"cmd",
- #"codeop",
- #"code",
- #"colorsys",
- #"_compat_pickle",
- #"compileall",
- #"concurrent",
- #"configparser",
- #"contextlib",
- #"cProfile",
- #"crypt",
- #"csv",
- #"ctypes",
- #"curses",
- #"datetime",
- #"dbm",
- #"decimal",
- #"difflib",
- #"dis",
- #"doctest",
- #"dummy_threading",
- "_dummy_thread",
- #"email",
- #"filecmp",
- #"fileinput",
- #"formatter",
- #"fractions",
- #"ftplib",
- #"functools",
- #"getopt",
- #"getpass",
- #"gettext",
- #"glob",
- #"gzip",
- "hashlib",
- #"heapq",
- "hmac",
- #"html",
- #"http",
- #"idlelib",
- #"imaplib",
- #"imghdr",
- "imp",
- "importlib",
- #"inspect",
- #"json",
- #"lib2to3",
- #"logging",
- #"macpath",
- #"macurl2path",
- #"mailbox",
- #"mailcap",
- #"_markupbase",
- #"mimetypes",
- #"modulefinder",
- #"multiprocessing",
- #"netrc",
- #"nntplib",
- #"nturl2path",
- #"numbers",
- #"opcode",
- #"optparse",
- #"os2emxpath",
- #"pdb",
- #"pickle",
- #"pickletools",
- #"pipes",
- #"pkgutil",
- #"platform",
- #"plat-linux2",
- #"plistlib",
- #"poplib",
- #"pprint",
- #"profile",
- #"pstats",
- #"pty",
- #"pyclbr",
- #"py_compile",
- #"pydoc_data",
- #"pydoc",
- #"_pyio",
- #"queue",
- #"quopri",
- #"reprlib",
- "rlcompleter",
- #"runpy",
- #"sched",
- #"shelve",
- #"shlex",
- #"smtpd",
- #"smtplib",
- #"sndhdr",
- #"socket",
- #"socketserver",
- #"sqlite3",
- #"ssl",
- #"stringprep",
- #"string",
- #"_strptime",
- #"subprocess",
- #"sunau",
- #"symbol",
- #"symtable",
- #"sysconfig",
- #"tabnanny",
- #"telnetlib",
- #"test",
- #"textwrap",
- #"this",
- #"_threading_local",
- #"threading",
- #"timeit",
- #"tkinter",
- #"tokenize",
- #"token",
- #"traceback",
- #"trace",
- #"tty",
- #"turtledemo",
- #"turtle",
- #"unittest",
- #"urllib",
- #"uuid",
- #"uu",
- #"wave",
- #"weakref",
- #"webbrowser",
- #"wsgiref",
- #"xdrlib",
- #"xml",
- #"xmlrpc",
- #"zipfile",
- ])
-
-if is_pypy:
- # these are needed to correctly display the exceptions that may happen
- # during the bootstrap
- REQUIRED_MODULES.extend(['traceback', 'linecache'])
-
-class Logger(object):
-
- """
- Logging object for use in command-line script. Allows ranges of
- levels, to avoid some redundancy of displayed information.
- """
-
- DEBUG = logging.DEBUG
- INFO = logging.INFO
- NOTIFY = (logging.INFO+logging.WARN)/2
- WARN = WARNING = logging.WARN
- ERROR = logging.ERROR
- FATAL = logging.FATAL
-
- LEVELS = [DEBUG, INFO, NOTIFY, WARN, ERROR, FATAL]
-
- def __init__(self, consumers):
- self.consumers = consumers
- self.indent = 0
- self.in_progress = None
- self.in_progress_hanging = False
-
- def debug(self, msg, *args, **kw):
- self.log(self.DEBUG, msg, *args, **kw)
- def info(self, msg, *args, **kw):
- self.log(self.INFO, msg, *args, **kw)
- def notify(self, msg, *args, **kw):
- self.log(self.NOTIFY, msg, *args, **kw)
- def warn(self, msg, *args, **kw):
- self.log(self.WARN, msg, *args, **kw)
- def error(self, msg, *args, **kw):
- self.log(self.ERROR, msg, *args, **kw)
- def fatal(self, msg, *args, **kw):
- self.log(self.FATAL, msg, *args, **kw)
- def log(self, level, msg, *args, **kw):
- if args:
- if kw:
- raise TypeError(
- "You may give positional or keyword arguments, not both")
- args = args or kw
- rendered = None
- for consumer_level, consumer in self.consumers:
- if self.level_matches(level, consumer_level):
- if (self.in_progress_hanging
- and consumer in (sys.stdout, sys.stderr)):
- self.in_progress_hanging = False
- sys.stdout.write('\n')
- sys.stdout.flush()
- if rendered is None:
- if args:
- rendered = msg % args
- else:
- rendered = msg
- rendered = ' '*self.indent + rendered
- if hasattr(consumer, 'write'):
- consumer.write(rendered+'\n')
- else:
- consumer(rendered)
-
- def start_progress(self, msg):
- assert not self.in_progress, (
- "Tried to start_progress(%r) while in_progress %r"
- % (msg, self.in_progress))
- if self.level_matches(self.NOTIFY, self._stdout_level()):
- sys.stdout.write(msg)
- sys.stdout.flush()
- self.in_progress_hanging = True
- else:
- self.in_progress_hanging = False
- self.in_progress = msg
-
- def end_progress(self, msg='done.'):
- assert self.in_progress, (
- "Tried to end_progress without start_progress")
- if self.stdout_level_matches(self.NOTIFY):
- if not self.in_progress_hanging:
- # Some message has been printed out since start_progress
- sys.stdout.write('...' + self.in_progress + msg + '\n')
- sys.stdout.flush()
- else:
- sys.stdout.write(msg + '\n')
- sys.stdout.flush()
- self.in_progress = None
- self.in_progress_hanging = False
-
- def show_progress(self):
- """If we are in a progress scope, and no log messages have been
- shown, write out another '.'"""
- if self.in_progress_hanging:
- sys.stdout.write('.')
- sys.stdout.flush()
-
- def stdout_level_matches(self, level):
- """Returns true if a message at this level will go to stdout"""
- return self.level_matches(level, self._stdout_level())
-
- def _stdout_level(self):
- """Returns the level that stdout runs at"""
- for level, consumer in self.consumers:
- if consumer is sys.stdout:
- return level
- return self.FATAL
-
- def level_matches(self, level, consumer_level):
- """
- >>> l = Logger([])
- >>> l.level_matches(3, 4)
- False
- >>> l.level_matches(3, 2)
- True
- >>> l.level_matches(slice(None, 3), 3)
- False
- >>> l.level_matches(slice(None, 3), 2)
- True
- >>> l.level_matches(slice(1, 3), 1)
- True
- >>> l.level_matches(slice(2, 3), 1)
- False
- """
- if isinstance(level, slice):
- start, stop = level.start, level.stop
- if start is not None and start > consumer_level:
- return False
- if stop is not None and stop <= consumer_level:
- return False
- return True
- else:
- return level >= consumer_level
-
- #@classmethod
- def level_for_integer(cls, level):
- levels = cls.LEVELS
- if level < 0:
- return levels[0]
- if level >= len(levels):
- return levels[-1]
- return levels[level]
-
- level_for_integer = classmethod(level_for_integer)
-
-# create a silent logger just to prevent this from being undefined
-# will be overridden with requested verbosity main() is called.
-logger = Logger([(Logger.LEVELS[-1], sys.stdout)])
-
-def mkdir(path):
- if not os.path.exists(path):
- logger.info('Creating %s', path)
- os.makedirs(path)
- else:
- logger.info('Directory %s already exists', path)
-
-def copyfileordir(src, dest):
- if os.path.isdir(src):
- shutil.copytree(src, dest, True)
- else:
- shutil.copy2(src, dest)
-
-def copyfile(src, dest, symlink=True):
- if not os.path.exists(src):
- # Some bad symlink in the src
- logger.warn('Cannot find file %s (bad symlink)', src)
- return
- if os.path.exists(dest):
- logger.debug('File %s already exists', dest)
- return
- if not os.path.exists(os.path.dirname(dest)):
- logger.info('Creating parent directories for %s' % os.path.dirname(dest))
- os.makedirs(os.path.dirname(dest))
- if not os.path.islink(src):
- srcpath = os.path.abspath(src)
- else:
- srcpath = os.readlink(src)
- if symlink and hasattr(os, 'symlink') and not is_win:
- logger.info('Symlinking %s', dest)
- try:
- os.symlink(srcpath, dest)
- except (OSError, NotImplementedError):
- logger.info('Symlinking failed, copying to %s', dest)
- copyfileordir(src, dest)
- else:
- logger.info('Copying to %s', dest)
- copyfileordir(src, dest)
-
-def writefile(dest, content, overwrite=True):
- if not os.path.exists(dest):
- logger.info('Writing %s', dest)
- f = open(dest, 'wb')
- f.write(content.encode('utf-8'))
- f.close()
- return
- else:
- f = open(dest, 'rb')
- c = f.read()
- f.close()
- if c != content.encode("utf-8"):
- if not overwrite:
- logger.notify('File %s exists with different content; not overwriting', dest)
- return
- logger.notify('Overwriting %s with new content', dest)
- f = open(dest, 'wb')
- f.write(content.encode('utf-8'))
- f.close()
- else:
- logger.info('Content %s already in place', dest)
-
-def rmtree(dir):
- if os.path.exists(dir):
- logger.notify('Deleting tree %s', dir)
- shutil.rmtree(dir)
- else:
- logger.info('Do not need to delete %s; already gone', dir)
-
-def make_exe(fn):
- if hasattr(os, 'chmod'):
- oldmode = os.stat(fn).st_mode & 0xFFF # 0o7777
- newmode = (oldmode | 0x16D) & 0xFFF # 0o555, 0o7777
- os.chmod(fn, newmode)
- logger.info('Changed mode of %s to %s', fn, oct(newmode))
-
-def _find_file(filename, dirs):
- for dir in reversed(dirs):
- files = glob.glob(os.path.join(dir, filename))
- if files and os.path.isfile(files[0]):
- return True, files[0]
- return False, filename
-
-def _install_req(py_executable, unzip=False, distribute=False,
- search_dirs=None, never_download=False):
-
- if search_dirs is None:
- search_dirs = file_search_dirs()
-
- if not distribute:
- egg_path = 'setuptools-*-py%s.egg' % sys.version[:3]
- found, egg_path = _find_file(egg_path, search_dirs)
- project_name = 'setuptools'
- bootstrap_script = EZ_SETUP_PY
- tgz_path = None
- else:
- # Look for a distribute egg (these are not distributed by default,
- # but can be made available by the user)
- egg_path = 'distribute-*-py%s.egg' % sys.version[:3]
- found, egg_path = _find_file(egg_path, search_dirs)
- project_name = 'distribute'
- if found:
- tgz_path = None
- bootstrap_script = DISTRIBUTE_FROM_EGG_PY
- else:
- # Fall back to sdist
- # NB: egg_path is not None iff tgz_path is None
- # iff bootstrap_script is a generic setup script accepting
- # the standard arguments.
- egg_path = None
- tgz_path = 'distribute-*.tar.gz'
- found, tgz_path = _find_file(tgz_path, search_dirs)
- bootstrap_script = DISTRIBUTE_SETUP_PY
-
- if is_jython and os._name == 'nt':
- # Jython's .bat sys.executable can't handle a command line
- # argument with newlines
- fd, ez_setup = tempfile.mkstemp('.py')
- os.write(fd, bootstrap_script)
- os.close(fd)
- cmd = [py_executable, ez_setup]
- else:
- cmd = [py_executable, '-c', bootstrap_script]
- if unzip and egg_path:
- cmd.append('--always-unzip')
- env = {}
- remove_from_env = ['__PYVENV_LAUNCHER__']
- if logger.stdout_level_matches(logger.DEBUG) and egg_path:
- cmd.append('-v')
-
- old_chdir = os.getcwd()
- if egg_path is not None and os.path.exists(egg_path):
- logger.info('Using existing %s egg: %s' % (project_name, egg_path))
- cmd.append(egg_path)
- if os.environ.get('PYTHONPATH'):
- env['PYTHONPATH'] = egg_path + os.path.pathsep + os.environ['PYTHONPATH']
- else:
- env['PYTHONPATH'] = egg_path
- elif tgz_path is not None and os.path.exists(tgz_path):
- # Found a tgz source dist, let's chdir
- logger.info('Using existing %s egg: %s' % (project_name, tgz_path))
- os.chdir(os.path.dirname(tgz_path))
- # in this case, we want to be sure that PYTHONPATH is unset (not
- # just empty, really unset), else CPython tries to import the
- # site.py that it's in virtualenv_support
- remove_from_env.append('PYTHONPATH')
- elif never_download:
- logger.fatal("Can't find any local distributions of %s to install "
- "and --never-download is set. Either re-run virtualenv "
- "without the --never-download option, or place a %s "
- "distribution (%s) in one of these "
- "locations: %r" % (project_name, project_name,
- egg_path or tgz_path,
- search_dirs))
- sys.exit(1)
- elif egg_path:
- logger.info('No %s egg found; downloading' % project_name)
- cmd.extend(['--always-copy', '-U', project_name])
- else:
- logger.info('No %s tgz found; downloading' % project_name)
- logger.start_progress('Installing %s...' % project_name)
- logger.indent += 2
- cwd = None
- if project_name == 'distribute':
- env['DONT_PATCH_SETUPTOOLS'] = 'true'
-
- def _filter_ez_setup(line):
- return filter_ez_setup(line, project_name)
-
- if not os.access(os.getcwd(), os.W_OK):
- cwd = tempfile.mkdtemp()
- if tgz_path is not None and os.path.exists(tgz_path):
- # the current working dir is hostile, let's copy the
- # tarball to a temp dir
- target = os.path.join(cwd, os.path.split(tgz_path)[-1])
- shutil.copy(tgz_path, target)
- try:
- call_subprocess(cmd, show_stdout=False,
- filter_stdout=_filter_ez_setup,
- extra_env=env,
- remove_from_env=remove_from_env,
- cwd=cwd)
- finally:
- logger.indent -= 2
- logger.end_progress()
- if cwd is not None:
- shutil.rmtree(cwd)
- if os.getcwd() != old_chdir:
- os.chdir(old_chdir)
- if is_jython and os._name == 'nt':
- os.remove(ez_setup)
-
-def file_search_dirs():
- here = os.path.dirname(os.path.abspath(__file__))
- dirs = ['.', here,
- join(here, 'virtualenv_support')]
- if os.path.splitext(os.path.dirname(__file__))[0] != 'virtualenv':
- # Probably some boot script; just in case virtualenv is installed...
- try:
- import virtualenv
- except ImportError:
- pass
- else:
- dirs.append(os.path.join(os.path.dirname(virtualenv.__file__), 'virtualenv_support'))
- return [d for d in dirs if os.path.isdir(d)]
-
-def install_setuptools(py_executable, unzip=False,
- search_dirs=None, never_download=False):
- _install_req(py_executable, unzip,
- search_dirs=search_dirs, never_download=never_download)
-
-def install_distribute(py_executable, unzip=False,
- search_dirs=None, never_download=False):
- _install_req(py_executable, unzip, distribute=True,
- search_dirs=search_dirs, never_download=never_download)
-
-_pip_re = re.compile(r'^pip-.*(zip|tar.gz|tar.bz2|tgz|tbz)$', re.I)
-def install_pip(py_executable, search_dirs=None, never_download=False):
- if search_dirs is None:
- search_dirs = file_search_dirs()
-
- filenames = []
- for dir in search_dirs:
- filenames.extend([join(dir, fn) for fn in os.listdir(dir)
- if _pip_re.search(fn)])
- filenames = [(os.path.basename(filename).lower(), i, filename) for i, filename in enumerate(filenames)]
- filenames.sort()
- filenames = [filename for basename, i, filename in filenames]
- if not filenames:
- filename = 'pip'
- else:
- filename = filenames[-1]
- easy_install_script = 'easy_install'
- if is_win:
- easy_install_script = 'easy_install-script.py'
- # There's two subtle issues here when invoking easy_install.
- # 1. On unix-like systems the easy_install script can *only* be executed
- # directly if its full filesystem path is no longer than 78 characters.
- # 2. A work around to [1] is to use the `python path/to/easy_install foo`
- # pattern, but that breaks if the path contains non-ASCII characters, as
- # you can't put the file encoding declaration before the shebang line.
- # The solution is to use Python's -x flag to skip the first line of the
- # script (and any ASCII decoding errors that may have occurred in that line)
- cmd = [py_executable, '-x', join(os.path.dirname(py_executable), easy_install_script), filename]
- # jython and pypy don't yet support -x
- if is_jython or is_pypy:
- cmd.remove('-x')
- if filename == 'pip':
- if never_download:
- logger.fatal("Can't find any local distributions of pip to install "
- "and --never-download is set. Either re-run virtualenv "
- "without the --never-download option, or place a pip "
- "source distribution (zip/tar.gz/tar.bz2) in one of these "
- "locations: %r" % search_dirs)
- sys.exit(1)
- logger.info('Installing pip from network...')
- else:
- logger.info('Installing existing %s distribution: %s' % (
- os.path.basename(filename), filename))
- logger.start_progress('Installing pip...')
- logger.indent += 2
- def _filter_setup(line):
- return filter_ez_setup(line, 'pip')
- try:
- call_subprocess(cmd, show_stdout=False,
- filter_stdout=_filter_setup)
- finally:
- logger.indent -= 2
- logger.end_progress()
-
-def filter_ez_setup(line, project_name='setuptools'):
- if not line.strip():
- return Logger.DEBUG
- if project_name == 'distribute':
- for prefix in ('Extracting', 'Now working', 'Installing', 'Before',
- 'Scanning', 'Setuptools', 'Egg', 'Already',
- 'running', 'writing', 'reading', 'installing',
- 'creating', 'copying', 'byte-compiling', 'removing',
- 'Processing'):
- if line.startswith(prefix):
- return Logger.DEBUG
- return Logger.DEBUG
- for prefix in ['Reading ', 'Best match', 'Processing setuptools',
- 'Copying setuptools', 'Adding setuptools',
- 'Installing ', 'Installed ']:
- if line.startswith(prefix):
- return Logger.DEBUG
- return Logger.INFO
-
-
-class UpdatingDefaultsHelpFormatter(optparse.IndentedHelpFormatter):
- """
- Custom help formatter for use in ConfigOptionParser that updates
- the defaults before expanding them, allowing them to show up correctly
- in the help listing
- """
- def expand_default(self, option):
- if self.parser is not None:
- self.parser.update_defaults(self.parser.defaults)
- return optparse.IndentedHelpFormatter.expand_default(self, option)
-
-
-class ConfigOptionParser(optparse.OptionParser):
- """
- Custom option parser which updates its defaults by by checking the
- configuration files and environmental variables
- """
- def __init__(self, *args, **kwargs):
- self.config = ConfigParser.RawConfigParser()
- self.files = self.get_config_files()
- self.config.read(self.files)
- optparse.OptionParser.__init__(self, *args, **kwargs)
-
- def get_config_files(self):
- config_file = os.environ.get('VIRTUALENV_CONFIG_FILE', False)
- if config_file and os.path.exists(config_file):
- return [config_file]
- return [default_config_file]
-
- def update_defaults(self, defaults):
- """
- Updates the given defaults with values from the config files and
- the environ. Does a little special handling for certain types of
- options (lists).
- """
- # Then go and look for the other sources of configuration:
- config = {}
- # 1. config files
- config.update(dict(self.get_config_section('virtualenv')))
- # 2. environmental variables
- config.update(dict(self.get_environ_vars()))
- # Then set the options with those values
- for key, val in config.items():
- key = key.replace('_', '-')
- if not key.startswith('--'):
- key = '--%s' % key # only prefer long opts
- option = self.get_option(key)
- if option is not None:
- # ignore empty values
- if not val:
- continue
- # handle multiline configs
- if option.action == 'append':
- val = val.split()
- else:
- option.nargs = 1
- if option.action == 'store_false':
- val = not strtobool(val)
- elif option.action in ('store_true', 'count'):
- val = strtobool(val)
- try:
- val = option.convert_value(key, val)
- except optparse.OptionValueError:
- e = sys.exc_info()[1]
- print("An error occured during configuration: %s" % e)
- sys.exit(3)
- defaults[option.dest] = val
- return defaults
-
- def get_config_section(self, name):
- """
- Get a section of a configuration
- """
- if self.config.has_section(name):
- return self.config.items(name)
- return []
-
- def get_environ_vars(self, prefix='VIRTUALENV_'):
- """
- Returns a generator with all environmental vars with prefix VIRTUALENV
- """
- for key, val in os.environ.items():
- if key.startswith(prefix):
- yield (key.replace(prefix, '').lower(), val)
-
- def get_default_values(self):
- """
- Overridding to make updating the defaults after instantiation of
- the option parser possible, update_defaults() does the dirty work.
- """
- if not self.process_default_values:
- # Old, pre-Optik 1.5 behaviour.
- return optparse.Values(self.defaults)
-
- defaults = self.update_defaults(self.defaults.copy()) # ours
- for option in self._get_all_options():
- default = defaults.get(option.dest)
- if isinstance(default, basestring):
- opt_str = option.get_opt_string()
- defaults[option.dest] = option.check_value(opt_str, default)
- return optparse.Values(defaults)
-
-
-def main():
- parser = ConfigOptionParser(
- version=virtualenv_version,
- usage="%prog [OPTIONS] DEST_DIR",
- formatter=UpdatingDefaultsHelpFormatter())
-
- parser.add_option(
- '-v', '--verbose',
- action='count',
- dest='verbose',
- default=0,
- help="Increase verbosity")
-
- parser.add_option(
- '-q', '--quiet',
- action='count',
- dest='quiet',
- default=0,
- help='Decrease verbosity')
-
- parser.add_option(
- '-p', '--python',
- dest='python',
- metavar='PYTHON_EXE',
- help='The Python interpreter to use, e.g., --python=python2.5 will use the python2.5 '
- 'interpreter to create the new environment. The default is the interpreter that '
- 'virtualenv was installed with (%s)' % sys.executable)
-
- parser.add_option(
- '--clear',
- dest='clear',
- action='store_true',
- help="Clear out the non-root install and start from scratch")
-
- parser.set_defaults(system_site_packages=False)
- parser.add_option(
- '--no-site-packages',
- dest='system_site_packages',
- action='store_false',
- help="Don't give access to the global site-packages dir to the "
- "virtual environment (default)")
-
- parser.add_option(
- '--system-site-packages',
- dest='system_site_packages',
- action='store_true',
- help="Give access to the global site-packages dir to the "
- "virtual environment")
-
- parser.add_option(
- '--unzip-setuptools',
- dest='unzip_setuptools',
- action='store_true',
- help="Unzip Setuptools or Distribute when installing it")
-
- parser.add_option(
- '--relocatable',
- dest='relocatable',
- action='store_true',
- help='Make an EXISTING virtualenv environment relocatable. '
- 'This fixes up scripts and makes all .pth files relative')
-
- parser.add_option(
- '--distribute', '--use-distribute', # the second option is for legacy reasons here. Hi Kenneth!
- dest='use_distribute',
- action='store_true',
- help='Use Distribute instead of Setuptools. Set environ variable '
- 'VIRTUALENV_DISTRIBUTE to make it the default ')
-
- parser.add_option(
- '--no-setuptools',
- dest='no_setuptools',
- action='store_true',
- help='Do not install distribute/setuptools (or pip) '
- 'in the new virtualenv.')
-
- parser.add_option(
- '--no-pip',
- dest='no_pip',
- action='store_true',
- help='Do not install pip in the new virtualenv.')
-
- parser.add_option(
- '--setuptools',
- dest='use_distribute',
- action='store_false',
- help='Use Setuptools instead of Distribute. Set environ variable '
- 'VIRTUALENV_SETUPTOOLS to make it the default ')
-
- # Set this to True to use distribute by default, even in Python 2.
- parser.set_defaults(use_distribute=False)
-
- default_search_dirs = file_search_dirs()
- parser.add_option(
- '--extra-search-dir',
- dest="search_dirs",
- action="append",
- default=default_search_dirs,
- help="Directory to look for setuptools/distribute/pip distributions in. "
- "You can add any number of additional --extra-search-dir paths.")
-
- parser.add_option(
- '--never-download',
- dest="never_download",
- action="store_true",
- help="Never download anything from the network. Instead, virtualenv will fail "
- "if local distributions of setuptools/distribute/pip are not present.")
-
- parser.add_option(
- '--prompt',
- dest='prompt',
- help='Provides an alternative prompt prefix for this environment')
-
- if 'extend_parser' in globals():
- extend_parser(parser)
-
- options, args = parser.parse_args()
-
- global logger
-
- if 'adjust_options' in globals():
- adjust_options(options, args)
-
- verbosity = options.verbose - options.quiet
- logger = Logger([(Logger.level_for_integer(2 - verbosity), sys.stdout)])
-
- if options.python and not os.environ.get('VIRTUALENV_INTERPRETER_RUNNING'):
- env = os.environ.copy()
- interpreter = resolve_interpreter(options.python)
- if interpreter == sys.executable:
- logger.warn('Already using interpreter %s' % interpreter)
- else:
- logger.notify('Running virtualenv with interpreter %s' % interpreter)
- env['VIRTUALENV_INTERPRETER_RUNNING'] = 'true'
- file = __file__
- if file.endswith('.pyc'):
- file = file[:-1]
- popen = subprocess.Popen([interpreter, file] + sys.argv[1:], env=env)
- raise SystemExit(popen.wait())
-
- # Force --distribute on Python 3, since setuptools is not available.
- if majver > 2:
- options.use_distribute = True
-
- if os.environ.get('PYTHONDONTWRITEBYTECODE') and not options.use_distribute:
- print(
- "The PYTHONDONTWRITEBYTECODE environment variable is "
- "not compatible with setuptools. Either use --distribute "
- "or unset PYTHONDONTWRITEBYTECODE.")
- sys.exit(2)
- if not args:
- print('You must provide a DEST_DIR')
- parser.print_help()
- sys.exit(2)
- if len(args) > 1:
- print('There must be only one argument: DEST_DIR (you gave %s)' % (
- ' '.join(args)))
- parser.print_help()
- sys.exit(2)
-
- home_dir = args[0]
-
- if os.environ.get('WORKING_ENV'):
- logger.fatal('ERROR: you cannot run virtualenv while in a workingenv')
- logger.fatal('Please deactivate your workingenv, then re-run this script')
- sys.exit(3)
-
- if 'PYTHONHOME' in os.environ:
- logger.warn('PYTHONHOME is set. You *must* activate the virtualenv before using it')
- del os.environ['PYTHONHOME']
-
- if options.relocatable:
- make_environment_relocatable(home_dir)
- return
-
- create_environment(home_dir,
- site_packages=options.system_site_packages,
- clear=options.clear,
- unzip_setuptools=options.unzip_setuptools,
- use_distribute=options.use_distribute,
- prompt=options.prompt,
- search_dirs=options.search_dirs,
- never_download=options.never_download,
- no_setuptools=options.no_setuptools,
- no_pip=options.no_pip)
- if 'after_install' in globals():
- after_install(options, home_dir)
-
-def call_subprocess(cmd, show_stdout=True,
- filter_stdout=None, cwd=None,
- raise_on_returncode=True, extra_env=None,
- remove_from_env=None):
- cmd_parts = []
- for part in cmd:
- if len(part) > 45:
- part = part[:20]+"..."+part[-20:]
- if ' ' in part or '\n' in part or '"' in part or "'" in part:
- part = '"%s"' % part.replace('"', '\\"')
- if hasattr(part, 'decode'):
- try:
- part = part.decode(sys.getdefaultencoding())
- except UnicodeDecodeError:
- part = part.decode(sys.getfilesystemencoding())
- cmd_parts.append(part)
- cmd_desc = ' '.join(cmd_parts)
- if show_stdout:
- stdout = None
- else:
- stdout = subprocess.PIPE
- logger.debug("Running command %s" % cmd_desc)
- if extra_env or remove_from_env:
- env = os.environ.copy()
- if extra_env:
- env.update(extra_env)
- if remove_from_env:
- for varname in remove_from_env:
- env.pop(varname, None)
- else:
- env = None
- try:
- proc = subprocess.Popen(
- cmd, stderr=subprocess.STDOUT, stdin=None, stdout=stdout,
- cwd=cwd, env=env)
- except Exception:
- e = sys.exc_info()[1]
- logger.fatal(
- "Error %s while executing command %s" % (e, cmd_desc))
- raise
- all_output = []
- if stdout is not None:
- stdout = proc.stdout
- encoding = sys.getdefaultencoding()
- fs_encoding = sys.getfilesystemencoding()
- while 1:
- line = stdout.readline()
- try:
- line = line.decode(encoding)
- except UnicodeDecodeError:
- line = line.decode(fs_encoding)
- if not line:
- break
- line = line.rstrip()
- all_output.append(line)
- if filter_stdout:
- level = filter_stdout(line)
- if isinstance(level, tuple):
- level, line = level
- logger.log(level, line)
- if not logger.stdout_level_matches(level):
- logger.show_progress()
- else:
- logger.info(line)
- else:
- proc.communicate()
- proc.wait()
- if proc.returncode:
- if raise_on_returncode:
- if all_output:
- logger.notify('Complete output from command %s:' % cmd_desc)
- logger.notify('\n'.join(all_output) + '\n----------------------------------------')
- raise OSError(
- "Command %s failed with error code %s"
- % (cmd_desc, proc.returncode))
- else:
- logger.warn(
- "Command %s had error code %s"
- % (cmd_desc, proc.returncode))
-
-
-def create_environment(home_dir, site_packages=False, clear=False,
- unzip_setuptools=False, use_distribute=False,
- prompt=None, search_dirs=None, never_download=False,
- no_setuptools=False, no_pip=False):
- """
- Creates a new environment in ``home_dir``.
-
- If ``site_packages`` is true, then the global ``site-packages/``
- directory will be on the path.
-
- If ``clear`` is true (default False) then the environment will
- first be cleared.
- """
- home_dir, lib_dir, inc_dir, bin_dir = path_locations(home_dir)
-
- py_executable = os.path.abspath(install_python(
- home_dir, lib_dir, inc_dir, bin_dir,
- site_packages=site_packages, clear=clear))
-
- install_distutils(home_dir)
-
- if not no_setuptools:
- if use_distribute:
- install_distribute(py_executable, unzip=unzip_setuptools,
- search_dirs=search_dirs, never_download=never_download)
- else:
- install_setuptools(py_executable, unzip=unzip_setuptools,
- search_dirs=search_dirs, never_download=never_download)
-
- if not no_pip:
- install_pip(py_executable, search_dirs=search_dirs, never_download=never_download)
-
- install_activate(home_dir, bin_dir, prompt)
-
-def is_executable_file(fpath):
- return os.path.isfile(fpath) and os.access(fpath, os.X_OK)
-
-def path_locations(home_dir):
- """Return the path locations for the environment (where libraries are,
- where scripts go, etc)"""
- # XXX: We'd use distutils.sysconfig.get_python_inc/lib but its
- # prefix arg is broken: http://bugs.python.org/issue3386
- if is_win:
- # Windows has lots of problems with executables with spaces in
- # the name; this function will remove them (using the ~1
- # format):
- mkdir(home_dir)
- if ' ' in home_dir:
- import ctypes
- GetShortPathName = ctypes.windll.kernel32.GetShortPathNameW
- size = max(len(home_dir)+1, 256)
- buf = ctypes.create_unicode_buffer(size)
- try:
- u = unicode
- except NameError:
- u = str
- ret = GetShortPathName(u(home_dir), buf, size)
- if not ret:
- print('Error: the path "%s" has a space in it' % home_dir)
- print('We could not determine the short pathname for it.')
- print('Exiting.')
- sys.exit(3)
- home_dir = str(buf.value)
- lib_dir = join(home_dir, 'Lib')
- inc_dir = join(home_dir, 'Include')
- bin_dir = join(home_dir, 'Scripts')
- if is_jython:
- lib_dir = join(home_dir, 'Lib')
- inc_dir = join(home_dir, 'Include')
- bin_dir = join(home_dir, 'bin')
- elif is_pypy:
- lib_dir = home_dir
- inc_dir = join(home_dir, 'include')
- bin_dir = join(home_dir, 'bin')
- elif not is_win:
- lib_dir = join(home_dir, 'lib', py_version)
- multiarch_exec = '/usr/bin/multiarch-platform'
- if is_executable_file(multiarch_exec):
- # In Mageia (2) and Mandriva distros the include dir must be like:
- # virtualenv/include/multiarch-x86_64-linux/python2.7
- # instead of being virtualenv/include/python2.7
- p = subprocess.Popen(multiarch_exec, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
- stdout, stderr = p.communicate()
- # stdout.strip is needed to remove newline character
- inc_dir = join(home_dir, 'include', stdout.strip(), py_version + abiflags)
- else:
- inc_dir = join(home_dir, 'include', py_version + abiflags)
- bin_dir = join(home_dir, 'bin')
- return home_dir, lib_dir, inc_dir, bin_dir
-
-
-def change_prefix(filename, dst_prefix):
- prefixes = [sys.prefix]
-
- if is_darwin:
- prefixes.extend((
- os.path.join("/Library/Python", sys.version[:3], "site-packages"),
- os.path.join(sys.prefix, "Extras", "lib", "python"),
- os.path.join("~", "Library", "Python", sys.version[:3], "site-packages"),
- # Python 2.6 no-frameworks
- os.path.join("~", ".local", "lib","python", sys.version[:3], "site-packages"),
- # System Python 2.7 on OSX Mountain Lion
- os.path.join("~", "Library", "Python", sys.version[:3], "lib", "python", "site-packages")))
-
- if hasattr(sys, 'real_prefix'):
- prefixes.append(sys.real_prefix)
- if hasattr(sys, 'base_prefix'):
- prefixes.append(sys.base_prefix)
- prefixes = list(map(os.path.expanduser, prefixes))
- prefixes = list(map(os.path.abspath, prefixes))
- # Check longer prefixes first so we don't split in the middle of a filename
- prefixes = sorted(prefixes, key=len, reverse=True)
- filename = os.path.abspath(filename)
- for src_prefix in prefixes:
- if filename.startswith(src_prefix):
- _, relpath = filename.split(src_prefix, 1)
- if src_prefix != os.sep: # sys.prefix == "/"
- assert relpath[0] == os.sep
- relpath = relpath[1:]
- return join(dst_prefix, relpath)
- assert False, "Filename %s does not start with any of these prefixes: %s" % \
- (filename, prefixes)
-
-def copy_required_modules(dst_prefix):
- import imp
- # If we are running under -p, we need to remove the current
- # directory from sys.path temporarily here, so that we
- # definitely get the modules from the site directory of
- # the interpreter we are running under, not the one
- # virtualenv.py is installed under (which might lead to py2/py3
- # incompatibility issues)
- _prev_sys_path = sys.path
- if os.environ.get('VIRTUALENV_INTERPRETER_RUNNING'):
- sys.path = sys.path[1:]
- try:
- for modname in REQUIRED_MODULES:
- if modname in sys.builtin_module_names:
- logger.info("Ignoring built-in bootstrap module: %s" % modname)
- continue
- try:
- f, filename, _ = imp.find_module(modname)
- except ImportError:
- logger.info("Cannot import bootstrap module: %s" % modname)
- else:
- if f is not None:
- f.close()
- # special-case custom readline.so on OS X, but not for pypy:
- if modname == 'readline' and sys.platform == 'darwin' and not (
- is_pypy or filename.endswith(join('lib-dynload', 'readline.so'))):
- dst_filename = join(dst_prefix, 'lib', 'python%s' % sys.version[:3], 'readline.so')
- else:
- dst_filename = change_prefix(filename, dst_prefix)
- copyfile(filename, dst_filename)
- if filename.endswith('.pyc'):
- pyfile = filename[:-1]
- if os.path.exists(pyfile):
- copyfile(pyfile, dst_filename[:-1])
- finally:
- sys.path = _prev_sys_path
-
-
-def subst_path(prefix_path, prefix, home_dir):
- prefix_path = os.path.normpath(prefix_path)
- prefix = os.path.normpath(prefix)
- home_dir = os.path.normpath(home_dir)
- if not prefix_path.startswith(prefix):
- logger.warn('Path not in prefix %r %r', prefix_path, prefix)
- return
- return prefix_path.replace(prefix, home_dir, 1)
-
-
-def install_python(home_dir, lib_dir, inc_dir, bin_dir, site_packages, clear):
- """Install just the base environment, no distutils patches etc"""
- if sys.executable.startswith(bin_dir):
- print('Please use the *system* python to run this script')
- return
-
- if clear:
- rmtree(lib_dir)
- ## FIXME: why not delete it?
- ## Maybe it should delete everything with #!/path/to/venv/python in it
- logger.notify('Not deleting %s', bin_dir)
-
- if hasattr(sys, 'real_prefix'):
- logger.notify('Using real prefix %r' % sys.real_prefix)
- prefix = sys.real_prefix
- elif hasattr(sys, 'base_prefix'):
- logger.notify('Using base prefix %r' % sys.base_prefix)
- prefix = sys.base_prefix
- else:
- prefix = sys.prefix
- mkdir(lib_dir)
- fix_lib64(lib_dir)
- stdlib_dirs = [os.path.dirname(os.__file__)]
- if is_win:
- stdlib_dirs.append(join(os.path.dirname(stdlib_dirs[0]), 'DLLs'))
- elif is_darwin:
- stdlib_dirs.append(join(stdlib_dirs[0], 'site-packages'))
- if hasattr(os, 'symlink'):
- logger.info('Symlinking Python bootstrap modules')
- else:
- logger.info('Copying Python bootstrap modules')
- logger.indent += 2
- try:
- # copy required files...
- for stdlib_dir in stdlib_dirs:
- if not os.path.isdir(stdlib_dir):
- continue
- for fn in os.listdir(stdlib_dir):
- bn = os.path.splitext(fn)[0]
- if fn != 'site-packages' and bn in REQUIRED_FILES:
- copyfile(join(stdlib_dir, fn), join(lib_dir, fn))
- # ...and modules
- copy_required_modules(home_dir)
- finally:
- logger.indent -= 2
- mkdir(join(lib_dir, 'site-packages'))
- import site
- site_filename = site.__file__
- if site_filename.endswith('.pyc'):
- site_filename = site_filename[:-1]
- elif site_filename.endswith('$py.class'):
- site_filename = site_filename.replace('$py.class', '.py')
- site_filename_dst = change_prefix(site_filename, home_dir)
- site_dir = os.path.dirname(site_filename_dst)
- writefile(site_filename_dst, SITE_PY)
- writefile(join(site_dir, 'orig-prefix.txt'), prefix)
- site_packages_filename = join(site_dir, 'no-global-site-packages.txt')
- if not site_packages:
- writefile(site_packages_filename, '')
-
- if is_pypy or is_win:
- stdinc_dir = join(prefix, 'include')
- else:
- stdinc_dir = join(prefix, 'include', py_version + abiflags)
- if os.path.exists(stdinc_dir):
- copyfile(stdinc_dir, inc_dir)
- else:
- logger.debug('No include dir %s' % stdinc_dir)
-
- platinc_dir = distutils.sysconfig.get_python_inc(plat_specific=1)
- if platinc_dir != stdinc_dir:
- platinc_dest = distutils.sysconfig.get_python_inc(
- plat_specific=1, prefix=home_dir)
- if platinc_dir == platinc_dest:
- # Do platinc_dest manually due to a CPython bug;
- # not http://bugs.python.org/issue3386 but a close cousin
- platinc_dest = subst_path(platinc_dir, prefix, home_dir)
- if platinc_dest:
- # PyPy's stdinc_dir and prefix are relative to the original binary
- # (traversing virtualenvs), whereas the platinc_dir is relative to
- # the inner virtualenv and ignores the prefix argument.
- # This seems more evolved than designed.
- copyfile(platinc_dir, platinc_dest)
-
- # pypy never uses exec_prefix, just ignore it
- if sys.exec_prefix != prefix and not is_pypy:
- if is_win:
- exec_dir = join(sys.exec_prefix, 'lib')
- elif is_jython:
- exec_dir = join(sys.exec_prefix, 'Lib')
- else:
- exec_dir = join(sys.exec_prefix, 'lib', py_version)
- for fn in os.listdir(exec_dir):
- copyfile(join(exec_dir, fn), join(lib_dir, fn))
-
- if is_jython:
- # Jython has either jython-dev.jar and javalib/ dir, or just
- # jython.jar
- for name in 'jython-dev.jar', 'javalib', 'jython.jar':
- src = join(prefix, name)
- if os.path.exists(src):
- copyfile(src, join(home_dir, name))
- # XXX: registry should always exist after Jython 2.5rc1
- src = join(prefix, 'registry')
- if os.path.exists(src):
- copyfile(src, join(home_dir, 'registry'), symlink=False)
- copyfile(join(prefix, 'cachedir'), join(home_dir, 'cachedir'),
- symlink=False)
-
- mkdir(bin_dir)
- py_executable = join(bin_dir, os.path.basename(sys.executable))
- if 'Python.framework' in prefix:
- # OS X framework builds cause validation to break
- # https://github.com/pypa/virtualenv/issues/322
- if os.environ.get('__PYVENV_LAUNCHER__'):
- os.unsetenv('__PYVENV_LAUNCHER__')
- if re.search(r'/Python(?:-32|-64)*$', py_executable):
- # The name of the python executable is not quite what
- # we want, rename it.
- py_executable = os.path.join(
- os.path.dirname(py_executable), 'python')
-
- logger.notify('New %s executable in %s', expected_exe, py_executable)
- pcbuild_dir = os.path.dirname(sys.executable)
- pyd_pth = os.path.join(lib_dir, 'site-packages', 'virtualenv_builddir_pyd.pth')
- if is_win and os.path.exists(os.path.join(pcbuild_dir, 'build.bat')):
- logger.notify('Detected python running from build directory %s', pcbuild_dir)
- logger.notify('Writing .pth file linking to build directory for *.pyd files')
- writefile(pyd_pth, pcbuild_dir)
- else:
- pcbuild_dir = None
- if os.path.exists(pyd_pth):
- logger.info('Deleting %s (not Windows env or not build directory python)' % pyd_pth)
- os.unlink(pyd_pth)
-
- if sys.executable != py_executable:
- ## FIXME: could I just hard link?
- executable = sys.executable
- shutil.copyfile(executable, py_executable)
- make_exe(py_executable)
- if is_win or is_cygwin:
- pythonw = os.path.join(os.path.dirname(sys.executable), 'pythonw.exe')
- if os.path.exists(pythonw):
- logger.info('Also created pythonw.exe')
- shutil.copyfile(pythonw, os.path.join(os.path.dirname(py_executable), 'pythonw.exe'))
- python_d = os.path.join(os.path.dirname(sys.executable), 'python_d.exe')
- python_d_dest = os.path.join(os.path.dirname(py_executable), 'python_d.exe')
- if os.path.exists(python_d):
- logger.info('Also created python_d.exe')
- shutil.copyfile(python_d, python_d_dest)
- elif os.path.exists(python_d_dest):
- logger.info('Removed python_d.exe as it is no longer at the source')
- os.unlink(python_d_dest)
- # we need to copy the DLL to enforce that windows will load the correct one.
- # may not exist if we are cygwin.
- py_executable_dll = 'python%s%s.dll' % (
- sys.version_info[0], sys.version_info[1])
- py_executable_dll_d = 'python%s%s_d.dll' % (
- sys.version_info[0], sys.version_info[1])
- pythondll = os.path.join(os.path.dirname(sys.executable), py_executable_dll)
- pythondll_d = os.path.join(os.path.dirname(sys.executable), py_executable_dll_d)
- pythondll_d_dest = os.path.join(os.path.dirname(py_executable), py_executable_dll_d)
- if os.path.exists(pythondll):
- logger.info('Also created %s' % py_executable_dll)
- shutil.copyfile(pythondll, os.path.join(os.path.dirname(py_executable), py_executable_dll))
- if os.path.exists(pythondll_d):
- logger.info('Also created %s' % py_executable_dll_d)
- shutil.copyfile(pythondll_d, pythondll_d_dest)
- elif os.path.exists(pythondll_d_dest):
- logger.info('Removed %s as the source does not exist' % pythondll_d_dest)
- os.unlink(pythondll_d_dest)
- if is_pypy:
- # make a symlink python --> pypy-c
- python_executable = os.path.join(os.path.dirname(py_executable), 'python')
- if sys.platform in ('win32', 'cygwin'):
- python_executable += '.exe'
- logger.info('Also created executable %s' % python_executable)
- copyfile(py_executable, python_executable)
-
- if is_win:
- for name in 'libexpat.dll', 'libpypy.dll', 'libpypy-c.dll', 'libeay32.dll', 'ssleay32.dll', 'sqlite.dll':
- src = join(prefix, name)
- if os.path.exists(src):
- copyfile(src, join(bin_dir, name))
-
- if os.path.splitext(os.path.basename(py_executable))[0] != expected_exe:
- secondary_exe = os.path.join(os.path.dirname(py_executable),
- expected_exe)
- py_executable_ext = os.path.splitext(py_executable)[1]
- if py_executable_ext == '.exe':
- # python2.4 gives an extension of '.4' :P
- secondary_exe += py_executable_ext
- if os.path.exists(secondary_exe):
- logger.warn('Not overwriting existing %s script %s (you must use %s)'
- % (expected_exe, secondary_exe, py_executable))
- else:
- logger.notify('Also creating executable in %s' % secondary_exe)
- shutil.copyfile(sys.executable, secondary_exe)
- make_exe(secondary_exe)
-
- if '.framework' in prefix:
- if 'Python.framework' in prefix:
- logger.debug('MacOSX Python framework detected')
- # Make sure we use the the embedded interpreter inside
- # the framework, even if sys.executable points to
- # the stub executable in ${sys.prefix}/bin
- # See http://groups.google.com/group/python-virtualenv/
- # browse_thread/thread/17cab2f85da75951
- original_python = os.path.join(
- prefix, 'Resources/Python.app/Contents/MacOS/Python')
- if 'EPD' in prefix:
- logger.debug('EPD framework detected')
- original_python = os.path.join(prefix, 'bin/python')
- shutil.copy(original_python, py_executable)
-
- # Copy the framework's dylib into the virtual
- # environment
- virtual_lib = os.path.join(home_dir, '.Python')
-
- if os.path.exists(virtual_lib):
- os.unlink(virtual_lib)
- copyfile(
- os.path.join(prefix, 'Python'),
- virtual_lib)
-
- # And then change the install_name of the copied python executable
- try:
- mach_o_change(py_executable,
- os.path.join(prefix, 'Python'),
- '@executable_path/../.Python')
- except:
- e = sys.exc_info()[1]
- logger.warn("Could not call mach_o_change: %s. "
- "Trying to call install_name_tool instead." % e)
- try:
- call_subprocess(
- ["install_name_tool", "-change",
- os.path.join(prefix, 'Python'),
- '@executable_path/../.Python',
- py_executable])
- except:
- logger.fatal("Could not call install_name_tool -- you must "
- "have Apple's development tools installed")
- raise
-
- if not is_win:
- # Ensure that 'python', 'pythonX' and 'pythonX.Y' all exist
- py_exe_version_major = 'python%s' % sys.version_info[0]
- py_exe_version_major_minor = 'python%s.%s' % (
- sys.version_info[0], sys.version_info[1])
- py_exe_no_version = 'python'
- required_symlinks = [ py_exe_no_version, py_exe_version_major,
- py_exe_version_major_minor ]
-
- py_executable_base = os.path.basename(py_executable)
-
- if py_executable_base in required_symlinks:
- # Don't try to symlink to yourself.
- required_symlinks.remove(py_executable_base)
-
- for pth in required_symlinks:
- full_pth = join(bin_dir, pth)
- if os.path.exists(full_pth):
- os.unlink(full_pth)
- os.symlink(py_executable_base, full_pth)
-
- if is_win and ' ' in py_executable:
- # There's a bug with subprocess on Windows when using a first
- # argument that has a space in it. Instead we have to quote
- # the value:
- py_executable = '"%s"' % py_executable
- # NOTE: keep this check as one line, cmd.exe doesn't cope with line breaks
- cmd = [py_executable, '-c', 'import sys;out=sys.stdout;'
- 'getattr(out, "buffer", out).write(sys.prefix.encode("utf-8"))']
- logger.info('Testing executable with %s %s "%s"' % tuple(cmd))
- try:
- proc = subprocess.Popen(cmd,
- stdout=subprocess.PIPE)
- proc_stdout, proc_stderr = proc.communicate()
- except OSError:
- e = sys.exc_info()[1]
- if e.errno == errno.EACCES:
- logger.fatal('ERROR: The executable %s could not be run: %s' % (py_executable, e))
- sys.exit(100)
- else:
- raise e
-
- proc_stdout = proc_stdout.strip().decode("utf-8")
- proc_stdout = os.path.normcase(os.path.abspath(proc_stdout))
- norm_home_dir = os.path.normcase(os.path.abspath(home_dir))
- if hasattr(norm_home_dir, 'decode'):
- norm_home_dir = norm_home_dir.decode(sys.getfilesystemencoding())
- if proc_stdout != norm_home_dir:
- logger.fatal(
- 'ERROR: The executable %s is not functioning' % py_executable)
- logger.fatal(
- 'ERROR: It thinks sys.prefix is %r (should be %r)'
- % (proc_stdout, norm_home_dir))
- logger.fatal(
- 'ERROR: virtualenv is not compatible with this system or executable')
- if is_win:
- logger.fatal(
- 'Note: some Windows users have reported this error when they '
- 'installed Python for "Only this user" or have multiple '
- 'versions of Python installed. Copying the appropriate '
- 'PythonXX.dll to the virtualenv Scripts/ directory may fix '
- 'this problem.')
- sys.exit(100)
- else:
- logger.info('Got sys.prefix result: %r' % proc_stdout)
-
- pydistutils = os.path.expanduser('~/.pydistutils.cfg')
- if os.path.exists(pydistutils):
- logger.notify('Please make sure you remove any previous custom paths from '
- 'your %s file.' % pydistutils)
- ## FIXME: really this should be calculated earlier
-
- fix_local_scheme(home_dir)
-
- if site_packages:
- if os.path.exists(site_packages_filename):
- logger.info('Deleting %s' % site_packages_filename)
- os.unlink(site_packages_filename)
-
- return py_executable
-
-
-def install_activate(home_dir, bin_dir, prompt=None):
- home_dir = os.path.abspath(home_dir)
- if is_win or is_jython and os._name == 'nt':
- files = {
- 'activate.bat': ACTIVATE_BAT,
- 'deactivate.bat': DEACTIVATE_BAT,
- 'activate.ps1': ACTIVATE_PS,
- }
-
- # MSYS needs paths of the form /c/path/to/file
- drive, tail = os.path.splitdrive(home_dir.replace(os.sep, '/'))
- home_dir_msys = (drive and "/%s%s" or "%s%s") % (drive[:1], tail)
-
- # Run-time conditional enables (basic) Cygwin compatibility
- home_dir_sh = ("""$(if [ "$OSTYPE" "==" "cygwin" ]; then cygpath -u '%s'; else echo '%s'; fi;)""" %
- (home_dir, home_dir_msys))
- files['activate'] = ACTIVATE_SH.replace('__VIRTUAL_ENV__', home_dir_sh)
-
- else:
- files = {'activate': ACTIVATE_SH}
-
- # suppling activate.fish in addition to, not instead of, the
- # bash script support.
- files['activate.fish'] = ACTIVATE_FISH
-
- # same for csh/tcsh support...
- files['activate.csh'] = ACTIVATE_CSH
-
- files['activate_this.py'] = ACTIVATE_THIS
- if hasattr(home_dir, 'decode'):
- home_dir = home_dir.decode(sys.getfilesystemencoding())
- vname = os.path.basename(home_dir)
- for name, content in files.items():
- content = content.replace('__VIRTUAL_PROMPT__', prompt or '')
- content = content.replace('__VIRTUAL_WINPROMPT__', prompt or '(%s)' % vname)
- content = content.replace('__VIRTUAL_ENV__', home_dir)
- content = content.replace('__VIRTUAL_NAME__', vname)
- content = content.replace('__BIN_NAME__', os.path.basename(bin_dir))
- writefile(os.path.join(bin_dir, name), content)
-
-def install_distutils(home_dir):
- distutils_path = change_prefix(distutils.__path__[0], home_dir)
- mkdir(distutils_path)
- ## FIXME: maybe this prefix setting should only be put in place if
- ## there's a local distutils.cfg with a prefix setting?
- home_dir = os.path.abspath(home_dir)
- ## FIXME: this is breaking things, removing for now:
- #distutils_cfg = DISTUTILS_CFG + "\n[install]\nprefix=%s\n" % home_dir
- writefile(os.path.join(distutils_path, '__init__.py'), DISTUTILS_INIT)
- writefile(os.path.join(distutils_path, 'distutils.cfg'), DISTUTILS_CFG, overwrite=False)
-
-def fix_local_scheme(home_dir):
- """
- Platforms that use the "posix_local" install scheme (like Ubuntu with
- Python 2.7) need to be given an additional "local" location, sigh.
- """
- try:
- import sysconfig
- except ImportError:
- pass
- else:
- if sysconfig._get_default_scheme() == 'posix_local':
- local_path = os.path.join(home_dir, 'local')
- if not os.path.exists(local_path):
- os.mkdir(local_path)
- for subdir_name in os.listdir(home_dir):
- if subdir_name == 'local':
- continue
- os.symlink(os.path.abspath(os.path.join(home_dir, subdir_name)), \
- os.path.join(local_path, subdir_name))
-
-def fix_lib64(lib_dir):
- """
- Some platforms (particularly Gentoo on x64) put things in lib64/pythonX.Y
- instead of lib/pythonX.Y. If this is such a platform we'll just create a
- symlink so lib64 points to lib
- """
- if [p for p in distutils.sysconfig.get_config_vars().values()
- if isinstance(p, basestring) and 'lib64' in p]:
- logger.debug('This system uses lib64; symlinking lib64 to lib')
- assert os.path.basename(lib_dir) == 'python%s' % sys.version[:3], (
- "Unexpected python lib dir: %r" % lib_dir)
- lib_parent = os.path.dirname(lib_dir)
- top_level = os.path.dirname(lib_parent)
- lib_dir = os.path.join(top_level, 'lib')
- lib64_link = os.path.join(top_level, 'lib64')
- assert os.path.basename(lib_parent) == 'lib', (
- "Unexpected parent dir: %r" % lib_parent)
- if os.path.lexists(lib64_link):
- return
- os.symlink('lib', lib64_link)
-
-def resolve_interpreter(exe):
- """
- If the executable given isn't an absolute path, search $PATH for the interpreter
- """
- if os.path.abspath(exe) != exe:
- paths = os.environ.get('PATH', '').split(os.pathsep)
- for path in paths:
- if os.path.exists(os.path.join(path, exe)):
- exe = os.path.join(path, exe)
- break
- if not os.path.exists(exe):
- logger.fatal('The executable %s (from --python=%s) does not exist' % (exe, exe))
- raise SystemExit(3)
- if not is_executable(exe):
- logger.fatal('The executable %s (from --python=%s) is not executable' % (exe, exe))
- raise SystemExit(3)
- return exe
-
-def is_executable(exe):
- """Checks a file is executable"""
- return os.access(exe, os.X_OK)
-
-############################################################
-## Relocating the environment:
-
-def make_environment_relocatable(home_dir):
- """
- Makes the already-existing environment use relative paths, and takes out
- the #!-based environment selection in scripts.
- """
- home_dir, lib_dir, inc_dir, bin_dir = path_locations(home_dir)
- activate_this = os.path.join(bin_dir, 'activate_this.py')
- if not os.path.exists(activate_this):
- logger.fatal(
- 'The environment doesn\'t have a file %s -- please re-run virtualenv '
- 'on this environment to update it' % activate_this)
- fixup_scripts(home_dir)
- fixup_pth_and_egg_link(home_dir)
- ## FIXME: need to fix up distutils.cfg
-
-OK_ABS_SCRIPTS = ['python', 'python%s' % sys.version[:3],
- 'activate', 'activate.bat', 'activate_this.py']
-
-def fixup_scripts(home_dir):
- # This is what we expect at the top of scripts:
- shebang = '#!%s/bin/python' % os.path.normcase(os.path.abspath(home_dir))
- # This is what we'll put:
- new_shebang = '#!/usr/bin/env python%s' % sys.version[:3]
- if is_win:
- bin_suffix = 'Scripts'
- else:
- bin_suffix = 'bin'
- bin_dir = os.path.join(home_dir, bin_suffix)
- home_dir, lib_dir, inc_dir, bin_dir = path_locations(home_dir)
- for filename in os.listdir(bin_dir):
- filename = os.path.join(bin_dir, filename)
- if not os.path.isfile(filename):
- # ignore subdirs, e.g. .svn ones.
- continue
- f = open(filename, 'rb')
- try:
- try:
- lines = f.read().decode('utf-8').splitlines()
- except UnicodeDecodeError:
- # This is probably a binary program instead
- # of a script, so just ignore it.
- continue
- finally:
- f.close()
- if not lines:
- logger.warn('Script %s is an empty file' % filename)
- continue
- if not lines[0].strip().startswith(shebang):
- if os.path.basename(filename) in OK_ABS_SCRIPTS:
- logger.debug('Cannot make script %s relative' % filename)
- elif lines[0].strip() == new_shebang:
- logger.info('Script %s has already been made relative' % filename)
- else:
- logger.warn('Script %s cannot be made relative (it\'s not a normal script that starts with %s)'
- % (filename, shebang))
- continue
- logger.notify('Making script %s relative' % filename)
- script = relative_script([new_shebang] + lines[1:])
- f = open(filename, 'wb')
- f.write('\n'.join(script).encode('utf-8'))
- f.close()
-
-def relative_script(lines):
- "Return a script that'll work in a relocatable environment."
- activate = "import os; activate_this=os.path.join(os.path.dirname(os.path.realpath(__file__)), 'activate_this.py'); execfile(activate_this, dict(__file__=activate_this)); del os, activate_this"
- # Find the last future statement in the script. If we insert the activation
- # line before a future statement, Python will raise a SyntaxError.
- activate_at = None
- for idx, line in reversed(list(enumerate(lines))):
- if line.split()[:3] == ['from', '__future__', 'import']:
- activate_at = idx + 1
- break
- if activate_at is None:
- # Activate after the shebang.
- activate_at = 1
- return lines[:activate_at] + ['', activate, ''] + lines[activate_at:]
-
-def fixup_pth_and_egg_link(home_dir, sys_path=None):
- """Makes .pth and .egg-link files use relative paths"""
- home_dir = os.path.normcase(os.path.abspath(home_dir))
- if sys_path is None:
- sys_path = sys.path
- for path in sys_path:
- if not path:
- path = '.'
- if not os.path.isdir(path):
- continue
- path = os.path.normcase(os.path.abspath(path))
- if not path.startswith(home_dir):
- logger.debug('Skipping system (non-environment) directory %s' % path)
- continue
- for filename in os.listdir(path):
- filename = os.path.join(path, filename)
- if filename.endswith('.pth'):
- if not os.access(filename, os.W_OK):
- logger.warn('Cannot write .pth file %s, skipping' % filename)
- else:
- fixup_pth_file(filename)
- if filename.endswith('.egg-link'):
- if not os.access(filename, os.W_OK):
- logger.warn('Cannot write .egg-link file %s, skipping' % filename)
- else:
- fixup_egg_link(filename)
-
-def fixup_pth_file(filename):
- lines = []
- prev_lines = []
- f = open(filename)
- prev_lines = f.readlines()
- f.close()
- for line in prev_lines:
- line = line.strip()
- if (not line or line.startswith('#') or line.startswith('import ')
- or os.path.abspath(line) != line):
- lines.append(line)
- else:
- new_value = make_relative_path(filename, line)
- if line != new_value:
- logger.debug('Rewriting path %s as %s (in %s)' % (line, new_value, filename))
- lines.append(new_value)
- if lines == prev_lines:
- logger.info('No changes to .pth file %s' % filename)
- return
- logger.notify('Making paths in .pth file %s relative' % filename)
- f = open(filename, 'w')
- f.write('\n'.join(lines) + '\n')
- f.close()
-
-def fixup_egg_link(filename):
- f = open(filename)
- link = f.readline().strip()
- f.close()
- if os.path.abspath(link) != link:
- logger.debug('Link in %s already relative' % filename)
- return
- new_link = make_relative_path(filename, link)
- logger.notify('Rewriting link %s in %s as %s' % (link, filename, new_link))
- f = open(filename, 'w')
- f.write(new_link)
- f.close()
-
-def make_relative_path(source, dest, dest_is_directory=True):
- """
- Make a filename relative, where the filename is dest, and it is
- being referred to from the filename source.
-
- >>> make_relative_path('/usr/share/something/a-file.pth',
- ... '/usr/share/another-place/src/Directory')
- '../another-place/src/Directory'
- >>> make_relative_path('/usr/share/something/a-file.pth',
- ... '/home/user/src/Directory')
- '../../../home/user/src/Directory'
- >>> make_relative_path('/usr/share/a-file.pth', '/usr/share/')
- './'
- """
- source = os.path.dirname(source)
- if not dest_is_directory:
- dest_filename = os.path.basename(dest)
- dest = os.path.dirname(dest)
- dest = os.path.normpath(os.path.abspath(dest))
- source = os.path.normpath(os.path.abspath(source))
- dest_parts = dest.strip(os.path.sep).split(os.path.sep)
- source_parts = source.strip(os.path.sep).split(os.path.sep)
- while dest_parts and source_parts and dest_parts[0] == source_parts[0]:
- dest_parts.pop(0)
- source_parts.pop(0)
- full_parts = ['..']*len(source_parts) + dest_parts
- if not dest_is_directory:
- full_parts.append(dest_filename)
- if not full_parts:
- # Special case for the current directory (otherwise it'd be '')
- return './'
- return os.path.sep.join(full_parts)
-
-
-
-############################################################
-## Bootstrap script creation:
-
-def create_bootstrap_script(extra_text, python_version=''):
- """
- Creates a bootstrap script, which is like this script but with
- extend_parser, adjust_options, and after_install hooks.
-
- This returns a string that (written to disk of course) can be used
- as a bootstrap script with your own customizations. The script
- will be the standard virtualenv.py script, with your extra text
- added (your extra text should be Python code).
-
- If you include these functions, they will be called:
-
- ``extend_parser(optparse_parser)``:
- You can add or remove options from the parser here.
-
- ``adjust_options(options, args)``:
- You can change options here, or change the args (if you accept
- different kinds of arguments, be sure you modify ``args`` so it is
- only ``[DEST_DIR]``).
-
- ``after_install(options, home_dir)``:
-
- After everything is installed, this function is called. This
- is probably the function you are most likely to use. An
- example would be::
-
- def after_install(options, home_dir):
- subprocess.call([join(home_dir, 'bin', 'easy_install'),
- 'MyPackage'])
- subprocess.call([join(home_dir, 'bin', 'my-package-script'),
- 'setup', home_dir])
-
- This example immediately installs a package, and runs a setup
- script from that package.
-
- If you provide something like ``python_version='2.5'`` then the
- script will start with ``#!/usr/bin/env python2.5`` instead of
- ``#!/usr/bin/env python``. You can use this when the script must
- be run with a particular Python version.
- """
- filename = __file__
- if filename.endswith('.pyc'):
- filename = filename[:-1]
- f = codecs.open(filename, 'r', encoding='utf-8')
- content = f.read()
- f.close()
- py_exe = 'python%s' % python_version
- content = (('#!/usr/bin/env %s\n' % py_exe)
- + '## WARNING: This file is generated\n'
- + content)
- return content.replace('##EXT' 'END##', extra_text)
-
-
-
-default_target_dir = 'venv'
-
-pip_install_packages = filter(len, open('requirements.txt').readlines())
-
-import os
-import subprocess
-import sys
-
-def adjust_options(options, args):
- if len(args)==0:
- os.chdir(os.path.dirname(__file__))
- args.append(default_target_dir)
-
-def after_install(options, home_dir):
- from os.path import join
- pip = join(home_dir, 'bin/pip')
- if not os.path.exists(pip):
- # on windows
- pip = join(home_dir, 'Scripts/pip.exe')
- if not os.path.exists(pip):
- print "error", pip, "is missing"
- if sys.version_info < (2, 7):
- subprocess.call([pip, 'install', 'importlib'])
- for prog in pip_install_packages:
- subprocess.call([pip, 'install', prog])
-
-
-
-def convert(s):
- b = base64.b64decode(s.encode('ascii'))
- return zlib.decompress(b).decode('utf-8')
-
-##file site.py
-SITE_PY = convert("""
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-UM3ckC/Q8aP8VLy+mjYY5MxOtAdgjULwf2RtvCc=
-""")
-
-##file ez_setup.py
-EZ_SETUP_PY = convert("""
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-qL7+C1AkCcw=
-""")
-
-##file distribute_from_egg.py
-DISTRIBUTE_FROM_EGG_PY = convert("""
-eJw9j8tqAzEMRfcG/4MgmxQyptkGusonZBmGoGTUGYFfWPKE6dfXTkM3gqt7rh47OKP3NMF3SQFW
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-hLLdWkDbi/DeEpCjNb3u/zccT2Ob8gtnwVyI
-""")
-
-##file distribute_setup.py
-DISTRIBUTE_SETUP_PY = convert("""
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-Ygf0IatroGl6ZBtD+lRuN+rEBcDhPE+KqzWJ3WFxOXoSwYSgnxf12NluHalaDqrHT6WpHhlOI7Cv
-M0/v7ykz7/m7Z7mTycyvWUwEttnliYprEA6TB9TqDL+N1QoHbUVm85e//bZASWI8A6nKz99gK9kg
-Gz8a9A8FqOcGeaunTqA/ULgA8cWD4Zv/6CgrZk94mSc5d8yi/zTTcljhlVBKW8arKDVoL8yIdqwJ
-r4PQ+ots1x6MrSNnkAqz6EnHNWfr7Guoo44NdCbiijCljl8p3zxe9PyRTcbVZUYN+Fl/gJCdsq9O
-DIda6/zizmR1YniuLz2ysisYp/I6pNsjQlB5nVjmf4sFh93KGyFyG/1yAbYBOCJYlbcN9tNRj5cY
-1CSekQZUW9VKOGJmnWdtGOA6y2D2edE7h3SYoBnoLqZw9Q/DJFVYqEoqRg+Xc1BOeYfzZ8mf8V6Z
-R27zWUAid4d0fiutlkpgb9cwHohTFHs5WR2LYsd6tDc1toqZPWIdUisH6tpX+JuEisNT54xVX08d
-M+CD1wCO9eJOyI4FYFUJkDCSdDj5Nqikc8MprZhkSsNYgYHdPQoetn3E1x2ajF+8qDtYyIbhhpxw
-hJkyTN41EWaR/hm3j/FaHnRjehKJy+u96okzEepxfCnctq+zXqpzu6/ZgF/YjHXOyl5/vPpXEmyp
-s0VqfxlQT1813Xtu7osgbskk2wbjgjohKWuZuk+I8RzvIJigiHqb9jNsc/647JMX6aG+drsvqDhF
-mVwadF03a0ZWUbwQpynSN6J6Ct+YfRXE1rx6zFKWyndVsrWCd9+KaZzWSKquIhZze5qjG61uPeSH
-kjHKxqWgsAFD532CAZE8BBq7hDv0bfJ+PtCyherocAXlZWZgo1KOjXuRUW1pZBMRK1MVRMR9uQOb
-KhfynqMVnkcHWvvhLt+oVPVkRRrgGPO3I00f5yrsYZIOJVEjpBzPqRSJ4aGUFHXO75Z8Q1p6MC89
-0lvv8cafN+yuu7phzizRrMXBuvSQ4pDb8f4l64vWLwi+V55DeiEmFTUQyZxDgZx2ZbK1mZ190g+e
-12rE2zhGO1mWinfIJIToSeiXjCRUndWkoPwBbzJUhIrjZ2onrLqNKp6K9BzfaQkWiX8RHhIJvFaU
-s4VqTSzYV/GaGSTQi4KWEMPT4M4geXUICWdJxTWkes9HJJwXP9xhwiIpAFcyNvDKCaV6+OzO9EGw
-Xegms5/9N2vuILnS0yYah7jzNPrSlBGJcxG8YflanhgspxHU+QXDuxjNEqOVPepSl9fF2bqCkAe3
-4l4FBxFKeeHXRF7b0ne39f7sHRH09vjKX7UrsZIvqhRfDpSRBc84BIDbk7CHoBpJBuotOn2gSGkT
-kXvcQGDu2uCbeoB0zQQhg6vrQKjiAHyEyWpHAfp4mQTTXBBR4JuX4v4N8FOQLFqfGg+eLSj7gOi0
-2pMNaxWucOZfSlGJX1LVe/c7VH1QW6h7lpKh8gq/BlCMt5cxXQ6APtyZjEOLZZBp6AGM+vl6Yuoc
-WEl4WohVCsQr09Ww6vz3PN6JJsyjR90RauiaoVRZ76aEhYxoDeVuGqo1fCep6VoKbkX46ygg3tHD
-XtGPP/6XTIuSrAD5ifoMCDz7z7MzJ/vL15GSvUYqtd+kK9cM3QEjDbLfpdm1b7eZSf6bhK/m5EeH
-RWhkOJ/xEDCczxHPq9loXZIUtYCJsCUhASN7LtfnGyINJeZxAC6pD8dOXQaIHth+qTUwwhsUoL9I
-c4AEBDNMxAU2eSNbMwiSQnF5BnAZEzZmi7or5IFZYp95Pa1zxj0ixfnnaBNFS9xn0OA6gpBysgXi
-rIwV3tkQsBPnqs8ATLawsyOAuvnqmOz/4iqxVFGcnAP3cyi4z4fFtrio3Svkx65+CGRxutqEoIRT
-5VvwlUW8RMZ670G5L4aF6k1pGwLE31/MSyL2bVfwpoF6uVbHLGK6NZV+e8gUY6o89r2js7L0aooZ
-iooIK35Nn+elDhjjT4cytKnsHui71g35qF8L/glDNOSjjPeuZ8lL8Tf7pmXFJcbWcydpcgjXTk03
-KLymggtomrVgWpLZPS5/xBEZS+WhE0Sakjkdp8YDF4jELUb1Lnj0QUAJNFy5AgkU0TSNJQ5b72qC
-8WJr0y4Dl9nwkIo7PcugabH114IrEJBr2uWqPLd3Z7csr5c6PUIbF8wWL5wruZPwGOtnwXOo1Rfz
-FnjX0ZDt3YAMMJNp6SPly+mn63dTS6KmfPTur6Rf/3MDmNTgjVgRmNXN1speCxxXbLUDJai5ztzU
-jlyh60S2Av6onMMYFcUu6qYEjqeuGmnxCw0qKDjGAzedrUZdHft3CoTPvqTNXkFpldL/TsLSV1PZ
-/zn6ipR/wVrbr/fUM4zhy8vHvBF4rExcM8RaLRbtwDhGPsSxepHeZMCCOzDhfwBqDMd7
-""")
-
-##file activate.sh
-ACTIVATE_SH = convert("""
-eJytVVFvokAQfudXTLEPtTlLeo9tvMSmJpq02hSvl7u2wRUG2QR2DSxSe7n/frOACEVNLlceRHa+
-nfl25pvZDswCnoDPQ4QoTRQsENIEPci4CsBMZBq7CAsuLOYqvmYKTTj3YxnBgiXBudGBjUzBZUJI
-BXEqgCvweIyuCjeG4eF2F5x14bcB9KQiQQWrjSddI1/oQIx6SYYeoFjzWIoIhYI1izlbhJjkKO7D
-M/QEmKfO9O7WeRo/zr4P7pyHwWxkwitcgwpQ5Ej96OX+PmiFwLeVjFUOrNYKaq1Nud3nR2n8nI2m
-k9H0friPTGVsUdptaxGrTEfpNVFEskxpXtUkkCkl1UNF9cgLBkx48J4EXyALuBtAwNYIjF5kcmUU
-abMKmMq1ULoiRbgsDEkTSsKSGFCJ6Z8vY/2xYiSacmtyAfCDdCNTVZoVF8vSTQOoEwSnOrngBkws
-MYGMBMg8/bMBLSYKS7pYEXP0PqT+ZmBT0Xuy+Pplj5yn4aM9nk72JD8/Wi+Gr98sD9eWSMOwkapD
-BbUv91XSvmyVkICt2tmXR4tWmrcUCsjWOpw87YidEC8i0gdTSOFhouJUNxR+4NYBG0MftoCTD9F7
-2rTtxG3oPwY1b2HncYwhrlmj6Wq924xtGDWqfdNxap+OYxplEurnMVo9RWks+rH8qKEtx7kZT5zJ
-4H7oOFclrN6uFe+d+nW2aIUsSgs/42EIPuOhXq+jEo3S6tX6w2ilNkDnIpHCWdEQhFgwj9pkk7FN
-l/y5eQvRSIQ5+TrL05lewxWpt/Lbhes5cJF3mLET1MGhcKCF+40tNWnUulxrpojwDo2sObdje3Bz
-N3QeHqf3D7OjEXMVV8LN3ZlvuzoWHqiUcNKHtwNd0IbvPGKYYM31nPKCgkUILw3KL+Y8l7aO1ArS
-Ad37nIU0fCj5NE5gQCuC5sOSu+UdI2NeXg/lFkQIlFpdWVaWZRfvqGiirC9o6liJ9FXGYrSY9mI1
-D/Ncozgn13vJvsznr7DnkJWXsyMH7e42ljdJ+aqNDF1bFnKWFLdj31xtaJYK6EXFgqmV/ymD/ROG
-+n8O9H8f5vsGOWXsL1+1k3g=
-""")
-
-##file activate.fish
-ACTIVATE_FISH = convert("""
-eJyVVWFv2jAQ/c6vuBoqQVWC9nVSNVGVCaS2VC2rNLWVZZILWAs2s52wVvvxsyEJDrjbmgpK7PP5
-3bt3d22YLbmGlGcIq1wbmCPkGhPYcLMEEsGciwGLDS+YwSjlekngLFVyBe73GXSXxqw/DwbuTS8x
-yyKpFr1WG15lDjETQhpQuQBuIOEKY5O9tlppLqxHKSDByjVAPwEy+mXtCq5MzjIUBTCRgEKTKwFG
-gpBqxTLYXgN2myspVigMaYF92tZSowGZJf4mFExxNs9Qb614CgZtmH0BpEOn11f0cXI/+za8pnfD
-2ZjA1sg9zlV/8QvcMhxbNu0QwgYokn/d+n02nt6Opzcjcnx1vXcIoN74O4ymWQXmHURfJw9jenc/
-vbmb0enj6P5+cuVhqlKm3S0u2XRtRbA2QQAhV7VhBF0rsgUX9Ur1rBUXJgVSy8O751k8mzY5OrKH
-RW3eaQhYGTr8hrXO59ALhxQ83mCsDLAid3T72CCSdJhaFE+fXgicXAARUiR2WeVO37gH3oYHzFKo
-9k7CaPZ1UeNwH1tWuXA4uFKYYcEa8vaKqXl7q1UpygMPhFLvlVKyNzsSM3S2km7UBOl4xweUXk5u
-6e3wZmQ9leY1XE/Ili670tr9g/5POBBpGIJXCCF79L1siarl/dbESa8mD8PL61GpzqpzuMS7tqeB
-1YkALrRBloBMbR9yLcVx7frQAgUqR7NZIuzkEu110gbNit1enNs82Rx5utq7Z3prU78HFRgulqNC
-OTwbqJa9vkJFclQgZSjbKeBgSsUtCtt9D8OwAbIVJuewQdfvQRaoFE9wd1TmCuRG7OgJ1bVXGHc7
-z5WDL/WW36v2oi37CyVBak61+yPBA9C1qqGxzKQqZ0oPuocU9hpud0PIp8sDHkXR1HKkNlzjuUWA
-a0enFUyzOWZA4yXGP+ZMI3Tdt2OuqU/SO4q64526cPE0A7ZyW2PMbWZiZ5HamIZ2RcCKLXhcDl2b
-vXL+eccQoRzem80mekPDEiyiWK4GWqZmwxQOmPM0eIfgp1P9cqrBsewR2p/DPMtt+pfcYM+Ls2uh
-hALufTAdmGl8B1H3VPd2af8fQAc4PgqjlIBL9cGQqNpXaAwe3LrtVn8AkZTUxg==
-""")
-
-##file activate.csh
-ACTIVATE_CSH = convert("""
-eJx9VG1P2zAQ/u5fcYQKNgTNPtN1WxlIQ4KCUEGaxuQ6yYVYSuzKdhqVX7+zk3bpy5YPUXL3PPfc
-ne98DLNCWshliVDV1kGCUFvMoJGugMjq2qQIiVSxSJ1cCofD1BYRnOVGV0CfZ0N2DD91DalQSjsw
-tQLpIJMGU1euvPe7QeJlkKzgWixlhnAt4aoUVsLnLBiy5NtbJWQ5THX1ZciYKKWwkOFaE04dUm6D
-r/zh7pq/3D7Nnid3/HEy+wFHY/gEJydg0aFaQrBFgz1c5DG1IhTs+UZgsBC2GMFBlaeH+8dZXwcW
-VPvCjXdlAvCfQsE7al0+07XjZvrSCUevR5dnkVeKlFYZmUztG4BdzL2u9KyLVabTU0bdfg7a0hgs
-cSmUg6UwUiQl2iHrcbcVGNvPCiLOe7+cRwG13z9qRGgx2z6DHjfm/Op2yqeT+xvOLzs0PTKHDz2V
-tkckFHoQfQRXoGJAj9el0FyJCmEMhzgMS4sB7KPOE2ExoLcSieYwDvR+cP8cg11gKkVJc2wRcm1g
-QhYFlXiTaTfO2ki0fQoiFM4tLuO4aZrhOzqR4dIPcWx17hphMBY+Srwh7RTyN83XOWkcSPh1Pg/k
-TXX/jbJTbMtUmcxZ+/bbqOsy82suFQg/BhdSOTRhMNBHlUarCpU7JzBhmkKmRejKOQzayQe6MWoa
-n1wqWmuh6LZAaHxcdeqIlVLhIBJdO9/kbl0It2oEXQj+eGjJOuvOIR/YGRqvFhttUB2XTvLXYN2H
-37CBdbW2W7j2r2+VsCn0doVWcFG1/4y1VwBjfwAyoZhD
-""")
-
-##file activate.bat
-ACTIVATE_BAT = convert("""
-eJx9UdEKgjAUfW6wfxjiIH+hEDKUFHSKLCMI7kNOEkIf9P9pTJ3OLJ/03HPPPed4Es9XS9qqwqgT
-PbGKKOdXL4aAFS7A4gvAwgijuiKlqOpGlATS2NeMLE+TjJM9RkQ+SmqAXLrBo1LLIeLdiWlD6jZt
-r7VNubWkndkXaxg5GO3UaOOKS6drO3luDDiO5my3iA0YAKGzPRV1ack8cOdhysI0CYzIPzjSiH5X
-0QcvC8Lfaj0emsVKYF2rhL5L3fCkVjV76kShi59NHwDniAHzkgDgqBcwOgTMx+gDQQqXCw==
-""")
-
-##file deactivate.bat
-DEACTIVATE_BAT = convert("""
-eJxzSE3OyFfIT0vj4ipOLVEI8wwKCXX0iXf1C7Pl4spMU0hJTcvMS01RiPf3cYmHyQYE+fsGhCho
-cCkAAUibEkTEVhWLMlUlLk6QGixStlyaeCyJDPHw9/Pw93VFsQguim4ZXAJoIUw5DhX47XUM8UCx
-EchHtwsohN1bILUgw61c/Vy4AJYPYm4=
-""")
-
-##file activate.ps1
-ACTIVATE_PS = convert("""
-eJylWdmS40Z2fVeE/oHT6rCloNUEAXDThB6wAyQAEjsB29GBjdgXYiWgmC/zgz/Jv+AEWNVd3S2N
-xuOKYEUxM+/Jmzfvcm7W//zXf/+wUMOoXtyi1F9kbd0sHH/hFc2iLtrK9b3FrSqyxaVQwr8uhqJd
-uHaeg9mqzRdR8/13Pyy8qPLdJh0+LMhi0QCoXxYfFh9WtttEnd34H8p6/f1300KauwrULws39e18
-0ZaLNm9rgN/ZVf3h++/e124Vlc0vKsspHy+Yyi5+XbzPhijvCtduoiL/kA1ukWV27n0o7Sb8LIFj
-CvWR5GQgUJdp1Pw8TS9+rPy6SDv/+e3d+0+4qw8f3v20+PliV37efEYBAB9FTKC+RHn/Cfxn3rdv
-00Fube5O+iyCtHDs9BfPfz3q4sfFv9d91Ljhfy7ei0VO+nVTtdOkv/jpt0l2AX6iG1jXgKnnDuD4
-ke2k/i8fzzz5UedkVcP4pwF+Wvz2FJl+3vt598urXf5Y6LNA5WcFOP7r0sW7b9a+W/xcu0Xpv5zk
-Kfq3P9Dz9di/fCxS72MXVU1rpx9L4Bxl85Wmn5a+zP76Zuh3pL9ROWr87PN+//GHIl+oOtvn9XSU
-qH+p0gQBFnx1uV+JLH5O5zv+PXW+WepXVVHZT0+oQezkIATcIm+ivPV/z5J/+cYj3ir4w0Lx09vC
-e5n/y5/Y5LPPfdrqb88ga/PabxZRVfmp39l588m/6u+/e+OpP+dF7n1WZpJ9//Z4v372fDDz9eHB
-7Juvs/BLMHzrxL9+9twXpJfhd1/DrpQ5Euu/vlss3wp9HXC/54C/Ld69m6zwdx3tC0d8daSv0V8B
-n4b9YYF53sJelJV/ix6LZspw/sJtqyl5LJ5r/23htA1Imfm/gt9R7dqVB1LjhydAX4Gb+zksQF59
-9+P7H//U+376afFuvh2/T6P85Xr/5c8C6OXyFY4BGuN+EE0+GeR201b+wkkLN5mmBY5TfMw8ngqL
-CztXxCSXKMCYrRIElWkEJlEPYsSOeKBVZCAQTKBhApMwRFQzmCThE0YQu2CdEhgjbgmk9GluHpfR
-/hhwJCZhGI5jt5FsAkOrObVyE6g2y1snyhMGFlDY1x+BoHpCMulTj5JYWNAYJmnKpvLxXgmQ8az1
-4fUGxxcitMbbhDFcsiAItg04E+OSBIHTUYD1HI4FHH4kMREPknuYRMyhh3AARWMkfhCketqD1CWJ
-mTCo/nhUScoQcInB1hpFhIKoIXLo5jLpwFCgsnLCx1QlEMlz/iFEGqzH3vWYcpRcThgWnEKm0QcS
-rA8ek2a2IYYeowUanOZOlrbWSJUC4c7y2EMI3uJPMnMF/SSXdk6E495VLhzkWHps0rOhKwqk+xBI
-DhJirhdUCTamMfXz2Hy303hM4DFJ8QL21BcPBULR+gcdYxoeiDqOFSqpi5B5PUISfGg46gFZBPo4
-jdh8lueaWuVSMTURfbAUnLINr/QYuuYoMQV6l1aWxuZVTjlaLC14UzqZ+ziTGDzJzhiYoPLrt3uI
-tXkVR47kAo09lo5BD76CH51cTt1snVpMOttLhY93yxChCQPI4OBecS7++h4p4Bdn4H97bJongtPk
-s9gQnXku1vzsjjmX4/o4YUDkXkjHwDg5FXozU0fW4y5kyeYW0uJWlh536BKr0kMGjtzTkng6Ep62
-uTWnQtiIqKnEsx7e1hLtzlXs7Upw9TwEnp0t9yzCGgUJIZConx9OHJArLkRYW0dW42G9OeR5Nzwk
-yk1mX7du5RGHT7dka7N3AznmSif7y6tuKe2N1Al/1TUPRqH6E2GLVc27h9IptMLkCKQYRqPQJgzV
-2m6WLsSipS3v3b1/WmXEYY1meLEVIU/arOGVkyie7ZsH05ZKpjFW4cpY0YkjySpSExNG2TS8nnJx
-nrQmWh2WY3cP1eISP9wbaVK35ZXc60yC3VN/j9n7UFoK6zvjSTE2+Pvz6Mx322rnftfP8Y0XKIdv
-Qd7AfK0nexBTMqRiErvCMa3Hegpfjdh58glW2oNMsKeAX8x6YJLZs9K8/ozjJkWL+JmECMvhQ54x
-9rsTHwcoGrDi6Y4I+H7yY4/rJVPAbYymUH7C2D3uiUS3KQ1nrCAUkE1dJMneDQIJMQQx5SONxoEO
-OEn1/Ig1eBBUeEDRuOT2WGGGE4bNypBLFh2PeIg3bEbg44PHiqNDbGIQm50LW6MJU62JHCGBrmc9
-2F7WBJrrj1ssnTAK4sxwRgh5LLblhwNAclv3Gd+jC/etCfyfR8TMhcWQz8TBIbG8IIyAQ81w2n/C
-mHWAwRzxd3WoBY7BZnsqGOWrOCKwGkMMNfO0Kci/joZgEocLjNnzgcmdehPHJY0FudXgsr+v44TB
-I3jnMGnsK5veAhgi9iXGifkHMOC09Rh9cAw9sQ0asl6wKMk8mpzFYaaDSgG4F0wisQDDBRpjCINg
-FIxhlhQ31xdSkkk6odXZFpTYOQpOOgw9ugM2cDQ+2MYa7JsEirGBrOuxsQy5nPMRdYjsTJ/j1iNw
-FeSt1jY2+dd5yx1/pzZMOQXUIDcXeAzR7QlDRM8AMkUldXOmGmvYXPABjxqkYKO7VAY6JRU7kpXr
-+Epu2BU3qFFXClFi27784LrDZsJwbNlDw0JzhZ6M0SMXE4iBHehCpHVkrQhpTFn2dsvsZYkiPEEB
-GSEAwdiur9LS1U6P2U9JhGp4hnFpJo4FfkdJHcwV6Q5dV1Q9uNeeu7rV8PAjwdFg9RLtroifOr0k
-uOiRTo/obNPhQIf42Fr4mtThWoSjitEdAmFW66UCe8WFjPk1YVNpL9srFbond7jrLg8tqAasIMpy
-zkH0SY/6zVAwJrEc14zt14YRXdY+fcJ4qOd2XKB0/Kghw1ovd11t2o+zjt+txndo1ZDZ2T+uMVHT
-VSXhedBAHoJIID9xm6wPQI3cXY+HR7vxtrJuCKh6kbXaW5KkVeJsdsjqsYsOwYSh0w5sMbu7LF8J
-5T7U6LJdiTx+ca7RKlulGgS5Z1JSU2Llt32cHFipkaurtBrvNX5UtvNZjkufZ/r1/XyLl6yOpytL
-Km8Fn+y4wkhlqZP5db0rooqy7xdL4wxzFVTX+6HaxuQJK5E5B1neSSovZ9ALB8091dDbbjVxhWNY
-Ve5hn1VnI9OF0wpvaRm7SZuC1IRczwC7GnkhPt3muHV1YxUJfo+uh1sYnJy+vI0ZwuPV2uqWJYUH
-bmBsi1zmFSxHrqwA+WIzLrHkwW4r+bad7xbOzJCnKIa3S3YvrzEBK1Dc0emzJW+SqysQfdEDorQG
-9ZJlbQzEHQV8naPaF440YXzJk/7vHGK2xwuP+Gc5xITxyiP+WQ4x18oXHjFzCBy9kir1EFTAm0Zq
-LYwS8MpiGhtfxiBRDXpxDWxk9g9Q2fzPPAhS6VFDAc/aiNGatUkPtZIStZFQ1qD0IlJa/5ZPAi5J
-ySp1ETDomZMnvgiysZSBfMikrSDte/K5lqV6iwC5q7YN9I1dBZXUytDJNqU74MJsUyNNLAPopWK3
-tzmLkCiDyl7WQnj9sm7Kd5kzgpoccdNeMw/6zPVB3pUwMgi4C7hj4AMFAf4G27oXH8NNT9zll/sK
-S6wVlQwazjxWKWy20ZzXb9ne8ngGalPBWSUSj9xkc1drsXkZ8oOyvYT3e0rnYsGwx85xZB9wKeKg
-cJKZnamYwiaMymZvzk6wtDUkxmdUg0mPad0YHtvzpjEfp2iMxvORhnx0kCVLf5Qa43WJsVoyfEyI
-pzmf8ruM6xBr7dnBgzyxpqXuUPYaKahOaz1LrxNkS/Q3Ae5AC+xl6NbxAqXXlzghZBZHmOrM6Y6Y
-ctAkltwlF7SKEsShjVh7QHuxMU0a08/eiu3x3M+07OijMcKFFltByXrpk8w+JNnZpnp3CfgjV1Ax
-gUYCnWwYow42I5wHCcTzLXK0hMZN2DrPM/zCSqe9jRSlJnr70BPE4+zrwbk/xVIDHy2FAQyHoomT
-Tt5jiM68nBQut35Y0qLclLiQrutxt/c0OlSqXAC8VrxW97lGoRWzhOnifE2zbF05W4xuyhg7JTUL
-aqJ7SWDywhjlal0b+NLTpERBgnPW0+Nw99X2Ws72gOL27iER9jgzj7Uu09JaZ3n+hmCjjvZpjNst
-vOWWTbuLrg+/1ltX8WpPauEDEvcunIgTxuMEHweWKCx2KQ9DU/UKdO/3za4Szm2iHYL+ss9AAttm
-gZHq2pkUXFbV+FiJCKrpBms18zH75vax5jSo7FNunrVWY3Chvd8KKnHdaTt/6ealwaA1x17yTlft
-8VBle3nAE+7R0MScC3MJofNCCkA9PGKBgGMYEwfB2QO5j8zUqa8F/EkWKCzGQJ5EZ05HTly1B01E
-z813G5BY++RZ2sxbQS8ZveGPJNabp5kXAeoign6Tlt5+L8i5ZquY9+S+KEUHkmYMRFBxRrHnbl2X
-rVemKnG+oB1yd9+zT+4c43jQ0wWmQRR6mTCkY1q3VG05Y120ZzKOMBe6Vy7I5Vz4ygPB3yY4G0FP
-8RxiMx985YJPXsgRU58EuHj75gygTzejP+W/zKGe78UQN3yOJ1aMQV9hFH+GAfLRsza84WlPLAI/
-9G/5JdcHftEfH+Y3/fHUG7/o8bv98dzzy3e8S+XCvgqB+VUf7sH0yDHpONdbRE8tAg9NWOzcTJ7q
-TuAxe/AJ07c1Rs9okJvl1/0G60qvbdDzz5zO0FuPFQIHNp9y9Bd1CufYVx7dB26mAxwa8GMNrN/U
-oGbNZ3EQ7inLzHy5tRg9AXJrN8cB59cCUBeCiVO7zKM0jU0MamhnRThkg/NMmBOGb6StNeD9tDfA
-7czsAWopDdnGoXUHtA+s/k0vNPkBcxEI13jVd/axp85va3LpwGggXXWw12Gwr/JGAH0b8CPboiZd
-QO1l0mk/UHukud4C+w5uRoNzpCmoW6GbgbMyaQNkga2pQINB18lOXOCJzSWPFOhZcwzdgrsQnne7
-nvjBi+7cP2BbtBeDOW5uOLGf3z94FasKIguOqJl+8ss/6Kumns4cuWbqq5592TN/RNIbn5Qo6qbi
-O4F0P9txxPAwagqPlftztO8cWBzdN/jz3b7GD6JHYP/Zp4ToAMaA74M+EGSft3hEGMuf8EwjnTk/
-nz/P7SLipB/ogQ6xNX0fDqNncMCfHqGLCMM0ZzFa+6lPJYQ5p81vW4HkCvidYf6kb+P/oB965g8K
-C6uR0rdjX1DNKc5pOSTquI8uQ6KXxYaKBn+30/09tK4kMpJPgUIQkbENEPbuezNPPje2Um83SgyX
-GTCJb6MnGVIpgncdQg1qz2bvPfxYD9fewCXDomx9S+HQJuX6W3VAL+v5WZMudRQZk9ZdOk6GIUtC
-PqEb/uwSIrtR7/edzqgEdtpEwq7p2J5OQV+RLrmtTvFwFpf03M/VrRyTZ73qVod7v7Jh2Dwe5J25
-JqFOU2qEu1sP+CRotklediycKfLjeIZzjJQsvKmiGSNQhxuJpKa+hoWUizaE1PuIRGzJqropwgVB
-oo1hr870MZLgnXF5ZIpr6mF0L8aSy2gVnTAuoB4WEd4d5NPVC9TMotYXERKlTcwQ2KiB/C48AEfH
-Qbyq4CN8xTFnTvf/ebOc3isnjD95s0QF0nx9s+y+zMmz782xL0SgEmRpA3x1w1Ff9/74xcxKEPdS
-IEFTz6GgU0+BK/UZ5Gwbl4gZwycxEw+Kqa5QmMkh4OzgzEVPnDAiAOGBFaBW4wkDmj1G4RyElKgj
-NlLCq8zsp085MNh/+R4t1Q8yxoSv8PUpTt7izZwf2BTHZZ3pIZpUIpuLkL1nNL6sYcHqcKm237wp
-T2+RCjgXweXd2Zp7ZM8W6dG5bZsqo0nrJBTx8EC0+CQQdzEGnabTnkzofu1pYkWl4E7XSniECdxy
-vLYavPMcL9LW5SToJFNnos+uqweOHriUZ1ntIYZUonc7ltEQ6oTRtwOHNwez2sVREskHN+bqG3ua
-eaEbJ8XpyO8CeD9QJc8nbLP2C2R3A437ISUNyt5Yd0TbDNcl11/DSsOzdbi/VhCC0KE6v1vqVNkq
-45ZnG6fiV2NwzInxCNth3BwL0+8814jE6+1W1EeWtpWbSZJOJNYXmWRXa7vLnAljE692eHjZ4y5u
-y1u63De0IzKca7As48Z3XshVF+3XiLNz0JIMh/JOpbiNLlMi672uO0wYzOCZjRxcxj3D+gVenGIE
-MvFUGGXuRps2RzMcgWIRolHXpGUP6sMsQt1hspUBnVKUn/WQj2u6j3SXd9Xz0QtEzoM7qTu5y7gR
-q9gNNsrlEMLdikBt9bFvBnfbUIh6voTw7eDsyTmPKUvF0bHqWLbHe3VRHyRZnNeSGKsB73q66Vsk
-taxWYmwz1tYVFG/vOQhlM0gUkyvIab3nv2caJ1udU1F3pDMty7stubTE4OJqm0i0ECfrJIkLtraC
-HwRWKzlqpfhEIqYH09eT9WrOhQyt8YEoyBlnXtAT37WHIQ03TIuEHbnRxZDdLun0iok9PUC79prU
-m5beZzfQUelEXnhzb/pIROKx3F7qCttYIFGh5dXNzFzID7u8vKykA8Uejf7XXz//S4nKvW//ofS/
-QastYw==
-""")
-
-##file distutils-init.py
-DISTUTILS_INIT = convert("""
-eJytV1uL4zYUfvevOE0ottuMW9q3gVDa3aUMXXbLMlDKMBiNrSTqOJKRlMxkf33PkXyRbGe7Dw2E
-UXTu37lpxLFV2oIyifAncxmOL0xLIfcG+gv80x9VW6maw7o/CANSWWBwFtqeWMPlGY6qPjV8A0bB
-C4eKSTgZ5LRgFeyErMEeOBhbN+Ipgeizhjtnhkn7DdyjuNLPoCS0l/ayQTG0djwZC08cLXozeMss
-aG5EzQ0IScpnWtHSTXuxByV/QCmxE7y+eS0uxWeoheaVVfqSJHiU7Mhhi6gULbOHorshkrEnKxpT
-0n3A8Y8SMpuwZx6aoix3ouFlmW8gHRSkeSJ2g7hU+kiHLDaQw3bmRDaTGfTnty7gPm0FHbIBg9U9
-oh1kZzAFLaue2R6htPCtAda2nGlDSUJ4PZBgCJBGVcwKTAMz/vJiLD+Oin5Z5QlvDPdulC6EsiyE
-NFzb7McNTKJzbJqzphx92VKRFY1idenzmq3K0emRcbWBD0ryqc4NZGmKOOOX9Pz5x+/l27tP797c
-f/z0d+4NruGNai8uAM0bfsYaw8itFk8ny41jsfpyO+BWlpqfhcG4yxLdi/0tQqoT4a8Vby382mt8
-p7XSo7aWGdPBc+b6utaBmCQ7rQKQoWtAuthQCiold2KfJIPTT8xwg9blPumc+YDZC/wYGdAyHpJk
-vUbHbHWAp5No6pK/WhhLEWrFjUwtPEv1Agf8YmnsuXUQYkeZoHm8ogP16gt2uHoxcEMdf2C6pmbw
-hUMsWGhanboh4IzzmsIpWs134jVPqD/c74bZHdY69UKKSn/+KfVhxLgUlToemayLMYQOqfEC61bh
-cbhwaqoGUzIyZRFHPmau5juaWqwRn3mpWmoEA5nhzS5gog/5jbcFQqOZvmBasZtwYlG93k5GEiyw
-buHhMWLjDarEGpMGB2LFs5nIJkhp/nUmZneFaRth++lieJtHepIvKgx6PJqIlD9X2j6pG1i9x3pZ
-5bHuCPFiirGHeO7McvoXkz786GaKVzC9DSpnOxJdc4xm6NSVq7lNEnKdVlnpu9BNYoKX2Iq3wvgh
-gGEUM66kK6j4NiyoneuPLSwaCWDxczgaolEWpiMyDVDb7dNuLAbriL8ig8mmeju31oNvQdpnvEPC
-1vAXbWacGRVrGt/uXN/gU0CDDwgooKRrHfTBb1/s9lYZ8ZqOBU0yLvpuP6+K9hLFsvIjeNhBi0KL
-MlOuWRn3FRwx5oHXjl0YImUx0+gLzjGchrgzca026ETmYJzPD+IpuKzNi8AFn048Thd63OdD86M6
-84zE8yQm0VqXdbbgvub2pKVnS76icBGdeTHHXTKspUmr4NYo/furFLKiMdQzFjHJNcdAnMhltBJK
-0/IKX3DVFqvPJ2dLE7bDBkH0l/PJ29074+F0CsGYOxsb7U3myTUncYfXqnLLfa6sJybX4g+hmcjO
-kMRBfA1JellfRRKJcyRpxdS4rIl6FdmQCWjo/o9Qz7yKffoP4JHjOvABcRn4CZIT2RH4jnxmfpVG
-qgLaAvQBNfuO6X0/Ux02nb4FKx3vgP+XnkX0QW9pLy/NsXgdN24dD3LxO2Nwil7Zlc1dqtP3d7/h
-kzp1/+7hGBuY4pk0XD/0Ao/oTe/XGrfyM773aB7iUhgkpy+dwAMalxMP0DrBcsVw/6p25+/hobP9
-GBknrWExDhLJ1bwt1NcCNblaFbMKCyvmX0PeRaQ=
-""")
-
-##file distutils.cfg
-DISTUTILS_CFG = convert("""
-eJxNj00KwkAMhfc9xYNuxe4Ft57AjYiUtDO1wXSmNJnK3N5pdSEEAu8nH6lxHVlRhtDHMPATA4uH
-xJ4EFmGbvfJiicSHFRzUSISMY6hq3GLCRLnIvSTnEefN0FIjw5tF0Hkk9Q5dRunBsVoyFi24aaLg
-9FDOlL0FPGluf4QjcInLlxd6f6rqkgPu/5nHLg0cXCscXoozRrP51DRT3j9QNl99AP53T2Q=
-""")
-
-##file activate_this.py
-ACTIVATE_THIS = convert("""
-eJyNU01v2zAMvetXEB4K21jmDOstQA4dMGCHbeihlyEIDMWmG62yJEiKE//7kXKdpN2KzYBt8euR
-fKSyLPs8wiEo8wh4wqZTGou4V6Hm0wJa1cSiTkJdr8+GsoTRHuCotBayiWqQEYGtMCgfD1KjGYBe
-5a3p0cRKiAe2NtLADikftnDco0ko/SFEVgEZ8aRC5GLux7i3BpSJ6J1H+i7A2CjiHq9z7JRZuuQq
-siwTIvpxJYCeuWaBpwZdhB+yxy/eWz+ZvVSU8C4E9FFZkyxFsvCT/ZzL8gcz9aXVE14Yyp2M+2W0
-y7n5mp0qN+avKXvbsyyzUqjeWR8hjGE+2iCE1W1tQ82hsCZN9UzlJr+/e/iab8WfqsmPI6pWeUPd
-FrMsd4H/55poeO9n54COhUs+sZNEzNtg/wanpjpuqHJaxs76HtZryI/K3H7KJ/KDIhqcbJ7kI4ar
-XL+sMgXnX0D+Te2Iy5xdP8yueSlQB/x/ED2BTAtyE3K4SYUN6AMNfbO63f4lBW3bUJPbTL+mjSxS
-PyRfJkZRgj+VbFv+EzHFi5pKwUEepa4JslMnwkowSRCXI+m5XvEOvtuBrxHdhLalG0JofYBok6qj
-YdN2dEngUlbC4PG60M1WEN0piu7Nq7on0mgyyUw3iV1etLo6r/81biWdQ9MWHFaePWZYaq+nmp+t
-s3az+sj7eA0jfgPfeoN1
-""")
-
-MH_MAGIC = 0xfeedface
-MH_CIGAM = 0xcefaedfe
-MH_MAGIC_64 = 0xfeedfacf
-MH_CIGAM_64 = 0xcffaedfe
-FAT_MAGIC = 0xcafebabe
-BIG_ENDIAN = '>'
-LITTLE_ENDIAN = '<'
-LC_LOAD_DYLIB = 0xc
-maxint = majver == 3 and getattr(sys, 'maxsize') or getattr(sys, 'maxint')
-
-
-class fileview(object):
- """
- A proxy for file-like objects that exposes a given view of a file.
- Modified from macholib.
- """
-
- def __init__(self, fileobj, start=0, size=maxint):
- if isinstance(fileobj, fileview):
- self._fileobj = fileobj._fileobj
- else:
- self._fileobj = fileobj
- self._start = start
- self._end = start + size
- self._pos = 0
-
- def __repr__(self):
- return '<fileview [%d, %d] %r>' % (
- self._start, self._end, self._fileobj)
-
- def tell(self):
- return self._pos
-
- def _checkwindow(self, seekto, op):
- if not (self._start <= seekto <= self._end):
- raise IOError("%s to offset %d is outside window [%d, %d]" % (
- op, seekto, self._start, self._end))
-
- def seek(self, offset, whence=0):
- seekto = offset
- if whence == os.SEEK_SET:
- seekto += self._start
- elif whence == os.SEEK_CUR:
- seekto += self._start + self._pos
- elif whence == os.SEEK_END:
- seekto += self._end
- else:
- raise IOError("Invalid whence argument to seek: %r" % (whence,))
- self._checkwindow(seekto, 'seek')
- self._fileobj.seek(seekto)
- self._pos = seekto - self._start
-
- def write(self, bytes):
- here = self._start + self._pos
- self._checkwindow(here, 'write')
- self._checkwindow(here + len(bytes), 'write')
- self._fileobj.seek(here, os.SEEK_SET)
- self._fileobj.write(bytes)
- self._pos += len(bytes)
-
- def read(self, size=maxint):
- assert size >= 0
- here = self._start + self._pos
- self._checkwindow(here, 'read')
- size = min(size, self._end - here)
- self._fileobj.seek(here, os.SEEK_SET)
- bytes = self._fileobj.read(size)
- self._pos += len(bytes)
- return bytes
-
-
-def read_data(file, endian, num=1):
- """
- Read a given number of 32-bits unsigned integers from the given file
- with the given endianness.
- """
- res = struct.unpack(endian + 'L' * num, file.read(num * 4))
- if len(res) == 1:
- return res[0]
- return res
-
-
-def mach_o_change(path, what, value):
- """
- Replace a given name (what) in any LC_LOAD_DYLIB command found in
- the given binary with a new name (value), provided it's shorter.
- """
-
- def do_macho(file, bits, endian):
- # Read Mach-O header (the magic number is assumed read by the caller)
- cputype, cpusubtype, filetype, ncmds, sizeofcmds, flags = read_data(file, endian, 6)
- # 64-bits header has one more field.
- if bits == 64:
- read_data(file, endian)
- # The header is followed by ncmds commands
- for n in range(ncmds):
- where = file.tell()
- # Read command header
- cmd, cmdsize = read_data(file, endian, 2)
- if cmd == LC_LOAD_DYLIB:
- # The first data field in LC_LOAD_DYLIB commands is the
- # offset of the name, starting from the beginning of the
- # command.
- name_offset = read_data(file, endian)
- file.seek(where + name_offset, os.SEEK_SET)
- # Read the NUL terminated string
- load = file.read(cmdsize - name_offset).decode()
- load = load[:load.index('\0')]
- # If the string is what is being replaced, overwrite it.
- if load == what:
- file.seek(where + name_offset, os.SEEK_SET)
- file.write(value.encode() + '\0'.encode())
- # Seek to the next command
- file.seek(where + cmdsize, os.SEEK_SET)
-
- def do_file(file, offset=0, size=maxint):
- file = fileview(file, offset, size)
- # Read magic number
- magic = read_data(file, BIG_ENDIAN)
- if magic == FAT_MAGIC:
- # Fat binaries contain nfat_arch Mach-O binaries
- nfat_arch = read_data(file, BIG_ENDIAN)
- for n in range(nfat_arch):
- # Read arch header
- cputype, cpusubtype, offset, size, align = read_data(file, BIG_ENDIAN, 5)
- do_file(file, offset, size)
- elif magic == MH_MAGIC:
- do_macho(file, 32, BIG_ENDIAN)
- elif magic == MH_CIGAM:
- do_macho(file, 32, LITTLE_ENDIAN)
- elif magic == MH_MAGIC_64:
- do_macho(file, 64, BIG_ENDIAN)
- elif magic == MH_CIGAM_64:
- do_macho(file, 64, LITTLE_ENDIAN)
-
- assert(len(what) >= len(value))
- do_file(open(path, 'r+b'))
-
-
-if __name__ == '__main__':
- main()
-
-## TODO:
-## Copy python.exe.manifest
-## Monkeypatch distutils.sysconfig
diff --git a/llvm/projects/hpvm-tensor-rt/table_fixer.py b/llvm/projects/hpvm-tensor-rt/table_fixer.py
deleted file mode 100644
index 3095d15aba0757aca3b74705ba57b5e189b5cecb..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/table_fixer.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# Fixes table format
-# Remove all instances of cifar10 --> each col should start with fp16 or fp32
-# Combine multiple tables
-
-def fix_columns(table_name, new_filename):
- table_file = open(table_name, "r")
-
- new_table_file = []
-
- for line in table_file:
- line = line.strip()
- if line.startswith("**"):
- col_names = line.split()
- new_col_names = []
-
- for col_name in col_names:
- if col_name.find("fp16") != -1:
- new_col_names.append(col_name[col_name.find("fp16") : ])
- elif col_name.find("fp32") != -1:
- new_col_names.append(col_name[col_name.find("fp32") : ])
- else:
- new_col_names.append(col_name)
- new_table_file.append(' '.join(new_col_names))
- else:
- new_table_file.append(line)
- table_file.close()
- table_file_new = open(new_filename, "w")
- table_file_new.write('\n'.join(new_table_file))
- table_file_new.close()
-
-def combine_tables(table1, table2, new_filename):
- table1_file = open(table1, "r")
- table2_file = open(table2, "r")
-
- table1_data = table1_file.read().strip().split('\n')
- table2_data = table2_file.read().strip().split('\n')
- new_contents = []
-
- table2_ind = 0
- for table1_line in table1_data:
- table2_line = table2_data[table2_ind]
-
- if table1_line.startswith("**"):
- assert table2_line.startswith("**")
- table2_lst = table2_line.strip().split()
- table2_cols = ' '.join(table2_lst[3 : ])
- new_contents.append(table1_line + ' ' + table2_cols)
- else:
- table2_lst = table2_line.strip().split()
- table2_cols = ' '.join(table2_lst[1 : ])
- new_contents.append(table1_line + ' ' + table2_cols)
- table2_ind += 1
-
- table1_file.close()
- table2_file.close()
-
- new_file = open(new_filename, "w")
- new_file.write('\n'.join(new_contents))
- new_file.close()
-
-import sys
-
-if __name__ == "__main__":
- num_args = len(sys.argv)
-
- if num_args != 4 and num_args != 5:
- print("python table_fixer.py <fix> <filename> OR <combine> <table1> <table2> <new name>")
- exit(1)
- elif sys.argv[1] == "fix":
- fix_columns(sys.argv[2], sys.argv[3])
- elif sys.argv[1] == "combine":
- combine_tables(sys.argv[2], sys.argv[3], sys.argv[4])
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/#error.h# b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/#error.h#
deleted file mode 100644
index d474152fd80ecc90e07092795e513e2d97da0129..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/#error.h#
+++ /dev/null
@@ -1,627 +0,0 @@
-
-#ifndef ERROR_HEADER
-#define ERROR_HEADER
-
-
-#include <stdio.h>
-#include <stdarg.h>
-#include <cstdio>
-#include <cstdlib>
-#include <cmath>
-#include <ctime>
-#include <cfloat>
-#include <algorithm>
-#include <sstream>
-#include <vector>
-#include <iostream>
-#include <random>
-#include <string>
-#include <time.h>
-
-#include <curand.h>
-#include <curand_kernel.h>
-#include <math.h>
-#include <assert.h>
-
-
-#include "../include/debug.h"
-#include "tensor.h"
-#include "profiling.h"
-#include "tensor_utils.cu"
-#include "global_data.h"
-
-
-
-
-void readOpenTunerFlags(char* file_name){
-
- total_ops = 0;
- op_counter = 0;
- op_accuracies.clear();
-
-
- FILE* fp = fopen(file_name, "r");
- if(fp == NULL){
- ERROR("File 'opentuner_flags' not found \n");
- }
-
- int retVal = 200;
- while(retVal != EOF){
-
- int op_acc;
- if(fp != NULL)
- retVal = fscanf(fp, "%d", &op_acc);
- else
- op_acc = 0;
-
- op_accuracies.push_back(op_acc);
- //printf("op_accuracies = %d, total_ops =%d \n", op_accuracies[total_ops], total_ops);
- total_ops++;
- }
-
- fclose(fp);
-}
-
-
-
-
-
-/*__device__ inline void atomicAdd(float* address, float value)
-
-{
-
- float old = value;
- float new_old;
-
- do{
- new_old = atomicExch(address, 0.0f);
- new_old += old;
- }
-
- while ((old = atomicExch(address, new_old))!=0.0f);
-
-};
-*/
-
-
-
-
-
-Norm_t* calculateNorms(Tensor* x, Tensor* x_orig){
-
- deviceToHostCopy(x);
- deviceToHostCopy(x_orig);
-
- // NOTE: Move floats to doubles - overflow is quite possible
- float l1_norm = 0.0;
- float l2_norm = 0.0;
- float inf_norm = -1.0;
- double total = 0.0;
-
- float* arr1 = (float*) x->host_data;
- float* arr2 = (float*) x_orig->host_data;
-
- for(unsigned int i = 0; i < x->num_elems; i++){
-
- total = total + arr2[i];
-
- float diff = abs(arr1[i] - arr2[i]);
- l1_norm += diff;
- l2_norm += (arr1[i] - arr2[i]) * (arr1[i] - arr2[i]);
-
- if(inf_norm < diff)
- inf_norm = diff;
- }
-
- l1_norm = l1_norm / (x->num_elems * 1.0);
- l2_norm = l2_norm / (x->num_elems * 1.0);
-
- double distribution_mean = total / (x->num_elems * 1.0);
- l1_norm = l1_norm / distribution_mean;
- l2_norm = l2_norm / distribution_mean;
-
-
- Norm_t* norms = (Norm_t*) malloc(sizeof(Norm_t));
- norms->l1_norm = l1_norm;
- norms->l2_norm = l2_norm;
- norms->inf_norm = inf_norm;
-
- INFO("l1_norm = %f \n", l1_norm);
- INFO("l2_norm = %f \n", l2_norm);
- INFO("inf_norm = %f \n", inf_norm);
-
- return norms;
-}
-
-
-
-Norm_t* calculateNorms2(Tensor* x, Tensor* x_orig){
-
- deviceToHostCopy(x);
- deviceToHostCopy(x_orig);
-
- // NOTE: Move all floats to doubles - overflow is quite possible
- double l0_norm_A = 0.0;
- double l0_norm_B = 0.0;
-
- double l1_norm_A = 0.0;
- double l1_norm_B = 0.0;
-
- double l2_norm_A = 0.0;
- double l2_norm_B = 0.0;
- float inf_norm = -1.0;
- float orig_inf_norm = -1.0;
- double total_diff = 0.0;
- double total_diff_squared = 0.0;
-
- float* arr1 = (float*) x->host_data;
- float* arr2 = (float*) x_orig->host_data;
-
- for(unsigned int i = 0; i < x->num_elems; i++){
-
- if(arr2[i] != 0.0)
- l0_norm_A = l0_norm_A + 1.0;
- if(arr1[i] != 0.0)
- l0_norm_B = l0_norm_B + 1.0;
-
- l1_norm_A = l1_norm_A + abs(arr2[i]);
- l1_norm_B = l1_norm_B + abs(arr1[i]);
-
- l2_norm_A = l2_norm_A + (arr2[i] * arr2[i]);
- l2_norm_B = l2_norm_B + (arr1[i] * arr1[i]);
-
- float diff = abs(arr1[i] - arr2[i]);
- total_diff = total_diff + diff;
- float diff_squared = diff * diff;
- total_diff_squared = total_diff_squared + diff_squared;
-
-
- if(orig_inf_norm < diff){
- orig_inf_norm = diff;
- }
-
- // Relative difference value
- float normalized_diff = diff / arr2[i];
- if(inf_norm < normalized_diff){
- inf_norm = normalized_diff;
- }
- }
-
- // Relative L1 and Mean L1 norms of the difference Matrix
- float mean_l1 = ( total_diff ) / x->num_elems;
- float relative_l1 = ( total_diff ) / l1_norm_A;
-
- // Computing Relative L2 norm - i.e., Euclidean distance
- double norm_root_A = sqrt(l2_norm_A);
- double diff_root = sqrt(total_diff_squared);
- float mean_l2 = diff_root / x->num_elems;
- float relative_l2 = diff_root / norm_root_A;
-
- // Packing computed norms in Norm_t struct
- Norm_t* norms = (Norm_t*) malloc(sizeof(Norm_t));
- // Mean metrics - not normalized for the distribution - suitable for precision tuning hardware
- norms->mean_l1 = mean_l1;
- norms->mean_l2 = mean_l2;
- norms->orig_inf_norm = orig_inf_norm;
-
- // Relative metrics (relative to distribution) - suitable for PROMISE
- norms->l1_norm = relative_l1;
- norms->l2_norm = relative_l2;
- norms->inf_norm = inf_norm;
-
- INFO("l1_norm = %f \n", relative_l1);
- INFO("l2_norm = %f \n", relative_l2);
- INFO("inf_norm = %f \n", inf_norm);
-
- return norms;
-}
-
-
-
-
-
-__global__ void normComputeKernel(float* A, float * B, double* l1_A, double* l2_A,
- double* l1_diff, double* l2_diff, unsigned int n){
-
- int i = blockIdx.x * blockDim.x + threadIdx.x;
-
- if(i < n){
-
- double diff = fabsf(A[i] - B[i]);
- double diff_squared = diff * diff;
-
- atomicAdd( l1_A, fabsf(A[i]) );
- atomicAdd( l2_A, (A[i] * A[i]) );
-
- atomicAdd( l1_diff, diff);
- atomicAdd( l2_diff, diff_squared);
- }
-}
-
-
-
-// Compute Norms on the GPU
-Norm_t* calculateNormsGPU(Tensor* x, Tensor* x_orig){
-
- hostToDeviceCopy(x);
- hostToDeviceCopy(x_orig);
-
- // FIXIT: Move all floats to doubles - overflow is possible
-
- double l1_norm_A;
- double l2_norm_A;
-
- double l1_diff;
- double l2_diff;
-
- // Device pointers
- double *l1_norm_A_d;
- double *l2_norm_A_d;
- double *l1_diff_d;
- double *l2_diff_d;
-
- cudaMalloc( (void**) &l1_norm_A_d, sizeof(double));
- cudaMalloc( (void**) &l2_norm_A_d, sizeof(double));
- cudaMalloc( (void**) &l1_diff_d, sizeof(double));
- cudaMalloc( (void**) &l2_diff_d, sizeof(double));
-
-
- float* arr1 = (float*) x->gpu_data;
- float* arr2 = (float*) x_orig->gpu_data;
-
- int blockSize = 1024;
- int gridSize = (int) ceil ((float) x->num_elems / blockSize);
- INFO("blockSize = %d, gridSize = %d \n", blockSize, gridSize);
-
- normComputeKernel<<<gridSize, blockSize>>>(arr1, arr2, l1_norm_A_d, l2_norm_A_d, l1_diff_d, l2_diff_d, x->num_elems);
-
- cudaMemcpy(&l1_norm_A, l1_norm_A_d, sizeof(double), cudaMemcpyDeviceToHost);
- cudaMemcpy(&l2_norm_A, l2_norm_A_d, sizeof(double), cudaMemcpyDeviceToHost);
- cudaMemcpy(&l1_diff, l1_diff_d, sizeof(double), cudaMemcpyDeviceToHost);
- cudaMemcpy(&l2_diff, l2_diff_d, sizeof(double), cudaMemcpyDeviceToHost);
-
-
- // Relative L1 and Mean L1 norms of the difference Matrix
- float mean_l1 = l1_diff / x->num_elems;
- float relative_l1 = l1_diff / l1_norm_A;
-
- // Computing Relative L2 norm - i.e., Euclidean distance
- double norm_root_A = sqrt(l2_norm_A);
- double diff_root = sqrt(l2_diff);
- float mean_l2 = diff_root / x->num_elems;
- float relative_l2 = diff_root / norm_root_A;
-
- // Packing computed norms in Norm_t struct
- Norm_t* norms = (Norm_t*) malloc(sizeof(Norm_t));
- // Mean metrics - not normalized for the distribution - suitable for precision tuning hardware
- norms->mean_l1 = mean_l1;
- norms->mean_l2 = mean_l2;
- norms->orig_inf_norm = 0.0;
-
- // Relative metrics (relative to distribution) - suitable for PROMISE
- norms->l1_norm = relative_l1;
- norms->l2_norm = relative_l2;
- norms->inf_norm = 0.0;
-
- INFO("l1_norm = %f \n", relative_l1);
- INFO("l2_norm = %f \n", relative_l2);
-
- return norms;
-}
-
-
-
-
-__global__ void vecConstMul(float* A, float mul_factor, int n){
-
- int id = blockIdx.x * blockDim.x + threadIdx.x;
-
- if(id < n)
- A[id] = A[id] * mul_factor;
-}
-
-
-__global__ void vecRound(float* A, int n){
-
- int id = blockIdx.x * blockDim.x + threadIdx.x;
-
- if(id < n)
- A[id] = roundf(A[id]);
-}
-
-
-__global__ void vecConstDiv(float* A, float div_factor, int n){
-
- int id = blockIdx.x * blockDim.x + threadIdx.x;
-
- if(id < n)
- A[id] = A[id] / div_factor;
-}
-
-
-
-__global__ void vecMul(float* A, float* B, int n){
-
- int id = blockIdx.x * blockDim.x + threadIdx.x;
-
- if(id < n)
- B[id] = A[id] * B[id];
-}
-
-
-/**** ERROR injecion routines ******/
-
-void initRandValues(Tensor* bias, int error_scale){
-
- float scaling_values[20];
-
- // FIXIT: Error knob 0 should be 0 zero
- scaling_values[0] = 0.016;
- scaling_values[1] = 0.018;
- scaling_values[2] = 0.022;
- scaling_values[3] = 0.026;
- scaling_values[4] = 0.030;
- scaling_values[5] = 0.035;
- scaling_values[6] = 0.04;
- scaling_values[7] = 0.06;
- scaling_values[8] = 0.08;
- scaling_values[9] = 0.1;
- //scaling_values[8] = 0.15;
- //scaling_values[9] = 0.2;
- scaling_values[10] = 0.25;
- scaling_values[11] = 0.3;
- scaling_values[12] = 0.35;
- scaling_values[13] = 0.4;
- scaling_values[14] = 0.45;
- // Values below are currently unused by Opentuner
- scaling_values[15] = 0.5;
- scaling_values[16] = 0.55;
- scaling_values[17] = 0.6;
- scaling_values[18] = 0.65;
- scaling_values[19] = 0.7;
-
- curandGenerator_t gen;
-
- struct timespec ts;
- if(timespec_get(&ts, TIME_UTC) == 0){
- printf("crashed \n");
- abort();
- }
-
- curandCreateGenerator(&gen, CURAND_RNG_PSEUDO_DEFAULT);
-
- curandSetPseudoRandomGeneratorSeed(gen, ts.tv_nsec^ts.tv_sec);
-
- curandGenerateNormal(gen, (float*) bias->gpu_data, bias->num_elems, 0.0, 1.0 * scaling_values[error_scale]);
-
-
- /*
- std::random_device rd;
- std::mt19937 mt(rd());
- std::normal_distribution<float> distribution(0.0, 1.0);
-
- float* data_arr = (float*) bias->host_data;
- for(int i = 0; i < bias->num_elems; i++){
- float rand_num = distribution(mt);
- data_arr[i] = scaling_values[error_scale] * rand_num;
- }
- */
-
-}
-
-
-
-void* addBitError(void* x_ptr, int error_scale){
-
- if(error_scale > 6 || error_scale < 0){
- ERROR("Error Scale out of bounds \n");
- }
-
- INFO("*** TensorBitError \n");
- profileEvent("tensorBitError");
-
- Tensor* x = (Tensor*) x_ptr;
-
- size_t* dim_sizes = x->dims.dim_sizes;
- Tensor* x_original = (Tensor*) create4DTensor(x->data_type, x->data_format,
- dim_sizes[0], dim_sizes[1],
- dim_sizes[2], dim_sizes[3]);
-
- // Copying x data into x_original - for computing Norms
- tensorCopy(x, x_original);
-
- // Quadratic Error
- float freq_factors[6];
- freq_factors[0] = 0.1;
- freq_factors[1] = 0.2;
- freq_factors[2] = 0.4;
- freq_factors[3] = 0.6;
- freq_factors[4] = 0.8;
- freq_factors[5] = 1.0;
-
- float error_freq = freq_factors[error_scale];
-
- deviceToHostCopy(x);
-
- unsigned char* data_arr = reinterpret_cast<unsigned char*>(x->host_data);
- // FIXIT: Need to be careful about floating point datatype assumptions
- int size_of_elem = 4;
-
- long int total_bytes = x->size_in_bytes;
- long int error_iterations = total_bytes * 0.01 * error_freq;
- INFO("total_bytes = %lu, error_iterations = %lu \n", total_bytes, error_iterations);
-
- srand(time(NULL));
-
- for(int i = 0; i < error_iterations; i++){
- // FIXIT: The rand() is only specific to int - need long
- long int index = rand() % total_bytes;
- int N = 5; // The operation below flips the Nth bit
- unsigned char fil = 1UL << N;
- unsigned char val = data_arr[index];
- char flipped = val^fil;
- data_arr[i] = flipped;
- }
-
-
- Norm_t* norms = calculateNorms2(x, x_original);
-
- profileEvent("tensorBitError_end", true);
-
- return (void*) norms;
-}
-
-
-void randomCeilAndFloor(float* x, size_t num_elems){
-
- INFO("randomCeilAndFloor\n");
-
- std::random_device rd;
- std::mt19937 mt(rd());
- std::normal_distribution<float> distribution(0.0, 1.0);
-
- for(size_t i = 0; i < num_elems; i++){
- float rand_num = distribution(mt);
- int val = abs(((int) rand_num) % 2);
- if(val == 0)
- x[i] = floor(x[i]);
- else if(val == 1)
- x[i] = ceil(x[i]);
- }
-
-}
-
-// Routine for Adding RoundOff Errors
-void* addRoundError(void* x_ptr, int error_scale){
-
- if(error_scale > 11 || error_scale < 0){
- ERROR("Error Scale out of bounds \n");
- }
-
- INFO("*** TensorRoundError \n");
- profileEvent("tensorRoundError");
-
- Tensor* x = (Tensor*) x_ptr;
-
- size_t* dim_sizes = x->dims.dim_sizes;
- Tensor* x_original = (Tensor*) create4DTensor(x->data_type, x->data_format,
- dim_sizes[0], dim_sizes[1],
- dim_sizes[2], dim_sizes[3]);
-
- // Copying x data into x_original - for computing Norms
- tensorCopy(x, x_original);
-
- float round_factors[12];
- round_factors[0] = 1000000; // FIXIT: This should be zero error
- round_factors[1] = 100;
- round_factors[2] = 10;
- round_factors[3] = 7; // Beyond this point, the error function is linear
- round_factors[4] = 3;
- round_factors[5] = 1;
- round_factors[6] = 0.7;
- round_factors[7] = 0.3;
- round_factors[8] = 0.1;
- round_factors[9] = 0.07;
- round_factors[10] = 0.03;
- round_factors[11] = 0.01;
-
- // THINK: Considering using error magnitudes in this scenario
-
-
- float round_factor = round_factors[error_scale];
- INFO("round_factor = %f \n", round_factor);
-
- hostToDeviceCopy(x);
-
- int blockSize = 128;
- int gridSize = (int) ceil ((float) x->num_elems / blockSize);
- INFO("blockSize = %d, gridSize = %d \n", blockSize, gridSize);
-
- // NOTE: Check if a large gridSize will work with really large tensors
- vecConstMul<<<gridSize, blockSize>>>((float*) x->gpu_data, round_factor, x->num_elems);
- //vecRound<<<gridSize, blockSize>>>((float*) x->gpu_data, x->num_elems);
-
- deviceToHostCopy(x);
- randomCeilAndFloor((float*) x->host_data, x->num_elems);
- hostToDeviceCopy(x);
-
- vecConstDiv<<<gridSize, blockSize>>>((float*) x->gpu_data, round_factor, x->num_elems);
-
- Norm_t* norms = calculateNorms2(x, x_original);
-
- profileEvent("tensorRoundError_end", true);
-
- return (void*) norms;
-}
-
-
-
-
-// Routine for Adding Gaussian Error
-void* addGaussianError(void* x_ptr, int error_scale){
-
- if(error_scale > 11 || error_scale < 0){
- ERROR("Error Scale out of bounds \n");
- }
-
- INFO("*** TensorAddError \n");
- profileEvent("tensorAddError");
-
- Tensor* x = (Tensor*) x_ptr;
-
- size_t* dim_sizes = x->dims.dim_sizes;
- Tensor* bias = (Tensor*) create4DTensor(x->data_type, x->data_format,
- dim_sizes[0], dim_sizes[1],
- dim_sizes[2], dim_sizes[3]);
-
- Tensor* x_original = (Tensor*) create4DTensor(x->data_type, x->data_format,
- dim_sizes[0], dim_sizes[1],
- dim_sizes[2], dim_sizes[3]);
-
- // Copying x data into x_original - for computing Norms
- tensorCopy(x, x_original);
-
- // NOTE: Error scale is used to generate the bias matrix
- initRandValues(bias, error_scale);
-
- hostToDeviceCopy(x);
- //hostToDeviceCopy(bias);
-
-
- int blockSize = 1024;
- int gridSize = (int) ceil ((float) x->num_elems / blockSize);
- INFO("blockSize = %d, gridSize = %d \n", blockSize, gridSize);
-
- // NOTE: Check if a large gridSize will work with really large tensors
- vecMul<<<gridSize, blockSize>>>((float*) x->gpu_data, (float*) bias->gpu_data, x->num_elems);
-
- float alpha = 1.0f, beta = 0.0f;
-
- // FIXIT: routine fails for 3D tensors
- checkCUDNN(cudnnAddTensor(cudnnHandle, &alpha, bias->tensor_desc,
- bias->gpu_data, &alpha, x->tensor_desc, x->gpu_data));
-
-
- //Norm_t* norms = calculateNorms2(x, x_original);
- Norm_t* norms = calculateNormsGPU(x, x_original);
-
-
- profileEvent("tensorAddError_end", true);
-
- return (void*) norms;
-}
-
-
-
-void* tensorAddError(void* x_ptr, int error_scale){
-
- void * new_x = addGaussianError(x_ptr, error_scale);
- //void * new_x = addRoundError(x_ptr, error_scale);
- //void * new_x = addBitError(x_ptr, error_scale);
- return new_x;
-}
-
-
-
-
-#endif
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/.#error.h b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/.#error.h
deleted file mode 120000
index a9c72af5b6737a57be1db44cd3231c6dda0857f0..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/.#error.h
+++ /dev/null
@@ -1 +0,0 @@
-hsharif3@tyler.cs.illinois.edu.21294:1541049775
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/approx_api.h b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/approx_api.h
deleted file mode 100644
index 811fa4090ff2f4399b31bfb0ec228801eee844e0..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/approx_api.h
+++ /dev/null
@@ -1,70 +0,0 @@
-
-
-#include "tensor.h"
-
-
-extern "C"{
-
- // NOTE: API for tensorGroupConvolution
- // API for Running Tensor Convolution with CUTLASS
- void* tensorConvCutlass(void* input, void* filter,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int conv_groups);
-
- void* tensorHalfConvCutlass(void* input, void* filter,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int conv_groups);
-
-
- // Perforated Tensor Conv with 'perforation_rate' parameter
- void* tensorConvPerf(void* input, void* filter,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int conv_groups, int row, int col);
-
- void* tensorConvolutionKernelSamp(void* input, void* filter_ptr,
- int vertical_pad, int horizontal_pad, int vertical_stride,
- int horizontal_stride, int conv_mode, int conv_groups, int skip_every);
-
- void* tensorConvPerfCuda(void* input, void* filter,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int conv_groups,
- int row, int col, int start);
-
- void* tensorConvPerfCudaHalf(void* input_ptr, void* filter_ptr,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int conv_groups,
- int row, int col, int start);
-
- void sampleFilter(Tensor* filter, int skip_rate, int skip_offset);
-
- void* tensorConvSampSim(void* input_ptr, void* filter_ptr,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int conv_groups,
- int skip_rate, int skip_offset);
-
- void* tensorConvInputHalf(void* input_ptr, void* filter_ptr,
- int vertical_pad, int horizontal_pad, int vertical_stride,
- int horizontal_stride, int conv_mode, int conv_groups,
- int skip_every, int skip_offset);
-
- void* tensorConvApproxHalf(void* input_ptr, void* filter_ptr,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int conv_groups,
- int row, int col,
- int skip_every, int skip_offset);
-
- void* tensorConvApprox(void* input_ptr, void* filter_ptr,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int conv_groups,
- int row, int col,
- int skip_every, int skip_offset);
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/approx_simulation.h b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/approx_simulation.h
deleted file mode 100644
index 384464f44ddc32524db5de07a59b4e38d1422a8e..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/approx_simulation.h
+++ /dev/null
@@ -1,1399 +0,0 @@
-
-
-#ifndef SIM_HEADER
-#define SIM_HEADER
-
-
-
-#include "tensor_runtime.h"
-#include "tensor_utils.cu"
-#include "debug.h"
-#include "profiling.h"
-#include "fp16_conversion.h"
-#include "global_data.h"
-#include "error.h"
-#include "tensor.h"
-#include "op_overheads.h"
-#include "half_precision_api.h"
-#include "approx_techniques2.h"
-#include <unordered_map>
-
-
-
-
-//N is new_data's size
-//n, c, h, w are the dimensions of new_data
-__global__
-void postInterpolateRow(int N, int n, int c, int h, int w, float* data, int int_row){
-
- int index = blockIdx.x * blockDim.x + threadIdx.x;
- int stride = blockDim.x * gridDim.x;
-
- for(int i = index; i < N; i += stride){
- int col = ((i % (c * h * w)) % (h * w)) % w;
- int row = ((i % (c * h * w)) % (h * w)) / w;
- int ch = (i % (c * h * w)) / (h * w);
- int n = i / (c * h * w);
-
- if((row % int_row == 1) && (row != 0) && (row != h-1))
- data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- (data[n * (c * h * w) + ch * (h * w) + (row - 1) * (w) + col] +
- data[n * (c * h * w) + ch * (h * w) + (row + 1) * (w) + col])/2;
-
- }
-}
-
-
-
-__global__
-void postInterpolateCol(int N, int n, int c, int h, int w, float* data, int int_col){
-
- int index = blockIdx.x * blockDim.x + threadIdx.x;
- int stride = blockDim.x * gridDim.x;
-
- for(int i = index; i < N; i += stride){
- int col = ((i % (c * h * w)) % (h * w)) % w;
- int row = ((i % (c * h * w)) % (h * w)) / w;
- int ch = (i % (c * h * w)) / (h * w);
- int n = i / (c * h * w);
-
- if((col % int_col == 1) && (col != 0) && (col != w-1))
- data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- (data[n * (c * h * w) + ch * (h * w) + row * (w) + (col-1) ] +
- data[n * (c * h * w) + ch * (h * w) + row * (w) + (col+1) ])/2;
-
- }
-}
-
-
-
-
-// A 'Simulation' of perforated tensor convolution
-void* tensorConvPerfSim(void* input_ptr, void* filter_ptr,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int conv_groups, int row, int col){
-
-
- INFO("*** TensorConvolution \n");
- profileEvent("tensorConv");
-
- Tensor* input = (Tensor*) input_ptr;
- Tensor* filter = (Tensor*) filter_ptr;
-
- cudnnConvolutionDescriptor_t convDesc;
- cudnnConvolutionFwdAlgo_t convAlgo;
- cudnnConvolutionMode_t mode;
-
- if(conv_mode == 0)
- mode = CUDNN_CONVOLUTION;
- else if(conv_mode == 1)
- mode = CUDNN_CROSS_CORRELATION;
-
- float alpha = 1.0f, beta = 0.0f;
-
- hostToDeviceCopy(input);
- hostToDeviceCopy(filter);
-
- INFO("vertical_stride = %lu, horizontal_stride = %lu \n", vertical_stride, horizontal_stride);
-
- checkCUDNN(cudnnCreateConvolutionDescriptor(&convDesc));
-
- //FIXME: Current hack to preserve backward compatibilty
- if(conv_groups == 0){
- conv_groups = 1;
- }
-
- // NOTE: Adding support for grouped convolution
- checkCUDNN(cudnnSetConvolutionGroupCount(convDesc, conv_groups));
-
- int new_v = vertical_stride + 0;
- int new_h = horizontal_stride + 0;
- cudnnDataType_t computeType = CUDNN_DATA_FLOAT;
-
- checkCUDNN(cudnnSetConvolution2dDescriptor(convDesc,
- vertical_pad, horizontal_pad, // conv padding
- new_v, new_h, // conv strides
- 1, 1, // upscaling values
- mode , // mode is configurable
- computeType)); // defines compute precision
-
- int n, c, h, w; // output dimensions
- // Find dimension of convolution output
- checkCUDNN(cudnnGetConvolution2dForwardOutputDim(convDesc,
- input->tensor_desc,
- filter->filter_desc,
- &n, &c, &h, &w));
-
-
- DEBUG("**Output Tensor Dims, n = %d, c = %d, h = %d, w = %d \n", n, c, h, w);
-
- Tensor* output;
- if(input->data_format == CUDNN_TENSOR_NCHW)
- output = (Tensor*) create4DTensor((cudnnDataType_t) input->data_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
- else if(input->data_format == CUDNN_TENSOR_NHWC){
- DEBUG("* NHWC Format \n");
- output = (Tensor*) create4DTensor((cudnnDataType_t) input->data_type,
- CUDNN_TENSOR_NHWC, n, h, w, c);
- }
- else
- ERROR("Unsupported Tensor Type");
-
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(output, DEVICE);
- // NOTE: Necessary to insert the above call for every output tensor
-
- DEBUG("tensor->data_type = %d, tensor->data_format = %d, N = %d, C = %d, H = %d, W = %d \n",
- output->data_type, output->data_format, output->dims.dim_sizes[0],
- output->dims.dim_sizes[1],
- output->dims.dim_sizes[2], output->dims.dim_sizes[3]);
-
- if(convDesc == NULL || input->tensor_desc == NULL ||
- filter->filter_desc == NULL || output->tensor_desc == NULL)
- ERROR("NULL descriptor! \n");
-
-
-
- // NOTE-FIXIT: function failing for NHWC formats - perhaps some CUDNN support is lacking
- checkCUDNN(cudnnGetConvolutionForwardAlgorithm(cudnnHandle,
- input->tensor_desc,
- filter->filter_desc,
- convDesc,
- output->tensor_desc,
- CUDNN_CONVOLUTION_FWD_PREFER_FASTEST,
- //CUDNN_CONVOLUTION_FWD_NO_WORKSPACE,
- 0,
- &convAlgo));
-
-
- DEBUG("ConvAlgo = %d, FFT = %d, GEMM = %d, WINOGRAD = %d \n", convAlgo,
- CUDNN_CONVOLUTION_FWD_ALGO_FFT, CUDNN_CONVOLUTION_FWD_ALGO_GEMM,
- CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD);
-
-
- // FIXIT: Algo shouldn't be hardcoded
- convAlgo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM;
-
- size_t workspace_size;
- checkCUDNN(cudnnGetConvolutionForwardWorkspaceSize(cudnnHandle,
- input->tensor_desc,
- filter->filter_desc,
- convDesc,
- output->tensor_desc,
- convAlgo,
- &workspace_size));
-
- // Allocating memory for the convolution workspace
- void* workspace;
- checkCudaErrors(cudaMalloc(&workspace, workspace_size));
- DEBUG("workspace size = %d \n", workspace_size);
-
-
- checkCUDNN(cudnnConvolutionForward(cudnnHandle, &alpha, input->tensor_desc,
- input->gpu_data, filter->filter_desc, filter->gpu_data,
- convDesc, convAlgo, workspace, workspace_size,
- &beta, output->tensor_desc, output->gpu_data));
-
-
- h = (2 * vertical_pad + input->dims.dim_sizes[2] - filter->dims.dim_sizes[2]) / vertical_stride + 1;
- w = (2 * horizontal_pad + input->dims.dim_sizes[3] - filter->dims.dim_sizes[3]) / horizontal_stride + 1;
-
-
- int numBlocks = (n * c * h * w + 127) / 128;
-
- if (row > 0)
- postInterpolateRow<<<numBlocks,128>>>(n * c * h * w, n, c, h, w,
- (float *) output->gpu_data, row);
-
- if (col > 0)
- postInterpolateCol<<<numBlocks,128>>>(n * c * h * w, n, c, h, w,
- (float *) output->gpu_data, col);
-
-
- profileEvent("tensorConv_end", true);
-
- return output;
-}
-
-
-
-
-
-//N is new_data's size
-//n, c, h, w are the dimensions of new_data
-__global__
-void sampleFilterElems(int N,
- int n, int c, int h, int w,
- float* data,
- int skip_elem, int skip_offset, float mul_factor){
-
- int index = blockIdx.x * blockDim.x + threadIdx.x;
- int stride = blockDim.x * gridDim.x;
-
- for(int i = index; i < N; i += stride){
- int col = ((i % (c * h * w)) % (h * w)) % w;
- int row = ((i % (c * h * w)) % (h * w)) / w;
- int ch = (i % (c * h * w)) / (h * w);
- int n = i / (c * h * w);
-
- //int local_index = row * w + col;
- int local_index = (ch * (h * w)) + (row * w) + col;
-
- if(local_index % skip_elem == skip_offset)
- data[n * (c * h * w) + ch * (h * w) + row * (w) + col] = 0;
- else
- data[n * (c * h * w) + ch * (h * w) + row * (w) + col] *= mul_factor;
-
- }
-}
-
-
-
-
-
-void sampleFilter(Tensor* filter, int skip_rate, int skip_offset){
-
- int n = filter->dims.dim_sizes[0];
- int c = filter->dims.dim_sizes[1];
- int h = filter->dims.dim_sizes[2];
- int w = filter->dims.dim_sizes[3];
-
- int numBlocks = (n * c * h * w + 127) / 128;
- int N = n * c * h * w;
- //float mul_factor = skip_rate / (skip_rate - 1);
- float mul_factor = (skip_rate * 1.0) / (skip_rate - 1);
-
- printf ("mul_factor = %f \n", mul_factor);
-
- sampleFilterElems<<<numBlocks,128>>>(N,
- n, c, h, w,
- (float *) filter->gpu_data,
- skip_rate, skip_offset, mul_factor);
-
-}
-
-
-
-// A 'Simulation' of perforated tensor convolution
-void* tensorConvSampSim(void* input_ptr, void* filter_ptr,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int conv_groups,
- int skip_rate, int skip_offset){
-
-
- INFO("*** TensorConvolution \n");
- profileEvent("tensorConv");
-
- Tensor* input = (Tensor*) input_ptr;
- Tensor* filter = (Tensor*) filter_ptr;
-
-
- cudnnConvolutionDescriptor_t convDesc;
- cudnnConvolutionFwdAlgo_t convAlgo;
- cudnnConvolutionMode_t mode;
-
- if(conv_mode == 0)
- mode = CUDNN_CONVOLUTION;
- else if(conv_mode == 1)
- mode = CUDNN_CROSS_CORRELATION;
-
- float alpha = 1.0f, beta = 0.0f;
-
- hostToDeviceCopy(input);
- hostToDeviceCopy(filter);
-
- convertToFP32(input);
- convertToFP32(filter);
-
-
- // Zeroing (+Scaling) Filter elements to 'Simulate' input sampling
- sampleFilter(filter, skip_rate, skip_offset);
-
-
- INFO("vertical_stride = %lu, horizontal_stride = %lu \n", vertical_stride, horizontal_stride);
-
- checkCUDNN(cudnnCreateConvolutionDescriptor(&convDesc));
-
- //FIXME: Current hack to preserve backward compatibilty
- if(conv_groups == 0){
- conv_groups = 1;
- }
-
- // NOTE: Adding support for grouped convolution
- checkCUDNN(cudnnSetConvolutionGroupCount(convDesc, conv_groups));
-
- int new_v = vertical_stride + 0;
- int new_h = horizontal_stride + 0;
- cudnnDataType_t computeType = CUDNN_DATA_FLOAT;
-
- checkCUDNN(cudnnSetConvolution2dDescriptor(convDesc,
- vertical_pad, horizontal_pad, // conv padding
- new_v, new_h, // conv strides
- 1, 1, // upscaling values
- mode , // mode is configurable
- computeType)); // defines compute precision
-
- int n, c, h, w; // output dimensions
- // Find dimension of convolution output
- checkCUDNN(cudnnGetConvolution2dForwardOutputDim(convDesc,
- input->tensor_desc,
- filter->filter_desc,
- &n, &c, &h, &w));
-
-
- DEBUG("**Output Tensor Dims, n = %d, c = %d, h = %d, w = %d \n", n, c, h, w);
-
- Tensor* output;
- output = (Tensor*) create4DTensor((cudnnDataType_t) float_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
-
-
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(output, DEVICE);
- // NOTE: Necessary to insert the above call for every output tensor
-
- DEBUG("tensor->data_type = %d, tensor->data_format = %d, N = %d, C = %d, H = %d, W = %d \n",
- output->data_type, output->data_format, output->dims.dim_sizes[0],
- output->dims.dim_sizes[1],
- output->dims.dim_sizes[2], output->dims.dim_sizes[3]);
-
- if(convDesc == NULL || input->tensor_desc == NULL ||
- filter->filter_desc == NULL || output->tensor_desc == NULL)
- ERROR("NULL descriptor! \n");
-
-
- // NOTE-FIXIT: function failing for NHWC formats - perhaps some CUDNN support is lacking
- checkCUDNN(cudnnGetConvolutionForwardAlgorithm(cudnnHandle,
- input->tensor_desc,
- filter->filter_desc,
- convDesc,
- output->tensor_desc,
- CUDNN_CONVOLUTION_FWD_PREFER_FASTEST,
- //CUDNN_CONVOLUTION_FWD_NO_WORKSPACE,
- 0,
- &convAlgo));
-
-
- DEBUG("ConvAlgo = %d, FFT = %d, GEMM = %d, WINOGRAD = %d \n", convAlgo,
- CUDNN_CONVOLUTION_FWD_ALGO_FFT, CUDNN_CONVOLUTION_FWD_ALGO_GEMM,
- CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD);
-
-
- // NOTE: Using GEMM-based Algo
- convAlgo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM;
-
- size_t workspace_size;
- checkCUDNN(cudnnGetConvolutionForwardWorkspaceSize(cudnnHandle,
- input->tensor_desc,
- filter->filter_desc,
- convDesc,
- output->tensor_desc,
- convAlgo,
- &workspace_size));
-
- // Allocating memory for the convolution workspace
- void* workspace;
- checkCudaErrors(cudaMalloc(&workspace, workspace_size));
- DEBUG("workspace size = %d \n", workspace_size);
-
-
- checkCUDNN(cudnnConvolutionForward(cudnnHandle, &alpha, input->tensor_desc,
- input->gpu_data, filter->filter_desc, filter->gpu_data,
- convDesc, convAlgo, workspace, workspace_size,
- &beta, output->tensor_desc, output->gpu_data));
-
-
-
-
- profileEvent("tensorConv_end", true);
-
- return output;
-}
-
-
-
-
-
-
-
-
-
-
-
-/************ NOTE: API for ApproxHPVM Wrapper runtime *******/
-
-
-void* PROMISE_Conv(void* input, float i_min, float i_max,
- void* filter, float w_min, float w_max,
- void* bias, float b_min, float b_max,
- int conv_pad_h, int conv_pad_w,
- int conv_stride_h, int conv_stride_w,
- int pool_id, int pool_size,
- int activation_id, // Relu, Tanh, ClipRelu
- float out_min, float out_max, int swing){
-
-
- Tensor* input_t = (Tensor*) input;
- Tensor* filter_t = (Tensor*) filter;
- Tensor* bias_t = (Tensor*) bias;
-
- int orig_type = input_t->cur_type;
-
- DEBUG("FP32 conversions \n");
-
- convertToFP32(input_t);
-
- convertToFP32(filter_t);
- convertToFP32(bias_t);
-
- DEBUG("DONE FP32 conversions \n");
-
-
- if(swing < 8){
- input = quantizeTensorPromise(input, i_min, i_max);
- filter = quantizeTensorPromise(filter, w_min, w_max);
- if(bias != NULL)
- bias = quantizeTensorPromise(bias, b_min, b_max);
- // aRead error
-
- input = addPromiseError(input, swing);
- }
-
-
- void* conv_out;
- conv_out = tensorConvolution(input, filter,
- conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w,
- 1, 0);
-
- void* conv_add;
- if(bias != NULL){
- conv_add = tensorAdd(conv_out, bias);
- }
- else{
- conv_add = conv_out;
- }
-
- void* pool_out;
- // NOTE: Skip pooling on negative pool sizes
- if(pool_size > 0){
- //FIXME: Currently only using MaxPooling
- pool_out = tensorPooling(conv_add, 0, pool_size, pool_size, 0, 0, pool_size, pool_size);
- }
- else{
- pool_out = conv_add;
- }
-
- void* activation_out;
- switch(activation_id){
- case -1:
- activation_out = pool_out;
- INFO("NO Activation Function \n");
- break;
- case 0:
- activation_out = tensorTanh(pool_out);
- break;
- case 1:
- activation_out = tensorRelu(pool_out);
- break;
- case 2:
- activation_out = tensorRelu2(pool_out, out_min, out_max);
- break;
- default:
- ERROR("Activation id %d NOT supported \n", activation_out);
- break;
- }
-
-
- if(swing < 8 && activation_id != -1){
- activation_out = quantizeTensorPromise(activation_out, out_min, out_max);
- }
-
-
-
- //NOTE: Convert back to FP16 if original type
- if (orig_type == half_type){
- convertToFP16((Tensor*) activation_out);
- }
-
-
- return activation_out;
-}
-
-
-
-void* PROMISE_FC(void* input, float i_min, float i_max,
- void* weights, float w_min, float w_max,
- void* bias, float b_min, float b_max,
- int activation_id,
- float out_min, float out_max, int swing){
-
-
- Tensor* input_t = (Tensor*) input;
- Tensor* weights_t = (Tensor*) weights;
- Tensor* bias_t = (Tensor*) bias;
-
- int orig_type = input_t->cur_type;
-
- convertToFP32(input_t);
- convertToFP32(weights_t);
- convertToFP32(bias_t);
-
-
- if(swing < 8){
- input = quantizeTensorPromise(input, i_min, i_max);
- weights = quantizeTensorPromise(weights, w_min, w_max);
- if(bias != NULL)
- bias = quantizeTensorPromise(bias, b_min, b_max);
-
- // NOTE: Modelling aRead error in PROMISE
- input = addPromiseError(input, swing);
- }
-
-
-
- void* gemm_out;
- gemm_out = tensorGemmGPU(input, weights);
-
-
- void* gemmbias_out;
- if(bias != NULL){
- gemmbias_out = tensorAdd(gemm_out, bias);
- }
- else{
- gemmbias_out = gemm_out;
- }
-
- void* activation_out;
- switch(activation_id){
-
- case -1:
- activation_out = gemmbias_out;
- INFO("No Activation Function \n");
- break;
- case 0:
- activation_out = tensorTanh(gemmbias_out);
- break;
- case 1:
- activation_out = tensorRelu(gemmbias_out);
- break;
- case 2:
- activation_out = tensorRelu2(gemmbias_out, out_min, out_max);
- break;
- default:
- ERROR("Activation id %d NOT supported \n", activation_out);
- break;
- }
-
-
- if(swing < 8 && activation_id != -1){
- activation_out = quantizeTensorPromise(activation_out, out_min, out_max);
- }
-
-
- //NOTE: Convert back to FP16 if original type
- if (orig_type == half_type){
- convertToFP16((Tensor*) activation_out);
- }
-
-
-
- return activation_out;
-}
-
-
-
-
-
-// NOTE: Enabling the macro below is used for testing against the old PROMISE wrapper
-//#define OLD_MODEL
-
-#ifndef OLD_MODEL
-
-
-
-bool isPromise(int swing){
-
- if(swing < 8)
- return true;
- else
- return false;
-}
-
-
-bool isFullPrecision(int swing){
-
- if(swing == 11)
- return true;
- else
- return false;
-}
-
-
-
-bool isHalfPrecision(int swing){
-
- if(swing == 12)
- return true;
- else
- return false;
-}
-
-
-bool isPerforation(int swing){
-
- if(swing >= 21 && swing <= 30)
- return true;
- else
- return false;
-}
-
-
-bool isSampling(int swing){
-
- if(swing >= 31 && swing <= 39)
- return true;
- else
- return false;
-}
-
-
-int getSwing(int swing){
-
- #ifdef PROMISE_TUNER_ENABLED
-
- // NOTE: Skip reading file-based error levels for ApproxHPVM wrapper runtime
- if(!approxhpvm_runtime_mode){
-
- if(op_counter >= total_ops){
- ERROR("No accuracy flag found \n");
- }
-
- swing = op_accuracies[op_counter];
- op_counter++;
- }
-
- #endif
-
- DEBUG("---- swing_value = %d \n", swing);
-
- return swing;
-}
-
-
-
-
-
-
-class PerfParams{
-
- public:
- int row;
- int col;
- int skip_offset;
-
- PerfParams(){
- row = 1;
- col = 1;
- skip_offset = 0;
- }
-
- PerfParams(int row1, int col1, int skip_offset1){
- row = row1;
- col = col1;
- skip_offset = skip_offset1;
- }
-
-};
-
-
-
-PerfParams getPerfParams(int swing){
-
- std::map<int, PerfParams> perf_knob_map;
-
- PerfParams params21(1, 2, 0);
- perf_knob_map[21] = params21;
-
- PerfParams params22(1, 2, 1);
- perf_knob_map[22] = params22;
-
- PerfParams params23(1, 3, 0);
- perf_knob_map[23] = params23;
-
- PerfParams params24(1, 3, 1);
- perf_knob_map[24] = params24;
-
- PerfParams params25(1, 3, 2);
- perf_knob_map[25] = params25;
-
- PerfParams params26(2, 1, 0);
- perf_knob_map[26] = params26;
-
- PerfParams params27(2, 1, 1);
- perf_knob_map[27] = params27;
-
- PerfParams params28(3, 1, 0);
- perf_knob_map[28] = params28;
-
- PerfParams params29(3, 1, 1);
- perf_knob_map[29] = params29;
-
- PerfParams params30(3, 1, 2);
- perf_knob_map[30] = params30;
-
-
- return perf_knob_map[swing];
-
-}
-
-
-
-
-class SampParams{
-
- public:
- int skip_rate;
- int skip_offset;
-
- SampParams(){
- skip_rate = 1;
- skip_offset = 0;
- }
-
- SampParams(int skip_rate1, int skip_offset1){
- skip_rate = skip_rate1;
- skip_offset = skip_offset1;
- }
-
-};
-
-
-
-SampParams getSampParams(int swing){
-
- std::map<int, SampParams> samp_knob_map;
-
- SampParams params31(2, 0);
- samp_knob_map[31] = params31;
-
- SampParams params32(2, 1);
- samp_knob_map[32] = params32;
-
- SampParams params33(4, 0);
- samp_knob_map[33] = params33;
-
- SampParams params34(4, 1);
- samp_knob_map[34] = params34;
-
- SampParams params35(4, 2);
- samp_knob_map[35] = params35;
-
- SampParams params36(4, 3);
- samp_knob_map[36] = params36;
-
- return samp_knob_map[swing];
-
-}
-
-
-
-
-
-
-/***** API for Autotuner Use - Not the ApproxHPVM Wrapper API */
-
-
-// NOTE: code to compute the gold result - for norm computations
-//bool compute_norms = false; // true; //false;
-
-void* ConvLayer_PROMISE(void* input, float i_min, float i_max,
- void* filter, float w_min, float w_max,
- void* bias, float b_min, float b_max,
- int conv_pad_h, int conv_pad_w,
- int conv_stride_h, int conv_stride_w,
- int pool_id, int pool_size,
- int activation_id, // Relu, Tanh, ClipRelu
- float out_min, float out_max, int swing){
-
- if(ONLINE_PROFILING){
- ERROR("Online Profiling cannot be enabled with PROMISE Simulation \n");
- }
-
-
-
- swing = getSwing(swing);
-
- if(isPromise(swing)){
-
- return PROMISE_Conv(input, i_min, i_max,
- filter, w_min, w_max,
- bias, b_min, b_max,
- conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w,
- pool_id, pool_size,
- activation_id,
- out_min, out_max, swing);
- }
-
-
-
-
-
- void* conv_out;
- if(isPerforation(swing)){
-
- PerfParams params = getPerfParams(swing);
- DEBUG("params.row = %d, params.col = %d, params.skip_offset = %d \n",
- params.row, params.col, params.skip_offset);
-
- conv_out = tensorConvPerfCudaHalf(input, filter,
- conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w, 1, 1,
- params.row, params.col, params.skip_offset);
-
- }
-
- if(isSampling(swing)){
-
- SampParams params = getSampParams(swing);
- DEBUG("params.skip_rate = %d, params.skip_offset = %d \n",
- params.skip_rate, params.skip_offset);
-
- /*
- conv_out = tensorConvolutionKernelSamp(input, filter,
- conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w,
- 1, 1,
- 2);
- */
-
-
- conv_out = tensorConvSampSim(input, filter,
- conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w, 1, 1,
- params.skip_rate, params.skip_offset);
-
-
- /* conv_out = tensorConvApproxHalf(input, filter,
- conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w, 1, 1,
- 1,1,
- 4, 3);
- // params.skip_rate, params.skip_offset);
- */
-
- }
-
-
- if (isHalfPrecision(swing)){
-
- conv_out = tensorHalfConvolution(input, filter,
- conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w,
- 1, 0);
- }
-
- if (isFullPrecision(swing)){
- conv_out = tensorConvolution(input, filter,
- conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w,
- 1, 0);
- }
-
-
-
-
-
- void* conv_add;
- if(bias != NULL){
- if( !isFullPrecision(swing) ){
- conv_add = tensorHalfAdd(conv_out, bias);
- }
- else{
- conv_add = tensorAdd(conv_out, bias);
- }
- }
- else{
- conv_add = conv_out;
- }
-
- void* pool_out;
- if(pool_size > 0){
- //FIXME: Currently only using MaxPooling
- pool_out = tensorHalfPooling(conv_add, 0, pool_size, pool_size,
- 0, 0, pool_size, pool_size);
- }
- else{
- pool_out = conv_add;
- }
-
- void* activation_out;
- switch(activation_id){
- case -1:
- activation_out = pool_out;
- INFO("NO Activation Function \n");
- break;
- case 0:
- activation_out = tensorHalfTanh(pool_out);
- break;
- case 1:
- activation_out = tensorHalfRelu(pool_out);
- break;
- case 2:
- activation_out = tensorHalfRelu2(pool_out, out_min, out_max);
- break;
- default:
- ERROR("Activation id %d NOT supported \n", activation_out);
- break;
- }
-
-
- return activation_out;
-}
-
-
-void* FCLayer_PROMISE(void* input, float i_min, float i_max,
- void* weights, float w_min, float w_max,
- void* bias, float b_min, float b_max,
- int activation_id,
- float out_min, float out_max, int swing){ //NOTE: min_val, max_val apply to 'ClippedRelu'
-
-
- swing = getSwing(swing);
-
- if(isPromise(swing)){
-
- return PROMISE_FC(input, i_min, i_max,
- weights, w_min, w_max,
- bias, b_min, b_max,
- activation_id,
- out_min, out_max, swing);
- }
-
-
-
- void* gemm_out;
- if(!isFullPrecision(swing)){
- gemm_out = tensorHalfGemm(input, weights);
- }
- else{
- gemm_out = tensorGemmGPU(input, weights);
- }
-
-
- void* gemmbias_out;
- if(bias != NULL){
- // Swing 8 corresponds to FP32
- if(!isFullPrecision(swing)){
- gemmbias_out = tensorHalfAdd(gemm_out, bias);
- }
- else{
- gemmbias_out = tensorAdd(gemm_out, bias);
- }
- }
- else{
- gemmbias_out = gemm_out;
- }
-
- void* activation_out;
- switch(activation_id){
-
- case -1:
- activation_out = gemmbias_out;
- INFO("No Activation Function \n");
- break;
- case 0:
- activation_out = tensorHalfTanh(gemmbias_out);
- break;
- case 1:
- activation_out = tensorHalfRelu(gemmbias_out);
- break;
- case 2:
- activation_out = tensorHalfRelu2(gemmbias_out, out_min, out_max);
- break;
- default:
- ERROR("Activation id %d NOT supported \n", activation_out);
- break;
- }
-
-
-
-
- return activation_out;
-}
-
-#endif
-
-
-
-#ifdef OLD_MODEL
-
-#endif
-
-#endif
-
-
-
-/************* NOTE: Outdated PROMISE routines - Used for Comparison ****/
-
-
-
-
-/*
-
-
-
-void* ConvLayer_PROMISE(void* input, float i_min, float i_max,
- void* filter, float w_min, float w_max,
- void* bias, float b_min, float b_max,
- int conv_pad_h, int conv_pad_w, int conv_stride_h, int conv_stride_w,
- int pool_id, int pool_size,
- int activation_id, // Relu, Tanh, ClipRelu
- float out_min, float out_max, int swing){
-
-
- DEBUG("\n\n**** NOTE: Conv OLD MODEL *** \n\n");
-
- #ifdef PROMISE_TUNER_ENABLED
-
- // NOTE: Skip reading file-based error levels for ApproxHPVM wrapper runtime
- if(!approxhpvm_runtime_mode){
-
- if(op_counter >= total_ops){
- ERROR("No accuracy flag found \n");
- }
-
- swing = op_accuracies[op_counter];
- op_counter++;
- }
-
- #endif
-
-
- if (swing < 0 || swing > 20){
- ERROR("Incorrect swing value");
- }
-
-
-
- if(swing < 8){
- input = quantizeTensorPromise(input, i_min, i_max);
- filter = quantizeTensorPromise(filter, w_min, w_max);
- if(bias != NULL)
- bias = quantizeTensorPromise(bias, b_min, b_max);
- // aRead error
-
- input = addPromiseError(input, swing);
- }
-
-
- void* conv_out;
- if(swing == 8 || (swing >= 12 && swing <= 15) ){
- //conv_out = tensorConvPerf(input, filter, conv_pad_h, conv_pad_w,
- // conv_stride_h, conv_stride_w, 1, 1, 1, 0);
-
- int rows = 2;
- switch(swing){
-
- case 12: rows = 5; break;
- case 13: rows = 4; break;
- case 14: rows = 3; break;
- case 15: rows = 2; break;
-
- default: rows = 2; break;
- }
-
- conv_out = tensorConvPerfSim(input, filter, conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w, 1, 1, rows, 0);
-
- /*void* gold = tensorConvolution(input, filter,
- conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w,
- 1, 0);
-
- Norm_t* norms = calculateNormsTreeReduction((struct Tensor*) conv_out, (struct Tensor*) gold);
-
- DEBUG("\n-------- l2_norm = %f \n", norms->l2_norm);
- */
-
-/*-------------
- }
- else if(swing == 9 || (swing >= 16 && swing <= 19) ){
- //conv_out = tensorConvPerf(input, filter, conv_pad_h, conv_pad_w,
- // conv_stride_h, conv_stride_w, 1, 1, 0, 1);
-
-
- int cols = 2;
- switch(swing){
-
- case 16: cols = 5; break;
- case 17: cols = 4; break;
- case 18: cols = 3; break;
- case 19: cols = 2; break;
-
- default: cols = 2; break;
- }
-
-
- conv_out = tensorConvPerfSim(input, filter, conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w, 1, 1, 0, cols);
-
-
- /*void* gold = tensorConvolution(input, filter,
- conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w,
- 1, 0);
-
- Norm_t* norms = calculateNormsTreeReduction((struct Tensor*)conv_out, (struct Tensor*) gold);
-
- DEBUG("\n-------- l2_norm = %f \n", norms->l2_norm);
- */
-
-/*-----
-
- }
- else if(swing == 10){
- conv_out = tensorHalfConvolution(input, filter,
- conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w,
- 1, 0);
- }
- else{
- conv_out = tensorConvolution(input, filter,
- conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w,
- 1, 0);
- }
-
- void* conv_add;
- if(bias != NULL){
- if(swing >= 8){
- conv_add = tensorHalfAdd(conv_out, bias);
- }
- else{
- conv_add = tensorAdd(conv_out, bias);
- }
- }
- else{
- conv_add = conv_out;
- }
-
- void* pool_out;
- // NOTE: Skip pooling on negative pool sizes
- if(pool_size > 0){
- //FIXME: Currently only using MaxPooling
- pool_out = tensorPooling(conv_add, 0, pool_size, pool_size, 0, 0, pool_size, pool_size);
- }
- else{
- pool_out = conv_add;
- }
-
- void* activation_out;
- switch(activation_id){
- case -1:
- activation_out = pool_out;
- INFO("NO Activation Function \n");
- break;
- case 0:
- activation_out = tensorTanh(pool_out);
- break;
- case 1:
- activation_out = tensorRelu(pool_out);
- break;
- case 2:
- activation_out = tensorHalfRelu2(pool_out, out_min, out_max);
- break;
- default:
- ERROR("Activation id %d NOT supported \n", activation_out);
- break;
- }
-
-
- if(swing < 8 && activation_id != -1){
- activation_out = quantizeTensorPromise(activation_out, out_min, out_max);
- }
-
- return activation_out;
-}
-
-
-void* FCLayer_PROMISE(void* input, float i_min, float i_max,
- void* weights, float w_min, float w_max,
- void* bias, float b_min, float b_max,
- int activation_id,
- float out_min, float out_max, int swing){
-
-
-
- #ifdef PROMISE_TUNER_ENABLED
-
- // NOTE: Skip reading file-based error levels for ApproxHPVM wrapper runtime
- if(!approxhpvm_runtime_mode){
-
- if(op_counter >= total_ops){
- ERROR("No accuracy flag found \n");
- }
-
- swing = op_accuracies[op_counter];
- op_counter++;
- }
-
- #endif
-
-
- if (swing < 0 || swing > 20){
- ERROR("Incorrect swing value");
- }
-
- if(swing < 8){
- input = quantizeTensorPromise(input, i_min, i_max);
- weights = quantizeTensorPromise(weights, w_min, w_max);
- if(bias != NULL)
- bias = quantizeTensorPromise(bias, b_min, b_max);
-
- // NOTE: Modelling aRead error in PROMISE
- input = addPromiseError(input, swing);
- }
-
-
-
- void* gemm_out;
- if(swing >= 8 && swing < 11){
- gemm_out = tensorHalfGemm(input, weights);
- }
- else{
- gemm_out = tensorGemmGPU(input, weights);
- }
-
-
- void* gemmbias_out;
- if(bias != NULL){
- // Swing 8 corresponds to FP32
- if(swing >= 8 && swing < 20){
- gemmbias_out = tensorHalfAdd(gemm_out, bias);
- }
- else{
- gemmbias_out = tensorAdd(gemm_out, bias);
- }
- }
- else{
- gemmbias_out = gemm_out;
- }
-
- void* activation_out;
- switch(activation_id){
-
- case -1:
- activation_out = gemmbias_out;
- INFO("No Activation Function \n");
- break;
- case 0:
- activation_out = tensorTanh(gemmbias_out);
- break;
- case 1:
- activation_out = tensorRelu(gemmbias_out);
- break;
- case 2:
- activation_out = tensorRelu2(gemmbias_out, out_min, out_max);
- break;
- default:
- ERROR("Activation id %d NOT supported \n", activation_out);
- break;
- }
-
-
- if(swing < 8 && activation_id != -1){
- activation_out = quantizeTensorPromise(activation_out, out_min, out_max);
- }
-
- return activation_out;
-}
-
-#endif
-
-
-
-
-
-
-#endif
-
-
-
-
-
-
-
-
- DEBUG("\n-------- l2_norm = %f \n", norms->l2_norm);
- */
-
-
- /*void* gold = tensorConvolution(input, filter,
- conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w,
- 1, 0);
-
- Norm_t* norms = calculateNormsTreeReduction((struct Tensor*)conv_out, (struct Tensor*) gold);
-
- DEBUG("\n-------- l2_norm = %f \n", norms->l2_norm);
- */
-
-
-
-
-
-
- /*#ifdef PROMISE_TUNER_ENABLED
-
- // NOTE: Skip reading file-based error levels for ApproxHPVM wrapper runtime
- if(!approxhpvm_runtime_mode){
-
- if(op_counter >= total_ops){
- ERROR("No accuracy flag found \n");
- }
-
- swing = op_accuracies[op_counter];
- op_counter++;
- }
-
- #endif
-
- */
-
-
-/* void* gold;
- if (compute_norms){
-
- gold = tensorConvolution(input, filter,
- conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w,
- 1, 0);
- }
-
-
-
-
- if (compute_norms){
-
- Norm_t* norms = calculateNormsTreeReduction((struct Tensor*) conv_out,
- (struct Tensor*) gold);
- add_norms(norms, "tensorConv", swing);
- add_conv_overheads(input, filter, conv_stride_h, conv_stride_w, swing);
- }
-
-
-
- if (compute_norms){
- Norm_t* norms = calculateNormsTreeReduction((struct Tensor*) activation_out,
- (struct Tensor*) activation_out);
- add_norms(norms, "tensorMul", swing);
- add_gemm_overheads(input, weights, swing);
- }
-
-
-
-*/
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/approx_techniques.h b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/approx_techniques.h
deleted file mode 100644
index 70f89a4a97106cee389a6b220054400306dac9f1..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/approx_techniques.h
+++ /dev/null
@@ -1,2101 +0,0 @@
-
-
-
-#include "tensor_utils.cu"
-
-
-
-__global__ void depthwise_conv(float* const __restrict__ y,
- const float* const __restrict__ x,
- const float* const __restrict__ w,
- const int B, const int M,
- const int H, const int W, const int KH,
- const int KW, const int H_out, const int W_out,
- const int H_pad, const int W_pad,
- const int H_stride, const int W_stride, const int start_batch)
-{
-
- #define y4d(i3, i2, i1, i0) y[(i3) * (M * H_out * W_out) + (i2) * (H_out * W_out) + (i1) * (W_out) + i0]
- #define x4d(i3, i2, i1, i0) x[(i3) * (M * H * W) + (i2) * (H * W) + (i1) * (W) + i0]
-
- const int num = 1;
-
- const int b = num * blockIdx.x + start_batch;
- const int m = blockIdx.y; //current filter/channel
-
- const int tx = threadIdx.x;
-
- const int start_h = (threadIdx.x / W_out) * H_stride - H_pad;
- const int start_w = (threadIdx.x % W_out) * W_stride - W_pad;
-
- float C[num] = { 0 };
-
- const float* weights = &w[m * KH * KW];
-
- for (int k = 0; k < KH * KW; k++) {
- int p = k / KW;
- int q = k % KW;
-
- #pragma unroll
- for (int i = 0; i < num; i++) {
- if (start_h + p > -1 && start_h + p < H &&
- start_w + q > -1 && start_w + q < W) {
-
- C[i] += x4d(b + i, m, start_h + p, start_w + q) * weights[k];
- }
-
- }
- }
-
- #pragma unroll
- for (int i = 0; i < num; i++) {
- if(b + i < B)
- y4d(b + i, m, 0, tx) = C[i];
-
- }
-
-
- #undef y4d
- #undef x4d
-}
-
-
-__global__ void depthwise_convNew(float* const __restrict__ y,
- const float* const __restrict__ x,
- const float* const __restrict__ w,
- const int B, const int M,
- const int H, const int W, const int KH,
- const int KW, const int H_out, const int W_out,
- const int H_pad, const int W_pad,
- const int H_stride, const int W_stride)
-{
-
- #define y4d(i3, i2, i1, i0) y[(i3) * (M * H_out * W_out) + (i2) * (H_out * W_out) + (i1) * (W_out) + i0]
- #define x4d(i3, i2, i1, i0) x[(i3) * (M * H * W) + (i2) * (H * W) + (i1) * (W) + i0]
-
- const int num = 12;
-
- const int b = num * blockIdx.x;
- const int m = (blockIdx.y * blockDim.x + threadIdx.x)/ (H_out * W_out);
-
- const int tx = (blockIdx.y * blockDim.x + threadIdx.x) % (H_out * W_out);
-
- const int start_h = (tx / W_out) * H_stride - H_pad;
- const int start_w = (tx % W_out) * W_stride - W_pad;
-
- float C[num] = { 0 };
-
- const float* weights = &w[m * KH * KW];
-
- for (int k = 0; k < KH * KW; k++) {
- int p = k / KW;
- int q = k % KW;
-
- if (start_h + p > -1 && start_h + p < H &&
- start_w + q > -1 && start_w + q < W) {
-
- #pragma unroll
- for (int i = 0; i < num; i++) {
- if(b + i < B)
- C[i] += x4d(b + i, m, start_h + p, start_w + q) * weights[k];
- }
-
- }
- }
-
- #pragma unroll
- for (int i = 0; i < num; i++) {
- if(b + i < B)
- y4d(b + i, m, 0, tx) = C[i];
-
- }
-
-
- #undef y4d
- #undef x4d
-}
-
-__global__ void depthwise_convNew8(float* const __restrict__ y,
- const float* const __restrict__ x,
- const float* const __restrict__ w,
- const int B, const int M,
- const int H, const int W, const int KH,
- const int KW, const int H_out, const int W_out,
- const int H_pad, const int W_pad,
- const int H_stride, const int W_stride)
-{
-
- #define y4d(i3, i2, i1, i0) y[(i3) * (M * H_out * W_out) + (i2) * (H_out * W_out) + (i1) * (W_out) + i0]
- #define x4d(i3, i2, i1, i0) x[(i3) * (M * H * W) + (i2) * (H * W) + (i1) * (W) + i0]
-
- const int num = 8;
-
- const int b = num * blockIdx.x;
- const int m = (blockIdx.y * blockDim.x + threadIdx.x)/ (H_out * W_out);
-
- if(m < M){
- const int tx = (blockIdx.y * blockDim.x + threadIdx.x) % (H_out * W_out);
-
- const int start_h = (tx / W_out) * H_stride - H_pad;
- const int start_w = (tx % W_out) * W_stride - W_pad;
-
- float c0 = 0;
- float c1 = 0;
- float c2 = 0;
- float c3 = 0;
- float c4 = 0;
- float c5 = 0;
- float c6 = 0;
- float c7 = 0;
-
- const float* weights = &w[m * KH * KW];
-
- for (int k = 0; k < KH * KW; k++) {
- int p = k / KW;
- int q = k % KW;
-
- if (start_h + p > -1 && start_h + p < H &&
- start_w + q > -1 && start_w + q < W) {
-
- c0 += x4d(b, m, start_h + p, start_w + q) * weights[k];
- if(b + 1 < B)
- c1 += x4d(b + 1, m, start_h + p, start_w + q) * weights[k];
- if(b + 2 < B)
- c2 += x4d(b + 2, m, start_h + p, start_w + q) * weights[k];
- if(b + 3 < B)
- c3 += x4d(b + 3, m, start_h + p, start_w + q) * weights[k];
- if(b + 4 < B)
- c4 += x4d(b + 4, m, start_h + p, start_w + q) * weights[k];
- if(b + 5 < B)
- c5 += x4d(b + 5, m, start_h + p, start_w + q) * weights[k];
- if(b + 6 < B)
- c6 += x4d(b + 6, m, start_h + p, start_w + q) * weights[k];
- if(b + 7 < B)
- c7 += x4d(b + 7, m, start_h + p, start_w + q) * weights[k];
-
-
- }
- }
-
- y4d(b, m, 0, tx) = c0;
- if(b + 1 < B)
- y4d(b + 1, m, 0, tx) = c1;
- if(b + 2 < B)
- y4d(b + 2, m, 0, tx) = c2;
- if(b + 3 < B)
- y4d(b + 3, m, 0, tx) = c3;
- if(b + 4 < B)
- y4d(b + 4, m, 0, tx) = c4;
- if(b + 5 < B)
- y4d(b + 5, m, 0, tx) = c5;
- if(b + 6 < B)
- y4d(b + 6, m, 0, tx) = c6;
- if(b + 7 < B)
- y4d(b + 7, m, 0, tx) = c7;
- }
-
- #undef y4d
- #undef x4d
-}
-
-__global__ void depthwise_convNew8_half(__half* const __restrict__ y,
- const __half* const __restrict__ x,
- const __half* const __restrict__ w,
- const int B, const int M,
- const int H, const int W, const int KH,
- const int KW, const int H_out, const int W_out,
- const int H_pad, const int W_pad,
- const int H_stride, const int W_stride)
-{
-
- #define y4d(i3, i2, i1, i0) y[(i3) * (M * H_out * W_out) + (i2) * (H_out * W_out) + (i1) * (W_out) + i0]
- #define x4d(i3, i2, i1, i0) x[(i3) * (M * H * W) + (i2) * (H * W) + (i1) * (W) + i0]
-
- const int num = 8;
-
- const int b = num * blockIdx.x;
- const int m = (blockIdx.y * blockDim.x + threadIdx.x)/ (H_out * W_out);
-
- if(m < M){
- const int tx = (blockIdx.y * blockDim.x + threadIdx.x) % (H_out * W_out);
-
- const int start_h = (tx / W_out) * H_stride - H_pad;
- const int start_w = (tx % W_out) * W_stride - W_pad;
-
- __half c0 = 0;
- __half c1 = 0;
- __half c2 = 0;
- __half c3 = 0;
- __half c4 = 0;
- __half c5 = 0;
- __half c6 = 0;
- __half c7 = 0;
-
- const __half* weights = &w[m * KH * KW];
-
- for (int k = 0; k < KH * KW; k++) {
- int p = k / KW;
- int q = k % KW;
-
- if (start_h + p > -1 && start_h + p < H &&
- start_w + q > -1 && start_w + q < W) {
-
- c0 = __hfma(x4d(b, m, start_h + p, start_w + q), weights[k], c0);
- if(b + 1 < B)
- c1 = __hfma(x4d(b + 1, m, start_h + p, start_w + q), weights[k], c1);
- if(b + 2 < B)
- c2 = __hfma(x4d(b + 2, m, start_h + p, start_w + q), weights[k], c2);
- if(b + 3 < B)
- c3 = __hfma(x4d(b + 3, m, start_h + p, start_w + q), weights[k], c3);
- if(b + 4 < B)
- c4 = __hfma(x4d(b + 4, m, start_h + p, start_w + q), weights[k], c4);
- if(b + 5 < B)
- c5 = __hfma(x4d(b + 5, m, start_h + p, start_w + q), weights[k], c5);
- if(b + 6 < B)
- c6 = __hfma(x4d(b + 6, m, start_h + p, start_w + q), weights[k], c6);
- if(b + 7 < B)
- c7 = __hfma(x4d(b + 7, m, start_h + p, start_w + q), weights[k], c7);
-
-
- }
- }
-
- y4d(b, m, 0, tx) = c0;
- if(b + 1 < B)
- y4d(b + 1, m, 0, tx) = c1;
- if(b + 2 < B)
- y4d(b + 2, m, 0, tx) = c2;
- if(b + 3 < B)
- y4d(b + 3, m, 0, tx) = c3;
- if(b + 4 < B)
- y4d(b + 4, m, 0, tx) = c4;
- if(b + 5 < B)
- y4d(b + 5, m, 0, tx) = c5;
- if(b + 6 < B)
- y4d(b + 6, m, 0, tx) = c6;
- if(b + 7 < B)
- y4d(b + 7, m, 0, tx) = c7;
- }
-
- #undef y4d
- #undef x4d
-}
-
-__global__ void depthwise_convNew8_half1(__half* const __restrict__ y,
- const __half* const __restrict__ x,
- const __half* const __restrict__ w,
- const int B, const int M,
- const int H, const int W, const int KH,
- const int KW, const int H_out, const int W_out,
- const int H_pad, const int W_pad,
- const int H_stride, const int W_stride)
-{
-
- #define y4d(i3, i2, i1, i0) y[(i3) * (M * H_out * W_out) + (i2) * (H_out * W_out) + (i1) * (W_out) + i0]
- #define x4d(i3, i2, i1, i0) x[(i3) * (M * H * W) + (i2) * (H * W) + (i1) * (W) + i0]
-
- const int num = 8;
-
- const int b = num * blockIdx.x;
- const int m = (blockIdx.y * blockDim.x + threadIdx.x)/ (H_out * W_out);
-
- if(m < M){
- const int tx = (blockIdx.y * blockDim.x + threadIdx.x) % (H_out * W_out);
-
- const int start_h = (tx / W_out) * H_stride - H_pad;
- const int start_w = (tx % W_out) * W_stride - W_pad;
-
- __half c0 = 0;
- __half c1 = 0;
- __half c2 = 0;
- __half c3 = 0;
- __half c4 = 0;
- __half c5 = 0;
- __half c6 = 0;
- __half c7 = 0;
-
- const __half* weights = &w[m * KH * KW];
-
- for (int k = 0; k < KH * KW; k++) {
- int p = k / KW;
- int q = k % KW;
-
- if (start_h + p > -1 && start_h + p < H &&
- start_w + q > -1 && start_w + q < W) {
-
- c0 = __hfma(x4d(b, m, start_h + p, start_w + q), weights[k], c0);
- }
- }
-
- if(b + 1 < B){
- for (int k = 0; k < KH * KW; k++) {
- int p = k / KW;
- int q = k % KW;
-
- if (start_h + p > -1 && start_h + p < H &&
- start_w + q > -1 && start_w + q < W) {
-
- c1 = __hfma(x4d(b + 1, m, start_h + p, start_w + q), weights[k], c1);
- }
- }
- }
-
- if(b + 2 < B){
- for (int k = 0; k < KH * KW; k++) {
- int p = k / KW;
- int q = k % KW;
-
- if (start_h + p > -1 && start_h + p < H &&
- start_w + q > -1 && start_w + q < W) {
-
- c2 = __hfma(x4d(b + 2, m, start_h + p, start_w + q), weights[k], c2);
- }
- }
- }
-
- if(b + 3 < B){
- for (int k = 0; k < KH * KW; k++) {
- int p = k / KW;
- int q = k % KW;
-
- if (start_h + p > -1 && start_h + p < H &&
- start_w + q > -1 && start_w + q < W) {
-
- c3 = __hfma(x4d(b + 3, m, start_h + p, start_w + q), weights[k], c3);
- }
- }
- }
-
- if(b + 4 < B){
- for (int k = 0; k < KH * KW; k++) {
- int p = k / KW;
- int q = k % KW;
-
- if (start_h + p > -1 && start_h + p < H &&
- start_w + q > -1 && start_w + q < W) {
-
- c4 = __hfma(x4d(b + 4, m, start_h + p, start_w + q), weights[k], c4);
- }
- }
- }
-
- if(b + 5 < B){
- for (int k = 0; k < KH * KW; k++) {
- int p = k / KW;
- int q = k % KW;
-
- if (start_h + p > -1 && start_h + p < H &&
- start_w + q > -1 && start_w + q < W) {
-
- c5 = __hfma(x4d(b + 5, m, start_h + p, start_w + q), weights[k], c5);
- }
- }
- }
-
- if(b + 6 < B){
- for (int k = 0; k < KH * KW; k++) {
- int p = k / KW;
- int q = k % KW;
-
- if (start_h + p > -1 && start_h + p < H &&
- start_w + q > -1 && start_w + q < W) {
-
- c6 = __hfma(x4d(b + 6, m, start_h + p, start_w + q), weights[k], c6);
- }
- }
- }
-
- if(b + 7 < B){
- for (int k = 0; k < KH * KW; k++) {
- int p = k / KW;
- int q = k % KW;
-
- if (start_h + p > -1 && start_h + p < H &&
- start_w + q > -1 && start_w + q < W) {
-
- c7 = __hfma(x4d(b + 7, m, start_h + p, start_w + q), weights[k], c7);
- }
- }
- }
-
-
-
- y4d(b, m, 0, tx) = c0;
- if(b + 1 < B)
- y4d(b + 1, m, 0, tx) = c1;
- if(b + 2 < B)
- y4d(b + 2, m, 0, tx) = c2;
- if(b + 3 < B)
- y4d(b + 3, m, 0, tx) = c3;
- if(b + 4 < B)
- y4d(b + 4, m, 0, tx) = c4;
- if(b + 5 < B)
- y4d(b + 5, m, 0, tx) = c5;
- if(b + 6 < B)
- y4d(b + 6, m, 0, tx) = c6;
- if(b + 7 < B)
- y4d(b + 7, m, 0, tx) = c7;
- }
-
- #undef y4d
- #undef x4d
-}
-
-
-__global__ void depthwise_convNew12(float* const __restrict__ y,
- const float* const __restrict__ x,
- const float* const __restrict__ w,
- const int B, const int M,
- const int H, const int W, const int KH,
- const int KW, const int H_out, const int W_out,
- const int H_pad, const int W_pad,
- const int H_stride, const int W_stride)
-{
-
- #define y4d(i3, i2, i1, i0) y[(i3) * (M * H_out * W_out) + (i2) * (H_out * W_out) + (i1) * (W_out) + i0]
- #define x4d(i3, i2, i1, i0) x[(i3) * (M * H * W) + (i2) * (H * W) + (i1) * (W) + i0]
-
- const int num = 12;
-
- const int b = num * blockIdx.x;
- const int m = (blockIdx.y * blockDim.x + threadIdx.x)/ (H_out * W_out);
-
- if(m < M){
- const int tx = (blockIdx.y * blockDim.x + threadIdx.x) % (H_out * W_out);
-
- const int start_h = (tx / W_out) * H_stride - H_pad;
- const int start_w = (tx % W_out) * W_stride - W_pad;
-
- float c0 = 0;
- float c1 = 0;
- float c2 = 0;
- float c3 = 0;
- float c4 = 0;
- float c5 = 0;
- float c6 = 0;
- float c7 = 0;
- float c8 = 0;
- float c9 = 0;
- float c10 = 0;
- float c11 = 0;
-
- const float* weights = &w[m * KH * KW];
-
- for (int k = 0; k < KH * KW; k++) {
- int p = k / KW;
- int q = k % KW;
-
- if (start_h + p > -1 && start_h + p < H &&
- start_w + q > -1 && start_w + q < W) {
-
- c0 += x4d(b, m, start_h + p, start_w + q) * weights[k];
- if(b + 1 < B)
- c1 += x4d(b + 1, m, start_h + p, start_w + q) * weights[k];
- if(b + 2 < B)
- c2 += x4d(b + 2, m, start_h + p, start_w + q) * weights[k];
- if(b + 3 < B)
- c3 += x4d(b + 3, m, start_h + p, start_w + q) * weights[k];
- if(b + 4 < B)
- c4 += x4d(b + 4, m, start_h + p, start_w + q) * weights[k];
- if(b + 5 < B)
- c5 += x4d(b + 5, m, start_h + p, start_w + q) * weights[k];
- if(b + 6 < B)
- c6 += x4d(b + 6, m, start_h + p, start_w + q) * weights[k];
- if(b + 7 < B)
- c7 += x4d(b + 7, m, start_h + p, start_w + q) * weights[k];
- if(b + 8 < B)
- c8 += x4d(b + 8, m, start_h + p, start_w + q) * weights[k];
- if(b + 9 < B)
- c9 += x4d(b + 9, m, start_h + p, start_w + q) * weights[k];
- if(b + 10 < B)
- c10 += x4d(b + 10, m, start_h + p, start_w + q) * weights[k];
- if(b + 11 < B)
- c11 += x4d(b + 11, m, start_h + p, start_w + q) * weights[k];
-
-
- }
- }
-
- y4d(b, m, 0, tx) = c0;
- if(b + 1 < B)
- y4d(b + 1, m, 0, tx) = c1;
- if(b + 2 < B)
- y4d(b + 2, m, 0, tx) = c2;
- if(b + 3 < B)
- y4d(b + 3, m, 0, tx) = c3;
- if(b + 4 < B)
- y4d(b + 4, m, 0, tx) = c4;
- if(b + 5 < B)
- y4d(b + 5, m, 0, tx) = c5;
- if(b + 6 < B)
- y4d(b + 6, m, 0, tx) = c6;
- if(b + 7 < B)
- y4d(b + 7, m, 0, tx) = c7;
- if(b + 8 < B)
- y4d(b + 8, m, 0, tx) = c8;
- if(b + 9 < B)
- y4d(b + 9, m, 0, tx) = c9;
- if(b + 10 < B)
- y4d(b + 10, m, 0, tx) = c10;
- if(b + 11 < B)
- y4d(b + 11, m, 0, tx) = c11;
-
- }
-
- #undef y4d
- #undef x4d
-}
-
-
-__global__ void depthwise_convNew12_half(__half* const __restrict__ y,
- const __half* const __restrict__ x,
- const __half* const __restrict__ w,
- const int B, const int M,
- const int H, const int W, const int KH,
- const int KW, const int H_out, const int W_out,
- const int H_pad, const int W_pad,
- const int H_stride, const int W_stride)
-{
-
- #define y4d(i3, i2, i1, i0) y[(i3) * (M * H_out * W_out) + (i2) * (H_out * W_out) + (i1) * (W_out) + i0]
- #define x4d(i3, i2, i1, i0) x[(i3) * (M * H * W) + (i2) * (H * W) + (i1) * (W) + i0]
-
- const int num = 12;
-
- const int b = num * blockIdx.x;
- const int m = (blockIdx.y * blockDim.x + threadIdx.x)/ (H_out * W_out);
-
- if(m < M){
- const int tx = (blockIdx.y * blockDim.x + threadIdx.x) % (H_out * W_out);
-
- const int start_h = (tx / W_out) * H_stride - H_pad;
- const int start_w = (tx % W_out) * W_stride - W_pad;
-
- __half c0 = 0;
- __half c1 = 0;
- __half c2 = 0;
- __half c3 = 0;
- __half c4 = 0;
- __half c5 = 0;
- __half c6 = 0;
- __half c7 = 0;
- __half c8 = 0;
- __half c9 = 0;
- __half c10 = 0;
- __half c11 = 0;
-
- const __half* weights = &w[m * KH * KW];
-
- for (int k = 0; k < KH * KW; k++) {
- int p = k / KW;
- int q = k % KW;
-
- if (start_h + p > -1 && start_h + p < H &&
- start_w + q > -1 && start_w + q < W) {
-
- c0 = __hfma(x4d(b, m, start_h + p, start_w + q), weights[k], c0);
- if(b + 1 < B)
- c1 = __hfma(x4d(b + 1, m, start_h + p, start_w + q), weights[k], c1);
- if(b + 2 < B)
- c2 = __hfma(x4d(b + 2, m, start_h + p, start_w + q), weights[k], c2);
- if(b + 3 < B)
- c3 = __hfma(x4d(b + 3, m, start_h + p, start_w + q), weights[k], c3);
- if(b + 4 < B)
- c4 = __hfma(x4d(b + 4, m, start_h + p, start_w + q), weights[k], c4);
- if(b + 5 < B)
- c5 = __hfma(x4d(b + 5, m, start_h + p, start_w + q), weights[k], c5);
- if(b + 6 < B)
- c6 = __hfma(x4d(b + 6, m, start_h + p, start_w + q), weights[k], c6);
- if(b + 7 < B)
- c7 = __hfma(x4d(b + 7, m, start_h + p, start_w + q), weights[k], c7);
- if(b + 8 < B)
- c8 = __hfma(x4d(b + 8, m, start_h + p, start_w + q), weights[k], c8);
- if(b + 9 < B)
- c9 = __hfma(x4d(b + 9, m, start_h + p, start_w + q), weights[k], c9);
- if(b + 10 < B)
- c10 = __hfma(x4d(b + 10, m, start_h + p, start_w + q), weights[k], c10);
- if(b + 11 < B)
- c11 = __hfma(x4d(b + 11, m, start_h + p, start_w + q), weights[k], c11);
-
-
- }
- }
-
- y4d(b, m, 0, tx) = c0;
- if(b + 1 < B)
- y4d(b + 1, m, 0, tx) = c1;
- if(b + 2 < B)
- y4d(b + 2, m, 0, tx) = c2;
- if(b + 3 < B)
- y4d(b + 3, m, 0, tx) = c3;
- if(b + 4 < B)
- y4d(b + 4, m, 0, tx) = c4;
- if(b + 5 < B)
- y4d(b + 5, m, 0, tx) = c5;
- if(b + 6 < B)
- y4d(b + 6, m, 0, tx) = c6;
- if(b + 7 < B)
- y4d(b + 7, m, 0, tx) = c7;
- if(b + 8 < B)
- y4d(b + 8, m, 0, tx) = c8;
- if(b + 9 < B)
- y4d(b + 9, m, 0, tx) = c9;
- if(b + 10 < B)
- y4d(b + 10, m, 0, tx) = c10;
- if(b + 11 < B)
- y4d(b + 11, m, 0, tx) = c11;
-
- }
-
- #undef y4d
- #undef x4d
-}
-
-
-__global__ void depthwise_convNew8_half2(__half* const __restrict__ y,
- const __half* const __restrict__ x,
- const __half* const __restrict__ w,
- const int B, const int M,
- const int H, const int W, const int KH,
- const int KW, const int H_out, const int W_out,
- const int H_pad, const int W_pad,
- const int H_stride, const int W_stride)
-{
-
- #define y4d(i3, i2, i1, i0) y[(i3) * (M * H_out * W_out) + (i2) * (H_out * W_out) + (i1) * (W_out) + i0]
- #define x4d(i3, i2, i1, i0) x[(i3) * (M * H * W) + (i2) * (H * W) + (i1) * (W) + i0]
-
- const int num = 8;
-
- const int b = num * blockIdx.x;
- const int m = (blockIdx.y * blockDim.x + threadIdx.x)/ (H_out * W_out);
-
- if(m < M){
- const int tx = (blockIdx.y * blockDim.x + threadIdx.x) % (H_out * W_out);
-
- const int start_h = (tx / W_out) * H_stride - H_pad;
- const int start_w = (tx % W_out) * W_stride - W_pad;
-
- __half2 c0 = __half2half2(0);
- __half2 c1 = __half2half2(0);
- __half2 c2 = __half2half2(0);
- __half2 c3 = __half2half2(0);
-
- const __half* weights = &w[m * KH * KW];
-
- for (int k = 0; k < KH * KW; k++) {
- int p = k / KW;
- int q = k % KW;
- if (start_h + p > -1 && start_h + p < H &&
- start_w + q > -1 && start_w + q < W) {
-
-
- __half2 t1;
- __half2 t2;
- __half2 t3;
- __half2 t4;
- if(b + 7 < B){
- t1 = __halves2half2(x4d(b + 1, m, start_h + p, start_w + q), x4d(b, m, start_h + p, start_w + q));
- t2 = __halves2half2(x4d(b + 3, m, start_h + p, start_w + q), x4d(b + 2, m, start_h + p, start_w + q));
- t3 = __halves2half2(x4d(b + 5, m, start_h + p, start_w + q), x4d(b + 4, m, start_h + p, start_w + q));
- t4 = __halves2half2(x4d(b + 7, m, start_h + p, start_w + q), x4d(b + 6, m, start_h + p, start_w + q));
- }
- else if(b + 6 < B){
- t1 = __halves2half2(x4d(b + 1, m, start_h + p, start_w + q), x4d(b, m, start_h + p, start_w + q));
- t2 = __halves2half2(x4d(b + 3, m, start_h + p, start_w + q), x4d(b + 2, m, start_h + p, start_w + q));
- t3 = __halves2half2(x4d(b + 5, m, start_h + p, start_w + q), x4d(b + 4, m, start_h + p, start_w + q));
- t4 = __halves2half2(0, x4d(b + 6, m, start_h + p, start_w + q));
-
- }
- else if(b + 5 < B){
- t1 = __halves2half2(x4d(b + 1, m, start_h + p, start_w + q), x4d(b, m, start_h + p, start_w + q));
- t2 = __halves2half2(x4d(b + 3, m, start_h + p, start_w + q), x4d(b + 2, m, start_h + p, start_w + q));
- t3 = __halves2half2(x4d(b + 5, m, start_h + p, start_w + q), x4d(b + 4, m, start_h + p, start_w + q));
- }
- else if(b + 4 < B){
- t1 = __halves2half2(x4d(b + 1, m, start_h + p, start_w + q), x4d(b, m, start_h + p, start_w + q));
- t2 = __halves2half2(x4d(b + 3, m, start_h + p, start_w + q), x4d(b + 2, m, start_h + p, start_w + q));
- t3 = __halves2half2(0, x4d(b + 4, m, start_h + p, start_w + q));
-
- }
- else if(b + 3 < B){
- t1 = __halves2half2(x4d(b + 1, m, start_h + p, start_w + q), x4d(b, m, start_h + p, start_w + q));
- t2 = __halves2half2(x4d(b + 3, m, start_h + p, start_w + q), x4d(b + 2, m, start_h + p, start_w + q));
- }
- else if(b + 2 < B){
- t1 = __halves2half2(x4d(b + 1, m, start_h + p, start_w + q), x4d(b, m, start_h + p, start_w + q));
- t2 = __halves2half2(0, x4d(b + 2, m, start_h + p, start_w + q));
-
- }
- else if(b + 1 < B){
- t1 = __halves2half2(x4d(b + 1, m, start_h + p, start_w + q), x4d(b, m, start_h + p, start_w + q));
- }
- else{
- t1 = __halves2half2(0, x4d(b, m, start_h + p, start_w + q));
-
- }
-
-
- c0 = __hfma2(t1, __halves2half2(weights[k], weights[k]), c0);
- c1 = __hfma2(t2, __halves2half2(weights[k], weights[k]), c1);
- c2 = __hfma2(t3, __halves2half2(weights[k], weights[k]), c2);
- c3 = __hfma2(t4, __halves2half2(weights[k], weights[k]), c3);
-
- }
- }
-
- y4d(b, m, 0, tx) = __high2half(c0);
- if(b + 1 < B)
- y4d(b + 1, m, 0, tx) = __low2half(c0);
- if(b + 2 < B)
- y4d(b + 2, m, 0, tx) = __high2half(c1);
- if(b + 3 < B)
- y4d(b + 3, m, 0, tx) = __low2half(c1);
- if(b + 4 < B)
- y4d(b + 4, m, 0, tx) = __high2half(c2);
- if(b + 5 < B)
- y4d(b + 5, m, 0, tx) = __low2half(c2);
- if(b + 6 < B)
- y4d(b + 6, m, 0, tx) = __high2half(c3);
- if(b + 7 < B)
- y4d(b + 7, m, 0, tx) = __low2half(c3);
- }
-
- #undef y4d
- #undef x4d
-}
-
-
-//When stride is 1
-__global__ void depthwise_conv4_half3(__half* const __restrict__ y,
- const __half* const __restrict__ x,
- const __half* const __restrict__ w,
- const int B, const int M,
- const int H, const int W, const int KH,
- const int KW, const int H_out, const int W_out,
- const int H_pad, const int W_pad,
- const int C_dim, const int H_dim, const int W_dim)
-{
-
-#define y4d(i3, i2, i1, i0) y[(i3) * (M * H_out * W_out) + (i2) * (H_out * W_out) + (i1) * (W_out) + i0]
-#define x4d(i3, i2, i1, i0) x[(i3) * (M * H * W) + (i2) * (H * W) + (i1) * (W) + i0]
-
- const int num = 1;
-
- const int b = num * blockIdx.x;
- const int m = (blockIdx.y * blockDim.x + threadIdx.x) / (H_out * W_out);
-
- if (m < M) {
- const int tx = (blockIdx.y * blockDim.x + threadIdx.x) % (H_out * W_out);
-
- const int start_h = (tx / W_out) - H_pad;
- const int start_w = (tx % W_out) - W_pad;
-
- const int bstart_h = (blockIdx.y * blockDim.x % (H_out * W_out)) / W_out - H_pad;
- const int bstart_w = (blockIdx.y * blockDim.x % (H_out * W_out)) % W_out - H_pad;
- const int bstartm = (blockIdx.y * blockDim.x / (H_out * W_out));
-
- extern __shared__ __half xdata[];
-
- for (int i = 0; i < C_dim * H_dim * W_dim; i += blockDim.x) {
- if (i / (H_dim * W_dim) + bstartm < M && (i % (H_dim * W_dim)) / W_dim + bstart_h > -1 &&
- (i % (H_dim * W_dim)) / W_dim + bstart_h < H && (i % (H_dim * W_dim)) % W_dim + bstart_w > -1 &&
- (i % (H_dim * W_dim)) % W_dim + bstart_w < W) {
- xdata[i] = x4d(b, i / (H_dim * W_dim) + bstartm, (i % (H_dim * W_dim)) / W_dim + bstart_h,
- (i % (H_dim * W_dim)) % W_dim + bstart_w);
- }
- }
- __syncthreads();
-
- __half c0;
- const __half* weights = &w[m * KH * KW];
-
- for (int k = 0; k < KH * KW; k++) {
- int p = k / KW;
- int q = k % KW;
- if (start_h + p > -1 && start_h + p < H &&
- start_w + q > -1 && start_w + q < W) {
-
-
- __half t1;
-
- //int total = C_dim * H_dim * W_dim;
- t1 = xdata[(m - bstartm) * H_dim * W_dim + (start_h + p - bstart_h) * W_dim +
- start_w + q - bstart_w];
-
-
- c0 = __hfma(t1, weights[k], c0);
- }
- }
-
- y4d(b, m, 0, tx) = c0;
-
-
- }
-
-#undef y4d
-#undef x4d
-}
-
-
-__global__ void depthwise_convNew4_half2(__half* const __restrict__ y,
- const __half* const __restrict__ x,
- const __half* const __restrict__ w,
- const int B, const int M,
- const int H, const int W, const int KH,
- const int KW, const int H_out, const int W_out,
- const int H_pad, const int W_pad,
- const int H_stride, const int W_stride)
-{
-
- #define y4d(i3, i2, i1, i0) y[(i3) * (M * H_out * W_out) + (i2) * (H_out * W_out) + (i1) * (W_out) + i0]
- #define x4d(i3, i2, i1, i0) x[(i3) * (M * H * W) + (i2) * (H * W) + (i1) * (W) + i0]
-
- const int num = 4;
-
- const int b = num * blockIdx.x;
- const int m = (blockIdx.y * blockDim.x + threadIdx.x)/ (H_out * W_out);
-
- if(m < M){
- const int tx = (blockIdx.y * blockDim.x + threadIdx.x) % (H_out * W_out);
-
- const int start_h = (tx / W_out) * H_stride - H_pad;
- const int start_w = (tx % W_out) * W_stride - W_pad;
-
- __half2 c0 = __half2half2(0);
- __half2 c1 = __half2half2(0);
-
- const __half* weights = &w[m * KH * KW];
-
- for (int k = 0; k < KH * KW; k++) {
- int p = k / KW;
- int q = k % KW;
- if (start_h + p > -1 && start_h + p < H &&
- start_w + q > -1 && start_w + q < W) {
-
-
- __half2 t1;
- __half2 t2;
- if(b + 3 < B){
- t1 = __halves2half2(x4d(b + 1, m, start_h + p, start_w + q), x4d(b, m, start_h + p, start_w + q));
- t2 = __halves2half2(x4d(b + 3, m, start_h + p, start_w + q), x4d(b + 2, m, start_h + p, start_w + q));
- }
- else if(b + 2 < B){
- t1 = __halves2half2(x4d(b + 1, m, start_h + p, start_w + q), x4d(b, m, start_h + p, start_w + q));
- t2 = __halves2half2(0, x4d(b + 2, m, start_h + p, start_w + q));
-
- }
- else if(b + 1 < B){
- t1 = __halves2half2(x4d(b + 1, m, start_h + p, start_w + q), x4d(b, m, start_h + p, start_w + q));
- }
- else{
- t1 = __halves2half2(0, x4d(b, m, start_h + p, start_w + q));
-
- }
-
-
- c0 = __hfma2(t1, __halves2half2(weights[k], weights[k]), c0);
- c1 = __hfma2(t2, __halves2half2(weights[k], weights[k]), c1);
-
- }
- }
-
- y4d(b, m, 0, tx) = __high2half(c0);
- if(b + 1 < B)
- y4d(b + 1, m, 0, tx) = __low2half(c0);
- if(b + 2 < B)
- y4d(b + 2, m, 0, tx) = __high2half(c1);
- if(b + 3 < B)
- y4d(b + 3, m, 0, tx) = __low2half(c1);
-
- }
-
- #undef y4d
- #undef x4d
-}
-
-
-void* tensorConvCutlass(void* input_ptr, void* filter_ptr,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int conv_groups){
-
-
- INFO("*** TensorConvolution \n");
- profileEvent("Conv");
-
- Tensor* input = (Tensor*)input_ptr;
- Tensor* filter = (Tensor*)filter_ptr;
-
- //FIXME: Current hack to preserve backward compatibilty
- if (conv_groups == 0) {
- conv_groups = 1;
- }
-
- Tensor* output;
-
- hostToDeviceCopy(input);
- hostToDeviceCopy(filter);
-
- convertToFP32(input);
- convertToFP32(filter);
-
-
- if (conv_groups > 32) {
- // TODO: Support other cases;
- hostToDeviceCopy(input);
- hostToDeviceCopy(filter);
-
- int n, c, h, w; // output dimensions
- n = input->dims.dim_sizes[0];
- c = input->dims.dim_sizes[1];
- const int KH = filter->dims.dim_sizes[2];
- const int KW = filter->dims.dim_sizes[3];
- h = (2 * vertical_pad + input->dims.dim_sizes[2] - KH) / vertical_stride + 1;
- w = (2 * horizontal_pad + input->dims.dim_sizes[3] - KW) / horizontal_stride + 1;
-
- output = (Tensor*)create4DTensor((cudnnDataType_t) float_type, //input->data_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
-
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(output, DEVICE);
- // NOTE: Necessary to insert the above call for every output tensor
-
-
-
- int blockSize;
- blockSize = 64;
-
- dim3 grid(((n + 7)/ 8), (c * h * w + blockSize - 1)/ blockSize);
- dim3 block(blockSize);
- depthwise_convNew8<<<grid, block>>> ((float*)output->gpu_data,
- (float*)input->gpu_data, (float*)filter->gpu_data,
- input->dims.dim_sizes[0], input->dims.dim_sizes[1],
- input->dims.dim_sizes[2], input->dims.dim_sizes[3],
- KH, KW, h, w, vertical_pad, horizontal_pad,
- vertical_stride, horizontal_stride);
-
- }
- else {
-
- cudnnConvolutionDescriptor_t convDesc;
- cudnnConvolutionFwdAlgo_t convAlgo;
- cudnnConvolutionMode_t mode;
- if (conv_mode == 0)
- mode = CUDNN_CONVOLUTION;
- else if (conv_mode == 1)
- mode = CUDNN_CROSS_CORRELATION;
-
- // FIXIT: Need to be more aware of the implications of alpha and beta
- float alpha = 1.0f, beta = 0.0f;
-
- // TODO: Support other cases;
- hostToDeviceCopy(input);
- hostToDeviceCopy(filter);
-
- INFO("vertical_stride = %lu, horizontal_stride = %lu \n", vertical_stride, horizontal_stride);
-
- checkCUDNN(cudnnCreateConvolutionDescriptor(&convDesc));
-
- // NOTE: Adding support for grouped convolution
- checkCUDNN(cudnnSetConvolutionGroupCount(convDesc, conv_groups));
-
-
- cudnnDataType_t computeType = CUDNN_DATA_FLOAT;
- // FIXIT: Think if upscaling values need to be configurable?
- // IMP-FIXIT: Either make mode configurable OR see if CUDNN_CONVOLUTION MODE should be used?
- checkCUDNN(cudnnSetConvolution2dDescriptor(convDesc,
- vertical_pad, horizontal_pad, // conv padding
- vertical_stride, horizontal_stride, // conv strides
- 1, 1, // upscaling values
- mode, // mode is configurable
- computeType)); // defines compute precision
-
- int n, c, h, w; // output dimensions
- // Find dimension of convolution output
- checkCUDNN(cudnnGetConvolution2dForwardOutputDim(convDesc,
- input->tensor_desc,
- filter->filter_desc,
- &n, &c, &h, &w));
-
-
- DEBUG("**Output Tensor Dims, n = %d, c = %d, h = %d, w = %d \n", n, c, h, w);
-
- if (input->data_format == CUDNN_TENSOR_NCHW)
- output = (Tensor*)create4DTensor((cudnnDataType_t) float_type, // input->data_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
- else if (input->data_format == CUDNN_TENSOR_NHWC) {
- DEBUG("* NHWC Format \n");
- output = (Tensor*)create4DTensor((cudnnDataType_t) float_type, //input->data_type,
- CUDNN_TENSOR_NHWC, n, h, w, c);
- }
- else
- ERROR("Unsupported Tensor Type");
-
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(output, DEVICE);
- // NOTE: Necessary to insert the above call for every output tensor
-
- DEBUG("tensor->data_type = %d, tensor->data_format = %d, N = %d, C = %d, H = %d, W = %d \n",
- output->data_type, output->data_format, output->dims.dim_sizes[0], output->dims.dim_sizes[1],
- output->dims.dim_sizes[2], output->dims.dim_sizes[3]);
-
- if (convDesc == NULL || input->tensor_desc == NULL ||
- filter->filter_desc == NULL || output->tensor_desc == NULL)
- ERROR("NULL descriptor! \n");
-
-
- // NOTE-FIXIT: function failing for NHWC formats - perhaps some CUDNN support is lacking
- checkCUDNN(cudnnGetConvolutionForwardAlgorithm(cudnnHandle,
- input->tensor_desc,
- filter->filter_desc,
- convDesc,
- output->tensor_desc,
- CUDNN_CONVOLUTION_FWD_PREFER_FASTEST,
- //CUDNN_CONVOLUTION_FWD_NO_WORKSPACE,
- 0,
- &convAlgo));
-
-
- DEBUG("ConvAlgo = %d, FFT = %d, GEMM = %d, WINOGRAD = %d \n", convAlgo,
- CUDNN_CONVOLUTION_FWD_ALGO_FFT, CUDNN_CONVOLUTION_FWD_ALGO_GEMM,
- CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD);
-
-
- // FIXIT: Algo shouldn't be hardcoded
- convAlgo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM;
-
- size_t workspace_size;
- checkCUDNN(cudnnGetConvolutionForwardWorkspaceSize(cudnnHandle,
- input->tensor_desc,
- filter->filter_desc,
- convDesc,
- output->tensor_desc,
- convAlgo,
- &workspace_size));
-
- // Allocating memory for the convolution workspace
- void* workspace;
- checkCudaErrors(cudaMalloc(&workspace, workspace_size));
- DEBUG("workspace size = %d \n", workspace_size);
-
-
- checkCUDNN(cudnnConvolutionForward(cudnnHandle, &alpha, input->tensor_desc,
- input->gpu_data, filter->filter_desc, filter->gpu_data,
- convDesc, convAlgo, workspace, workspace_size,
- &beta, output->tensor_desc, output->gpu_data));
- }
-
- cudaDeviceSynchronize();
- profileEvent("Conv_end", true);
-
-
- #ifdef ERROR_INJECTION_ENABLED
-
- if (op_counter >= total_ops) {
- ERROR("No accuracy flag found \n");
- }
-
- int op_acc = op_accuracies[op_counter];
-
- // Skip errorInjection if explicitly requested
- if (skip_tensors.find(op_counter) != skip_tensors.end()) {
- op_acc = 0;
- }
-
- void* error_norms = tensorAddError(output, op_acc);
- add_norms(error_norms, "tensorConv", op_acc);
- add_conv_overheads(input, filter, vertical_stride, horizontal_stride, op_acc);
-
- op_counter++;
-
- #endif
-
- return output;
-
-
-}
-
-// FIXME: Need to properly fix the new HALF type conversion
-void* tensorHalfConvCutlass(void* input_ptr, void* filter_ptr,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int conv_groups){
-
- INFO("*** TensorHConvolution \n");
- profileEvent("#Conv");
-
- Tensor* input = (Tensor*) input_ptr;
- Tensor* filter = (Tensor*) filter_ptr;
-
- cudnnConvolutionDescriptor_t convDesc;
- cudnnConvolutionFwdAlgo_t convAlgo;
- cudnnConvolutionMode_t mode;
-
- if(conv_mode == 0)
- mode = CUDNN_CONVOLUTION;
- else if(conv_mode == 1)
- mode = CUDNN_CROSS_CORRELATION;
-
- // FIXIT: Need to be more aware of the implications of alpha and beta
- float alpha = 1.0f, beta = 0.0f;
- // NOTE: compute in half precision
- cudnnDataType_t computeType = CUDNN_DATA_HALF;
-
- // NOTE: Moving inputs to GPU global memory
- hostToDeviceCopy(input);
- hostToDeviceCopy(filter);
-
-
- /***** CONVERSIONS from FP32 to FP16 - on the GPU */
- size_t* input_dims = input->dims.dim_sizes;
- size_t* filter_dims = filter->dims.dim_sizes;
-
-
- profileEvent("F2H_start");
-
- convertToFP16(input);
- convertToFP16(filter);
-
- profileEvent("F2H_end");
- /******* END OF INPUT DATA CONVERSIONS*/
-
-
- Tensor *output;
- if(conv_groups > 1){
- int n = input->dims.dim_sizes[0];
- int c = input->dims.dim_sizes[1];
- const int KH = filter->dims.dim_sizes[2];
- const int KW = filter->dims.dim_sizes[3];
- int h = (2 * vertical_pad + input->dims.dim_sizes[2] - KH) / vertical_stride + 1;
- int w = (2 * horizontal_pad + input->dims.dim_sizes[3] - KW) / horizontal_stride + 1;
-
- DEBUG("**Output Tensor Dims, n = %d, c = %d, h = %d, w = %d \n", n, c, h, w);
-
-
- output = (Tensor*) create4DTensor((cudnnDataType_t) half_type, //input->data_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
-
-
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(output, DEVICE);
- // NOTE: Necessary to insert the above call for every output tensor
-
- int blockSize;
- blockSize = 128;
-
- dim3 grid(((n + 7)/ 8), (c * h * w + blockSize - 1)/ blockSize);
- dim3 block(blockSize);
- depthwise_convNew8_half2<<<grid, block>>> ((__half*) output->gpu_half_data,
- (__half*) input->gpu_half_data,
- (__half*) filter->gpu_half_data,
- input->dims.dim_sizes[0], input->dims.dim_sizes[1],
- input->dims.dim_sizes[2], input->dims.dim_sizes[3],
- KH, KW, h, w,
- vertical_pad, horizontal_pad,
- vertical_stride, horizontal_stride);
- cudaDeviceSynchronize();
-
-
- }
- else{
- checkCUDNN(cudnnCreateConvolutionDescriptor(&convDesc));
-
- //FIXME: Current hack to preserve backward compatibilty
- if(conv_groups == 0){
- conv_groups = 1;
- }
-
- // NOTE: Adding support for grouped convolution
- checkCUDNN(cudnnSetConvolutionGroupCount(convDesc, conv_groups));
-
-
- checkCUDNN(cudnnSetConvolution2dDescriptor(convDesc,
- vertical_pad, horizontal_pad, // conv padding
- vertical_stride, horizontal_stride, // conv strides
- 1, 1, // upscaling values
- mode, // mode is configurable
- computeType)); // defines compute precision
-
- int n, c, h, w; // output dimensions
- // Find dimension of convolution output
- checkCUDNN(cudnnGetConvolution2dForwardOutputDim(convDesc,
- input->tensor_half_desc,
- filter->filter_half_desc,
- &n, &c, &h, &w));
- DEBUG("**Output Tensor Dims, n = %d, c = %d, h = %d, w = %d \n", n, c, h, w);
-
-
- output = (Tensor*) create4DTensor((cudnnDataType_t) half_type, //input->data_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
-
-
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(output, DEVICE);
- // NOTE: Necessary to insert the above call for every output tensor
-
- DEBUG("tensor->data_type = %d, tensor->data_format = %d, N = %d, H = %d, W = %d, C = %d \n",
- output->data_type, output->data_format,
- output->dims.dim_sizes[0], output->dims.dim_sizes[1],
- output->dims.dim_sizes[2], output->dims.dim_sizes[3]);
-
- if(convDesc == NULL || input->tensor_desc == NULL ||
- filter->filter_desc == NULL || output->tensor_desc == NULL)
- ERROR("NULL descriptor! \n");
-
-
- // NOTE: The following algo works with TRUE half precision
- convAlgo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM;
- //convAlgo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM;
-
-
- size_t workspace_size;
- checkCUDNN(cudnnGetConvolutionForwardWorkspaceSize(cudnnHandle,
- input->tensor_half_desc,
- filter->filter_half_desc,
- convDesc,
- output->tensor_half_desc,
- convAlgo,
- &workspace_size));
-
- // Allocating memory for the convolution workspace
- DEBUG("workspace size = %d \n", workspace_size);
- void* workspace;
- checkCudaErrors(cudaMalloc(&workspace, workspace_size));
-
-
-
-
- checkCUDNN(cudnnConvolutionForward(cudnnHandle,
- &alpha,
- input->tensor_half_desc,
- input->gpu_half_data,
- filter->filter_half_desc,
- filter->gpu_half_data,
- convDesc, convAlgo, workspace, workspace_size,
- &beta,
- output->tensor_half_desc,
- output->gpu_half_data));
-
- }
-
- profileEvent("H2F_start");
-
- convertToFP32_offline(output);
-
- profileEvent("H2F_end");
-
-
-
- profileEvent("#Conv_end");
-
-
-
- return output;
-
-}
-
-void* tensorHalfConvCutlass2(void* input_ptr, void* filter_ptr,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int conv_groups){
-
- INFO("*** TensorHConvolution \n");
- profileEvent("#Conv");
-
- Tensor* input = (Tensor*)input_ptr;
- Tensor* filter = (Tensor*)filter_ptr;
-
- cudnnConvolutionDescriptor_t convDesc;
- cudnnConvolutionFwdAlgo_t convAlgo;
- cudnnConvolutionMode_t mode;
- if (conv_mode == 0)
- mode = CUDNN_CONVOLUTION;
- else if (conv_mode == 1)
- mode = CUDNN_CROSS_CORRELATION;
-
- // FIXIT: Need to be more aware of the implications of alpha and beta
- float alpha = 1.0f, beta = 0.0f;
- // NOTE: compute in half precision
- cudnnDataType_t computeType = CUDNN_DATA_HALF;
-
- // NOTE: Moving inputs to GPU global memory
- hostToDeviceCopy(input);
- hostToDeviceCopy(filter);
-
-
- /***** CONVERSIONS from FP32 to FP16 - on the GPU */
- size_t* input_dims = input->dims.dim_sizes;
- size_t* filter_dims = filter->dims.dim_sizes;
-
-
- profileEvent("F2H_start");
-
- Tensor* input_half = (Tensor*)create4DTensor(CUDNN_DATA_HALF, CUDNN_TENSOR_NCHW,
- input_dims[0], input_dims[1],
- input_dims[2], input_dims[3]);
-
-
- changeTensorPlacement(input_half, DEVICE);
- Tensor* filter_half = (Tensor*)create4DTensor(CUDNN_DATA_HALF, CUDNN_TENSOR_NCHW,
- filter_dims[0], filter_dims[1],
- filter_dims[2], filter_dims[3]);
-
-
- changeTensorPlacement(filter_half, DEVICE);
-
-
- f2h((float*)input->gpu_data, input->num_elems, (half*)input_half->gpu_data);
- f2h((float*)filter->gpu_data, filter->num_elems, (half*)filter_half->gpu_data);
-
-
- /******* END OF INPUT DATA CONVERSIONS*/
- profileEvent("F2H_end");
-
- Tensor* output;
- Tensor* output_half;
-
-
- if (conv_groups > 1 && horizontal_stride == 1 && vertical_stride == 1) {
- int n = input->dims.dim_sizes[0];
- int c = input->dims.dim_sizes[1];
- const int KH = filter->dims.dim_sizes[2];
- const int KW = filter->dims.dim_sizes[3];
- int h = (2 * vertical_pad + input->dims.dim_sizes[2] - KH) / vertical_stride + 1;
- int w = (2 * horizontal_pad + input->dims.dim_sizes[3] - KW) / horizontal_stride + 1;
-
- DEBUG("**Output Tensor Dims, n = %d, c = %d, h = %d, w = %d \n", n, c, h, w);
-
-
- output = (Tensor*)create4DTensor((cudnnDataType_t)input->data_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
- // FIXIT: more checks for data types needed
- output_half = (Tensor*)create4DTensor(CUDNN_DATA_HALF,
- CUDNN_TENSOR_NCHW, n, c, h, w);
-
-
-
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(output, DEVICE);
- // NOTE: Necessary to insert the above call for every output tensor
-
- int blockSize;
- blockSize = 128;
-
- dim3 grid(((n + 3) / 4), (c * h * w + blockSize - 1) / blockSize);
- dim3 block(blockSize);
- int C_dim = blockSize / (h * w) + 1 + 1;
- int H_dim = blockSize % (h * w) / w + 1 + KH + 1;
- int W_dim = blockSize % (h * w) % w + 1 + KW + 1;
- depthwise_conv4_half3 << <grid, block, sizeof(__half)* C_dim* H_dim* W_dim >> > ((__half*)output_half->gpu_data,
- (__half*)input_half->gpu_data, (__half*)filter_half->gpu_data,
- input->dims.dim_sizes[0], input->dims.dim_sizes[1],
- input->dims.dim_sizes[2], input->dims.dim_sizes[3],
- KH, KW, h, w,
- vertical_pad, horizontal_pad, C_dim, H_dim, W_dim);
- cudaDeviceSynchronize();
-
-
- }
- else {
- checkCUDNN(cudnnCreateConvolutionDescriptor(&convDesc));
-
- //FIXME: Current hack to preserve backward compatibilty
- if (conv_groups == 0) {
- conv_groups = 1;
- }
-
- // NOTE: Adding support for grouped convolution
- checkCUDNN(cudnnSetConvolutionGroupCount(convDesc, conv_groups));
-
-
- // FIXIT: Think if upscaling values need to be configurable?
- // IMP-FIXIT: CUDNN Cross correlation is only used in the Lenet context
- // IMP-FIXIT: Either make mode configurable OR see if CUDNN_CONVOLUTION MODE should be used?
- checkCUDNN(cudnnSetConvolution2dDescriptor(convDesc,
- vertical_pad, horizontal_pad, // conv padding
- vertical_stride, horizontal_stride, // conv strides
- 1, 1, // upscaling values
- mode, // mode is configurable
- computeType)); // defines compute precision
-
- int n, c, h, w; // output dimensions
- // Find dimension of convolution output
- checkCUDNN(cudnnGetConvolution2dForwardOutputDim(convDesc,
- input->tensor_desc,
- filter->filter_desc,
- &n, &c, &h, &w));
- DEBUG("**Output Tensor Dims, n = %d, c = %d, h = %d, w = %d \n", n, c, h, w);
-
-
- output = (Tensor*)create4DTensor((cudnnDataType_t)input->data_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
- // FIXIT: more checks for data types needed
- output_half = (Tensor*)create4DTensor(CUDNN_DATA_HALF,
- CUDNN_TENSOR_NCHW, n, c, h, w);
-
-
-
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(output, DEVICE);
- // NOTE: Necessary to insert the above call for every output tensor
-
- DEBUG("tensor->data_type = %d, tensor->data_format = %d, N = %d, H = %d, W = %d, C = %d \n",
- output->data_type, output->data_format, output->dims.dim_sizes[0], output->dims.dim_sizes[1],
- output->dims.dim_sizes[2], output->dims.dim_sizes[3]);
-
- if (convDesc == NULL || input->tensor_desc == NULL ||
- filter->filter_desc == NULL || output->tensor_desc == NULL)
- ERROR("NULL descriptor! \n");
-
-
- // NOTE: The following algo works with TRUE half precision
- convAlgo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM;
- //convAlgo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM;
-
-
- size_t workspace_size;
- checkCUDNN(cudnnGetConvolutionForwardWorkspaceSize(cudnnHandle,
- input_half->tensor_desc,
- filter_half->filter_desc,
- convDesc,
- output_half->tensor_desc,
- convAlgo,
- &workspace_size));
-
- // Allocating memory for the convolution workspace
- DEBUG("workspace size = %d \n", workspace_size);
- void* workspace;
- checkCudaErrors(cudaMalloc(&workspace, workspace_size));
-
-
-
-
- checkCUDNN(cudnnConvolutionForward(cudnnHandle,
- &alpha,
- input_half->tensor_desc,
- input_half->gpu_data,
- filter_half->filter_desc,
- filter_half->gpu_data,
- convDesc, convAlgo, workspace, workspace_size,
- &beta,
- output_half->tensor_desc,
- output_half->gpu_data));
-
- }
-
- profileEvent("H2F_start");
-
- // NOTE: Transforming half precision output to single precision
- h2f((half*)output_half->gpu_data, output->num_elems, (float*)output->gpu_data);
-
- profileEvent("H2F_end");
-
- profileEvent("#Conv_end");
-
-
- freeTensor(input_half);
- freeTensor(filter_half);
- freeTensor(output_half);
-
- return output;
-
-}
-
-//N is new_data's size
-//n, c, h, w are the dimensions of new_data
-__global__
-void interpolateCol(int N, int old_w, int n, int c, int h, int w, float *old_data, float *new_data){
-
- int index = blockIdx.x * blockDim.x + threadIdx.x;
- int stride = blockDim.x * gridDim.x;
-
- for(int i = index; i < N; i += stride){
- int col = ((i % (c * h * w)) % (h * w)) % w;
- int row = ((i % (c * h * w)) % (h * w)) / w;
- int ch = (i % (c * h * w)) / (h * w);
- int n = i / (c * h * w);
-
- if(col % 2 == 0)
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[n * (c * h * old_w) + ch * (h * old_w) + row * (old_w) + col / 2];
- else if(col == w - 1)
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[n * (c * h * old_w) + ch * (h * old_w) + row * (old_w) + (col-1) / 2];
- else
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- (old_data[n * (c * h * old_w) + ch * (h * old_w) + row * (old_w) + (col-1) / 2] +
- old_data[n * (c * h * old_w) + ch * (h * old_w) + row * (old_w) + (col+1) / 2])/2;
-
- }
-}
-
-//N is new_data's size
-//n, c, h, w are the dimensions of new_data
-__global__
-void interpolateRow(int N, int old_h, int n, int c, int h, int w, float *old_data, float *new_data){
-
- int index = blockIdx.x * blockDim.x + threadIdx.x;
- int stride = blockDim.x * gridDim.x;
-
- for(int i = index; i < N; i += stride){
- int col = ((i % (c * h * w)) % (h * w)) % w;
- int row = ((i % (c * h * w)) % (h * w)) / w;
- int ch = (i % (c * h * w)) / (h * w);
- int n = i / (c * h * w);
-
- if(row % 2 == 0)
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[n * (c * old_h * w) + ch * (old_h * w) + (row/2) * (w) + col];
- else if(row == h - 1)
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[n * (c * old_h * w) + ch * (old_h * w) + (row-1)/2 * (w) + col];
- else
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- (old_data[n * (c * old_h * w) + ch * (old_h * w) + (row -1)/2 * (w) + col] +
- old_data[n * (c * old_h * w) + ch * (old_h * w) + ((row+1) / 2) * (w) + col])/2;
-
- }
-}
-
-//N is new_data's size
-//n, c, h, w are the dimensions of new_data
-__global__
-void interpolateXCol(int N, int old_w, int n, int c, int h, int w,
- float *old_data, float *new_data, int num){
-
- int index = blockIdx.x * blockDim.x + threadIdx.x;
- int stride = blockDim.x * gridDim.x;
-
- for(int i = index; i < N; i += stride){
- int col = ((i % (c * h * w)) % (h * w)) % w;
- int row = ((i % (c * h * w)) % (h * w)) / w;
- int ch = (i % (c * h * w)) / (h * w);
- int n = i / (c * h * w);
-
- if(col % num == 0)
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[n * (c * h * old_w) + ch * (h * old_w) + row * (old_w) + col / num];
- else{
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[n * (c * h * old_w) + ch * (h * old_w) + row * (old_w) + (col-(col%num)) / num];
- }
-
- }
-}
-
-//N is new_data's size
-//n, c, h, w are the dimensions of new_data
-__global__
-void interpolateXRow(int N, int old_h, int n, int c, int h, int w,
- float *old_data, float *new_data, int num){
-
- int index = blockIdx.x * blockDim.x + threadIdx.x;
- int stride = blockDim.x * gridDim.x;
-
- for(int i = index; i < N; i += stride){
- int col = ((i % (c * h * w)) % (h * w)) % w;
- int row = ((i % (c * h * w)) % (h * w)) / w;
- int ch = (i % (c * h * w)) / (h * w);
- int n = i / (c * h * w);
-
- if(row % num == 0)
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[n * (c * old_h * w) + ch * (old_h * w) + (row/num) * (w) + col];
- else{
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[n * (c * old_h * w) + ch * (old_h * w) + (row - (row % num))/num * (w) + col];
- }
-
- }
-}
-
-
-// Perforated Tensor Conv with 'perforation_rate' parameter
-void* tensorConvPerf(void* input_ptr, void* filter_ptr,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int conv_groups, int row, int col){
- llvm_hpvm_initTensorRt(0);
-
- INFO("*** TensorConvolution \n");
- profileEvent("tensorConv");
-
- Tensor* input = (Tensor*) input_ptr;
- Tensor* filter = (Tensor*) filter_ptr;
-
- cudnnConvolutionDescriptor_t convDesc;
- cudnnConvolutionFwdAlgo_t convAlgo;
- cudnnConvolutionMode_t mode;
- if(conv_mode == 0)
- mode = CUDNN_CONVOLUTION;
- else if(conv_mode == 1)
- mode = CUDNN_CROSS_CORRELATION;
-
- // FIXIT: Need to be more aware of the implications of alpha and beta
- float alpha = 1.0f, beta = 0.0f;
-
- // TODO: Support other cases;
- hostToDeviceCopy(input);
- hostToDeviceCopy(filter);
-
- INFO("vertical_stride = %lu, horizontal_stride = %lu \n", vertical_stride, horizontal_stride);
-
- checkCUDNN(cudnnCreateConvolutionDescriptor(&convDesc));
-
- //FIXME: Current hack to preserve backward compatibilty
- if(conv_groups == 0){
- conv_groups = 1;
- }
-
- // NOTE: Adding support for grouped convolution
- checkCUDNN(cudnnSetConvolutionGroupCount(convDesc, conv_groups));
-
- int new_v = vertical_stride + row;
- int new_h = horizontal_stride + col;
- cudnnDataType_t computeType = CUDNN_DATA_FLOAT;
- // FIXIT: Think if upscaling values need to be configurable?
- // IMP-FIXIT: Either make mode configurable OR see if CUDNN_CONVOLUTION MODE should be used?
- checkCUDNN(cudnnSetConvolution2dDescriptor(convDesc,
- vertical_pad, horizontal_pad, // conv padding
- new_v, new_h, // conv strides
- 1, 1, // upscaling values
- mode , // mode is configurable
- computeType)); // defines compute precision
-
- int n, c, h, w; // output dimensions
- // Find dimension of convolution output
- checkCUDNN(cudnnGetConvolution2dForwardOutputDim(convDesc,
- input->tensor_desc,
- filter->filter_desc,
- &n, &c, &h, &w));
-
-
- DEBUG("**Output Tensor Dims, n = %d, c = %d, h = %d, w = %d \n", n, c, h, w);
-
- Tensor* output;
- if(input->data_format == CUDNN_TENSOR_NCHW)
- output = (Tensor*) create4DTensor((cudnnDataType_t) input->data_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
- else if(input->data_format == CUDNN_TENSOR_NHWC){
- DEBUG("* NHWC Format \n");
- output = (Tensor*) create4DTensor((cudnnDataType_t) input->data_type,
- CUDNN_TENSOR_NHWC, n, h, w, c);
- }
- else
- ERROR("Unsupported Tensor Type");
-
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(output, DEVICE);
- // NOTE: Necessary to insert the above call for every output tensor
-
- DEBUG("tensor->data_type = %d, tensor->data_format = %d, N = %d, C = %d, H = %d, W = %d \n",
- output->data_type, output->data_format, output->dims.dim_sizes[0], output->dims.dim_sizes[1],
- output->dims.dim_sizes[2], output->dims.dim_sizes[3]);
-
- if(convDesc == NULL || input->tensor_desc == NULL ||
- filter->filter_desc == NULL || output->tensor_desc == NULL)
- ERROR("NULL descriptor! \n");
-
-
- // Debugging info prints
- printTensorDescInfo(input);
- printTensorDescInfo(filter);
- printTensorDescInfo(output);
-
- // NOTE-FIXIT: function failing for NHWC formats - perhaps some CUDNN support is lacking
- checkCUDNN(cudnnGetConvolutionForwardAlgorithm(cudnnHandle,
- input->tensor_desc,
- filter->filter_desc,
- convDesc,
- output->tensor_desc,
- CUDNN_CONVOLUTION_FWD_PREFER_FASTEST,
- //CUDNN_CONVOLUTION_FWD_NO_WORKSPACE,
- 0,
- &convAlgo));
-
-
- DEBUG("ConvAlgo = %d, FFT = %d, GEMM = %d, WINOGRAD = %d \n", convAlgo,
- CUDNN_CONVOLUTION_FWD_ALGO_FFT, CUDNN_CONVOLUTION_FWD_ALGO_GEMM,
- CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD);
-
-
- // FIXIT: Algo shouldn't be hardcoded
- convAlgo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM;
-
- size_t workspace_size;
- checkCUDNN(cudnnGetConvolutionForwardWorkspaceSize(cudnnHandle,
- input->tensor_desc,
- filter->filter_desc,
- convDesc,
- output->tensor_desc,
- convAlgo,
- &workspace_size));
-
- // Allocating memory for the convolution workspace
- void* workspace;
- checkCudaErrors(cudaMalloc(&workspace, workspace_size));
- DEBUG("workspace size = %d \n", workspace_size);
-
-
- checkCUDNN(cudnnConvolutionForward(cudnnHandle, &alpha, input->tensor_desc,
- input->gpu_data, filter->filter_desc, filter->gpu_data,
- convDesc, convAlgo, workspace, workspace_size,
- &beta, output->tensor_desc, output->gpu_data));
-
-
- int old_w = w;
- int old_h = h;
- h = (2 * vertical_pad + input->dims.dim_sizes[2] - filter->dims.dim_sizes[2]) / vertical_stride + 1;
- w = (2 * horizontal_pad + input->dims.dim_sizes[3] - filter->dims.dim_sizes[3]) / horizontal_stride + 1;
-
- Tensor* new_output;
- if(input->data_format == CUDNN_TENSOR_NCHW)
- new_output = (Tensor*) create4DTensor((cudnnDataType_t) float_type, //input->data_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
- else if(input->data_format == CUDNN_TENSOR_NHWC){
- DEBUG("* NHWC Format \n");
- new_output = (Tensor*) create4DTensor((cudnnDataType_t) input->data_type,
- CUDNN_TENSOR_NHWC, n, h, w, c);
- }
- else
- ERROR("Unsupported Tensor Type");
-
-
- int numBlocks = (n * c * h * w + 127) / 128;
- if(vertical_stride == 0 && row == 0)
- return output;
-
- if(vertical_stride == 1 && row == 1){
- interpolateRow<<<numBlocks,128>>>(n * c * h * w, old_h, n, c, h, w,
- (float *)output->gpu_data, (float *)new_output->gpu_data);
- }
- else if(horizontal_stride == 1 && col == 1){
- interpolateCol<<<numBlocks,128>>>(n * c * h * w, old_w, n, c, h, w,
- (float *)output->gpu_data, (float *)new_output->gpu_data);
- }
- else if (col > 0){
- interpolateXCol<<<numBlocks,128>>>(n * c * h * w, old_w, n, c, h, w,
- (float *)output->gpu_data, (float *)new_output->gpu_data, col + 1);
- }
- else{
- interpolateXRow<<<numBlocks,128>>>(n * c * h * w, old_h, n, c, h, w,
- (float *)output->gpu_data, (float *)new_output->gpu_data, row + 1);
- }
-
-
- cudaDeviceSynchronize();
-
- profileEvent("tensorConv_end", true);
-
-
- changeTensorPlacement(new_output, DEVICE);
- return new_output;
-
-}
-
-
-
-//N is new_data's size
-//n, c, h, w are the dimensions of new_data
-__global__
-void interpolateColHalf(int N, int old_w, int n, int c, int h, int w, __half *old_data, __half *new_data){
-
- int index = blockIdx.x * blockDim.x + threadIdx.x;
- int stride = blockDim.x * gridDim.x;
-
- for(int i = index; i < N; i += stride){
- int col = ((i % (c * h * w)) % (h * w)) % w;
- int row = ((i % (c * h * w)) % (h * w)) / w;
- int ch = (i % (c * h * w)) / (h * w);
- int n = i / (c * h * w);
-
- if(col % 2 == 0)
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[n * (c * h * old_w) + ch * (h * old_w) + row * (old_w) + col / 2];
- else if(col == w - 1)
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[n * (c * h * old_w) + ch * (h * old_w) + row * (old_w) + (col-1) / 2];
- else
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- __hdiv(__hadd(old_data[n * (c * h * old_w) + ch * (h * old_w) + row * (old_w) + (col-1) / 2],
- old_data[n * (c * h * old_w) + ch * (h * old_w) + row * (old_w) + (col+1) / 2]),2);
-
- }
-}
-
-//N is new_data's size
-//n, c, h, w are the dimensions of new_data
-__global__
-void interpolateRowHalf(int N, int old_h, int n, int c, int h, int w, __half *old_data, __half *new_data){
-
- int index = blockIdx.x * blockDim.x + threadIdx.x;
- int stride = blockDim.x * gridDim.x;
-
- for(int i = index; i < N; i += stride){
- int col = ((i % (c * h * w)) % (h * w)) % w;
- int row = ((i % (c * h * w)) % (h * w)) / w;
- int ch = (i % (c * h * w)) / (h * w);
- int n = i / (c * h * w);
-
- if(row % 2 == 0)
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[n * (c * old_h * w) + ch * (old_h * w) + (row/2) * (w) + col];
- else if(row == h - 1)
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[n * (c * old_h * w) + ch * (old_h * w) + (row-1)/2 * (w) + col];
- else
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- __hdiv(__hadd(old_data[n * (c * old_h * w) + ch * (old_h * w) + (row -1)/2 * (w) + col],
- old_data[n * (c * old_h * w) + ch * (old_h * w) + ((row+1) / 2) * (w) + col]), 2);
-
- }
-}
-
-//N is new_data's size
-//n, c, h, w are the dimensions of new_data
-__global__
-void interpolateXColHalf(int N, int old_w, int n, int c, int h, int w,
- __half *old_data, __half *new_data, int num){
-
- int index = blockIdx.x * blockDim.x + threadIdx.x;
- int stride = blockDim.x * gridDim.x;
-
- for(int i = index; i < N; i += stride){
- int col = ((i % (c * h * w)) % (h * w)) % w;
- int row = ((i % (c * h * w)) % (h * w)) / w;
- int ch = (i % (c * h * w)) / (h * w);
- int n = i / (c * h * w);
-
- if(col % num == 0)
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[n * (c * h * old_w) + ch * (h * old_w) + row * (old_w) + col / num];
- else{
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[n * (c * h * old_w) + ch * (h * old_w) + row * (old_w) + (col-(col%num)) / num];
- }
-
- }
-}
-
-//N is new_data's size
-//n, c, h, w are the dimensions of new_data
-__global__
-void interpolateXRowHalf(int N, int old_h, int n, int c, int h, int w,
- __half *old_data, __half *new_data, int num){
-
- int index = blockIdx.x * blockDim.x + threadIdx.x;
- int stride = blockDim.x * gridDim.x;
-
- for(int i = index; i < N; i += stride){
- int col = ((i % (c * h * w)) % (h * w)) % w;
- int row = ((i % (c * h * w)) % (h * w)) / w;
- int ch = (i % (c * h * w)) / (h * w);
- int n = i / (c * h * w);
-
- if(row % num == 0)
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[n * (c * old_h * w) + ch * (old_h * w) + (row/num) * (w) + col];
- else{
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[n * (c * old_h * w) + ch * (old_h * w) + (row - (row % num))/num * (w) + col];
- }
-
- }
-}
-
-
-//produces N COL MAJOR matrixes with H_out*W_out rows and reduced_filter_elem cols
-__global__ void convToGemmApprox(float * const __restrict__ output,
- const float * const __restrict input, const int N, const int C,
- const int H, const int W,
- const int KH, const int KW, const int V_pad,
- const int H_pad, const int H_out,
- const int W_out, const int V_stride,
- const int H_stride, const int reduced_filter_elem,
- const int skip_every) {
-
- const int tx = blockDim.x * blockIdx.x + threadIdx.x; //thread id
- const int n = tx / (C * H_out * W_out); //output image number
- const int c = tx % (C * H_out * W_out) / (H_out * W_out); //output chan number
- const int h = tx % (H_out * W_out) / W_out; //output height index (row number)
- const int w = tx % W_out; //output width index (col number)
- const int inH = h * V_stride - V_pad; //input height index (row number)
- const int inW = w * H_stride - H_pad; //input width index (col number)
- if(n < N) { //is thread id within bounds?
- for(int i = 0; i < KH; i++) {
- for(int j = 0; j < KW; j++) {
- const int filter_elem_num = (c * KH + i) * KW + j; //index of this filter element
- if(filter_elem_num % skip_every != skip_every-1) { //are we including this filter element?
- const int output_col = filter_elem_num - (filter_elem_num/skip_every); //calculate output column, taking skipping into account
- if(inH + i >= 0 && inH + i < H && inW + j >= 0 && inW + j < W)
- output[((n * reduced_filter_elem + output_col) * H_out + h) * W_out + w] = input[((n * C + c) * H + (inH + i)) * W + (inW + j)];
- else
- output[((n * reduced_filter_elem + output_col) * H_out + h) * W_out + w] = 0;
- }
- }
- }
- }
-}
-
-
-//produces COL MAJOR matrix with reduced_filter_elem rows and NF cols
-__global__ void createReducedFilters(float * const __restrict__ output,
- const float * const __restrict input, const int NF,
- const int num_filter_elem, const int reduced_filter_elem,
- const int skip_every) {
- const int tx = blockDim.x * blockIdx.x + threadIdx.x; //thread id
- const int fIdx = tx / num_filter_elem; //filter index
- const int offset = tx % num_filter_elem; //offset within filter
- if(fIdx < NF) { //is thread id within bounds?
- if(offset % skip_every != skip_every-1) { //are we including this filter element?
- const int output_row = offset - (offset/skip_every); //calculate output row, taking skipping into account
- output[fIdx*reduced_filter_elem + output_row] = input[tx] * 2;
- }
- }
-}
-
-
-void* tensorConvolutionKernelSamp(void* input_ptr, void* filter_ptr,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int conv_groups, int skip_every){
-
- INFO("*** TensorConvolution (w/ kernel sampling) \n");
- profileEvent("Conv");
- Tensor* input = (Tensor*)input_ptr;
- Tensor* filter = (Tensor*)filter_ptr;
- //FIXME: Current hack to preserve backward compatibilty
- if (conv_groups == 0) {
- conv_groups = 1;
- }
-
- Tensor* output;
- // TODO: Support other cases;
- hostToDeviceCopy(input);
- hostToDeviceCopy(filter);
-
-
- convertToFP32(input);
- convertToFP32(filter);
-
-
- int n, c, h, w; // output dimensions
- n = input->dims.dim_sizes[0];
- c = filter->dims.dim_sizes[0]; //number of filters
- const int KH = filter->dims.dim_sizes[2];
- const int KW = filter->dims.dim_sizes[3];
- h = (2 * vertical_pad + input->dims.dim_sizes[2] - KH) / vertical_stride + 1;
- w = (2 * horizontal_pad + input->dims.dim_sizes[3] - KW) / horizontal_stride + 1;
- output = (Tensor*)create4DTensor((cudnnDataType_t)input->data_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(output, DEVICE);
- // NOTE: Necessary to insert the above call for every output tensor
- //total number of filter elem
- const int num_filter_elem = KH * KW * input->dims.dim_sizes[1];
- //reduced number after skipping
- const int reduced_filter_elem = num_filter_elem - (num_filter_elem/skip_every);
- float * convData;
- int convDataSize = sizeof(float) * n * reduced_filter_elem * h * w;
- checkCudaErrors(cudaMalloc(&convData, convDataSize));
- float * reducedFilter;
- checkCudaErrors(cudaMalloc(&reducedFilter, sizeof(float) * c * reduced_filter_elem));
- const int filtBlockSize = 128;
- const int filtGridSize = (c * num_filter_elem + filtBlockSize - 1) / filtBlockSize;
- createReducedFilters<<<filtGridSize, filtBlockSize>>>(reducedFilter,
- (float *)filter->gpu_data, c,
- num_filter_elem, reduced_filter_elem,
- skip_every);
- const int blockSize = 128;
- const int gridSize = (n * input->dims.dim_sizes[1] * h * w + blockSize - 1) / blockSize;
- convToGemmApprox<<<gridSize, blockSize>>>(convData, (float *)input->gpu_data, n,
- input->dims.dim_sizes[1],
- input->dims.dim_sizes[2],
- input->dims.dim_sizes[3],
- KH, KW, vertical_pad, horizontal_pad, h, w,
- vertical_stride, horizontal_stride,
- reduced_filter_elem, skip_every);
- checkCudaErrors(cudaDeviceSynchronize());
- //Do the matrix multiplication. Want to multiply convData by filter->gpu_data[f * chan * KH * KW]
- float alpha = 1.0f, beta = 0.0f;
- checkCudaErrors(cublasSgemmStridedBatched(cublasHandle,
- CUBLAS_OP_N, CUBLAS_OP_N,
- h * w, c, reduced_filter_elem,
- &alpha,
- convData, h * w, reduced_filter_elem * h * w,
- reducedFilter, reduced_filter_elem, 0,
- &beta,
- (float *)output->gpu_data, h * w, c * h * w,
- n));
- cudaFree(convData);
- cudaFree(reducedFilter);
- profileEvent("Conv_end", true);
- #ifdef ERROR_INJECTION_ENABLED
- if (op_counter >= total_ops) {
- ERROR("No accuracy flag found \n");
- }
- int op_acc = op_accuracies[op_counter];
- // Skip errorInjection if explicitly requested
- if (skip_tensors.find(op_counter) != skip_tensors.end()) {
- op_acc = 0;
- }
- void* error_norms = tensorAddError(output, op_acc);
- add_norms(error_norms, "tensorConv", op_acc);
- add_conv_overheads(input, filter, vertical_stride, horizontal_stride, op_acc);
- op_counter++;
- #endif
- return output;
-}
-
-
- /*
- if (c > 255) {
- dim3 grid((n / 16), c);
- dim3 block(h * w);
- depthwise_conv << <grid, block >> > ((float*)output->gpu_data,
- (float*)input->gpu_data, (float*)filter->gpu_data,
- input->dims.dim_sizes[0], input->dims.dim_sizes[1], input->dims.dim_sizes[2], input->dims.dim_sizes[3],
- KH, KW, h, w, vertical_pad, horizontal_pad, vertical_stride, horizontal_stride);
-
- }*/
-
- /*
- dim3 grid((n / 12), c);
- dim3 block(h * w);
- depthwise_conv12 <<<grid, block >>> ((float*)output->gpu_data,
- (float*)input->gpu_data, (float*)filter->gpu_data,
- input->dims.dim_sizes[0], input->dims.dim_sizes[1], input->dims.dim_sizes[2], input->dims.dim_sizes[3],
- KH, KW, h, w, vertical_pad, horizontal_pad, vertical_stride, horizontal_stride);
- if(n % 12 > 0){
- dim3 grid2((n % 12), c);
- dim3 block(h * w);
- depthwise_conv <<<grid, block >>> ((float*)output->gpu_data,
- (float*)input->gpu_data, (float*)filter->gpu_data,
- input->dims.dim_sizes[0], input->dims.dim_sizes[1], input->dims.dim_sizes[2], input->dims.dim_sizes[3],
- KH, KW, h, w, vertical_pad, horizontal_pad, vertical_stride, horizontal_stride, 12 * (n/12));
- }
- */
-
-
-
-
-
-
-
-//Tensor* input_half = (Tensor*) create4DTensor(CUDNN_DATA_HALF, CUDNN_TENSOR_NCHW,
- // input_dims[0], input_dims[1],
- // input_dims[2], input_dims[3]);
-
-
- //changeTensorPlacement(input_half, DEVICE);
- //Tensor* filter_half = (Tensor*) create4DTensor(CUDNN_DATA_HALF, CUDNN_TENSOR_NCHW,
- // filter_dims[0], filter_dims[1],
- // filter_dims[2], filter_dims[3]);
-
-
- //changeTensorPlacement(filter_half, DEVICE);
-
-
- //f2h((float*) input->gpu_data, input->num_elems, (half*) input_half->gpu_data);
- //f2h((float*) filter->gpu_data, filter->num_elems, (half*) filter_half->gpu_data);
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/approx_techniques2.h b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/approx_techniques2.h
deleted file mode 100644
index 6042cc7dae94090fc554669975c6e28ea8ebdda0..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/approx_techniques2.h
+++ /dev/null
@@ -1,1673 +0,0 @@
-
-#include "tensor_utils.cu"
-
-
-//produces N COL MAJOR matrixes with H_out*W_out rows and reduced_filter_elem cols
-__global__ void convToGemmApproxHalf(__half * const __restrict__ output,
- const __half * const __restrict input, const int N, const int C,
- const int H, const int W, const int KH,
- const int KW, const int V_pad,
- const int H_pad, const int H_out,
- const int W_out, const int V_stride,
- const int H_stride, const int reduced_filter_elem,
- const int skip_every) {
- const int tx = blockDim.x * blockIdx.x + threadIdx.x; //thread id
- const int n = tx / (C * H_out * W_out); //output image number
- const int c = tx % (C * H_out * W_out) / (H_out * W_out); //output chan number
- const int h = tx % (H_out * W_out) / W_out; //output height index (row number)
- const int w = tx % W_out; //output width index (col number)
- const int inH = h * V_stride - V_pad; //input height index (row number)
- const int inW = w * H_stride - H_pad; //input width index (col number)
- if(n < N) { //is thread id within bounds?
- for(int i = 0; i < KH; i++) {
- for(int j = 0; j < KW; j++) {
- const int filter_elem_num = (c * KH + i) * KW + j; //index of this filter element
- if(filter_elem_num % skip_every != skip_every-1) { //are we including this filter element?
- const int output_col = filter_elem_num - (filter_elem_num/skip_every); //calculate output column, taking skipping into account
- if(inH + i >= 0 && inH + i < H && inW + j >= 0 && inW + j < W)
- output[((n * reduced_filter_elem + output_col) * H_out + h) * W_out + w] = input[((n * C + c) * H + (inH + i)) * W + (inW + j)];
- else
- output[((n * reduced_filter_elem + output_col) * H_out + h) * W_out + w] = 0;
- }
- }
- }
- }
-}
-
-
-//This skips every xth row
-//H_eff is the number of rows calculated exactly
-__global__
-void convToGemmPerfRow(float * const __restrict__ output,
- const float * const __restrict input, const int N, const int C,
- const int H, const int W, const int KH, const int KW, const int V_pad,
- const int H_pad, const int H_out, const int W_out, const int V_stride,
- const int H_stride, const int x, const int start, const int H_eff){
-
- const int tx = blockDim.x * blockIdx.x + threadIdx.x; //thread id
- const int n = tx / (C * H_eff * W_out); //output image number
- const int c = tx % (C * H_eff * W_out) / (H_eff * W_out); //output chan number
- const int h = tx % (H_eff * W_out) / W_out; //output height index (row number)
- const int w = tx % W_out; //output width index (col number)
- int past_start = (h % (x - 1) >= (x - 1 - start));
- const int inH = (h / (x - 1) * x + h % (x-1) +
- past_start) * V_stride - V_pad; //input height index (row number)
- const int inW = w * H_stride - H_pad; //input width index (col number)
- if(n < N) { //is thread id within bounds?
- for(int i = 0; i < KH; i++) {
- for(int j = 0; j < KW; j++) {
- const int filter_elem_num = (c * KH + i) * KW + j; //index of this filter element
-
- if(inH + i >= 0 && inH + i < H && inW + j >= 0 && inW + j < W)
- output[((n * C * KH * KW + filter_elem_num) * H_eff + h) * W_out + w] =
- input[((n * C + c) * H + (inH + i)) * W + (inW + j)];
- else
- output[((n * C * KH * KW + filter_elem_num) * H_eff + h) * W_out + w] = 0;
-
- }
- }
- }
-
-}
-
-
-//For use in tensorConvPerfCuda
-//Interpolates every xth row starting from x - 1 - start
-//N is total number of elements in final output array
-__global__
-void approxInterpolateRow(int N, int old_h, int n, int c, int h, int w,
- float *old_data, float *new_data, int x, int start){
-
- int index = blockIdx.x * blockDim.x + threadIdx.x;
- int stride = blockDim.x * gridDim.x;
-
- for(int i = index; i < N; i += stride){
- int col = ((i % (c * h * w)) % (h * w)) % w;
- int row = ((i % (c * h * w)) % (h * w)) / w;
- int ch = (i % (c * h * w)) / (h * w);
- int n = i / (c * h * w);
- int past_start = ((row % x) >= (x - 1 - start));
-
- if(row == h-1)
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[n * (c * old_h * w) + ch * (old_h * w) + (old_h - 1) * (w) + col];
- else if (row == 0)
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[n * (c * old_h * w) + ch * (old_h * w) + 0 * (w) + col];
- else if(row % x == x - 1 - start){
- int past_startO = ((row - 1) % x) > (x - 1 - start);
- int oldIdx1 = n * (c * old_h * w) + ch * (old_h * w) +
- ((x-1) * ((row - 1) / x) + (row-1) % x - past_startO) * (w) + col;
-
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- (old_data[oldIdx1] + old_data[oldIdx1 + 1 * w]) / 2;
- }
- else
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[n * (c * old_h * w) + ch * (old_h * w) +
- ((x-1) * (row / x) + row % x - past_start ) * (w) + col];
-
-
- }
-
-}
-
-
-//This skips every xth row
-//W_eff is the number of cols calculated exactly
-__global__
-void convToGemmPerfCol(float * const __restrict__ output,
- const float * const __restrict input, const int N, const int C,
- const int H, const int W, const int KH, const int KW, const int V_pad,
- const int H_pad, const int H_out, const int W_out, const int V_stride,
- const int H_stride, const int x, const int start, const int W_eff){
-
- const int tx = blockDim.x * blockIdx.x + threadIdx.x; //thread id
- const int n = tx / (C * H_out * W_eff); //output image number
- const int c = tx % (C * H_out * W_eff) / (H_out * W_eff); //output chan number
- const int h = tx % (H_out * W_eff) / W_eff; //output height index (row number)
- const int w = tx % W_eff; //output width index (col number)
- int past_start = (w % (x - 1)) >= (x - 1 - start);
- const int inH = h * V_stride - V_pad; //input height index (row number)
- const int inW = (w / (x - 1) * x + w % (x-1) +
- past_start) * H_stride - H_pad; //input width index (col number)
- if(n < N) { //is thread id within bounds?
- for(int i = 0; i < KH; i++) {
- for(int j = 0; j < KW; j++) {
- const int filter_elem_num = (c * KH + i) * KW + j; //index of this filter element
-
- if(inH + i >= 0 && inH + i < H && inW + j >= 0 && inW + j < W)
- output[((n * C * KH * KW + filter_elem_num) * H_out + h) * W_eff + w] =
- input[((n * C + c) * H + (inH + i)) * W + (inW + j)];
- else
- output[((n * C * KH * KW + filter_elem_num) * H_out + h) * W_eff + w] = 0;
-
- }
- }
- }
-
-}
-
-
-//For use in tensorConvPerfCuda
-//Interpolates every xth col starting from x - 1 - start
-//N is total number of elements in final output array
-__global__
-void approxInterpolateCol(int N, int old_w, int n, int c, int h, int w,
- float *old_data, float *new_data, int x, int start){
-
- int index = blockIdx.x * blockDim.x + threadIdx.x;
- int stride = blockDim.x * gridDim.x;
-
- for(int i = index; i < N; i += stride){
- int col = ((i % (c * h * w)) % (h * w)) % w;
- int row = ((i % (c * h * w)) % (h * w)) / w;
- int ch = (i % (c * h * w)) / (h * w);
- int n = i / (c * h * w);
- int past_start = ((col % x) >= (x - 1 - start));
-
- if(col == w-1)
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[n * (c * h * old_w) + ch * (h * old_w) + row * (old_w) + old_w - 1];
- else if (col == 0)
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[n * (c * h * old_w) + ch * (h * old_w) + row * (old_w)];
- else if(col % x == x - 1 - start){
- int past_startO = ((col - 1) % x) > (x - 1 - start);
- int oldIdx1 = n * (c * h * old_w) + ch * (h * old_w) + row * old_w +
- ((x-1) * ((col - 1) / x) + (col-1) % x - past_startO);
-
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- (old_data[oldIdx1] + old_data[oldIdx1 + 1]) / 2;
- }
- else
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[n * (c * h * old_w) + ch * (h * old_w) + row * old_w +
- ((x-1) * (col / x) + col % x - past_start)];
-
- }
-
-}
-
-
-
-//start has to be less than row or less than col
-//row and col have to be >= 0
-//row = col = 1 means no perforation
-void* tensorConvPerfCuda(void* input_ptr, void* filter_ptr,
- int vertical_pad, int horizontal_pad, int vertical_stride,
- int horizontal_stride, int conv_mode, int conv_groups,
- int row, int col, int start){
-
- INFO("*** TensorConvolution (output perforation) \n");
- profileEvent("Conv");
- Tensor* input = (Tensor*)input_ptr;
- Tensor* filter = (Tensor*)filter_ptr;
- //FIXME: Current hack to preserve backward compatibilty
- if (conv_groups == 0) {
- conv_groups = 1;
- }
-
- Tensor* output;
- // TODO: Support other cases;
- hostToDeviceCopy(input);
- hostToDeviceCopy(filter);
-
- profileEvent("H2F_start");
- convertToFP32(input);
- convertToFP32(filter);
- profileEvent("H2F_end");
-
- int n, c, h, w; // output dimensions
- n = input->dims.dim_sizes[0];
- c = filter->dims.dim_sizes[0]; //number of filters
- const int KH = filter->dims.dim_sizes[2];
- const int KW = filter->dims.dim_sizes[3];
-
- h = (2 * vertical_pad + input->dims.dim_sizes[2] - KH) / vertical_stride + 1;
- int h_eff = h - h / row;
- if(h % row > row - 1 - start)
- h_eff = h_eff - 1;
-
- w = (2 * horizontal_pad + input->dims.dim_sizes[3] - KW) / horizontal_stride + 1;
- int w_eff = w - w / col;
- if(w % col > col - 1 - start)
- w_eff = w_eff - 1;
-
-
- Tensor *new_output;
- if(row > 1){
- output = (Tensor*) create4DTensor((cudnnDataType_t) float_type, // input->data_type,
- CUDNN_TENSOR_NCHW, n, c, h_eff, w);
-
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(output, DEVICE);
- // NOTE: Necessary to insert the above call for every output tensor
- //total number of filter elem
- const int num_filter_elem = KH * KW * input->dims.dim_sizes[1];
-
- float * convData;
- int convDataSize = sizeof(float) * n * num_filter_elem * h_eff * w;
- checkCudaErrors(cudaMalloc(&convData, convDataSize));
-
- const int blockSize = 128;
- const int gridSize = (n * input->dims.dim_sizes[1] * h_eff * w + blockSize - 1) / blockSize;
-
- convToGemmPerfRow<<<gridSize, blockSize>>>(convData, (float *)input->gpu_data, n,
- input->dims.dim_sizes[1], input->dims.dim_sizes[2],
- input->dims.dim_sizes[3], KH, KW, vertical_pad,
- horizontal_pad, h, w,
- vertical_stride, horizontal_stride, row, start, h_eff);
-
-
- checkCudaErrors(cudaDeviceSynchronize());
-
- float alpha = 1.0f, beta = 0.0f;
- checkCudaErrors(cublasSgemmStridedBatched(cublasHandle,
- CUBLAS_OP_N, CUBLAS_OP_N,
- h_eff * w, c, num_filter_elem,
- &alpha,
- convData, h_eff * w, num_filter_elem * h_eff * w,
- (float *)filter->gpu_data, num_filter_elem, 0,
- &beta,
- (float *)output->gpu_data, h_eff * w, c * h_eff * w,
- n));
-
- new_output = (Tensor*) create4DTensor((cudnnDataType_t) float_type, // input->data_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(new_output, DEVICE);
-
- //interpolate
- int numBlocks = (n * c * h * w + 127) / 128;
- approxInterpolateRow<<<numBlocks,128>>>(n * c * h * w, h_eff, n, c, h, w,
- (float *) output->gpu_data, (float *) new_output->gpu_data,
- row, start);
- cudaDeviceSynchronize();
-
- freeTensor(output);
- cudaFree(convData);
- }
- else if(col > 1){
-
- output = (Tensor*)create4DTensor((cudnnDataType_t) float_type, //input->data_type,
- CUDNN_TENSOR_NCHW, n, c, h, w_eff);
-
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(output, DEVICE);
- // NOTE: Necessary to insert the above call for every output tensor
- //total number of filter elem
- const int num_filter_elem = KH * KW * input->dims.dim_sizes[1];
-
- float * convData;
- int convDataSize = sizeof(float) * n * num_filter_elem * h * w_eff;
- checkCudaErrors(cudaMalloc(&convData, convDataSize));
-
- const int blockSize = 128;
- const int gridSize = (n * input->dims.dim_sizes[1] * h * w_eff + blockSize - 1) / blockSize;
-
- convToGemmPerfCol<<<gridSize, blockSize>>>(convData, (float *)input->gpu_data, n,
- input->dims.dim_sizes[1], input->dims.dim_sizes[2],
- input->dims.dim_sizes[3], KH, KW,
- vertical_pad, horizontal_pad, h, w,
- vertical_stride, horizontal_stride, col, start, w_eff);
-
-
- checkCudaErrors(cudaDeviceSynchronize());
-
- float alpha = 1.0f, beta = 0.0f;
- checkCudaErrors(cublasSgemmStridedBatched(cublasHandle,
- CUBLAS_OP_N, CUBLAS_OP_N,
- h * w_eff, c, num_filter_elem,
- &alpha,
- convData, h * w_eff, num_filter_elem * h * w_eff,
- (float *)filter->gpu_data, num_filter_elem, 0,
- &beta,
- (float *)output->gpu_data, h * w_eff, c * h * w_eff,
- n));
-
- new_output = (Tensor*) create4DTensor((cudnnDataType_t) float_type, // input->data_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(new_output, DEVICE);
-
- //interpolate
- int numBlocks = (n * c * h * w + 127) / 128;
- approxInterpolateCol<<<numBlocks,128>>>(n * c * h * w, w_eff, n, c, h, w,
- (float *)output->gpu_data, (float *)new_output->gpu_data,
- col, start);
- cudaDeviceSynchronize();
-
- freeTensor(output);
- cudaFree(convData);
- }
- else{
- output = (Tensor*)create4DTensor((cudnnDataType_t) float_type, // input->data_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
-
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(output, DEVICE);
- // NOTE: Necessary to insert the above call for every output tensor
- //total number of filter elem
- const int num_filter_elem = KH * KW * input->dims.dim_sizes[1];
-
- float * convData;
- int convDataSize = sizeof(float) * n * num_filter_elem * h * w;
- checkCudaErrors(cudaMalloc(&convData, convDataSize));
-
- const int blockSize = 128;
- const int gridSize = (n * input->dims.dim_sizes[1] * h * w + blockSize - 1) / blockSize;
- convToGemmApprox<<<gridSize, blockSize>>>(convData, (float *)input->gpu_data, n,
- input->dims.dim_sizes[1], input->dims.dim_sizes[2],
- input->dims.dim_sizes[3], KH, KW,
- vertical_pad, horizontal_pad, h, w,
- vertical_stride, horizontal_stride,
- num_filter_elem, c * h * w);
- checkCudaErrors(cudaDeviceSynchronize());
- //Do the matrix multiplication. Want to multiply convData by filter->gpu_data[f * chan * KH * KW]
- float alpha = 1.0f, beta = 0.0f;
- checkCudaErrors(cublasSgemmStridedBatched(cublasHandle,
- CUBLAS_OP_N, CUBLAS_OP_N,
- h * w, c, num_filter_elem,
- &alpha,
- convData, h * w, num_filter_elem * h * w,
- (float *)filter->gpu_data, num_filter_elem, 0,
- &beta,
- (float *)output->gpu_data, h * w, c * h * w,
- n));
-
- new_output = output;
- cudaFree(convData);
- }
-
-
- profileEvent("Conv_end"); //, true);
-
-
- return new_output;
-}
-
-__global__
-void convToGemmPerfRowHalf(__half * const __restrict__ output,
- const __half * const __restrict input, const int N, const int C,
- const int H, const int W, const int KH, const int KW, const int V_pad,
- const int H_pad, const int H_out, const int W_out, const int V_stride,
- const int H_stride, const int x, const int start, const int H_eff){
-
- const int tx = blockDim.x * blockIdx.x + threadIdx.x; //thread id
- const int n = tx / (C * H_eff * W_out); //output image number
- const int c = tx % (C * H_eff * W_out) / (H_eff * W_out); //output chan number
- const int h = tx % (H_eff * W_out) / W_out; //output height index (row number)
- const int w = tx % W_out; //output width index (col number)
- int past_start = (h % (x - 1) >= (x - 1 - start));
- const int inH = (h / (x - 1) * x + h % (x-1) +
- past_start) * V_stride - V_pad; //input height index (row number)
- const int inW = w * H_stride - H_pad; //input width index (col number)
- if(n < N) { //is thread id within bounds?
- for(int i = 0; i < KH; i++) {
- for(int j = 0; j < KW; j++) {
- const int filter_elem_num = (c * KH + i) * KW + j; //index of this filter element
-
- if(inH + i >= 0 && inH + i < H && inW + j >= 0 && inW + j < W)
- output[((filter_elem_num * N + n) * H_eff + h) * W_out + w] =
- input[((n * C + c) * H + (inH + i)) * W + (inW + j)];
- else
- output[((filter_elem_num * N + n) * H_eff + h) * W_out + w] = 0;
-
- }
- }
- }
-
-}
-
-
-//For use in tensorConvPerfCuda
-//Interpolates every xth row starting from x - 1 - start
-//N is total number of elements in final output array
-__global__
-void approxInterpolateRowHalf(int N, int old_h, int b, int c, int h, int w,
- __half *old_data, __half *new_data, int x, int start){
-
- int index = blockIdx.x * blockDim.x + threadIdx.x;
- int stride = blockDim.x * gridDim.x;
-
- for(int i = index; i < N; i += stride){
- int col = ((i % (c * h * w)) % (h * w)) % w;
- int row = ((i % (c * h * w)) % (h * w)) / w;
- int ch = (i % (c * h * w)) / (h * w);
- int n = i / (c * h * w);
- int past_start = ((row % x) >= (x - 1 - start));
-
- if(row == h-1)
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[ch * (b * old_h * w) + n * (old_h * w) + (old_h - 1) * (w) + col];
- else if (row == 0)
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[ch * (b * old_h * w) + n * (old_h * w) + 0 * (w) + col];
- else if(row % x == x - 1 - start){
- int past_startO = ((row - 1) % x) > (x - 1 - start);
- int oldIdx1 = ch * (b * old_h * w) + n * (old_h * w) +
- ((x-1) * ((row - 1) / x) + (row-1) % x - past_startO) * (w) + col;
-
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- __hdiv(__hadd(old_data[oldIdx1], old_data[oldIdx1 + 1 * w]), 2);
- }
- else
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[ch * (b * old_h * w) + n * (old_h * w) +
- ((x-1) * (row / x) + row % x - past_start ) * (w) + col];
-
-
- }
-
-}
-
-
-//This skips every xth row
-//W_eff is the number of cols calculated exactly
-__global__
-void convToGemmPerfColHalf(__half * const __restrict__ output,
- const __half * const __restrict input, const int N, const int C,
- const int H, const int W, const int KH, const int KW, const int V_pad,
- const int H_pad, const int H_out, const int W_out, const int V_stride,
- const int H_stride, const int x, const int start, const int W_eff){
-
- const int tx = blockDim.x * blockIdx.x + threadIdx.x; //thread id
- const int n = tx / (C * H_out * W_eff); //output image number
- const int c = tx % (C * H_out * W_eff) / (H_out * W_eff); //output chan number
- const int h = tx % (H_out * W_eff) / W_eff; //output height index (row number)
- const int w = tx % W_eff; //output width index (col number)
- int past_start = (w % (x - 1)) >= (x - 1 - start);
- const int inH = h * V_stride - V_pad; //input height index (row number)
- const int inW = (w / (x - 1) * x + w % (x-1) +
- past_start) * H_stride - H_pad; //input width index (col number)
- if(n < N) { //is thread id within bounds?
- for(int i = 0; i < KH; i++) {
- for(int j = 0; j < KW; j++) {
- const int filter_elem_num = (c * KH + i) * KW + j; //index of this filter element
-
- if(inH + i >= 0 && inH + i < H && inW + j >= 0 && inW + j < W)
- output[((filter_elem_num * N + n) * H_out + h) * W_eff + w] =
- input[((n * C + c) * H + (inH + i)) * W + (inW + j)];
- else
- output[((filter_elem_num * N + n) * H_out + h) * W_eff + w] = 0;
-
- }
- }
- }
-
-}
-
-
-//For use in tensorConvPerfCuda
-//Interpolates every xth col starting from x - 1 - start
-//N is total number of elements in final output array
-__global__
-void approxInterpolateColHalf(int N, int old_w, int b, int c, int h, int w,
- __half *old_data, __half *new_data, int x, int start){
-
-
- int index = blockIdx.x * blockDim.x + threadIdx.x;
- int stride = blockDim.x * gridDim.x;
-
- for(int i = index; i < N; i += stride){
- int col = ((i % (c * h * w)) % (h * w)) % w;
- int row = ((i % (c * h * w)) % (h * w)) / w;
- int ch = (i % (c * h * w)) / (h * w);
- int n = i / (c * h * w);
- int past_start = ((col % x) >= (x - 1 - start));
-
- if(col == w-1)
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[ch * (b * h * old_w) + n * (h * old_w) + row * (old_w) + old_w - 1];
- else if (col == 0)
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[ch * (b * h * old_w) + n * (h * old_w) + row * (old_w)];
- else if(col % x == x - 1 - start){
- int past_startO = ((col - 1) % x) > (x - 1 - start);
- int oldIdx1 = ch * (b * h * old_w) + n * (h * old_w) + row * old_w +
- ((x-1) * ((col - 1) / x) + (col-1) % x - past_startO);
-
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- __hdiv(__hadd(old_data[oldIdx1], old_data[oldIdx1 + 1]), 2);
- }
- else
- new_data[n * (c * h * w) + ch * (h * w) + row * (w) + col] =
- old_data[ch * (b * h * old_w) + n * (h * old_w) + row * old_w +
- ((x-1) * (col / x) + col % x - past_start)];
-
- }
-}
-
-__global__
-void switchMatrix(int N, int n, int c, int h, int w, __half *old_data, __half *new_data){
-
- int i = blockIdx.x * blockDim.x + threadIdx.x;
- if(i < N){
- int col = ((i % (c * h * w)) % (h * w)) % w;
- int row = ((i % (c * h * w)) % (h * w)) / w;
- int ch = (i % (c * h * w)) / (h * w);
- int n_new = i / (c * h * w);
-
- new_data[((n_new * c + ch) * h + row ) * w + col] =
- old_data[((ch * n + n_new) * h + row ) * w + col];
- }
-
-}
-
-
-//produces N COL MAJOR matrixes with H_out*W_out rows and reduced_filter_elem cols
-__global__ void convToGemmApproxHalfN(__half * const __restrict__ output,
- const __half * const __restrict input, const int N, const int C,
- const int H, const int W, const int KH, const int KW, const int V_pad,
- const int H_pad, const int H_out, const int W_out, const int V_stride,
- const int H_stride, const int reduced_filter_elem,
- const int skip_every) {
- const int tx = blockDim.x * blockIdx.x + threadIdx.x; //thread id
- const int n = tx / (C * H_out * W_out); //output image number
- const int c = tx % (C * H_out * W_out) / (H_out * W_out); //output chan number
- const int h = tx % (H_out * W_out) / W_out; //output height index (row number)
- const int w = tx % W_out; //output width index (col number)
- const int inH = h * V_stride - V_pad; //input height index (row number)
- const int inW = w * H_stride - H_pad; //input width index (col number)
- if(n < N) { //is thread id within bounds?
- for(int i = 0; i < KH; i++) {
- for(int j = 0; j < KW; j++) {
- const int filter_elem_num = (c * KH + i) * KW + j; //index of this filter element
- const int output_col = filter_elem_num; //calculate output column, taking skipping into account
- if(inH + i >= 0 && inH + i < H && inW + j >= 0 && inW + j < W)
- output[((output_col * N + n) * H_out + h) * W_out + w] =
- input[((n * C + c) * H + (inH + i)) * W + (inW + j)];
- else
- output[((output_col * N + n) * H_out + h) * W_out + w] = 0;
-
- }
- }
- }
-}
-
-//start has to be less than row or less than col
-//row and col have to be >= 0
-//row = col = 1 means no perforation
-void* tensorConvPerfCudaHalf(void* input_ptr, void* filter_ptr,
- int vertical_pad, int horizontal_pad, int vertical_stride,
- int horizontal_stride, int conv_mode, int conv_groups,
- int row, int col, int start){
-
- INFO("*** TensorConvolution half perforation \n");
- profileEvent("#Conv");
-
- Tensor* input = (Tensor*)input_ptr;
- Tensor* filter = (Tensor*)filter_ptr;
- //FIXME: Current hack to preserve backward compatibilty
- if (conv_groups == 0) {
- conv_groups = 1;
- }
-
- hostToDeviceCopy(input);
- hostToDeviceCopy(filter);
-
- profileEvent("F2H_start");
- convertToFP16(input);
- convertToFP16(filter);
- profileEvent("F2H_end");
-
- Tensor* output_half;
- int n, c, h, w; // output dimensions
- n = input->dims.dim_sizes[0];
- c = filter->dims.dim_sizes[0]; //number of filters
- const int KH = filter->dims.dim_sizes[2];
- const int KW = filter->dims.dim_sizes[3];
-
- h = (2 * vertical_pad + input->dims.dim_sizes[2] - KH) / vertical_stride + 1;
- int h_eff = h - h / row;
- if(h % row > row - 1 - start)
- h_eff = h_eff - 1;
-
- w = (2 * horizontal_pad + input->dims.dim_sizes[3] - KW) / horizontal_stride + 1;
- int w_eff = w - w / col;
- if(w % col > col - 1 - start)
- w_eff = w_eff - 1;
-
-
- Tensor *new_output;
- if(row > 1){
- output_half = (Tensor*)create4DTensor((cudnnDataType_t) half_type, CUDNN_TENSOR_NCHW,
- n, c, h_eff, w);
-
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(output_half, DEVICE);
- // NOTE: Necessary to insert the above call for every output tensor
- //total number of filter elem
- const int num_filter_elem = KH * KW * input->dims.dim_sizes[1];
-
- __half * convData;
- int convDataSize = sizeof(__half) * n * num_filter_elem * h_eff * w;
- checkCudaErrors(cudaMalloc(&convData, convDataSize));
-
- const int blockSize = 256;
- const int gridSize = (n * input->dims.dim_sizes[1] * h_eff * w + blockSize - 1) / blockSize;
-
- convToGemmPerfRowHalf<<<gridSize, blockSize>>>(convData, (__half *)input->gpu_half_data, n,
- input->dims.dim_sizes[1], input->dims.dim_sizes[2],
- input->dims.dim_sizes[3], KH, KW, vertical_pad,
- horizontal_pad, h, w,
- vertical_stride, horizontal_stride, row, start, h_eff);
-
-
- checkCudaErrors(cudaDeviceSynchronize());
-
- const __half alf = approx_float_to_half(1.0);
- const __half bet = approx_float_to_half(0.0);
- const __half *alpha_half = &alf;
- const __half *beta_half = &bet;
-
- checkCudaErrors(cublasGemmEx(cublasHandle, CUBLAS_OP_N, CUBLAS_OP_N,
- n * h_eff * w, c, num_filter_elem,
- alpha_half,
- convData, CUDA_R_16F, n * h_eff * w,
- (__half*) filter->gpu_half_data, CUDA_R_16F, num_filter_elem,
- beta_half,
- (__half*) output_half->gpu_half_data, CUDA_R_16F, n * h_eff * w,
- CUDA_R_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP) );
-
-
- new_output = (Tensor*)create4DTensor((cudnnDataType_t) half_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
-
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(new_output, DEVICE);
-
- //interpolate
- int numBlocks = (n * c * h * w + 255) / 256;
- approxInterpolateRowHalf<<<numBlocks,256>>>(n * c * h * w, h_eff, n, c, h, w,
- (__half *)output_half->gpu_half_data,
- (__half *)new_output->gpu_half_data,
- row, start);
- cudaDeviceSynchronize();
-
- freeTensor(output_half);
- cudaFree(convData);
- }
- else if(col > 1){
- output_half = (Tensor*)create4DTensor((cudnnDataType_t) half_type,
- CUDNN_TENSOR_NCHW, n, c, h, w_eff);
-
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(output_half, DEVICE);
- // NOTE: Necessary to insert the above call for every output tensor
- //total number of filter elem
- const int num_filter_elem = KH * KW * input->dims.dim_sizes[1];
-
- __half * convData;
- int convDataSize = sizeof(__half) * n * num_filter_elem * h * w_eff;
- checkCudaErrors(cudaMalloc(&convData, convDataSize));
-
- const int blockSize = 256;
- const int gridSize = (n * input->dims.dim_sizes[1] * h * w_eff + blockSize - 1) / blockSize;
-
- convToGemmPerfColHalf<<<gridSize, blockSize>>>(convData, (__half *)input->gpu_half_data, n,
- input->dims.dim_sizes[1], input->dims.dim_sizes[2],
- input->dims.dim_sizes[3], KH, KW, vertical_pad,
- horizontal_pad, h, w,
- vertical_stride, horizontal_stride, col, start, w_eff);
-
-
- checkCudaErrors(cudaDeviceSynchronize());
-
- const __half alf = approx_float_to_half(1.0);
- const __half bet = approx_float_to_half(0.0);
- const __half *alpha_half = &alf;
- const __half *beta_half = &bet;
-
-
- checkCudaErrors(cublasGemmEx(cublasHandle, CUBLAS_OP_N, CUBLAS_OP_N,
- n * h * w_eff, c, num_filter_elem,
- alpha_half,
- convData, CUDA_R_16F, n * h * w_eff,
- (__half*) filter->gpu_half_data, CUDA_R_16F, num_filter_elem,
- beta_half,
- (__half*) output_half->gpu_half_data, CUDA_R_16F, n * h * w_eff,
- CUDA_R_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP) );
-
-
- new_output = (Tensor*)create4DTensor((cudnnDataType_t) half_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
-
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(new_output, DEVICE);
-
- //interpolate
- int numBlocks = (n * c * h * w + 255) / 256;
- approxInterpolateColHalf<<<numBlocks,256>>>(n * c * h * w, w_eff, n, c, h, w,
- (__half *)output_half->gpu_half_data,
- (__half *)new_output->gpu_half_data,
- col, start);
-
- cudaDeviceSynchronize();
-
- freeTensor(output_half);
- cudaFree(convData);
-
- }
- else{
- output_half = (Tensor*)create4DTensor((cudnnDataType_t) half_type,
- CUDNN_TENSOR_NCHW, c, n, h, w);
-
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(output_half, DEVICE);
- // NOTE: Necessary to insert the above call for every output tensor
- //total number of filter elem
- const int num_filter_elem = KH * KW * input->dims.dim_sizes[1];
-
- __half * convData;
- int convDataSize = sizeof(__half) * n * num_filter_elem * h * w;
- checkCudaErrors(cudaMalloc(&convData, convDataSize));
-
- const int blockSize = 256;
- const int gridSize = (n * input->dims.dim_sizes[1] * h * w + blockSize - 1) / blockSize;
- convToGemmApproxHalfN<<<gridSize, blockSize>>>(convData, (__half *)input->gpu_half_data, n,
- input->dims.dim_sizes[1], input->dims.dim_sizes[2],
- input->dims.dim_sizes[3], KH, KW,
- vertical_pad, horizontal_pad, h, w,
- vertical_stride, horizontal_stride,
- num_filter_elem, c * h * w);
- checkCudaErrors(cudaDeviceSynchronize());
- //Do the matrix multiplication. Want to multiply convData by filter->gpu_data[f * chan * KH * KW]
- const __half alf = approx_float_to_half(1.0);
- const __half bet = approx_float_to_half(0.0);
- const __half *alpha_half = &alf;
- const __half *beta_half = &bet;
-
- checkCudaErrors(cublasGemmEx(cublasHandle, CUBLAS_OP_N, CUBLAS_OP_N,
- n * h * w, c, num_filter_elem,
- alpha_half,
- convData, CUDA_R_16F, n * h * w,
- (__half*) filter->gpu_half_data, CUDA_R_16F, num_filter_elem,
- beta_half,
- (__half*) output_half->gpu_half_data, CUDA_R_16F, n * h * w,
- CUDA_R_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP) );
-
-
-
- // profileEvent("gemm_end", true);
- new_output = (Tensor*)create4DTensor((cudnnDataType_t) half_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
- changeTensorPlacement(new_output, DEVICE);
-
-
- int numBlocks = (n * c * h * w + 255) / 256;
- switchMatrix<<<numBlocks,256>>>(n * c * h * w, n, c, h, w,
- (__half *)output_half->gpu_half_data,
- (__half *)new_output->gpu_half_data);
-
- checkCudaErrors(cudaDeviceSynchronize());
-
- cudaFree(convData);
- freeTensor(output_half);
- }
-
- //profileEvent("Conv_end", true);
-
- profileEvent("H2F_start");
- convertToFP32_offline(new_output);
- profileEvent("H2F_end");
-
- profileEvent("#Conv_end"); //, true);
-
- return new_output;
-}
-
-
-//produces COL MAJOR matrix with reduced_filter_elem rows and NF cols
-__global__ void createReducedFiltersHalf(__half * output,
- const __half * const __restrict input, const int NF,
- const int num_filter_elem, const int reduced_filter_elem,
- const int skip_every, const int skip_offset) {
- const int tx = blockDim.x * blockIdx.x + threadIdx.x; //thread id
- const int fIdx = tx / num_filter_elem; //filter index
- const int offset = tx % num_filter_elem; //offset within filter
- if(fIdx < NF) { //is thread id within bounds?
- if(offset % skip_every != skip_every-1-skip_offset) { //are we including this filter element?
- const int output_row = offset - ((offset + skip_every)/skip_every); //correct for skip_every = 2
- output[fIdx*reduced_filter_elem + output_row] =
- __hmul((skip_every * 1.0 / (skip_every - 1)), input[tx]);
- }
- }
-}
-
-
-//COL Major matrix with N*H*W columns and reduced_filter_elem rows
-//skip_every = 1 means no perforation
-__global__ void convToGemmHalfInput(__half * const __restrict__ output,
- const __half * const __restrict input,
- const int N, const int C,
- const int H, const int W,
- const int KH, const int KW, const int V_pad,
- const int H_pad, const int H_out,
- const int W_out, const int V_stride,
- const int H_stride, const int reduced_filter_elem,
- const int skip_every, const int skip_offset) {
- const int tx = blockDim.x * blockIdx.x + threadIdx.x; //thread id
- const int n = tx / (C * H_out * W_out); //output image number
- const int c = tx % (C * H_out * W_out) / (H_out * W_out); //output chan number
- const int h = tx % (H_out * W_out) / W_out; //output height index (row number)
- const int w = tx % W_out; //output width index (col number)
- const int inH = h * V_stride - V_pad; //input height index (row number)
- const int inW = w * H_stride - H_pad; //input width index (col number)
- if(n < N) { //is thread id within bounds?
- for(int i = 0; i < KH; i++) {
- for(int j = 0; j < KW; j++) {
- const int filter_elem_num = (c * KH + i) * KW + j; //index of this filter element
-
- if(filter_elem_num % skip_every != skip_every-1-skip_offset) {
- int output_col = filter_elem_num -
- ((filter_elem_num + skip_every)/skip_every);
- if(skip_every == 1)
- output_col = filter_elem_num;
- if(inH + i >= 0 && inH + i < H && inW + j >= 0 && inW + j < W)
- output[((output_col*N + n) * H_out + h) * W_out + w] =
- input[((n * C + c) * H + (inH + i)) * W + (inW + j)];
- else
- output[((output_col*N + n) * H_out + h) * W_out + w] = 0;
- }
- }
- }
- }
-}
-
-
-//COL Major matrix with N*H*W columns and reduced_filter_elem rows
-//Can only be used when skipping every other element in input sampling
-__global__ void convToGemmHalfInput2(__half * const __restrict__ output,
- const __half * const __restrict input,
- const int N, const int C,
- const int H, const int W,
- const int KH, const int KW, const int V_pad,
- const int H_pad, const int H_out,
- const int W_out, const int V_stride,
- const int H_stride, const int reduced_filter_elem,
- const int skip_every, const int skip_offset) {
- const int tx = blockDim.x * blockIdx.x + threadIdx.x; //thread id
- const int n = tx / (C * H_out * W_out); //output image number
- const int c = tx % (C * H_out * W_out) / (H_out * W_out); //output chan number
- const int h = tx % (H_out * W_out) / W_out; //output height index (row number)
- const int w = tx % W_out; //output width index (col number)
- const int inH = h * V_stride - V_pad; //input height index (row number)
- const int inW = w * H_stride - H_pad; //input width index (col number)
- if(n < N) { //is thread id within bounds?
- const int filter_elem_num = c * KH * KW;
- for(int l = (filter_elem_num % 2) + skip_offset; l < KH * KW; l+=2) {
- int i = l / KW;
- int j = l % KW;
-
- const int new_idx = filter_elem_num + i * KW + j;
- const int output_col = new_idx - ((new_idx + skip_every)/2); //new output column
- if(inH + i >= 0 && inH + i < H && inW + j >= 0 && inW + j < W)
- output[((output_col*N + n) * H_out + h) * W_out + w] =
- input[((n * C + c) * H + (inH + i)) * W + (inW + j)];
- else
- output[((output_col*N + n) * H_out + h) * W_out + w] = 0;
-
- }
- }
-}
-
-//Baseline: skip_offset = skip_every = 1
-void* tensorConvInputHalf(void* input_ptr, void* filter_ptr,
- int vertical_pad, int horizontal_pad, int vertical_stride,
- int horizontal_stride, int conv_mode, int conv_groups,
- int skip_every, int skip_offset){
-
- INFO("*** TensorHConvolution input sampling \n");
- profileEvent("#Conv");
-
- Tensor* input = (Tensor*)input_ptr;
- Tensor* filter = (Tensor*)filter_ptr;
- //FIXME: Current hack to preserve backward compatibilty
- if (conv_groups == 0) {
- conv_groups = 1;
- }
-
- hostToDeviceCopy(input);
- hostToDeviceCopy(filter);
-
- profileEvent("F2H_start");
- convertToFP16(input);
- convertToFP16(filter);
- profileEvent("F2H_end");
-
- Tensor* output;
- Tensor* new_output;
- // TODO: Support other cases;
- int n, c, h, w; // output dimensions
- n = input->dims.dim_sizes[0];
- c = filter->dims.dim_sizes[0]; //number of filters
- const int KH = filter->dims.dim_sizes[2];
- const int KW = filter->dims.dim_sizes[3];
- h = (2 * vertical_pad + input->dims.dim_sizes[2] - KH) / vertical_stride + 1;
- w = (2 * horizontal_pad + input->dims.dim_sizes[3] - KW) / horizontal_stride + 1;
- output = (Tensor*)create4DTensor((cudnnDataType_t) half_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
- new_output = (Tensor*)create4DTensor((cudnnDataType_t) half_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
-
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(output, DEVICE);
- changeTensorPlacement(new_output, DEVICE);
- // NOTE: Necessary to insert the above call for every output tensor
-
- //total number of filter elem
- const int num_filter_elem = KH * KW * input->dims.dim_sizes[1];
- //reduced number after skipping
- int reduced_filter_elem;
- if(skip_offset != skip_every){
- reduced_filter_elem = num_filter_elem - (num_filter_elem/skip_every);
- if(num_filter_elem % skip_every > skip_every - 1 - skip_offset)
- reduced_filter_elem = reduced_filter_elem - 1;
- }
- else
- reduced_filter_elem = num_filter_elem;
-
- __half * convData;
- int convDataSize = sizeof(__half) * n * reduced_filter_elem * h * w;
- checkCudaErrors(cudaMalloc(&convData, convDataSize));
- __half * reducedFilter;
- checkCudaErrors(cudaMalloc(&reducedFilter, sizeof(__half) * c * reduced_filter_elem));
- const int filtBlockSize = 128;
- const int filtGridSize = (c * num_filter_elem + filtBlockSize - 1) / filtBlockSize;
- if(skip_offset != skip_every)
- createReducedFiltersHalf<<<filtGridSize, filtBlockSize>>>(reducedFilter,
- (__half *)filter->gpu_half_data,
- c, num_filter_elem, reduced_filter_elem,
- skip_every, skip_offset);
- checkCudaErrors(cudaDeviceSynchronize());
-
- const int blockSize = 256;
- const int gridSize = (n * input->dims.dim_sizes[1] * h * w + blockSize - 1) / blockSize;
- if(skip_every == 2){
- convToGemmHalfInput2<<<gridSize, blockSize>>>(convData, (__half *)input->gpu_half_data, n,
- input->dims.dim_sizes[1],
- input->dims.dim_sizes[2],
- input->dims.dim_sizes[3],
- KH, KW, vertical_pad, horizontal_pad,
- h, w, vertical_stride, horizontal_stride,
- reduced_filter_elem, skip_every,
- skip_offset);
- }
- else{
- convToGemmHalfInput<<<gridSize, blockSize>>>(convData, (__half *)input->gpu_half_data, n,
- input->dims.dim_sizes[1],
- input->dims.dim_sizes[2],
- input->dims.dim_sizes[3],
- KH, KW, vertical_pad, horizontal_pad,
- h, w, vertical_stride, horizontal_stride,
- reduced_filter_elem, skip_every,
- skip_offset);
- }
-
- checkCudaErrors(cudaDeviceSynchronize());
- //Do the matrix multiplication. Want to multiply convData by filter->gpu_data[f * chan * KH * KW]
- const __half alf = approx_float_to_half(1.0);
- const __half bet = approx_float_to_half(0.0);
- const __half *alpha_half = &alf;
- const __half *beta_half = &bet;
-
- if(skip_offset != skip_every)
- checkCudaErrors(cublasGemmEx(cublasHandle, CUBLAS_OP_N, CUBLAS_OP_N,
- n * h * w, c, reduced_filter_elem,
- alpha_half,
- convData, CUDA_R_16F, n * h * w,
- reducedFilter, CUDA_R_16F, reduced_filter_elem,
- beta_half,
- (__half*) output->gpu_half_data, CUDA_R_16F, n * h * w,
- CUDA_R_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP) );
- else
- checkCudaErrors(cublasGemmEx(cublasHandle, CUBLAS_OP_N, CUBLAS_OP_N,
- n * h * w, c, reduced_filter_elem,
- alpha_half,
- convData, CUDA_R_16F, n * h * w,
- (__half*) filter->gpu_half_data, CUDA_R_16F,
- reduced_filter_elem,
- beta_half,
- (__half*) output->gpu_half_data, CUDA_R_16F, n * h * w,
- CUDA_R_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP) );
-
-
- int numBlocks = (n * c * h * w + 255) / 256;
- switchMatrix<<<numBlocks,256>>>(n * c * h * w, n, c, h, w,
- (__half *)output->gpu_half_data,
- (__half *)new_output->gpu_half_data);
-
- checkCudaErrors(cudaDeviceSynchronize());
-
- cudaFree(convData);
- cudaFree(reducedFilter);
- freeTensor(output);
-
- profileEvent("H2F_start");
-
- // NOTE: Transforming half precision output to single precision
- convertToFP32_offline(new_output);
-
- profileEvent("H2F_end");
-
- profileEvent("#Conv_end", true);
-
- return new_output;
-
-}
-
-//COL Major matrix with N*H*W columns and reduced_filter_elem rows
-//skip_every = 1 means no perforation
-__global__ void convToGemmFullInput(float * const __restrict__ output,
- const float * const __restrict input,
- const int N, const int C,
- const int H, const int W,
- const int KH, const int KW, const int V_pad,
- const int H_pad, const int H_out,
- const int W_out, const int V_stride,
- const int H_stride, const int reduced_filter_elem,
- const int skip_every, const int skip_offset) {
- const int tx = blockDim.x * blockIdx.x + threadIdx.x; //thread id
- const int n = tx / (C * H_out * W_out); //output image number
- const int c = tx % (C * H_out * W_out) / (H_out * W_out); //output chan number
- const int h = tx % (H_out * W_out) / W_out; //output height index (row number)
- const int w = tx % W_out; //output width index (col number)
- const int inH = h * V_stride - V_pad; //input height index (row number)
- const int inW = w * H_stride - H_pad; //input width index (col number)
- if(n < N) { //is thread id within bounds?
- for(int i = 0; i < KH; i++) {
- for(int j = 0; j < KW; j++) {
- const int filter_elem_num = (c * KH + i) * KW + j; //index of this filter element
-
- if(filter_elem_num % skip_every != skip_every-1-skip_offset) {
- int output_col = filter_elem_num -
- ((filter_elem_num + skip_every)/skip_every);
- if(skip_every == 1)
- output_col = filter_elem_num;
- if(inH + i >= 0 && inH + i < H && inW + j >= 0 && inW + j < W)
- output[((output_col*N + n) * H_out + h) * W_out + w] =
- input[((n * C + c) * H + (inH + i)) * W + (inW + j)];
- else
- output[((output_col*N + n) * H_out + h) * W_out + w] = 0;
- }
- }
- }
- }
-}
-
-
-//COL Major matrix with N*H*W columns and reduced_filter_elem rows
-//Can only be used when skipping every other element in input sampling
-__global__ void convToGemmFullInput2(float * const __restrict__ output,
- const float * const __restrict input,
- const int N, const int C,
- const int H, const int W,
- const int KH, const int KW, const int V_pad,
- const int H_pad, const int H_out,
- const int W_out, const int V_stride,
- const int H_stride, const int reduced_filter_elem,
- const int skip_every, const int skip_offset) {
- const int tx = blockDim.x * blockIdx.x + threadIdx.x; //thread id
- const int n = tx / (C * H_out * W_out); //output image number
- const int c = tx % (C * H_out * W_out) / (H_out * W_out); //output chan number
- const int h = tx % (H_out * W_out) / W_out; //output height index (row number)
- const int w = tx % W_out; //output width index (col number)
- const int inH = h * V_stride - V_pad; //input height index (row number)
- const int inW = w * H_stride - H_pad; //input width index (col number)
- if(n < N) { //is thread id within bounds?
- const int filter_elem_num = c * KH * KW;
- for(int l = (filter_elem_num % 2) + skip_offset; l < KH * KW; l+=2) {
- int i = l / KW;
- int j = l % KW;
-
- const int new_idx = filter_elem_num + i * KW + j;
- const int output_col = new_idx - ((new_idx + skip_every)/2); //new output column
- if(inH + i >= 0 && inH + i < H && inW + j >= 0 && inW + j < W)
- output[((output_col*N + n) * H_out + h) * W_out + w] =
- input[((n * C + c) * H + (inH + i)) * W + (inW + j)];
- else
- output[((output_col*N + n) * H_out + h) * W_out + w] = 0;
-
- }
- }
-}
-
-
-//produces COL MAJOR matrix with reduced_filter_elem rows and NF cols
-__global__ void createReducedFiltersFull(float * output,
- const float * const __restrict input, const int NF,
- const int num_filter_elem, const int reduced_filter_elem,
- const int skip_every, const int skip_offset) {
- const int tx = blockDim.x * blockIdx.x + threadIdx.x; //thread id
- const int fIdx = tx / num_filter_elem; //filter index
- const int offset = tx % num_filter_elem; //offset within filter
- if(fIdx < NF) { //is thread id within bounds?
- if(offset % skip_every != skip_every-1-skip_offset) { //are we including this filter element?
- const int output_row = offset - ((offset + skip_every)/skip_every); //correct for skip_every = 2
- output[fIdx*reduced_filter_elem + output_row] =
- (skip_every * 1.0 / (skip_every - 1)) * input[tx];
- }
- }
-}
-
-__global__
-void switchMatrixFull(int N, int n, int c, int h, int w,
- float *old_data, float *new_data){
-
- int i = blockIdx.x * blockDim.x + threadIdx.x;
- if(i < N){
- int col = ((i % (c * h * w)) % (h * w)) % w;
- int row = ((i % (c * h * w)) % (h * w)) / w;
- int ch = (i % (c * h * w)) / (h * w);
- int n_new = i / (c * h * w);
-
- new_data[((n_new * c + ch) * h + row ) * w + col] =
- old_data[((ch * n + n_new) * h + row ) * w + col];
- }
-
-}
-
-void* tensorConvApprox(void* input_ptr, void* filter_ptr,
- int vertical_pad, int horizontal_pad, int vertical_stride,
- int horizontal_stride, int conv_mode, int conv_groups,
- int row, int col, int skip_every, int offset){
-
- INFO("*** TensorConvolution approximation \n");
- profileEvent("Conv");
-
- Tensor* input = (Tensor*)input_ptr;
- Tensor* filter = (Tensor*)filter_ptr;
- //FIXME: Current hack to preserve backward compatibilty
- if (conv_groups == 0) {
- conv_groups = 1;
- }
-
- hostToDeviceCopy(input);
- hostToDeviceCopy(filter);
-
- //profileEvent("H2F_start");
- convertToFP32(input);
- convertToFP32(filter);
- //profileEvent("H2F_end");
-
- int n, c, h, w; // output dimensions
- n = input->dims.dim_sizes[0];
- c = filter->dims.dim_sizes[0]; //number of filters
- const int KH = filter->dims.dim_sizes[2];
- const int KW = filter->dims.dim_sizes[3];
-
- h = (2 * vertical_pad + input->dims.dim_sizes[2] - KH) / vertical_stride + 1;
- int h_eff = h - h / row;
- if(h % row > row - 1 - offset)
- h_eff = h_eff - 1;
-
- w = (2 * horizontal_pad + input->dims.dim_sizes[3] - KW) / horizontal_stride + 1;
- int w_eff = w - w / col;
- if(w % col > col - 1 - offset)
- w_eff = w_eff - 1;
-
-
- Tensor *new_output = (Tensor*)create4DTensor((cudnnDataType_t) float_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(new_output, DEVICE);
-
- if(row > 1){
- Tensor *output = (Tensor*) create4DTensor((cudnnDataType_t) float_type, // input->data_type,
- CUDNN_TENSOR_NCHW, n, c, h_eff, w);
-
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(output, DEVICE);
- // NOTE: Necessary to insert the above call for every output tensor
- //total number of filter elem
- const int num_filter_elem = KH * KW * input->dims.dim_sizes[1];
-
- float * convData;
- int convDataSize = sizeof(float) * n * num_filter_elem * h_eff * w;
- checkCudaErrors(cudaMalloc(&convData, convDataSize));
-
- const int blockSize = 128;
- const int gridSize = (n * input->dims.dim_sizes[1] * h_eff * w + blockSize - 1) / blockSize;
-
- convToGemmPerfRow<<<gridSize, blockSize>>>(convData, (float *)input->gpu_data, n,
- input->dims.dim_sizes[1], input->dims.dim_sizes[2],
- input->dims.dim_sizes[3], KH, KW, vertical_pad,
- horizontal_pad, h, w,
- vertical_stride, horizontal_stride,
- row, offset, h_eff);
-
-
- checkCudaErrors(cudaDeviceSynchronize());
-
- float alpha = 1.0f, beta = 0.0f;
- checkCudaErrors(cublasSgemmStridedBatched(cublasHandle,
- CUBLAS_OP_N, CUBLAS_OP_N,
- h_eff * w, c, num_filter_elem,
- &alpha,
- convData, h_eff * w, num_filter_elem * h_eff * w,
- (float *)filter->gpu_data, num_filter_elem, 0,
- &beta,
- (float *)output->gpu_data, h_eff * w, c * h_eff * w,
- n));
-
- new_output = (Tensor*) create4DTensor((cudnnDataType_t) float_type, // input->data_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(new_output, DEVICE);
-
- //interpolate
- int numBlocks = (n * c * h * w + 127) / 128;
- approxInterpolateRow<<<numBlocks,128>>>(n * c * h * w, h_eff, n, c, h, w,
- (float *) output->gpu_data, (float *) new_output->gpu_data,
- row, offset);
- cudaDeviceSynchronize();
-
- freeTensor(output);
- cudaFree(convData);
- }
- else if(col > 1){
-
- Tensor *output = (Tensor*)create4DTensor((cudnnDataType_t) float_type, //input->data_type,
- CUDNN_TENSOR_NCHW, n, c, h, w_eff);
-
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(output, DEVICE);
- // NOTE: Necessary to insert the above call for every output tensor
- //total number of filter elem
- const int num_filter_elem = KH * KW * input->dims.dim_sizes[1];
-
- float * convData;
- int convDataSize = sizeof(float) * n * num_filter_elem * h * w_eff;
- checkCudaErrors(cudaMalloc(&convData, convDataSize));
-
- const int blockSize = 128;
- const int gridSize = (n * input->dims.dim_sizes[1] * h * w_eff + blockSize - 1) / blockSize;
-
- convToGemmPerfCol<<<gridSize, blockSize>>>(convData, (float *)input->gpu_data, n,
- input->dims.dim_sizes[1], input->dims.dim_sizes[2],
- input->dims.dim_sizes[3], KH, KW,
- vertical_pad, horizontal_pad, h, w,
- vertical_stride, horizontal_stride,
- col, offset, w_eff);
-
-
- checkCudaErrors(cudaDeviceSynchronize());
-
- float alpha = 1.0f, beta = 0.0f;
- checkCudaErrors(cublasSgemmStridedBatched(cublasHandle,
- CUBLAS_OP_N, CUBLAS_OP_N,
- h * w_eff, c, num_filter_elem,
- &alpha,
- convData, h * w_eff, num_filter_elem * h * w_eff,
- (float *)filter->gpu_data, num_filter_elem, 0,
- &beta,
- (float *)output->gpu_data, h * w_eff, c * h * w_eff,
- n));
-
- new_output = (Tensor*) create4DTensor((cudnnDataType_t) float_type, // input->data_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(new_output, DEVICE);
-
- //interpolate
- int numBlocks = (n * c * h * w + 127) / 128;
- approxInterpolateCol<<<numBlocks,128>>>(n * c * h * w, w_eff, n, c, h, w,
- (float *)output->gpu_data, (float *)new_output->gpu_data,
- col, offset);
- cudaDeviceSynchronize();
-
- freeTensor(output);
- cudaFree(convData);
- }
- else{
- Tensor *output = (Tensor*)create4DTensor((cudnnDataType_t) float_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
-
- //total number of filter elem
- const int num_filter_elem = KH * KW * input->dims.dim_sizes[1];
- //reduced number after skipping
- int reduced_filter_elem;
- if(offset != skip_every){
- reduced_filter_elem = num_filter_elem - (num_filter_elem/skip_every);
- if(num_filter_elem % skip_every > skip_every - 1 - offset)
- reduced_filter_elem = reduced_filter_elem - 1;
- }
- else
- reduced_filter_elem = num_filter_elem;
-
-
- float * convData;
- int convDataSize = sizeof(float) * n * reduced_filter_elem * h * w;
- checkCudaErrors(cudaMalloc(&convData, convDataSize));
- float * reducedFilter;
- checkCudaErrors(cudaMalloc(&reducedFilter, sizeof(float) * c * reduced_filter_elem));
- const int filtBlockSize = 128;
- const int filtGridSize = (c * num_filter_elem + filtBlockSize - 1) / filtBlockSize;
- if(offset != skip_every)
- createReducedFiltersFull<<<filtGridSize, filtBlockSize>>>(reducedFilter,
- (float *)filter->gpu_data,
- c, num_filter_elem, reduced_filter_elem,
- skip_every, offset);
- checkCudaErrors(cudaDeviceSynchronize());
-
- const int blockSize = 128;
- const int gridSize = (n * input->dims.dim_sizes[1] * h * w + blockSize - 1) / blockSize;
- if(skip_every == 2){
- convToGemmFullInput2<<<gridSize, blockSize>>>(convData, (float *)input->gpu_data, n,
- input->dims.dim_sizes[1],
- input->dims.dim_sizes[2],
- input->dims.dim_sizes[3],
- KH, KW, vertical_pad, horizontal_pad,
- h, w, vertical_stride, horizontal_stride,
- reduced_filter_elem, skip_every,
- offset);
- }
- else{
- convToGemmFullInput<<<gridSize, blockSize>>>(convData, (float *)input->gpu_data, n,
- input->dims.dim_sizes[1],
- input->dims.dim_sizes[2],
- input->dims.dim_sizes[3],
- KH, KW, vertical_pad, horizontal_pad,
- h, w, vertical_stride, horizontal_stride,
- reduced_filter_elem, skip_every,
- offset);
- }
-
- checkCudaErrors(cudaDeviceSynchronize());
- //Do the matrix multiplication. Want to multiply convData by filter->gpu_data[f * chan * KH * KW]
- const float alpha = 1.0;
- const float beta = 0.0;
-
- if(offset != skip_every)
- checkCudaErrors(cublasGemmEx(cublasHandle, CUBLAS_OP_N, CUBLAS_OP_N,
- n * h * w, c, reduced_filter_elem,
- &alpha,
- convData, CUDA_R_32F, n * h * w,
- reducedFilter, CUDA_R_32F, reduced_filter_elem,
- &beta,
- (float *) output->gpu_data, CUDA_R_32F, n * h * w,
- CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP) );
- else
- checkCudaErrors(cublasGemmEx(cublasHandle, CUBLAS_OP_N, CUBLAS_OP_N,
- n * h * w, c, reduced_filter_elem,
- &alpha,
- convData, CUDA_R_32F, n * h * w,
- (float *) filter->gpu_data, CUDA_R_32F,
- reduced_filter_elem,
- &beta,
- (float *) output->gpu_data, CUDA_R_32F, n * h * w,
- CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP) );
-
- int numBlocks = (n * c * h * w + 255) / 256;
- switchMatrixFull<<<numBlocks,256>>>(n * c * h * w, n, c, h, w,
- (float *)output->gpu_data,
- (float *)new_output->gpu_data);
-
- checkCudaErrors(cudaDeviceSynchronize());
-
- cudaFree(convData);
- cudaFree(reducedFilter);
- freeTensor(output);
- }
-
- profileEvent("Conv_end");
-
- return new_output;
-
-}
-
-void* tensorConvApproxHalf(void* input_ptr, void* filter_ptr,
- int vertical_pad, int horizontal_pad, int vertical_stride,
- int horizontal_stride, int conv_mode, int conv_groups,
- int row, int col, int skip_every, int offset){
-
- INFO("*** TensorConvolution half approximation \n");
- profileEvent("#Conv");
-
- Tensor* input = (Tensor*)input_ptr;
- Tensor* filter = (Tensor*)filter_ptr;
- //FIXME: Current hack to preserve backward compatibilty
- if (conv_groups == 0) {
- conv_groups = 1;
- }
-
- hostToDeviceCopy(input);
- hostToDeviceCopy(filter);
-
- profileEvent("F2H_start");
- convertToFP16(input);
- convertToFP16(filter);
- profileEvent("F2H_end");
-
- int n, c, h, w; // output dimensions
- n = input->dims.dim_sizes[0];
- c = filter->dims.dim_sizes[0]; //number of filters
- const int KH = filter->dims.dim_sizes[2];
- const int KW = filter->dims.dim_sizes[3];
-
- h = (2 * vertical_pad + input->dims.dim_sizes[2] - KH) / vertical_stride + 1;
- int h_eff = h - h / row;
- if(h % row > row - 1 - offset)
- h_eff = h_eff - 1;
-
- w = (2 * horizontal_pad + input->dims.dim_sizes[3] - KW) / horizontal_stride + 1;
- int w_eff = w - w / col;
- if(w % col > col - 1 - offset)
- w_eff = w_eff - 1;
-
-
- Tensor *new_output = (Tensor*)create4DTensor((cudnnDataType_t) half_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(new_output, DEVICE);
-
- if(row > 1){
- Tensor *output_half = (Tensor*)create4DTensor((cudnnDataType_t) half_type,
- CUDNN_TENSOR_NCHW,
- n, c, h_eff, w);
-
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(output_half, DEVICE);
-
- //total number of filter elem
- const int num_filter_elem = KH * KW * input->dims.dim_sizes[1];
-
- __half * convData;
- int convDataSize = sizeof(__half) * n * num_filter_elem * h_eff * w;
- checkCudaErrors(cudaMalloc(&convData, convDataSize));
-
- const int blockSize = 256;
- const int gridSize = (n * input->dims.dim_sizes[1] * h_eff * w + blockSize - 1) / blockSize;
-
- convToGemmPerfRowHalf<<<gridSize, blockSize>>>(convData, (__half *)input->gpu_half_data, n,
- input->dims.dim_sizes[1], input->dims.dim_sizes[2],
- input->dims.dim_sizes[3], KH, KW, vertical_pad,
- horizontal_pad, h, w, vertical_stride,
- horizontal_stride, row, offset, h_eff);
-
-
- checkCudaErrors(cudaDeviceSynchronize());
-
- const __half alf = approx_float_to_half(1.0);
- const __half bet = approx_float_to_half(0.0);
- const __half *alpha_half = &alf;
- const __half *beta_half = &bet;
-
- checkCudaErrors(cublasGemmEx(cublasHandle, CUBLAS_OP_N, CUBLAS_OP_N,
- n * h_eff * w, c, num_filter_elem,
- alpha_half,
- convData, CUDA_R_16F, n * h_eff * w,
- (__half*) filter->gpu_half_data, CUDA_R_16F, num_filter_elem,
- beta_half,
- (__half*) output_half->gpu_half_data, CUDA_R_16F, n * h_eff * w,
- CUDA_R_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP) );
-
- //interpolate
- int numBlocks = (n * c * h * w + 255) / 256;
- approxInterpolateRowHalf<<<numBlocks,256>>>(n * c * h * w, h_eff, n, c, h, w,
- (__half *)output_half->gpu_half_data,
- (__half *)new_output->gpu_half_data,
- row, offset);
- cudaDeviceSynchronize();
-
- freeTensor(output_half);
- cudaFree(convData);
- }
- else if(col > 1){
- Tensor *output_half = (Tensor*)create4DTensor((cudnnDataType_t) half_type,
- CUDNN_TENSOR_NCHW, n, c, h, w_eff);
-
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(output_half, DEVICE);
- // NOTE: Necessary to insert the above call for every output tensor
- //total number of filter elem
- const int num_filter_elem = KH * KW * input->dims.dim_sizes[1];
-
- __half * convData;
- int convDataSize = sizeof(__half) * n * num_filter_elem * h * w_eff;
- checkCudaErrors(cudaMalloc(&convData, convDataSize));
-
- const int blockSize = 256;
- const int gridSize = (n * input->dims.dim_sizes[1] * h * w_eff + blockSize - 1) / blockSize;
-
- convToGemmPerfColHalf<<<gridSize, blockSize>>>(convData, (__half *)input->gpu_half_data, n,
- input->dims.dim_sizes[1], input->dims.dim_sizes[2],
- input->dims.dim_sizes[3], KH, KW, vertical_pad,
- horizontal_pad, h, w, vertical_stride,
- horizontal_stride, col, offset, w_eff);
-
-
- checkCudaErrors(cudaDeviceSynchronize());
-
- const __half alf = approx_float_to_half(1.0);
- const __half bet = approx_float_to_half(0.0);
- const __half *alpha_half = &alf;
- const __half *beta_half = &bet;
-
-
- checkCudaErrors(cublasGemmEx(cublasHandle, CUBLAS_OP_N, CUBLAS_OP_N,
- n * h * w_eff, c, num_filter_elem,
- alpha_half,
- convData, CUDA_R_16F, n * h * w_eff,
- (__half*) filter->gpu_half_data, CUDA_R_16F, num_filter_elem,
- beta_half,
- (__half*) output_half->gpu_half_data, CUDA_R_16F, n * h * w_eff,
- CUDA_R_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP) );
-
- //interpolate
- int numBlocks = (n * c * h * w + 255) / 256;
- approxInterpolateColHalf<<<numBlocks,256>>>(n * c * h * w, w_eff, n, c, h, w,
- (__half *)output_half->gpu_half_data,
- (__half *)new_output->gpu_half_data,
- col, offset);
-
- cudaDeviceSynchronize();
-
- freeTensor(output_half);
- cudaFree(convData);
-
- }
- else{
- Tensor *output = (Tensor*)create4DTensor((cudnnDataType_t) half_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
-
- //total number of filter elem
- const int num_filter_elem = KH * KW * input->dims.dim_sizes[1];
- //reduced number after skipping
- int reduced_filter_elem;
- if(offset != skip_every){
- reduced_filter_elem = num_filter_elem - (num_filter_elem/skip_every);
- if(num_filter_elem % skip_every > skip_every - 1 - offset)
- reduced_filter_elem = reduced_filter_elem - 1;
- }
- else
- reduced_filter_elem = num_filter_elem;
-
- __half * convData;
- int convDataSize = sizeof(__half) * n * reduced_filter_elem * h * w;
- checkCudaErrors(cudaMalloc(&convData, convDataSize));
- __half * reducedFilter;
- checkCudaErrors(cudaMalloc(&reducedFilter, sizeof(__half) * c * reduced_filter_elem));
- const int filtBlockSize = 128;
- const int filtGridSize = (c * num_filter_elem + filtBlockSize - 1) / filtBlockSize;
- if(offset != skip_every)
- createReducedFiltersHalf<<<filtGridSize, filtBlockSize>>>(reducedFilter,
- (__half *)filter->gpu_half_data,
- c, num_filter_elem, reduced_filter_elem,
- skip_every, offset);
- checkCudaErrors(cudaDeviceSynchronize());
-
- const int blockSize = 256;
- const int gridSize = (n * input->dims.dim_sizes[1] * h * w + blockSize - 1) / blockSize;
- if(skip_every == 2){
- convToGemmHalfInput2<<<gridSize, blockSize>>>(convData, (__half *)input->gpu_half_data, n,
- input->dims.dim_sizes[1],
- input->dims.dim_sizes[2],
- input->dims.dim_sizes[3],
- KH, KW, vertical_pad, horizontal_pad,
- h, w, vertical_stride, horizontal_stride,
- reduced_filter_elem, skip_every,
- offset);
- }
- else{
- convToGemmHalfInput<<<gridSize, blockSize>>>(convData, (__half *)input->gpu_half_data, n,
- input->dims.dim_sizes[1],
- input->dims.dim_sizes[2],
- input->dims.dim_sizes[3],
- KH, KW, vertical_pad, horizontal_pad,
- h, w, vertical_stride, horizontal_stride,
- reduced_filter_elem, skip_every,
- offset);
- }
-
- checkCudaErrors(cudaDeviceSynchronize());
- //Do the matrix multiplication. Want to multiply convData by filter->gpu_data[f * chan * KH * KW]
- const __half alf = approx_float_to_half(1.0);
- const __half bet = approx_float_to_half(0.0);
- const __half *alpha_half = &alf;
- const __half *beta_half = &bet;
-
- if(offset != skip_every)
- checkCudaErrors(cublasGemmEx(cublasHandle, CUBLAS_OP_N, CUBLAS_OP_N,
- n * h * w, c, reduced_filter_elem,
- alpha_half,
- convData, CUDA_R_16F, n * h * w,
- reducedFilter, CUDA_R_16F, reduced_filter_elem,
- beta_half,
- (__half*) output->gpu_half_data, CUDA_R_16F, n * h * w,
- CUDA_R_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP) );
- else
- checkCudaErrors(cublasGemmEx(cublasHandle, CUBLAS_OP_N, CUBLAS_OP_N,
- n * h * w, c, reduced_filter_elem,
- alpha_half,
- convData, CUDA_R_16F, n * h * w,
- (__half*) filter->gpu_half_data, CUDA_R_16F,
- reduced_filter_elem,
- beta_half,
- (__half*) output->gpu_half_data, CUDA_R_16F, n * h * w,
- CUDA_R_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP) );
-
-
- int numBlocks = (n * c * h * w + 255) / 256;
- switchMatrix<<<numBlocks,256>>>(n * c * h * w, n, c, h, w,
- (__half *)output->gpu_half_data,
- (__half *)new_output->gpu_half_data);
-
- checkCudaErrors(cudaDeviceSynchronize());
-
- cudaFree(convData);
- cudaFree(reducedFilter);
- freeTensor(output);
-
- }
-
- profileEvent("H2F_start");
- convertToFP32_offline(new_output);
- profileEvent("H2F_end");
-
- profileEvent("#Conv_end");
-
- return new_output;
-}
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/approxhpvm_img_runtime_utils.h b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/approxhpvm_img_runtime_utils.h
deleted file mode 100644
index dc5cddf8a2121a937dbe8cd4582fe1022fd99f48..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/approxhpvm_img_runtime_utils.h
+++ /dev/null
@@ -1,238 +0,0 @@
-#ifndef APPROXHPVM_IMG_RUNTIME_UTILS
-#define APPROXHPVM_IMG_RUNTIME_UTILS
-
-#include "configuration.h"
-#include "hpvm-rt-controller.h"
-
-#include "img_tensor_runtime.h"
-
-
-// Utilities header for ApproxHPVM image runtime API (wrapper runtime API)
-
-void* handleTensorFftApproximationTuples(
- std::vector< std::pair<GPUNodeConfiguration::APPROX, int> > &approxTuples,
- void* input) {
-
- if (approxTuples.size() == 1) {
- enum GPUNodeConfiguration::APPROX approx = approxTuples[0].first;
- int param = approxTuples[0].second;
- switch (approx) {
- case GPUNodeConfiguration::APPROX::FP32 :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorFft(input); //TODO: correct name here
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorFft", pinfo.first); // and here
- RC->addToCurrentIterationComputeEnergy("tensorFft", pinfo.second); // and here
- return t_out;
- }
- default :
- CUSTOM_ASSERT(false && "Unknown approximation type");
- ERROR("Unknown approximation type");
- abort();
- // TODO additional approx methods implemented here
- }
- } else if (approxTuples.size() == 2) {
- ERROR("Currently unsupported case");
- abort();
- } else {
- ERROR("Unsupported case");
- abort();
- }
- return NULL;
-}
-
-void* handleTensorReduceApproximationTuples(
- std::vector< std::pair<GPUNodeConfiguration::APPROX, int> > &approxTuples,
- void* input) {
- if (approxTuples.size() == 1) {
- enum GPUNodeConfiguration::APPROX approx = approxTuples[0].first;
- int param = approxTuples[0].second;
- switch (approx) {
- case GPUNodeConfiguration::APPROX::FP32 :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorReduce(input); //TODO: correct name here
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorReduce", pinfo.first); // and here
- RC->addToCurrentIterationComputeEnergy("tensorReduce", pinfo.second); // and here
- return t_out;
- }
- case GPUNodeConfiguration::APPROX::REDUCTION_SAMPLING :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorReductionSamplingReduce(input); //TODO: correct name here
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorReductionSamplingReduce",
- pinfo.first); // and here
- RC->addToCurrentIterationComputeEnergy("tensorReductionSamplingReduce",
- pinfo.second); // and here
- return t_out;
- }
- default :
- CUSTOM_ASSERT(false && "Unknown approximation type");
- ERROR("Unknown approximation type");
- abort();
- // TODO additional approx methods implemented here
- }
- } else if (approxTuples.size() == 2) {
- ERROR("Currently unsupported case");
- abort();
- } else {
- ERROR("Unsupported case");
- abort();
- }
- return NULL;
-}
-
-void* handleTensorProjectiveTApproximationTuples(
- std::vector< std::pair<GPUNodeConfiguration::APPROX, int> > &approxTuples,
- void* input) {
- if (approxTuples.size() == 1) {
- enum GPUNodeConfiguration::APPROX approx = approxTuples[0].first;
- int param = approxTuples[0].second;
- switch (approx) {
- case GPUNodeConfiguration::APPROX::FP32 :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorProjectiveT(input); //TODO: correct name here
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorProjectiveT", pinfo.first); // and here
- RC->addToCurrentIterationComputeEnergy("tensorProjectiveT", pinfo.second); // and here
- return t_out;
- }
- default :
- CUSTOM_ASSERT(false && "Unknown approximation type");
- ERROR("Unknown approximation type");
- abort();
- // TODO additional approx methods implemented here
- }
- } else if (approxTuples.size() == 2) {
- ERROR("Currently unsupported case");
- abort();
- } else {
- ERROR("Unsupported case");
- abort();
- }
- return NULL;
-}
-
-void* handleTensorMap1ApproximationTuples(
- std::vector< std::pair<GPUNodeConfiguration::APPROX, int> > &approxTuples,
- void* input) {
- if (approxTuples.size() == 1) {
- enum GPUNodeConfiguration::APPROX approx = approxTuples[0].first;
- int param = approxTuples[0].second;
- switch (approx) {
- case GPUNodeConfiguration::APPROX::FP32 :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorMap1(input); //TODO: correct name here
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorMap1", pinfo.first); // and here
- RC->addToCurrentIterationComputeEnergy("tensorMap1", pinfo.second); // and here
- return t_out;
- }
- default :
- CUSTOM_ASSERT(false && "Unknown approximation type");
- ERROR("Unknown approximation type");
- abort();
- // TODO additional approx methods implemented here
- }
- } else if (approxTuples.size() == 2) {
- ERROR("Currently unsupported case");
- abort();
- } else {
- ERROR("Unsupported case");
- abort();
- }
- return NULL;
-}
-
-void* handleTensorMap2ApproximationTuples(
- std::vector< std::pair<GPUNodeConfiguration::APPROX, int> > &approxTuples,
- void* input) {
- if (approxTuples.size() == 1) {
- enum GPUNodeConfiguration::APPROX approx = approxTuples[0].first;
- int param = approxTuples[0].second;
- switch (approx) {
- case GPUNodeConfiguration::APPROX::FP32 :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorMap2(input); //TODO: correct name here
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorMap2", pinfo.first); // and here
- RC->addToCurrentIterationComputeEnergy("tensorMap2", pinfo.second); // and here
- return t_out;
- }
- default :
- CUSTOM_ASSERT(false && "Unknown approximation type");
- ERROR("Unknown approximation type");
- abort();
- // TODO additional approx methods implemented here
- }
- } else if (approxTuples.size() == 2) {
- ERROR("Currently unsupported case");
- abort();
- } else {
- ERROR("Unsupported case");
- abort();
- }
- return NULL;
-}
-
-void* handleTensorMap3ApproximationTuples(
- std::vector< std::pair<GPUNodeConfiguration::APPROX, int> > &approxTuples,
- void* input) {
- if (approxTuples.size() == 1) {
- enum GPUNodeConfiguration::APPROX approx = approxTuples[0].first;
- int param = approxTuples[0].second;
- switch (approx) {
- case GPUNodeConfiguration::APPROX::FP32 :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorMap3(input); //TODO: correct name here
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorMap3", pinfo.first); // and here
- RC->addToCurrentIterationComputeEnergy("tensorMap3", pinfo.second); // and here
- return t_out;
- }
- default :
- CUSTOM_ASSERT(false && "Unknown approximation type");
- ERROR("Unknown approximation type");
- abort();
- // TODO additional approx methods implemented here
- }
- } else if (approxTuples.size() == 2) {
- ERROR("Currently unsupported case");
- abort();
- } else {
- ERROR("Unsupported case");
- abort();
- }
- return NULL;
-}
-
-
-#endif
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/approxhpvm_runtime_utils.h b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/approxhpvm_runtime_utils.h
deleted file mode 100644
index 5e282d130f2a261ba76ebefa2b92af38682a7def..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/approxhpvm_runtime_utils.h
+++ /dev/null
@@ -1,612 +0,0 @@
-
-
-
-#ifndef APPROXHPVM_RUNTIME_UTILS
-#define APPROXHPVM_RUNTIME_UTILS
-
-
-#include "configuration.h"
-#include "hpvm-rt-controller.h"
-
-
-// Utilities header for ApproxHPVM runtime API (wrapper runtime API)
-
-void* handleTensorAddApproximationTuples(
- std::vector< std::pair<GPUNodeConfiguration::APPROX, int> > &approxTuples,
- void* input, void* bias) {
-
- if (approxTuples.size() == 1) {
- enum GPUNodeConfiguration::APPROX approx = approxTuples[0].first;
- int param = approxTuples[0].second;
- switch (approx) {
- case GPUNodeConfiguration::APPROX::FP32 :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorAdd(input, bias);
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorAdd", pinfo.first);
- RC->addToCurrentIterationComputeEnergy("tensorAdd", pinfo.second);
- return t_out;
- }
- case GPUNodeConfiguration::APPROX::FP16 :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorHalfAdd(input, bias);
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorHalfAdd", pinfo.first);
- RC->addToCurrentIterationComputeEnergy("tensorHalfAdd", pinfo.second);
- return t_out;
- }
- default :
- CUSTOM_ASSERT(false && "Unknown approximation type");
- ERROR("Unknown approximation type");
- abort();
- // TODO additional approx methods implemented here
- }
- } else if (approxTuples.size() == 2) {
- ERROR("Currently unsupported case");
- abort();
- } else {
- ERROR("Unsupported case");
- abort();
- }
- return NULL;
-}
-
-void* handleTensorMulApproximationTuples(
- std::vector< std::pair<GPUNodeConfiguration::APPROX, int> > &approxTuples,
- void* lhs, void* rhs) {
-
- if (approxTuples.size() == 1) {
- enum GPUNodeConfiguration::APPROX approx = approxTuples[0].first;
- int param = approxTuples[0].second;
- switch (approx) {
- case GPUNodeConfiguration::APPROX::FP32 :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorGemmGPU(lhs, rhs);
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorGemmGPU", pinfo.first);
- RC->addToCurrentIterationComputeEnergy("tensorGemmGPU", pinfo.second);
- return t_out;
- }
- case GPUNodeConfiguration::APPROX::FP16 :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorHalfGemmGPU(lhs, rhs);
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorHalfGemmGPU", pinfo.first);
- RC->addToCurrentIterationComputeEnergy("tensorHalfGemmGPU", pinfo.second);
- return t_out;
- }
- default :
- CUSTOM_ASSERT(false && "Unknown approximation type");
- ERROR("Unknown approximation type");
- abort();
- // TODO additional approx methods implemented here
- }
- } else if (approxTuples.size() == 2) {
- ERROR("Currently unsupported case");
- abort();
- } else {
- ERROR("Unsupported case");
- abort();
- }
- return NULL;
-}
-
-void* handleTensorConvApproximationTuples(
- std::vector< std::pair<GPUNodeConfiguration::APPROX, int> > &approxTuples,
- void* input, void* filter,
- int conv_pad_h, int conv_pad_w,
- int conv_stride_h, int conv_stride_w) {
-
- if (approxTuples.size() == 1) {
- enum GPUNodeConfiguration::APPROX approx = approxTuples[0].first;
- int param = approxTuples[0].second;
- switch (approx) {
- case GPUNodeConfiguration::APPROX::FP32 :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorConvApprox(input, filter,
- conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w,
- 1, 1,
- 1, 1, 1, 1);
-// t_out = tensorConvPerfCuda(input, filter,
-// conv_pad_h, conv_pad_w,
-// conv_stride_h, conv_stride_w,
-// 1, 1,
-// 1, 1, 0);
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorConvApprox", pinfo.first);
- RC->addToCurrentIterationComputeEnergy("tensorConvApprox", pinfo.second);
- return t_out;
- }
- case GPUNodeConfiguration::APPROX::FP16 :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorConvApproxHalf(input, filter,
- conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w,
- 1, 1,
- 1, 1, 1, 1);
-// t_out = tensorConvPerfCudaHalf(input, filter,
-// conv_pad_h, conv_pad_w,
-// conv_stride_h, conv_stride_w,
-// 1, 1,
-// 1, 1, 0);
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorConvApproxHalf", pinfo.first);
- RC->addToCurrentIterationComputeEnergy("tensorConvApproxHalf", pinfo.second);
- return t_out;
- }
- case GPUNodeConfiguration::APPROX::PERFORATION :
- {
- int row = 0, col = 0, offset = 0;
- switch (param) {
- case 21:
- row = 1; col = 2; offset = 0;
- break;
- case 22:
- row = 1; col = 2; offset = 1;
- break;
- case 23:
- row = 1; col = 3; offset = 0;
- break;
- case 24:
- row = 1; col = 3; offset = 1;
- break;
- case 25:
- row = 1; col = 3; offset = 2;
- break;
- case 26:
- row = 2; col = 1; offset = 0;
- break;
- case 27:
- row = 2; col = 1; offset = 1;
- break;
- case 28:
- row = 3; col = 1; offset = 0;
- break;
- case 29:
- row = 3; col = 1; offset = 1;
- break;
- case 30:
- row = 3; col = 1; offset = 2;
- break;
- default:
- DEBUG("Unsupported Option: Select default, 1-2-0.\n");
- row = 1; col = 2; offset = 0;
- break;
- }
- void* t_out;
- RC->resume_profiler();
- t_out = tensorConvApproxHalf(input, filter,
- conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w,
- 1, 1,
- row, col, 1, offset);
-// t_out = tensorConvPerfCudaHalf(input, filter,
-// conv_pad_h, conv_pad_w,
-// conv_stride_h, conv_stride_w,
-// 1, 1,
-// row, col, offset);
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorConvApproxHalf(_perf)", pinfo.first);
- RC->addToCurrentIterationComputeEnergy("tensorConvApproxHalf(_perf)", pinfo.second);
- return t_out;
- }
- case GPUNodeConfiguration::APPROX::INPUT_SAMPLING :
- {
- int skip_rate = 2, offset = 0;
- switch (param) {
- case 31:
- skip_rate = 2; offset = 0;
- break;
- case 32:
- skip_rate = 2; offset = 1;
- break;
- case 33:
- skip_rate = 4; offset = 0;
- break;
- case 34:
- skip_rate = 4; offset = 1;
- break;
- case 35:
- skip_rate = 4; offset = 2;
- break;
- case 36:
- skip_rate = 4; offset = 3;
- break;
- default:
- DEBUG("Unsupported Option: Select default, 2-0.\n");
- skip_rate = 2; offset = 0;
- break;
- }
- void* t_out;
- RC->resume_profiler();
- t_out = tensorConvApproxHalf(input, filter,
- conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w,
- 1, 1,
- 1, 1, skip_rate, skip_rate - 1/*offset*/); //FIXME
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorConvApproxHalf(_samp)", pinfo.first);
- RC->addToCurrentIterationComputeEnergy("tensorConvApproxHalf(_samp)", pinfo.second);
- // Overwrite the result writen in t_out with the simulation result,
- // to propagate that to the next layer.
- // TODO: Remove this call when bug is fixed in
-// t_out = tensorConvSampSim(input, filter,
-// conv_pad_h, conv_pad_w,
-// conv_stride_h, conv_stride_w,
-// 1, 1, skip_rate, offset);
- return t_out;
- }
- default :
- CUSTOM_ASSERT(false && "Unknown approximation type");
- ERROR("Unknown approximation type");
- abort();
- // TODO additional approx methods implemented here
- }
- } else if (approxTuples.size() == 2) {
- ERROR("Currently unsupported case");
- abort();
- } else {
- ERROR("Unsupported case");
- abort();
- }
- return NULL;
-}
-
-void* handleTensorGroupConvApproximationTuples(
- std::vector< std::pair<GPUNodeConfiguration::APPROX, int> > &approxTuples,
- void* input, void* filter,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int conv_groups) {
-
- if (approxTuples.size() == 1) {
- enum GPUNodeConfiguration::APPROX approx = approxTuples[0].first;
- int param = approxTuples[0].second;
- switch (approx) {
- case GPUNodeConfiguration::APPROX::FP32 :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorConvCutlass(input, filter,
- vertical_pad, horizontal_pad,
- vertical_stride, horizontal_stride,
- conv_mode, conv_groups);
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorConvCutlass", pinfo.first);
- RC->addToCurrentIterationComputeEnergy("tensorConvCutlass", pinfo.second);
- return t_out;
- }
- case GPUNodeConfiguration::APPROX::FP16 :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorHalfConvCutlass(input, filter,
- vertical_pad, horizontal_pad,
- vertical_stride, horizontal_stride,
- conv_mode, conv_groups);
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorHalfConvCutlass", pinfo.first);
- RC->addToCurrentIterationComputeEnergy("tensorHalfConvCutlass", pinfo.second);
- return t_out;
- }
- default :
- CUSTOM_ASSERT(false && "Unknown approximation type");
- ERROR("Unknown approximation type");
- abort();
- // TODO additional approx methods implemented here
- }
- } else if (approxTuples.size() == 2) {
- ERROR("Currently unsupported case");
- abort();
- } else {
- ERROR("Unsupported case");
- abort();
- }
- return NULL;
-}
-
-void* handleTensorBatchNormApproximationTuples(
- std::vector< std::pair<GPUNodeConfiguration::APPROX, int> > &approxTuples,
- void* input_ptr, void* gamma_ptr, void* beta_ptr,
- void* mean_ptr, void* variance_ptr, double epsilon) {
-
- if (approxTuples.size() == 1) {
- enum GPUNodeConfiguration::APPROX approx = approxTuples[0].first;
- int param = approxTuples[0].second;
- switch (approx) {
- case GPUNodeConfiguration::APPROX::FP32 :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorBatchNorm(input_ptr, gamma_ptr, beta_ptr,
- mean_ptr, variance_ptr, epsilon);
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorBatchNorm", pinfo.first);
- RC->addToCurrentIterationComputeEnergy("tensorBatchNorm", pinfo.second);
- return t_out;
- }
- case GPUNodeConfiguration::APPROX::FP16 :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorHalfBatchNorm(input_ptr, gamma_ptr, beta_ptr,
- mean_ptr, variance_ptr, epsilon);
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorHalfBatchNorm", pinfo.first);
- RC->addToCurrentIterationComputeEnergy("tensorHalfBatchNorm", pinfo.second);
- return t_out;
- }
- default :
- CUSTOM_ASSERT(false && "Unknown approximation type");
- ERROR("Unknown approximation type");
- abort();
- // TODO additional approx methods implemented here
- }
- } else if (approxTuples.size() == 2) {
- ERROR("Currently unsupported case");
- abort();
- } else {
- ERROR("Unsupported case");
- abort();
- }
- return NULL;
-}
-
-void* handleTensorReluApproximationTuples(
- std::vector< std::pair<GPUNodeConfiguration::APPROX, int> > &approxTuples,
- void* input) {
-
- if (approxTuples.size() == 1) {
- enum GPUNodeConfiguration::APPROX approx = approxTuples[0].first;
- int param = approxTuples[0].second;
- switch (approx) {
- case GPUNodeConfiguration::APPROX::FP32 :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorRelu(input);
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorRelu", pinfo.first);
- RC->addToCurrentIterationComputeEnergy("tensorRelu", pinfo.second);
- return t_out;
- }
- case GPUNodeConfiguration::APPROX::FP16 :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorHalfRelu(input);
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorHalfRelu", pinfo.first);
- RC->addToCurrentIterationComputeEnergy("tensorHalfRelu", pinfo.second);
- return t_out;
- }
- default :
- CUSTOM_ASSERT(false && "Unknown approximation type");
- ERROR("Unknown approximation type");
- abort();
- // TODO additional approx methods implemented here
- }
- } else if (approxTuples.size() == 2) {
- ERROR("Currently unsupported case");
- abort();
- } else {
- ERROR("Unsupported case");
- abort();
- }
- return NULL;
-}
-
-void* handleTensorClippedReluApproximationTuples(
- std::vector< std::pair<GPUNodeConfiguration::APPROX, int> > &approxTuples,
- void* input, float min, float max) {
-
- if (approxTuples.size() == 1) {
- enum GPUNodeConfiguration::APPROX approx = approxTuples[0].first;
- int param = approxTuples[0].second;
- switch (approx) {
- case GPUNodeConfiguration::APPROX::FP32 :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorRelu2(input, min, max);
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorRelu2", pinfo.first);
- RC->addToCurrentIterationComputeEnergy("tensorRelu2", pinfo.second);
- return t_out;
- }
- case GPUNodeConfiguration::APPROX::FP16 :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorHalfRelu2(input, min, max);
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorHalfRelu2", pinfo.first);
- RC->addToCurrentIterationComputeEnergy("tensorHalfRelu2", pinfo.second);
- return t_out;
- }
- default :
- CUSTOM_ASSERT(false && "Unknown approximation type");
- ERROR("Unknown approximation type");
- abort();
- // TODO additional approx methods implemented here
- }
- } else if (approxTuples.size() == 2) {
- ERROR("Currently unsupported case");
- abort();
- } else {
- ERROR("Unsupported case");
- abort();
- }
- return NULL;
-}
-
-void* handleTensorTanhApproximationTuples(
- std::vector< std::pair<GPUNodeConfiguration::APPROX, int> > &approxTuples,
- void* input) {
-
- if (approxTuples.size() == 1) {
- enum GPUNodeConfiguration::APPROX approx = approxTuples[0].first;
- int param = approxTuples[0].second;
- switch (approx) {
- case GPUNodeConfiguration::APPROX::FP32 :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorTanh(input);
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorTanh", pinfo.first);
- RC->addToCurrentIterationComputeEnergy("tensorTanh", pinfo.second);
- return t_out;
- }
- case GPUNodeConfiguration::APPROX::FP16 :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorHalfTanh(input);
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorHalfTanh", pinfo.first);
- RC->addToCurrentIterationComputeEnergy("tensorHalfTanh", pinfo.second);
- return t_out;
- }
- default :
- CUSTOM_ASSERT(false && "Unknown approximation type");
- ERROR("Unknown approximation type");
- abort();
- // TODO additional approx methods implemented here
- }
- } else if (approxTuples.size() == 2) {
- ERROR("Currently unsupported case");
- abort();
- } else {
- ERROR("Unsupported case");
- abort();
- }
- return NULL;
-}
-
-void* handleTensorPoolingApproximationTuples(
- std::vector< std::pair<GPUNodeConfiguration::APPROX, int> > &approxTuples,
- void* input_ptr, int poolFunction,
- int window_height, int window_width,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride) {
-
- if (approxTuples.size() == 1) {
- enum GPUNodeConfiguration::APPROX approx = approxTuples[0].first;
- int param = approxTuples[0].second;
- switch (approx) {
- case GPUNodeConfiguration::APPROX::FP32 :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorPooling(input_ptr,
- poolFunction,
- window_height, window_width,
- vertical_pad, horizontal_pad,
- vertical_stride, horizontal_stride);
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorPooling", pinfo.first);
- RC->addToCurrentIterationComputeEnergy("tensorPooling", pinfo.second);
- return t_out;
- }
- case GPUNodeConfiguration::APPROX::FP16 :
- {
- void* t_out;
- RC->resume_profiler();
- t_out = tensorHalfPooling(input_ptr,
- poolFunction,
- window_height, window_width,
- vertical_pad, horizontal_pad,
- vertical_stride, horizontal_stride);
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorHalfPooling", pinfo.first);
- RC->addToCurrentIterationComputeEnergy("tensorHalfPooling", pinfo.second);
- return t_out;
- }
- default :
- CUSTOM_ASSERT(false && "Unknown approximation type");
- ERROR("Unknown approximation type");
- abort();
- // TODO additional approx methods implemented here
- }
- } else if (approxTuples.size() == 2) {
- ERROR("Currently unsupported case");
- abort();
- } else {
- ERROR("Unsupported case");
- abort();
- }
- return NULL;
-}
-
-void* handleTensorSoftmaxApproximationTuples(
- std::vector< std::pair<GPUNodeConfiguration::APPROX, int> > &approxTuples,
- void* input_ptr) {
- //TODO: if approximation choices are added for softmax operation,
- // implement this like the other handle* functions
- void* t_out;
- RC->resume_profiler();
- t_out = tensorSoftmax(input_ptr);
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationComputeTime("tensorSoftmax", pinfo.first);
- RC->addToCurrentIterationComputeEnergy("tensorSoftmax", pinfo.second);
- return t_out;
-}
-
-
-#endif
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/configuration.h b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/configuration.h
deleted file mode 100644
index 99c465434a2879d85624b7ff6bb4141dd8fe4634..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/configuration.h
+++ /dev/null
@@ -1,360 +0,0 @@
-#ifndef LLVM_HPVM_CONFIGURATION_H
-#define LLVM_HPVM_CONFIGURATION_H
-
-#include <map>
-#include <vector>
-
-#include "debug.h"
-
-// Configuration related class definitions - in Configuration.h
-
-// Describes the internal choices made for an ApproxHPVM node
-class NodeConfiguration {
-public:
- enum NODE_CONFIGURATION_TARGET
- {
- PROMISE,
- GPU,
- END
- };
-
-protected:
- enum NODE_CONFIGURATION_TARGET NODE_CONFIGURATION_TARGET_ID;
-
-public:
- bool isPROMISENodeConfiguration() {
- return NODE_CONFIGURATION_TARGET_ID == PROMISE;
- }
-
- bool isGPUNodeConfiguration() {
- return NODE_CONFIGURATION_TARGET_ID == GPU;
- }
-
- virtual void print() = 0;
-};
-
-class PROMISENodeConfiguration : public NodeConfiguration {
-public:
- // Approximation methods available for this HW type
- enum APPROX
- {
- SWING_LEVEL,
- END
- };
-
-private:
- // A vector, containing pairs of approximation method and tunable parameter
- // (expressed as int, or ignored when not applicable)
- std::vector< std::pair<enum APPROX, int> > ApproxChoices;
-
-public:
- void pushNewApproximationChoice(enum APPROX approx, int u) {
- ApproxChoices.push_back(std::make_pair(approx, u));
- }
-
- std::vector<std::pair<enum APPROX, int> > &getApproxChoices() {
- return ApproxChoices;
- }
-
- PROMISENodeConfiguration() {
- NODE_CONFIGURATION_TARGET_ID = PROMISE;
- }
- ~PROMISENodeConfiguration() {}
-
- void print() override;
-};
-
-class GPUNodeConfiguration : public NodeConfiguration {
-public:
- // Approximation methods available for this HW type
- enum APPROX
- {
- FP32,
- FP16,
- PERFORATION,
- INPUT_SAMPLING,
- REDUCTION_SAMPLING,
-// ADDITIONAL_APPROXIMATION_METHOD
- APPROX_END
- };
-
- // Operations to be approximated in the node using this configuration
- enum TENSOR_OP
- {
- ADD,
- BATCHNORM,
- CONV,
- GROUP_CONV,
- MUL,
- RELU,
- CLIPPED_RELU,
- TANH,
- POOL_MAX,
- POOL_MEAN,
- POOL_MIN,
- SOFTMAX,
- FFT,
- REDUCE,
- PROJECTIVE_T,
- MAP1,
- MAP2,
- MAP3,
-// STENCIL,
-// COSINE_T,
-// ADDITIONAL_TENSOR_OPERATION
- TENSOR_OP_END
- };
-
-private:
- // A vector, containing pairs of approximation method and tunable parameter
- // (expressed as int, or ignored when not applicable) for each operation
- std::vector< std::pair< enum TENSOR_OP, std::vector< std::pair<enum APPROX, int> > > > ApproxChoices;
-
-public:
- void pushNewTensorOperation(enum TENSOR_OP top) {
- std::vector< std::pair<enum APPROX, int> > emptyVec;
- ApproxChoices.push_back(std::make_pair(top, emptyVec));
- }
-
- void pushNewApproximationChoiceForOperation(enum APPROX approx, int u) {
- unsigned size = ApproxChoices.size();
- CUSTOM_ASSERT(size >=1 && "Cannot apply approximation choice to non existent operation.");
- ApproxChoices[size-1].second.push_back(std::make_pair(approx, u));
- }
-
- std::vector< std::pair< enum TENSOR_OP, std::vector< std::pair<enum APPROX, int> > > > &getApproxChoices() {
- return ApproxChoices;
- }
-
- GPUNodeConfiguration() {
- NODE_CONFIGURATION_TARGET_ID = GPU;
- }
- ~GPUNodeConfiguration() {}
-
- void print() override;
-};
-
-// Configuration : Includes configuration information :
-// - name
-// - speedup
-// - energy
-// - accuracy (compared to golden output)
-// - accuracy loss (compared to baseline)
-// - a hardware choice and set or operations-approximation choices, described in setup
-struct Configuration {
- std::string name;
- float speedup;
- float energy;
- float accuracy;
- float accuracyLoss;
- std::map<std::string, NodeConfiguration * > setup;
-
- Configuration(std::string &n, float f, float e, float a, float al) :
- name(n), speedup(f), energy(e), accuracy(a), accuracyLoss(al) {}
-
- float getSpeedup() {
- return speedup;
- }
-
- float getEnergy() {
- return energy;
- }
-
- float getAccuracy() {
- return accuracy;
- }
-
- float getAccuracyLoss() {
- return accuracyLoss;
- }
-
- void print();
-};
-
-// Comparison operator definition, in increasing accuracy loss
-// (for std sort, used in pareto optimal computation)
-struct ConfigurationLessThan {
- bool operator()(const struct Configuration &a, const struct Configuration &b) const {
- return (a.accuracyLoss < b.accuracyLoss) ;
- }
-};
-
-// Comparison operator definition, in increasing accuracy loss
-// (for std lower bound, used in pareto optimal frontier search)
-struct ConfigurationLessThan_AL {
- bool operator()(const struct Configuration *a, const float &b) const {
- return (a->accuracyLoss < b) ;
- }
-};
-
-// Comparison operator definition, in increasing speedup
-// (for std lower bound, used in pareto optimal frontier search)
-struct ConfigurationLessThan_SP {
- bool operator()(const struct Configuration *a, const float &b) const {
- return (a->speedup < b) ;
- }
-};
-
-// Comparison operator definition, in decreasing energy
-// (for std lower bound, used in pareto optimal frontier search)
-struct ConfigurationLessThan_E {
- bool operator()(const struct Configuration *a, const float &b) const {
- return (a->energy < b) ;
- }
-};
-
-enum SEARCH_KIND
- {
- SPEEDUP,
- ENERGY,
- ACCURACY_LOSS,
- END
- };
-
-//****** HEADER Ends - Source Starts
-
-
-// Helper configuration print methods
-
-void PROMISENodeConfiguration::print() {
-
- printf(" promise");
- for (auto &it : ApproxChoices) {
- printf(" ");
- switch (it.first) {
- case APPROX::SWING_LEVEL :
- printf("swing_level");
- break;
- default:
- ERROR("Unknown approximation option");
- break;
- // TODO additional approx methods to be printed here
- }
- printf(" %d", it.second);
- }
-
- printf("\n");
-
-}
-
-void GPUNodeConfiguration::print() {
-
- printf(" gpu");
- for (auto &it : ApproxChoices) {
-
- printf(" ");
- switch (it.first) {
- case TENSOR_OP::ADD :
- printf("add");
- break;
- case TENSOR_OP::BATCHNORM :
- printf("batchnorm");
- break;
- case TENSOR_OP::CONV :
- printf("conv");
- break;
- case TENSOR_OP::GROUP_CONV :
- printf("group_conv");
- break;
- case TENSOR_OP::MUL :
- printf("mul");
- break;
- case TENSOR_OP::RELU :
- printf("relu");
- break;
- case TENSOR_OP::CLIPPED_RELU :
- printf("clipped_relu");
- break;
- case TENSOR_OP::TANH :
- printf("tanh");
- break;
- case TENSOR_OP::POOL_MAX :
- printf("pool_max");
- break;
- case TENSOR_OP::POOL_MEAN :
- printf("pool_mean");
- break;
- case TENSOR_OP::POOL_MIN :
- printf("pool_min");
- break;
- case TENSOR_OP::SOFTMAX :
- printf("softmax");
- break;
- case TENSOR_OP::FFT :
- printf("fft");
- break;
- case TENSOR_OP::REDUCE :
- printf("reduce");
- break;
- case TENSOR_OP::PROJECTIVE_T :
- printf("projectiveT");
- break;
- case TENSOR_OP::MAP1 :
- printf("map1");
- break;
- case TENSOR_OP::MAP2 :
- printf("map2");
- break;
- case TENSOR_OP::MAP3 :
- printf("map3");
- break;
- default :
- ERROR("Unknown tensor operation.");
- break;
- // TODO additional operations to be printed here
- }
-
- auto &approxVec = it.second;
- for (auto &inner_it : approxVec) {
- printf(" ");
- switch (inner_it.first) {
- case APPROX::FP32 :
- printf("fp32");
- break;
- case APPROX::FP16 :
- printf("fp16");
- break;
- case APPROX::PERFORATION :
- printf("perf");
- break;
- case APPROX::INPUT_SAMPLING :
- printf("samp");
- break;
- case APPROX::REDUCTION_SAMPLING :
- printf("red_samp");
- break;
- default:
- ERROR("Unknown approximation option");
- break;
- // TODO additional approx methods to be printed here
- }
-
- printf(" %d", inner_it.second);
- }
-
- }
-
- printf("\n");
-
-}
-
-void Configuration::print() {
-
- printf("+++++\n");
- printf("%s %f %f %f %f\n", name.c_str(), speedup, energy, accuracy, accuracyLoss);
- for (std::map<std::string, NodeConfiguration* >::const_iterator it = setup.begin();
- it != setup.end(); ++it) {
- printf("%s :", it->first.c_str());
-
- it->second->print();
- }
-
- printf("-----\n");
-}
-
-
-
-
-
-
-#endif
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/debug.h b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/debug.h
deleted file mode 100644
index 33864fed94f3a86d065f2f166adbfc36127cc42d..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/debug.h
+++ /dev/null
@@ -1,123 +0,0 @@
-
-
-#ifndef RUNTIME_DEBUG
-#define RUNTIME_DEBUG
-
-#define LOG_DEBUG 1 // Sets the debug logging to true
-#define LOG_INFO 1 // Sets the info logging to true
-#define ASSERT_FLAG // Sets assertions to true (opposite of NDEBUG macro)
-
-#include "tensor.h"
-
-
-#define FatalError(s) do { \
- std::stringstream _where, _message; \
- _where << __FILE__ << ':' << __LINE__; \
- _message << std::string(s) + "\n" << __FILE__ << ':' << __LINE__; \
- std::cerr << _message.str() << "\nAborting...\n"; \
- cudaDeviceReset(); \
- exit(1); \
-} while(0)
-
-
-#define checkCUDNN(status) do { \
- std::stringstream _error; \
- if (status != CUDNN_STATUS_SUCCESS) { \
- _error << "CUDNN failure: " << cudnnGetErrorString(status); \
- FatalError(_error.str()); \
- } \
-} while(0)
-
-
-#define checkCudaErrors(status) do { \
- std::stringstream _error; \
- if (status != 0) { \
- _error << "Cuda failure: " << status; \
- FatalError(_error.str()); \
- } \
-} while(0)
-
-
-
-void INFO(const char* format, ...){
- if(!LOG_INFO) // Don't print if logging info is disabled
- return;
- va_list args;
- va_start(args, format);
- printf("INFO: ");
- vprintf(format, args);
- va_end(args);
-}
-
-void DEBUG(const char* format, ...){
- if(!LOG_DEBUG) // Don't print if logging info is disabled
- return;
- va_list args;
- va_start(args, format);
- printf("DEBUG: ");
- vprintf(format, args);
- va_end(args);
-}
-
-void ERROR(const char* format, ...){
- if(!LOG_DEBUG) // Don't print if logging info is disabled
- return;
- va_list args;
- va_start(args, format);
- printf("ERROR!: ");
- vprintf(format, args);
- va_end(args);
-
- abort();
-}
-
-#ifdef ASSERT_FLAG
-#define CUSTOM_ASSERT(x) do { \
- if (!(x)) { \
- std::stringstream _message; \
- _message << "Assertion failed at " \
- << __FILE__ << ':' << __LINE__ \
- << " inside function " << __FUNCTION__ << "\n" \
- << "Condition: " << #x << "\n"; \
- std::cerr << _message.str(); \
- abort(); \
- } \
-} while (0)
-#else
-#define CUSTOM_ASSERT(x) do { } while (0)
-#endif
-
-void fillOnes(struct Tensor* tensor){
- // initialization is specific to the floating point type
- if(tensor->data_type == CUDNN_DATA_FLOAT){
- float* data_arr = (float*) tensor->host_data;
- for(unsigned int i = 0; i < tensor->num_elems; i++){
- data_arr[i] = 1.0;
- }
- }
-}
-
-
-void printTensorDescInfo(struct Tensor* tensor){
-
- cudnnDataType_t dType;
- int nStride, cStride, hStride, wStride;
- int size1, size2, size3, size4;
- cudnnGetTensor4dDescriptor(tensor->tensor_desc,
- &dType,
- &size1, &size2, &size3, &size4,
- &nStride, &cStride, &hStride, &wStride);
-
- DEBUG("dType = %d, size1 = %d, size2 = %d, size3 = %d, size4 = %d \n",
- dType, size1, size2, size3, size4);
-
- DEBUG("nStride = %d, cStride = %d, hStride = %d, wStride = %d \n",
- nStride, cStride, hStride, wStride);
-
-}
-
-
-
-
-
-#endif
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/error.h b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/error.h
deleted file mode 100644
index e2e78f1d10c048d73755df73d553b3932ab72d24..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/error.h
+++ /dev/null
@@ -1,1032 +0,0 @@
-
-#ifndef ERROR_HEADER
-#define ERROR_HEADER
-
-
-#include <stdio.h>
-#include <stdarg.h>
-#include <cstdio>
-#include <cstdlib>
-#include <cmath>
-#include <ctime>
-#include <cfloat>
-#include <algorithm>
-#include <sstream>
-#include <vector>
-#include <iostream>
-#include <random>
-#include <string>
-#include <time.h>
-
-#include <curand.h>
-#include <curand_kernel.h>
-#include <math.h>
-#include <assert.h>
-
-
-#include "../include/debug.h"
-#include "tensor.h"
-#include "profiling.h"
-#include "tensor_utils.cu"
-#include "global_data.h"
-
-
-void readSkipTensors(int* skip_tensor_ids, int op_count){
-
- for(int i = 0; i < op_count; i++){
- int tensor_id = skip_tensor_ids[i];
- skip_tensors[tensor_id] = 1;
- }
-
-}
-
-
-
-
-void readOpenTunerFlags(const char* file_name){
-
- total_ops = 0;
- op_counter = 0;
- op_accuracies.clear();
-
- FILE* fp = fopen(file_name, "r");
- if(fp == NULL){
- INFO("\nWARNING: File 'opentuner_flags' not found \n\n\n");
- return;
- }
-
- int retVal = 200;
- while(retVal != EOF){
-
- int op_acc;
- if(fp != NULL)
- retVal = fscanf(fp, "%d", &op_acc);
- else
- op_acc = 0;
-
- op_accuracies.push_back(op_acc);
- total_ops++;
- }
-
- fclose(fp);
-}
-
-
-void readQuantRanges(char* file_name){
-
- total_ops = 0;
- op_counter = 0;
- quant_ranges.clear();
-
- FILE* fp = fopen(file_name, "r");
- if(fp == NULL){
- ERROR("File %s not found \n", file_name);
- }
-
- int retVal = 200;
- while(retVal != EOF && retVal != -1){
-
- int min;
- int max;
- if(fp != NULL){
- retVal = fscanf(fp, "%d", &min);
- printf("min =% d \n", min);
-
- retVal = fscanf(fp, "%d", &max);
- printf("max =% d \n", max);
- }
-
- if(retVal != -1){
- struct Range* range = (struct Range*) malloc(sizeof(struct Range));
- range->min = min;
- range->max = max;
- quant_ranges.push_back(range);
- total_ops++;
- }
- }
-
- fclose(fp);
-}
-
-
-
-
-
-/*__device__ inline void atomicAdd(float* address, float value)
-
-{
-
- float old = value;
- float new_old;
-
- do{
- new_old = atomicExch(address, 0.0f);
- new_old += old;
- }
-
- while ((old = atomicExch(address, new_old))!=0.0f);
-
-};
-*/
-
-
-
-
-
-Norm_t* calculateNorms(Tensor* x, Tensor* x_orig){
-
- deviceToHostCopy(x);
- deviceToHostCopy(x_orig);
-
- // NOTE: Move floats to doubles - overflow is quite possible
- float l1_norm = 0.0;
- float l2_norm = 0.0;
- float inf_norm = -1.0;
- double total = 0.0;
-
- float* arr1 = (float*) x->host_data;
- float* arr2 = (float*) x_orig->host_data;
-
- for(unsigned int i = 0; i < x->num_elems; i++){
-
- total = total + arr2[i];
-
- float diff = abs(arr1[i] - arr2[i]);
- l1_norm += diff;
- l2_norm += (arr1[i] - arr2[i]) * (arr1[i] - arr2[i]);
-
- if(inf_norm < diff)
- inf_norm = diff;
- }
-
- l1_norm = l1_norm / (x->num_elems * 1.0);
- l2_norm = l2_norm / (x->num_elems * 1.0);
-
- double distribution_mean = total / (x->num_elems * 1.0);
- l1_norm = l1_norm / distribution_mean;
- l2_norm = l2_norm / distribution_mean;
-
-
- Norm_t* norms = (Norm_t*) malloc(sizeof(Norm_t));
- norms->l1_norm = l1_norm;
- norms->l2_norm = l2_norm;
- norms->inf_norm = inf_norm;
-
- INFO("l1_norm = %f \n", l1_norm);
- INFO("l2_norm = %f \n", l2_norm);
- INFO("inf_norm = %f \n", inf_norm);
-
- return norms;
-}
-
-
-
-Norm_t* calculateNorms2(Tensor* x, Tensor* x_orig){
-
- deviceToHostCopy(x);
- deviceToHostCopy(x_orig);
-
- // NOTE: Move all floats to doubles - overflow is quite possible
- double l0_norm_A = 0.0;
- double l0_norm_B = 0.0;
-
- double l1_norm_A = 0.0;
- double l1_norm_B = 0.0;
-
- double l2_norm_A = 0.0;
- double l2_norm_B = 0.0;
- float inf_norm = -1.0;
- float orig_inf_norm = -1.0;
- double total_diff = 0.0;
- double total_diff_squared = 0.0;
-
- float* arr1 = (float*) x->host_data;
- float* arr2 = (float*) x_orig->host_data;
-
- for(unsigned int i = 0; i < x->num_elems; i++){
-
- if(arr2[i] != 0.0)
- l0_norm_A = l0_norm_A + 1.0;
- if(arr1[i] != 0.0)
- l0_norm_B = l0_norm_B + 1.0;
-
- l1_norm_A = l1_norm_A + abs(arr2[i]);
- l1_norm_B = l1_norm_B + abs(arr1[i]);
-
- l2_norm_A = l2_norm_A + (arr2[i] * arr2[i]);
- l2_norm_B = l2_norm_B + (arr1[i] * arr1[i]);
-
- float diff = abs(arr1[i] - arr2[i]);
- total_diff = total_diff + diff;
- float diff_squared = diff * diff;
- total_diff_squared = total_diff_squared + diff_squared;
-
-
- if(orig_inf_norm < diff){
- orig_inf_norm = diff;
- }
-
- // Relative difference value
- float normalized_diff = diff / arr2[i];
- if(inf_norm < normalized_diff){
- inf_norm = normalized_diff;
- }
- }
-
- // Relative L1 and Mean L1 norms of the difference Matrix
- float mean_l1 = ( total_diff ) / x->num_elems;
- float relative_l1 = ( total_diff ) / l1_norm_A;
-
- // Computing Relative L2 norm - i.e., Euclidean distance
- double norm_root_A = sqrt(l2_norm_A);
- double diff_root = sqrt(total_diff_squared);
- float mean_l2 = diff_root / x->num_elems;
- float relative_l2 = diff_root / norm_root_A;
-
- // Packing computed norms in Norm_t struct
- Norm_t* norms = (Norm_t*) malloc(sizeof(Norm_t));
- // Mean metrics - not normalized for the distribution - suitable for precision tuning hardware
- norms->mean_l1 = mean_l1;
- norms->mean_l2 = mean_l2;
- norms->orig_inf_norm = orig_inf_norm;
-
- // Relative metrics (relative to distribution) - suitable for PROMISE
- norms->l1_norm = relative_l1;
- norms->l2_norm = relative_l2;
- norms->inf_norm = inf_norm;
-
- INFO("l1_norm = %f \n", relative_l1);
- INFO("l2_norm = %f \n", relative_l2);
- INFO("inf_norm = %f \n", inf_norm);
-
- return norms;
-}
-
-
-
-
-
-__global__ void normComputeKernel(float* A, float * B, double* l1_A, double* l2_A,
- double* l1_diff, double* l2_diff, unsigned int n){
-
- int i = blockIdx.x * blockDim.x + threadIdx.x;
-
- if(i < n){
-
- double diff = fabsf(A[i] - B[i]);
- double diff_squared = diff * diff;
-
- atomicAdd( l1_A, fabsf(A[i]) );
- atomicAdd( l2_A, (A[i] * A[i]) );
-
- atomicAdd( l1_diff, diff);
- atomicAdd( l2_diff, diff_squared);
- }
-}
-
-
-
-__inline__ __device__ double warpReduceSum(double val) {
-
- for (int offset = warpSize/2; offset > 0; offset /= 2)
- val += __shfl_down(val, offset);
-
- return val;
-}
-
-__inline__ __device__ double blockReduceSum(double val) {
-
- static __shared__ double shared[32]; // Shared mem for 32 partial sums
- int lane = threadIdx.x % warpSize;
- int wid = threadIdx.x / warpSize;
-
- val = warpReduceSum(val); // Each warp performs partial reduction
-
- if (lane == 0)
- shared[wid]=val; // Write reduced value to shared memory
-
-
- __syncthreads(); // Wait for all partial reductions
-
-
- //read from shared memory only if that warp existed
- val = (threadIdx.x < blockDim.x / warpSize) ? shared[lane] : 0;
-
- if (wid == 0) val = warpReduceSum(val); //Final reduce within first warp
-
- return val;
-
-}
-
-
-
-__global__ void deviceReduceBlockAtomicKernel(float* A, float* B, int N,
- double* A_l1, double* A_l2,
- double* diff_l1, double* diff_l2) {
-
- double sum_A_l1 = double(0);
- double sum_A_l2 = double(0);
- double sum_diff_l1 = double(0);
- double sum_diff_l2 = double(0);
-
- for(int i = blockIdx.x * blockDim.x + threadIdx.x; i < N; i += blockDim.x * gridDim.x) {
-
- sum_A_l1 += fabsf(A[i]);
- sum_A_l2 += (A[i] * A[i]);
- double diff1 = A[i] - B[i];
- sum_diff_l1 += fabsf(diff1);
- double diff2 = diff1 * diff1;
- sum_diff_l2 += diff2;
- }
-
- sum_A_l1 = blockReduceSum(sum_A_l1);
- sum_A_l2 = blockReduceSum(sum_A_l2);
- sum_diff_l1 = blockReduceSum(sum_diff_l1);
- sum_diff_l2 = blockReduceSum(sum_diff_l2);
-
- if (threadIdx.x == 0){
- atomicAdd(A_l1, sum_A_l1);
- atomicAdd(A_l2, sum_A_l2);
- atomicAdd(diff_l1, sum_diff_l1);
- atomicAdd(diff_l2, sum_diff_l2);
- }
-}
-
-
-void deviceReduce(float* A, float* B, int N,
- double* A_l1, double* A_l2,
- double* diff_l1, double* diff_l2) {
-
- int threads = 512;
- int blocks = min((N + threads - 1) / threads, 1024);
-
- deviceReduceBlockAtomicKernel<<<blocks, threads>>>(A, B, N, A_l1, A_l2, diff_l1, diff_l2);
- //-- deviceReduceKernel<<<1, 1024>>>(out, out, blocks);
-}
-
-
-
-// Compute Norms on the GPU
-Norm_t* calculateNormsTreeReduction(Tensor* x, Tensor* x_orig){
-
- hostToDeviceCopy(x);
- hostToDeviceCopy(x_orig);
-
- // FIXIT: Move all floats to doubles - overflow is possible
- double l1_norm_A;
- double l2_norm_A;
-
- double l1_diff;
- double l2_diff;
-
- // Device pointers
- double *l1_norm_A_d;
- double *l2_norm_A_d;
- double *l1_diff_d;
- double *l2_diff_d;
-
- cudaMalloc( (void**) &l1_norm_A_d, sizeof(double));
- cudaMalloc( (void**) &l2_norm_A_d, sizeof(double));
- cudaMalloc( (void**) &l1_diff_d, sizeof(double));
- cudaMalloc( (void**) &l2_diff_d, sizeof(double));
-
-
- float* arr1 = (float*) x->gpu_data;
- float* arr2 = (float*) x_orig->gpu_data;
-
- //normComputeKernel<<<gridSize, blockSize>>>(arr1, arr2, l1_norm_A_d, l2_norm_A_d, l1_diff_d, l2_diff_d, x->num_elems);
- deviceReduce(arr1, arr2, x->num_elems, l1_norm_A_d, l2_norm_A_d, l1_diff_d, l2_diff_d);
-
- cudaMemcpy(&l1_norm_A, l1_norm_A_d, sizeof(double), cudaMemcpyDeviceToHost);
- cudaMemcpy(&l2_norm_A, l2_norm_A_d, sizeof(double), cudaMemcpyDeviceToHost);
- cudaMemcpy(&l1_diff, l1_diff_d, sizeof(double), cudaMemcpyDeviceToHost);
- cudaMemcpy(&l2_diff, l2_diff_d, sizeof(double), cudaMemcpyDeviceToHost);
-
- INFO("l1_norm_A = %f, l2_norm_A = %f, l1_diff = %f, l2_diff = %f \n",
- l1_norm_A, l2_norm_A,l1_diff, l2_diff);
-
- // Relative L1 and Mean L1 norms of the difference Matrix
- float mean_l1 = l1_diff / x->num_elems;
- float relative_l1 = l1_diff / l1_norm_A;
-
- // Computing Relative L2 norm - i.e., Euclidean distance
- double norm_root_A = sqrt(l2_norm_A);
- double diff_root = sqrt(l2_diff);
- float mean_l2 = diff_root / x->num_elems;
- float relative_l2 = diff_root / norm_root_A;
-
- // Packing computed norms in Norm_t struct
- Norm_t* norms = (Norm_t*) malloc(sizeof(Norm_t));
- // Mean metrics - not normalized for the distribution - suitable for precision tuning hardware
- norms->mean_l1 = mean_l1;
- norms->mean_l2 = mean_l2;
- norms->orig_inf_norm = 0.0;
-
- // Relative metrics (relative to distribution)
- norms->l1_norm = relative_l1;
- norms->l2_norm = relative_l2;
- norms->inf_norm = 0.0;
-
- INFO("l1_norm = %f \n", relative_l1);
- INFO("l2_norm = %f \n", relative_l2);
-
- return norms;
-}
-
-
-
-
-// Compute Norms on the GPU
-Norm_t* calculateNormsGPU(Tensor* x, Tensor* x_orig){
-
- hostToDeviceCopy(x);
- hostToDeviceCopy(x_orig);
-
- // FIXIT: Move all floats to doubles - overflow is possible
-
- double l1_norm_A;
- double l2_norm_A;
-
- double l1_diff;
- double l2_diff;
-
- // Device pointers
- double *l1_norm_A_d;
- double *l2_norm_A_d;
- double *l1_diff_d;
- double *l2_diff_d;
-
- cudaMalloc( (void**) &l1_norm_A_d, sizeof(double));
- cudaMalloc( (void**) &l2_norm_A_d, sizeof(double));
- cudaMalloc( (void**) &l1_diff_d, sizeof(double));
- cudaMalloc( (void**) &l2_diff_d, sizeof(double));
-
-
- float* arr1 = (float*) x->gpu_data;
- float* arr2 = (float*) x_orig->gpu_data;
-
- int blockSize = 1024;
- int gridSize = (int) ceil ((float) x->num_elems / blockSize);
- INFO("blockSize = %d, gridSize = %d \n", blockSize, gridSize);
-
- normComputeKernel<<<gridSize, blockSize>>>(arr1, arr2, l1_norm_A_d, l2_norm_A_d, l1_diff_d, l2_diff_d, x->num_elems);
-
- cudaMemcpy(&l1_norm_A, l1_norm_A_d, sizeof(double), cudaMemcpyDeviceToHost);
- cudaMemcpy(&l2_norm_A, l2_norm_A_d, sizeof(double), cudaMemcpyDeviceToHost);
- cudaMemcpy(&l1_diff, l1_diff_d, sizeof(double), cudaMemcpyDeviceToHost);
- cudaMemcpy(&l2_diff, l2_diff_d, sizeof(double), cudaMemcpyDeviceToHost);
-
-
- // Relative L1 and Mean L1 norms of the difference Matrix
- float mean_l1 = l1_diff / x->num_elems;
- float relative_l1 = l1_diff / l1_norm_A;
-
- // Computing Relative L2 norm - i.e., Euclidean distance
- double norm_root_A = sqrt(l2_norm_A);
- double diff_root = sqrt(l2_diff);
- float mean_l2 = diff_root / x->num_elems;
- float relative_l2 = diff_root / norm_root_A;
-
- // Packing computed norms in Norm_t struct
- Norm_t* norms = (Norm_t*) malloc(sizeof(Norm_t));
- // Mean metrics - not normalized for the distribution - suitable for precision tuning hardware
- norms->mean_l1 = mean_l1;
- norms->mean_l2 = mean_l2;
- norms->orig_inf_norm = 0.0;
-
- // Relative metrics (relative to distribution) - suitable for PROMISE
- norms->l1_norm = relative_l1;
- norms->l2_norm = relative_l2;
- norms->inf_norm = 0.0;
-
- INFO("l1_norm = %f \n", relative_l1);
- INFO("l2_norm = %f \n", relative_l2);
-
- return norms;
-}
-
-
-
-
-__global__ void vecConstMul(float* A, float mul_factor, int n){
-
- int id = blockIdx.x * blockDim.x + threadIdx.x;
-
- if(id < n)
- A[id] = A[id] * mul_factor;
-}
-
-
-__global__ void vecRound(float* A, int n){
-
- int id = blockIdx.x * blockDim.x + threadIdx.x;
-
- if(id < n)
- A[id] = roundf(A[id]);
-}
-
-
-__global__ void vecConstDiv(float* A, float div_factor, int n){
-
- int id = blockIdx.x * blockDim.x + threadIdx.x;
-
- if(id < n)
- A[id] = A[id] / div_factor;
-}
-
-
-
-__global__ void vecMul(float* A, float* B, int n){
-
- int id = blockIdx.x * blockDim.x + threadIdx.x;
-
- if(id < n)
- B[id] = A[id] * B[id];
-}
-
-
-/**** ERROR injecion routines ******/
-
-void initRandValues(Tensor* bias, int error_scale){
-
- float scaling_values[20];
-
- // FIXIT: Error knob 0 should be 0 zero
- scaling_values[0] = 0.000;
- scaling_values[1] = 0.0005;
- scaling_values[2] = 0.03;
- scaling_values[3] = 0.06;
- scaling_values[4] = 0.08;
- scaling_values[5] = 0.105;
- scaling_values[6] = 0.134;
- scaling_values[7] = 0.16;
- scaling_values[8] = 0.2;
- scaling_values[9] = 0.23;
- scaling_values[10] = 0.26;
- scaling_values[11] = 0.3;
- scaling_values[12] = 0.35;
- scaling_values[13] = 0.4;
- scaling_values[14] = 0.45;
- scaling_values[15] = 0.55;
- scaling_values[16] = 0.65;
- scaling_values[17] = 0.7;
- scaling_values[18] = 0.8;
- scaling_values[19] = 0.9;
-
-
- curandGenerator_t gen;
-
- struct timespec ts;
-
- if(timespec_get(&ts, TIME_UTC) == 0){
- printf("crashed \n");
- abort();
- }
-
- curandCreateGenerator(&gen, CURAND_RNG_PSEUDO_DEFAULT);
-
- curandSetPseudoRandomGeneratorSeed(gen, ts.tv_nsec^ts.tv_sec);
-
- curandGenerateNormal(gen, (float*) bias->gpu_data, bias->num_elems, 0.0, 1.0 * scaling_values[error_scale]);
-
-}
-
-
-
-void initRandValues2(Tensor* bias, int error_scale){
-
- float scaling_values[20];
-
- // FIXIT: Error knob 0 should be 0 zero
- scaling_values[0] = 0.000;
- scaling_values[1] = 0.0005;
- scaling_values[2] = 0.0008;
- scaling_values[3] = 0.001;
- scaling_values[4] = 0.005;
- scaling_values[5] = 0.01;
- scaling_values[6] = 0.02;
- scaling_values[7] = 0.03;
- scaling_values[8] = 0.04;
- scaling_values[9] = 0.05;
- scaling_values[10] = 0.06;
- scaling_values[11] = 0.08;
- scaling_values[12] = 0.1;
- scaling_values[13] = 0.12;
- scaling_values[14] = 0.15;
- scaling_values[15] = 0.2;
- scaling_values[16] = 0.55;
- scaling_values[17] = 0.6;
- scaling_values[18] = 0.65;
- scaling_values[19] = 0.7;
-
-
- curandGenerator_t gen;
-
- struct timespec ts;
-
- if(timespec_get(&ts, TIME_UTC) == 0){
- printf("crashed \n");
- abort();
- }
-
- curandCreateGenerator(&gen, CURAND_RNG_PSEUDO_DEFAULT);
-
- curandSetPseudoRandomGeneratorSeed(gen, ts.tv_nsec^ts.tv_sec);
-
- curandGenerateNormal(gen, (float*) bias->gpu_data, bias->num_elems, 0.0, 1.0 * scaling_values[error_scale]);
-
-}
-
-
-void* addBitError(void* x_ptr, int error_scale){
-
- if(error_scale > 6 || error_scale < 0){
- ERROR("Error Scale out of bounds \n");
- }
-
- INFO("*** TensorBitError \n");
- profileEvent("tensorBitError");
-
- Tensor* x = (Tensor*) x_ptr;
-
- size_t* dim_sizes = x->dims.dim_sizes;
- Tensor* x_original = (Tensor*) create4DTensor(x->data_type, x->data_format,
- dim_sizes[0], dim_sizes[1],
- dim_sizes[2], dim_sizes[3]);
-
- // Copying x data into x_original - for computing Norms
- tensorCopy(x, x_original);
-
- // Quadratic Error
- float freq_factors[6];
- freq_factors[0] = 0.1;
- freq_factors[1] = 0.2;
- freq_factors[2] = 0.4;
- freq_factors[3] = 0.6;
- freq_factors[4] = 0.8;
- freq_factors[5] = 1.0;
-
- float error_freq = freq_factors[error_scale];
-
- deviceToHostCopy(x);
-
- unsigned char* data_arr = reinterpret_cast<unsigned char*>(x->host_data);
- // FIXIT: Need to be careful about floating point datatype assumptions
- long int total_bytes = x->size_in_bytes;
- long int error_iterations = total_bytes * 0.01 * error_freq;
- INFO("total_bytes = %lu, error_iterations = %lu \n", total_bytes, error_iterations);
-
- srand(time(NULL));
-
- for(int i = 0; i < error_iterations; i++){
- // FIXIT: The rand() is only specific to int - need long
- long int index = rand() % total_bytes;
- int N = 5; // The operation below flips the Nth bit
- unsigned char fil = 1UL << N;
- unsigned char val = data_arr[index];
- char flipped = val^fil;
- data_arr[i] = flipped;
- }
-
-
- Norm_t* norms = calculateNorms2(x, x_original);
-
-
- profileEvent("tensorBitError_end", true);
-
- return (void*) norms;
-
-}
-
-
-void randomCeilAndFloor(float* x, size_t num_elems){
-
- INFO("randomCeilAndFloor\n");
-
- std::random_device rd;
- std::mt19937 mt(rd());
- std::normal_distribution<float> distribution(0.0, 1.0);
-
- for(size_t i = 0; i < num_elems; i++){
- float rand_num = distribution(mt);
- int val = abs(((int) rand_num) % 2);
- if(val == 0)
- x[i] = floor(x[i]);
- else if(val == 1)
- x[i] = ceil(x[i]);
- }
-
-}
-
-// Routine for Adding RoundOff Errors
-void* addRoundError(void* x_ptr, int error_scale){
-
- if(error_scale > 11 || error_scale < 0){
- ERROR("Error Scale out of bounds \n");
- }
-
- INFO("*** TensorRoundError \n");
- profileEvent("tensorRoundError");
-
- Tensor* x = (Tensor*) x_ptr;
-
- size_t* dim_sizes = x->dims.dim_sizes;
- Tensor* x_original = (Tensor*) create4DTensor(x->data_type, x->data_format,
- dim_sizes[0], dim_sizes[1],
- dim_sizes[2], dim_sizes[3]);
-
- // Copying x data into x_original - for computing Norms
- tensorCopy(x, x_original);
-
- float round_factors[12];
- round_factors[0] = 1000000; // FIXIT: This should be zero error
- round_factors[1] = 100;
- round_factors[2] = 10;
- round_factors[3] = 7; // Beyond this point, the error function is linear
- round_factors[4] = 3;
- round_factors[5] = 1;
- round_factors[6] = 0.7;
- round_factors[7] = 0.3;
- round_factors[8] = 0.1;
- round_factors[9] = 0.07;
- round_factors[10] = 0.03;
- round_factors[11] = 0.01;
-
- // THINK: Considering using error magnitudes in this scenario
-
-
- float round_factor = round_factors[error_scale];
- INFO("round_factor = %f \n", round_factor);
-
- hostToDeviceCopy(x);
-
- int blockSize = 128;
- int gridSize = (int) ceil ((float) x->num_elems / blockSize);
- INFO("blockSize = %d, gridSize = %d \n", blockSize, gridSize);
-
- // NOTE: Check if a large gridSize will work with really large tensors
- vecConstMul<<<gridSize, blockSize>>>((float*) x->gpu_data, round_factor, x->num_elems);
- //vecRound<<<gridSize, blockSize>>>((float*) x->gpu_data, x->num_elems);
-
- deviceToHostCopy(x);
- randomCeilAndFloor((float*) x->host_data, x->num_elems);
- hostToDeviceCopy(x);
-
- vecConstDiv<<<gridSize, blockSize>>>((float*) x->gpu_data, round_factor, x->num_elems);
-
- Norm_t* norms = calculateNorms2(x, x_original);
-
- profileEvent("tensorRoundError_end", true);
-
- return (void*) norms;
-}
-
-
-
-
-// Routine for Adding Gaussian Error
-void* addGaussianError(void* x_ptr, int error_scale){
-
- if(error_scale > 20 || error_scale < 0){
- ERROR("Error Scale out of bounds \n");
- }
-
- INFO("*** TensorAddError \n");
- profileEvent("tensorAddError");
-
- Tensor* x = (Tensor*) x_ptr;
-
- size_t* dim_sizes = x->dims.dim_sizes;
- Tensor* bias = (Tensor*) create4DTensor(x->cur_type, x->data_format,
- dim_sizes[0], dim_sizes[1],
- dim_sizes[2], dim_sizes[3]);
-
- Tensor* x_original = (Tensor*) create4DTensor(x->cur_type, x->data_format,
- dim_sizes[0], dim_sizes[1],
- dim_sizes[2], dim_sizes[3]);
-
- // Copying x data into x_original - for computing Norms
- tensorCopy(x, x_original);
-
- // NOTE: Error scale is used to generate the bias matrix
- initRandValues(bias, error_scale);
-
- hostToDeviceCopy(x);
- //hostToDeviceCopy(bias);
-
-
- int blockSize = 1024;
- int gridSize = (int) ceil ((float) x->num_elems / blockSize);
- INFO("blockSize = %d, gridSize = %d \n", blockSize, gridSize);
-
- // NOTE: Check if a large gridSize will work with really large tensors
- vecMul<<<gridSize, blockSize>>>((float*) x->gpu_data, (float*) bias->gpu_data, x->num_elems);
-
- float alpha = 1.0f;
-
- // FIXIT: routine fails for 3D tensors
- checkCUDNN(cudnnAddTensor(cudnnHandle, &alpha, bias->tensor_desc,
- bias->gpu_data, &alpha, x->tensor_desc, x->gpu_data));
-
-
- //Norm_t* norms = calculateNorms2(x, x_original);
- //Norm_t* norms = calculateNormsGPU(x, x_original);
-
- Norm_t* norms = calculateNormsTreeReduction(x, x_original);
-
- freeTensor(x_original);
- freeTensor(bias);
-
-
- profileEvent("tensorAddError_end", true);
-
- return (void*) norms;
-}
-
-
-
-void initPromiseRandValues(Tensor* bias, int error_scale){
-
- float scaling_values[10];
-
- // FIXIT: Error knob 0 should be 0 zero
- scaling_values[0] = 0.75;
- scaling_values[1] = 0.64;
- scaling_values[2] = 0.336;
- scaling_values[3] = 0.21;
- scaling_values[4] = 0.168;
- scaling_values[5] = 0.14;
- scaling_values[6] = 0.11;
- scaling_values[7] = 0.0784;
- scaling_values[8] = 0.005;
- scaling_values[9] = 0.000;
-
-
- curandGenerator_t gen;
- struct timespec ts;
- if(timespec_get(&ts, TIME_UTC) == 0){
- printf("crashed \n");
- abort();
- }
-
- curandCreateGenerator(&gen, CURAND_RNG_PSEUDO_DEFAULT);
- curandSetPseudoRandomGeneratorSeed(gen, ts.tv_nsec^ts.tv_sec);
- curandGenerateNormal(gen, (float*) bias->gpu_data, bias->num_elems, 0.0, 1.0 * scaling_values[error_scale]);
-
-}
-
-
-// NOTE: Assumption is that x_ptr is FP32 tensor - doesn't work with FP16
-// Routine for Adding PROMISE bitline swing error
-void* addPromiseError(void* x_ptr, int error_scale){
-
- if(error_scale > 10 || error_scale < 0){
- ERROR("Error Scale out of bounds for PROMISE - 8 Swing values \n");
- }
-
- INFO("*** addPromiseError \n");
- profileEvent("addPromiseError");
-
- Tensor* x = (Tensor*) x_ptr;
-
- size_t* dim_sizes = x->dims.dim_sizes;
- Tensor* bias = (Tensor*) create4DTensor(x->cur_type, x->data_format,
- dim_sizes[0], dim_sizes[1],
- dim_sizes[2], dim_sizes[3]);
-
- // NOTE: Error scale is used to generate the bias matrix
- initPromiseRandValues(bias, error_scale);
-
- hostToDeviceCopy(x);
- //hostToDeviceCopy(bias);
-
- int blockSize = 1024;
- int gridSize = (int) ceil ((float) x->num_elems / blockSize);
- INFO("blockSize = %d, gridSize = %d \n", blockSize, gridSize);
-
- // NOTE: Check if a large gridSize will work with really large tensors
- vecMul<<<gridSize, blockSize>>>((float*) x->gpu_data, (float*) bias->gpu_data, x->num_elems);
-
- float alpha = 1.0f;
- //float beta = 0.0f;
- checkCUDNN(cudnnAddTensor(cudnnHandle, &alpha, bias->tensor_desc,
- bias->gpu_data, &alpha, x->tensor_desc, x->gpu_data));
-
- profileEvent("addPromiseError_end", true);
-
- return (void*) x;
-}
-
-
-
-
-__global__ void quantizeAndClip(float* A, int n, float mul_factor, float min, float max){
-
- int id = blockIdx.x * blockDim.x + threadIdx.x;
- if(id < n){
- int temp = (A[id] - min) / mul_factor;
- float result = temp * 1.0 * mul_factor;
- result = result + min;
- A[id] = result;
-
- if(A[id] > max){
- A[id] = max;
- }
- if(A[id] < min){
- A[id] = min;
- }
-
- }
-}
-
-
-__global__ void quantizeElem(float* A, int n, float mul_factor, float min){
-
- int id = blockIdx.x * blockDim.x + threadIdx.x;
- if(id < n){
- int temp = (A[id] - min) / mul_factor;
- float result = temp * 1.0 * mul_factor;
- result = result + min;
- A[id] = result;
- }
-}
-
-
-void* quantizeTensorPromise(void* input_ptr, float min, float max){
-
- INFO("QuantizeTensorPROMISE \n");
- Tensor* input = (Tensor*) input_ptr;
-
-
- int quantize_range = 256;
- float input_range = max - min;
- float mul_factor = input_range / quantize_range;
- INFO("mul_factor = %f \n", mul_factor);
-
- int blockSize = 1024;
- int gridSize = (int) ceil ((float) input->num_elems / blockSize);
- INFO("blockSize = %d, gridSize = %d \n", blockSize, gridSize);
-
- hostToDeviceCopy(input);
-
- quantizeAndClip<<<gridSize, blockSize>>>((float*) input->gpu_data,
- input->num_elems, mul_factor, min, max);
-
-
- return input;
-}
-
-
-void* tensorAddError(void* x_ptr, int error_scale){
-
- void * new_x = addGaussianError(x_ptr, error_scale);
- //void * new_x = addRoundError(x_ptr, error_scale);
- //void * new_x = addBitError(x_ptr, error_scale);
- return new_x;
-}
-
-
-
-/*void* tensorGemmModel(void* lhs_ptr, void* rhs_ptr){
-
- void* gold_output = tensorGemmGPU(lhs_ptr, rhs_ptr);
-
- Tensor* x = (Tensor*) lhs_ptr;
- size_t* dim_sizes = x->dims.dim_sizes;
-
- Tensor* x_original = (Tensor*) create4DTensor(x->data_type, x->data_format,
- dim_sizes[0], dim_sizes[1],
- dim_sizes[2], dim_sizes[3]);
-
- // Copying x data into x_original
- tensorCopy(x, x_original);
-
- // NOTE: creating result tensor allocation that is to be reused
- void* gemm_result = tensorGemmGPU((void*)x, rhs_ptr);
-
- int error_level1 = 5;
- int error_level2 = 5;
-
- for(int i = 0; i < error_level1; i++){
- Norm_t* x_norms = (Norm_t*) tensorAddError(x, 2);
-
- for(int j = 0; j < error_level2; j++){
- // NOTE: Reusing the result tensor - to keep memory usage in limits
- void* gemm_result = tensorGemmGPU((void*)x, rhs_ptr, gemm_result);
- Norm_t* compute_norms = (Norm_t*) tensorAddError(gemm_result, 2);
- Norm_t* output_norms = calculateNormsTreeReduction((Tensor*) gemm_result, (Tensor*) gold_output);
- double l1_norm = output_norms->l1_norm;
- double l2_norm = output_norms->l2_norm;
- printf("*********** l1_norm = %f, l2_norm = %f \n\n", l1_norm, l2_norm);
- }
- // Copying x data into x_original
- tensorCopy(x_original, x);
- }
-
-
- return gold_output;
-}
-*/
-
-
-#endif
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/fp16_conversion.h b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/fp16_conversion.h
deleted file mode 100644
index 4c2fbe806d1758118f6d55c079f9c75de42599d8..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/fp16_conversion.h
+++ /dev/null
@@ -1,124 +0,0 @@
-// Copyright (c) 1993-2016, NVIDIA CORPORATION. All rights reserved.
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions
-// are met:
-// * Redistributions of source code must retain the above copyright
-// notice, this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright
-// notice, this list of conditions and the following disclaimer in the
-// documentation and/or other materials provided with the distribution.
-// * Neither the name of NVIDIA CORPORATION nor the names of its
-// contributors may be used to endorse or promote products derived
-// from this software without specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
-// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
-// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
-// CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
-// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
-// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
-// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
-// OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-// This code modified from the public domain code here:
-// https://gist.github.com/rygorous/2156668
-// The URL above includes more robust conversion routines
-// that handle Inf and NaN correctly.
-//
-// It is recommended to use the more robust versions in production code.
-
-
-#ifndef FP16_CONV_HEADER
-#define FP16_CONV_HEADER
-
-
-
-typedef unsigned uint;
-
-union FP32
-{
- uint u;
- float f;
- struct
- {
- uint Mantissa : 23;
- uint Exponent : 8;
- uint Sign : 1;
- };
-};
-
-union FP16
-{
- unsigned short u;
- struct
- {
- uint Mantissa : 10;
- uint Exponent : 5;
- uint Sign : 1;
- };
-};
-
-// Approximate solution. This is faster but converts some sNaNs to
-// infinity and doesn't round correctly. Handle with care.
-// Approximate solution. This is faster but converts some sNaNs to
-// infinity and doesn't round correctly. Handle with care.
-static half approx_float_to_half(float fl)
-{
- FP32 f32infty = { 255 << 23 };
- FP32 f16max = { (127 + 16) << 23 };
- FP32 magic = { 15 << 23 };
- FP32 expinf = { (255 ^ 31) << 23 };
- uint sign_mask = 0x80000000u;
- FP16 o = { 0 };
-
- FP32 f = *((FP32*)&fl);
-
- uint sign = f.u & sign_mask;
- f.u ^= sign;
-
- if (!(f.f < f32infty.u)) // Inf or NaN
- o.u = f.u ^ expinf.u;
- else
- {
- if (f.f > f16max.f) f.f = f16max.f;
- f.f *= magic.f;
- }
-
- o.u = f.u >> 13; // Take the mantissa bits
- o.u |= sign >> 16;
- return *((half*)&o);
-}
-
-// from half->float code - just for verification.
-static float half_to_float(half hf)
-{
- FP16 h = *((FP16*)&hf);
-
- static const FP32 magic = { 113 << 23 };
- static const uint shifted_exp = 0x7c00 << 13; // exponent mask after shift
- FP32 o;
-
- o.u = (h.u & 0x7fff) << 13; // exponent/mantissa bits
- uint exp = shifted_exp & o.u; // just the exponent
- o.u += (127 - 15) << 23; // exponent adjust
-
- // handle exponent special cases
- if (exp == shifted_exp) // Inf/NaN?
- o.u += (128 - 16) << 23; // extra exp adjust
- else if (exp == 0) // Zero/Denormal?
- {
- o.u += 1 << 23; // extra exp adjust
- o.f -= magic.f; // renormalize
- }
-
- o.u |= (h.u & 0x8000) << 16; // sign bit
- return o.f;
-}
-
-
-
-#endif
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/fp16_emu.h b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/fp16_emu.h
deleted file mode 100644
index 64aee8231b54d52710192fc7d598d6ed162f1338..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/fp16_emu.h
+++ /dev/null
@@ -1,274 +0,0 @@
-/*
- * Copyright 1993-2014 NVIDIA Corporation. All rights reserved.
- *
- * NOTICE TO LICENSEE:
- *
- * This source code and/or documentation ("Licensed Deliverables") are
- * subject to NVIDIA intellectual property rights under U.S. and
- * international Copyright laws.
- *
- * These Licensed Deliverables contained herein is PROPRIETARY and
- * CONFIDENTIAL to NVIDIA and is being provided under the terms and
- * conditions of a form of NVIDIA software license agreement by and
- * between NVIDIA and Licensee ("License Agreement") or electronically
- * accepted by Licensee. Notwithstanding any terms or conditions to
- * the contrary in the License Agreement, reproduction or disclosure
- * of the Licensed Deliverables to any third party without the express
- * written consent of NVIDIA is prohibited.
- *
- * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
- * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
- * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
- * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
- * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
- * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
- * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
- * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
- * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
- * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
- * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
- * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
- * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
- * OF THESE LICENSED DELIVERABLES.
- *
- * U.S. Government End Users. These Licensed Deliverables are a
- * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
- * 1995), consisting of "commercial computer software" and "commercial
- * computer software documentation" as such terms are used in 48
- * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
- * only as a commercial end item. Consistent with 48 C.F.R.12.212 and
- * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
- * U.S. Government End Users acquire the Licensed Deliverables with
- * only those rights set forth herein.
- *
- * Any use of the Licensed Deliverables in individual and commercial
- * software must include, in the user documentation and internal
- * comments to the code, the above Disclaimer and U.S. Government End
- * Users Notice.
- */
-
-// Conversion from/to 16-bit floating point (half-precision).
-
-#if !defined(_FP16_EMU_H_)
-#define _FP16_EMU_H_
-
-#include <driver_types.h>
-#include <cuda_fp16.h>
-
-// Necessary to ensure visibility of CUDART_VERSION macro
-#include <cuda_runtime_api.h>
-
-// Definition of '__half_raw' was not provided before CUDA 9.0.
-// '__half_raw' is our type where the unsigned 16-bit integer
-// data member 'x' can be accessed in both CUDA 9.0 and 8.0.
-#if CUDART_VERSION < 9000
-typedef __half __half_raw;
-#endif
-
-// Internally, in CUDNN we use half1 struct as the FP16 type.
-typedef __half half1;
-
-#define HLF_EPSILON 4.887581E-04
-#define HLF_MIN 6.103516E-05
-#define HLF_MAX 6.550400E+04
-
-half1 cpu_float2half_rn(float f);
-
-float cpu_half2float(half1 h);
-
-static __inline__ __device__ __host__ half1 habs(half1 h)
-{
- __half_raw hr = reinterpret_cast<__half_raw&>(h);
- hr.x &= 0x7fffU;
- return reinterpret_cast<half1&>(hr);
-}
-
-static __inline__ __device__ __host__ half1 hneg(half1 h)
-{
- __half_raw hr = reinterpret_cast<__half_raw&>(h);
- hr.x ^= 0x8000U;
- return reinterpret_cast<half1&>(hr);
-}
-
-static __inline__ __device__ __host__ int ishnan(half1 h)
-{
- // When input is NaN, exponent is all ones and mantissa is non-zero.
- __half_raw hr = reinterpret_cast<__half_raw&>(h);
- return (hr.x & 0x7c00U) == 0x7c00U && (hr.x & 0x03ffU) != 0;
-}
-
-static __inline__ __device__ __host__ int ishinf(half1 h)
-{
- // When input is +/- inf, exponent is all ones and mantissa is zero.
- __half_raw hr = reinterpret_cast<__half_raw&>(h);
- return (hr.x & 0x7c00U) == 0x7c00U && (hr.x & 0x03ffU) == 0;
-}
-
-static __inline__ __device__ __host__ int ishequ(half1 x, half1 y)
-{
- __half_raw xr = reinterpret_cast<__half_raw&>(x);
- __half_raw yr = reinterpret_cast<__half_raw&>(y);
- return ishnan(x) == 0 && ishnan(y) == 0 && xr.x == yr.x;
-}
-
-// Returns 0.0000 in FP16 binary form
-static __inline__ __device__ __host__ half1 hzero()
-{
- __half_raw hr;
- hr.x = 0x0000U;
- return reinterpret_cast<half1&>(hr);
-}
-
-// Returns 1.0000 in FP16 binary form
-static __inline__ __device__ __host__ half1 hone()
-{
- __half_raw hr;
- hr.x = 0x3c00U;
- return reinterpret_cast<half1&>(hr);
-}
-
-// Returns quiet NaN, the most significant fraction bit #9 is set
-static __inline__ __device__ __host__ half1 hnan()
-{
- __half_raw hr;
- hr.x = 0x7e00U;
- return reinterpret_cast<half1&>(hr);
-}
-
-// Largest positive FP16 value, corresponds to 6.5504e+04
-static __inline__ __device__ __host__ half1 hmax()
-{
- // Exponent all ones except LSB (0x1e), mantissa is all ones (0x3ff)
- __half_raw hr;
- hr.x = 0x7bffU;
- return reinterpret_cast<half1&>(hr);
-}
-
-// Smallest positive (normalized) FP16 value, corresponds to 6.1035e-05
-static __inline__ __device__ __host__ half1 hmin()
-{
- // Exponent is 0x01 (5 bits), mantissa is all zeros (10 bits)
- __half_raw hr;
- hr.x = 0x0400U;
- return reinterpret_cast<half1&>(hr);
-}
-
-
-
-
-
-
-
-
-
-
-
-#define STATIC_ASSERT(cond) do { typedef char compile_time_assert[(cond) ? 1 : -1]; } while (0)
-
-// Host functions for converting between FP32 and FP16 formats
-// Paulius Micikevicius (pauliusm@nvidia.com)
-
-half1 cpu_float2half_rn(float f)
-{
- unsigned x = *((int*)(void*)(&f));
- unsigned u = (x & 0x7fffffff), remainder, shift, lsb, lsb_s1, lsb_m1;
- unsigned sign, exponent, mantissa;
-
- __half_raw hr;
-
- // Get rid of +NaN/-NaN case first.
- if (u > 0x7f800000) {
- hr.x = 0x7fffU;
- return reinterpret_cast<half1&>(hr);
- }
-
- sign = ((x >> 16) & 0x8000);
-
- // Get rid of +Inf/-Inf, +0/-0.
- if (u > 0x477fefff) {
- hr.x = sign | 0x7c00U;
- return reinterpret_cast<half1&>(hr);
- }
- if (u < 0x33000001) {
- hr.x = sign | 0x0000U;
- return reinterpret_cast<half1&>(hr);
- }
-
- exponent = ((u >> 23) & 0xff);
- mantissa = (u & 0x7fffff);
-
- if (exponent > 0x70) {
- shift = 13;
- exponent -= 0x70;
- } else {
- shift = 0x7e - exponent;
- exponent = 0;
- mantissa |= 0x800000;
- }
- lsb = (1 << shift);
- lsb_s1 = (lsb >> 1);
- lsb_m1 = (lsb - 1);
-
- // Round to nearest even.
- remainder = (mantissa & lsb_m1);
- mantissa >>= shift;
- if (remainder > lsb_s1 || (remainder == lsb_s1 && (mantissa & 0x1))) {
- ++mantissa;
- if (!(mantissa & 0x3ff)) {
- ++exponent;
- mantissa = 0;
- }
- }
-
- hr.x = (sign | (exponent << 10) | mantissa);
-
- return reinterpret_cast<half1&>(hr);
-}
-
-
-float cpu_half2float(half1 h)
-{
- STATIC_ASSERT(sizeof(int) == sizeof(float));
-
- __half_raw hr = reinterpret_cast<__half_raw&>(h);
-
- unsigned sign = ((hr.x >> 15) & 1);
- unsigned exponent = ((hr.x >> 10) & 0x1f);
- unsigned mantissa = ((hr.x & 0x3ff) << 13);
-
- if (exponent == 0x1f) { /* NaN or Inf */
- mantissa = (mantissa ? (sign = 0, 0x7fffff) : 0);
- exponent = 0xff;
- } else if (!exponent) { /* Denorm or Zero */
- if (mantissa) {
- unsigned int msb;
- exponent = 0x71;
- do {
- msb = (mantissa & 0x400000);
- mantissa <<= 1; /* normalize */
- --exponent;
- } while (!msb);
- mantissa &= 0x7fffff; /* 1.mantissa is implicit */
- }
- } else {
- exponent += 0x70;
- }
-
- int temp = ((sign << 31) | (exponent << 23) | mantissa);
-
- return reinterpret_cast<float&>(temp);
-}
-
-
-
-
-
-
-
-#endif // _FP16_EMU_H_
-
-
-
-
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/fp16_gemm.cu b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/fp16_gemm.cu
deleted file mode 100644
index 0649b5f58b7044cc9982e24975006c6d3cb35565..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/fp16_gemm.cu
+++ /dev/null
@@ -1,279 +0,0 @@
-
-
-#ifndef FP16_UTILS_HEADER
-#define FP16_UTILS_HEADER
-
-#include <iostream>
-#include <string>
-#include <cublas_v2.h>
-#include <cuda_fp16.h>
-#include "fp16_emu.h"
-
-inline cudaError_t checkCuda(cudaError_t result) {
- if (result != cudaSuccess)
- std::cerr << "CUDA Runtime Error: " << cudaGetErrorString(result) << "\n";
- return result;
-}
-
-inline cublasStatus_t checkCublas(cublasStatus_t result) {
- if (result != CUBLAS_STATUS_SUCCESS)
- std::cerr << "cuBLAS Error: " << result << "\n";
- return result;
-}
-
-template <typename T>
-inline void printArray(const T * const __restrict__ array,
- const unsigned elements) {
- for (unsigned i = 0; i < elements; i++)
- std::cout << std::to_string(array[i]) << "\n";
-}
-
-// initialization
-template <typename T>
-__global__ void initKernel(T * const __restrict__ array,
- const unsigned elements) {
- const unsigned idx = blockIdx.x * blockDim.x + threadIdx.x;
- if (idx < elements)
- array[idx] = 1.2;
-}
-
-template <typename T>
-void init(T * const __restrict__ array,
- const unsigned elements) {
- const unsigned block_size = 512;
- const unsigned num_blocks = (elements + block_size - 1) / block_size;
- initKernel<<<num_blocks, block_size>>>(array, elements);
- checkCuda(cudaDeviceSynchronize());
-}
-
-// float to half
-__global__ void f2hKernel(const float * const __restrict__ input,
- const unsigned elements,
- half * const __restrict__ output) {
- const unsigned idx = blockIdx.x * blockDim.x + threadIdx.x;
- if (idx < elements)
- output[idx] = __float2half_rn(input[idx]);
-}
-
-void f2h(const float * const __restrict__ input,
- const unsigned elements,
- half * const __restrict__ output) {
- const unsigned block_size = 512;
- const unsigned num_blocks = (elements + block_size - 1) / block_size;
- f2hKernel<<<num_blocks, block_size>>>(input, elements, output);
- checkCuda(cudaDeviceSynchronize());
-}
-
-// half to float
-__global__ void h2fKernel(const half * const __restrict__ input,
- const unsigned elements,
- float * const __restrict__ output) {
- const unsigned idx = blockIdx.x * blockDim.x + threadIdx.x;
- if (idx < elements)
- output[idx] = __half2float(input[idx]);
-}
-
-void h2f(const half * const __restrict__ input,
- const unsigned elements,
- float * const __restrict__ output) {
- const unsigned block_size = 512;
- const unsigned num_blocks = (elements + block_size - 1) / block_size;
- h2fKernel<<<num_blocks, block_size>>>(input, elements, output);
- checkCuda(cudaDeviceSynchronize());
-}
-
-void sgemm(const float * const __restrict__ a,
- const unsigned num_rows_a,
- const unsigned num_cols_a,
- const float * const __restrict__ b,
- const unsigned num_rows_b,
- const unsigned num_cols_b,
- float * const __restrict__ c) {
- const unsigned iterations = 10;
- float kernel_time;
- cudaEvent_t start;
- cudaEvent_t stop;
- cudaEventCreate(&start);
- cudaEventCreate(&stop);
-
- cublasHandle_t handle;
- checkCublas(cublasCreate(&handle));
-
- // Enable Tensor Cores
- checkCublas(cublasSetMathMode(handle, CUBLAS_TENSOR_OP_MATH));
-
- const float alpha_ = 1.0;
- const float beta_ = 0.0;
- const float *alpha = &alpha_;
- const float *beta = &beta_;
-
- cudaEventRecord(start, 0);
- for (unsigned i = 0; i < iterations; i++) {
- checkCublas(cublasGemmEx(handle,
- CUBLAS_OP_N,
- CUBLAS_OP_N,
- // Dimensions
- num_rows_a,
- num_cols_b,
- num_cols_a,
- alpha,
- // A
- a,
- CUDA_R_32F,
- num_rows_a,
- // B
- b,
- CUDA_R_32F,
- num_rows_b,
- beta,
- // C
- c,
- CUDA_R_32F,
- num_rows_a,
- // Compute precision and algorithm
- CUDA_R_32F,
- CUBLAS_GEMM_DEFAULT_TENSOR_OP));
- }
- cudaEventRecord(stop, 0);
- cudaEventSynchronize(stop);
- cudaEventElapsedTime(&kernel_time, start, stop);
-
- std::cout << "FP32 GEMM: " << std::to_string(kernel_time / iterations) << " ms\n";
-}
-
-void hgemm(const float * const __restrict__ af,
- const unsigned num_rows_a,
- const unsigned num_cols_a,
- const float * const __restrict__ bf,
- const unsigned num_rows_b,
- const unsigned num_cols_b,
- float * const __restrict__ cf) {
- const unsigned iterations = 10;
-
- const unsigned num_elements_a = num_rows_a * num_cols_a;
- const unsigned num_elements_b = num_rows_b * num_cols_b;
- const unsigned num_elements_c = num_rows_a * num_cols_b;
-
- float to_fp16_time;
- float to_fp32_time;
- float kernel_time;
- float total_time;
-
- cudaEvent_t start;
- cudaEvent_t stop;
- cudaEventCreate(&start);
- cudaEventCreate(&stop);
-
- half *a;
- half *b;
- half *c;
-
- checkCuda(cudaMallocManaged(&a, sizeof(half) * num_elements_a));
- checkCuda(cudaMallocManaged(&b, sizeof(half) * num_elements_b));
- checkCuda(cudaMallocManaged(&c, sizeof(half) * num_elements_c));
-
- init(a, num_elements_a);
- init(b, num_elements_b);
- init(c, num_elements_c);
-
- // Convert floats to halfs
- cudaEventRecord(start, 0);
- f2h(af, num_elements_a, a);
- f2h(bf, num_elements_b, b);
- cudaEventRecord(stop, 0);
- cudaEventSynchronize(stop);
- cudaEventElapsedTime(&to_fp16_time, start, stop);
-
- cublasHandle_t handle;
- checkCublas(cublasCreate(&handle));
- checkCublas(cublasSetMathMode(handle, CUBLAS_TENSOR_OP_MATH));
-
- const half alpha_ = cpu_float2half_rn(1.0);
- const half beta_ = cpu_float2half_rn(0.0);
- const half *alpha = &alpha_;
- const half *beta = &beta_;
-
- cudaEventRecord(start, 0);
- for (unsigned i = 0; i < iterations; i++) {
- checkCublas(cublasGemmEx(handle,
- CUBLAS_OP_N,
- CUBLAS_OP_N,
- // Dimensions
- num_rows_a,
- num_cols_b,
- num_cols_a,
- alpha,
- // A
- a,
- CUDA_R_16F,
- num_rows_a,
- // B
- b,
- CUDA_R_16F,
- num_rows_b,
- beta,
- // C
- c,
- CUDA_R_16F,
- num_rows_a,
- // Compute precision and algorithm
- CUDA_R_16F,
- CUBLAS_GEMM_DEFAULT_TENSOR_OP));
- }
- cudaEventRecord(stop, 0);
- cudaEventSynchronize(stop);
- cudaEventElapsedTime(&kernel_time, start, stop);
-
- cudaEventRecord(start, 0);
- h2f(c, num_elements_c, cf);
- cudaEventRecord(stop, 0);
- cudaEventSynchronize(stop);
- cudaEventElapsedTime(&to_fp32_time, start, stop);
-
- total_time = to_fp16_time + (kernel_time / iterations) + to_fp32_time;
- std::cout << "FP16 GEMM: " << std::to_string(total_time) << " ms\n";
- std::cout << "\tTo FP16: " << std::to_string(to_fp16_time) << " ms\n";
- std::cout << "\tKernel : " << std::to_string(kernel_time / iterations) << " ms\n";
- std::cout << "\tTo FP32: " << std::to_string(to_fp32_time) << " ms\n";
-}
-
-
-/*int main() {
- const unsigned num_rows_a = 5000 * 14 * 14;
- const unsigned num_cols_a = 800;
- const unsigned num_rows_b = num_cols_a;
- const unsigned num_cols_b = 64;
-
- const unsigned num_elements_a = num_rows_a * num_cols_a;
- const unsigned num_elements_b = num_rows_b * num_cols_b;
- const unsigned num_elements_c = num_rows_a * num_cols_b;
-
- float *a;
- float *b;
- float *c;
-
- checkCuda(cudaMallocManaged(&a, sizeof(float) * num_elements_a));
- checkCuda(cudaMallocManaged(&b, sizeof(float) * num_elements_b));
- checkCuda(cudaMallocManaged(&c, sizeof(float) * num_elements_c));
-
- init(a, num_elements_a);
- init(b, num_elements_b);
- init(c, num_elements_c);
-
- // FP32
- sgemm(a, num_rows_a, num_cols_a, b, num_rows_b, num_cols_b, c);
- printArray(c, 16);
-
- // FP16
- hgemm(a, num_rows_a, num_cols_a, b, num_rows_b, num_cols_b, c);
- printArray(c, 16);
-
- checkCuda(cudaFree(a));
- checkCuda(cudaFree(b));
- checkCuda(cudaFree(c));
-
- return 0;
-}
-*/
-
-#endif
\ No newline at end of file
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/global_data.h b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/global_data.h
deleted file mode 100644
index 230cb31f4de4740428737e52ad2834908566a07b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/global_data.h
+++ /dev/null
@@ -1,61 +0,0 @@
-
-#ifndef GLOBAL_DATA_HEADER
-#define GLOBAL_DATA_HEADER
-
-
-#include <stdio.h>
-#include <stdarg.h>
-#include <cstdio>
-#include <cstdlib>
-
-#include <cuda_runtime.h>
-#include <device_launch_parameters.h>
-
-#include <cublas_v2.h>
-#include <cudnn.h>
-#include <cublas_api.h>
-#include "tensor.h"
-#include <string>
-#include <unordered_map>
-
-#define ERROR_INJECTION_ENABLED 0
-#define PROMISE_MODE 1
-
-
-#ifdef NO_INJECTION
-#undef ERROR_INJECTION_ENABLED
-#endif
-
-
-//#define ERROR_INJECTION_ENABLED 1
-/* Data declarations */
-cudnnHandle_t cudnnHandle;
-cublasHandle_t cublasHandle;
-
-bool runtime_initialized = false;
-// NOTE: Layers Mode is True or Approxhpvm wrappper runtime mode
-bool approxhpvm_runtime_mode = false;
-
-
-int op_counter = 0;
-int total_ops = 0;
-// NOTE: Both vectors asssume a linear CFG
-// FIXME: Each operation should have an ID passed to the runtime
-std::vector<int> op_accuracies;
-std::vector<Range*> quant_ranges;
-
-std::vector<void*> tensors_ptr;
-std::vector<void*> host_ptr;
-std::vector<void*> obj_ptr;
-
-std::unordered_map<void*, int> tracked_tensors;
-
-// Autotuning data
-std::unordered_map<int, int> skip_tensors;
-
-// Profiling Data
-std::unordered_map<std::string, int> func_counters;
-std::string profile_data = "";
-
-
-#endif
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/half_precision_api.h b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/half_precision_api.h
deleted file mode 100644
index 94e1a635b5a6baec9fec6c91509caee5cf287e01..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/half_precision_api.h
+++ /dev/null
@@ -1,618 +0,0 @@
-
-
-#ifndef HALF_API_HEADER
-#define HALF_API_HEADER
-
-
-#include <stdio.h>
-#include <stdarg.h>
-#include <cstdio>
-#include <cstdlib>
-#include <cmath>
-#include <ctime>
-#include <cfloat>
-#include <iostream>
-#include <map>
-#include <memory>
-#include <random>
-#include <sstream>
-#include <string>
-#include <vector>
-
-#include <cuda_runtime.h>
-#include <device_launch_parameters.h>
-
-#include <cublas_v2.h>
-#include <cudnn.h>
-#include <cublas_api.h>
-#include <cuda_fp16.h>
-#include <driver_types.h>
-
-
-// Tensor runtime header files
-#include "../include/tensor_runtime.h"
-#include "../include/tensor_utils.cu"
-#include "../include/debug.h"
-#include "../include/profiling.h"
-#include "../include/global_data.h"
-#include "../include/tensor.h"
-#include "../include/fp16_gemm.cu"
-
-
-
-void* tensorHalfGemm(void* lhs_ptr, void* rhs_ptr){
-
- INFO("*** TensorHalfGemm \n");
- profileEvent("#Mul");
-
- Tensor* lhs = (Tensor*) lhs_ptr;
- Tensor* rhs = (Tensor*) rhs_ptr;
-
- INFO("rhs->dims.num_dims = %d \n", rhs->dims.num_dims);
- INFO("lhs->dims.num_dims = %d \n", lhs->dims.num_dims);
-
- hostToDeviceCopy(lhs);
- hostToDeviceCopy(rhs);
-
-
- profileEvent("F2H_start");
-
- convertToFP16(lhs);
- convertToFP16(rhs);
-
- profileEvent("F2H_end");
-
-
- // 'm' holds the batch dimension - assuming NCHW format Tensors
- int m = lhs->dims.dim_sizes[0];
- // The rhs last dimension must contain the neurons
- int n = rhs->dims.dim_sizes[rhs->dims.num_dims-1]; // output neurons
- int k = 1;
-
- for (int j = 1 ; j < lhs->dims.num_dims; j++){
- k = k * lhs->dims.dim_sizes[j]; // input neurons
- }
-
- int rhs_k = rhs->dims.dim_sizes[rhs->dims.num_dims-2];
- // Dimension-note: Check if k is same across the two tensors
- INFO("m = %d, n = %d, k = %d \n", m, n, k);
- if(rhs_k != k){
- ERROR("rhs=%d and lhs=%d columns/rows don't match", rhs_k, k);
- }
-
- // NOTE: Creating a 4D tensor to be compatible with later called cuDNN routines
- Tensor* output = (Tensor*) create4DTensor(half_type, CUDNN_TENSOR_NCHW,
- m, n, 1, 1);
-
- changeTensorPlacement(output, DEVICE);
-
- //convertToFP16(output);
-
-
- // INFO: cuBlas uses column-major format
- // INFO: The leading dimension is just the FIRST Dimension
- // IMP: output is N * M in column-major format, M*N in row-major - what cuDNN expects
- const __half alf = approx_float_to_half(1.0);
- const __half bet = approx_float_to_half(0.0);
- const __half *alpha_half = &alf;
- const __half *beta_half = &bet;
-
-
- checkCudaErrors(cublasGemmEx(cublasHandle, CUBLAS_OP_N, CUBLAS_OP_N,
- n, m, k,
- alpha_half,
- (__half*) rhs->gpu_half_data, CUDA_R_16F, n,
- (__half*) lhs->gpu_half_data, CUDA_R_16F, k,
- beta_half,
- (__half*) output->gpu_half_data, CUDA_R_16F, n,
- CUDA_R_16F, CUBLAS_GEMM_DEFAULT_TENSOR_OP) );
-
-
- profileEvent("H2F_start");
-
- convertToFP32_offline(output);
-
- //h2f((half*) output_half->gpu_data, output->num_elems, (float*) output->gpu_data);
-
-
- profileEvent("H2F_end");
-
-
- profileEvent("#tensorHalfGemm_end");
-
-
-
- return output;
-}
-
-
-
-void* tensorHalfGemmGPU(void* lhs_ptr, void* rhs_ptr){
- return tensorHalfGemm(lhs_ptr, rhs_ptr);
-}
-
-
-
-// FIXIT: Generalize all of the routines for types {half, float, double}
-void* tensorHalfConvolution(void* input_ptr, void* filter_ptr,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int conv_groups){
-
- INFO("*** TensorHConvolution \n");
- profileEvent("#Conv");
-
- Tensor* input = (Tensor*) input_ptr;
- Tensor* filter = (Tensor*) filter_ptr;
-
- cudnnConvolutionDescriptor_t convDesc;
- cudnnConvolutionFwdAlgo_t convAlgo;
- cudnnConvolutionMode_t mode;
- if(conv_mode == 0)
- mode = CUDNN_CONVOLUTION;
- else if(conv_mode == 1)
- mode = CUDNN_CROSS_CORRELATION;
-
- // FIXIT: Need to be more aware of the implications of alpha and beta
- float alpha = 1.0f, beta = 0.0f;
- // NOTE: compute in half precision
- cudnnDataType_t computeType = CUDNN_DATA_HALF;
-
- // NOTE: Moving inputs to GPU global memory
- hostToDeviceCopy(input);
- hostToDeviceCopy(filter);
-
-
- /***** CONVERSIONS from FP32 to FP16 - on the GPU */
- profileEvent("F2H_start");
-
- convertToFP16(input);
- convertToFP16(filter);
-
- profileEvent("F2H_end");
- /******* END OF INPUT DATA CONVERSIONS*/
-
-
-
- checkCUDNN(cudnnCreateConvolutionDescriptor(&convDesc));
-
- //FIXME: Current hack to preserve backward compatibilty
- if(conv_groups == 0){
- conv_groups = 1;
- }
-
- // NOTE: Adding support for grouped convolution
- checkCUDNN(cudnnSetConvolutionGroupCount(convDesc, conv_groups));
-
-
- // FIXIT: Think if upscaling values need to be configurable?
- // IMP-FIXIT: CUDNN Cross correlation is only used in the Lenet context
- // IMP-FIXIT: Either make mode configurable OR see if CUDNN_CONVOLUTION MODE should be used?
- checkCUDNN(cudnnSetConvolution2dDescriptor(convDesc,
- vertical_pad, horizontal_pad, // conv padding
- vertical_stride, horizontal_stride, // conv strides
- 1, 1, // upscaling values
- mode, // mode is configurable
- computeType)); // defines compute precision
-
- int n, c, h, w; // output dimensions
- // Find dimension of convolution output
- checkCUDNN(cudnnGetConvolution2dForwardOutputDim(convDesc,
- input->tensor_desc,
- filter->filter_desc,
- &n, &c, &h, &w));
-
- DEBUG("**Output Tensor Dims, n = %d, c = %d, h = %d, w = %d \n", n, c, h, w);
-
-
- Tensor* output = (Tensor*) create4DTensor((cudnnDataType_t) half_type, // input->data_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
-
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(output, DEVICE);
-
- //convertToFP16(output);
-
-
- // NOTE: Necessary to insert the above call for every output tensor
-
- DEBUG("tensor->data_type = %d, tensor->data_format = %d, N = %d, H = %d, W = %d, C = %d \n",
- output->data_type, output->data_format,
- output->dims.dim_sizes[0], output->dims.dim_sizes[1],
- output->dims.dim_sizes[2], output->dims.dim_sizes[3]);
-
- if(convDesc == NULL || input->tensor_half_desc == NULL ||
- filter->filter_half_desc == NULL || output->tensor_half_desc == NULL)
- ERROR("NULL descriptor! \n");
-
-
- // NOTE: The following algo works with TRUE half precision
- convAlgo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM;
- //convAlgo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM;
-
-
- size_t workspace_size;
- checkCUDNN(cudnnGetConvolutionForwardWorkspaceSize(cudnnHandle,
- input->tensor_half_desc,
- filter->filter_half_desc,
- convDesc,
- output->tensor_half_desc,
- convAlgo,
- &workspace_size));
-
- // Allocating memory for the convolution workspace
- DEBUG("workspace size = %d \n", workspace_size);
- void* workspace;
- checkCudaErrors(cudaMalloc(&workspace, workspace_size));
-
-
-
-
- checkCUDNN(cudnnConvolutionForward(cudnnHandle,
- &alpha,
- input->tensor_half_desc,
- input->gpu_half_data,
- filter->filter_half_desc,
- filter->gpu_half_data,
- convDesc, convAlgo,
- workspace, workspace_size,
- &beta,
- output->tensor_half_desc,
- output->gpu_half_data));
-
-
- profileEvent("H2F_start");
-
- convertToFP32_offline(output);
-
- profileEvent("H2F_end");
-
-
-
- profileEvent("#tensorHalfConv_end");
-
-
- return output;
-}
-
-
-
-
-void* tensorHalfBatchNorm(void* input_ptr, void* gamma_ptr, void* beta_ptr,
- void* mean_ptr, void* variance_ptr, double epsilon){
-
- INFO("*** TensorHalfBatchNorm \n");
- profileEvent("#BatchNorm");
-
- Tensor* input = (Tensor*) input_ptr;
- Tensor* gamma = (Tensor*) gamma_ptr;
- Tensor* beta = (Tensor*) beta_ptr;
- Tensor* mean = (Tensor*) mean_ptr;
- Tensor* variance = (Tensor*) variance_ptr;
-
- float alpha_val = 1.0f, beta_val = 0.0f;
- hostToDeviceCopy(input);
- hostToDeviceCopy(gamma);
- hostToDeviceCopy(beta);
- hostToDeviceCopy(mean);
- hostToDeviceCopy(variance);
-
-
- profileEvent("F2H_start");
-
- convertToFP16(input);
-
- profileEvent("F2H_end");
-
-
-
- checkCUDNN(cudnnBatchNormalizationForwardInference(cudnnHandle, CUDNN_BATCHNORM_SPATIAL,
- &alpha_val, &beta_val,
- input->tensor_half_desc,
- input->gpu_half_data,
- input->tensor_half_desc,
- input->gpu_half_data,
- gamma->tensor_desc, gamma->gpu_data,
- beta->gpu_data, mean->gpu_data,
- variance->gpu_data, epsilon));
-
-
-
- profileEvent("H2F_start");
-
- convertToFP32_offline(input);
-
- profileEvent("H2F_end");
-
-
-
- profileEvent("#tensorHalfBatchNorm_end", true);
-
-
- return input;
-}
-
-
-
-
-void* tensorHalfPooling(void* input_ptr,
- int poolFunction,
- int window_height, int window_width,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride){
-
-
-
- INFO("*** TensorHalfPooling \n");
- profileEvent("#Pool");
-
- Tensor* input = (Tensor*) input_ptr;
-
- hostToDeviceCopy(input);
-
- /** floating point to half conversion */
- profileEvent("F2H_start");
-
- convertToFP16(input);
-
- profileEvent("F2H_end");
- //*** end of data conversions
-
- cudnnPoolingDescriptor_t poolDesc;
- // FIXIT: Need to be more aware of the implications of alpha and beta
- float alpha = 1.0f, beta = 0.0f;
-
-
- checkCUDNN(cudnnCreatePoolingDescriptor(&poolDesc));
-
- int n = input->dims.dim_sizes[0];
- int c = input->dims.dim_sizes[1];
- int h = (input->dims.dim_sizes[2] + (2 * vertical_pad) - window_height) / vertical_stride;
- h = h + 1;
- int w = (input->dims.dim_sizes[3] + (2 * horizontal_pad) - window_width) / horizontal_stride;
- w = w + 1;
-
- DEBUG("n = %d, c = %d, h = %d, w = %d \n", n, c, h, w);
-
- // FIXIT: Don't be specific to floats
- Tensor* output = (Tensor*) create4DTensor(half_type, CUDNN_TENSOR_NCHW, n, c, h, w);
- // Changing output tensor placement from host to device
- changeTensorPlacement(output, DEVICE);
-
- //convertToFP16(output);
-
- // FIXIT: Fix being specific to CUDNN_DATA_FLOAT and NCHW format
- // FIXIT: Is this setTensor even needed?
- checkCUDNN(cudnnSetTensor4dDescriptor(output->tensor_half_desc,
- CUDNN_TENSOR_NCHW,
- CUDNN_DATA_HALF,
- n, c,
- h, w));
-
- cudnnPoolingMode_t pool_mode;
- if(poolFunction == 0)
- pool_mode = CUDNN_POOLING_MAX;
- else if(poolFunction == 1)
- pool_mode = CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING;
-
-
- // FIXIT: Make the pool function (max, min, avg) configurable
- checkCUDNN(cudnnSetPooling2dDescriptor(poolDesc,
- pool_mode,
- CUDNN_PROPAGATE_NAN,
- window_height, window_width,
- vertical_pad, horizontal_pad,
- vertical_stride, horizontal_stride));
-
-
- checkCUDNN(cudnnPoolingForward(cudnnHandle, poolDesc, &alpha,
- input->tensor_half_desc,
- input->gpu_half_data, &beta,
- output->tensor_half_desc, output->gpu_half_data));
-
-
-
- profileEvent("H2F_start");
-
- convertToFP32_offline(output);
-
- profileEvent("H2F_end");
-
-
- profileEvent("#tensorHalfPooling_end", true);
-
- return output;
-}
-
-
-
-
-
-void* tensorHalfRelu2(void* input_ptr, float min, float max){
-
- INFO("*** TensorClippedRelu \n");
- profileEvent("#Relu");
-
- Tensor* input = (Tensor*) input_ptr;
-
- cudnnActivationDescriptor_t reluDesc;
- float alpha = 1.0f, beta = 0.0f;
- hostToDeviceCopy(input);
-
-
- //**** Floating point to half conversions
- profileEvent("F2H_start");
-
- convertToFP16(input);
-
- profileEvent("F2H_end");
- /*** End of data type conversion **/
-
-
- checkCUDNN(cudnnCreateActivationDescriptor(&reluDesc));
-
- checkCUDNN(cudnnSetActivationDescriptor(reluDesc, CUDNN_ACTIVATION_CLIPPED_RELU,
- CUDNN_PROPAGATE_NAN, 2.0));
-
- checkCUDNN(cudnnActivationForward(cudnnHandle, reluDesc, &alpha,
- input->tensor_half_desc, input->gpu_half_data, &beta,
- input->tensor_half_desc, input->gpu_half_data));
-
-
- profileEvent("H2F_start");
- // NOTE: Transforming half precision output to single precision
-
- convertToFP32_offline(input);
-
- profileEvent("H2F_end");
-
- profileEvent("#tensorHalfClippedRelu_end");
-
-
- return input;
-}
-
-
-
-
-void* tensorHalfRelu(void* input_ptr){
-
- INFO("*** TensorHalfRelu \n");
- profileEvent("#Relu");
-
- Tensor* input = (Tensor*) input_ptr;
-
- cudnnActivationDescriptor_t reluDesc;
- float alpha = 1.0f, beta = 0.0f;
- hostToDeviceCopy(input);
-
-
- //**** Floating point to half conversions
- profileEvent("F2H_start");
-
- convertToFP16(input);
-
- profileEvent("F2H_end");
- /*** End of data type conversion **/
-
-
- checkCUDNN(cudnnCreateActivationDescriptor(&reluDesc));
-
- checkCUDNN(cudnnSetActivationDescriptor(reluDesc, CUDNN_ACTIVATION_RELU,
- CUDNN_PROPAGATE_NAN, 0.0));
-
- checkCUDNN(cudnnActivationForward(cudnnHandle, reluDesc, &alpha,
- input->tensor_half_desc, input->gpu_half_data, &beta,
- input->tensor_half_desc, input->gpu_half_data));
-
-
- profileEvent("H2F_start");
-
- convertToFP32_offline(input);
-
- profileEvent("H2F_end");
-
-
- profileEvent("#tensorHalfRelu_end");
-
-
- return input;
-}
-
-
-
-
-
-
-void* tensorHalfTanh(void* input_ptr){
-
- INFO("*** TensorHalfTanh \n");
- profileEvent("#Tanh");
-
-
- Tensor* input = (Tensor*) input_ptr;
-
- cudnnActivationDescriptor_t tanhDesc;
- float alpha = 1.0f, beta = 0.0f;
- hostToDeviceCopy(input);
-
-
- //**** Data conversion from float to half
- profileEvent("F2H_start");
-
- convertToFP16(input);
-
- profileEvent("F2H_end");
- /**** End of data type conversion ****/
-
-
- checkCUDNN(cudnnCreateActivationDescriptor(&tanhDesc));
-
- checkCUDNN(cudnnSetActivationDescriptor(tanhDesc, CUDNN_ACTIVATION_TANH,
- CUDNN_PROPAGATE_NAN, 0.0));
-
- checkCUDNN(cudnnActivationForward(cudnnHandle, tanhDesc, &alpha,
- input->tensor_half_desc, input->gpu_half_data, &beta,
- input->tensor_half_desc, input->gpu_half_data));
-
- profileEvent("H2F_start");
-
- convertToFP32_offline(input);
-
- profileEvent("H2F_end");
-
-
- profileEvent("#tensorHalfTanh_end");
-
-
- return input;
-}
-
-
-
-void* tensorHalfAdd(void* x_ptr, void* bias_ptr){
-
- Tensor* x = (Tensor*) x_ptr;
- Tensor* bias = (Tensor*) bias_ptr;
-
- INFO("*** TensorHalfAdd \n");
- profileEvent("#Add");
-
- float alpha = 1.0f;
- // float beta = 0.0f;
- hostToDeviceCopy(x);
- hostToDeviceCopy(bias);
-
-
- //**** Data conversion from float to half
- profileEvent("F2H_start");
-
- convertToFP16(x);
- convertToFP16(bias);
-
- profileEvent("F2H_end");
- /*** End of data type conversions ****/
-
-
- // FIXIT: routine fails for 3D tensors
- checkCUDNN(cudnnAddTensor(cudnnHandle, &alpha, bias->tensor_half_desc,
- bias->gpu_half_data, &alpha,
- x->tensor_half_desc, x->gpu_half_data));
-
-
- profileEvent("H2F_start");
-
- convertToFP32_offline(x);
-
- profileEvent("H2F_end");
-
-
- profileEvent("#tensorHalfAdd_end");
-
-
- return x;
-}
-
-
-
-#endif
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/hpvm-rt-controller.h b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/hpvm-rt-controller.h
deleted file mode 100644
index af107016a5b8009fef0622487a285ac38a437ad4..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/hpvm-rt-controller.h
+++ /dev/null
@@ -1,1397 +0,0 @@
-
-#ifndef LLVM_HPVM_RT_CONTROLLER_H
-#define LLVM_HPVM_RT_CONTROLLER_H
-
-#include <fstream>
-#include <iostream>
-#include <map>
-#include <vector>
-#include <cstring>
-#include <cstdlib>
-
-#include "configuration.h"
-
-#include "profiler.h"
-#include "promise_timing_model.h"
-#include <ctime>
-
-#include <sys/stat.h>
-
-#define ACTIVE_PROFILING
-
-/*
- * Check if a file exists
- * Return true if the file exists, false else
- */
-bool fileExists(const std::string& file) {
- struct stat buf;
- return (stat(file.c_str(), &buf) == 0);
-}
-
-class ProfileInfo {
- private:
- // Members
- double time_total; // Total execution time of application
- double time_compute; // Compute
- double time_control; // Control
- double time_config; // Apply configuration
-
- double energy_total; // Total energy consumed by applcation
- double energy_compute; // Compute
- double energy_control; // Control
- double energy_config; // Apply configuration
-
- // Execution time of one loop iteration
- double time_compute_current_iteration; // Compute
- double time_control_current_iteration; // Control
- double time_config_current_iteration; // Apply configuration
-
- // Energy comsumed by one loop iteration
- double energy_compute_current_iteration; // Compute
- double energy_control_current_iteration; // Control
- double energy_config_current_iteration; // Apply configuration
-
- // Vectors, where compute time and energy information
- // - for each loop iteration (outer vector)
- // - per operation (inner vector)
- // (tensor operation for GPU, or whole layer for PROMISE)
- // is stored
- std::vector< std::vector< std::pair< std::string, double > > > tensor_time_info;
- std::vector< std::vector< std::pair< std::string, double > > > tensor_energy_info;
-
- // Vectors, where total compute time and energy information per iteration are stored
- std::vector< double > compute_time_info;
- std::vector< double > compute_energy_info;
-
- // Vectors, where control time and energy information per iteration are stored
- std::vector< double > control_time_info;
- std::vector< double > control_energy_info;
-
- // Vectors, where control time and energy information per iteration are stored
- std::vector< double > config_time_info;
- std::vector< double > config_energy_info;
-
- bool in_iteration;
-
- // Set to the path of the file where results will be written by printToFile.
- std::string out_file_name;
-
- // Functions
- void resetCurrentIterationTime() {
- time_compute_current_iteration = 0.0;
- time_control_current_iteration = 0.0;
- time_config_current_iteration = 0.0;
- }
-
- void resetCurrentIterationEnergy() {
- energy_compute_current_iteration = 0.0;
- energy_control_current_iteration = 0.0;
- energy_config_current_iteration = 0.0;
- }
-
- void start_iteration() {
- if (!in_iteration) {
- resetCurrentIterationTime();
- resetCurrentIterationEnergy();
- tensor_time_info.push_back(std::vector< std::pair< std::string, double > > ());
- tensor_energy_info.push_back(std::vector< std::pair< std::string, double > > ());
- in_iteration = true;
- }
- }
-
- public:
- void end_iteration() {
- // Update time counters
- time_compute += time_compute_current_iteration;
- time_control += time_control_current_iteration;
- time_config += time_config_current_iteration;
-
- time_total += (time_compute_current_iteration +
- time_control_current_iteration +
- time_config_current_iteration);
-
- // Update energy counters
- energy_compute += energy_compute_current_iteration;
- energy_control += energy_control_current_iteration;
- energy_config += energy_config_current_iteration;
-
- energy_total += (energy_compute_current_iteration +
- energy_control_current_iteration +
- energy_config_current_iteration);
-
- // Save current iteration counters
- compute_time_info.push_back(time_compute_current_iteration);
- compute_energy_info.push_back(energy_compute_current_iteration);
- control_time_info.push_back(time_control_current_iteration);
- control_energy_info.push_back(energy_control_current_iteration);
- config_time_info.push_back(time_config_current_iteration);
- config_energy_info.push_back(energy_config_current_iteration);
-
- // Note end of iteration
- in_iteration = false;
- }
-
- void addToCurrentIterationComputeTime(const char *s, double t) {
- start_iteration();
- time_compute_current_iteration += t;
- tensor_time_info.back().push_back(std::make_pair(std::string(s), t));
- }
-
- void addToCurrentIterationControlTime(double t) {
- start_iteration();
- time_control_current_iteration += t;
- }
-
- void addToCurrentIterationConfigTime(double t) {
- start_iteration();
- time_config_current_iteration += t;
- }
-
- void addToCurrentIterationComputeEnergy(const char *s, double e) {
- start_iteration();
- energy_compute_current_iteration += e;
- tensor_energy_info.back().push_back(std::make_pair(std::string(s), e));
- }
-
- void addToCurrentIterationControlEnergy(double e) {
- start_iteration();
- energy_control_current_iteration += e;
- }
-
- void addToCurrentIterationConfigEnergy(double e) {
- start_iteration();
- energy_config_current_iteration += e;
- }
-
- double getTotalTime() {
- return time_total;
- }
-
- double getTotalEnergy() {
- return energy_total;
- }
-
- double getCurrentIterationComputeTime() {
- return time_compute_current_iteration;
- }
-
- double getCurrentIterationComputeEnergy() {
- return energy_compute_current_iteration;
- }
-
- void set_out_file_name(std::string &str) {
- out_file_name = str;
- }
-
- void printToFile() {
-
- INFO("Writing Runtime Profile Info File...\n");
-
- std::ofstream s_out(out_file_name.c_str());
-
- if (!s_out) {
- ERROR("Failed to open output file.");
- abort();
- }
-
- // By construction, tensor_time_info and tensor_energy_info are expected
- // to have equal sizes, in outer and inner vectors both,
- // and all time_info and energy_info vectors must have the same size.
- unsigned iterations = tensor_time_info.size();
- CUSTOM_ASSERT((tensor_time_info.size() == iterations) &&
- (tensor_energy_info.size() == iterations) &&
- (control_time_info.size() == iterations) &&
- (control_energy_info.size() == iterations) &&
- (config_time_info.size() == iterations) &&
- (config_energy_info.size() == iterations) &&
- "time_info and energy_info size: \
- iteration number does not match.");
-
- for (unsigned i = 0; i < tensor_time_info.size(); i++ ) {
- // time_info.size() == energy_info.size(), since we passed the assertion
- s_out << "Iteration " << i << "\n";
-
- CUSTOM_ASSERT((tensor_time_info[i].size() == tensor_energy_info[i].size()) &&
- "time_info and energy_info size: operation number does not match.");
- for (unsigned j = 0; j < tensor_time_info[i].size(); j++) {
- // time_info[i].size() == energy_info[i].size(), we passed the assertion
- CUSTOM_ASSERT((tensor_time_info[i][j].first == tensor_energy_info[i][j].first) &&
- "time_info and energy_info: operation does not match.");
- s_out << tensor_time_info[i][j].first << " "
- << tensor_time_info[i][j].second << " "
- << tensor_energy_info[i][j].second << "\n";
- }
-
- s_out << "\nIteration Compute Time : " << compute_time_info[i] << "\n";
- s_out << "Iteration Compute Energy: " << compute_energy_info[i] << "\n";
- s_out << "Iteration Control Time : " << control_time_info[i] << "\n";
- s_out << "Iteration Control Energy: " << control_energy_info[i] << "\n";
- s_out << "Iteration Config Time : " << config_time_info[i] << "\n";
- s_out << "Iteration Control Energy: " << config_energy_info[i] << "\n\n\n";
-
- }
- s_out << "\n\nTotal Compute Time : " << time_compute << "\n";
- s_out << "Total Compute Energy: " << energy_compute << "\n";
-
- s_out << "\nTotal Control Time : " << time_control << "\n";
- s_out << "Total Control Energy: " << energy_control << "\n";
-
- s_out << "\nTotal Config Time : " << time_config << "\n";
- s_out << "Total Config Energy: " << energy_config << "\n";
-
- s_out << "\nTotal Time : " << time_total << "\n";
- s_out << "Total Energy: " << energy_total << "\n";
-
- s_out.close();
-
- INFO("Done writing profile.\n");
-
- }
-
- ProfileInfo() : time_total(0.0), energy_total(0.0),
- time_compute_current_iteration(0.0),
- time_control_current_iteration(0.0),
- time_config_current_iteration(0.0),
- energy_compute_current_iteration(0.0),
- energy_control_current_iteration(0.0),
- energy_config_current_iteration(0.0),
- in_iteration(false) {}
-
-};
-
-class Slowdowns {
- private:
- std::vector<float> slowdowns;
- unsigned idx;
-
- public:
- Slowdowns() {
- idx = 0;
-
- std::ifstream s_in("slowdowns.txt");
- if (!s_in) {
- DEBUG("slowdowns file not found. Initializing slowdowns randomly.\n");
- for (unsigned i = 0; i < 10; i++) {
- slowdowns.push_back( 1.0 + (rand()/(RAND_MAX/(5.0-1.0))) );
- }
- } else {
- for (std::string line; std::getline(s_in, line); ) {
- float s = std::stof(line);
- slowdowns.push_back(s);
- }
- }
- }
-
- unsigned getSlowdownsNumber() {
- return slowdowns.size();
- }
-
- float getNextSlowdown() {
- float tmp = slowdowns[idx];
- idx = (idx + 1) % slowdowns.size();
- return tmp;
- }
-
-};
-
-class RuntimeController;
-
-RuntimeController *RC;
-
-class RuntimeController {
- private:
- // Members
- // Map from node names to quantization ranges
- std::map<std::string, std::vector<float> > QuantizationMap;
-
- // Configurations.
- // Configurations initially read - all generated from autotuner
- std::vector<struct Configuration> InitialConfigurations;
-
- // The ones in non dominated set (of pareto optimal points)
- // for accuracy loss-speedup
- std::vector<struct Configuration *> SpeedupConfigurations;
- // The ones in non dominated set (of pareto optimal points)
- // for accuracy loss-energy
- std::vector<struct Configuration *> EnergyConfigurations;
- // The ones in non dominated set (of pareto optimal points)
- // for accuracy loss-speedup-energy
- std::vector<struct Configuration *> ThreeDCurveConfigurations;
-
- std::vector<struct Configuration *> *Configurations;
- unsigned configurationIdx = 0;
-
- double baseline_time = 0.0; // Execution time of baseline configuration
- Slowdowns *slowdowns;
-
- /*** Objects used to gather timing and energy information for execution ***/
- ProfileInfo *PI;
- Profiler *profiler;
- Promise *promise;
-
- //Functions
-
- // Private functions of profiler
- void start_profiler() {
- if (profiler)
- profiler->start_profiler();
- }
- void stop_profiler() {
- if (profiler)
- profiler->stop_profiler();
- }
-
- void setProfileInfoFilename(const char *);
- void readQuantizationFile(const char *);
- void readConfigurationFile(const char *);
-
- void computeParetoConfigurationPoints();
- void compute3DParetoConfigurationPoints();
-
- public:
- // For testing purposes only - do not use widely
- std::vector<struct Configuration *> &getSpeedupConfigurations() {
- return SpeedupConfigurations;
- }
- // For testing purposes only - do not use widely
- std::vector<struct Configuration *> &getEnergyConfigurations() {
- return EnergyConfigurations;
- }
- // For testing purposes only - do not use widely
- std::vector<struct Configuration *> &getThreeDCurveConfigurations() {
- return ThreeDCurveConfigurations;
- }
- // For testing purposes only - do not use widely
- unsigned getConfigurationIdx() {
- return configurationIdx;
- }
-
- std::vector<float> &getQuantizationRanges(const char *data) {
- std::string s(data);
- // All nodes are expected to have quantization ranges
- return QuantizationMap.at(s);
- }
-
- NodeConfiguration *getNodeConfiguration(const char *data) {
- std::string s(data);
- // All nodes are expected to have a configuration
- return (*Configurations)[configurationIdx]->setup.at(s);
- }
-
- // Functions for runtime control
- void findNextConfiguration();
- void findTargetConfiguration(float, enum SEARCH_KIND);
- float getGoalSpeedup();
- double getBaselineTime();
- Slowdowns *getSlowdowns();
-
- void init(const char *Cstr, const char *Qstr) {
- // We initialize the path to the profile info output file,
- // based on the path given for the configuration file
- setProfileInfoFilename(Cstr);
-
- readQuantizationFile(Qstr);
- readConfigurationFile(Cstr);
- Configurations = NULL;
- computeParetoConfigurationPoints();
-// compute3DParetoConfigurationPoints(); Not using 3D curve
- INFO("Speedup Configurations\n");
- printConfigurations(SpeedupConfigurations);
-// INFO("Energy Configurations\n");
-// printConfigurations(EnergyConfigurations);
-// INFO("3D Configurations\n");
-// printConfigurations(ThreeDCurveConfigurations);
- configurationIdx = 0; //TODO: initialize using pareto curve - findTargetConfiguration ?
- Configurations = &SpeedupConfigurations;
-
- // Initializations for different runtime control strategies
- srand(static_cast <unsigned> (time(0)));
- slowdowns = new Slowdowns();
-
- // Start profiling thread in the background, ready to time
- start_profiler();
- pause_profiler();
- reset_profiler();
- }
-
- // Exposing functionality of ProfileInfo
- void end_iteration() {
- if (PI)
- PI->end_iteration();
- }
-
- void addToCurrentIterationComputeTime(const char *s, double t) {
- if (PI)
- PI->addToCurrentIterationComputeTime(s, t);
- }
-
- void addToCurrentIterationControlTime(double t) {
- if (PI)
- PI->addToCurrentIterationControlTime(t);
- }
-
- void addToCurrentIterationConfigTime(double t) {
- if (PI)
- PI->addToCurrentIterationConfigTime(t);
- }
-
- void addToCurrentIterationComputeEnergy(const char *s, double e) {
- if (PI)
- PI->addToCurrentIterationComputeEnergy(s, e);
- }
-
- void addToCurrentIterationControlEnergy(double e) {
- if (PI)
- PI->addToCurrentIterationControlEnergy(e);
- }
-
- void addToCurrentIterationConfigEnergy(double e) {
- if (PI)
- PI->addToCurrentIterationConfigEnergy(e);
- }
-
- double getCurrentIterationComputeTime() {
- return (PI ? PI->getCurrentIterationComputeTime() : 0.0) ;
- }
-
- double getCurrentIterationComputeEnergy() {
- return (PI ? PI->getCurrentIterationComputeEnergy() : 0.0) ;
- }
-
- void writeProfileInfo() {
- if (PI)
- PI->printToFile();
- }
-
- // Exposing functionality of (gpu) profiler
- void resume_profiler() {
- if (profiler)
- profiler->resume_profiler();
- }
-
- void pause_profiler() {
- if (profiler)
- profiler->pause_profiler();
- }
-
- void reset_profiler() {
- if (profiler)
- profiler->reset();
- }
-
- std::pair<double, double> get_time_energy() const {
- return (profiler ? profiler->get_time_energy()
- : std::make_pair(0.0, 0.0)) ;
- }
-
- // Exposing functionality of promise simulator
- std::pair<double, double> fc_profile(const unsigned num_rows_a,
- const unsigned num_cols_a,
- const unsigned num_rows_b,
- const unsigned num_cols_b,
- const unsigned voltage_swing,
- const unsigned patch_factor) {
- return (promise ? promise->fc_profile(num_rows_a,
- num_cols_a,
- num_rows_b,
- num_cols_b,
- voltage_swing,
- patch_factor)
- : std::make_pair(0.0, 0.0)) ;
- }
-
- std::pair<double, double> conv_profile(const unsigned n,
- const unsigned c,
- const unsigned h,
- const unsigned w,
- const unsigned c_out,
- const unsigned c_in,
- const unsigned k_h,
- const unsigned k_w,
- const unsigned s_h,
- const unsigned s_w,
- const unsigned voltage_swing,
- const unsigned patch_factor) {
- return (promise ? promise->conv_profile(n, c, h, w,
- c_out, c_in, k_h, k_w,
- s_h, s_w,
- voltage_swing, patch_factor)
- : std::make_pair(0.0, 0.0)) ;
- }
-
- // Constructor and descructor
- RuntimeController() {
- configurationIdx = 0;
-#ifdef ACTIVE_PROFILING
- PI = new ProfileInfo();
- profiler = new Profiler();
- promise = new Promise();
-#else
- PI = NULL;
- profiler = NULL;
- promise = NULL;
-#endif
-
- }
-
- ~RuntimeController() {
-
- stop_profiler();
- writeProfileInfo();
-
- if (PI) {
- delete PI;
- }
- if (profiler) {
- delete profiler;
- }
- if (promise) {
- delete promise;
- }
-
- for (std::vector<struct Configuration>::iterator it = InitialConfigurations.begin(),
- ie = InitialConfigurations.end(); it != ie; ++it) {
- std::map<std::string, NodeConfiguration * > ConfSetup = it->setup;
- for (std::map<std::string, NodeConfiguration* >::const_iterator it = ConfSetup.begin();
- it != ConfSetup.end(); ++it) {
- delete it->second;
- }
- }
- // Handle freeing memory, for all configurations
- // A way to do that is to not free the initial configurations in the pareto curve,
- // and free all at once in the end
- // This is done because configurations are stored in different containers, but
- // share the node setup
- }
-
- // Helper Functions
- void printQuantizationMap();
- void printConfigurations(std::vector<struct Configuration> &);
- void printConfigurations(std::vector<struct Configuration *> &);
-
-};
-
-void RuntimeController::setProfileInfoFilename(const char *str) {
-
- if (PI) {
- std::string file_path = std::string(str);
- size_t idx = file_path.find_last_of("/");
- file_path.erase(idx + 1);
- file_path.append("profile_info_");
-
- bool found = false;
- std::string profile_filename;
- for (unsigned i = 0; !found; i++) {
- profile_filename = file_path;
- profile_filename.append(std::to_string(i));
- profile_filename.append(".txt");
- found = !fileExists(profile_filename);
- }
-
- PI->set_out_file_name(profile_filename);
- }
-}
-
-#define NODE_NAME_BUFFER_SIZE 10
-
-void RuntimeController::readQuantizationFile(const char *str) {
-
- INFO("Reading Quantization Ranges File...\n");
-
- if (std::string(str).empty()) {
- INFO("Empty quantization file string.\n");
- return;
- }
-
- std::ifstream qin(str);
-
- if (!qin) {
- ERROR("Failed to open PROMISE quantization file.");
- abort();
- }
-
- while (!qin.eof()) {
- char NodeName[NODE_NAME_BUFFER_SIZE];
- std::vector<float> QuantRangeVector;
-
- qin >> NodeName;
-
- float qrange;
- for (unsigned i = 0; i < 8; i++ ) {
- qin >> qrange;
- QuantRangeVector.push_back(qrange);
- }
- // See if we need to insert this in map instead - my lookup test seemed to work without it
- // std::string s(NodeName);
- QuantizationMap.insert(std::pair< std::string, std::vector<float> > (NodeName,
- QuantRangeVector));
- }
-
- qin.close();
- INFO("DONE.\n");
-}
-
-void RuntimeController::printQuantizationMap() {
-
- DEBUG("Quantization Ranges Map:\n");
-
- for (std::map<std::string, std::vector<float> >::const_iterator it = QuantizationMap.begin();
- it != QuantizationMap.end(); ++it) {
- DEBUG("%s :", it->first.c_str());
-
- for (unsigned i = 0; i < it->second.size() ; i++) {
- DEBUG(" %f", it->second[i]);
- }
-
- DEBUG("\n");
- }
-
-}
-
-
-void RuntimeController::readConfigurationFile(const char *str) {
-
- INFO("Reading Configuration File...\n");
-
- std::ifstream qin(str);
-
- if (!qin) {
- ERROR("Failed to open configuration file.");
- abort();
- }
-
- bool readingConfiguration = false;
- bool readingFirstLine = false;
-
- // Read baseline_time from first line of configuration file
- std::string first_line;
- std::getline(qin, first_line);
- DEBUG("first_line: %s\n", first_line.c_str());
- baseline_time = std::stod(first_line);
- DEBUG("Baseline time: %lf\n\n", baseline_time);
-
- for (std::string line; std::getline(qin, line); ) {
- DEBUG("line: %s\n", line.c_str());
-
- // Tokenize using ' ' as delimiter
- // Vector to store tokens
- std::vector<std::string> tokens;
-
- for (auto i = strtok(&line[0], " "); i != NULL; i = strtok(NULL, " "))
- tokens.push_back(i);
-
- for (unsigned i = 0; i < tokens.size(); i++ )
- DEBUG("t: %s\n", tokens[i].c_str());
-
- DEBUG("\n");
-
- if (tokens[0] == "+++++") { // Found new configuration start token
- // Mark the start of a new configuration
- readingConfiguration = true;
- readingFirstLine = true;
- continue;
- }
-
- if (tokens[0] == "-----") { // Found configuration end token
- readingConfiguration = false;
- // Mark the end of current configuration
- continue;
- }
-
- if (readingFirstLine) {
- // Read first line, to create the new configuration struct
- readingFirstLine = false;
- InitialConfigurations.push_back(Configuration(tokens[0],
- std::stof(tokens[1]),
- std::stof(tokens[2]),
- std::stof(tokens[3]),
- std::stof(tokens[4])));
- continue;
- }
-
- if (tokens[1] == "promise") {
- DEBUG("Found promise configuration\n");
-
- // There must be at least one approximation option
- CUSTOM_ASSERT((tokens.size() >= 2) && "Not enough approximation options.");
-
- PROMISENodeConfiguration *NodeConf = new PROMISENodeConfiguration();
- InitialConfigurations.back().setup.insert(std::make_pair(tokens[0], NodeConf));
-
- // In increments of two, to handle pairs of approx option - tunable parameter
- for (unsigned idx = 2; idx < tokens.size(); idx += 2) {
- if (tokens[idx] == "swing_level") {
- DEBUG("Found swing voltage option\n");
- int vswing = std::stoi(tokens[idx+1]);
- DEBUG("vswing: %d\n", vswing);
- NodeConf->pushNewApproximationChoice(PROMISENodeConfiguration::APPROX::SWING_LEVEL,
- vswing);
- }
- // TODO: other approximation options handled here
- }
-
- } else if (tokens[1] == "gpu") {
- DEBUG("Found gpu configuration\n");
-
- // There must be at least one operation, with an approximation option
- CUSTOM_ASSERT((tokens.size() >= 5) && "Not enough operations - approximation options.");
-
- GPUNodeConfiguration *NodeConf = new GPUNodeConfiguration();
- InitialConfigurations.back().setup.insert(std::make_pair(tokens[0], NodeConf));
-
- unsigned idx = 2;
- while (idx < tokens.size()) {
- if (tokens[idx] == "add") {
- DEBUG("Found add operation\n");
- NodeConf->pushNewTensorOperation(GPUNodeConfiguration::TENSOR_OP::ADD);
- idx++;
- } else if (tokens[idx] == "batchnorm") {
- DEBUG("Found batchnorm operation\n");
- NodeConf->pushNewTensorOperation(GPUNodeConfiguration::TENSOR_OP::BATCHNORM);
- idx++;
- } else if (tokens[idx] == "conv") {
- DEBUG("Found conv operation\n");
- NodeConf->pushNewTensorOperation(GPUNodeConfiguration::TENSOR_OP::CONV);
- idx++;
- } else if (tokens[idx] == "group_conv") {
- DEBUG("Found group_conv operation\n");
- NodeConf->pushNewTensorOperation(GPUNodeConfiguration::TENSOR_OP::GROUP_CONV);
- idx++;
- } else if (tokens[idx] == "mul") {
- DEBUG("Found mul operation\n");
- NodeConf->pushNewTensorOperation(GPUNodeConfiguration::TENSOR_OP::MUL);
- idx++;
- } else if (tokens[idx] == "relu") {
- DEBUG("Found relu operation\n");
- NodeConf->pushNewTensorOperation(GPUNodeConfiguration::TENSOR_OP::RELU);
- idx++;
- } else if (tokens[idx] == "clipped_relu") {
- DEBUG("Found clipped_relu operation\n");
- NodeConf->pushNewTensorOperation(GPUNodeConfiguration::TENSOR_OP::CLIPPED_RELU);
- idx++;
- } else if (tokens[idx] == "tanh") {
- DEBUG("Found tanh operation\n");
- NodeConf->pushNewTensorOperation(GPUNodeConfiguration::TENSOR_OP::TANH);
- idx++;
- } else if (tokens[idx] == "pool_max") {
- DEBUG("Found pool_max operation\n");
- NodeConf->pushNewTensorOperation(GPUNodeConfiguration::TENSOR_OP::POOL_MAX);
- idx++;
- } else if (tokens[idx] == "pool_mean") {
- DEBUG("Found pool_mean operation\n");
- NodeConf->pushNewTensorOperation(GPUNodeConfiguration::TENSOR_OP::POOL_MEAN);
- idx++;
- } else if (tokens[idx] == "pool_min") {
- DEBUG("Found pool_min operation\n");
- NodeConf->pushNewTensorOperation(GPUNodeConfiguration::TENSOR_OP::POOL_MIN);
- idx++;
- } else if (tokens[idx] == "softmax") {
- DEBUG ("Found softmax operation\n");
- NodeConf->pushNewTensorOperation(GPUNodeConfiguration::TENSOR_OP::SOFTMAX);
- idx++;
- } else if (tokens[idx] == "fft") {
- DEBUG ("Found fft operation\n");
- NodeConf->pushNewTensorOperation(GPUNodeConfiguration::TENSOR_OP::FFT);
- idx++;
- } else if (tokens[idx] == "reduce") {
- DEBUG ("Found reduce operation\n");
- NodeConf->pushNewTensorOperation(GPUNodeConfiguration::TENSOR_OP::REDUCE);
- idx++;
- } else if (tokens[idx] == "projectiveT") {
- DEBUG ("Found projectiveT operation\n");
- NodeConf->pushNewTensorOperation(GPUNodeConfiguration::TENSOR_OP::PROJECTIVE_T);
- idx++;
- } else if (tokens[idx] == "map1") {
- DEBUG ("Found map1 operation\n");
- NodeConf->pushNewTensorOperation(GPUNodeConfiguration::TENSOR_OP::MAP1);
- idx++;
- } else if (tokens[idx] == "map2") {
- DEBUG ("Found map2 operation\n");
- NodeConf->pushNewTensorOperation(GPUNodeConfiguration::TENSOR_OP::MAP2);
- idx++;
- } else if (tokens[idx] == "map3") {
- DEBUG ("Found map3 operation\n");
- NodeConf->pushNewTensorOperation(GPUNodeConfiguration::TENSOR_OP::MAP3);
- idx++;
- } else /*Not a new operation. This means an approximation option*/
- if (tokens[idx] == "fp32") {
- DEBUG("Found fp32 option\n");
- int fp32 = std::stoi(tokens[idx+1]);
- DEBUG("fp32 parameter: %d, ignoring\n", fp32);
- NodeConf->pushNewApproximationChoiceForOperation(GPUNodeConfiguration::APPROX::FP32,
- fp32);
- idx += 2;
- } else if (tokens[idx] == "fp16") {
- DEBUG("Found fp16 option\n");
- int fp16 = std::stoi(tokens[idx+1]);
- DEBUG("fp16 parameter: %d, ignoring\n", fp16);
- NodeConf->pushNewApproximationChoiceForOperation(GPUNodeConfiguration::APPROX::FP16,
- fp16);
- idx += 2;
- } else if (tokens[idx] == "perf") {
- DEBUG("Found perf option\n");
- int perf = std::stoi(tokens[idx+1]);
- DEBUG("perf parameter: %d\n", perf);
- NodeConf->pushNewApproximationChoiceForOperation(GPUNodeConfiguration::APPROX::PERFORATION, perf);
- idx += 2;
- } else if (tokens[idx] == "samp") {
- DEBUG("Found samp option\n");
- int samp = std::stoi(tokens[idx+1]);
- DEBUG("samp parameter: %d\n", samp);
- NodeConf->pushNewApproximationChoiceForOperation(GPUNodeConfiguration::APPROX::INPUT_SAMPLING, samp);
- idx += 2;
- } else if (tokens[idx] == "red_samp") {
- DEBUG("Found red_samp option\n");
- int red_samp = std::stoi(tokens[idx+1]);
- DEBUG("red_samp parameter: %d\n", red_samp);
- NodeConf->pushNewApproximationChoiceForOperation(GPUNodeConfiguration::APPROX::REDUCTION_SAMPLING, red_samp);
- idx += 2;
- }
- // TODO: other approximation options handled here
-
- }
-
- } else {
- DEBUG ("Invalid Configuration File\n");
- exit(1);
- }
-
- }
-
- qin.close();
- DEBUG("DONE.\n");
-
-}
-
-#define AL_THRESHOLD 0.1
-void RuntimeController::computeParetoConfigurationPoints() {
-
- // Keep indices of pareto optimal points (configurations from
- // InitialConfigurations vector that were copied to Configurations vector.)
- // The others' setup pointer needs to be deleted
- std::vector<unsigned> Indices;
-
- // Sort the configurations according to accuracy loss
- INFO("Sorting autotuner configurations...\n");
- std::sort(InitialConfigurations.begin(),
- InitialConfigurations.end(),
- ConfigurationLessThan());
- INFO("Done sorting.\n");
-
- for (unsigned start_idx = 0; start_idx < InitialConfigurations.size(); ) {
- // Points to first Configuration with different (higher) accuracy loss
- // compared to the one pointed by start_idx
- unsigned end_idx = start_idx + 1;
- while ((end_idx < InitialConfigurations.size()) &&
- (InitialConfigurations[end_idx].accuracyLoss -
- InitialConfigurations[start_idx].accuracyLoss < AL_THRESHOLD)) {
- end_idx++;
- }
- DEBUG("start_idx = %d, end_idx = %d\n", start_idx, end_idx);
- // Now, all elements in [start_idx, end_idx) have equal accuracy loss,
- // that is lower from later ones.
-
- // Find the best speedup and energy between them as well
- float sp = -1.0; //FLT_MIN
- unsigned sp_idx = 0;
-
- float en = -1.0; //FLT_MIN
- unsigned en_idx = 0;
-
- for (unsigned i = start_idx; i < end_idx; i++) {
- if (InitialConfigurations[i].speedup > sp) {
- sp = InitialConfigurations[i].speedup;
- sp_idx = i;
- }
- if (InitialConfigurations[i].energy > en) {
- en = InitialConfigurations[i].energy;
- en_idx = i;
- }
- }
- DEBUG("accuracy loss = %f, speedup = %f, at sp_idx = %d\n",
- InitialConfigurations[sp_idx].accuracyLoss, sp, sp_idx);
- // Found best speedup for this accuracy point (not dominated by any of these).
- DEBUG("accuracy loss = %f, energy = %f, at en_idx = %d\n",
- InitialConfigurations[en_idx].accuracyLoss, en, en_idx);
- // Found best energy for this accuracy point (not dominated by any of these).
-
- // Now, we need to check that it is not dominated.
- // - better accuracy loss of all in initial configurations out of
- // start_idx, end_idx range
- // - better or equal speedup to the ones within this range
- // We only need to check the points already in Configurations, that have
- // already been inserted in pareto frontier. These have better accuracy
- // loss, so this one will only be added if it shows better speedup
- // The one in curve with best speedup so far is the last one (with worst
- // = highest accuracy loss), so compare only with that one.
-
- // Similar handling of energy vector
-
- bool sp_notDominated = true;
- if (!SpeedupConfigurations.empty()) {
- if (SpeedupConfigurations.back()->speedup >= sp)
- sp_notDominated = false;
- }
-
- bool en_notDominated = true;
- if (!EnergyConfigurations.empty()) {
- if (EnergyConfigurations.back()->energy >= en)
- en_notDominated = false;
- }
-
- DEBUG("sp_notDominated = %d\n", sp_notDominated);
- DEBUG("en_notDominated = %d\n", en_notDominated);
-
- // If not dominated, insert in pareto frontier set
- if (sp_notDominated) {
- SpeedupConfigurations.push_back(&InitialConfigurations[sp_idx]);
- }
- if (en_notDominated) {
- EnergyConfigurations.push_back(&InitialConfigurations[en_idx]);
- }
-
- // Keep track of unnecessary configurations
- for (unsigned i = start_idx; i < end_idx; i++) {
- if (((i != sp_idx) || (!sp_notDominated)) &&
- ((i != en_idx) || (!en_notDominated)))
- Indices.push_back(i);
- }
-
- // Continue from next accuracy loss level
- start_idx = end_idx;
-
- }
-
- // All elements in InitialConfigurations whose index is in Indices are no
- // longer needed.
-// for (std::vector<unsigned>::iterator idx_it = Indices.begin(), idx_e = Indices.end();
-// idx_it != idx_e; ++idx_it) {
-// std::map<std::string, NodeConfiguration * > ConfSetup =
-// InitialConfigurations[*idx_it].setup;
-// for (std::map<std::string, NodeConfiguration* >::const_iterator it = ConfSetup.begin();
-// it != ConfSetup.end(); ++it) {
-// delete it->second;
-// }
-// }
-// InitialConfigurations.clear();
-
-}
-
-void RuntimeController::compute3DParetoConfigurationPoints() {
-
- // Sort the configurations according to accuracy loss
- INFO("Sorting autotuner configurations...\n");
- std::sort(InitialConfigurations.begin(),
- InitialConfigurations.end(),
- ConfigurationLessThan());
- INFO("Done sorting.\n");
-
- for (unsigned start_idx = 0; start_idx < InitialConfigurations.size(); ) {
- // Points to first Configuration with different (higher) accuracy loss
- // compared to the one pointed by start_idx
- unsigned end_idx = start_idx + 1;
- while ((end_idx < InitialConfigurations.size()) &&
- (InitialConfigurations[end_idx].accuracyLoss -
- InitialConfigurations[start_idx].accuracyLoss < AL_THRESHOLD)) {
- end_idx++;
- }
- DEBUG("start_idx = %d, end_idx = %d\n", start_idx, end_idx);
- // Now, all elements in [start_idx, end_idx) have equal accuracy loss,
- // that is lower from later ones and worse than those already in curve
- // (so they cannot displace them).
-
- // Find candidates from [start_idx, end_idx) to be inserted
- // Keep their indices. If a point is dominated (strictly worse),
- // its index will not be inserted
- std::vector<unsigned> Indices;
-
- for (unsigned i = start_idx; i < end_idx; i++) {
- bool dominated = false;
- for (unsigned j = i+1; (j < end_idx) && !dominated; j++) {
- if ((InitialConfigurations[i].speedup < InitialConfigurations[j].speedup) &&
- (InitialConfigurations[i].energy < InitialConfigurations[j].energy)) {
- dominated = true;
- }
- }
- if (!dominated) {
- DEBUG("accuracy loss = %f, speedup = %f, energy = %f, at idx = %d\n",
- InitialConfigurations[i].accuracyLoss,
- InitialConfigurations[i].speedup,
- InitialConfigurations[i].energy,
- i);
- Indices.push_back(i);
- }
- }
-
- for (std::vector<unsigned>::iterator idx_it = Indices.begin(), idx_e = Indices.end();
- idx_it != idx_e; ++idx_it) {
- Configuration &CandidateConfiguration = InitialConfigurations[*idx_it];
-
- if (!ThreeDCurveConfigurations.empty()) {
- bool notDominated = true;
- for (unsigned i = 0; (i < ThreeDCurveConfigurations.size()) && notDominated; i++) {
- if ((CandidateConfiguration.speedup <= ThreeDCurveConfigurations[i]->speedup) &&
- (CandidateConfiguration.energy <= ThreeDCurveConfigurations[i]->energy)) {
- // This configuration is not better, in at least one characteristic,
- // compared to the existing ones in the curve.
- notDominated = false;
- }
- }
- if (notDominated) {
- ThreeDCurveConfigurations.push_back(&CandidateConfiguration);
- }
- } else {
- // If the curve is empty, we know that this is a point that must be
- // inserted. It has the best accuracy loss, and belongs here because
- // it is not dominated by any point in this accuracy range.
- ThreeDCurveConfigurations.push_back(&CandidateConfiguration);
- }
- }
-
- // Continue from next accuracy loss level
- start_idx = end_idx;
- }
-
-}
-
-
-void RuntimeController::printConfigurations(std::vector<struct Configuration> &Confs) {
-
- for (std::vector<struct Configuration>::iterator it = Confs.begin(),
- ie = Confs.end(); it != ie; ++it) {
- it->print();
- }
-
-}
-
-void RuntimeController::printConfigurations(std::vector<struct Configuration *> &Confs) {
-
- for (std::vector<struct Configuration *>::iterator it = Confs.begin(),
- ie = Confs.end(); it != ie; ++it) {
- (*it)->print();
- }
-
-}
-
-void RuntimeController::findNextConfiguration() {
- configurationIdx = (configurationIdx + 1) % Configurations->size() ;
- DEBUG("findNextConfiguration: Updated configurationIdx to %u.\n", configurationIdx);
-}
-
-void RuntimeController::findTargetConfiguration(float goal, enum SEARCH_KIND sk) {
- // We search in range begin(), end()-1 . It is OK to decrement end(), because
- // the configurations vector always points to one of the pareto curves, and
- // they are never empty - we have always pushed at least one configuration.
-
- DEBUG("findTargetConfiguration: goalVal: %f, search kind: %d.\n", goal, sk);
- std::vector<struct Configuration *>::iterator low_it;
- switch (sk) {
- case SPEEDUP:
- {
- Configurations = &SpeedupConfigurations;
- low_it = std::lower_bound(Configurations->begin(),
- Configurations->end()-1,
- goal,
- ConfigurationLessThan_SP());
- configurationIdx = low_it - Configurations->begin();
- break;
- }
- case ENERGY:
- {
- Configurations = &EnergyConfigurations;
- low_it = std::lower_bound(Configurations->begin(),
- Configurations->end()-1,
- goal,
- ConfigurationLessThan_E());
- configurationIdx = low_it - Configurations->begin();
- break;
- }
- case ACCURACY_LOSS:
- {
- Configurations = &SpeedupConfigurations;
- low_it = std::lower_bound(Configurations->begin(),
- Configurations->end()-1,
- goal,
- ConfigurationLessThan_AL());
- if ((*low_it)->accuracyLoss > goal)
- --low_it;
- configurationIdx = low_it - Configurations->begin();
- break;
- }
- default:
- {
- CUSTOM_ASSERT(false && "Unknown search option for optimization target");
- ERROR("Unknown search option for optimization target.");
- abort();
- }
- }
- // After search, low_it points to the Configuration to the element with the
- // goal value or the immediately lower value if it does not exist
-
- DEBUG("findTargetConfiguration: Updated configurationIdx to %u.\n", configurationIdx);
-
-}
-
-#define MAX_GOAL_SPEEDUP 9
-float RuntimeController::getGoalSpeedup() {
- return 1.0 + (rand() / (RAND_MAX / (MAX_GOAL_SPEEDUP - 1.0) ) );
-}
-
-double RuntimeController::getBaselineTime() {
- return baseline_time;
-}
-
-Slowdowns *RuntimeController::getSlowdowns() {
- return slowdowns;
-}
-
-// Functions to be inserted with initializeTensorRT and clearTensorRT
-void llvm_hpvm_initializeRuntimeController(const char *ConfigFile, const char *QRangeFile) {
- RC = new RuntimeController();
- RC->init(ConfigFile, QRangeFile);
- return;
-}
-
-void llvm_hpvm_clearRuntimeController() {
- delete RC;
- return;
-}
-
-//*** Methods to compute accuracy of a tensor by the runtime controller ***//
-uint32_t* labels_from_file = NULL;
-
-uint32_t* hpvm_rt_readLabelsBatch_cached(const char* labels_file, int start, int end) {
-
- // Initialize buffer
- if (!labels_from_file) {
- FILE* file = fopen(labels_file, "rb");
- if (file == NULL) {
- ERROR("Data file %s is not found. Aborting...\n", labels_file);
- abort();
- }
- // Get number of labels
- fseek(file, 0, SEEK_END);
- long size = ftell(file);
- fseek(file, 0, SEEK_SET); // return file pointer to beginning
-
- // Allocate memory for labels
- labels_from_file = (uint32_t*) malloc(size);
- if (labels_from_file == NULL) {
- ERROR("Memory allocation for labels unsucessfull. Aborting...\n");
- abort();
- }
-
- // Copy the labels file into the allocated buffer
- size_t result = fread(labels_from_file, 1, size, file);
- if (result != size) {
- // We did not read as many elemets as there are in the file
- ERROR("Reading labels file unsucessfull. Aborting...\n");
- abort();
- }
-
- fclose(file);
- }
-
-// int num_labels = end - start;
-// uint32_t* labels = (uint32_t*) malloc(sizeof(uint32_t) * num_labels);
-// for (unsigned i = start; i < end; i++) {
-// labels[i-start] = labels_from_file[i];
-// }
-// return labels;
-
- // Return pointer to labels
- return &labels_from_file[start];
-
-}
-
-//*** Copied from dnn_sources/include/utils.h ***//
-float hpvm_rt_computeAccuracy3(uint32_t* labels, void* result_ptr) {
-
- struct Tensor* result = (struct Tensor*) result_ptr;
-
- size_t batch_dim = result->dims.dim_sizes[0];
- size_t num_classes = result->dims.dim_sizes[1];
- float* data = (float*) result->host_data;
- int num_errors = 0;
-
- printf("batch_dim = %lu, num_classes = %lu \n", batch_dim, num_classes);
-
- for(int i = 0; i < batch_dim; i++){
-
- int chosen = 0;
- for (int id = 1; id < num_classes; ++id){
- if (data[i * num_classes + chosen] < data[i * num_classes + id]) chosen = id;
- }
-
- if(chosen != labels[i])
- num_errors++;
- }
-
- float accuracy = ((batch_dim - num_errors) * 1.0 / batch_dim * 1.0) * 100.0;
- printf("****** Accuracy = %f \n\n", accuracy);
-
- FILE* fp = fopen("final_accuracy", "w+");
- if(fp != NULL){
-
- std::ostringstream ss;
- ss << std::fixed << accuracy;
- std::string print_str = ss.str();
-
- fwrite(print_str.c_str(), 1, print_str.length(), fp);
- }
-
- fclose(fp);
-
- return accuracy;
-}
-
-#define llvm_hpvm_invokeRtControl_BASE llvm_hpvm_invokeRtControl
-
-void llvm_hpvm_invokeRtControl_BASE(void* result, const char* str, int start, int end) {
-
- RC->resume_profiler();
-
- uint32_t* labels_cached = hpvm_rt_readLabelsBatch_cached(str, start, end);
- hpvm_rt_computeAccuracy3(labels_cached, result);
-
- // Read stats for iteration that was just completed
- double current_iteration_time = RC->getCurrentIterationComputeTime();
- double current_iteration_energy = RC->getCurrentIterationComputeEnergy();
-
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationControlTime(pinfo.first);
- RC->addToCurrentIterationControlEnergy(pinfo.second);
-
- INFO("current iteration time = %f, current iteration energy = %f\n\n",
- current_iteration_time, current_iteration_energy);
-
- // Note the end of iteration
- RC->end_iteration();
-}
-
-void llvm_hpvm_invokeRtControl_ITERATE(void* result, const char* str, int start, int end) {
-
- uint32_t* labels_cached = hpvm_rt_readLabelsBatch_cached(str, start, end);
- hpvm_rt_computeAccuracy3(labels_cached, result);
-
- // Read stats for iteration that was just completed
- double current_iteration_time = RC->getCurrentIterationComputeTime();
- double current_iteration_energy = RC->getCurrentIterationComputeEnergy();
-
- RC->resume_profiler();
- RC->findNextConfiguration();
- // Still use findNext configuration, to update the configurationIdx,
- // to point to next location
- enum SEARCH_KIND k = ACCURACY_LOSS;
- float goalVal = RC->getSpeedupConfigurations()[RC->getConfigurationIdx()]->accuracyLoss;
- RC->findTargetConfiguration(goalVal, k);
-
- RC->pause_profiler();
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationControlTime(pinfo.first);
- RC->addToCurrentIterationControlEnergy(pinfo.second);
-
- INFO("current iteration time = %f, current iteration energy = %f\n\n",
- current_iteration_time, current_iteration_energy);
-
- // Note the end of iteration
- RC->end_iteration();
-}
-
-void llvm_hpvm_invokeRtControl_ADJUST(void* result, const char* str, int start, int end) {
-
- uint32_t* labels_cached = hpvm_rt_readLabelsBatch_cached(str, start, end);
- hpvm_rt_computeAccuracy3(labels_cached, result);
-
- // Read stats for iteration that was just completed
- double current_iteration_energy = RC->getCurrentIterationComputeEnergy();
-
- RC->resume_profiler();
- double current_iteration_time = RC->getCurrentIterationComputeTime();
- double baseline_time = RC->getBaselineTime();
- double target_speedup = current_iteration_time / baseline_time;
- RC->findTargetConfiguration(target_speedup, SPEEDUP);
- RC->pause_profiler();
-
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationControlTime(pinfo.first);
- RC->addToCurrentIterationControlEnergy(pinfo.second);
-
- INFO("current iteration time = %f, current iteration energy = %f\n",
- current_iteration_time, current_iteration_energy);
- INFO("target speedup = %lf\n\n", target_speedup);
-
- // Note the end of iteration
- RC->end_iteration();
-}
-
-void llvm_hpvm_invokeRtControl_SLOWDOWN(void* result, const char* str, int start, int end) {
-
- uint32_t* labels_cached = hpvm_rt_readLabelsBatch_cached(str, start, end);
- hpvm_rt_computeAccuracy3(labels_cached, result);
-
- // Read stats for iteration that was just completed
- double current_iteration_time = RC->getCurrentIterationComputeTime();
- double current_iteration_energy = RC->getCurrentIterationComputeEnergy();
-
- std::string prev_conf_name =
- RC->getSpeedupConfigurations()[RC->getConfigurationIdx()]->name;
-
- RC->resume_profiler();
- float slowdown = RC->getSlowdowns()->getNextSlowdown();
- RC->findTargetConfiguration(slowdown, SPEEDUP);
- RC->pause_profiler();
-
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationControlTime(pinfo.first);
- RC->addToCurrentIterationControlEnergy(pinfo.second);
-
- std::string next_conf_name =
- RC->getSpeedupConfigurations()[RC->getConfigurationIdx()]->name;
- float next_conf_speedup =
- RC->getSpeedupConfigurations()[RC->getConfigurationIdx()]->speedup;
-
- INFO("current iteration time = %f, current iteration energy = %f\n",
- current_iteration_time, current_iteration_energy);
- INFO("slowdown (target speedup) = %f\n", slowdown);
- INFO("Previous configuration: %s\n", prev_conf_name.c_str());
- INFO("Swapping to next configuration: %s with speedup %f\n\n",
- next_conf_name.c_str(), next_conf_speedup);
-
- // Note the end of iteration
- RC->end_iteration();
-}
-
-void llvm_hpvm_invokeRtControl_RAND(void* result, const char* str, int start, int end) {
-
- uint32_t* labels_cached = hpvm_rt_readLabelsBatch_cached(str, start, end);
- hpvm_rt_computeAccuracy3(labels_cached, result);
-
- // Read stats for iteration that was just completed
- double current_iteration_time = RC->getCurrentIterationComputeTime();
- double current_iteration_energy = RC->getCurrentIterationComputeEnergy();
-
- RC->resume_profiler();
- RC->findTargetConfiguration(RC->getGoalSpeedup(), SPEEDUP);
- RC->pause_profiler();
-
- std::pair<double, double> pinfo = RC->get_time_energy();
- RC->reset_profiler();
- RC->addToCurrentIterationControlTime(pinfo.first);
- RC->addToCurrentIterationControlEnergy(pinfo.second);
-
- INFO("current iteration time = %f, current iteration energy = %f\n\n",
- current_iteration_time, current_iteration_energy);
-
- // Note the end of iteration
- RC->end_iteration();
-}
-
-
-
-#endif
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/img_tensor_runtime.h b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/img_tensor_runtime.h
deleted file mode 100644
index 9c098719e52e31fcd06b6425964c8e1d48a15210..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/img_tensor_runtime.h
+++ /dev/null
@@ -1,26 +0,0 @@
-#ifndef IMG_TENSOR_RUNTIME_H
-#define IMG_TENSOR_RUNTIME_H
-
-// *** Runtime declaration *** //
-void* tensorFft(void* input);
-void* tensorReduce(void* input);
-void* tensorReductionSamplingReduce(void* input);
-void* tensorProjectiveT(void* input);
-void* tensorMap1(void* input);
-void* tensorMap2(void* input);
-void* tensorMap3(void* input);
-
-// *** Wrapper API declaration *** //
-void* wrapper_tensorFft(const char* hpvm_node_id, void* input);
-void* wrapper_tensorReduce(const char* hpvm_node_id, void* input);
-void* wrapper_tensorProjectiveT(const char* hpvm_node_id, void* input);
-void* wrapper_tensorMap1(const char* hpvm_node_id, void* input);
-void* wrapper_tensorMap2(const char* hpvm_node_id, void* input);
-void* wrapper_tensorMap3(const char* hpvm_node_id, void* input);
-
-// Tentative
-void* wrapper_tensorStencil(const char* hpvm_node_id, void* input);
-void* wrapper_tensorCosineT(const char* hpvm_node_id, void* input);
-
-
-#endif
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/op_overheads.h b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/op_overheads.h
deleted file mode 100644
index ddd718515a358cc122120e1f418233b8cf5947c6..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/op_overheads.h
+++ /dev/null
@@ -1,229 +0,0 @@
-
-
-#ifndef OP_OVERHEADS_HEADER
-#define OP_OVERHEADS_HEADER
-
-
-#include <math.h>
-#include <sstream>
-#include "tensor.h"
-
-
-float scale_down_factor = 10000.0;
-std::string result_str = "";
-
-
-// TODO: Every routine needs testing
-
-static float scaleDownComps(double total_comps){
-
- total_comps = total_comps / scale_down_factor;
- return total_comps;
-}
-
-// private function
-static float getScaledComps(double total_comps, int error_scale, int factor_type){
-
- double scaled_comps;
-
- // Logarithmic error factor scaling - higher error, lower cost
- if(factor_type == 1){
- float error_factor = log2((float) error_scale + 3);
- scaled_comps = total_comps / error_factor;
- }
- // Linear error factor scaling
- if(factor_type == 2){
- scaled_comps = total_comps / (error_scale + 1);
- }
- // Quadratic error factor scaling (scaling down)
- if(factor_type == 3){
- error_scale = (error_scale + 1) * (error_scale + 1);
- scaled_comps = total_comps / error_scale;
- }
-
-
- return scaled_comps;
-}
-
-
-static void addNormToResult(float comps){
-
- std::ostringstream ss;
- ss << std::fixed << comps;
-
- result_str.append( std::string(ss.str()) );
- result_str.append("\t");
-}
-
-
-
-static void addCompsToResult(float total_comps, float opt_comps1, float opt_comps2, float opt_comps3){
-
- std::ostringstream ss;
- ss << std::fixed << total_comps;
- result_str.append( std::string(ss.str()) );
- result_str.append("\t");
-
- std::ostringstream ss2;
- ss2 << std::fixed << opt_comps1;
- result_str.append( std::string(ss2.str()) );
- result_str.append("\t");
-
- std::ostringstream ss3;
- ss3 << std::fixed << opt_comps2;
- result_str.append( std::string(ss3.str()) );
- result_str.append("\t");
-
- std::ostringstream ss4;
- ss4 << std::fixed << opt_comps3;
- result_str.append( std::string(ss4.str()) );
- result_str.append("\n");
-}
-
-
-void dumpCompOverheads(double total_comps, int error_scale){
-
- total_comps = scaleDownComps(total_comps);
-
- float scaled_comps1 = getScaledComps(total_comps, error_scale, 1); // Log scaling
- float scaled_comps2 = getScaledComps(total_comps, error_scale, 2); // Linear scaling
- float scaled_comps3 = getScaledComps(total_comps, error_scale, 3); // Quadratic scaling
-
- //INFO("error_scale = %d, total_comps = %f, scaled_comps = %f \n",
- // error_scale, total_comps, scaled_comps1);
-
- addCompsToResult(total_comps, scaled_comps1, scaled_comps2, scaled_comps3);
-}
-
-
-
-void add_conv_overheads(void* input_ptr, void* filter_ptr,
- int vertical_stride, int horizontal_stride,
- int error_scale){
-
- Tensor* input = (Tensor*) input_ptr;
- Tensor* filter = (Tensor*) filter_ptr;
-
- double kernel_comps = filter->dims.dim_sizes[0] * filter->dims.dim_sizes[1] *
- filter->dims.dim_sizes[2] * filter->dims.dim_sizes[3];
-
- double H_in = input->dims.dim_sizes[2] / vertical_stride;
- double W_in = input->dims.dim_sizes[3] / horizontal_stride;
- double N_in = input->dims.dim_sizes[0]; // batch Dimension
-
- double total_comps = N_in * H_in * W_in * kernel_comps;
-
- dumpCompOverheads(total_comps, error_scale);
-
-}
-
-
-void add_gemm_overheads(void* lhs_ptr, void* rhs_ptr, int error_scale){
-
- Tensor* lhs = (Tensor*) lhs_ptr;
- Tensor* rhs = (Tensor*) rhs_ptr;
-
- int m = lhs->dims.dim_sizes[0];
- // The rhs last dimension must contain the neurons
- int n = rhs->dims.dim_sizes[rhs->dims.num_dims-1]; // output neurons
- int k = 1;
-
- // Flattening the dimensions after the batch dimension
- for (int j = 1 ; j < lhs->dims.num_dims; j++){
- k = k * lhs->dims.dim_sizes[j]; // input neurons
- }
-
- int rhs_k = rhs->dims.dim_sizes[rhs->dims.num_dims-2];
- // Dimension-note: Check if k is same across the two tensors
-
- //printf("m = %d, n = %d, k = %d \n", m, n, k);
-
- if(rhs_k != k){
- printf("rhs=%d and lhs=%d columns/rows don't match", rhs_k, k);
- abort();
- }
-
- double m_d = m;
- double n_d = n;
- double rhs_k_d = rhs_k;
-
- double total_comps = m_d * n_d * rhs_k_d * 1.0;
- dumpCompOverheads(total_comps, error_scale);
-
-}
-
-
-void add_bias_overheads(void* input_ptr, int error_scale){
-
- Tensor* input = (Tensor*) input_ptr;
- double total_comps = input->num_elems;
-
- dumpCompOverheads(total_comps, error_scale);
- }
-
-
-void add_relu_overheads(void* input_ptr, int error_scale){
-
- Tensor* input = (Tensor*) input_ptr;
- double total_comps = input->num_elems;
-
- dumpCompOverheads(total_comps, error_scale);
-}
-
-
-void add_pool_overheads(void* input_ptr, int kernel_size,
- int stride_size, int error_scale){
-
- Tensor* input = (Tensor*) input_ptr;
-
- int num_dims = input->dims.num_dims;
- double H = input->dims.dim_sizes[num_dims-2];
- double W = input->dims.dim_sizes[num_dims-1];
- double C = input->dims.dim_sizes[1]; // channel dimension
- double N = input->dims.dim_sizes[0]; // batch dimension
-
- H = H / stride_size;
- W = W / stride_size;
-
- double total_comps = N * C * H * W * kernel_size * kernel_size;
-
- dumpCompOverheads(total_comps, error_scale);
-
-}
-
-
-void add_norms(void* norms_ptr, char* op_name, int error_value){
-
- // Print operation name - {tensorAdd, tensorPool, tensorGemm}
- result_str.append(op_name);
- result_str.append("\t");
-
- addNormToResult(error_value);
-
- Norm_t* norms = (Norm_t*) norms_ptr;
-
- addNormToResult(norms->mean_l1);
- addNormToResult(norms->mean_l2);
- addNormToResult(norms->orig_inf_norm);
-
- addNormToResult(norms->l1_norm);
- addNormToResult(norms->l2_norm);
- addNormToResult(norms->inf_norm);
-}
-
-
-void dump_result(char* file_name){
-
- FILE* fp = fopen(file_name, "w+");
- if(fp != NULL){
- fwrite(result_str.c_str(), 1, result_str.length(), fp);
- fclose(fp);
- }
- else{
- ERROR("Could not create file \n");
- }
-
- result_str = "";
-}
-
-#endif
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/profiling.h b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/profiling.h
deleted file mode 100644
index 7075d51cd60b610379536ddaa1c2255a8fc68de0..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/profiling.h
+++ /dev/null
@@ -1,95 +0,0 @@
-
-#ifndef PROFILING_HEADER
-#define PROFILING_HEADER
-
-
-
-#include <stdio.h>
-#include <stdarg.h>
-#include <ctime>
-#include <chrono>
-#include <iostream>
-#include <map>
-#include <memory>
-#include <random>
-#include <string>
-#include <unordered_map>
-#include <cuda_runtime.h>
-
-#include "global_data.h"
-
-
-/***** Profiling routines ***/
-
-
-std::chrono::time_point<std::chrono::high_resolution_clock> start_time;
-// previous_time maintains time for the latest timed operation
-std::chrono::time_point<std::chrono::high_resolution_clock> previous_time;
-
-extern "C"{
-
- void startProfiling(){
- start_time = std::chrono::high_resolution_clock::now();
- }
-
- void stopProfiling(){
-
- FILE* fp = fopen("profile_data.txt", "w+");
- if(fp != NULL){
- fwrite(profile_data.c_str(), 1, profile_data.length(), fp);
- fclose(fp);
- }
-
- profile_data = "";
- func_counters.clear();
- }
-
-
- void profileEvent(const char* event_name, bool compare_previous = false){
-
- checkCudaErrors(cudaDeviceSynchronize());
-
- auto it = func_counters.find(event_name);
- if(it == func_counters.end()){
- func_counters[event_name] = 1;
- }
- else{
- int counter = func_counters[event_name];
- counter++;
- func_counters[event_name] = counter;
- }
-
- std::stringstream ss;
- ss << func_counters[event_name];
- std::string event_count = ss.str();
-
-
- std::chrono::time_point<std::chrono::high_resolution_clock> zero_time;
- std::chrono::time_point<std::chrono::high_resolution_clock> time_reading =
- std::chrono::high_resolution_clock::now();
- std::chrono::duration<double, std::ratio<1>> current_time =
- time_reading - zero_time;
-
- INFO("AbsoluteTime, Event = %s, Time = %f \n", event_name, current_time.count());
- profile_data.append(event_name);
- profile_data.append(event_count);
- profile_data.append("\t");
- profile_data.append(std::to_string(current_time.count()));
-
- if(compare_previous){
- std::chrono::duration<double, std::ratio<1>> duration_time =
- time_reading - previous_time;
-
- profile_data.append("\t");
- profile_data.append(std::to_string(duration_time.count()));
- INFO("TimeDuration, Event = %s, Time = %f \n", event_name, duration_time.count());
- }
-
- profile_data.append("\n");
-
- previous_time = time_reading; // set the previous time reading to the current profiled time
- }
-
-}
-
-#endif
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/rt-controller-api.h b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/rt-controller-api.h
deleted file mode 100644
index b9fe649325b7da11d3e03db6e21cad386d56747b..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/rt-controller-api.h
+++ /dev/null
@@ -1,11 +0,0 @@
-
-
-
-extern "C"{
-
- void llvm_hpvm_initializeRuntimeController(const char *, const char *);
- void llvm_hpvm_clearRuntimeController();
- void llvm_hpvm_invokeRtControl(void* result, const char* str, int start, int end);
-}
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/tensor.h b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/tensor.h
deleted file mode 100644
index 0b16a51921eeb62bdfceb3540ff6b42c82626d72..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/tensor.h
+++ /dev/null
@@ -1,78 +0,0 @@
-
-
-#ifndef TENSOR_HEADER
-#define TENSOR_HEADER
-
-#include <cuda_runtime.h>
-#include <device_launch_parameters.h>
-#include <cublas_v2.h>
-#include <cudnn.h>
-#include <cublas_api.h>
-#include <cuda_fp16.h>
-#include <driver_types.h>
-
-
-
-struct Norm_t{
- float mean_l1;
- float mean_l2;
- float orig_inf_norm;
- float l0_norm;
- float l1_norm;
- float l2_norm;
- float inf_norm;
-};
-
-
-struct Dimension{
- int num_dims;
- size_t* dim_sizes;
-};
-
-enum data_location_t{
- HOST,
- DEVICE
-};
-
-
-struct Tensor{
- int data_type;
- int cur_type;
- int data_format;
- data_location_t data_placement; // Maintains the location of the tensor {host, device...}
- cudnnTensorDescriptor_t tensor_desc;
- cudnnFilterDescriptor_t filter_desc; // FIXIT: Rethink if this should be in tensor struct
- cudnnTensorDescriptor_t tensor_half_desc;
- cudnnFilterDescriptor_t filter_half_desc; // FIXIT: Rethink if this should be in tensor struct
- void* host_data;
- void* gpu_data; // Pointer to GPU FP32 data
- void* gpu_half_data; // Pointer to GPU FP16 data
- size_t num_elems; // Total elements
- size_t size_in_bytes; // Total size in bytes
- struct Dimension dims;
-};
-
-
-
-struct Range{
- float min;
- float max;
-};
-
-
-// NOTE: Currently only NCHW is supported due to limited cuDNN support
-enum Tensor_format_t{
- nchw,
- nhwc
-};
-
-enum Tensor_type_t{
- float_type,
- double_type,
- half_type,
- int_type
-};
-
-
-#endif
-
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/tensor_cpu.h b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/tensor_cpu.h
deleted file mode 100644
index 07fb766493a8ddeccc90db60c4345dda7889e193..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/tensor_cpu.h
+++ /dev/null
@@ -1,39 +0,0 @@
-
-
-#ifndef TENSOR_HEADER
-#define TENSOR_HEADER
-
-
-struct Dimension{
- int num_dims;
- size_t* dim_sizes;
-};
-
-
-struct Tensor{
- int data_type;
- int data_format;
- void* host_data;
- void* gpu_data; // Pointers should not be device specific - Think: Better design
- size_t num_elems; // Total elements
- size_t size_in_bytes; // Total size in bytes
- struct Dimension dims;
-};
-
-
-enum Tensor_format_t{
- nchw,
- nhwc
-};
-
-enum Tensor_type_t{
- float_type,
- double_type,
- half_type,
- int_type
-};
-
-
-
-#endif
-
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/tensor_cpu_runtime.h b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/tensor_cpu_runtime.h
deleted file mode 100644
index 42969d27712d92c451ae066a44fac0c03cedf170..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/tensor_cpu_runtime.h
+++ /dev/null
@@ -1,96 +0,0 @@
-
-#include <stdio.h>
-#include <cstdlib>
-#include <cmath>
-#include <memory>
-#include <string>
-
-
-#ifndef CUDNN_HEADER
-#define CUDNN_HEADER
-
-
-extern "C"{
- /**** Initialization Routine - Must be inserted at program start (in the backend) ****/
- void llvm_hpvm_initTensorRt(int gpuid = 0);
- void llvm_hpvm_cleanupTensorRt();
-
- // Routine to moving tensor data (from and to GPU,CPU)
- void hpvm_request_tensor(void* tensor, int destination);
-
-
- // NOTE: Currently only using 4-D tensors - 2D and 3D tensors not supported for cuDNN operations
- // NOTE: The only data format supported as of now is: NCHW (batch_dimension, channels, Height, Width)
- void* create4DTensor(int data_type, int data_format, size_t dim1_size, size_t dim2_size,
- size_t dim3_size, size_t dim4_size);
-
- void initTensorData(void* tensor, void* data_ptr, size_t size_in_bytes);
-
- /********** Tensor Operation API ******/
-
- // NOTE: For conv_mode, only value '1' is supported
- void* tensorConvolutionCPU(void* input, void* filter,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int compute_precision);
-
- void* tensorPoolingCPU(void* input,
- int poolFunction,
- int window_height, int window_width,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride);
-
- void* tensorGemmCPU(void* lhs, void* rhs);
-
- void* tensorAddCPU(void* x, void* bias);
-
- void* tensorReluCPU(void* input);
-
- void* tensorRelu2CPU(void* input, float min, float max);
-
- void* tensorTanhCPU(void* input);
-
- void* tensorSoftmaxCPU(void* input);
-
-}
-
-
-/*
-void dummyFunction(){
-
- void* initRT = (void*) &llvm_hpvm_initTensorRt;
- void* cleanRT = (void*) &llvm_hpvm_cleanupTensorRt;
- void* request_tensorPtr = (void*) &hpvm_request_tensor;
- void* startProf = (void*) &startProfiling;
- void* stopProf = (void*) &stopProfiling;
- void* create2Dptr = (void*) &create2DTensor;
- void* create3Dptr = (void*) &create3DTensor;
- void* create4Dptr = (void*) &create4DTensor;
- void* initTensorPtr = (void*) &initTensorData;
- void* tensorSplitPtr = (void*) &tensorSplit;
- void* tensorConcatPtr = (void*) &tensorConcat;
- void* tensorConvPtr = (void*) &tensorConvolution;
- void* tensorHConvPtr = (void*) &tensorHalfConvolution;
- void* tensorPoolPtr = (void*) &tensorPooling;
- void* tensorHalfPoolPtr = (void*) &tensorHalfPooling;
- void* tensorLRNPtr = (void*) &tensorLRN;
- void* tensorGemmPr = (void*) &tensorGemm;
- void* tensorGemmCPUPtr = (void*) &tensorGemmCPU;
- void* tensorGemmGPUPtr = (void*) &tensorGemmGPU;
- void* tensorHgemmPtr = (void*) &tensorHalfGemm;
- void* tensorGemmBiasPtr = (void*) &tensorGemmBias;
- void* tensorAddPtr = (void*) &tensorAdd;
- void* tensorHalfAddPtr = (void*) &tensorHalfAdd;
- void* tensorReluPtr = (void*) &tensorRelu;
- //FIXME: --void* tensorHalfReluPtr = (void*) &tensorHalfRelu;
- void* tensorRelu2Ptr = (void*) &tensorRelu2;
- void* tensorHalfRelu2Ptr = (void*) &tensorHalfRelu2;
- void* tensorTanhPtr = (void*) &tensorTanh;
- void* tensorHalfTanhPtr = (void*) &tensorHalfTanh;
- void* tensorSoftmaxPtr = (void*) &tensorSoftmax;
- void* tensorAddErrorPtr = (void*) &tensorAddError;
-}
-*/
-
-
-#endif
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/tensor_runtime.cc b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/tensor_runtime.cc
deleted file mode 100644
index 2216172eab78414b46814e0d457908f5584c606a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/tensor_runtime.cc
+++ /dev/null
@@ -1,114 +0,0 @@
-
-#include <stdio.h>
-#include <cstdlib>
-#include <cmath>
-#include <memory>
-#include <string>
-
-
-#ifndef CUDNN_HEADER
-#define CUDNN_HEADER
-
-
-extern "C"{
- /**** Initialization Routine - Must be inserted at program start (in the backend) ****/
- void llvm_hpvm_initTensorRt(int gpuid = 0);
- void llvm_hpvm_cleanupTensorRt();
-
- // Routine to moving tensor data (from and to GPU,CPU)
- void hpvm_request_tensor(void* tensor, int destination);
-
- /****** Profiling API - defines profiling scope */
- void startProfiling();
- void stopProfiling();
-
- /****** Routines for tensor creation and initialization *******/
- void* create2DTensor(int data_type, size_t dim1_size, size_t dim2_size);
- void* create3DTensor(int data_type, size_t dim1_size, size_t dim2_size,
- size_t dim3_size);
-
- // NOTE: Currently only using 4-D tensors - 2D and 3D tensors not supported for cuDNN operations
- // NOTE: The only data format supported as of now is: CUDNN_NCHW
- void* create4DTensor(int data_type, int data_format, size_t dim1_size, size_t dim2_size,
- size_t dim3_size, size_t dim4_size);
- void initTensorData(void* tensor, void* data_ptr, size_t size_in_bytes);
-
- /********** Tensor Operation API ******/
-
- void** tensorSplit(void* tensor, int num_splits, int split_dim);
- void* tensorConcat(void** tensors, int num_splits, int split_dim);
-
- // NOTE: For conv_mode, only value '1' is supported
- void* tensorConvolution(void* input, void* filter,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int compute_precision);
- void* tensorHConvolution(void* input, void* filter,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int compute_precision);
-
- void* tensorPooling(void* input,
- int poolFunction,
- int window_height, int window_width,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride);
-
- void* tensorLRN(void* input, unsigned int LRN_window,
- double LRN_alpha, double LRN_beta, double LRN_k);
-
-
- /* 4 different Gemm versions */
- void* tensorGemm(void* lhs, void* rhs);
- void* tensorGemmCPU(void* lhs, void* rhs);
- void* tensorGemmGPU(void* lhs, void* rhs);
- void* tensorHgemm(void* lhs, void* rhs);
-
-
- // NOTE: In-place operation
- void* tensorGemmBias(void* input, void* bias);
- // NOTE: In place operation
- void* tensorAdd(void* x, void* bias);
- // NOTE: In-place operation
- void* tensorRelu(void* input);
- // NOTE: In-place operation
- void* tensorSoftmax(void* input);
-
- /* Error injection API - used for accuracy tuning */
- void* tensorAddError(void* x_ptr);
-}
-
-
-
-void emptyFunction(){
-
- void* initRT = (void*) &llvm_hpvm_initTensorRt;
- void* cleanRT = (void*) &llvm_hpvm_cleanupTensorRt;
- void* request_tensorPtr = (void*) &hpvm_request_tensor;
- void* startProf = (void*) &startProfiling;
- void* stopProf = (void*) &stopProfiling;
- void* create2Dptr = (void*) &create2DTensor;
- void* create3Dptr = (void*) &create3DTensor;
- void* create4Dptr = (void*) &create4DTensor;
- void* initTensorPtr = (void*) &initTensorData;
- void* tensorSplitPtr = (void*) &tensorSplit;
- void* tensorConcatPtr = (void*) &tensorConcat;
- void* tensorConvPtr = (void*) &tensorConvolution;
- void* tensorHConvPtr = (void*) &tensorHConvolution;
- void* tensorPoolPtr = (void*) &tensorPooling;
- void* tensorLRNPtr = (void*) &tensorLRN;
- void* tensorGemmPr = (void*) &tensorGemm;
- void* tensorGemmCPUPtr = (void*) &tensorGemmCPU;
- void* tensorGemmGPUPtr = (void*) &tensorGemmGPU;
- void* tensorHgemmPtr = (void*) &tensorHgemm;
- void* tensorGemmBiasPtr = (void*) &tensorGemmBias;
- void* tensorAddPtr = (void*) &tensorAdd;
- void* tensorReluPtr = (void*) &tensorRelu;
- void* tensorSoftmaxPtr = (void*) &tensorSoftmax;
- void* tensorAddErrorPtr = (void*) &tensorAddError;
-
-}
-
-
-
-#endif
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/tensor_runtime.h b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/tensor_runtime.h
deleted file mode 100644
index 06c492c9e8fb45e0a51de153e8cf434a79a50e23..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/tensor_runtime.h
+++ /dev/null
@@ -1,229 +0,0 @@
-
-
-#include <stdio.h>
-#include <cstdlib>
-#include <cmath>
-#include <memory>
-#include <string>
-#include "approx_api.h"
-#include "testing_api.h"
-#include "tensor.h"
-#include "rt-controller-api.h"
-
-#include "img_tensor_runtime.h"
-
-#ifndef CUDNN_HEADER
-#define CUDNN_HEADER
-
-
-extern "C"{
- /**** Initialization Routine - Must be inserted at program start (in the backend) ****/
- void llvm_hpvm_initTensorRt(int gpuid = 0);
- void llvm_hpvm_cleanupTensorRt();
-
- void llvm_hpvm_initApproxhpvmRt(int gpuid = 0);
- void llvm_hpvm_cleanupApproxhpvmRt();
-
- // Routine to moving tensor data (from and to GPU,CPU)
- void hpvm_request_tensor(void* tensor, int destination);
-
- /****** Profiling API - defines profiling scope */
- void startProfiling();
- void stopProfiling();
-
- /****** Routines for tensor creation and initialization *******/
- void* create2DTensor(int data_type, size_t dim1_size, size_t dim2_size);
- void* create3DTensor(int data_type, size_t dim1_size, size_t dim2_size,
- size_t dim3_size);
-
- // NOTE: Currently only using 4-D tensors - 2D and 3D tensors not supported for cuDNN operations
- // NOTE: The only data format supported as of now is: CUDNN_NCHW
- void* create4DTensor(int data_type, int data_format, size_t dim1_size, size_t dim2_size,
- size_t dim3_size, size_t dim4_size);
- void initTensorData(void* tensor, void* data_ptr, size_t size_in_bytes);
-
- void changeTensorPlacement(struct Tensor* tensor,
- data_location_t data_placement);
-
- void tensorCopy(void* srcTensor, void* dstTensor);
-
- void freeTensor(void*);
-
- /********** Tensor Operation API ******/
-
- void** tensorSplit(void* tensor, int num_splits, int split_dim);
- void* tensorConcat(void** tensors, int num_splits, int split_dim);
-
- // NOTE: For conv_mode, only value '1' is supported
- void* tensorConvolution(void* input, void* filter,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int conv_groups);
- void* tensorHalfConvolution(void* input, void* filter,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int conv_groups);
-
- void* tensorPooling(void* input,
- int poolFunction,
- int window_height, int window_width,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride);
-
- void* tensorHalfPooling(void* input,
- int poolFunction,
- int window_height, int window_width,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride);
-
-
- void* tensorLRN(void* input, unsigned int LRN_window,
- double LRN_alpha, double LRN_beta, double LRN_k);
-
-
- /* 4 different Gemm versions */
- void* tensorGemm(void* lhs, void* rhs);
- void* tensorGemmCPU(void* lhs, void* rhs);
- void* tensorGemmGPU(void* lhs, void* rhs); // , void* result_tensor = NULL);
- void* tensorHalfGemmGPU(void* lhs, void* rhs);
- void* tensorHalfGemm(void* lhs, void* rhs);
-
-
- // NOTE: In-place operation
- void* tensorGemmBias(void* input, void* bias);
- // NOTE: In place operation
- void* tensorAdd(void* x, void* bias);
- // NOTE: In place operation
- void* tensorHalfAdd(void* x, void* bias);
- // NOTE: In-place operation
- void* tensorRelu(void* input);
- // NOTE: In-place operation
- void* tensorHalfRelu(void* input);
- // NOTE: In-place operation
-
- void* tensorTanh(void* input);
- // NOTE: In-place operation
- void* tensorHalfTanh(void* input);
-
- // NOTE: In-place operation
- void* tensorRelu2(void* input, float min, float max);
- // NOTE: In-place operation
- void* tensorHalfRelu2(void* input, float min, float max);
- // NOTE: In-place operation
- void* tensorSoftmax(void* input);
-
- // NOTE: In-place operation
- void* tensorBatchNorm(void* input_ptr, void* gamma_ptr, void* beta_ptr,
- void* mean_ptr, void* variance_ptr, double epsilon);
-
- void* tensorHalfBatchNorm(void* input_ptr, void* gamma_ptr, void* beta_ptr,
- void* mean_ptr, void* variance_ptr, double epsilon);
-
-
- /* Error injection API - used for accuracy tuning */
- void* tensorAddError(void* x_ptr, int error_scale);
-
- void* tensorGemmModel(void* lhs, void* rhs);
-
- /*** Error Injection API End **/
-
-
- /**** PROMISE API *****/
-
- /*************
- --- Synopsys:
-
- input: input activation tensor
- filter: filter tensor
- bias: bias tensor
- conv_pad_h, conv_pad_w: convolution padding in height and width
- conv_stride_h, conv_stride_w: convolution stride - vertical and horizontal
- pool_id: {0, 1} 0: max_pooling , 1: avg_pooling
- pool_size: Size of pooling window. Note: Pass '0' for *NO* Pooling
- activation_id: {-1,0,1,2} -1: NO Activation, 0: Tanh, 1: Relu, 2: ClippedRelu
- Swing: PROMISE swing level
-
- *************/
-
- void* ConvLayer_PROMISE(void* input, float i_min, float i_max,
- void* filter, float w_min, float w_max,
- void* bias, float b_min, float b_max,
- int conv_pad_h, int conv_pad_w, int conv_stride_h, int conv_stride_w,
- int pool_id, int pool_size,
- int activation_id, // Relu, Tanh, ClipRelu
- float out_min, float out_max, int swing); // NOTE: min_val, max_val apply to 'ClippedRelu'
-
-
- void* FCLayer_PROMISE(void* input, float i_min, float i_max,
- void* weights, float w_min, float w_max,
- void* bias, float b_min, float b_max,
- int activation_id,
- float out_min, float out_max, int swing); // NOTE: min_val, max_val apply to 'ClippedRelu'
-
-
- /**** Wrapper Runtime API ***/
-
- void* wrapper_ConvLayer(const char* hpvm_node_id,
- void* input,
- void* filter,
- void* bias,
- int conv_pad_h, int conv_pad_w,
- int conv_stride_h, int conv_stride_w,
- int pool_id, int pool_size,
- int activation_id, // Relu, Tanh, ClipRelu
- float out_min, float out_max);
-
-
- void* wrapper_FCLayer(const char* hpvm_node_id,
- void* input,
- void* weights,
- void* bias,
- int activation_id,
- float out_min, float out_max);
-
-
- void* wrapper_tensorGroupConvolution(const char* hpvm_node_id, void* input, void* filter,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int conv_groups);
-
-
- void* wrapper_tensorRelu(const char* hpvm_node_id, void* input_ptr);
-
- void* wrapper_tensorTanh(const char* hpvm_node_id, void* input_ptr);
-
- void* wrapper_tensorBatchNorm(const char* hpvm_node_id,
- void* input_ptr, void* gamma_ptr, void* beta_ptr,
- void* mean_ptr, void* variance_ptr, double epsilon);
-
- void* wrapper_tensorAdd(const char* hpvm_node_id, void* input_ptr, void* bias_ptr);
-
-
- void* wrapper_tensorPooling(const char* hpvm_node_id,
- void* input_ptr,
- int poolFunction,
- int window_height, int window_width,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride);
-
-
- void* wrapper_tensorSoftmax(const char* hpvm_node_id, void* input_ptr);
-
-
-}
-
-
-void dumpAccuracyNorms();
-void readOpenTunerFlags(const char* file_name);
-void clearOpCounter();
-void clearTensorMap();
-void startMemTracking();
-void freeOutputTensors();
-void freeBatchMemory();
-void* quantizeTensorPromise(void* input_ptr, float min, float max);
-void* addPromiseError(void* x_ptr, int error_scale);
-void readSkipTensors(int* skip_tensor_ids, int op_count);
-
-
-
-#endif
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/tensor_signatures.cc b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/tensor_signatures.cc
deleted file mode 100644
index e8947881765637d68ca9d95d716c97d486e8380a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/tensor_signatures.cc
+++ /dev/null
@@ -1,58 +0,0 @@
-
-#include "tensor_runtime.h"
-
-
-void dummyFunction(){
-
- void* initRT = (void*) &llvm_hpvm_initTensorRt;
- void* cleanRT = (void*) &llvm_hpvm_cleanupTensorRt;
-
- void* initApproxRT = (void*) &llvm_hpvm_initApproxhpvmRt;
- void* cleanApproxRT = (void*) &llvm_hpvm_cleanupApproxhpvmRt;
-
- void* initRTController = (void*) &llvm_hpvm_initializeRuntimeController;
- void* cleanRTController = (void*) &llvm_hpvm_clearRuntimeController;
-
- void* request_tensorPtr = (void*) &hpvm_request_tensor;
- void* startProf = (void*) &startProfiling;
- void* stopProf = (void*) &stopProfiling;
- void* create2Dptr = (void*) &create2DTensor;
- void* create3Dptr = (void*) &create3DTensor;
- void* create4Dptr = (void*) &create4DTensor;
- void* initTensorPtr = (void*) &initTensorData;
- void* tensorSplitPtr = (void*) &tensorSplit;
- void* tensorConcatPtr = (void*) &tensorConcat;
- void* tensorConvPtr = (void*) &tensorConvolution;
- void* tensorHConvPtr = (void*) &tensorHalfConvolution;
- void* tensorPoolPtr = (void*) &tensorPooling;
- void* tensorHalfPoolPtr = (void*) &tensorHalfPooling;
- void* tensorLRNPtr = (void*) &tensorLRN;
- void* tensorGemmPr = (void*) &tensorGemm;
- void* tensorGemmCPUPtr = (void*) &tensorGemmCPU;
- void* tensorGemmGPUPtr = (void*) &tensorGemmGPU;
- void* tensorHgemmPtr = (void*) &tensorHalfGemm;
- void* tensorGemmBiasPtr = (void*) &tensorGemmBias;
- void* tensorAddPtr = (void*) &tensorAdd;
- void* tensorHalfAddPtr = (void*) &tensorHalfAdd;
- void* tensorReluPtr = (void*) &tensorRelu;
- //FIXME: --void* tensorHalfReluPtr = (void*) &tensorHalfRelu;
- void* tensorRelu2Ptr = (void*) &tensorRelu2;
- void* tensorHalfRelu2Ptr = (void*) &tensorHalfRelu2;
- void* tensorTanhPtr = (void*) &tensorTanh;
- void* tensorHalfTanhPtr = (void*) &tensorHalfTanh;
- void* tensorSoftmaxPtr = (void*) &tensorSoftmax;
- void* tensorBatchNormPtr = (void*) &tensorBatchNorm;
- void* tensorAddErrorPtr = (void*) &tensorAddError;
- void* ConvLayer = (void*) &ConvLayer_PROMISE;
- void* FCLayer = (void*) &FCLayer_PROMISE;
-
- void* ConvLayer2 = (void*) &wrapper_ConvLayer;
- void* FCLayer2 = (void*) &wrapper_FCLayer;
- void* AddWrapper = (void*) &wrapper_tensorAdd;
- void* ReluWrapper = (void*) &wrapper_tensorRelu;
- void* TanhWrapper = (void*) &wrapper_tensorTanh;
- void* BatchNormWrapper = (void*) &wrapper_tensorBatchNorm;
- void* PoolingWrapper = (void*) &wrapper_tensorPooling;
- void* softmaxWrapper = (void*) &wrapper_tensorSoftmax;
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/tensor_utils.cu b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/tensor_utils.cu
deleted file mode 100644
index c65fd8b0163b80459476b4ab3c61ce48580cc0ff..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/tensor_utils.cu
+++ /dev/null
@@ -1,575 +0,0 @@
-
-#ifndef TENSOR_UTILS_HEADER
-#define TENSOR_UTILS_HEADER
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <stdarg.h>
-#include <cstdio>
-#include <cstdlib>
-#include <cstring>
-#include <cmath>
-#include <ctime>
-#include <cfloat>
-#include <algorithm>
-#include <chrono>
-#include <iomanip>
-#include <iostream>
-#include <map>
-#include <memory>
-#include <random>
-#include <sstream>
-#include <string>
-
-#include <cuda_runtime.h>
-#include <device_launch_parameters.h>
-
-#include <cublas_v2.h>
-#include <cudnn.h>
-#include <cublas_api.h>
-#include <vector>
-
-//#include "../../dnn_sources/include/types.h"
-#include "tensor_runtime.h"
-#include "debug.h"
-#include "tensor.h"
-#include "global_data.h"
-#include "fp16_gemm.cu"
-
-
-// used to map HPVM tensors to runtime tensors (with extra runtime-specific information)
-std::vector<Tensor*> tensorsArr;
-int total_tensors = 0;
-
-
-
-/*void addRuntimeTensor(struct Tensor* hpvm_tensor, struct Tensor* tensor){
-
- hpvm_tensor->tensor_id = total_tensors;
- total_tensors++;
- tensorsArr.push_back(tensor);
-}
-
-
-struct Tensor* getRuntimeTensor(struct Tensor* hpvm_tensor){
- int tensor_id = hpvm_tensor->tensor_id;
- if(tensor_id < total_tensors)
- return tensorsArr[tensor_id];
- else
- ERROR("Tensor not found in runtime. Aborting ...");
- return NULL;
-}
-*/
-
-
-/*void freeCudaPtr(void* cuda_ptr){
-
- int i;
- for(i = 0; i < tensors_ptr.size(); i++){
- if(tensors_ptr[i] == cuda_ptr)
- break;
- }
-
- tensors_ptr.erase(tensors_ptr.begin()+i);
- cudaFree(cuda_ptr);
-}
-*/
-
-
-void freeTensor(void* tensor_ptr){
-
- Tensor* tensor = (Tensor*) tensor_ptr;
-
- for(int i = 0; i < tensors_ptr.size(); i++){
- if(tensors_ptr[i] == tensor->gpu_data)
- tensors_ptr.erase(tensors_ptr.begin()+i);
- }
-
- for(int i = 0; i < tensors_ptr.size(); i++){
- if(tensors_ptr[i] == tensor->gpu_half_data)
- tensors_ptr.erase(tensors_ptr.begin()+i);
- }
-
- for(int i = 0; i < host_ptr.size(); i++){
- if(host_ptr[i] == tensor->host_data)
- host_ptr.erase(host_ptr.begin()+i);
- }
-
- for(int i = 0; i < obj_ptr.size(); i++){
- if(obj_ptr[i] == tensor_ptr)
- obj_ptr.erase(obj_ptr.begin()+i);
- }
-
-
- cudaFree(tensor->gpu_data);
- cudaFree(tensor->gpu_half_data);
-
- tensor->gpu_data = NULL;
- tensor->gpu_half_data = NULL;
- free(tensor->host_data);
- tensor->host_data = NULL;
-
-}
-
-
-// Returns the size of the target cudnn datatype
-int getTypeSize(int data_type){
- if(data_type == CUDNN_DATA_FLOAT)
- return 4;
-
- if(data_type == CUDNN_DATA_HALF)
- return 2;
-
- INFO("Given type = %d, expected type = %d \n", data_type, CUDNN_DATA_FLOAT);
- return 1;
- // TODO: Add support for more data types
-}
-
-
-void setSizeInBytes(struct Tensor* tensor, int data_type, size_t num_elems){
- int type_size = getTypeSize(data_type);
- size_t size_in_bytes = type_size * num_elems;
- tensor->size_in_bytes = size_in_bytes;
-
- DEBUG("***--- size_in_bytes = %d \n", size_in_bytes);
-}
-
-
-// NOTE: Always allocates FP32 on Host, FP32/FP16 for Device (GPU)
-void allocateMem(struct Tensor* tensor, int data_type, size_t num_elems){
- setSizeInBytes(tensor, data_type, num_elems);
- tensor->data_type = data_type;
- tensor->cur_type = data_type; // type maintained for hanlding FP32 <-> FP16 conversions
- tensor->num_elems = num_elems;
-
- size_t size_on_host = num_elems * 4; // NOTE: On host, always FP32
- tensor->host_data = (void*) malloc(size_on_host); // Allocate memory on the host
- tensor->data_placement = HOST; // By defaut data is on the host
-
- //printf("Allocating Sizes = %lu \n", tensor->size_in_bytes);
-
- if(data_type == float_type){
- checkCudaErrors(cudaMalloc(&tensor->gpu_data, tensor->size_in_bytes)); // Allocate FP32
- tensor->gpu_half_data = NULL;
- }
- else{
- checkCudaErrors(cudaMalloc(&tensor->gpu_half_data, tensor->size_in_bytes)); // Allocate FP16
- tensor->gpu_data = NULL;
- }
-
- tensors_ptr.push_back(tensor->gpu_data);
- tensors_ptr.push_back(tensor->gpu_half_data);
-
- tracked_tensors[tensor] = 1; // For FP16-FP32 data handling
-
- host_ptr.push_back(tensor->host_data);
- obj_ptr.push_back(tensor);
- //host_ptr.push_back(tensor->host_data);
-}
-
-
-void setCudnnDataFormat(struct Tensor* tensor, int data_format){
-
- switch(data_format){
- case 0:
- data_format = CUDNN_TENSOR_NCHW; break;
- case 1:
- data_format = CUDNN_TENSOR_NHWC; break;
-
- default:
- break;
- }
-
- tensor->data_format = data_format;
- DEBUG("tensor->data_format = %d \n", tensor->data_format);
-}
-
-
-void set4DFilterDescriptor(struct Tensor* tensor, int data_format, size_t dim1_size,
- size_t dim2_size, size_t dim3_size, size_t dim4_size){
-
- setCudnnDataFormat(tensor, data_format);
-
- checkCUDNN(cudnnCreateFilterDescriptor(&tensor->filter_desc));
-
- checkCUDNN(cudnnCreateFilterDescriptor(&tensor->filter_half_desc));
-
-
- checkCUDNN(cudnnSetFilter4dDescriptor(tensor->filter_desc,
- (cudnnDataType_t) CUDNN_DATA_FLOAT, //tensor->data_type,
- (cudnnTensorFormat_t) tensor->data_format,
- dim1_size,
- dim2_size,
- dim3_size,
- dim4_size));
-
- checkCUDNN(cudnnSetFilter4dDescriptor(tensor->filter_half_desc,
- (cudnnDataType_t) CUDNN_DATA_HALF,
- (cudnnTensorFormat_t) tensor->data_format,
- dim1_size,
- dim2_size,
- dim3_size,
- dim4_size));
-
-}
-
-
-
-void set4DTensorDescriptor(struct Tensor* tensor, int data_format, size_t dim1_size,
- size_t dim2_size, size_t dim3_size, size_t dim4_size){
-
- setCudnnDataFormat(tensor, data_format);
-
- checkCUDNN(cudnnCreateTensorDescriptor(&tensor->tensor_desc));
-
- checkCUDNN(cudnnCreateTensorDescriptor(&tensor->tensor_half_desc));
-
- // For certain operations, the strides may need to change - in which case the descriptor
- // needs to be reinitialized
- cudnnSetTensor4dDescriptor(tensor->tensor_desc,
- (cudnnTensorFormat_t) tensor->data_format, // Data format
- (cudnnDataType_t) CUDNN_DATA_FLOAT, //tensor->data_type, // Data type
- dim1_size, dim2_size,
- dim3_size, dim4_size);
-
-
- cudnnSetTensor4dDescriptor(tensor->tensor_half_desc,
- (cudnnTensorFormat_t) tensor->data_format, // Data format
- (cudnnDataType_t) CUDNN_DATA_HALF, // Data type
- dim1_size, dim2_size,
- dim3_size, dim4_size);
-
-
- cudnnDataType_t dType;
- int nStride, cStride, hStride, wStride;
- int size1, size2, size3, size4;
- cudnnGetTensor4dDescriptor(tensor->tensor_desc,
- &dType,
- &size1, &size2, &size3, &size4,
- &nStride, &cStride, &hStride, &wStride);
-
- INFO("nStride = %d, cStride = %d, hStride = %d, wStride = %d \n",
- nStride, cStride, hStride, wStride);
-}
-
-
-// FIXIT: Striding still not working - hence 2D and 3D tensor support is missing
-void setTensorDescriptor(struct Tensor* tensor, int num_dims,
- size_t* dim_sizes){
-
- checkCUDNN(cudnnCreateTensorDescriptor(&tensor->tensor_desc));
-
- int* strides = (int*) malloc(sizeof(int) * num_dims);
- strides[num_dims - 1] = 1;
- for(int i = num_dims - 2; i >= 0; i--){
- strides[i] = strides[i+1] * dim_sizes[i+1];
- }
-
- for(int i = 0; i < num_dims; i++){
- INFO("strides[%d] = %d \n", i, strides[i]);
- }
-
- int* const_dims = (int*) malloc(sizeof(int) * num_dims);
- for(int j = 0 ; j < num_dims; j++){
- const_dims[j] = (int) dim_sizes[j];
- INFO("const_dim = %d \n", const_dims[j]);
- }
-
- INFO("data_type = %d, cuDNN_value = %d \n", tensor->data_type, CUDNN_DATA_FLOAT);
- // For certain operations, the strides may need to change - in which case the descriptor
- // needs to be reinitialized
- checkCUDNN(cudnnSetTensorNdDescriptor(tensor->tensor_desc,
- (cudnnDataType_t) tensor->data_type, // Data type
- num_dims,
- (const int*) const_dims,
- (const int*) strides));
-}
-
-
-
-
-extern "C"{
-
- void* create2DTensor(int data_type, size_t dim1_size, size_t dim2_size){
- struct Tensor* tensor = (struct Tensor*) malloc(sizeof(Tensor));
- size_t num_elems = dim1_size * dim2_size;
- allocateMem(tensor, data_type, num_elems);
- // Setting the tensor dimensions
- size_t* dim_sizes = (size_t*) malloc(sizeof(size_t) * 2);
- dim_sizes[0] = dim1_size;
- dim_sizes[1] = dim2_size;
- tensor->dims.dim_sizes = dim_sizes;
- tensor->dims.num_dims = 2;
-
- return tensor;
- }
-
-
- void* create3DTensor(int data_type, size_t dim1_size, size_t dim2_size,
- size_t dim3_size){
- struct Tensor* tensor = (struct Tensor*) malloc(sizeof(Tensor));
- size_t num_elems = dim1_size * dim2_size * dim3_size;
- allocateMem(tensor, data_type, num_elems);
- // Setting the tensor dimensions
- size_t* dim_sizes = (size_t*) malloc(sizeof(size_t) * 3);
- dim_sizes[0] = dim1_size;
- dim_sizes[1] = dim2_size;
- dim_sizes[2] = dim3_size;
- tensor->dims.dim_sizes = dim_sizes;
- tensor->dims.num_dims = 3;
-
- return tensor;
- }
-
-
- void* create4DTensor(int data_type, int data_format, size_t dim1_size, size_t dim2_size,
- size_t dim3_size, size_t dim4_size){
- struct Tensor* tensor = (struct Tensor*) malloc(sizeof(Tensor));
- size_t num_elems = dim1_size * dim2_size * dim3_size * dim4_size;
- allocateMem(tensor, data_type, num_elems);
- // Setting the tensor dimensions
- size_t* dim_sizes = (size_t*) malloc(sizeof(size_t) * 4);
- dim_sizes[0] = dim1_size;
- dim_sizes[1] = dim2_size;
- dim_sizes[2] = dim3_size;
- dim_sizes[3] = dim4_size;
- tensor->dims.dim_sizes = dim_sizes;
- tensor->dims.num_dims = 4;
- // Done setting tensor dimensions
- //setTensorDescriptor(tensor, 4, dim_sizes);
- set4DTensorDescriptor(tensor, data_format, dim1_size, dim2_size, dim3_size, dim4_size);
- // FIXIT: filter descriptor should be invoked only for filters
- set4DFilterDescriptor(tensor, data_format, dim1_size, dim2_size, dim3_size, dim4_size);
-
- return tensor;
- }
-
-
- void initTensorData(void* tensor_ptr, void* data_ptr, size_t size_in_bytes){
-
- Tensor* tensor = (Tensor*) tensor_ptr;
-
- size_t host_size_in_bytes = tensor->num_elems * 4;
- //if(tensor->size_in_bytes != size_in_bytes){
- if(host_size_in_bytes != size_in_bytes){
- ERROR("The destination and source sizes don't match");
- }
-
- std::memcpy(tensor->host_data, data_ptr, size_in_bytes);
-
- changeTensorPlacement(tensor, HOST);
-
- tensor->cur_type = float_type;
- }
-
-
-
- void hostToDeviceCopy(struct Tensor* tensor){
-
- if(tensor->data_placement != DEVICE){
- cudaMemcpy(tensor->gpu_data, tensor->host_data, tensor->size_in_bytes,
- cudaMemcpyHostToDevice);
- INFO("Moving %d bytes from host to GPU \n", tensor->size_in_bytes);
- tensor->data_placement = DEVICE;
- }
- else{
- DEBUG("No data movement required - Data on Device \n");
- }
-
- }
-
-
- void deviceToHostCopy(struct Tensor* tensor){
-
- if(tensor->data_placement != HOST){
- cudaMemcpy(tensor->host_data, tensor->gpu_data, tensor->size_in_bytes,
- cudaMemcpyDeviceToHost);
- INFO("Moving %d bytes from GPU to host \n", tensor->size_in_bytes);
- tensor->data_placement = HOST;
- }
- else{
- DEBUG("No data movement required - Data on Host \n");
- }
-
- }
-
-
- //void tensorCopy(struct Tensor* srcTensor, struct Tensor* dstTensor){
-
- void tensorCopy(void* srcTensor_ptr, void* dstTensor_ptr){
-
- struct Tensor* srcTensor = (struct Tensor*) srcTensor_ptr;
- struct Tensor* dstTensor = (struct Tensor*) dstTensor_ptr;
-
-
- if(srcTensor->data_placement == HOST){
- memcpy(dstTensor->host_data, srcTensor->host_data, srcTensor->size_in_bytes);
- INFO("Moving %d bytes from host to host \n", srcTensor->size_in_bytes);
- dstTensor->data_placement = HOST;
- }
- else if (srcTensor->data_placement == DEVICE){
- cudaMemcpy(dstTensor->gpu_data, srcTensor->gpu_data, srcTensor->size_in_bytes,
- cudaMemcpyDeviceToDevice);
- INFO("Moving %d bytes from GPU to GPU \n", srcTensor->size_in_bytes);
- dstTensor->data_placement = DEVICE;
- }
-
- }
-
-
- void hpvm_request_tensor(void* tensor_ptr, int destination){
-
- Tensor* tensor = (Tensor*) tensor_ptr;
- // If destination is the host
- if(destination == 0){
- if(tensor->data_placement != HOST){
- cudaMemcpy(tensor->host_data, tensor->gpu_data, tensor->size_in_bytes,
- cudaMemcpyDeviceToHost);
- DEBUG("Moving %d bytes from GPU to host \n", tensor->size_in_bytes);
- tensor->data_placement = HOST;
- }
- else{
- DEBUG("No data movement required - Data on Host \n");
- }
- }
- // If destination is the GPU
- else if(destination == 1){
-
- if(tensor->data_placement != DEVICE){
- cudaMemcpy(tensor->gpu_data, tensor->host_data, tensor->size_in_bytes,
- cudaMemcpyHostToDevice);
- INFO("Moving %d bytes from host to GPU \n", tensor->size_in_bytes);
- tensor->data_placement = DEVICE;
- }
- else{
- DEBUG("No data movement required - Data on Device \n");
- }
- }
-
- }
-
-}
-
-
-
-
-bool ONLINE_PROFILING = false; // true;
-
-
-void convertToFP16(struct Tensor* tensor){
-
- if(tensor == NULL)
- return;
-
- printf("**** cur_type = %d , half_type = %d \n", tensor->cur_type, half_type);
-
- if (ONLINE_PROFILING){
- if (tensor->cur_type == half_type)
- return;
- }
-
- DEBUG("ConvertoFP16 \n");
-
- setSizeInBytes(tensor, half_type, tensor->num_elems);
- size_t size_in_bytes = tensor->size_in_bytes;
- DEBUG("size_in_bytes = %d \n", size_in_bytes);
-
- if(tensor->gpu_half_data == NULL)
- checkCudaErrors(cudaMalloc(&tensor->gpu_half_data, size_in_bytes)); // Allocate memory on GPU
-
-
- // If Tensor is one of Tracked (has to free per batch) then track all data types
- if(tracked_tensors.find(tensor) != tracked_tensors.end())
- tensors_ptr.push_back(tensor->gpu_half_data);
-
- f2h((float*) tensor->gpu_data, tensor->num_elems, (half*) tensor->gpu_half_data);
-
- tensor->cur_type = half_type;
-}
-
-
-
-void convertToFP32(struct Tensor* tensor){
-
- if(tensor == NULL)
- return;
-
- // Need this check for both offline and online profiling path
- if (tensor->cur_type == float_type)
- return;
-
- DEBUG("ConvertoFP32 \n");
-
- setSizeInBytes(tensor, float_type, tensor->num_elems);
- size_t size_in_bytes = tensor->size_in_bytes;
-
- // If FP32 data array doesn't exist, allocate
- if(tensor->gpu_data == NULL){
- checkCudaErrors(cudaMalloc(&tensor->gpu_data, size_in_bytes)); // Allocate memory on GPU
- DEBUG("NOTE: Allocating new FP32 Array with size = %lu \n", size_in_bytes);
- }
-
-
- // If Tensor is one of Tracked (has to free per batch) then track all data types
- if(tracked_tensors.find(tensor) != tracked_tensors.end())
- tensors_ptr.push_back(tensor->gpu_data);
-
- h2f((half*) tensor->gpu_half_data, tensor->num_elems, (float*) tensor->gpu_data);
-
- tensor->cur_type = float_type;
-
-}
-
-
-
-void convertToFP32_offline(struct Tensor* tensor){
-
- if(tensor == NULL)
- return;
-
- if(ONLINE_PROFILING){
- return;
- }
-
- DEBUG("ConvertoFP32 \n");
-
- setSizeInBytes(tensor, float_type, tensor->num_elems);
- size_t size_in_bytes = tensor->size_in_bytes;
-
- // If FP32 data array doesn't exist, allocate
- if(tensor->gpu_data == NULL){
- checkCudaErrors(cudaMalloc(&tensor->gpu_data, size_in_bytes)); // Allocate memory on GPU
- DEBUG("NOTE: Allocating new FP32 Array with size = %lu \n", size_in_bytes);
- }
-
- // If Tensor is one of Tracked (has to free per batch) then track all data types
- if(tracked_tensors.find(tensor) != tracked_tensors.end())
- tensors_ptr.push_back(tensor->gpu_data);
-
- h2f((half*) tensor->gpu_half_data, tensor->num_elems, (float*) tensor->gpu_data);
-
- tensor->cur_type = float_type;
-
- //freeCudaPtr(tensor->gpu_half_data);
- //cudaFree(tensor->gpu_half_data);
- //tensor->gpu_half_data = NULL;
-}
-
-
-
-
-
-// Called from within the runtime to change the data placement
-// This routine is required to change the output data placements from host to device
-void changeTensorPlacement(struct Tensor* tensor, data_location_t data_placement){
-
- if(tensor == NULL)
- ERROR("Tensor == NULL");
- tensor->data_placement = data_placement;
-}
-
-
-#endif
-
-
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/testing_api.h b/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/testing_api.h
deleted file mode 100644
index 8b137891791fe96927ad78e64b0aad7bded08bdc..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/include/testing_api.h
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/src/img_tensor_runtime.cu b/llvm/projects/hpvm-tensor-rt/tensor_runtime/src/img_tensor_runtime.cu
deleted file mode 100644
index 0460e490fd2b188b85f53cf9b109f09ac3d6b83a..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/src/img_tensor_runtime.cu
+++ /dev/null
@@ -1,137 +0,0 @@
-#include "../include/debug.h"
-#include "../include/img_tensor_runtime.h"
-#include "../include/approxhpvm_img_runtime_utils.h"
-
-// *** Runtime implementation *** //
-void* tensorFft(void* input) {
-
-}
-
-void* tensorReduce(void* input) {
-
-}
-
-void* tensorReductionSamplingReduce(void* input) {
-
-}
-
-void* tensorProjectiveT(void* input) {
-
-}
-
-void* tensorMap1(void* input) {
-
-}
-
-void* tensorMap2(void* input) {
-
-}
-
-void* tensorMap3(void* input) {
-
-}
-
-
-// *** Wrapper API implementation *** //
-
-void* wrapper_tensorFft(const char* hpvm_node_id, void* input) {
- GPUNodeConfiguration *GPUConf =
- (GPUNodeConfiguration *)RC->getNodeConfiguration(hpvm_node_id);
- std::vector< std::pair< GPUNodeConfiguration::TENSOR_OP,
- std::vector< std::pair<GPUNodeConfiguration::APPROX,
- int> > > > &ApproxChoices =
- GPUConf->getApproxChoices();
- // Approximation choices must be for a fft operation
- CUSTOM_ASSERT(ApproxChoices.size() == 1 &&
- ApproxChoices[0].first == GPUNodeConfiguration::TENSOR_OP::FFT &&
- "Invalid configuration generated for tensor fft wrapper operation");
- return handleTensorFftApproximationTuples(ApproxChoices[0].second,
- input);
-}
-
-void* wrapper_tensorReduce(const char* hpvm_node_id, void* input) {
- GPUNodeConfiguration *GPUConf =
- (GPUNodeConfiguration *)RC->getNodeConfiguration(hpvm_node_id);
- std::vector< std::pair< GPUNodeConfiguration::TENSOR_OP,
- std::vector< std::pair<GPUNodeConfiguration::APPROX,
- int> > > > &ApproxChoices =
- GPUConf->getApproxChoices();
- // Approximation choices must be for a reduce operation
- CUSTOM_ASSERT(ApproxChoices.size() == 1 &&
- ApproxChoices[0].first == GPUNodeConfiguration::TENSOR_OP::REDUCE &&
- "Invalid configuration generated for tensor reduce wrapper operation");
- return handleTensorReduceApproximationTuples(ApproxChoices[0].second,
- input);
-}
-
-void* wrapper_tensorProjectiveT(const char* hpvm_node_id, void* input) {
- GPUNodeConfiguration *GPUConf =
- (GPUNodeConfiguration *)RC->getNodeConfiguration(hpvm_node_id);
- std::vector< std::pair< GPUNodeConfiguration::TENSOR_OP,
- std::vector< std::pair<GPUNodeConfiguration::APPROX,
- int> > > > &ApproxChoices =
- GPUConf->getApproxChoices();
- // Approximation choices must be for a projectiveT operation
- CUSTOM_ASSERT(ApproxChoices.size() == 1 &&
- ApproxChoices[0].first == GPUNodeConfiguration::TENSOR_OP::PROJECTIVE_T &&
- "Invalid configuration generated for tensor projectiveT wrapper operation");
- return handleTensorProjectiveTApproximationTuples(ApproxChoices[0].second,
- input);
-}
-
-void* wrapper_tensorMap1(const char* hpvm_node_id, void* input) {
- GPUNodeConfiguration *GPUConf =
- (GPUNodeConfiguration *)RC->getNodeConfiguration(hpvm_node_id);
- std::vector< std::pair< GPUNodeConfiguration::TENSOR_OP,
- std::vector< std::pair<GPUNodeConfiguration::APPROX,
- int> > > > &ApproxChoices =
- GPUConf->getApproxChoices();
- // Approximation choices must be for a map1 operation
- CUSTOM_ASSERT(ApproxChoices.size() == 1 &&
- ApproxChoices[0].first == GPUNodeConfiguration::TENSOR_OP::MAP1 &&
- "Invalid configuration generated for tensor map1 wrapper operation");
- return handleTensorMap1ApproximationTuples(ApproxChoices[0].second,
- input);
-}
-
-void* wrapper_tensorMap2(const char* hpvm_node_id, void* input) {
- GPUNodeConfiguration *GPUConf =
- (GPUNodeConfiguration *)RC->getNodeConfiguration(hpvm_node_id);
- std::vector< std::pair< GPUNodeConfiguration::TENSOR_OP,
- std::vector< std::pair<GPUNodeConfiguration::APPROX,
- int> > > > &ApproxChoices =
- GPUConf->getApproxChoices();
- // Approximation choices must be for a map2 operation
- CUSTOM_ASSERT(ApproxChoices.size() == 1 &&
- ApproxChoices[0].first == GPUNodeConfiguration::TENSOR_OP::MAP2 &&
- "Invalid configuration generated for tensor map2 wrapper operation");
- return handleTensorMap2ApproximationTuples(ApproxChoices[0].second,
- input);
-}
-
-void* wrapper_tensorMap3(const char* hpvm_node_id, void* input) {
- GPUNodeConfiguration *GPUConf =
- (GPUNodeConfiguration *)RC->getNodeConfiguration(hpvm_node_id);
- std::vector< std::pair< GPUNodeConfiguration::TENSOR_OP,
- std::vector< std::pair<GPUNodeConfiguration::APPROX,
- int> > > > &ApproxChoices =
- GPUConf->getApproxChoices();
- // Approximation choices must be for a map3 operation
- CUSTOM_ASSERT(ApproxChoices.size() == 1 &&
- ApproxChoices[0].first == GPUNodeConfiguration::TENSOR_OP::MAP3 &&
- "Invalid configuration generated for tensor map3 wrapper operation");
- return handleTensorMap3ApproximationTuples(ApproxChoices[0].second,
- input);
-}
-
-// Tentative
-void* wrapper_tensorStencil(const char* hpvm_node_id, void* input) {
- ERROR("Stencil operation currently unsupported.\n");
- abort();
-}
-
-void* wrapper_tensorCosineT(const char* hpvm_node_id, void* input) {
- ERROR("CosineT operation currently unsupported.\n");
- abort();
-}
-
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/src/tensor_cpu_runtime.cc b/llvm/projects/hpvm-tensor-rt/tensor_runtime/src/tensor_cpu_runtime.cc
deleted file mode 100644
index 7397e78013c1e0284314ba8e47012c435345da59..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/src/tensor_cpu_runtime.cc
+++ /dev/null
@@ -1,465 +0,0 @@
-/* This file includes the API implementation of the HPVM tensor runtime built on
- *cublas, cudnn
- **
- ** Author: Hashim Sharif
- ** Email: hsharif3@illinois.edu
- */
-
-#include <algorithm>
-#include <cfloat>
-#include <cmath>
-#include <cstdio>
-#include <cstdlib>
-#include <cstring>
-#include <ctime>
-#include <iostream>
-#include <limits>
-#include <map>
-#include <memory>
-#include <sstream>
-#include <stdarg.h>
-#include <stdio.h>
-#include <stdlib.h>
-#include <string>
-#include <vector>
-
-// Tensor runtime header files
-#include "../include/tensor_cpu.h"
-#include "../include/tensor_cpu_runtime.h"
-
-
-extern "C"{
-
- void llvm_hpvm_initTensorRt(int gpuid) {
- // NOTE: Do Nothing
- }
-
- void llvm_hpvm_cleanupTensorRt() {
- // NOTE: Do Nothing
- }
-
- void hpvm_request_tensor(void *tensor, int destination) {
- // NOTE: Do Nothing
- }
-
- // Returns the size of the target cudnn datatype
- int getTypeSize(int data_type) __attribute__((always_inline));
- inline int getTypeSize(int data_type) {
- // Float/Int data type - Full Precision
- if (data_type == 0)
- return 4;
- // Half data type
- if (data_type == 1)
- return 2;
-
- return 1;
- }
-
- void setSizeInBytes(struct Tensor *tensor, int data_type, size_t num_elems) __attribute__((always_inline));
- inline void setSizeInBytes(struct Tensor *tensor, int data_type, size_t num_elems) {
- int type_size = getTypeSize(data_type);
- size_t size_in_bytes = type_size * num_elems;
- tensor->size_in_bytes = size_in_bytes;
- }
-
- void allocateMemCPU(struct Tensor *tensor, int data_type, size_t num_elems) __attribute__((always_inline));
- inline void allocateMemCPU(struct Tensor *tensor, int data_type, size_t num_elems) {
- setSizeInBytes(tensor, data_type, num_elems);
- tensor->data_type = data_type;
- tensor->num_elems = num_elems;
- tensor->host_data =
- (void *)malloc(tensor->size_in_bytes); // Allocate memory on the host
- }
-
- void initTensorData(void *tensor_ptr, void *data_ptr, size_t size_in_bytes) {
-
- Tensor *tensor = (Tensor *)tensor_ptr;
- if (tensor->size_in_bytes != size_in_bytes) {
- printf("The destination and source sizes don't match");
- }
- memcpy(tensor->host_data, data_ptr, size_in_bytes);
- }
-
-
- void *create4DTensorInternal(int data_type, int data_format, size_t dim1_size,
- size_t dim2_size, size_t dim3_size, size_t dim4_size) __attribute__((always_inline));
- inline void *create4DTensorInternal(int data_type, int data_format, size_t dim1_size,
- size_t dim2_size, size_t dim3_size, size_t dim4_size) {
-
- struct Tensor *tensor = (struct Tensor *)malloc(sizeof(Tensor));
- size_t num_elems = dim1_size * dim2_size * dim3_size * dim4_size;
-
- allocateMemCPU(tensor, data_type, num_elems);
- // Setting the tensor dimensions
- size_t *dim_sizes = (size_t *)malloc(sizeof(size_t) * 4);
- dim_sizes[0] = dim1_size;
- dim_sizes[1] = dim2_size;
- dim_sizes[2] = dim3_size;
- dim_sizes[3] = dim4_size;
- tensor->dims.dim_sizes = dim_sizes;
- tensor->dims.num_dims = 4;
-
- return tensor;
- }
-
- void* create4DTensor(int data_type, int data_format, size_t dim1_size,
- size_t dim2_size, size_t dim3_size, size_t dim4_size) {
-
- return create4DTensorInternal(data_type, data_format, dim1_size, dim2_size, dim3_size, dim4_size);
- }
-
-
- void* __attribute__((always_inline)) tensorAddCPU(void *x_ptr, void *bias_ptr) {
-
- Tensor *x = (Tensor *)x_ptr;
- Tensor *bias = (Tensor *)bias_ptr;
-
- float *x_data = (float *)x->host_data;
- float *bias_data = (float *)bias->host_data;
-
- int n = x->dims.dim_sizes[0];
- int c = x->dims.dim_sizes[1];
- int h = x->dims.dim_sizes[2];
- int w = x->dims.dim_sizes[3];
-
- size_t num_elems = x->num_elems;
- size_t num_elems2 = bias->num_elems;
-
- if (num_elems == num_elems2) {
- for (size_t i = 0; i < num_elems; i++) {
- x_data[i] += bias_data[i];
- }
- } else {
-
- for (int i = 0; i < n; i++) {
- for (int j = 0; j < c; j++) {
- for (int k = 0; k < h; k++) {
- for (int l = 0; l < w; l++) {
- x_data[i * (c * h * w) + j * (h * w) + k * w + l] += bias_data[j];
- }
- }
- }
- }
- }
-
- return x;
- }
-
- void *tensorGemmCPU(void *lhs_ptr, void *rhs_ptr) {
-
- Tensor *lhs = (Tensor *)lhs_ptr;
- Tensor *rhs = (Tensor *)rhs_ptr;
-
- // 'm' holds the batch dimension - assuming NCHW format Tensors
- int m = lhs->dims.dim_sizes[0];
- // The rhs must be a 2D tensor
- int n = rhs->dims.dim_sizes[rhs->dims.num_dims - 1]; // output neurons
- int k = 1;
- // Flattening the dimensions after the batch dimension
- // NOTE: Allowing any number of dimensions > 2 for lhs
- for (int j = 1; j < lhs->dims.num_dims; j++) {
- k = k * lhs->dims.dim_sizes[j]; // input neurons
- }
-
- int rhs_k = rhs->dims.dim_sizes[rhs->dims.num_dims - 2];
-
- // NOTE: Creating a 4D tensor to be compatible with later called cuDNN
- // routines
- Tensor *output = (Tensor *)create4DTensorInternal(0, 0, m, n, 1, 1);
-
- float *lhs_arr = (float *)lhs->host_data;
- float *rhs_arr = (float *)rhs->host_data;
- float *output_arr = (float *)output->host_data;
-
- for (int i = 0; i < m; i++) {
- for (int j = 0; j < n; j++) {
- float sum = 0.0;
- for (int l = 0; l < k; l++) {
- float mul = lhs_arr[i * k + l] * rhs_arr[l * n + j];
- sum = sum + mul;
- }
- output_arr[i * n + j] = sum;
- }
- }
-
- return output;
- }
-
- float power(float num, int exp) __attribute__((always_inline));
- inline float power(float num, int exp){
- bool neg = false;
- if (exp < 0) {
- neg = true;
- exp = -1 * exp;
- }
-
- float pow = 1;
- for (int i = 0; i < exp; i++) {
- pow = pow * num;
- }
-
- if(neg)
- return 1 / pow;
- else
- return pow;
- }
-
- float epow(float x) __attribute__((always_inline));
- inline float epow(float x){
-
- bool neg = false;
- if (x < 0) {
- x = -1 * x;
- neg = true;
- }
-
- float sum = 0.0;
- float fac = 1;
-
- //whole number part
- float pow = 1;
- for (int i = x; i > 0; i--,x--) {
- pow = pow * 2.71828;
- }
- //x++;
-
- // First 15 terms in Taylor Series
- for (int i = 0; i < 15; i++) {
- sum = sum + power(x, i) / fac;
- fac = fac * (i + 1);
- }
-
- if(neg)
- return 1 / (sum * pow);
- else
- return sum * pow;
- }
-
- float custom_tanh(float num) __attribute__((always_inline));
- inline float custom_tanh(float num){
- float value = epow(2 * num);
- value = (value - 1) / (value + 1);
- return value;
- }
-
- float max(float v1, float v2) __attribute__((always_inline));
- inline float max(float v1, float v2){
- if (v1 < v2)
- return v2;
- else
- return v1;
- }
-
- void *tensorReluCPU(void *input_ptr) {
- Tensor *input = (Tensor *)input_ptr;
-
- float *input_data = (float *)input->host_data;
- size_t num_elems = input->num_elems;
- for (size_t i = 0; i < num_elems; i++) {
- if (input_data[i] < 0) {
- input_data[i] = 0;
- }
- }
-
- return input;
- }
-
- void *tensorTanhCPU(void *input_ptr) {
- Tensor *input = (Tensor *)input_ptr;
-
- float *input_data = (float *)input->host_data;
- size_t num_elems = input->num_elems;
- for (size_t i = 0; i < num_elems; i++) {
- input_data[i] = custom_tanh(input_data[i]);
- }
-
- return input;
- }
-
- void *tensorRelu2CPU(void *input_ptr, float min, float max) {
- Tensor *input = (Tensor *)input_ptr;
-
- float *input_data = (float *)input->host_data;
- size_t num_elems = input->num_elems;
- for (size_t i = 0; i < num_elems; i++) {
- if (input_data[i] < min) {
- input_data[i] = min;
- }
- if (input_data[i] > max) {
- input_data[i] = max;
- }
- }
-
- return input;
- }
-
- void *tensorPoolingCPU(void *input_ptr, int poolFunction, int window_height,
- int window_width, int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride) {
-
- Tensor *input = (Tensor *)input_ptr;
- float *input_data = (float *)input->host_data;
-
- int batch_size = input->dims.dim_sizes[0];
- int channels = input->dims.dim_sizes[1];
- int image_height = input->dims.dim_sizes[2];
- int image_width = input->dims.dim_sizes[3];
-
- int output_height =
- 1 + ((image_height - window_height + 2 * vertical_pad) / vertical_stride);
- int output_width = 1 + ((image_width - window_width + 2 * horizontal_pad) /
- horizontal_stride);
-
- int center_x = (window_width - 1) / 2 - horizontal_pad;
- int center_y = (window_height - 1) / 2 - vertical_pad;
- int x_radius = (window_width - 1) / 2;
- int y_radius = (window_height - 1) / 2;
-
- Tensor *output = (Tensor *) create4DTensorInternal(0, 0, batch_size, channels,
- output_height, output_width);
- float *output_data = (float *)output->host_data;
-
- for (int b = 0; b < batch_size; b++) {
- for (int ch = 0; ch < channels; ch++) {
- int ii = 0, jj = 0;
- for (int r = center_y; r < image_height + vertical_pad - y_radius;
- r += vertical_stride) {
- for (int c = center_x; c < image_width + horizontal_pad - x_radius;
- c += horizontal_stride) {
- float val;
- if (poolFunction == 0)
- val = -3.40282e+38; // assuming values never fall below min value of float
- else
- val = 0;
-
- for (int i = r - y_radius, ki = 0; ki < window_height; i++, ki++) {
- for (int j = c - x_radius, kj = 0; kj < window_width; j++, kj++) {
- if (i >= 0 && j >= 0 && i < image_height && j < image_width) {
- if (poolFunction == 0)
- val = max(
- val,
- input_data[b * (channels * image_height * image_width) +
- ch * (image_height * image_width) +
- i * image_width + j]);
- else
- val +=
- input_data[b * (channels * image_height * image_width) +
- ch * (image_height * image_width) +
- i * image_width + j];
- }
- }
- }
- if (poolFunction == 1)
- val /= window_height * window_width;
-
- output_data[b * (channels * output_height * output_width) +
- ch * (output_height * output_width) + ii * output_width +
- jj] = val;
- jj++;
- if (jj == output_width) {
- jj = 0;
- ii++;
- }
- }
- }
- }
- }
-
- return output;
- }
-
- void *tensorSoftmaxCPU(void *input_ptr) {
- Tensor *input = (Tensor *)input_ptr;
-
- float *logits = (float *)input->host_data;
-
- int n = input->dims.dim_sizes[0];
- int c = input->dims.dim_sizes[1];
-
- for (int i = 0; i < n; i++) {
- float x = 0;
- for (int j = 0; j < c; j++)
- x += epow(logits[i * c + j]);
-
- for (int j = 0; j < c; j++)
- logits[i * c + j] = epow(logits[i * c + j]) / x;
- }
-
- return input;
- }
-
- void* __attribute__((always_inline)) tensorConvolutionCPU(void *input_ptr, void *filter_ptr, int vertical_pad,
- int horizontal_pad, int vertical_stride,
- int horizontal_stride, int conv_mode,
- int compute_precision) {
-
- Tensor *input = (Tensor *)input_ptr;
- Tensor *filter = (Tensor *)filter_ptr;
-
- float *image = (float *)input->host_data;
- float *kernels = (float *)filter->host_data;
-
- int batch_size = input->dims.dim_sizes[0];
- int channels = input->dims.dim_sizes[1];
- int image_height = input->dims.dim_sizes[2];
- int image_width = input->dims.dim_sizes[3];
- int num_filters = filter->dims.dim_sizes[0];
- int kernel_height = filter->dims.dim_sizes[2];
- int kernel_width = filter->dims.dim_sizes[3];
-
- // kernel centers
- int center_x = (kernel_width - 1) / 2 - horizontal_pad;
- int center_y = (kernel_height - 1) / 2 - vertical_pad;
-
- int x_radius = (kernel_width - 1) / 2;
- int y_radius = (kernel_height - 1) / 2;
- int output_height =
- 1 + ((image_height - kernel_height + 2 * vertical_pad) / vertical_stride);
- int output_width = 1 + ((image_width - kernel_width + 2 * horizontal_pad) /
- horizontal_stride);
-
- Tensor *output = (Tensor *) create4DTensorInternal(0, 0, batch_size, num_filters,
- output_height, output_width);
- float *output_data = (float *)output->host_data;
-
- for (int b = 0; b < batch_size; b++) {
- for (int f = 0; f < num_filters; f++) {
- int ii = 0, jj = 0;
- for (int r = center_y; r < image_height + vertical_pad - y_radius;
- r += vertical_stride) {
- for (int c = center_x; c < image_width + horizontal_pad - x_radius;
- c += horizontal_stride) {
-
- float sum = 0;
- for (int ch = 0; ch < channels; ch++) {
- for (int i = r - y_radius, ki = 0; ki < kernel_height; i++, ki++) {
- for (int j = c - x_radius, kj = 0; kj < kernel_width; j++, kj++) {
- if (i >= 0 && j >= 0 && i < image_height && j < image_width) {
- sum += image[b * (channels * image_height * image_width) +
- ch * (image_height * image_width) +
- i * image_width + j] *
- kernels[f * (channels * kernel_height * kernel_width) +
- ch * (kernel_height * kernel_width) +
- ki * kernel_width + kj];
- }
- }
- }
- }
- output_data[b * (num_filters * output_height * output_width) +
- f * (output_height * output_width) + ii * output_width +
- jj] = sum;
- jj++;
- if (jj == output_width) {
- jj = 0;
- ii++;
- }
- }
- }
- }
- }
-
- return output;
- }
-
-
-}
diff --git a/llvm/projects/hpvm-tensor-rt/tensor_runtime/src/tensor_runtime.cu b/llvm/projects/hpvm-tensor-rt/tensor_runtime/src/tensor_runtime.cu
deleted file mode 100644
index 5f17cefeed77f15cd0af13ac8e0ae0d3474b6873..0000000000000000000000000000000000000000
--- a/llvm/projects/hpvm-tensor-rt/tensor_runtime/src/tensor_runtime.cu
+++ /dev/null
@@ -1,2122 +0,0 @@
-/* This file includes the API implementation of the HPVM tensor runtime built on cublas, cudnn
-**
-** Author: Hashim Sharif
-** Email: hsharif3@illinois.edu
-*/
-
-#include <stdio.h>
-#include <stdarg.h>
-#include <cstdio>
-#include <cstdlib>
-#include <cmath>
-#include <ctime>
-#include <cfloat>
-#include <algorithm>
-#include <chrono>
-#include <iomanip>
-#include <iostream>
-#include <map>
-#include <memory>
-#include <random>
-#include <sstream>
-#include <string>
-#include <vector>
-
-#include <cuda_runtime.h>
-#include <device_launch_parameters.h>
-
-#include <cublas_v2.h>
-#include <cudnn.h>
-#include <cublas_api.h>
-#include <cuda_fp16.h>
-#include <driver_types.h>
-
-
-// Tensor runtime header files
-#include "../include/tensor_runtime.h"
-#include "../include/tensor_utils.cu"
-#include "../include/debug.h"
-#include "../include/profiling.h"
-#include "../include/fp16_conversion.h"
-#include "../include/global_data.h"
-#include "../include/error.h"
-#include "../include/tensor.h"
-#include "../include/op_overheads.h"
-#include "../include/half_precision_api.h"
-#include "../include/approx_techniques.h"
-#include "../include/hpvm-rt-controller.h"
-#include "../include/approxhpvm_runtime_utils.h"
-
-#include "../include/approx_simulation.h"
-
-// Image tensor runtime implementation
-#include "img_tensor_runtime.cu"
-
-//** Potential Improvements:
-// 1) Add support for dataypes beyond floats and half
-// 2) Support for more CUDNN operations
-
-
-
-void llvm_hpvm_initTensorRt(int gpuid){
-
- if(!runtime_initialized){
-
- printf("INITIALIZING GPU %d \n", gpuid);
- // NOTE: Setting the target GPU. Can we use multiple GPUs?
- checkCudaErrors(cudaSetDevice(gpuid));
- // Initializing cuDNN and cuBlas handles
- checkCudaErrors(cublasCreate(&cublasHandle));
- checkCUDNN(cudnnCreate(&cudnnHandle));
-
-
-#ifdef PROMISE_TUNER_ENABLED
- // readOpenTunerFlags("opentuner_flags");
- readOpenTunerFlags("promise_flags");
-#endif
-
-
-#ifdef ERROR_INJECTION_ENABLED
- readOpenTunerFlags("opentuner_flags");
-#endif
-
-
- runtime_initialized = true;
- }
-
-}
-
-
-void llvm_hpvm_cleanupTensorRt(){
- DEBUG("\**** llvm_hpvm_cleanupTensorRt ***\n");
- dumpAccuracyNorms();
-}
-
-
-void llvm_hpvm_initApproxhpvmRt(int gpuid){
- llvm_hpvm_initTensorRt(gpuid);
- approxhpvm_runtime_mode = true;
-}
-
-void llvm_hpvm_cleanupApproxhpvmRt(){
-
-}
-
-
-
-void dumpAccuracyNorms(){
-
- #ifdef ERROR_INJECTION_ENABLED
-
-
- #endif
-
- dump_result("accuracy_summary");
-
-}
-
-
-// Returns the number of GPUs active on the platform
-int getGPUCount(){
- int num_gpus;
- checkCudaErrors(cudaGetDeviceCount(&num_gpus));
- return num_gpus;
-}
-
-
-
-void clearTensorMap(){
-
- tensors_ptr.clear();
- host_ptr.clear();
- obj_ptr.clear();
-}
-
-
-void startMemTracking(){
-
- tensors_ptr.clear();
- host_ptr.clear();
- obj_ptr.clear();
-
- tracked_tensors.clear();
-}
-
-
-void freeOutputTensors(){
-
- DEBUG("**** Freeing Ouput Tensors *** \n");
- for(int i = 0; i < tensors_ptr.size(); i++){
- cudaFree(tensors_ptr[i]);
- tensors_ptr[i] = NULL;
- }
-
- for(int i = 0; i < host_ptr.size(); i++){
- free(host_ptr[i]);
- host_ptr[i] = NULL;
- }
-
- for(int i = 0; i < obj_ptr.size(); i++){
- free(obj_ptr[i]);
- obj_ptr[i] = NULL;
- }
-}
-
-
-
-void clearOpCounter(){
- total_ops = 0;
- op_counter = 0;
- op_accuracies.clear();
-}
-
-
-
-void freeBatchMemory(){
- // Free allocated memory for the current mini-batch
- freeOutputTensors();
- // Reinitialize couter for OpenTuner flags - next mini-batch of execution
- op_counter = 0;
- // Clearing profiling data map
- func_counters.clear();
-}
-
-
-
-
-// FIXIT: Fix any assumptions on the NCHW format
-// TODO: benchmark split performance and check if it is prohibitively high?
-void** tensorSplit(void* tensor_ptr, int num_splits, int split_dim){
-
- INFO("*** TensorSplit \n");
- profileEvent("tensorSplit");
-
- Tensor* tensor = (Tensor*) tensor_ptr;
-
- deviceToHostCopy(tensor); // Splitting done on the host
-
- Tensor** splits = (Tensor**) malloc(sizeof(Tensor*) * num_splits);
- size_t* dim_sizes = (size_t*) malloc(sizeof(size_t) * tensor->dims.num_dims);
- for(unsigned int i = 0; i < tensor->dims.num_dims; i++){
- dim_sizes[i] = tensor->dims.dim_sizes[i];
- }
-
-
- dim_sizes[split_dim] = tensor->dims.dim_sizes[split_dim] / num_splits;
- if(dim_sizes[split_dim] < 1)
- ERROR("Split Dimension < 1 after splitting");
-
- size_t copy_size = getTypeSize(tensor->data_type);
- for(unsigned int i = split_dim; i < tensor->dims.num_dims; i++){
- copy_size = copy_size * dim_sizes[i];
- }
-
- for(unsigned int i = 0; i < num_splits; i++){
- // FIXIT: Don't be specific to 4D tensors
- // NOTE: Using same data format (NHWC/NCHW) for the split tensors
- INFO("dim_sizes[0] = %d, dim_sizes[1] = %d, dim_sizes[2] = %d, dim_sizes[3] = %d \n",
- dim_sizes[0], dim_sizes[1], dim_sizes[2], dim_sizes[3]);
-
- Tensor* split = (Tensor*) create4DTensor(tensor->data_type, tensor->data_format,
- dim_sizes[0], dim_sizes[1], dim_sizes[2], dim_sizes[3]);
-
- size_t copy_start = i * copy_size;
- size_t copy_stride = num_splits * copy_size;
- INFO("copy_size = %d, copy_start = %d, copy_stride = %d, tensor->size_in_bytes = %d \n",
- copy_size, copy_start, copy_stride, tensor->size_in_bytes);
-
- int index = 0;
- while(copy_start + copy_size <= tensor->size_in_bytes){
- memcpy(((char*) split->host_data + (index * copy_size)),
- ((char*)tensor->host_data + copy_start),
- copy_size);
- copy_start += copy_stride;
- index++;
- }
-
- splits[i] = split;
- }
-
- profileEvent("tensorSplit_end", true);
-
- return (void**) splits;
-}
-
-
-void* tensorConcat(void** tensors_ptr, int num_splits, int split_dim){
-
- INFO("*** TensorConcat \n");
- profileEvent("tensorConcat");
-
- Tensor** tensors = (Tensor**) tensors_ptr;
-
- for(int i = 0; i < num_splits; i++){
- deviceToHostCopy(tensors[i]); // Concatenation done on the host
- }
-
- // The no of dimensions of concatenated tensor are the same
- size_t* dim_sizes = (size_t*) malloc(sizeof(size_t) * tensors[0]->dims.num_dims);
- for(unsigned int i = 0; i < tensors[0]->dims.num_dims; i++){
- dim_sizes[i] = tensors[0]->dims.dim_sizes[i];
- }
-
- size_t copy_size = getTypeSize(tensors[0]->data_type);
- for(unsigned int i = split_dim; i < tensors[0]->dims.num_dims; i++){
- copy_size = copy_size * dim_sizes[i];
- }
-
- dim_sizes[split_dim] = dim_sizes[split_dim] * num_splits;
- if(dim_sizes[split_dim] < 1)
- ERROR("Split Dimension < 1 after concat");
-
- Tensor* output = (Tensor*) create4DTensor(tensors[0]->data_type, tensors[0]->data_format,
- dim_sizes[0], dim_sizes[1], dim_sizes[2], dim_sizes[3]);
-
- INFO("dim_sizes[0] = %d, dim_sizes[1] = %d, dim_sizes[2] = %d, dim_sizes[3] = %d \n",
- dim_sizes[0], dim_sizes[1], dim_sizes[2], dim_sizes[3]);
-
-
- int num_copies = 1;
- for(unsigned int i = 0; i < split_dim; i++){
- num_copies = num_copies * dim_sizes[i];
- }
-
- size_t copy_stride = num_splits * copy_size;
- INFO("copy_size = %d, num_copies = %d, copy_stride = %d, output->size_in_bytes = %d \n",
- copy_size, num_copies, copy_stride, output->size_in_bytes);
-
- for(unsigned int i = 0; i < num_copies; i++){
- // FIXIT: Don't be specific to 4D tensors
- size_t copy_start = i * copy_stride;
-
- for(int j = 0; j < num_splits; j++){
- struct Tensor* split = tensors[j];
- memcpy(((char*) output->host_data + copy_start + (j * copy_size)),
- ((char*) split->host_data + (i * copy_size)),
- copy_size);
- }
- }
-
- profileEvent("tensorConcat_end", true);
-
- return output;
-}
-
-
-
-void* tensorLRN(void* input_ptr, unsigned int LRN_window,
- double LRN_alpha, double LRN_beta, double LRN_k){
-
- INFO("*** TensorLRN \n");
- profileEvent("tensorLRN");
-
- Tensor* input = (Tensor*) input_ptr;
-
- hostToDeviceCopy(input);
-
- float alpha = 1.0f, beta = 0.0f;
- cudnnLRNDescriptor_t LRNDesc;
- checkCUDNN(cudnnCreateLRNDescriptor(&LRNDesc));
-
- INFO("window = %d, LRN_alpha = %f, LRN_beta = %f, LRN_k = %f \n",
- LRN_window, LRN_alpha, LRN_beta, LRN_k);
-
-
- checkCUDNN(cudnnSetLRNDescriptor(LRNDesc, LRN_window, LRN_alpha, LRN_beta, LRN_k));
-
- size_t* dim_sizes = input->dims.dim_sizes;
- Tensor* output = (Tensor*) create4DTensor((cudnnDataType_t) float_type,
- CUDNN_TENSOR_NCHW, dim_sizes[0], dim_sizes[1],
- dim_sizes[2], dim_sizes[3]);
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(output, DEVICE);
- // NOTE: Necessary to insert the above call for every output tensor
-
- printTensorDescInfo(input);
- printTensorDescInfo(output);
-
- checkCUDNN(cudnnLRNCrossChannelForward(cudnnHandle, LRNDesc, CUDNN_LRN_CROSS_CHANNEL_DIM1,
- &alpha, input->tensor_desc, input->gpu_data,
- &beta, output->tensor_desc, output->gpu_data));
-
- profileEvent("tensorLRN_end", true);
-
- return output;
-}
-
-
-void printTensorDims2(void* tensor_ptr){
-
- struct Tensor* tensor = (struct Tensor*) tensor_ptr;
-
- printf("Num_elems = %lu \n", tensor->num_elems);
- for (int i = 0; i < tensor->dims.num_dims; i++){
- printf("dim[%d] = %lu \n", i, tensor->dims.dim_sizes[i]);
- }
-}
-
-
-
-
-// FIXIT: tensorAdd currently only works for 4D tensors
-void* tensorAdd(void* x_ptr, void* bias_ptr){
-
- Tensor* x = (Tensor*) x_ptr;
- Tensor* bias = (Tensor*) bias_ptr;
-
- INFO("*** TensorAdd \n");
- profileEvent("Add");
-
- float alpha = 1.0f;
- //float beta = 0.0f;
- hostToDeviceCopy(x);
- hostToDeviceCopy(bias);
-
- convertToFP32(x);
- convertToFP32(bias);
-
-
- INFO("x->num_elems = %d \n", x->num_elems);
- INFO("bias->num_elems = %d \n", bias->num_elems);
-
- if(cudnnHandle == NULL){
- ERROR("cudnnHandle NOT initialized!! \n");
- }
-
- // FIXIT: routine fails for 3D tensors
- checkCUDNN(cudnnAddTensor(cudnnHandle, &alpha, bias->tensor_desc,
- bias->gpu_data, &alpha, x->tensor_desc, x->gpu_data));
-
- profileEvent("Add_end", true);
-
- #ifdef ERROR_INJECTION_ENABLED
- if(op_counter >= total_ops){
- ERROR("No accuracy flag found \n");
- }
-
- int op_acc = op_accuracies[op_counter];
-
- // Forcing 0 error for (Resnet-like) equal dimension adds (Testing-only)
-
- //-- if (bias->dims.dim_sizes[0] > 1)
- //-- op_acc = 0;
- // Skip errorInjection if explicitly requested
- //-- if (skip_tensors.find(op_counter) != skip_tensors.end()){
- //-- op_acc = 0;
- // }
-
- void* error_norms = tensorAddError(x, op_acc);
- add_norms(error_norms, "tensorAdd", op_acc);
- add_bias_overheads(x, op_acc);
- op_counter++;
-
- #endif
-
-
- return x;
-}
-
-
-// FIXIT: Generalize all of the routines for types {half, float, double}
-void* tensorConvolution(void* input_ptr, void* filter_ptr,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int conv_groups){
-
- INFO("*** TensorConvolution \n");
- profileEvent("Conv");
-
- Tensor* input = (Tensor*) input_ptr;
- Tensor* filter = (Tensor*) filter_ptr;
-
- cudnnConvolutionDescriptor_t convDesc;
- cudnnConvolutionFwdAlgo_t convAlgo;
- cudnnConvolutionMode_t mode;
- if(conv_mode == 0)
- mode = CUDNN_CONVOLUTION;
- else if(conv_mode == 1)
- mode = CUDNN_CROSS_CORRELATION;
-
- mode = CUDNN_CROSS_CORRELATION;
- // FIXIT: Need to be more aware of the implications of alpha and beta
- float alpha = 1.0f, beta = 0.0f;
-
- // TODO: Support other cases;
- hostToDeviceCopy(input);
- hostToDeviceCopy(filter);
-
- convertToFP32(input);
- convertToFP32(filter);
-
-
- INFO("vertical_stride = %lu, horizontal_stride = %lu \n", vertical_stride, horizontal_stride);
-
- checkCUDNN(cudnnCreateConvolutionDescriptor(&convDesc));
-
- //FIXME: Current hack to preserve backward compatibilty
- if(conv_groups == 0){
- conv_groups = 1;
- }
-
-
-
- cudnnDataType_t computeType = CUDNN_DATA_FLOAT;
- // FIXIT: Think if upscaling values need to be configurable?
- // IMP-FIXIT: Either make mode configurable OR see if CUDNN_CONVOLUTION MODE should be used?
- checkCUDNN(cudnnSetConvolution2dDescriptor(convDesc,
- vertical_pad, horizontal_pad, // conv padding
- vertical_stride, horizontal_stride, // conv strides
- 1, 1, // upscaling values
- mode , // mode is configurable
- computeType)); // defines compute precision
-
- // NOTE: Adding support for grouped convolution
- checkCUDNN(cudnnSetConvolutionGroupCount(convDesc, conv_groups));
-
- int n, c, h, w; // output dimensions
- // Find dimension of convolution output
-
- if(input->tensor_desc == NULL || filter->filter_desc == NULL)
- ERROR("Input or Filter descriptor is NULL");
-
- checkCUDNN(cudnnGetConvolution2dForwardOutputDim(convDesc,
- input->tensor_desc,
- filter->filter_desc,
- &n, &c, &h, &w));
-
-
- DEBUG("**Output Tensor Dims, n = %d, c = %d, h = %d, w = %d \n", n, c, h, w);
-
- Tensor* output;
- if(input->data_format == CUDNN_TENSOR_NCHW)
- output = (Tensor*) create4DTensor((cudnnDataType_t) float_type,
- CUDNN_TENSOR_NCHW, n, c, h, w);
- else if(input->data_format == CUDNN_TENSOR_NHWC){
- DEBUG("* NHWC Format \n");
- output = (Tensor*) create4DTensor((cudnnDataType_t) float_type,
- CUDNN_TENSOR_NHWC, n, h, w, c);
- }
- else
- ERROR("Unsupported Tensor Type");
-
- // NOTE: Changing output tensor placement from host to device
- changeTensorPlacement(output, DEVICE);
- // NOTE: Necessary to insert the above call for every output tensor
-
- DEBUG("tensor->data_type = %d, tensor->data_format = %d, N = %d, C = %d, H = %d, W = %d \n",
- output->data_type, output->data_format, output->dims.dim_sizes[0],
- output->dims.dim_sizes[1],
- output->dims.dim_sizes[2], output->dims.dim_sizes[3]);
-
- if(convDesc == NULL || input->tensor_desc == NULL ||
- filter->filter_desc == NULL || output->tensor_desc == NULL)
- ERROR("NULL descriptor! \n");
-
-
- // Debugging info prints
- printTensorDescInfo(input);
- printTensorDescInfo(filter);
- printTensorDescInfo(output);
-
- // NOTE-FIXIT: function failing for NHWC formats - perhaps some CUDNN support is lacking
- checkCUDNN(cudnnGetConvolutionForwardAlgorithm(cudnnHandle,
- input->tensor_desc,
- filter->filter_desc,
- convDesc,
- output->tensor_desc,
- CUDNN_CONVOLUTION_FWD_PREFER_FASTEST,
- //CUDNN_CONVOLUTION_FWD_NO_WORKSPACE,
- 0,
- &convAlgo));
-
-
- DEBUG("ConvAlgo = %d, FFT = %d, GEMM = %d, WINOGRAD = %d \n", convAlgo,
- CUDNN_CONVOLUTION_FWD_ALGO_FFT, CUDNN_CONVOLUTION_FWD_ALGO_GEMM,
- CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD);
-
-
- // FIXIT: Algo shouldn't be hardcoded
- //convAlgo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM;
- convAlgo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM;
-
- size_t workspace_size;
- checkCUDNN(cudnnGetConvolutionForwardWorkspaceSize(cudnnHandle,
- input->tensor_desc,
- filter->filter_desc,
- convDesc,
- output->tensor_desc,
- convAlgo,
- &workspace_size));
-
- // Allocating memory for the convolution workspace
- void* workspace;
- checkCudaErrors(cudaMalloc(&workspace, workspace_size));
- DEBUG("workspace size = %d \n", workspace_size);
-
-
- checkCUDNN(cudnnConvolutionForward(cudnnHandle, &alpha, input->tensor_desc,
- input->gpu_data, filter->filter_desc, filter->gpu_data,
- convDesc, convAlgo, workspace, workspace_size,
- &beta, output->tensor_desc, output->gpu_data));
-
- profileEvent("Conv_end", true);
-
-
- #ifdef ERROR_INJECTION_ENABLED
-
- if(op_counter >= total_ops){
- ERROR("No accuracy flag found \n");
- }
-
- int op_acc = op_accuracies[op_counter];
-
- // Ignore Error Injection for Depthwise Convolution
- /*if (conv_groups > 1){
- op_acc = 0;
- }
- */
-
-
- void* error_norms = tensorAddError(output, op_acc);
- add_norms(error_norms, "tensorConv", op_acc);
- add_conv_overheads(input, filter, vertical_stride, horizontal_stride, op_acc);
-
- op_counter++;
-
- #endif
-
-
- return output;
-}
-
-
-
-// NOTE: Supports Max and Avg Pooling
-void* tensorPooling(void* input_ptr,
- int poolFunction,
- int window_height, int window_width,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride){
-
- INFO("*** TensorPooling \n");
- profileEvent("Pool");
-
- Tensor* input = (Tensor*) input_ptr;
-
- cudnnPoolingDescriptor_t poolDesc;
- // FIXIT: Need to be more aware of the implications of alpha and beta
- float alpha = 1.0f, beta = 0.0f;
-
- hostToDeviceCopy(input);
-
- convertToFP32(input);
-
-
- checkCUDNN(cudnnCreatePoolingDescriptor(&poolDesc));
-
- int n = input->dims.dim_sizes[0];
- int c = input->dims.dim_sizes[1];
- int h = (input->dims.dim_sizes[2] + (2 * vertical_pad) - window_height) / vertical_stride;
- h = h + 1;
- int w = (input->dims.dim_sizes[3] + (2 * horizontal_pad) - window_width) / horizontal_stride;
- w = w + 1;
-
- DEBUG("n = %d, c = %d, h = %d, w = %d \n", n, c, h, w);
-
- // FIXIT: Don't be specific to floats
- Tensor* output = (Tensor*) create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, n, c, h, w);
- // Changing output tensor placement from host to device
- changeTensorPlacement(output, DEVICE);
-
- // FIXIT: Fix being specific to CUDNN_DATA_FLOAT and NCHW format
- // FIXIT: Is this setTensor even needed?
- checkCUDNN(cudnnSetTensor4dDescriptor(output->tensor_desc,
- CUDNN_TENSOR_NCHW,
- CUDNN_DATA_FLOAT,
- n, c,
- h, w));
-
-
- cudnnPoolingMode_t pool_mode;
- if(poolFunction == 0)
- pool_mode = CUDNN_POOLING_MAX;
- else if(poolFunction == 1)
- pool_mode = CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING;
-
-
-
- // FIXIT: Make the pool function (max, min, avg) configurable
- checkCUDNN(cudnnSetPooling2dDescriptor(poolDesc,
- //CUDNN_POOLING_MAX,
- pool_mode,
- CUDNN_PROPAGATE_NAN,
- window_height, window_width,
- vertical_pad, horizontal_pad,
- vertical_stride, horizontal_stride));
-
- checkCUDNN(cudnnPoolingForward(cudnnHandle, poolDesc, &alpha, input->tensor_desc,
- input->gpu_data, &beta, output->tensor_desc, output->gpu_data));
-
- profileEvent("Pool_end", true);
-
-
- #ifdef ERROR_INJECTION_ENABLED
-
- if(op_counter >= total_ops){
- ERROR("No accuracy flag found \n");
- }
-
- int op_acc = op_accuracies[op_counter];
- void* error_norms = tensorAddError(output, op_acc);
- add_norms(error_norms, "tensorPooling", op_acc);
- add_pool_overheads(input, window_height, vertical_stride, op_acc);
-
- op_counter++;
-
- #endif
-
-
- return output;
-}
-
-
-
-
-void* tensorGemmCPU(void* lhs_ptr, void* rhs_ptr){
-
- INFO("*** TensorGemmCPU \n");
-
- Tensor* lhs = (Tensor*) lhs_ptr;
- Tensor* rhs = (Tensor*) rhs_ptr;
-
- // The operation is done on the CPU
- deviceToHostCopy(lhs);
- deviceToHostCopy(rhs);
-
- if(lhs->data_type != CUDNN_DATA_FLOAT){
- ERROR("Currently only Floating point is supported ");
- }
-
- profileEvent("tensorGemmCPU");
-
- INFO("rhs->dims.num_dims = %d \n", rhs->dims.num_dims);
- INFO("lhs->dims.num_dims = %d \n", lhs->dims.num_dims);
-
- // FIXIT: Need to be more aware of the implications of alpha and beta
- //float alpha = 1.0f;
- // float beta = 0.0f;
- // 'm' holds the batch dimension - assuming NCHW format Tensors
- int m = lhs->dims.dim_sizes[0];
- // The rhs must be a 2D tensor
- int n = rhs->dims.dim_sizes[rhs->dims.num_dims-1]; // output neurons
- int k = 1;
- // Flattening the dimensions after the batch dimension
- // NOTE: Allowing any number of dimensions > 2 for lhs
- for (int j = 1 ; j < lhs->dims.num_dims; j++){
- k = k * lhs->dims.dim_sizes[j]; // input neurons
- }
-
- int rhs_k = rhs->dims.dim_sizes[rhs->dims.num_dims-2];
- // Dimension-note: Check if k is same across the two tensors
- INFO("m = %d, n = %d, k = %d \n", m, n, k);
- if(rhs_k != k){
- ERROR("rhs=%d and lhs=%d columns/rows don't match", rhs_k, k);
- }
-
- // NOTE: Creating a 4D tensor to be compatible with later called cuDNN routines
- Tensor* output = (Tensor*) create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, m, n, 1, 1);
- // Changing output tensor placement from host to device
- changeTensorPlacement(output, HOST);
-
- float* lhs_arr = (float*) lhs->host_data;
- float* rhs_arr = (float*) rhs->host_data;
- float* output_arr = (float*) output->host_data;
-
- for(int i = 0; i < m; i++){
- for(int j = 0; j < n; j++){
- float sum = 0.0;
- for(int l = 0; l < k; l++){
- float mul = lhs_arr[i*k+l] * rhs_arr[l*n+j];
- sum = sum + mul;
- }
- output_arr[i*n+j] = sum;
- }
- }
-
-
- profileEvent("tensorGemmCPU_end", true);
-
- return output;
-}
-
-
-
-// Reference: https://gist.github.com/peterwittek/6303527
-void* tensorGemmGPU(void* lhs_ptr, void* rhs_ptr ){ //, void* result_tensor){
-
- INFO("*** TensorGemmGPU \n");
- profileEvent("Mul");
-
- Tensor* lhs = (Tensor*) lhs_ptr;
- Tensor* rhs = (Tensor*) rhs_ptr;
-
-
- INFO("rhs->dims.num_dims = %d \n", rhs->dims.num_dims);
- INFO("lhs->dims.num_dims = %d \n", lhs->dims.num_dims);
-
- // FIXIT: Need to be more aware of the implications of alpha and beta
- float alpha = 1.0f, beta = 0.0f;
- // 'm' holds the batch dimension - assuming NCHW format Tensors
- int m = lhs->dims.dim_sizes[0];
- // The rhs last dimension must contain the neurons
- int n = rhs->dims.dim_sizes[rhs->dims.num_dims-1]; // output neurons
- int k = 1;
-
- // Flattening the dimensions after the batch dimension
- // NOTE: Allowing any number of dimensions > 2 for lhs
- for (int j = 1 ; j < lhs->dims.num_dims; j++){
- k = k * lhs->dims.dim_sizes[j]; // input neurons
- }
-
- int rhs_k = rhs->dims.dim_sizes[rhs->dims.num_dims-2];
- // Dimension-note: Check if k is same across the two tensors
- INFO("m = %d, n = %d, k = %d \n", m, n, k);
- if(rhs_k != k){
- ERROR("rhs=%d and lhs=%d columns/rows don't match", rhs_k, k);
- }
-
- Tensor* output = NULL;
- DEBUG("Creating new TENSOR * \n");
- output = (Tensor*) create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, m, n, 1, 1);
-
-
- /* else{
- DEBUG("Reusing TENSOR *\n");
- // FIXIT: Add Assertion to check for null pointer and dimension matching
- output = (Tensor*) result_tensor;
- // FIXIT: output value is trashing - Is this deallocated?
- INFO("output->num_elems = %lu \n", output->data_type);
- }
- */
-
- DEBUG("Changing placement *\n");
- // Changing output tensor placement from host to device
- changeTensorPlacement(output, DEVICE);
-
- DEBUG("Changed Placement * \n\n");
-
- hostToDeviceCopy(lhs);
- hostToDeviceCopy(rhs);
-
- convertToFP32(lhs);
- convertToFP32(rhs);
-
-
- DEBUG("CuBlasSgemm *\n");
-
- // INFO: cuBlas uses column-major format
- // INFO: The leading dimension is just the FIRST Dimension
- // IMP: output is N * M in column-major format, M*N in row-major - what cuDNN expects
- checkCudaErrors(cublasSgemm(cublasHandle, CUBLAS_OP_N, CUBLAS_OP_N,
- n, m, k,
- &alpha,
- (float*) rhs->gpu_data, n,
- (float*) lhs->gpu_data, k,
- &beta,
- (float*) output->gpu_data, n));
-
-
- profileEvent("Mul_end", true);
-
-
-
- #ifdef ERROR_INJECTION_ENABLED
-
- if(op_counter >= total_ops){
- ERROR("No accuracy flag found \n");
- }
-
- int op_acc = op_accuracies[op_counter];
-
- void* error_norms = tensorAddError(output, op_acc);
- add_norms(error_norms, "tensorGemm", op_acc);
- add_gemm_overheads(lhs_ptr, rhs_ptr, op_acc);
-
- op_counter++;
-
- #endif
-
-
- return output;
-}
-
-
-
-
-
-
-
-void* tensorGemm(void* lhs_ptr, void* rhs_ptr){
-
- INFO("*** TensorGemm \n");
- profileEvent("tensorGemm");
-
- Tensor* lhs = (Tensor*) lhs_ptr;
- Tensor* rhs = (Tensor*) rhs_ptr;
-
- INFO("rhs->dims.num_dims = %d \n", rhs->dims.num_dims);
- INFO("lhs->dims.num_dims = %d \n", lhs->dims.num_dims);
-
- // FIXIT: Need to be more aware of the implications of alpha and beta
- float alpha = 1.0f, beta = 0.0f;
- // 'm' holds the batch dimension - assuming NCHW format Tensors
- int m = lhs->dims.dim_sizes[0];
- // The rhs last dimension must contain the neurons
- int n = rhs->dims.dim_sizes[rhs->dims.num_dims-1]; // output neurons
- int k = 1;
- // Flattening the dimensions after the batch dimension
- // NOTE: Allowing any number of dimensions > 2 for lhs
- for (int j = 1 ; j < lhs->dims.num_dims; j++){
- k = k * lhs->dims.dim_sizes[j]; // input neurons
- }
-
- int rhs_k = rhs->dims.dim_sizes[rhs->dims.num_dims-2];
- // Dimension-note: Check if k is same across the two tensors
- INFO("m = %d, n = %d, k = %d \n", m, n, k);
- if(rhs_k != k){
- ERROR("rhs=%d and lhs=%d columns/rows don't match", rhs_k, k);
- }
-
- // NOTE: Creating a 4D tensor to be compatible with later called cuDNN routines
- Tensor* output = (Tensor*) create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, m, n, 1, 1);
- // Changing output tensor placement from host to device
- changeTensorPlacement(output, DEVICE);
-
- hostToDeviceCopy(lhs);
- hostToDeviceCopy(rhs);
-
- // NOTE: cuBlas uses column-major format
- // NOTE: The leading dimension is the FIRST Dimension
- // NOTE: The output is N * M in column-major format, M*N in row-major - what cuDNN expects
- checkCudaErrors(cublasSgemm(cublasHandle, CUBLAS_OP_T, CUBLAS_OP_N,
- n, m, k,
- &alpha,
- (float*) rhs->gpu_data, k,
- (float*) lhs->gpu_data, k,
- &beta,
- (float*) output->gpu_data, n));
-
- profileEvent("tensorGemm_end", true);
-
- return output;
-}
-
-
-
-
-// FIXIT: Add dimension check assertions throughout the code
-void* tensorGemmBias(void* input_ptr, void* bias_ptr){
-
- INFO("*** TensorGemmBias \n");
- profileEvent("tensorGemmBias");
-
- Tensor* input = (Tensor*) input_ptr;
- Tensor* bias = (Tensor*) bias_ptr;
-
- // NOTE: beta is set to 1 to append to input
- // C = A * B + Beta * C
- float alpha = 1.0f, beta = 1.0f;
- // 'm' holds the batch dimension - assuming NCHW format Tensors
- int m = input->dims.dim_sizes[0];
- // The bias must be a 2D tensor
- int n = bias->dims.dim_sizes[bias->dims.num_dims - 1]; // output neurons
-
- INFO("m = %d, n = %d \n", m, n);
-
- hostToDeviceCopy(input);
- hostToDeviceCopy(bias);
-
- struct Tensor* onevec = (Tensor*) create2DTensor(CUDNN_DATA_FLOAT, m, 1);
- fillOnes(onevec);
- hostToDeviceCopy(onevec);
-
- // NOTE: cuBlas uses column-major format
- // NOTE: The leading dimension is just the FIRST Dimension
- checkCudaErrors(cublasSgemm(cublasHandle, CUBLAS_OP_N, CUBLAS_OP_N,
- n, m, 1,
- &alpha,
- (float*) bias->gpu_data, n,
- (float*) onevec->gpu_data, 1,
- &beta,
- (float*) input->gpu_data, n));
-
- profileEvent("tensorGemmBias_end", true);
-
- return input;
-}
-
-
-void* tensorRelu(void* input_ptr){
-
- INFO("*** TensorRelu \n");
- profileEvent("Relu");
-
- Tensor* input = (Tensor*) input_ptr;
-
- cudnnActivationDescriptor_t reluDesc;
- float alpha = 1.0f, beta = 0.0f;
-
- hostToDeviceCopy(input);
-
- convertToFP32(input);
-
-
- checkCUDNN(cudnnCreateActivationDescriptor(&reluDesc));
-
- checkCUDNN(cudnnSetActivationDescriptor(reluDesc, CUDNN_ACTIVATION_RELU,
- CUDNN_PROPAGATE_NAN, 0.0));
-
- checkCUDNN(cudnnActivationForward(cudnnHandle, reluDesc, &alpha,
- input->tensor_desc, input->gpu_data, &beta,
- input->tensor_desc, input->gpu_data));
-
- profileEvent("Relu_end", true);
-
-
- #ifdef ERROR_INJECTION_ENABLED
-
- if(op_counter >= total_ops){
- ERROR("No accuracy flag found \n");
- }
-
- int op_acc = op_accuracies[op_counter];
-
- void* error_norms = tensorAddError(input, op_acc);
- add_norms(error_norms, "tensorRelu", op_acc);
- add_relu_overheads(input, op_acc);
- op_counter++;
- #endif
-
-
- return input;
-}
-
-
-// Think: Should Softmax be broken into multiple IR operations?
-void* tensorSoftmax(void* input_ptr){
-
- INFO("*** TensorSoftmax \n");
- profileEvent("Softmax");
-
- Tensor* input = (Tensor*) input_ptr;
- float alpha = 1.0f, beta = 0.0f;
-
- hostToDeviceCopy(input);
- convertToFP32(input);
-
-
- // IMP: CUDNN_SOFTMAX_ACCURATE can be replaced with a less acurate CUDNN_SOFTMAX_FAST
- checkCUDNN(cudnnSoftmaxForward(cudnnHandle, CUDNN_SOFTMAX_ACCURATE, CUDNN_SOFTMAX_MODE_CHANNEL,
- &alpha, input->tensor_desc, input->gpu_data, &beta,
- input->tensor_desc, input->gpu_data));
-
- deviceToHostCopy(input);
- profileEvent("Softmax_end", true);
-
- return input;
-}
-
-
-
-__global__ void clipValues(float* A, float min, float max, int n){
-
- int id = blockIdx.x * blockDim.x + threadIdx.x;
-
- if(id < n){
- A[id] = fmaxf(min, A[id]);
- A[id] = fminf(max, A[id]);
- }
-}
-
-
-
-void* tensorRelu2(void* input_ptr, float min, float max){
-
- INFO("*** TensorClippedRelu *** \n");
- profileEvent("Relu");
-
- cudnnActivationDescriptor_t reluDesc;
- float alpha = 1.0f, beta = 0.0f;
-
- Tensor* input = (Tensor*) input_ptr;
-
- hostToDeviceCopy(input);
-
- convertToFP32(input);
-
-
- checkCUDNN(cudnnCreateActivationDescriptor(&reluDesc));
-
- checkCUDNN(cudnnSetActivationDescriptor(reluDesc, CUDNN_ACTIVATION_CLIPPED_RELU,
- CUDNN_PROPAGATE_NAN, max));
-
- checkCUDNN(cudnnActivationForward(cudnnHandle, reluDesc, &alpha,
- input->tensor_desc, input->gpu_data, &beta,
- input->tensor_desc, input->gpu_data));
-
-
-
- profileEvent("Relu_end", true);
-
-
- #ifdef ERROR_INJECTION_ENABLED
-
- if(op_counter >= total_ops){
- ERROR("No accuracy flag found \n");
- }
-
- int op_acc = op_accuracies[op_counter];
- void* error_norms = tensorAddError(input, op_acc);
- add_norms(error_norms, "tensorClippedRelu", op_acc);
- add_relu_overheads(input, op_acc);
- op_counter++;
- #endif
-
-
- return input;
-}
-
-
-void* tensorTanh(void* input_ptr){
-
- INFO("*** TensorTanh \n");
- profileEvent("Tanh");
-
- Tensor* input = (Tensor*) input_ptr;
-
- cudnnActivationDescriptor_t tanhDesc;
- float alpha = 1.0f, beta = 0.0f;
-
- hostToDeviceCopy(input);
-
- convertToFP32(input);
-
-
- checkCUDNN(cudnnCreateActivationDescriptor(&tanhDesc));
-
- checkCUDNN(cudnnSetActivationDescriptor(tanhDesc, CUDNN_ACTIVATION_TANH,
- CUDNN_PROPAGATE_NAN, 0.0));
-
- checkCUDNN(cudnnActivationForward(cudnnHandle, tanhDesc, &alpha,
- input->tensor_desc, input->gpu_data, &beta,
- input->tensor_desc, input->gpu_data));
-
- profileEvent("Tanh_end", true);
-
-
- #ifdef ERROR_INJECTION_ENABLED
-
- if(op_counter >= total_ops){
- ERROR("No accuracy flag found \n");
- }
-
- int op_acc = op_accuracies[op_counter];
- void* error_norms = tensorAddError(input, op_acc);
- add_norms(error_norms, "tensorTanh", op_acc);
- add_relu_overheads(input, op_acc);
- op_counter++;
- #endif
-
-
- return input;
-}
-
-
-
-
-void* tensorBatchNorm(void* input_ptr, void* gamma_ptr, void* beta_ptr,
- void* mean_ptr, void* variance_ptr, double epsilon){
-
- INFO("*** TensorBatchNorm \n");
- profileEvent("BatchNorm");
-
- Tensor* input = (Tensor*) input_ptr;
- Tensor* gamma = (Tensor*) gamma_ptr;
- Tensor* beta = (Tensor*) beta_ptr;
- Tensor* mean = (Tensor*) mean_ptr;
- Tensor* variance = (Tensor*) variance_ptr;
-
- if (input == NULL || gamma == NULL || beta == NULL || mean == NULL || variance == NULL){
- ERROR("NULL Input Tensor");
- }
-
- float alpha_val = 1.0f, beta_val = 0.0f;
- hostToDeviceCopy(input);
- hostToDeviceCopy(gamma);
- hostToDeviceCopy(beta);
- hostToDeviceCopy(mean);
- hostToDeviceCopy(variance);
-
- convertToFP32(input);
-
-
-
- checkCUDNN(cudnnBatchNormalizationForwardInference(cudnnHandle, CUDNN_BATCHNORM_SPATIAL,
- &alpha_val, &beta_val,
- input->tensor_desc, input->gpu_data,
- input->tensor_desc, input->gpu_data,
- gamma->tensor_desc, gamma->gpu_data,
- beta->gpu_data, mean->gpu_data,
- variance->gpu_data,
- epsilon));
-
- profileEvent("BatchNorm_end", true);
-
-
- #ifdef ERROR_INJECTION_ENABLED
-
- if(op_counter >= total_ops){
- ERROR("No accuracy flag found \n");
- }
-
- int op_acc = op_accuracies[op_counter];
- //op_acc = 0;
- void* error_norms = tensorAddError(input, op_acc);
- add_norms(error_norms, "tensorBatchNorm", op_acc);
- add_relu_overheads(input, op_acc);
- op_counter++;
- #endif
-
-
- return input;
-}
-
-
-
-
-/************* GPU Layer API *************/
-
-void* ConvLayer_GPU(void* input,
- void* filter,
- void* bias,
- int conv_pad_h, int conv_pad_w, int conv_stride_h, int conv_stride_w,
- int pool_id, int pool_size,
- int activation_id, // Relu, Tanh, ClipRelu
- float out_min, float out_max){ // NOTE: min_val, max_val apply to 'ClippedRelu'
-
- void* conv_out = tensorConvolution(input, filter,
- conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w,
- 1, 0);
- void* conv_add;
- if(bias != NULL){
- conv_add = tensorAdd(conv_out, bias);
- }
- else{
- conv_add = conv_out;
- }
-
- void* activation_out;
- switch(activation_id){
- case -1:
- activation_out = conv_add;
- INFO("NO Activation Function \n");
- break;
- case 0:
- activation_out = tensorTanh(conv_add);
- break;
- case 1:
- activation_out = tensorRelu(conv_add);
- break;
- case 2:
- activation_out = tensorRelu2(conv_add, out_min, out_max);
- break;
- default:
- ERROR("Activation id %d NOT supported \n", activation_out);
- break;
- }
-
-
- void* pool_out = activation_out;
- // NOTE: Skip pooling on negative pool sizes
- if(pool_size > 0){
- //FIXME: Currently only using MaxPooling
- pool_out = tensorPooling(activation_out, 0, pool_size, pool_size, 0, 0, pool_size, pool_size);
- }
- else{
- pool_out = activation_out;
- }
-
- return pool_out;
-}
-
-
-void* FCLayer_GPU(void* input,
- void* weights,
- void* bias,
- int activation_id,
- float out_min, float out_max){ // NOTE: min_val, max_val apply to 'ClippedRelu'
-
- void* gemm_out = tensorGemmGPU(input, weights);
-
- void* gemmbias_out;
- if(bias != NULL){
- gemmbias_out = tensorAdd(gemm_out, bias);
- }
- else{
- gemmbias_out = gemm_out;
- }
-
- void* activation_out;
- switch(activation_id){
-
- case -1:
- activation_out = gemmbias_out;
- INFO("No Activation Function \n");
- break;
- case 0:
- activation_out = tensorTanh(gemmbias_out);
- break;
- case 1:
- activation_out = tensorRelu(gemmbias_out);
- break;
- case 2:
- activation_out = tensorRelu2(gemmbias_out, out_min, out_max);
- break;
- default:
- ERROR("Activation id %d NOT supported \n", activation_out);
- break;
- }
-
- return activation_out;
-}
-
-
-/*********** PROMISE API **************/
-
-/*
-void* ConvLayer_PROMISE(void* input, float i_min, float i_max,
- void* filter, float w_min, float w_max,
- void* bias, float b_min, float b_max,
- int conv_pad_h, int conv_pad_w, int conv_stride_h, int conv_stride_w,
- int pool_id, int pool_size,
- int activation_id, // Relu, Tanh, ClipRelu
- float out_min, float out_max, int swing){
-
-
- #ifdef PROMISE_TUNER_ENABLED
-
- // NOTE: Skip reading file-based error levels for ApproxHPVM wrapper runtime
- if(!approxhpvm_runtime_mode){
-
- if(op_counter >= total_ops){
- ERROR("No accuracy flag found \n");
- }
-
- swing = op_accuracies[op_counter];
- op_counter++;
- }
-
- #endif
-
-
- if (swing < 0 || swing > 20){
- ERROR("Incorrect swing value");
- }
-
-
-
- if(swing < 8){
- input = quantizeTensorPromise(input, i_min, i_max);
- filter = quantizeTensorPromise(filter, w_min, w_max);
- if(bias != NULL)
- bias = quantizeTensorPromise(bias, b_min, b_max);
- // aRead error
-
- input = addPromiseError(input, swing);
- }
-
-
- void* conv_out;
- if(swing == 8 || (swing >= 12 && swing <= 15) ){
- //conv_out = tensorConvPerf(input, filter, conv_pad_h, conv_pad_w,
- // conv_stride_h, conv_stride_w, 1, 1, 1, 0);
-
- int rows = 2;
- switch(swing){
-
- case 12: rows = 5; break;
- case 13: rows = 4; break;
- case 14: rows = 3; break;
- case 15: rows = 2; break;
-
- default: rows = 2; break;
- }
-
- conv_out = tensorConvPerf2(input, filter, conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w, 1, 1, rows, 0);
-
- /*void* gold = tensorConvolution(input, filter,
- conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w,
- 1, 0);
-
- Norm_t* norms = calculateNormsTreeReduction((struct Tensor*) conv_out, (struct Tensor*) gold);
-
- DEBUG("\n-------- l2_norm = %f \n", norms->l2_norm);
- */
-
-
-
- /* -----
- }
- else if(swing == 9 || (swing >= 16 && swing <= 19) ){
- //conv_out = tensorConvPerf(input, filter, conv_pad_h, conv_pad_w,
- // conv_stride_h, conv_stride_w, 1, 1, 0, 1);
-
-
- int cols = 2;
- switch(swing){
-
- case 16: cols = 5; break;
- case 17: cols = 4; break;
- case 18: cols = 3; break;
- case 19: cols = 2; break;
-
- default: cols = 2; break;
- }
-
-
- conv_out = tensorConvPerf2(input, filter, conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w, 1, 1, 0, cols);
-
-
- /*void* gold = tensorConvolution(input, filter,
- conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w,
- 1, 0);
-
- Norm_t* norms = calculateNormsTreeReduction((struct Tensor*)conv_out, (struct Tensor*) gold);
-
- DEBUG("\n-------- l2_norm = %f \n", norms->l2_norm);
- */
-
- /*------
- }
- else if(swing == 10){
- conv_out = tensorHalfConvolution(input, filter,
- conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w,
- 1, 0);
- }
- else{
- conv_out = tensorConvolution(input, filter,
- conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w,
- 1, 0);
- }
-
- void* conv_add;
- if(bias != NULL){
- if(swing >= 8){
- conv_add = tensorHalfAdd(conv_out, bias);
- }
- else{
- conv_add = tensorAdd(conv_out, bias);
- }
- }
- else{
- conv_add = conv_out;
- }
-
- void* pool_out;
- // NOTE: Skip pooling on negative pool sizes
- if(pool_size > 0){
- //FIXME: Currently only using MaxPooling
- pool_out = tensorHalfPooling(conv_add, 0, pool_size, pool_size, 0, 0, pool_size, pool_size);
- }
- else{
- pool_out = conv_add;
- }
-
- void* activation_out;
- switch(activation_id){
- case -1:
- activation_out = pool_out;
- INFO("NO Activation Function \n");
- break;
- case 0:
- activation_out = tensorHalfTanh(pool_out);
- break;
- case 1:
- activation_out = tensorHalfRelu(pool_out);
- break;
- case 2:
- activation_out = tensorHalfRelu2(pool_out, out_min, out_max);
- break;
- default:
- ERROR("Activation id %d NOT supported \n", activation_out);
- break;
- }
-
-
- if(swing < 8 && activation_id != -1){
- activation_out = quantizeTensorPromise(activation_out, out_min, out_max);
- }
-
- return activation_out;
-}
-
-
-void* FCLayer_PROMISE(void* input, float i_min, float i_max,
- void* weights, float w_min, float w_max,
- void* bias, float b_min, float b_max,
- int activation_id,
- float out_min, float out_max, int swing){ //NOTE: min_val, max_val apply to 'ClippedRelu'
-
-
-
- #ifdef PROMISE_TUNER_ENABLED
-
- // NOTE: Skip reading file-based error levels for ApproxHPVM wrapper runtime
- if(!approxhpvm_runtime_mode){
-
- if(op_counter >= total_ops){
- ERROR("No accuracy flag found \n");
- }
-
- swing = op_accuracies[op_counter];
- op_counter++;
- }
-
- #endif
-
-
- if (swing < 0 || swing > 20){
- ERROR("Incorrect swing value");
- }
-
- if(swing < 8){
- input = quantizeTensorPromise(input, i_min, i_max);
- weights = quantizeTensorPromise(weights, w_min, w_max);
- if(bias != NULL)
- bias = quantizeTensorPromise(bias, b_min, b_max);
-
- // NOTE: Modelling aRead error in PROMISE
- input = addPromiseError(input, swing);
- }
-
-
-
- void* gemm_out;
- if(swing >= 8 && swing < 11){
- gemm_out = tensorHalfGemm(input, weights);
- }
- else{
- gemm_out = tensorGemmGPU(input, weights);
- }
-
-
- void* gemmbias_out;
- if(bias != NULL){
- // Swing 8 corresponds to FP32
- if(swing >= 8 && swing < 20){
- gemmbias_out = tensorHalfAdd(gemm_out, bias);
- }
- else{
- gemmbias_out = tensorAdd(gemm_out, bias);
- }
- }
- else{
- gemmbias_out = gemm_out;
- }
-
- void* activation_out;
- switch(activation_id){
-
- case -1:
- activation_out = gemmbias_out;
- INFO("No Activation Function \n");
- break;
- case 0:
- activation_out = tensorTanh(gemmbias_out);
- break;
- case 1:
- activation_out = tensorRelu(gemmbias_out);
- break;
- case 2:
- activation_out = tensorRelu2(gemmbias_out, out_min, out_max);
- break;
- default:
- ERROR("Activation id %d NOT supported \n", activation_out);
- break;
- }
-
-
- if(swing < 8 && activation_id != -1){
- activation_out = quantizeTensorPromise(activation_out, out_min, out_max);
- }
-
- return activation_out;
-}
-
-*****/
-
-
-
-/**** Wrapper Runtime API ***/
-
-void* wrapper_ConvLayer(const char* hpvm_node_id,
- void* input,
- void* filter,
- void* bias,
- int conv_pad_h, int conv_pad_w,
- int conv_stride_h, int conv_stride_w,
- int pool_id, int pool_size,
- int activation_id,
- // NOTE: out_min, out_max are only relevant for ClippedRelu
- float out_min, float out_max){
-
- NodeConfiguration *NodeConf = RC->getNodeConfiguration(hpvm_node_id);
-
- if (NodeConf->isPROMISENodeConfiguration()) {
- DEBUG("PROMISE Configuration for ConvLayer\n");
- // Mapped to PROMISE - get a PROMISE node configuration
- PROMISENodeConfiguration *PROMISEConf = (PROMISENodeConfiguration *)NodeConf;
- std::vector<float> &QRanges = RC->getQuantizationRanges(hpvm_node_id);
-
- std::vector<std::pair<PROMISENodeConfiguration::APPROX, int> > &approxTuples =
- PROMISEConf->getApproxChoices();
-
- if (approxTuples.size() == 1) {
- enum PROMISENodeConfiguration::APPROX approx = approxTuples[0].first;
- int param = approxTuples[0].second;
- if (approx == PROMISENodeConfiguration::APPROX::SWING_LEVEL) {
- DEBUG("Approximation choice for ConvLayer: swing level %d\n", param);
-
- struct Tensor* input_tensor_cast = (struct Tensor*) input;
- struct Tensor* filter_tensor_cast = (struct Tensor*) filter;
- std::pair<double, double> pinfo =
- RC->conv_profile(input_tensor_cast->dims.dim_sizes[0], //n
- input_tensor_cast->dims.dim_sizes[1], //c
- input_tensor_cast->dims.dim_sizes[2], //h
- input_tensor_cast->dims.dim_sizes[3], //w
- filter_tensor_cast->dims.dim_sizes[0], //c_out
- filter_tensor_cast->dims.dim_sizes[1], //c_in
- filter_tensor_cast->dims.dim_sizes[2], //k_h
- filter_tensor_cast->dims.dim_sizes[3], //k_w
- conv_stride_h, //s_h
- conv_stride_w, //s_w
- param, //voltage_swing
- filter_tensor_cast->dims.dim_sizes[2] *
- filter_tensor_cast->dims.dim_sizes[3] /*patch_factor: k_h*k_w*/);
- RC->addToCurrentIterationComputeTime("ConvLayer_PROMISE", pinfo.first);
- RC->addToCurrentIterationComputeEnergy("ConvLayer_PROMISE", pinfo.second);
- void* t_out;
- t_out = PROMISE_Conv(input, QRanges[0], QRanges[1],
- filter, QRanges[2], QRanges[3],
- bias, QRanges[4], QRanges[5],
- conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w,
- pool_id, pool_size,
- activation_id,
- QRanges[6], QRanges[7], param);
-
- return t_out;
- } else {
- CUSTOM_ASSERT(false && "Unknown approximation type");
- ERROR("Unknown approximation type");
- abort();
- }
- // TODO additional approx methods implemented here
-
- } else if (approxTuples.size() == 2) {
- ERROR("Currently unsupported case");
- abort();
- } else {
- ERROR("Unsupported case");
- abort();
- }
- }
- else
- if (NodeConf->isGPUNodeConfiguration()) {
- DEBUG("GPU Configuration for ConvLayer\n");
- // Mapped to GPU - get a GPU node configuration
- GPUNodeConfiguration *GPUConf = (GPUNodeConfiguration *)NodeConf;
-
- std::vector< std::pair< GPUNodeConfiguration::TENSOR_OP,
- std::vector< std::pair<GPUNodeConfiguration::APPROX,
- int> > > > &ApproxChoices =
- GPUConf->getApproxChoices();
-
- // Check for convolution as first operation
- CUSTOM_ASSERT((ApproxChoices.size() >= 1) &&
- (ApproxChoices[0].first == GPUNodeConfiguration::TENSOR_OP::CONV) &&
- "Incorrect number/type of operations in provided Conv layer configuration");
-
- void* conv_out = handleTensorConvApproximationTuples(ApproxChoices[0].second,
- input, filter, conv_pad_h, conv_pad_w,
- conv_stride_h, conv_stride_w);
- void* add_out;
- if (bias != NULL) {
- // Check for add as second operation
- CUSTOM_ASSERT((ApproxChoices.size() >= 2) &&
- (ApproxChoices[1].first == GPUNodeConfiguration::TENSOR_OP::ADD) &&
- "Incorrect number/type of operations in provided Conv layer configuration");
- add_out = handleTensorAddApproximationTuples(ApproxChoices[1].second,
- conv_out, bias);
- } else {
- add_out = conv_out;
- }
-
- void* activation_out;
- switch (activation_id) {
- case -1:
- { // No activation
- INFO("No activation Function\n");
- activation_out = add_out;
- }
- break;
- case 0:
- { // TanH activation
- CUSTOM_ASSERT((ApproxChoices.size() >= 3) &&
- (ApproxChoices[2].first == GPUNodeConfiguration::TENSOR_OP::TANH) &&
- "Incorrect number/type of operations in provided Conv layer configuration");
- activation_out = handleTensorTanhApproximationTuples(ApproxChoices[2].second,
- add_out);
- }
- break;
- case 1:
- { // ReLU activation
- CUSTOM_ASSERT((ApproxChoices.size() >= 3) &&
- (ApproxChoices[2].first == GPUNodeConfiguration::TENSOR_OP::RELU) &&
- "Incorrect number/type of operations in provided Conv layer configuration");
- activation_out = handleTensorReluApproximationTuples(ApproxChoices[2].second,
- add_out);
- }
- break;
- case 2:
- { // Clipped ReLU activation
- CUSTOM_ASSERT((ApproxChoices.size() >= 3) &&
- (ApproxChoices[2].first == GPUNodeConfiguration::TENSOR_OP::CLIPPED_RELU) &&
- "Incorrect number/type of operations in provided Conv layer configuration");
- activation_out =
- handleTensorClippedReluApproximationTuples(ApproxChoices[2].second,
- add_out, out_min, out_max);
- }
- break;
- default:
- {
- ERROR("Activation id %d NOT supported \n", activation_id);
- }
- break;
- }
-
- void* pool_out;
-
- if (pool_size > 0) {
- switch (pool_id) {
- case 0:
- {
- // If we remove the asserts, we can have all cases handled by a single call
- CUSTOM_ASSERT((ApproxChoices.back().first == GPUNodeConfiguration::TENSOR_OP::POOL_MAX) &&
- "Expected POOL_MAX in provided Conv layer configuration");
- pool_out =
- handleTensorPoolingApproximationTuples(ApproxChoices.back().second,
- activation_out, pool_id,
- pool_size, pool_size, 0, 0,
- pool_size, pool_size);
- }
- break;
- case 1:
- {
- CUSTOM_ASSERT((ApproxChoices.back().first == GPUNodeConfiguration::TENSOR_OP::POOL_MEAN) &&
- "Expected POOL_MEAN in provided Conv layer configuration");
- pool_out =
- handleTensorPoolingApproximationTuples(ApproxChoices.back().second,
- activation_out, pool_id,
- pool_size, pool_size, 0, 0,
- pool_size, pool_size);
- }
- break;
- case 2:
- {
- CUSTOM_ASSERT((ApproxChoices.back().first == GPUNodeConfiguration::TENSOR_OP::POOL_MIN) &&
- "Expected POOL_MIN in provided Conv layer configuration");
- pool_out =
- handleTensorPoolingApproximationTuples(ApproxChoices.back().second,
- activation_out, pool_id,
- pool_size, pool_size, 0, 0,
- pool_size, pool_size);
- }
- break;
- default:
- {
- ERROR("Pool id %d NOT supported \n", pool_id);
- }
- break;
- }
- } else {
- pool_out = activation_out;
- }
- return pool_out;
- }
- else {
- ERROR("Unsupported Configuration");
- abort();
- }
-
- return NULL;
-}
-
-
-void* wrapper_FCLayer(const char* hpvm_node_id,
- void* input,
- void* weights,
- void* bias,
- int activation_id,
- // NOTE: out_min and out_max are only relevant for ClippedRelu
- float out_min, float out_max){
-
- NodeConfiguration *NodeConf = RC->getNodeConfiguration(hpvm_node_id);
-
- if (NodeConf->isPROMISENodeConfiguration()) {
- DEBUG("PROMISE Configuration for FCLayer\n");
- // Mapped to PROMISE - get a PROMISE node configuration
- PROMISENodeConfiguration *PROMISEConf = (PROMISENodeConfiguration *)NodeConf;
- std::vector<float> &QRanges = RC->getQuantizationRanges(hpvm_node_id);
-
- std::vector<std::pair<PROMISENodeConfiguration::APPROX, int> > &approxTuples =
- PROMISEConf->getApproxChoices();
-
- if (approxTuples.size() == 1) {
- enum PROMISENodeConfiguration::APPROX approx = approxTuples[0].first;
- int param = approxTuples[0].second;
- if (approx == PROMISENodeConfiguration::APPROX::SWING_LEVEL) {
- DEBUG("Approximation choice for FCLayer: swing level %d\n", param);
-
- struct Tensor* input_tensor_cast = (struct Tensor*) input;
- struct Tensor* weights_tensor_cast = (struct Tensor*) weights;
- CUSTOM_ASSERT((input_tensor_cast->dims.dim_sizes[1] *
- input_tensor_cast->dims.dim_sizes[2] *
- input_tensor_cast->dims.dim_sizes[3] ==
- weights_tensor_cast->dims.dim_sizes[2]) &&
- "Dimensions for matrix multiplication do not match.");
- std::pair<double, double> pinfo =
- RC->fc_profile(input_tensor_cast->dims.dim_sizes[0], //num_rows_a,
- input_tensor_cast->dims.dim_sizes[1] *
- input_tensor_cast->dims.dim_sizes[2] *
- input_tensor_cast->dims.dim_sizes[3], //num_cols_a,
- weights_tensor_cast->dims.dim_sizes[2], //num_rows_b,
- weights_tensor_cast->dims.dim_sizes[3], //num_cols_b,
- param, //voltage_swing,
- 1 /*patch_factor*/);
- RC->addToCurrentIterationComputeTime("FCLayer_PROMISE", pinfo.first);
- RC->addToCurrentIterationComputeEnergy("FCLayer_PROMISE", pinfo.second);
- void* t_out;
- t_out = PROMISE_FC(input, QRanges[0], QRanges[1],
- weights, QRanges[2], QRanges[3],
- bias, QRanges[4], QRanges[5],
- activation_id,
- QRanges[6], QRanges[7], param);
- return t_out;
- } else {
- CUSTOM_ASSERT(false && "Unknown approximation type");
- ERROR("Unknown approximation type");
- abort();
- }
- // TODO additional approx methods implemented here
-
- } else if (approxTuples.size() == 2) {
- ERROR("Currently unsupported case");
- abort();
- } else {
- ERROR("Unsupported case");
- abort();
- }
- }
- else
- if (NodeConf->isGPUNodeConfiguration()) {
- DEBUG("GPU Configuration for FCLayer\n");
- // Mapped to GPU - get a GPU node configuration
- GPUNodeConfiguration *GPUConf = (GPUNodeConfiguration *)NodeConf;
-
- std::vector< std::pair< GPUNodeConfiguration::TENSOR_OP,
- std::vector< std::pair<GPUNodeConfiguration::APPROX,
- int> > > > &ApproxChoices =
- GPUConf->getApproxChoices();
-
- // Approximation choices must be for a FC wrapper operation
- CUSTOM_ASSERT((ApproxChoices.size() == 2 || ApproxChoices.size() == 3) &&
- ApproxChoices[0].first == GPUNodeConfiguration::TENSOR_OP::MUL &&
- ApproxChoices[1].first == GPUNodeConfiguration::TENSOR_OP::ADD &&
- "Invalid configuration generated for FC layer wrapper operation");
-
- void* gemm_out = handleTensorMulApproximationTuples(ApproxChoices[0].second,
- input, weights);
- void* add_out = handleTensorAddApproximationTuples(ApproxChoices[1].second,
- gemm_out, bias);
-
- void* activation_out;
- switch (activation_id) {
- case -1:
- { // No activation
- CUSTOM_ASSERT((ApproxChoices.size() == 2) &&
- "Incorrect number of operations in provided FC layer configuration");
- INFO("No activation Function\n");
- activation_out = add_out;
- }
- break;
- case 0:
- { // TanH activation
- CUSTOM_ASSERT((ApproxChoices.size() == 3) &&
- (ApproxChoices[2].first == GPUNodeConfiguration::TENSOR_OP::TANH) &&
- "Incorrect number/type of operations in provided FC layer configuration");
- activation_out = handleTensorTanhApproximationTuples(ApproxChoices[1].second,
- add_out);
- }
- break;
- case 1:
- { // ReLU activation
- CUSTOM_ASSERT((ApproxChoices.size() == 3) &&
- (ApproxChoices[2].first == GPUNodeConfiguration::TENSOR_OP::RELU) &&
- "Incorrect number/type of operations in provided FC layer configuration");
- activation_out = handleTensorReluApproximationTuples(ApproxChoices[1].second,
- add_out);
- }
- break;
- case 2:
- { // Clipped ReLU activation
- CUSTOM_ASSERT((ApproxChoices.size() == 3) &&
- (ApproxChoices[2].first == GPUNodeConfiguration::TENSOR_OP::CLIPPED_RELU) &&
- "Incorrect number/type of operations in provided FC layer configuration");
- activation_out =
- handleTensorClippedReluApproximationTuples(ApproxChoices[1].second,
- add_out, out_min, out_max);
- }
- break;
- default:
- {
- ERROR("Activation id %d NOT supported \n", activation_id);
- }
- break;
- }
- return activation_out;
- }
- else {
- ERROR("Unsupported Configuration");
- abort();
- }
-
- return NULL;
-}
-
-
-
-
-void* wrapper_tensorRelu(const char* hpvm_node_id, void* input_ptr){
-// return tensorRelu(input_ptr);
-
- // Only mapped to GPU - get a GPU configuration
- GPUNodeConfiguration *GPUConf =
- (GPUNodeConfiguration *)RC->getNodeConfiguration(hpvm_node_id);
-
- std::vector< std::pair< GPUNodeConfiguration::TENSOR_OP,
- std::vector< std::pair<GPUNodeConfiguration::APPROX,
- int> > > > &ApproxChoices =
- GPUConf->getApproxChoices();
-
- // Approximation choices must be for a relu operation
- CUSTOM_ASSERT(ApproxChoices.size() == 1 &&
- ApproxChoices[0].first == GPUNodeConfiguration::TENSOR_OP::RELU &&
- "Invalid configuration generated for tensor relu wrapper operation");
-
- return handleTensorReluApproximationTuples(ApproxChoices[0].second,
- input_ptr);
-
-}
-
-void* wrapper_tensorClippedRelu(const char* hpvm_node_id,
- void* input_ptr,
- float out_min, float out_max){
- // Only mapped to GPU - get a GPU configuration
- GPUNodeConfiguration *GPUConf =
- (GPUNodeConfiguration *)RC->getNodeConfiguration(hpvm_node_id);
-
- std::vector< std::pair< GPUNodeConfiguration::TENSOR_OP,
- std::vector< std::pair<GPUNodeConfiguration::APPROX,
- int> > > > &ApproxChoices =
- GPUConf->getApproxChoices();
-
- // Approximation choices must be for a relu operation
- CUSTOM_ASSERT(ApproxChoices.size() == 1 &&
- ApproxChoices[0].first == GPUNodeConfiguration::TENSOR_OP::CLIPPED_RELU &&
- "Invalid configuration generated for tensor clipped relu wrapper operation");
-
- return handleTensorClippedReluApproximationTuples(ApproxChoices[0].second,
- input_ptr, out_min, out_max);
-
-}
-
-void* wrapper_tensorTanh(const char* hpvm_node_id, void* input_ptr){
-// return tensorTanh(input_ptr);
-
- GPUNodeConfiguration *GPUConf =
- (GPUNodeConfiguration *)RC->getNodeConfiguration(hpvm_node_id);
-
- std::vector< std::pair< GPUNodeConfiguration::TENSOR_OP,
- std::vector< std::pair<GPUNodeConfiguration::APPROX,
- int> > > > &ApproxChoices =
- GPUConf->getApproxChoices();
-
- // Approximation choices must be for a tanh operation
- CUSTOM_ASSERT(ApproxChoices.size() == 1 &&
- ApproxChoices[0].first == GPUNodeConfiguration::TENSOR_OP::TANH &&
- "Invalid configuration generated for tensor tanh wrapper operation");
-
- return handleTensorTanhApproximationTuples(ApproxChoices[0].second,
- input_ptr);
-
-}
-
-
-void* wrapper_tensorBatchNorm(const char* hpvm_node_id,
- void* input_ptr, void* gamma_ptr, void* beta_ptr,
- void* mean_ptr, void* variance_ptr, double epsilon){
-// return tensorBatchNorm(input_ptr, gamma_ptr, beta_ptr, mean_ptr, variance_ptr, epsilon);
-
- // Only mapped to GPU - get a GPU configuration
- GPUNodeConfiguration *GPUConf =
- (GPUNodeConfiguration *)RC->getNodeConfiguration(hpvm_node_id);
-
- std::vector< std::pair< GPUNodeConfiguration::TENSOR_OP,
- std::vector< std::pair<GPUNodeConfiguration::APPROX,
- int> > > > &ApproxChoices =
- GPUConf->getApproxChoices();
-
- // Approximation choices must be for a batchnorm operation
- CUSTOM_ASSERT(ApproxChoices.size() == 1 &&
- ApproxChoices[0].first == GPUNodeConfiguration::TENSOR_OP::BATCHNORM &&
- "Invalid configuration generated for tensor batchnorm wrapper operation");
-
- return handleTensorBatchNormApproximationTuples(ApproxChoices[0].second,
- input_ptr, gamma_ptr, beta_ptr,
- mean_ptr, variance_ptr, epsilon);
-
-}
-
-
-void* wrapper_tensorAdd(const char* hpvm_node_id, void* input_ptr, void* bias_ptr){
-// return tensorAdd(input_ptr, bias_ptr);
-
- // Only mapped to GPU - get a GPU configuration
- GPUNodeConfiguration *GPUConf =
- (GPUNodeConfiguration *)RC->getNodeConfiguration(hpvm_node_id);
-
- std::vector< std::pair< GPUNodeConfiguration::TENSOR_OP,
- std::vector< std::pair<GPUNodeConfiguration::APPROX,
- int> > > > &ApproxChoices =
- GPUConf->getApproxChoices();
-
- // Approximation choices must be for an add operation
- CUSTOM_ASSERT(ApproxChoices.size() == 1 &&
- ApproxChoices[0].first == GPUNodeConfiguration::TENSOR_OP::ADD &&
- "Invalid configuration generated for tensor add wrapper operation");
-
- return handleTensorAddApproximationTuples(ApproxChoices[0].second,
- input_ptr, bias_ptr);
-
-}
-
-
-void* wrapper_tensorPooling(const char* hpvm_node_id,
- void* input_ptr,
- int poolFunction,
- int window_height, int window_width,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride){
-
-// return tensorPooling(input_ptr, poolFunction, window_height, window_width,
-// vertical_pad, horizontal_pad, vertical_stride, horizontal_stride);
-
- // Only mapped to GPU - get a GPU configuration
- GPUNodeConfiguration *GPUConf =
- (GPUNodeConfiguration *)RC->getNodeConfiguration(hpvm_node_id);
-
- std::vector< std::pair< GPUNodeConfiguration::TENSOR_OP,
- std::vector< std::pair<GPUNodeConfiguration::APPROX,
- int> > > > &ApproxChoices =
- GPUConf->getApproxChoices();
-
- // Approximation choices must be for a single operation
- CUSTOM_ASSERT(ApproxChoices.size() == 1 &&
- "Invalid configuration generated for tensor pool wrapper operation");
- enum GPUNodeConfiguration::TENSOR_OP top = ApproxChoices[0].first;
- // Approximation choices must be for a pool operation
- CUSTOM_ASSERT((top == GPUNodeConfiguration::TENSOR_OP::POOL_MAX ||
- top == GPUNodeConfiguration::TENSOR_OP::POOL_MEAN ||
- top == GPUNodeConfiguration::TENSOR_OP::POOL_MIN) &&
- "Invalid configuration generated for tensor pool wrapper operation");
-
- return handleTensorPoolingApproximationTuples(ApproxChoices[0].second,
- input_ptr, poolFunction,
- window_height, window_width,
- vertical_pad, horizontal_pad,
- vertical_stride, horizontal_stride);
-
-}
-
-
-void* wrapper_tensorGroupConvolution(const char* hpvm_node_id,
- void* input, void* filter,
- int vertical_pad, int horizontal_pad,
- int vertical_stride, int horizontal_stride,
- int conv_mode, int conv_groups){
- // Only mapped to GPU - get a GPU configuration
- GPUNodeConfiguration *GPUConf =
- (GPUNodeConfiguration *)RC->getNodeConfiguration(hpvm_node_id);
-
- std::vector< std::pair< GPUNodeConfiguration::TENSOR_OP,
- std::vector< std::pair<GPUNodeConfiguration::APPROX,
- int> > > > &ApproxChoices =
- GPUConf->getApproxChoices();
-
- // Approximation choices must be for a group_conv operation
- CUSTOM_ASSERT(ApproxChoices.size() == 1 &&
- ApproxChoices[0].first == GPUNodeConfiguration::TENSOR_OP::GROUP_CONV &&
- "Invalid configuration generated for tensor group_conv wrapper operation");
-
- return handleTensorGroupConvApproximationTuples(ApproxChoices[0].second,
- input, filter,
- vertical_pad, horizontal_pad,
- vertical_stride, horizontal_stride,
- conv_mode, conv_groups);
-
-}
-
-
-
-void* wrapper_tensorSoftmax(const char* hpvm_node_id, void* input_ptr){
-// return tensorSoftmax(input_ptr);
-
- // Only mapped to GPU - get a GPU configuration
- GPUNodeConfiguration *GPUConf =
- (GPUNodeConfiguration *)RC->getNodeConfiguration(hpvm_node_id);
-
- std::vector< std::pair< GPUNodeConfiguration::TENSOR_OP,
- std::vector< std::pair<GPUNodeConfiguration::APPROX,
- int> > > > &ApproxChoices =
- GPUConf->getApproxChoices();
-
- // Approximation choices must be for a softmax operation
- CUSTOM_ASSERT(ApproxChoices.size() == 1 &&
- ApproxChoices[0].first == GPUNodeConfiguration::TENSOR_OP::SOFTMAX &&
- "Invalid configuration generated for tensor softmax wrapper operation");
-
- return handleTensorSoftmaxApproximationTuples(ApproxChoices[0].second, input_ptr);
-
-
-}
-
-
-
diff --git a/llvm/projects/soc_simulator/CMakeLists.txt b/llvm/projects/soc_simulator/CMakeLists.txt
deleted file mode 100644
index 6a78ce1ce5ea16a119838c71b195b4c1e0209010..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/CMakeLists.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-cmake_minimum_required(VERSION 3.5)
-set(libsrc src/promise_timing_model.cpp)
-set (CMAKE_CXX_STANDARD 11)
-add_library(promise_profiler STATIC ${libsrc})
-target_include_directories(promise_profiler PRIVATE include)
diff --git a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_confs1.txt b/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_confs1.txt
deleted file mode 100644
index f8add8f3feabc93963e6f75fbce1ace36412ad07..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_confs1.txt
+++ /dev/null
@@ -1,16 +0,0 @@
-9 9 9,9 9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9 9,9 9
-9 9 9,7,7,7,9 9 9,9 9 9 9,9 9
-9 9 9,7,7,7,9 9 9,8 8 8 8,9 9
-9 9 9,7,7,7,8 8 8,9 9 9 9,9 9
-9 9 9,7,7,7,9 9 9,7,9 9
-9 9 9,7,7,7,9 9 9,7,9 9
-9 9 9,7,9 9 9,7,9 9 9,7,9 9
-9 9 9,7,7,7,9 9 9,9 9 9 9,9 9
-9 9 9,7,7,7,8 8 8,9 9 9 9,9 9
-9 9 9,7,7,7,9 9 9,7,9 9
-9 9 9,7,7,7,9 9 9,7,9 9
-9 9 9,7,7,7,9 9 9,7,9 9
-9 9 9,7,7,7,9 9 9,9 9 9 9,9 9
-9 9 9,7,7,7,9 9 9,8 8 8 8,9 9
-9 9 9,7,7,7,9 9 9,7,9 9
-9 9 9,7,7,7,8 8 8,9 9 9 9,9 9
diff --git a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_confs2.txt b/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_confs2.txt
deleted file mode 100644
index da9dce2be8e9649ece5376e363ede5b3a84629fe..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_confs2.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-9 9 9,9 9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9 9,9 9
-9 9 9,6,8 8 8,6,6,6,9 9
-9 9 9,6,8 8 8,6,6,6,9 9
-9 9 9,6,8 8 8,6,6,7,9 9
-9 9 9,6,8 8 8,9 9 9 9,6,6,9 9
-9 9 9,6,8 8 8,6,8 8 8,6,9 9
-9 9 9,6,8 8 8,6,6,6,9 9
-9 9 9,6,8 8 8,6,6,6,9 9
-9 9 9,6,8 8 8,6,6,6,9 9
-9 9 9,6,8 8 8,6,6,6,9 9
-9 9 9,6,8 8 8,6,6,7,9 9
-9 9 9,6,8 8 8,9 9 9 9,6,6,9 9
-9 9 9,6,8 8 8,6,8 8 8,6,9 9
-9 9 9,6,8 8 8,6,6,6,9 9
-9 9 9,6,8 8 8,6,6,7,9 9
-9 9 9,6,8 8 8,6,6,6,9 9
-9 9 9,7,7,6,8 8 8,6,9 9
-9 9 9,6,8 8 8,9 9 9 9,6,6,9 9
-9 9 9,6,8 8 8,6,6,6,9 9
diff --git a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_fp16.csv b/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_fp16.csv
deleted file mode 100644
index 2414bed90d2339bb232800ceccf1711302aa4174..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_fp16.csv
+++ /dev/null
@@ -1,70 +0,0 @@
-Add1,36.2217,116.852,62.3719,54.4799,6479.76,3464.4,3015.35,25.6183,86.2375,47.824,39.0211,4798.11,2666.76,2164.16
-Add1_f2h,56.9247,172.83,93.6324,79.1972,6048.31,3271.98,2776.33,40.2841,129.638,73.389,57.1983,4518.79,2553.55,1999.31
-Add1_h2f,41.6054,134.411,71.4991,62.9117,6462.47,3437.92,3024.54,29.4206,98.8293,54.3763,45.0458,4752.33,2615.03,2165.77
-Add2,35.0236,153.259,99.7851,53.4736,8759.13,5703.66,3055.47,24.7684,108.633,70.7873,37.8589,6213.07,4049.51,2164.3
-Add2_f2h,158.537,686.903,462.538,224.365,8696.58,5858.33,2838.26,112.305,486.692,327.861,158.872,6171.91,4160.96,2011.47
-Add2_h2f,41.5412,177.63,114.226,63.4042,8551.44,5498.99,3052.45,29.3753,125.993,81.1067,44.9049,6064.86,3904.13,2161.65
-Add3,21.2537,100.994,63.1299,37.8636,9509.91,5945.29,3564.62,15.0519,71.5273,44.7163,26.8142,6729.98,4208.46,2521.83
-Add3_f2h,28.3975,126.566,79.763,46.8029,8911.18,5615.63,3295.55,20.0846,89.624,56.5024,33.1257,6306.83,3975.68,2331.45
-Add3_h2f,20.9649,93.9519,58.396,35.5559,8962.73,5570.82,3391.91,14.8261,66.498,41.3441,25.1571,6342.95,3943.66,2399.6
-Add4,19.4384,96.5969,63.7597,32.8372,9942.78,6562.79,3380,13.7631,68.3882,45.1432,23.2456,7033.19,4642.48,2390.77
-Add4_f2h,76.34,366.744,245.383,121.361,9608.93,6429.36,3179.57,54.0262,259.574,173.68,85.8957,6795.77,4547.27,2248.55
-Add4_h2f,20.8326,97.3319,63.8484,33.4835,9343.97,6129.52,3214.45,14.7322,68.8549,45.1702,23.6853,6609.33,4335.85,2273.54
-Add5,19.2262,98.1635,63.363,34.8005,10213,6592.51,3620.45,13.5983,69.4422,44.8239,24.6189,7224.11,4663.35,2560.82
-Add5_f2h,14.7236,68.2983,44.2206,24.0777,9277.35,6006.75,3270.59,10.4122,48.3256,31.2902,17.0358,6563.72,4249.95,2313.82
-Add5_h2f,10.5498,48.5475,31.279,17.2686,9203.37,5929.71,3273.66,7.46111,34.3694,22.1444,12.2253,6514.35,4197.26,2317.14
-Add6,18.7741,98.9177,65.9587,32.959,10538.9,7027.44,3511.51,13.2788,70.0688,46.7239,23.3455,7463.67,4977.02,2486.72
-Add6_f2h,39.6841,197.748,132.575,65.1736,9965.93,6681.4,3284.52,28.0732,139.953,93.8285,46.1249,7049.93,4726.52,2323.44
-Add6_h2f,10.548,49.6626,33.0275,16.635,9451.94,6285.89,3166.05,7.48932,35.1512,23.3772,11.7744,6695.42,4452.75,2242.74
-Add7,0.658159,3.49495,2.27537,1.21958,11081.8,7217.49,3864.29,0.52552,3.31175,2.15602,1.15575,11595.3,7554.3,4041.16
-Add7_f2h,0.887661,1.49455,0.974188,0.52036,2879.78,1876.88,1002.9,0.714309,1.9336,1.26097,0.672641,3070.05,2000.93,1069.15
-Add7_h2f,0.140755,0.0381309,0.0248393,0.0132916,504.337,328.401,175.936,0.101868,0.096878,0.0630995,0.0337795,1295.66,843.266,452.404
-Conv1,200.956,558.231,289.217,269.014,5491.56,2830.66,2660.9,142.184,439.06,248.366,197.045,4285.98,2415.45,1937.47
-Conv1_f2h,6.64024,16.2201,8.18921,8.03089,4750.32,2365.55,2384.77,4.70953,13.1579,7.43711,5.95826,3789.64,2126.15,1744.54
-Conv1_h2f,41.8978,123.674,68.3327,55.3409,5907.39,3264.34,2643.05,29.6316,93.3619,54.0372,40.0462,4460.74,2582.14,1913.01
-Conv2,610.501,2404.06,1472.21,931.848,8000.29,4920.48,3079.81,433.644,1704.25,1048.11,658.96,5732.95,3554.54,2187.14
-Conv2_f2h,56.8745,201.057,104.016,97.0408,7053.45,3645.39,3408.06,40.2486,145.587,76.9607,69.1825,5092.26,2687.52,2424.64
-Conv2_h2f,41.8404,192.576,135.567,57.0093,9205.52,6480.36,2725.15,29.5868,136.713,96.2778,40.438,6535.05,4602.22,1932.98
-Conv3,188.912,837.371,508.274,329.097,8868.48,5383.62,3484.86,133.617,592.676,359.91,232.821,6277.65,3812.98,2465.25
-Conv3_f2h,15.6031,66.3758,39.8497,26.5262,8494.73,5098.38,3396.35,11.0572,47.1949,28.3617,18.8384,6017.1,3613.78,2403.98
-Conv3_h2f,21.0237,93.63,59.667,33.963,8906.6,5675.84,3230.76,14.8682,66.2814,42.2505,24.0337,6303.73,4018.24,2285.77
-Conv4,284.927,1346.18,857.932,488.249,9448.68,6021.77,3426.9,201.502,952.374,607.015,345.376,6683.2,4259.76,2423.56
-Conv4_f2h,28.3107,127.724,77.4371,50.2866,9022.12,5469.86,3552.26,20.0267,90.4336,54.8443,35.5938,6384.94,3872.03,2513.22
-Conv4_h2f,20.9833,102.055,69.6208,32.434,9727.36,6635.93,3091.43,14.8387,72.2015,49.2568,22.9451,6881.39,4694.6,2186.84
-Conv5,86.4203,417.756,265.595,152.161,9668.23,6146.81,3521.41,61.1232,295.494,187.871,107.626,6837.22,4347.11,2490.16
-Conv5_f2h,8.38767,36.7727,23.2235,13.5492,8765.45,5535.76,3229.69,5.9326,26.0583,16.4576,9.60107,6207.8,3920.67,2287.22
-Conv5_h2f,10.6053,48.7992,31.7579,17.0412,9202.4,5988.83,3213.57,7.5001,34.5413,22.48,12.0618,6512.52,4238.46,2274.14
-Conv6,139.892,696.675,453.631,243.043,9961.36,6486.38,3474.97,98.9321,492.663,320.792,171.874,7044.22,4587,2457.26
-Conv6_f2h,14.7838,68.8585,43.7444,25.1141,9313.71,5916.85,3396.85,10.4574,48.7457,30.9686,17.7776,6589.63,4186.51,2403.19
-Conv6_h2f,10.568,51.057,34.5719,16.4851,9662.37,6542.6,3119.77,7.47346,36.2259,24.5301,11.6961,6854.97,4641.76,2213.26
-Mul1,4.29982,22.6027,14.73,7.87274,10529.9,6862.81,3667.08,3.06206,16.1877,10.549,5.63885,7504.4,4891.15,2613.32
-Mul1_f2h,5.01057,21.4017,13.9545,7.44724,8536.09,5565.65,2970.44,3.54931,15.2502,9.94396,5.30637,6067.7,3956.33,2111.43
-Mul1_h2f,0.219266,0.10134,0.0660095,0.03533,811.894,528.683,283.211,0.163586,0.22974,0.14973,0.080012,1619.98,1054.57,565.431
-Pool1,43.674,198.246,119.456,78.7903,9078.2,5470.13,3608.07,30.8997,140.487,84.7207,55.7843,6429.35,3877.11,2553.05
-Pool1_f2h,54.8347,240.102,146.135,93.9672,8752.72,5326.68,3426.04,38.7916,170.226,103.7,66.5481,6199.29,3775.75,2424.32
-Pool1_h2f,10.6202,44.9693,27.1714,17.7978,8469.49,5117.56,3351.94,7.50988,31.8572,19.2617,12.5996,6000.37,3628.12,2373
-Pool2,22.3065,111.077,70.4439,40.6332,9958.57,6315.5,3643.07,15.7792,78.602,49.8515,28.7515,7043.82,4467.22,2576.69
-Pool2_f2h,27.6028,129.232,82.458,46.7739,9362.89,5974.06,3388.83,19.5202,91.4233,58.3375,33.0868,6622.4,4225.7,2396.77
-Pool2_h2f,5.39931,22.7498,14.4354,8.3144,8425.89,5346.44,3079.45,3.81822,16.1451,10.2449,5.9004,5978.26,3793.49,2184.86
-Pool3,12.0697,64.0871,41.917,22.1701,10618.3,6945.08,3673.23,8.53664,45.3661,29.6731,15.6935,7513.77,4914.65,2599.18
-Pool3_f2h,14.3906,68.8152,45.1258,23.6894,9563.88,6271.6,3292.28,10.1772,48.6924,31.9299,16.7629,6766.23,4437.02,2329.26
-Pool3_h2f,2.78859,10.4233,6.81839,3.60494,7474.86,4889.52,2585.34,1.97384,7.50692,4.91099,2.59601,5378.12,3518.12,1860.05
-Softmax1,2.26715,7.67319,4.99613,2.67706,6769.48,4407.84,2361.64,1.60403,5.63971,3.67227,1.96749,4973.77,3238.83,1734.98
-Tanh1,33.4466,118.003,61.6149,56.3879,7064.04,3690.25,3373.79,23.6527,85.7445,45.9758,40.1967,5137.61,2756.88,2406.17
-Tanh1_f2h,57.0977,185.926,97.1494,88.7762,6536.44,3420.5,3115.94,40.4191,135.633,73.0493,63.3674,4779.37,2580.1,2226.23
-Tanh1_h2f,41.7089,145.509,76.2065,69.3025,6978.55,3654.51,3324.04,29.4985,105.384,56.4528,49.367,5054.37,2707.22,2368.07
-Tanh2,36.2146,162.298,101.111,61.1867,8963.85,5584.56,3379.29,25.609,115.01,71.7167,43.3095,6352.26,3961.22,2391.95
-Tanh2_f2h,56.9829,242.874,153.169,89.7055,8520.54,5373.23,3147.31,40.3301,172.418,108.85,63.5934,6040.38,3813.04,2228.24
-Tanh2_h2f,41.625,182.762,113.157,69.605,8781.86,5437.35,3344.5,29.4345,129.486,80.2412,49.263,6222.04,3855.85,2367.07
-Tanh3,18.3396,88.5345,54.3058,34.2287,9654.14,5921.68,3732.46,12.9703,62.6686,38.4496,24.2216,6831.73,4191.48,2640.53
-Tanh3_f2h,28.9295,129.858,80.0577,49.7999,8969.72,5529.15,3440.56,20.4875,92.1118,56.8106,35.3056,6347.03,3913.57,2433.76
-Tanh3_h2f,20.8836,94.2318,57.6446,36.5872,9024.13,5520.31,3503.81,14.7676,66.6859,40.8043,25.8844,6385.64,3907.25,2478.66
-Tanh4,18.4717,92.4694,59.7134,32.7559,10012,6465.38,3546.6,13.0617,65.4106,42.2419,23.1693,7082.06,4573.57,2508.57
-Tanh4_f2h,28.0864,130.933,85.0439,45.8888,9321.9,6054.74,3267.16,19.8684,92.6641,60.1914,32.4736,6593.39,4282.78,2310.68
-Tanh4_h2f,20.8569,97.8308,62.9465,34.8843,9381.09,6035.98,3345.11,14.7494,69.2071,44.5324,24.6756,6635.71,4269.83,2365.97
-Tanh5,9.39612,49.9132,31.9349,17.9784,10620.9,6795.34,3825.57,6.64567,35.5193,22.7251,12.7945,7550.97,4831.1,2719.94
-Tanh5_f2h,14.9164,69.1465,44.357,24.7895,9279.12,5952.49,3326.62,10.5528,49.0071,31.4389,17.5687,6574.75,4217.83,2356.99
-Tanh5_h2f,10.5006,48.3962,30.92,17.4763,9217.58,5889.04,3328.55,7.42558,34.2539,21.8851,12.3692,6523.36,4167.82,2355.62
-Tanh6,9.37294,50.5591,33.3473,17.2118,10787.6,7115.14,3672.42,6.62805,35.791,23.607,12.1843,7635.55,5036.22,2599.38
-Tanh6_f2h,14.3859,68.827,45.5284,23.2985,9568.27,6329.29,3238.98,10.1735,48.7028,32.2173,16.4857,6769.57,4478.09,2291.52
-Tanh6_h2f,10.5241,49.2841,32.4417,16.8424,9386.9,6178.99,3207.92,7.44696,34.9422,23.0012,11.9412,6656.18,4381.49,2274.73
diff --git a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_fp32.csv b/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_fp32.csv
deleted file mode 100644
index e2cbba7f6782a0d77ab4c7279a3a88f5acfb532d..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_fp32.csv
+++ /dev/null
@@ -1,24 +0,0 @@
-Add1,47.2501,154.498,89.5402,64.9578,6426.98,3696.33,2730.65,33.4463,121.478,76.4718,47.0638,4993.53,3124.31,1963.93
-Add2,45.3358,217.323,149.508,67.8141,9594.62,6601.66,2992.96,32.0593,156.332,108.33,48.2216,6906.53,4787.12,2129.06
-Add3,26.3653,138.399,95.847,42.5524,10500.7,7272.43,3228.23,18.6463,98.9696,68.8605,30.2012,7508.99,5224.96,2291.01
-Add4,26.3854,150.498,109.738,40.7597,11414.1,8323.45,3090.67,18.6598,107.025,78.1688,28.8878,8120.56,5932,2190.98
-Add5,24.2343,136.978,99.389,37.5888,11306.5,8204.06,3102.4,17.1387,97.2822,70.6568,26.6431,8030.37,5832.9,2198.92
-Add6,24.3243,144.761,108.644,36.1167,11913.1,8941.05,2972.07,17.2054,102.532,76.9757,25.5618,8441.39,6337.58,2104.27
-Add7,0.256741,0.108633,0.0804227,0.0282101,837.078,619.959,217.119,0.184457,0.239262,0.17707,0.0622105,1850.45,1370.8,479.791
-Conv1,115.33,355.301,207.624,147.677,5963.71,3435.06,2528.65,81.7778,290.851,188.764,108.126,4791.55,3087.45,1825.79
-Conv2,310.308,1282.26,816.061,466.203,8263.6,5259,3004.6,219.438,941.609,613.698,332.739,6067.24,3954.22,2144.15
-Conv3,165.302,812.529,542.858,269.671,10211.6,6887.29,3324.32,119.55,575.142,383.942,192.317,7329.84,4983.3,2359.48
-Conv4,303.007,1619.75,1129.02,490.726,10843.2,7579.9,3263.26,215.314,1146.48,800.497,347.114,7743.98,5437.17,2313.98
-Conv5,156.337,882.718,630.491,252.227,11295,8067.89,3227.12,110.575,626.941,448.461,178.656,8022.03,5738.71,2285.56
-Conv6,248.321,1443.4,1054.07,389.33,11626.8,8490.88,3135.95,175.601,1023.01,747.582,275.556,8241.2,6022.64,2219.57
-Mul1,2.20702,9.19011,6.8038,2.38631,8310.13,6155.01,2155.12,1.56606,6.74557,4.99224,1.75375,6073,4498.52,1574.87
-Pool1,39.1015,194.307,129.484,64.8227,9955.51,6637,3318.52,27.6862,139.26,93.4326,46.0372,7137.28,4792.17,2355.71
-Pool2,45.8717,263.305,188.721,74.5842,11476,8224.81,3251.15,32.4473,187.287,134.477,52.87,8157.05,5856.35,2303.32
-Pool3,23.8305,140.946,104.705,36.2403,11829.1,8787.6,3041.49,16.856,99.855,74.2068,25.6543,8377.95,6226.11,2152.36
-Softmax1,2.09044,8.55352,6.33795,2.21557,8172.34,6055.47,2116.87,1.4798,6.29816,4.66796,1.63058,6001.96,4448.28,1554.05
-Tanh1,39.4831,135.034,76.518,58.5163,6760.26,3809.4,2950.86,27.9698,104.297,63.7634,42.2063,5167.76,3142.26,2115.25
-Tanh2,38.504,188.321,126.889,61.4314,9782.72,6591.66,3191.06,27.2277,135.275,91.8445,43.6415,7027.36,4771.38,2266.95
-Tanh3,19.3912,101.904,69.9865,31.9174,10512.4,7220.09,3292.33,13.7121,72.8245,50.2415,22.6485,7513.77,5184.09,2336.42
-Tanh4,19.2511,109.512,79.122,30.3901,11374.4,8217.68,3156.73,13.6147,77.9259,56.4047,21.5465,8090.47,5855.69,2237.41
-Tanh5,9.61697,52.0084,37.5149,14.4935,10817.2,7802.82,3014.35,6.80077,36.934,26.6693,10.2721,7682.21,5547.37,2136.37
-Tanh6,9.63435,54.9648,40.9982,13.9666,11410.6,8511.23,2899.37,6.81383,38.9614,29.0708,9.8929,8087.16,6034.3,2053.33
diff --git a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_layers.txt b/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_layers.txt
deleted file mode 100644
index 98dfa6fa380a34ee7ff5ce0615656deab585ac5b..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_layers.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-Conv1,2000,3,32,32,32,3,3,3,1,1
-Conv2,2000,32,32,32,32,32,3,3,1,1
-Conv3,2000,32,16,16,64,32,3,3,1,1
-Conv4,2000,64,16,16,64,64,3,3,1,1
-Conv5,2000,64,8,8,128,64,3,3,1,1
-Conv6,2000,128,8,8,128,128,3,3,1,1
-FC1,2000,2048,2048,10
diff --git a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_ops.txt b/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_ops.txt
deleted file mode 100644
index 7a26ba6faa8bf17a0a2067c8a80f69a514ad07c0..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_ops.txt
+++ /dev/null
@@ -1,30 +0,0 @@
-#Conv1,3
-Conv1
-Add1
-Tanh1
-#Conv2,4
-Conv2
-Add2
-Tanh2
-Pool1
-#Conv3,3
-Conv3
-Add3
-Tanh3
-#Conv4,4
-Conv4
-Add4
-Tanh4
-Pool2
-#Conv5,3
-Conv5
-Add5
-Tanh5
-#Conv6,4
-Conv6
-Add6
-Tanh6
-Pool3
-#FC1,2
-Mul1
-Add7
diff --git a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_promise_confs1.txt b/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_promise_confs1.txt
deleted file mode 100644
index c9e13831df88fd375fd8439e20106a483a8342bc..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_promise_confs1.txt
+++ /dev/null
@@ -1,23 +0,0 @@
-9 9 9,9 9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9 9,9 9
-9 9 9,7,8 8 8,6,8 8 8,4,7
-9 9 9,7,8 8 8,6,8 8 8,5,7
-9 9 9,7,4,5,8 8 8,5,7
-9 9 9,7,4,6,8 8 8,6,7
-9 9 9,5,7,6,8 8 8,8 8 8 8,7
-9 9 9,7,7,6,8 8 8,6,7
-9 9 9,6,8 8 8,7,8 8 8,7,5
-9 9 9,9 9 9 9,7,6,8 8 8,5,6
-9 9 9,5,8 8 8,4,8 8 8,7,6
-9 9 9,8 8 8 8,7,6,8 8 8,5,7
-9 9 9,7,7,4,8 8 8,6,7
-8 8 8,8 8 8 8,8 8 8,6,8 8 8,5,7
-9 9 9,7,7,6,8 8 8,8 8 8 8,7
-8 8 8,6,4,6,8 8 8,8 8 8 8,7
-8 8 8,9 9 9 9,5,5,8 8 8,5,7
-9 9 9,7,5,6,8 8 8,8 8 8 8,7
-9 9 9,6,8 8 8,8 8 8 8,8 8 8,7,5
-8 8 8,9 9 9 9,4,4,8 8 8,8 8 8 8,4
-8 8 8,8 8 8 8,6,6,8 8 8,8 8 8 8,7
-8 8 8,7,8 8 8,5,8 8 8,8 8 8 8,7
-9 9 9,8 8 8 8,7,6,8 8 8,8 8 8 8,7
-9 9 9,8 8 8 8,5,4,8 8 8,8 8 8 8,8 8
diff --git a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_promise_confs2.txt b/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_promise_confs2.txt
deleted file mode 100644
index 9188c561894dd811fa5adc5606c61afaf3972a1f..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_promise_confs2.txt
+++ /dev/null
@@ -1,37 +0,0 @@
-9 9 9,9 9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9 9,9 9
-9 9 9,7,7,8 8 8 8,8 8 8,5,8 8
-8 8 8,7,7,8 8 8 8,8 8 8,5,4
-8 8 8,6,7,8 8 8 8,8 8 8,7,7
-8 8 8,6,5,6,8 8 8,5,6
-8 8 8,7,7,6,8 8 8,4,6
-8 8 8,7,7,7,8 8 8,5,7
-8 8 8,7,6,6,8 8 8,5,4
-8 8 8,7,5,7,8 8 8,5,7
-8 8 8,7,8 8 8,6,8 8 8,4,7
-9 9 9,7,8 8 8,6,8 8 8,5,7
-8 8 8,7,7,5,8 8 8,5,7
-9 9 9,7,4,5,8 8 8,5,7
-8 8 8,7,4,6,8 8 8,6,7
-8 8 8,7,7,6,8 8 8,5,7
-8 8 8,5,7,6,8 8 8,8 8 8 8,7
-8 8 8,7,7,6,8 8 8,6,7
-8 8 8,9 9 9 9,7,6,8 8 8,4,8 8
-9 9 9,6,8 8 8,7,8 8 8,7,5
-8 8 8,7,7,6,8 8 8,8 8 8 8,8 8
-9 9 9,9 9 9 9,7,6,8 8 8,5,6
-8 8 8,8 8 8 8,8 8 8,6,8 8 8,5,8 8
-8 8 8,8 8 8 8,7,6,8 8 8,5,8 8
-8 8 8,5,8 8 8,4,8 8 8,7,6
-9 9 9,8 8 8 8,7,6,8 8 8,5,7
-8 8 8,7,7,4,8 8 8,6,7
-8 8 8,8 8 8 8,8 8 8,6,8 8 8,5,7
-9 9 9,7,7,6,8 8 8,8 8 8 8,7
-8 8 8,6,4,6,8 8 8,8 8 8 8,7
-8 8 8,9 9 9 9,5,5,8 8 8,5,7
-9 9 9,7,5,6,8 8 8,8 8 8 8,7
-9 9 9,6,8 8 8,8 8 8 8,8 8 8,7,5
-8 8 8,9 9 9 9,4,4,8 8 8,8 8 8 8,4
-8 8 8,8 8 8 8,6,6,8 8 8,8 8 8 8,7
-8 8 8,7,8 8 8,5,8 8 8,8 8 8 8,7
-8 8 8,8 8 8 8,7,6,8 8 8,8 8 8 8,7
-9 9 9,8 8 8 8,5,4,8 8 8,8 8 8 8,8 8
diff --git a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_promise_results1.csv b/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_promise_results1.csv
deleted file mode 100644
index 038318438fafe7dffdde74d6066e2bcb67cc686f..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_promise_results1.csv
+++ /dev/null
@@ -1,297 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,644.833,1882.211,1052.832,2143.065,1071.7044,1784.0718,9.298743,8588.015943,0.999999988355867
-c1,644.833,52.371161,1026.8995,32.782291,565.8327,17.406593,0.087285,2340.21253,3.66975882144521
-c2,644.833,52.371161,1026.8995,32.782291,565.8327,19.420124,0.087285,2342.226061,3.6666040564304
-c3,644.833,52.371161,11.604395,21.577915,565.8327,19.420124,0.087285,1315.72658,6.52720361572359
-c4,644.833,52.371161,11.604395,32.782291,565.8327,29.504062,0.087285,1337.014894,6.4232757160837
-c5,644.833,25.893498,26.185581,32.782291,565.8327,910.2389,0.087285,2205.853255,3.89328507424738
-c6,644.833,52.371161,26.185581,32.782291,565.8327,29.504062,0.087285,1351.59608,6.3539806268171
-c7,644.833,39.338749,1026.8995,43.642634,565.8327,39.278371,0.043156,2359.86811,3.6391930306142
-c8,644.833,1882.211,26.185581,32.782291,565.8327,19.420124,0.065565,3171.330261,2.70801681483981
-c9,644.833,25.893498,1026.8995,19.340658,565.8327,39.278371,0.065565,2322.143292,3.69831422666943
-c10,644.833,2917.863,26.185581,32.782291,565.8327,19.420124,0.087285,4207.003981,2.04136144811123
-c11,644.833,52.371161,26.185581,19.340658,565.8327,29.504062,0.087285,1338.154447,6.41780574766451
-c12,793.086,2917.863,1026.8995,32.782291,565.8327,19.420124,0.087285,5355.9709,1.60344705805913
-c13,644.833,52.371161,26.185581,32.782291,565.8327,910.2389,0.087285,2232.330918,3.84710684649905
-c14,793.086,39.338749,11.604395,32.782291,565.8327,910.2389,0.087285,2352.97032,3.64986141347242
-c15,793.086,1882.211,12.946749,21.577915,565.8327,19.420124,0.087285,3295.161773,2.60625009453064
-c16,644.833,52.371161,12.946749,32.782291,565.8327,910.2389,0.087285,2219.092086,3.87005821442697
-c17,644.833,39.338749,1026.8995,1646.3233,565.8327,39.278371,0.043156,3962.548776,2.1672959026487
-c18,793.086,1882.211,11.604395,19.340658,565.8327,910.2389,0.038681,4182.352334,2.0533936530993
-c19,793.086,2917.863,19.669375,32.782291,565.8327,910.2389,0.087285,5239.559551,1.63907208144122
-c20,793.086,52.371161,1026.8995,21.577915,565.8327,910.2389,0.087285,3370.093461,2.54830193511058
-c21,644.833,2917.863,26.185581,32.782291,565.8327,910.2389,0.087285,5097.822757,1.68464385364185
-c22,644.833,2917.863,12.946749,19.340658,565.8327,910.2389,26.09765,5097.152657,1.68486532627572
-c3,1315.72658
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,202.0632,433.2493,211.0585,394.5152,190.18827,306.11015,2.463761,1739.648381,0.999999942517123
-c1,202.0632,27.159744,228.5053,22.719616,115.04262,20.759040,0.054060,616.30358,2.82271295378278
-c2,202.0632,27.159744,228.5053,22.719616,115.04262,20.759040,0.054060,616.30358,2.82271295378278
-c3,202.0632,27.159744,13.608704,22.719616,115.04262,20.759040,0.054060,401.406984,4.33387563484056
-c4,202.0632,27.159744,13.608704,22.719616,115.04262,20.759040,0.054060,401.406984,4.33387563484056
-c5,202.0632,27.159744,13.608704,22.719616,115.04262,180.10874,0.054060,560.756684,3.10232248746187
-c6,202.0632,27.159744,13.608704,22.719616,115.04262,20.759040,0.054060,401.406984,4.33387563484056
-c7,202.0632,27.159744,228.5053,22.719616,115.04262,20.759040,0.054060,616.30358,2.82271295378278
-c8,202.0632,433.2493,13.608704,22.719616,115.04262,20.759040,0.054060,807.49654,2.1543722844469
-c9,202.0632,27.159744,228.5053,22.719616,115.04262,20.759040,0.054060,616.30358,2.82271295378278
-c10,202.0632,725.4132,13.608704,22.719616,115.04262,20.759040,0.054060,1099.66044,1.58198672928648
-c11,202.0632,27.159744,13.608704,22.719616,115.04262,20.759040,0.054060,401.406984,4.33387563484056
-c12,270.6243,725.4132,228.5053,22.719616,115.04262,20.759040,0.054060,1383.118136,1.25777271654742
-c13,202.0632,27.159744,13.608704,22.719616,115.04262,180.10874,0.054060,560.756684,3.10232248746187
-c14,270.6243,27.159744,13.608704,22.719616,115.04262,180.10874,0.054060,629.317784,2.76433965286771
-c15,270.6243,433.2493,13.608704,22.719616,115.04262,20.759040,0.054060,876.05764,1.98576908983191
-c16,202.0632,27.159744,13.608704,22.719616,115.04262,180.10874,0.054060,560.756684,3.10232248746187
-c17,202.0632,27.159744,228.5053,345.1436,115.04262,20.759040,0.054060,938.727564,1.85319816142118
-c18,270.6243,433.2493,13.608704,22.719616,115.04262,180.10874,0.054060,1035.40734,1.68015827759553
-c19,270.6243,725.4132,13.608704,22.719616,115.04262,180.10874,0.054060,1327.57124,1.31039916920774
-c20,270.6243,27.159744,228.5053,22.719616,115.04262,180.10874,0.054060,844.21438,2.06067109983709
-c21,202.0632,725.4132,13.608704,22.719616,115.04262,180.10874,0.054060,1259.01014,1.38175872262961
-c22,202.0632,725.4132,13.608704,22.719616,115.04262,180.10874,4.957979,1263.914059,1.37639757305702
-c3,401.406984
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,644.833,1882.211,1052.832,2143.065,1071.7044,1784.0718,9.298743,8588.015943,0.999999988355867
-c1,644.833,168.647541,1026.8995,107.363804,565.8327,72.035782,2.304538,2587.916865,3.31850521448241
-c2,644.833,168.647541,1026.8995,107.363804,565.8327,74.049313,2.304538,2589.930396,3.31592525601119
-c3,644.833,168.647541,61.067218,96.159428,565.8327,74.049313,2.304538,1612.893738,5.32460087617992
-c4,644.833,168.647541,61.067218,107.363804,565.8327,84.133251,2.304538,1634.182052,5.25523787693405
-c5,644.833,142.169878,75.648404,107.363804,565.8327,910.2389,2.304538,2448.391224,3.50761573888015
-c6,644.833,168.647541,75.648404,107.363804,565.8327,84.133251,2.304538,1648.763238,5.20876207340801
-c7,644.833,155.615129,1026.8995,118.224147,565.8327,93.90756,2.260409,2607.572445,3.29349070631513
-c8,644.833,1882.211,75.648404,107.363804,565.8327,74.049313,2.282818,3352.221039,2.56188824868573
-c9,644.833,142.169878,1026.8995,93.922171,565.8327,93.90756,2.282818,2569.847627,3.34183844932537
-c10,644.833,3118.92,75.648404,107.363804,565.8327,74.049313,2.304538,4588.951759,1.87145479117566
-c11,644.833,168.647541,75.648404,93.922171,565.8327,84.133251,2.304538,1635.321605,5.25157583168016
-c12,809.3061,2917.863,1026.8995,107.363804,565.8327,74.049313,2.304538,5503.618955,1.56043066519981
-c13,644.833,168.647541,75.648404,107.363804,565.8327,910.2389,2.304538,2474.868887,3.47008911910536
-c14,809.3061,155.615129,61.067218,107.363804,565.8327,910.2389,2.304538,2611.728389,3.2882499000837
-c15,809.3061,2074.787,62.409572,96.159428,565.8327,74.049313,2.304538,3684.848651,2.33062915829839
-c16,644.833,168.647541,62.409572,107.363804,565.8327,910.2389,2.304538,2461.630055,3.48875151921431
-c17,644.833,155.615129,1026.8995,1646.3233,565.8327,93.90756,2.260409,4135.671598,2.07657100711189
-c18,809.3061,2074.787,61.067218,93.922171,565.8327,910.2389,2.255934,4517.410023,1.90109281848793
-c19,809.3061,2917.863,69.132198,107.363804,565.8327,910.2389,2.304538,5382.04124,1.59568004042867
-c20,809.3061,168.647541,1026.8995,96.159428,565.8327,910.2389,2.304538,3579.388707,2.3992967531789
-c21,644.833,3118.92,75.648404,107.363804,565.8327,910.2389,2.304538,5425.141346,1.58300313982265
-c22,644.833,3118.92,62.409572,93.922171,565.8327,910.2389,26.09765,5422.253993,1.58384608978154
-c3,1612.893738
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0
-c1,0,63.122342,0,47.189800,0,39.390943,0.559103,150.262188,0
-c2,0,63.122342,0,47.189800,0,39.390943,0.559103,150.262188,0
-c3,0,63.122342,29.446347,47.189800,0,39.390943,0.559103,179.708535,0
-c4,0,63.122342,29.446347,47.189800,0,39.390943,0.559103,179.708535,0
-c5,0,63.122342,29.446347,47.189800,0,0,0.559103,140.317592,0
-c6,0,63.122342,29.446347,47.189800,0,39.390943,0.559103,179.708535,0
-c7,0,63.122342,0,47.189800,0,39.390943,0.559103,150.262188,0
-c8,0,0,29.446347,47.189800,0,39.390943,0.559103,116.586193,0
-c9,0,63.122342,0,47.189800,0,39.390943,0.559103,150.262188,0
-c10,0,0,29.446347,47.189800,0,39.390943,0.559103,116.586193,0
-c11,0,63.122342,29.446347,47.189800,0,39.390943,0.559103,179.708535,0
-c12,0,0,0,47.189800,0,39.390943,0.559103,87.139846,0
-c13,0,63.122342,29.446347,47.189800,0,0,0.559103,140.317592,0
-c14,0,63.122342,29.446347,47.189800,0,0,0.559103,140.317592,0
-c15,0,0,29.446347,47.189800,0,39.390943,0.559103,116.586193,0
-c16,0,63.122342,29.446347,47.189800,0,0,0.559103,140.317592,0
-c17,0,63.122342,0,0,0,39.390943,0.559103,103.072388,0
-c18,0,0,29.446347,47.189800,0,0,0.559103,77.19525,0
-c19,0,0,29.446347,47.189800,0,0,0.559103,77.19525,0
-c20,0,63.122342,0,47.189800,0,0,0.559103,110.871245,0
-c21,0,0,29.446347,47.189800,0,0,0.559103,77.19525,0
-c22,0,0,29.446347,47.189800,0,0,0,76.636147,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0
-c1,0,53.154038,0,27.391713,0,15.238246,1.658150,97.442147,0
-c2,0,53.154038,0,27.391713,0,15.238246,1.658150,97.442147,0
-c3,0,53.154038,20.016476,27.391713,0,15.238246,1.658150,117.458623,0
-c4,0,53.154038,20.016476,27.391713,0,15.238246,1.658150,117.458623,0
-c5,0,53.154038,20.016476,27.391713,0,0,1.658150,102.220377,0
-c6,0,53.154038,20.016476,27.391713,0,15.238246,1.658150,117.458623,0
-c7,0,53.154038,0,27.391713,0,15.238246,1.658150,97.442147,0
-c8,0,0,20.016476,27.391713,0,15.238246,1.658150,64.304585,0
-c9,0,53.154038,0,27.391713,0,15.238246,1.658150,97.442147,0
-c10,0,0,20.016476,27.391713,0,15.238246,1.658150,64.304585,0
-c11,0,53.154038,20.016476,27.391713,0,15.238246,1.658150,117.458623,0
-c12,0,0,0,27.391713,0,15.238246,1.658150,44.288109,0
-c13,0,53.154038,20.016476,27.391713,0,0,1.658150,102.220377,0
-c14,0,53.154038,20.016476,27.391713,0,0,1.658150,102.220377,0
-c15,0,0,20.016476,27.391713,0,15.238246,1.658150,64.304585,0
-c16,0,53.154038,20.016476,27.391713,0,0,1.658150,102.220377,0
-c17,0,53.154038,0,0,0,15.238246,1.658150,70.050434,0
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diff --git a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_promise_results2.csv b/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_promise_results2.csv
deleted file mode 100644
index a46a2216abbef609554dea8b8df6f572e60dff2e..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_promise_results2.csv
+++ /dev/null
@@ -1,451 +0,0 @@
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-c11,270.6243,27.159744,13.608704,22.719616,115.04262,20.759040,0.054060,469.968084,3.70163011077363
-c12,202.0632,27.159744,13.608704,22.719616,115.04262,20.759040,0.054060,401.406984,4.33387563484056
-c13,270.6243,27.159744,13.608704,22.719616,115.04262,20.759040,0.054060,469.968084,3.70163011077363
-c14,270.6243,27.159744,13.608704,22.719616,115.04262,20.759040,0.054060,469.968084,3.70163011077363
-c15,270.6243,27.159744,13.608704,22.719616,115.04262,180.10874,0.054060,629.317784,2.76433965286771
-c16,270.6243,27.159744,13.608704,22.719616,115.04262,20.759040,0.054060,469.968084,3.70163011077363
-c17,270.6243,433.2493,13.608704,22.719616,115.04262,20.759040,4.957979,880.961559,1.97471520267376
-c18,202.0632,27.159744,228.5053,22.719616,115.04262,20.759040,0.054060,616.30358,2.82271295378278
-c19,270.6243,27.159744,13.608704,22.719616,115.04262,180.10874,4.957979,634.221703,2.74296527298668
-c20,202.0632,433.2493,13.608704,22.719616,115.04262,20.759040,0.054060,807.49654,2.1543722844469
-c21,270.6243,725.4132,228.5053,22.719616,115.04262,20.759040,4.957979,1388.022055,1.25332897225981
-c22,270.6243,725.4132,13.608704,22.719616,115.04262,20.759040,4.957979,1173.125459,1.48291746578509
-c23,270.6243,27.159744,228.5053,22.719616,115.04262,20.759040,0.054060,684.86468,2.5401340991721
-c24,202.0632,725.4132,13.608704,22.719616,115.04262,20.759040,0.054060,1099.66044,1.58198672928648
-c25,270.6243,27.159744,13.608704,22.719616,115.04262,20.759040,0.054060,469.968084,3.70163011077363
-c26,270.6243,725.4132,228.5053,22.719616,115.04262,20.759040,0.054060,1383.118136,1.25777271654742
-c27,202.0632,27.159744,13.608704,22.719616,115.04262,180.10874,0.054060,560.756684,3.10232248746187
-c28,270.6243,27.159744,13.608704,22.719616,115.04262,180.10874,0.054060,629.317784,2.76433965286771
-c29,270.6243,433.2493,13.608704,22.719616,115.04262,20.759040,0.054060,876.05764,1.98576908983191
-c30,202.0632,27.159744,13.608704,22.719616,115.04262,180.10874,0.054060,560.756684,3.10232248746187
-c31,202.0632,27.159744,228.5053,345.1436,115.04262,20.759040,0.054060,938.727564,1.85319816142118
-c32,270.6243,433.2493,13.608704,22.719616,115.04262,180.10874,0.054060,1035.40734,1.68015827759553
-c33,270.6243,725.4132,13.608704,22.719616,115.04262,180.10874,0.054060,1327.57124,1.31039916920774
-c34,270.6243,27.159744,228.5053,22.719616,115.04262,180.10874,0.054060,844.21438,2.06067109983709
-c35,270.6243,725.4132,13.608704,22.719616,115.04262,180.10874,0.054060,1327.57124,1.31039916920774
-c36,202.0632,725.4132,13.608704,22.719616,115.04262,180.10874,4.957979,1263.914059,1.37639757305702
-c12,401.406984
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,644.833,1882.211,1052.832,2143.065,1071.7044,1784.0718,9.298743,8588.015943,0.999999988355867
-c1,644.833,168.647541,75.648404,1646.3233,565.8327,74.049313,26.09765,3201.431908,2.6825545323279
-c2,809.3061,168.647541,75.648404,1646.3233,565.8327,74.049313,2.255934,3342.063292,2.56967475947865
-c3,809.3061,155.615129,75.648404,1646.3233,565.8327,93.90756,2.304538,3348.937731,2.56439993107773
-c4,809.3061,155.615129,62.409572,107.363804,565.8327,74.049313,2.282818,1776.859436,4.83325539751613
-c5,809.3061,168.647541,75.648404,107.363804,565.8327,72.035782,2.282818,1801.117149,4.76816040031162
-c6,809.3061,168.647541,75.648404,118.224147,565.8327,74.049313,2.304538,1814.012743,4.73426413497559
-c7,809.3061,168.647541,69.132198,107.363804,565.8327,74.049313,2.255934,1796.58759,4.78018189192869
-c8,809.3061,168.647541,62.409572,118.224147,565.8327,74.049313,2.304538,1800.773911,4.76906923941607
-c9,809.3061,168.647541,1026.8995,107.363804,565.8327,72.035782,2.304538,2752.389965,3.12020307448683
-c10,644.833,168.647541,1026.8995,107.363804,565.8327,74.049313,2.304538,2589.930396,3.31592525601119
-c11,809.3061,168.647541,75.648404,96.159428,565.8327,74.049313,2.304538,1791.948024,4.79255834919471
-c12,644.833,168.647541,61.067218,96.159428,565.8327,74.049313,2.304538,1612.893738,5.32460087617992
-c13,809.3061,168.647541,61.067218,107.363804,565.8327,84.133251,2.304538,1798.655152,4.77468705214667
-c14,809.3061,168.647541,75.648404,107.363804,565.8327,74.049313,2.304538,1803.1524,4.76277849100395
-c15,809.3061,142.169878,75.648404,107.363804,565.8327,910.2389,2.304538,2612.864324,3.28682034326631
-c16,809.3061,168.647541,75.648404,107.363804,565.8327,84.133251,2.304538,1813.236338,4.73629128723697
-c17,809.3061,2074.787,75.648404,107.363804,565.8327,72.035782,26.09765,3731.07144,2.30175590334566
-c18,644.833,155.615129,1026.8995,118.224147,565.8327,93.90756,2.260409,2607.572445,3.29349070631513
-c19,809.3061,168.647541,75.648404,107.363804,565.8327,910.2389,26.09765,2663.135099,3.22477655142134
-c20,644.833,1882.211,75.648404,107.363804,565.8327,74.049313,2.282818,3352.221039,2.56188824868573
-c21,809.3061,2917.863,1026.8995,107.363804,565.8327,74.049313,26.09765,5527.412067,1.55371368798451
-c22,809.3061,2917.863,75.648404,107.363804,565.8327,74.049313,26.09765,4576.160971,1.87668567818206
-c23,809.3061,142.169878,1026.8995,93.922171,565.8327,93.90756,2.282818,2734.320727,3.14082234176685
-c24,644.833,3118.92,75.648404,107.363804,565.8327,74.049313,2.304538,4588.951759,1.87145479117566
-c25,809.3061,168.647541,75.648404,93.922171,565.8327,84.133251,2.304538,1799.794705,4.77166392476613
-c26,809.3061,2917.863,1026.8995,107.363804,565.8327,74.049313,2.304538,5503.618955,1.56043066519981
-c27,644.833,168.647541,75.648404,107.363804,565.8327,910.2389,2.304538,2474.868887,3.47008911910536
-c28,809.3061,155.615129,61.067218,107.363804,565.8327,910.2389,2.304538,2611.728389,3.2882499000837
-c29,809.3061,2074.787,62.409572,96.159428,565.8327,74.049313,2.304538,3684.848651,2.33062915829839
-c30,644.833,168.647541,62.409572,107.363804,565.8327,910.2389,2.304538,2461.630055,3.48875151921431
-c31,644.833,155.615129,1026.8995,1646.3233,565.8327,93.90756,2.260409,4135.671598,2.07657100711189
-c32,809.3061,2074.787,61.067218,93.922171,565.8327,910.2389,2.255934,4517.410023,1.90109281848793
-c33,809.3061,2917.863,69.132198,107.363804,565.8327,910.2389,2.304538,5382.04124,1.59568004042867
-c34,809.3061,168.647541,1026.8995,96.159428,565.8327,910.2389,2.304538,3579.388707,2.3992967531789
-c35,809.3061,2917.863,75.648404,107.363804,565.8327,910.2389,2.304538,5388.557446,1.5937504368632
-c36,644.833,3118.92,62.409572,93.922171,565.8327,910.2389,26.09765,5422.253993,1.58384608978154
-c12,1612.893738
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0
-c1,0,63.122342,29.446347,0,0,39.390943,0,131.959632,0
-c2,0,63.122342,29.446347,0,0,39.390943,0.559103,132.518735,0
-c3,0,63.122342,29.446347,0,0,39.390943,0.559103,132.518735,0
-c4,0,63.122342,29.446347,47.189800,0,39.390943,0.559103,179.708535,0
-c5,0,63.122342,29.446347,47.189800,0,39.390943,0.559103,179.708535,0
-c6,0,63.122342,29.446347,47.189800,0,39.390943,0.559103,179.708535,0
-c7,0,63.122342,29.446347,47.189800,0,39.390943,0.559103,179.708535,0
-c8,0,63.122342,29.446347,47.189800,0,39.390943,0.559103,179.708535,0
-c9,0,63.122342,0,47.189800,0,39.390943,0.559103,150.262188,0
-c10,0,63.122342,0,47.189800,0,39.390943,0.559103,150.262188,0
-c11,0,63.122342,29.446347,47.189800,0,39.390943,0.559103,179.708535,0
-c12,0,63.122342,29.446347,47.189800,0,39.390943,0.559103,179.708535,0
-c13,0,63.122342,29.446347,47.189800,0,39.390943,0.559103,179.708535,0
-c14,0,63.122342,29.446347,47.189800,0,39.390943,0.559103,179.708535,0
-c15,0,63.122342,29.446347,47.189800,0,0,0.559103,140.317592,0
-c16,0,63.122342,29.446347,47.189800,0,39.390943,0.559103,179.708535,0
-c17,0,0,29.446347,47.189800,0,39.390943,0,116.02709,0
-c18,0,63.122342,0,47.189800,0,39.390943,0.559103,150.262188,0
-c19,0,63.122342,29.446347,47.189800,0,0,0,139.758489,0
-c20,0,0,29.446347,47.189800,0,39.390943,0.559103,116.586193,0
-c21,0,0,0,47.189800,0,39.390943,0,86.580743,0
-c22,0,0,29.446347,47.189800,0,39.390943,0,116.02709,0
-c23,0,63.122342,0,47.189800,0,39.390943,0.559103,150.262188,0
-c24,0,0,29.446347,47.189800,0,39.390943,0.559103,116.586193,0
-c25,0,63.122342,29.446347,47.189800,0,39.390943,0.559103,179.708535,0
-c26,0,0,0,47.189800,0,39.390943,0.559103,87.139846,0
-c27,0,63.122342,29.446347,47.189800,0,0,0.559103,140.317592,0
-c28,0,63.122342,29.446347,47.189800,0,0,0.559103,140.317592,0
-c29,0,0,29.446347,47.189800,0,39.390943,0.559103,116.586193,0
-c30,0,63.122342,29.446347,47.189800,0,0,0.559103,140.317592,0
-c31,0,63.122342,0,0,0,39.390943,0.559103,103.072388,0
-c32,0,0,29.446347,47.189800,0,0,0.559103,77.19525,0
-c33,0,0,29.446347,47.189800,0,0,0.559103,77.19525,0
-c34,0,63.122342,0,47.189800,0,0,0.559103,110.871245,0
-c35,0,0,29.446347,47.189800,0,0,0.559103,77.19525,0
-c36,0,0,29.446347,47.189800,0,0,0,76.636147,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0
-c1,0,53.154038,20.016476,0,0,15.238246,0,88.40876,0
-c2,0,53.154038,20.016476,0,0,15.238246,1.658150,90.06691,0
-c3,0,53.154038,20.016476,0,0,15.238246,1.658150,90.06691,0
-c4,0,53.154038,20.016476,27.391713,0,15.238246,1.658150,117.458623,0
-c5,0,53.154038,20.016476,27.391713,0,15.238246,1.658150,117.458623,0
-c6,0,53.154038,20.016476,27.391713,0,15.238246,1.658150,117.458623,0
-c7,0,53.154038,20.016476,27.391713,0,15.238246,1.658150,117.458623,0
-c8,0,53.154038,20.016476,27.391713,0,15.238246,1.658150,117.458623,0
-c9,0,53.154038,0,27.391713,0,15.238246,1.658150,97.442147,0
-c10,0,53.154038,0,27.391713,0,15.238246,1.658150,97.442147,0
-c11,0,53.154038,20.016476,27.391713,0,15.238246,1.658150,117.458623,0
-c12,0,53.154038,20.016476,27.391713,0,15.238246,1.658150,117.458623,0
-c13,0,53.154038,20.016476,27.391713,0,15.238246,1.658150,117.458623,0
-c14,0,53.154038,20.016476,27.391713,0,15.238246,1.658150,117.458623,0
-c15,0,53.154038,20.016476,27.391713,0,0,1.658150,102.220377,0
-c16,0,53.154038,20.016476,27.391713,0,15.238246,1.658150,117.458623,0
-c17,0,0,20.016476,27.391713,0,15.238246,0,62.646435,0
-c18,0,53.154038,0,27.391713,0,15.238246,1.658150,97.442147,0
-c19,0,53.154038,20.016476,27.391713,0,0,0,100.562227,0
-c20,0,0,20.016476,27.391713,0,15.238246,1.658150,64.304585,0
-c21,0,0,0,27.391713,0,15.238246,0,42.629959,0
-c22,0,0,20.016476,27.391713,0,15.238246,0,62.646435,0
-c23,0,53.154038,0,27.391713,0,15.238246,1.658150,97.442147,0
-c24,0,0,20.016476,27.391713,0,15.238246,1.658150,64.304585,0
-c25,0,53.154038,20.016476,27.391713,0,15.238246,1.658150,117.458623,0
-c26,0,0,0,27.391713,0,15.238246,1.658150,44.288109,0
-c27,0,53.154038,20.016476,27.391713,0,0,1.658150,102.220377,0
-c28,0,53.154038,20.016476,27.391713,0,0,1.658150,102.220377,0
-c29,0,0,20.016476,27.391713,0,15.238246,1.658150,64.304585,0
-c30,0,53.154038,20.016476,27.391713,0,0,1.658150,102.220377,0
-c31,0,53.154038,0,0,0,15.238246,1.658150,70.050434,0
-c32,0,0,20.016476,27.391713,0,0,1.658150,49.066339,0
-c33,0,0,20.016476,27.391713,0,0,1.658150,49.066339,0
-c34,0,53.154038,0,27.391713,0,0,1.658150,82.203901,0
-c35,0,0,20.016476,27.391713,0,0,1.658150,49.066339,0
-c36,0,0,20.016476,27.391713,0,0,0,47.408189,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0
-c1,0,14.128858,5.690699,0,0,5.486035,0,25.305592,0
-c2,0,14.128858,5.690699,0,0,5.486035,0.351348,25.65694,0
-c3,0,14.128858,5.690699,0,0,5.486035,0.351348,25.65694,0
-c4,0,14.128858,5.690699,8.244707,0,5.486035,0.351348,33.901647,0
-c5,0,14.128858,5.690699,8.244707,0,5.486035,0.351348,33.901647,0
-c6,0,14.128858,5.690699,8.244707,0,5.486035,0.351348,33.901647,0
-c7,0,14.128858,5.690699,8.244707,0,5.486035,0.351348,33.901647,0
-c8,0,14.128858,5.690699,8.244707,0,5.486035,0.351348,33.901647,0
-c9,0,14.128858,0,8.244707,0,5.486035,0.351348,28.210948,0
-c10,0,14.128858,0,8.244707,0,5.486035,0.351348,28.210948,0
-c11,0,14.128858,5.690699,8.244707,0,5.486035,0.351348,33.901647,0
-c12,0,14.128858,5.690699,8.244707,0,5.486035,0.351348,33.901647,0
-c13,0,14.128858,5.690699,8.244707,0,5.486035,0.351348,33.901647,0
-c14,0,14.128858,5.690699,8.244707,0,5.486035,0.351348,33.901647,0
-c15,0,14.128858,5.690699,8.244707,0,0,0.351348,28.415612,0
-c16,0,14.128858,5.690699,8.244707,0,5.486035,0.351348,33.901647,0
-c17,0,0,5.690699,8.244707,0,5.486035,0,19.421441,0
-c18,0,14.128858,0,8.244707,0,5.486035,0.351348,28.210948,0
-c19,0,14.128858,5.690699,8.244707,0,0,0,28.064264,0
-c20,0,0,5.690699,8.244707,0,5.486035,0.351348,19.772789,0
-c21,0,0,0,8.244707,0,5.486035,0,13.730742,0
-c22,0,0,5.690699,8.244707,0,5.486035,0,19.421441,0
-c23,0,14.128858,0,8.244707,0,5.486035,0.351348,28.210948,0
-c24,0,0,5.690699,8.244707,0,5.486035,0.351348,19.772789,0
-c25,0,14.128858,5.690699,8.244707,0,5.486035,0.351348,33.901647,0
-c26,0,0,0,8.244707,0,5.486035,0.351348,14.08209,0
-c27,0,14.128858,5.690699,8.244707,0,0,0.351348,28.415612,0
-c28,0,14.128858,5.690699,8.244707,0,0,0.351348,28.415612,0
-c29,0,0,5.690699,8.244707,0,5.486035,0.351348,19.772789,0
-c30,0,14.128858,5.690699,8.244707,0,0,0.351348,28.415612,0
-c31,0,14.128858,0,0,0,5.486035,0.351348,19.966241,0
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diff --git a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_results1.csv b/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_results1.csv
deleted file mode 100644
index 80534cd08b0ee64c04faf405926188256a6da2a4..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_results1.csv
+++ /dev/null
@@ -1,220 +0,0 @@
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-c10,202.0632,27.159744,13.608704,22.719616,190.18827,20.759040,2.463761,478.962335,3.63211862533645
-c11,202.0632,27.159744,13.608704,22.719616,190.18827,20.759040,2.463761,478.962335,3.63211862533645
-c12,202.0632,27.159744,13.608704,22.719616,190.18827,306.11015,2.463761,764.313445,2.27609257009831
-c13,202.0632,27.159744,13.608704,22.719616,190.18827,180.10874,2.463761,638.312035,2.72538823188752
-c14,202.0632,27.159744,13.608704,22.719616,190.18827,20.759040,2.463761,478.962335,3.63211862533645
-c15,202.0632,27.159744,13.608704,22.719616,115.04262,306.11015,2.463761,689.167795,2.52427368370084
-c4,478.962335
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-Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,644.833,1882.211,1052.832,2143.065,1071.7044,1784.0718,9.298743,8588.015943,0.999999988355867
-c1,644.833,168.647541,75.648404,118.224147,1071.7044,1784.0718,9.298743,3872.428035,2.21773410470276
-c2,644.833,168.647541,75.648404,118.224147,1071.7044,979.0974,9.400083,3067.554975,2.79962893347563
-c3,644.833,168.647541,75.648404,118.224147,565.8327,1835.1288,9.298743,3417.613335,2.51286931841079
-c4,644.833,168.647541,75.648404,118.224147,1071.7044,93.90756,9.298743,2182.263795,3.93537003598733
-c5,644.833,168.647541,75.648404,118.224147,1071.7044,93.90756,9.298743,2182.263795,3.93537003598733
-c6,644.833,168.647541,1052.832,118.224147,1071.7044,93.90756,9.298743,3159.447391,2.71820182720676
-c7,644.833,168.647541,75.648404,118.224147,1071.7044,1784.0718,9.298743,3872.428035,2.21773410470276
-c8,644.833,168.647541,75.648404,118.224147,565.8327,1835.1288,9.298743,3417.613335,2.51286931841079
-c9,644.833,168.647541,75.648404,118.224147,1071.7044,93.90756,9.298743,2182.263795,3.93537003598733
-c10,644.833,168.647541,75.648404,118.224147,1071.7044,93.90756,9.298743,2182.263795,3.93537003598733
-c11,644.833,168.647541,75.648404,118.224147,1071.7044,93.90756,9.298743,2182.263795,3.93537003598733
-c12,644.833,168.647541,75.648404,118.224147,1071.7044,1784.0718,9.298743,3872.428035,2.21773410470276
-c13,644.833,168.647541,75.648404,118.224147,1071.7044,979.0974,9.400083,3067.554975,2.79962893347563
-c14,644.833,168.647541,75.648404,118.224147,1071.7044,93.90756,9.298743,2182.263795,3.93537003598733
-c15,644.833,168.647541,75.648404,118.224147,565.8327,1835.1288,9.298743,3417.613335,2.51286931841079
-c4,2182.263795
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0
-c1,0,63.122342,29.446347,47.189800,0,0,0,139.758489,0
-c2,0,63.122342,29.446347,47.189800,0,0,0,139.758489,0
-c3,0,63.122342,29.446347,47.189800,0,0,0,139.758489,0
-c4,0,63.122342,29.446347,47.189800,0,39.390943,0,179.149432,0
-c5,0,63.122342,29.446347,47.189800,0,39.390943,0,179.149432,0
-c6,0,63.122342,0,47.189800,0,39.390943,0,149.703085,0
-c7,0,63.122342,29.446347,47.189800,0,0,0,139.758489,0
-c8,0,63.122342,29.446347,47.189800,0,0,0,139.758489,0
-c9,0,63.122342,29.446347,47.189800,0,39.390943,0,179.149432,0
-c10,0,63.122342,29.446347,47.189800,0,39.390943,0,179.149432,0
-c11,0,63.122342,29.446347,47.189800,0,39.390943,0,179.149432,0
-c12,0,63.122342,29.446347,47.189800,0,0,0,139.758489,0
-c13,0,63.122342,29.446347,47.189800,0,0,0,139.758489,0
-c14,0,63.122342,29.446347,47.189800,0,39.390943,0,179.149432,0
-c15,0,63.122342,29.446347,47.189800,0,0,0,139.758489,0
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-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
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-c1,0,53.154038,20.016476,27.391713,0,0,0,100.562227,0
-c2,0,53.154038,20.016476,27.391713,0,0,0,100.562227,0
-c3,0,53.154038,20.016476,27.391713,0,0,0,100.562227,0
-c4,0,53.154038,20.016476,27.391713,0,15.238246,0,115.800473,0
-c5,0,53.154038,20.016476,27.391713,0,15.238246,0,115.800473,0
-c6,0,53.154038,0,27.391713,0,15.238246,0,95.783997,0
-c7,0,53.154038,20.016476,27.391713,0,0,0,100.562227,0
-c8,0,53.154038,20.016476,27.391713,0,0,0,100.562227,0
-c9,0,53.154038,20.016476,27.391713,0,15.238246,0,115.800473,0
-c10,0,53.154038,20.016476,27.391713,0,15.238246,0,115.800473,0
-c11,0,53.154038,20.016476,27.391713,0,15.238246,0,115.800473,0
-c12,0,53.154038,20.016476,27.391713,0,0,0,100.562227,0
-c13,0,53.154038,20.016476,27.391713,0,0,0,100.562227,0
-c14,0,53.154038,20.016476,27.391713,0,15.238246,0,115.800473,0
-c15,0,53.154038,20.016476,27.391713,0,0,0,100.562227,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0
-c1,0,14.128858,5.690699,8.244707,0,0,0,28.064264,0
-c2,0,14.128858,5.690699,8.244707,0,0,0,28.064264,0
-c3,0,14.128858,5.690699,8.244707,0,0,0,28.064264,0
-c4,0,14.128858,5.690699,8.244707,0,5.486035,0,33.550299,0
-c5,0,14.128858,5.690699,8.244707,0,5.486035,0,33.550299,0
-c6,0,14.128858,0,8.244707,0,5.486035,0,27.8596,0
-c7,0,14.128858,5.690699,8.244707,0,0,0,28.064264,0
-c8,0,14.128858,5.690699,8.244707,0,0,0,28.064264,0
-c9,0,14.128858,5.690699,8.244707,0,5.486035,0,33.550299,0
-c10,0,14.128858,5.690699,8.244707,0,5.486035,0,33.550299,0
-c11,0,14.128858,5.690699,8.244707,0,5.486035,0,33.550299,0
-c12,0,14.128858,5.690699,8.244707,0,0,0,28.064264,0
-c13,0,14.128858,5.690699,8.244707,0,0,0,28.064264,0
-c14,0,14.128858,5.690699,8.244707,0,5.486035,0,33.550299,0
-c15,0,14.128858,5.690699,8.244707,0,0,0,28.064264,0
-c0,0
-
-Patch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
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-c14,0,0,0,0,0,0,0,0,0
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-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
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-c1,0,0,0,0,0,0,0,0,0
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-c7,0,0,0,0,0,0,0,0,0
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-c11,0,0,0,0,0,0,0,0,0
-c12,0,0,0,0,0,0,0,0,0
-c13,0,0,0,0,0,68.8585,0.10134,68.95984,0
-c14,0,0,0,0,0,0,0,0,0
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-Quantization Time
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-c14,0,0,0,0,0,0,0,0,0
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-
-Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,202.0632,433.2493,211.0585,394.5152,190.18827,306.11015,2.463761,1739.648381,0.999999942517123
-c1,202.0632,41.288602,19.299403,30.964323,190.18827,306.11015,2.463761,792.377709,2.19547842107729
-c2,202.0632,41.288602,19.299403,30.964323,190.18827,194.89254,2.683027,681.379365,2.55312710517333
-c3,202.0632,41.288602,19.299403,30.964323,115.04262,316.67815,2.463761,727.800059,2.39028304609095
-c4,202.0632,41.288602,19.299403,30.964323,190.18827,26.245075,2.463761,512.512634,3.394351526493
-c5,202.0632,41.288602,19.299403,30.964323,190.18827,26.245075,2.463761,512.512634,3.394351526493
-c6,202.0632,41.288602,211.0585,30.964323,190.18827,26.245075,2.463761,704.271731,2.47013767188425
-c7,202.0632,41.288602,19.299403,30.964323,190.18827,306.11015,2.463761,792.377709,2.19547842107729
-c8,202.0632,41.288602,19.299403,30.964323,115.04262,316.67815,2.463761,727.800059,2.39028304609095
-c9,202.0632,41.288602,19.299403,30.964323,190.18827,26.245075,2.463761,512.512634,3.394351526493
-c10,202.0632,41.288602,19.299403,30.964323,190.18827,26.245075,2.463761,512.512634,3.394351526493
-c11,202.0632,41.288602,19.299403,30.964323,190.18827,26.245075,2.463761,512.512634,3.394351526493
-c12,202.0632,41.288602,19.299403,30.964323,190.18827,306.11015,2.463761,792.377709,2.19547842107729
-c13,202.0632,41.288602,19.299403,30.964323,190.18827,194.89254,2.683027,681.379365,2.55312710517333
-c14,202.0632,41.288602,19.299403,30.964323,190.18827,26.245075,2.463761,512.512634,3.394351526493
-c15,202.0632,41.288602,19.299403,30.964323,115.04262,316.67815,2.463761,727.800059,2.39028304609095
-c4,512.512634
-
-Unpatch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0
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diff --git a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_results2.csv b/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_results2.csv
deleted file mode 100644
index 0fb57641e8484f4b2d7403c55ac35414dbf58bba..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_results2.csv
+++ /dev/null
@@ -1,253 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,644.833,1882.211,1052.832,2143.065,1071.7044,1784.0718,9.298743,8588.015943,0.999999988355867
-c1,644.833,39.338749,1026.8995,32.782291,16.391145,29.504062,9.298743,1799.04749,4.77364578387835
-c2,644.833,39.338749,1026.8995,32.782291,16.391145,29.504062,9.298743,1799.04749,4.77364578387835
-c3,644.833,39.338749,1026.8995,32.782291,16.391145,39.278371,9.298743,1808.821799,4.74785049194055
-c4,644.833,39.338749,1026.8995,2143.065,16.391145,29.504062,9.298743,3909.330199,2.19679978056518
-c5,644.833,39.338749,1026.8995,32.782291,565.8327,29.504062,9.298743,2348.489045,3.65682590497986
-c6,644.833,39.338749,1026.8995,32.782291,16.391145,29.504062,9.298743,1799.04749,4.77364578387835
-c7,644.833,39.338749,1026.8995,32.782291,16.391145,29.504062,9.298743,1799.04749,4.77364578387835
-c8,644.833,39.338749,1026.8995,32.782291,16.391145,29.504062,9.298743,1799.04749,4.77364578387835
-c9,644.833,39.338749,1026.8995,32.782291,16.391145,29.504062,9.298743,1799.04749,4.77364578387835
-c10,644.833,39.338749,1026.8995,32.782291,16.391145,39.278371,9.298743,1808.821799,4.74785049194055
-c11,644.833,39.338749,1026.8995,2143.065,16.391145,29.504062,9.298743,3909.330199,2.19679978056518
-c12,644.833,39.338749,1026.8995,32.782291,565.8327,29.504062,9.298743,2348.489045,3.65682590497986
-c13,644.833,39.338749,1026.8995,32.782291,16.391145,29.504062,9.298743,1799.04749,4.77364578387835
-c14,644.833,39.338749,1026.8995,32.782291,16.391145,39.278371,9.298743,1808.821799,4.74785049194055
-c15,644.833,39.338749,1026.8995,32.782291,16.391145,29.504062,9.298743,1799.04749,4.77364578387835
-c16,644.833,52.371161,26.185581,32.782291,565.8327,29.504062,9.298743,1360.807538,6.31096982643479
-c17,644.833,39.338749,1026.8995,2143.065,16.391145,29.504062,9.298743,3909.330199,2.19679978056518
-c18,644.833,39.338749,1026.8995,32.782291,16.391145,29.504062,9.298743,1799.04749,4.77364578387835
-c16,1360.807538
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,202.0632,433.2493,211.0585,394.5152,190.18827,306.11015,2.463761,1739.648381,0.999999942517123
-c1,202.0632,27.159744,228.5053,22.719616,11.532800,20.759040,2.463761,515.203461,3.37662336343984
-c2,202.0632,27.159744,228.5053,22.719616,11.532800,20.759040,2.463761,515.203461,3.37662336343984
-c3,202.0632,27.159744,228.5053,22.719616,11.532800,20.759040,2.463761,515.203461,3.37662336343984
-c4,202.0632,27.159744,228.5053,394.5152,11.532800,20.759040,2.463761,886.999045,1.9612740224231
-c5,202.0632,27.159744,228.5053,22.719616,115.04262,20.759040,2.463761,618.713281,2.81171934278887
-c6,202.0632,27.159744,228.5053,22.719616,11.532800,20.759040,2.463761,515.203461,3.37662336343984
-c7,202.0632,27.159744,228.5053,22.719616,11.532800,20.759040,2.463761,515.203461,3.37662336343984
-c8,202.0632,27.159744,228.5053,22.719616,11.532800,20.759040,2.463761,515.203461,3.37662336343984
-c9,202.0632,27.159744,228.5053,22.719616,11.532800,20.759040,2.463761,515.203461,3.37662336343984
-c10,202.0632,27.159744,228.5053,22.719616,11.532800,20.759040,2.463761,515.203461,3.37662336343984
-c11,202.0632,27.159744,228.5053,394.5152,11.532800,20.759040,2.463761,886.999045,1.9612740224231
-c12,202.0632,27.159744,228.5053,22.719616,115.04262,20.759040,2.463761,618.713281,2.81171934278887
-c13,202.0632,27.159744,228.5053,22.719616,11.532800,20.759040,2.463761,515.203461,3.37662336343984
-c14,202.0632,27.159744,228.5053,22.719616,11.532800,20.759040,2.463761,515.203461,3.37662336343984
-c15,202.0632,27.159744,228.5053,22.719616,11.532800,20.759040,2.463761,515.203461,3.37662336343984
-c16,202.0632,27.159744,13.608704,22.719616,115.04262,20.759040,2.463761,403.816685,4.30801404404228
-c17,202.0632,27.159744,228.5053,394.5152,11.532800,20.759040,2.463761,886.999045,1.9612740224231
-c18,202.0632,27.159744,228.5053,22.719616,11.532800,20.759040,2.463761,515.203461,3.37662336343984
-c16,403.816685
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,644.833,1882.211,1052.832,2143.065,1071.7044,1784.0718,9.298743,8588.015943,0.999999988355867
-c1,644.833,155.615129,1026.8995,107.363804,49.35844,84.133251,9.298743,2077.501867,4.13381844128959
-c2,644.833,155.615129,1026.8995,107.363804,49.35844,84.133251,9.298743,2077.501867,4.13381844128959
-c3,644.833,155.615129,1026.8995,107.363804,49.35844,93.90756,9.298743,2087.276176,4.11446057321067
-c4,644.833,155.615129,1026.8995,2245.12,49.35844,84.133251,9.298743,4215.258063,2.03736416867239
-c5,644.833,155.615129,1026.8995,107.363804,565.8327,84.133251,9.298743,2593.976127,3.31075352719491
-c6,644.833,155.615129,1026.8995,107.363804,49.35844,84.133251,9.298743,2077.501867,4.13381844128959
-c7,644.833,155.615129,1026.8995,107.363804,49.35844,84.133251,9.298743,2077.501867,4.13381844128959
-c8,644.833,155.615129,1026.8995,107.363804,49.35844,84.133251,9.298743,2077.501867,4.13381844128959
-c9,644.833,155.615129,1026.8995,107.363804,49.35844,84.133251,9.298743,2077.501867,4.13381844128959
-c10,644.833,155.615129,1026.8995,107.363804,49.35844,93.90756,9.298743,2087.276176,4.11446057321067
-c11,644.833,155.615129,1026.8995,2245.12,49.35844,84.133251,9.298743,4215.258063,2.03736416867239
-c12,644.833,155.615129,1026.8995,107.363804,565.8327,84.133251,9.298743,2593.976127,3.31075352719491
-c13,644.833,155.615129,1026.8995,107.363804,49.35844,84.133251,9.298743,2077.501867,4.13381844128959
-c14,644.833,155.615129,1026.8995,107.363804,49.35844,93.90756,9.298743,2087.276176,4.11446057321067
-c15,644.833,155.615129,1026.8995,107.363804,49.35844,84.133251,9.298743,2077.501867,4.13381844128959
-c16,644.833,168.647541,75.648404,107.363804,565.8327,84.133251,9.298743,1655.757443,5.18675936540788
-c17,644.833,155.615129,1026.8995,2245.12,49.35844,84.133251,9.298743,4215.258063,2.03736416867239
-c18,644.833,155.615129,1026.8995,107.363804,49.35844,84.133251,9.298743,2077.501867,4.13381844128959
-c16,1655.757443
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0
-c1,0,63.122342,0,47.189800,22.543231,39.390943,0,172.246316,0
-c2,0,63.122342,0,47.189800,22.543231,39.390943,0,172.246316,0
-c3,0,63.122342,0,47.189800,22.543231,39.390943,0,172.246316,0
-c4,0,63.122342,0,0,22.543231,39.390943,0,125.056516,0
-c5,0,63.122342,0,47.189800,0,39.390943,0,149.703085,0
-c6,0,63.122342,0,47.189800,22.543231,39.390943,0,172.246316,0
-c7,0,63.122342,0,47.189800,22.543231,39.390943,0,172.246316,0
-c8,0,63.122342,0,47.189800,22.543231,39.390943,0,172.246316,0
-c9,0,63.122342,0,47.189800,22.543231,39.390943,0,172.246316,0
-c10,0,63.122342,0,47.189800,22.543231,39.390943,0,172.246316,0
-c11,0,63.122342,0,0,22.543231,39.390943,0,125.056516,0
-c12,0,63.122342,0,47.189800,0,39.390943,0,149.703085,0
-c13,0,63.122342,0,47.189800,22.543231,39.390943,0,172.246316,0
-c14,0,63.122342,0,47.189800,22.543231,39.390943,0,172.246316,0
-c15,0,63.122342,0,47.189800,22.543231,39.390943,0,172.246316,0
-c16,0,63.122342,29.446347,47.189800,0,39.390943,0,179.149432,0
-c17,0,63.122342,0,0,22.543231,39.390943,0,125.056516,0
-c18,0,63.122342,0,47.189800,22.543231,39.390943,0,172.246316,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0
-c1,0,53.154038,0,27.391713,10.424064,15.238246,0,106.208061,0
-c2,0,53.154038,0,27.391713,10.424064,15.238246,0,106.208061,0
-c3,0,53.154038,0,27.391713,10.424064,15.238246,0,106.208061,0
-c4,0,53.154038,0,0,10.424064,15.238246,0,78.816348,0
-c5,0,53.154038,0,27.391713,0,15.238246,0,95.783997,0
-c6,0,53.154038,0,27.391713,10.424064,15.238246,0,106.208061,0
-c7,0,53.154038,0,27.391713,10.424064,15.238246,0,106.208061,0
-c8,0,53.154038,0,27.391713,10.424064,15.238246,0,106.208061,0
-c9,0,53.154038,0,27.391713,10.424064,15.238246,0,106.208061,0
-c10,0,53.154038,0,27.391713,10.424064,15.238246,0,106.208061,0
-c11,0,53.154038,0,0,10.424064,15.238246,0,78.816348,0
-c12,0,53.154038,0,27.391713,0,15.238246,0,95.783997,0
-c13,0,53.154038,0,27.391713,10.424064,15.238246,0,106.208061,0
-c14,0,53.154038,0,27.391713,10.424064,15.238246,0,106.208061,0
-c15,0,53.154038,0,27.391713,10.424064,15.238246,0,106.208061,0
-c16,0,53.154038,20.016476,27.391713,0,15.238246,0,115.800473,0
-c17,0,53.154038,0,0,10.424064,15.238246,0,78.816348,0
-c18,0,53.154038,0,27.391713,10.424064,15.238246,0,106.208061,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0
-c1,0,14.128858,0,8.244707,3.455880,5.486035,0,31.31548,0
-c2,0,14.128858,0,8.244707,3.455880,5.486035,0,31.31548,0
-c3,0,14.128858,0,8.244707,3.455880,5.486035,0,31.31548,0
-c4,0,14.128858,0,0,3.455880,5.486035,0,23.070773,0
-c5,0,14.128858,0,8.244707,0,5.486035,0,27.8596,0
-c6,0,14.128858,0,8.244707,3.455880,5.486035,0,31.31548,0
-c7,0,14.128858,0,8.244707,3.455880,5.486035,0,31.31548,0
-c8,0,14.128858,0,8.244707,3.455880,5.486035,0,31.31548,0
-c9,0,14.128858,0,8.244707,3.455880,5.486035,0,31.31548,0
-c10,0,14.128858,0,8.244707,3.455880,5.486035,0,31.31548,0
-c11,0,14.128858,0,0,3.455880,5.486035,0,23.070773,0
-c12,0,14.128858,0,8.244707,0,5.486035,0,27.8596,0
-c13,0,14.128858,0,8.244707,3.455880,5.486035,0,31.31548,0
-c14,0,14.128858,0,8.244707,3.455880,5.486035,0,31.31548,0
-c15,0,14.128858,0,8.244707,3.455880,5.486035,0,31.31548,0
-c16,0,14.128858,5.690699,8.244707,0,5.486035,0,33.550299,0
-c17,0,14.128858,0,0,3.455880,5.486035,0,23.070773,0
-c18,0,14.128858,0,8.244707,3.455880,5.486035,0,31.31548,0
-c0,0
-
-Patch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0,0
-c3,0,0,0,0,0,0,0,0,0
-c4,0,0,0,0,0,0,0,0,0
-c5,0,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,0,0,0,0
-c7,0,0,0,0,0,0,0,0,0
-c8,0,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0,0
-c11,0,0,0,0,0,0,0,0,0
-c12,0,0,0,0,0,0,0,0,0
-c13,0,0,0,0,0,0,0,0,0
-c14,0,0,0,0,0,0,0,0,0
-c15,0,0,0,0,0,0,0,0,0
-c16,0,0,0,0,0,0,0,0,0
-c17,0,0,0,0,0,0,0,0,0
-c18,0,0,0,0,0,0,0,0,0
-c0,0
-
-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0,0
-c3,0,0,0,0,0,0,0,0,0
-c4,0,0,0,102.055,0,0,0,102.055,0
-c5,0,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,0,0,0,0
-c7,0,0,0,0,0,0,0,0,0
-c8,0,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0,0
-c11,0,0,0,102.055,0,0,0,102.055,0
-c12,0,0,0,0,0,0,0,0,0
-c13,0,0,0,0,0,0,0,0,0
-c14,0,0,0,0,0,0,0,0,0
-c15,0,0,0,0,0,0,0,0,0
-c16,0,0,0,0,0,0,0,0,0
-c17,0,0,0,102.055,0,0,0,102.055,0
-c18,0,0,0,0,0,0,0,0,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0,0
-c3,0,0,0,0,0,0,0,0,0
-c4,0,0,0,20.9833,0,0,0,20.9833,0
-c5,0,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,0,0,0,0
-c7,0,0,0,0,0,0,0,0,0
-c8,0,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0,0
-c11,0,0,0,20.9833,0,0,0,20.9833,0
-c12,0,0,0,0,0,0,0,0,0
-c13,0,0,0,0,0,0,0,0,0
-c14,0,0,0,0,0,0,0,0,0
-c15,0,0,0,0,0,0,0,0,0
-c16,0,0,0,0,0,0,0,0,0
-c17,0,0,0,20.9833,0,0,0,20.9833,0
-c18,0,0,0,0,0,0,0,0,0
-c0,0
-
-Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,202.0632,433.2493,211.0585,394.5152,190.18827,306.11015,2.463761,1739.648381,0.999999942517123
-c1,202.0632,41.288602,228.5053,30.964323,14.98868,26.245075,2.463761,546.518941,3.18314322190286
-c2,202.0632,41.288602,228.5053,30.964323,14.98868,26.245075,2.463761,546.518941,3.18314322190286
-c3,202.0632,41.288602,228.5053,30.964323,14.98868,26.245075,2.463761,546.518941,3.18314322190286
-c4,202.0632,41.288602,228.5053,415.4985,14.98868,26.245075,2.463761,931.053118,1.86847362467308
-c5,202.0632,41.288602,228.5053,30.964323,115.04262,26.245075,2.463761,646.572881,2.69056770406557
-c6,202.0632,41.288602,228.5053,30.964323,14.98868,26.245075,2.463761,546.518941,3.18314322190286
-c7,202.0632,41.288602,228.5053,30.964323,14.98868,26.245075,2.463761,546.518941,3.18314322190286
-c8,202.0632,41.288602,228.5053,30.964323,14.98868,26.245075,2.463761,546.518941,3.18314322190286
-c9,202.0632,41.288602,228.5053,30.964323,14.98868,26.245075,2.463761,546.518941,3.18314322190286
-c10,202.0632,41.288602,228.5053,30.964323,14.98868,26.245075,2.463761,546.518941,3.18314322190286
-c11,202.0632,41.288602,228.5053,415.4985,14.98868,26.245075,2.463761,931.053118,1.86847362467308
-c12,202.0632,41.288602,228.5053,30.964323,115.04262,26.245075,2.463761,646.572881,2.69056770406557
-c13,202.0632,41.288602,228.5053,30.964323,14.98868,26.245075,2.463761,546.518941,3.18314322190286
-c14,202.0632,41.288602,228.5053,30.964323,14.98868,26.245075,2.463761,546.518941,3.18314322190286
-c15,202.0632,41.288602,228.5053,30.964323,14.98868,26.245075,2.463761,546.518941,3.18314322190286
-c16,202.0632,41.288602,19.299403,30.964323,115.04262,26.245075,2.463761,437.366984,3.97754756734277
-c17,202.0632,41.288602,228.5053,415.4985,14.98868,26.245075,2.463761,931.053118,1.86847362467308
-c18,202.0632,41.288602,228.5053,30.964323,14.98868,26.245075,2.463761,546.518941,3.18314322190286
-c16,437.366984
-
-Unpatch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0,0
-c3,0,0,0,0,0,0,0,0,0
-c4,0,0,0,0,0,0,0,0,0
-c5,0,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,0,0,0,0
-c7,0,0,0,0,0,0,0,0,0
-c8,0,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0,0
-c11,0,0,0,0,0,0,0,0,0
-c12,0,0,0,0,0,0,0,0,0
-c13,0,0,0,0,0,0,0,0,0
-c14,0,0,0,0,0,0,0,0,0
-c15,0,0,0,0,0,0,0,0,0
-c16,0,0,0,0,0,0,0,0,0
-c17,0,0,0,0,0,0,0,0,0
-c18,0,0,0,0,0,0,0,0,0
-c0,0
-
diff --git a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_tensors.txt b/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_tensors.txt
deleted file mode 100644
index 55ad19b1c8cd2123e86ce7cf7dc43dad7516e413..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet2_cifar10/alexnet2_tensors.txt
+++ /dev/null
@@ -1,30 +0,0 @@
-#Conv1,3
-Conv1,115.33,355.301,200.956,558.231,6.64024,16.2201,41.8978,123.674
-Add1,47.2501,154.498,36.2217,116.852,56.9247,172.83,41.6054,134.411
-Tanh1,39.4831,135.034,33.4466,118.003,57.0977,185.926,41.7089,145.509
-#Conv2,4
-Conv2,310.308,1282.26,610.501,2404.06,56.8745,201.057,41.8404,192.576
-Add2,45.3358,217.323,35.0236,153.259,158.537,686.903,41.5412,177.63
-Tanh2,38.504,188.321,36.2146,162.298,56.9829,242.874,41.625,182.762
-Pool1,39.1015,194.307,43.674,198.246,54.8347,240.102,10.6202,44.9693
-#Conv3,3
-Conv3,165.302,812.529,188.912,837.371,15.6031,66.3758,21.0237,93.63
-Add3,26.3653,138.399,21.2537,100.994,28.3975,126.566,20.9649,93.9519
-Tanh3,19.3912,101.904,18.3396,88.5345,28.9295,129.858,20.8836,94.2318
-#Conv4,4
-Conv4,303.007,1619.75,284.927,1346.18,28.3107,127.724,20.9833,102.055
-Add4,26.3854,150.498,19.4384,96.5969,76.34,366.744,20.8326,97.3319
-Tanh4,19.2511,109.512,18.4717,92.4694,28.0864,130.933,20.8569,97.8308
-Pool2,45.8717,263.305,22.3065,111.077,27.6028,129.232,5.39931,22.7498
-#Conv5,3
-Conv5,156.337,882.718,86.4203,417.756,8.38767,36.7727,10.6053,48.7992
-Add5,24.2343,136.978,19.2262,98.1635,14.7236,68.2983,10.5498,48.5475
-Tanh5,9.61697,52.0084,9.39612,49.9132,14.9164,69.1465,10.5006,48.3962
-#Conv6,4
-Conv6,248.321,1443.4,139.892,696.675,14.7838,68.8585,10.568,51.057
-Add6,24.3243,144.761,18.7741,98.9177,39.6841,197.748,10.548,49.6626
-Tanh6,9.63435,54.9648,9.37294,50.5591,14.3859,68.827,10.5241,49.2841
-Pool3,23.8305,140.946,12.0697,64.0871,14.3906,68.8152,2.78859,10.4233
-#FC1,2
-Mul1,2.20702,9.19011,4.29982,22.6027,5.01057,21.4017,0.219266,0.10134
-Add7,0.256741,0.108633,0.658159,3.49495,0.887661,1.49455,0.140755,0.0381309
diff --git a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_confs1.txt b/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_confs1.txt
deleted file mode 100644
index ff963b918fa017a3635a326bbbd7111246cf9384..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_confs1.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-9 9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9
-8 8 8 8,6,6,8 8 8,7,7
-8 8 8 8,6,6,8 8 8,7,7
-8 8 8 8,6,8 8 8,8 8 8,7,7
-8 8 8 8,6,7,8 8 8,7,7
-8 8 8 8,6,6,8 8 8,7,7
-8 8 8 8,6,6,8 8 8,7,7
-8 8 8 8,6,6,8 8 8,7,7
-8 8 8 8,6,8 8 8,8 8 8,7,7
-8 8 8 8,6,8 8 8,8 8 8,7,7
-8 8 8 8,6,8 8 8,8 8 8,7,7
-8 8 8 8,6,8 8 8,8 8 8,7,7
-8 8 8 8,6,6,8 8 8,7,7
-8 8 8 8,6,6,8 8 8,7,7
-8 8 8 8,6,6,6,7,7
-8 8 8 8,6,6,8 8 8,7,7
-8 8 8 8,6,6,8 8 8,7,7
-8 8 8 8,6,6,8 8 8,7,7
-8 8 8 8,6,8 8 8,8 8 8,7,7
-8 8 8 8,6,7,8 8 8,7,7
-8 8 8 8,6,8 8 8,8 8 8,7,7
-8 8 8 8,6,6,8 8 8,7,7
-8 8 8 8,6,6,8 8 8,7,7
-8 8 8 8,6,8 8 8,8 8 8,7,7
-8 8 8 8,6,6,8 8 8,7,7
-8 8 8 8,6,7,8 8 8,7,7
-8 8 8 8,6,8 8 8,8 8 8,7,7
-8 8 8 8,6,8 8 8,8 8 8,7,7
-8 8 8 8,6,8 8 8,8 8 8,7,7
-8 8 8 8,6,8 8 8,8 8 8,7,7
-8 8 8 8,7,7,8 8 8,8 8 8 8,7
diff --git a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_confs2.txt b/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_confs2.txt
deleted file mode 100644
index 68b20e726f79648fa9c91169eb1f0a1111b6a889..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_confs2.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-9 9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9
-8 8 8 8,8 8 8 8,8 8 8,8 8 8,8 8 8 8,7
-8 8 8 8,7,4,6,8 8 8 8,7
-8 8 8 8,8 8 8 8,7,4,6,7
-8 8 8 8,4,4,6,4,7
-8 8 8 8,4,4,7,7,7
-8 8 8 8,4,4,8 8 8,5,7
-8 8 8 8,7,7,7,8 8 8 8,7
-8 8 8 8,7,5,7,4,7
-8 8 8 8,8 8 8 8,8 8 8,6,4,7
-8 8 8 8,8 8 8 8,4,6,5,7
-8 8 8 8,7,4,6,8 8 8 8,7
-8 8 8 8,8 8 8 8,7,4,6,7
-8 8 8 8,4,4,6,4,7
-8 8 8 8,4,4,7,7,7
-8 8 8 8,4,4,8 8 8,5,7
-8 8 8 8,7,7,7,8 8 8 8,7
-8 8 8 8,7,5,7,4,7
-8 8 8 8,8 8 8 8,8 8 8,6,4,7
-8 8 8 8,4,8 8 8,8 8 8,7,7
-8 8 8 8,8 8 8 8,4,6,5,7
-8 8 8 8,8 8 8 8,8 8 8,8 8 8,8 8 8 8,7
-8 8 8 8,7,4,6,8 8 8 8,7
-8 8 8 8,8 8 8 8,4,6,5,7
-8 8 8 8,4,4,6,4,7
-8 8 8 8,7,5,7,4,7
-8 8 8 8,7,7,7,8 8 8 8,7
-8 8 8 8,8 8 8 8,8 8 8,6,4,7
-8 8 8 8,8 8 8 8,7,4,6,7
-8 8 8 8,4,4,8 8 8,5,7
-8 8 8 8,4,4,7,7,7
diff --git a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_fp16.csv b/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_fp16.csv
deleted file mode 100644
index d6a6ef63a3128bc63e9fb6b5d07ce78c7d520338..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_fp16.csv
+++ /dev/null
@@ -1,61 +0,0 @@
-Add1,93.2842,353.575,204.306,149.269,3790.29,2190.14,1600.15,0.49058,4.5223,3.4086,1.24796,43.6026,34.3362,10.1933
-Add1_f2h,309.757,1205.25,778.891,426.362,3892.05,2515.4,1376.65,9.65112,25.7956,14.7813,11.0587,35.4623,28.6884,7.22661
-Add1_h2f,83.2903,330.731,185.213,145.518,3970.82,2223.7,1747.13,0.806036,4.16027,2.76063,1.51937,31.9222,24.6197,8.42906
-Add2,57.2394,332.553,242.25,90.3031,5809.85,4232.21,1577.64,0.457316,4.377,3.61364,0.896967,59.0466,51.9109,9.25098
-Add2_f2h,183.024,1140.1,895.094,245.002,6230.37,4891.61,1338.76,5.77653,29.1735,22.2262,6.9702,39.1547,34.6242,5.22016
-Add2_h2f,62.1204,356.499,249.999,106.501,5738.83,4024.41,1714.41,0.394536,3.84656,3.14568,0.877756,48.4454,42.9786,7.80049
-Add3,64.5391,421.019,313.098,107.922,6523.48,4851.29,1672.19,0.585465,4.31465,3.23825,1.10633,31.0225,23.7417,8.2678
-Add3_f2h,39.2865,257.541,197.461,60.0801,6555.52,5026.24,1529.28,0.47706,3.33185,2.49827,0.847902,40.88,31.2198,10.5659
-Add3_h2f,31.1203,195.786,142.213,53.5726,6291.23,4569.77,1721.46,0.306809,2.38301,1.71741,0.675567,39.8861,28.601,11.885
-Add4,40.9697,274.189,212.979,61.2102,6692.3,5198.28,1494.02,0.384841,4.60445,3.90429,0.821323,78.8032,70.489,12.2778
-Add4_f2h,75.4038,524.503,420.111,104.392,6958.45,5574.07,1384.38,5.55358,35.9191,28.1478,7.80022,43.8327,40.6864,11.0572
-Add4_h2f,20.652,132.57,101.351,31.2198,6419.34,4907.63,1511.71,0.183388,1.80855,1.4875,0.410811,74.4576,63.8749,15.3763
-Add5,41.049,260.675,199.276,61.3997,6350.43,4854.66,1495.77,0.362686,4.17524,3.61668,0.698418,90.2431,81.3189,11.135
-Add5_f2h,79.1767,523.707,412.572,111.134,6618.91,5215.04,1403.88,7.23508,41.7069,31.8753,9.84169,56.7552,52.1865,6.95965
-Add5_h2f,20.6328,125.982,94.7906,31.1915,6105.9,4594.17,1511.73,0.195554,2.38048,1.96829,0.481221,98.2565,84.4038,17.013
-Add6,0.580578,3.19613,2.21839,0.977745,5485.52,3807.55,1677.98,0.0359955,1.55104,1.07733,0.47379,2596.77,1804.25,792.663
-Add6_f2h,0.460408,1.00469,0.697219,0.307468,1898.57,1317.21,581.362,0.176587,1.16926,0.811923,0.35736,1980.93,1374.61,606.356
-Add6_h2f,0.0886822,0.0103369,0.00716523,0.00317167,111.312,77.1828,34.1295,0.015021,0.0600414,0.0415981,0.0184434,608.231,421.665,186.568
-Conv1,759.668,1563.71,760.258,803.448,2058.68,1000.97,1057.71,11.5002,10.4346,3.29189,8.66694,18.8258,13.6161,6.05864
-Conv1_f2h,5.44249,6.60174,1.40281,5.19893,1213.19,257.967,955.226,0.17219,0.353329,0.126049,0.277371,56.2627,24.0056,40.2598
-Conv1_h2f,83.2352,403.353,303.997,99.3552,4845.94,3652.27,1193.67,0.413148,2.25226,1.68684,0.670959,12.8815,9.56103,5.46339
-Conv2,1196.88,6519.17,4850.19,1668.97,5447.23,4052.73,1394.49,11.586,14.405,8.37537,10.139,44.9168,38.8365,6.77628
-Conv2_f2h,26.722,110.701,57.7904,52.9111,4142.93,2162.81,1980.12,0.279116,1.38408,0.889919,0.604014,52.084,36.0813,18.6573
-Conv2_h2f,62.1589,473.091,401.012,72.0794,7611.02,6451.42,1159.6,0.35738,2.96054,2.47914,0.520919,25.9077,21.4465,5.48728
-Conv3,561.436,3134.15,2164.67,969.482,5582.78,3855.92,1726.86,5.64894,12.6119,7.66204,6.48434,42.9576,35.3499,8.45526
-Conv3_f2h,20.4033,103.834,61.6107,42.2228,5089.07,3019.68,2069.39,0.33686,2.07671,1.24786,0.849022,58.3312,37.7391,22.1356
-Conv3_h2f,31.0982,213.521,168.425,45.0957,6866.05,5415.94,1450.12,0.193664,1.81345,1.41647,0.418016,45.1015,35.0321,10.9083
-Conv4,627.538,4183.57,3200.17,983.396,6666.76,5099.68,1567.08,3.19725,10.3525,6.96967,4.85478,29.6592,25.6687,6.16061
-Conv4_f2h,39.4494,231.112,155.03,76.0814,5858.45,3929.88,1928.58,0.383614,2.64826,1.77466,0.893289,38.1649,27.1767,11.8054
-Conv4_h2f,20.805,157.506,130.042,27.4631,7570.58,6250.58,1320,0.262449,2.5472,2.06942,0.51889,78.0758,64.5926,16.6892
-Conv5,430.284,2806.26,2156.62,649.637,6522.06,5012.26,1509.8,3.00065,16.2034,13.6006,4.30614,43.4971,39.7871,5.8117
-Conv5_f2h,26.6141,161.682,116.58,45.1028,6075.21,4380.5,1694.71,0.247547,2.32135,1.82405,0.588892,78.8117,64.6347,17.3373
-Conv5_h2f,20.7426,148.724,120.885,27.8386,7169.94,5827.84,1342.1,0.210998,2.14481,1.7637,0.390591,69.9498,57.905,12.7277
-Mul1,6.85001,40.5555,28.1774,12.3781,5920.38,4113.44,1806.94,0.171041,1.77782,1.24768,0.543706,208.025,148.206,62.2562
-Mul1_f2h,7.36824,40.3988,28.1257,12.2731,5482.81,3817.12,1665.69,0.13546,1.47559,1.05633,0.430894,175.413,126.752,50.5788
-Mul1_h2f,0.141077,0.0604596,0.0419153,0.0185444,379.141,262.838,116.304,0.0403448,0.169651,0.117675,0.0519786,996.445,691.124,305.345
-Pool1,77.3703,318.571,162.02,156.551,4117.58,2094.15,2023.43,0.455239,2.35316,1.53673,0.961891,33.6923,22.5917,12.0676
-Pool1_f2h,210.001,844.263,423.672,420.591,4020.89,2017.89,2003,4.20962,10.864,4.63699,6.43175,33.3606,22.9331,10.8583
-Pool1_h2f,20.9052,88.6981,46.9757,41.7224,4246.27,2248.89,1997.38,0.667696,1.1714,0.660301,0.539743,117.997,63.6641,55.0082
-Pool2,58.7924,314.375,188.669,125.706,5347.29,3209.14,2138.15,0.411831,2.52633,1.74937,0.927995,36.5478,28.4951,9.66495
-Pool2_f2h,76.8368,405.93,248.377,157.553,5283.1,3232.6,2050.5,0.716538,3.90927,2.59879,1.50885,31.914,26.7871,6.83944
-Pool2_h2f,15.6216,82.0579,49.566,32.4919,5255.37,3174.44,2080.93,0.215716,3.1694,1.92435,1.25917,232.498,141.245,92.0377
-Pool3,20.5304,123.325,86.6382,36.6872,6007.12,4220.09,1787.03,0.282783,2.47786,1.92759,0.626736,98.3816,79.4265,23.2579
-Pool3_f2h,26.4508,154.684,109.843,44.8406,5849.24,4153.88,1695.36,0.618053,2.67039,1.80558,0.988641,86.0654,72.4368,16.4758
-Pool3_h2f,5.2822,29.1224,20.4156,8.70687,5513.14,3864.89,1648.25,0.11154,1.36843,0.975666,0.401594,228.306,164.606,65.5302
-Softmax1,2.04532,9.65196,6.69876,2.9532,4718.43,3274.79,1443.63,0.0592166,1.26487,0.87714,0.388768,599.51,416.222,183.818
-Tanh1,71.349,309.397,166.636,142.762,4336.38,2335.5,2000.89,0.319789,2.76113,1.82636,1.0288,32.4436,22.8151,10.8671
-Tanh1_f2h,100.913,396.573,209.559,187.014,3930.03,2076.77,1853.26,0.899812,3.09604,1.99119,1.45311,33.4653,24.7837,9.33032
-Tanh1_h2f,83.2272,381.858,208.746,173.112,4588.14,2508.14,2080,0.436928,2.54257,1.61456,1.04033,21.0556,14.923,7.84162
-Tanh2,53.7113,299.392,193.858,105.534,5574.12,3609.27,1964.84,0.516207,3.73585,2.74272,1.11904,46.5019,38.4508,10.2748
-Tanh2_f2h,76.8765,416.385,277.089,139.296,5416.46,3604.5,1811.96,0.713004,3.82792,2.92357,1.24867,42.9582,37.8125,7.71353
-Tanh2_h2f,62.0292,344.851,220.072,124.778,5559.5,3547.89,2011.61,0.268756,2.54138,2.0299,0.664609,34.7016,29.7044,7.08025
-Tanh3,27.4497,170.841,118.94,51.9014,6223.81,4333.02,1890.79,0.272378,2.30827,1.59613,0.719755,58.4892,40.4211,18.4657
-Tanh3_f2h,39.0187,237.416,168.18,69.2353,6084.79,4310.35,1774.44,0.483226,2.94737,2.05624,0.902876,39.7976,28.4209,12.0028
-Tanh3_h2f,31.0592,188.341,129.589,58.7521,6063.92,4172.31,1891.61,0.214138,1.88422,1.30556,0.593641,41.1045,29.1294,12.6756
-Tanh4,18.3085,118.735,87.9986,30.7359,6485.13,4806.35,1678.77,0.220835,2.58413,2.07212,0.565112,112.566,92.977,23.0207
-Tanh4_f2h,26.1769,164.262,123.396,40.8663,6275.42,4714.21,1561.21,0.358447,2.34225,1.86472,0.587341,71.3708,61.4761,14.4163
-Tanh4_h2f,20.6587,128.708,94.4883,34.2199,6230.16,4573.71,1656.45,0.207862,2.25217,1.81483,0.480835,85.0819,70.6279,17.1689
-Tanh5,18.3283,114.102,83.1648,30.9372,6224.98,4537.15,1687.83,0.260623,5.957,4.43105,1.5561,299.19,223.089,77.8918
-Tanh5_f2h,26.1523,156.042,115.325,40.7168,5968.06,4410.79,1557.27,0.343513,4.99963,3.81509,1.25126,209.878,159.503,52.6995
-Tanh5_h2f,20.633,123.091,88.9037,34.1876,5965.83,4308.87,1656.96,0.190143,2.18341,1.76558,0.481496,94.4781,78.5292,18.9483
diff --git a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_fp32.csv b/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_fp32.csv
deleted file mode 100644
index fe62cc77eb9744a531345697df044276e4bb26e2..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_fp32.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-Add1,114.477,689.511,545.497,144.014,6023.13,4765.12,1258.01,0.494926,3.39383,2.63662,0.913302,15.2664,12.3757,5.32532
-Add2,139.245,1083.98,913.705,170.273,7784.72,6561.88,1222.83,0.616984,5.07127,4.2664,0.881209,16.6482,14.6652,3.17889
-Add3,73.7075,582.264,483.313,98.9509,7899.66,6557.19,1342.47,0.766894,6.39355,5.25146,1.16607,26.1761,21.787,5.48411
-Add4,47.9113,399.265,338.936,60.3286,8333.36,7074.23,1259.12,0.344035,4.04787,2.81768,2.21291,52.9096,30.2717,43.7817
-Add5,47.955,388.123,327.404,60.7191,8093.5,6827.34,1266.16,0.385326,4.51233,3.74464,0.829162,71.7827,59.7486,13.7432
-Add6,0.1721,0.083548,0.0671001,0.0164478,452.189,363.168,89.0211,0.0152622,0.264957,0.212801,0.0521557,1369.25,1099.72,269.533
-Conv1,925.494,3271.78,2320.75,951.03,3535.33,2507.72,1027.61,7.38366,11.5946,7.18956,6.71324,22.3312,19.251,4.89111
-Conv2,2080.05,13944.8,11447.4,2497.39,6704.35,5503.69,1200.66,13.4036,25.0061,20.4344,10.1874,42.1223,38.0878,4.46151
-Conv3,1014.71,6863.91,5475.41,1388.5,6764.61,5396.22,1368.39,5.96381,14.6827,14.1842,4.69302,43.9951,39.249,5.4172
-Conv4,1226.86,9535.92,7945,1590.92,7772.68,6475.96,1296.73,4.45174,35.4147,15.5377,26.2279,31.4207,24.4877,19.7888
-Conv5,861.092,6591.9,5495.97,1095.93,7655.42,6382.69,1272.73,4.21251,19.5607,11.6479,15.1544,34.6117,29.1745,17.0262
-Mul1,3.73053,25.3858,20.4052,4.98065,6805.05,5469.93,1335.12,0.0924027,1.617,1.29761,0.32099,403.014,323.605,79.8704
-Pool1,175.352,985.339,672.448,312.891,5619.71,3835.24,1784.47,3.94384,18.2525,12.0852,6.20264,25.3347,19.4031,6.99576
-Pool2,132.287,891.752,674.086,217.667,6741.51,5096,1645.51,3.25049,19.5357,14.6978,4.86596,26.8879,22.2401,5.69913
-Pool3,44.6908,343.93,279.325,64.6046,7695.89,6250.31,1445.57,0.968672,7.36666,5.9023,1.48972,37.1193,30.6839,9.10475
-Softmax1,1.92222,11.9898,9.62903,2.36074,6235.93,5008.1,1227.83,0.0948851,1.8978,1.52456,0.373476,928.239,745.748,182.625
-Tanh1,76.4029,470.583,342.12,128.463,6159.2,4477.81,1681.39,0.423554,3.56257,2.71704,0.923294,25.9953,20.7846,7.04828
-Tanh2,55.9198,411.855,325.555,86.2999,7365.1,5821.83,1543.28,0.307608,2.90099,2.28812,0.637094,29.5989,23.7191,6.64274
-Tanh3,27.9644,214.918,172.203,42.7143,7685.42,6157.97,1527.45,0.27067,2.55707,2.04195,0.526121,57.2266,45.9891,11.875
-Tanh4,18.6218,150.823,125.161,25.6619,8099.35,6721.29,1378.06,0.144119,1.9824,1.59565,0.785064,92.3216,73.7595,41.0994
-Tanh5,18.6201,147.003,121.178,25.8248,7894.73,6507.83,1386.9,0.18229,2.27179,1.84236,0.444801,85.7818,70.1289,16.9109
diff --git a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_layers.txt b/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_layers.txt
deleted file mode 100644
index bc8c3f5668a2fdb5eb8a568f34b334fe02016954..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_layers.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-Conv1,2000,3,32,32,64,3,11,11,1,1
-Conv2,2000,64,16,16,192,64,5,5,1,1
-Conv3,2000,192,8,8,384,192,3,3,1,1
-Conv4,2000,384,8,8,256,384,3,3,1,1
-Conv5,2000,256,8,8,256,256,3,3,1,1
-FC1,2000,4096,4096,10
diff --git a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_ops.txt b/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_ops.txt
deleted file mode 100644
index 9d047b9e469f980534fa95b39fd43e2984bf9d43..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_ops.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-#Conv1,4
-Conv1
-Add1
-Tanh1
-Pool1
-#Conv2,4
-Conv2
-Add2
-Tanh2
-Pool2
-#Conv3,3
-Conv3
-Add3
-Tanh3
-#Conv4,3
-Conv4
-Add4
-Tanh4
-#Conv5,4
-Conv5
-Add5
-Tanh5
-Pool3
-#FC1,2
-Mul1
-Add6
diff --git a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_promise_confs1.txt b/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_promise_confs1.txt
deleted file mode 100644
index f9a86825a6429e3145247f1680c64999ecfb918a..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_promise_confs1.txt
+++ /dev/null
@@ -1,16 +0,0 @@
-9 9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9
-8 8 8 8,4,6,6,7,7
-8 8 8 8,6,9 9 9,6,9 9 9 9,3
-8 8 8 8,4,3,8 8 8,8 8 8 8,4
-9 9 9 9,4,3,9 9 9,7,9 9
-8 8 8 8,8 8 8 8,9 9 9,7,3,4
-8 8 8 8,4,6,8 8 8,9 9 9 9,7
-8 8 8 8,9 9 9 9,6,6,9 9 9 9,4
-9 9 9 9,7,9 9 9,8 8 8,9 9 9 9,5
-8 8 8 8,9 9 9 9,7,8 8 8,5,5
-8 8 8 8,8 8 8 8,7,7,8 8 8 8,5
-9 9 9 9,6,6,8 8 8,9 9 9 9,7
-8 8 8 8,4,4,8 8 8,8 8 8 8,8 8
-8 8 8 8,9 9 9 9,6,6,9 9 9 9,8 8
-8 8 8 8,8 8 8 8,8 8 8,7,8 8 8 8,9 9
-9 9 9 9,8 8 8 8,9 9 9,9 9 9,7,7
diff --git a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_promise_confs2.txt b/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_promise_confs2.txt
deleted file mode 100644
index c73097f5494c9545b8d6a2bd7f737a7a9ad2dcc8..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_promise_confs2.txt
+++ /dev/null
@@ -1,52 +0,0 @@
-9 9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9
-8 8 8 8,3,1,4,7,3
-8 8 8 8,3,1,6,7,3
-8 8 8 8,4,1,3,7,4
-9 9 9 9,4,1,9 9 9,3,3
-8 8 8 8,3,1,7,7,4
-9 9 9 9,7,1,7,4,9 9
-8 8 8 8,3,1,6,5,9 9
-9 9 9 9,1,5,7,2,9 9
-8 8 8 8,3,2,2,7,3
-8 8 8 8,9 9 9 9,1,7,3,9 9
-8 8 8 8,4,1,6,7,8 8
-8 8 8 8,4,1,6,7,5
-8 8 8 8,1,8 8 8,6,7,3
-8 8 8 8,5,1,6,7,7
-9 9 9 9,7,1,6,7,8 8
-8 8 8 8,3,1,9 9 9,7,8 8
-8 8 8 8,3,7,2,7,3
-8 8 8 8,3,8 8 8,2,4,8 8
-9 9 9 9,1,8 8 8,9 9 9,4,8 8
-8 8 8 8,5,4,3,2,9 9
-8 8 8 8,5,1,6,8 8 8 8,3
-8 8 8 8,9 9 9 9,1,8 8 8,7,3
-9 9 9 9,4,2,4,9 9 9 9,2
-9 9 9 9,2,3,8 8 8,7,2
-9 9 9 9,3,2,6,7,3
-8 8 8 8,6,4,6,2,3
-8 8 8 8,6,4,9 9 9,2,3
-8 8 8 8,7,4,4,3,4
-9 9 9 9,4,7,6,2,8 8
-8 8 8 8,7,3,3,5,5
-8 8 8 8,3,2,6,7,7
-9 9 9 9,7,4,6,3,4
-9 9 9 9,7,9 9 9,3,7,3
-8 8 8 8,3,9 9 9,5,6,3
-9 9 9 9,6,7,3,4,5
-8 8 8 8,3,9 9 9,6,3,3
-8 8 8 8,2,5,7,9 9 9 9,3
-8 8 8 8,3,5,6,7,3
-8 8 8 8,4,7,6,5,3
-8 8 8 8,3,3,6,7,4
-8 8 8 8,3,4,7,5,6
-8 8 8 8,7,7,6,7,3
-8 8 8 8,5,4,7,4,7
-8 8 8 8,7,3,4,7,7
-8 8 8 8,4,3,7,4,5
-9 9 9 9,3,9 9 9,5,7,3
-8 8 8 8,7,2,7,9 9 9 9,7
-8 8 8 8,3,4,4,8 8 8 8,3
-8 8 8 8,3,5,6,7,5
-9 9 9 9,3,4,9 9 9,7,3
-8 8 8 8,7,4,7,7,3
diff --git a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_promise_results1.csv b/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_promise_results1.csv
deleted file mode 100644
index 4854a2efa2b2e321decb3ef95ab6bda3b1e22f42..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_promise_results1.csv
+++ /dev/null
@@ -1,220 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,5417.213,16332.387,7661.092,10086.008,7470.956,25.469348,46993.125348,0.999999997872029
-c1,2545.253,150.857135,137.685622,177.024371,157.113483,0.174571,3168.108182,14.8331815597962
-c2,2545.253,255.701869,7661.092,177.024371,7470.956,0.057413,18110.084653,2.59485949342205
-c3,2545.253,150.857135,60.283643,4576.494,3304.362,0.077363,10637.327141,4.41775685595832
-c4,5417.213,150.857135,60.283643,10086.008,157.113483,25.469348,15896.944609,2.95611051105908
-c5,2545.253,7465.49,7661.092,235.670225,51.671694,0.077363,17959.254282,2.61665235919258
-c6,2545.253,150.857135,137.685622,4576.494,7470.956,0.174571,14881.420328,3.15783870063778
-c7,2545.253,16332.387,137.685622,177.024371,7470.956,0.077363,26663.383356,1.7624591952311
-c8,5417.213,340.412547,7661.092,4576.494,7470.956,0.086312,25466.253859,1.8453096958688
-c9,2545.253,16332.387,183.299064,4576.494,77.680494,0.086312,23715.19987,1.98156142083756
-c10,2545.253,7465.49,183.299064,235.670225,3304.362,0.086312,13734.160601,3.42162337918315
-c11,5417.213,255.701869,137.685622,4576.494,7470.956,0.174571,17858.225062,2.63145552941035
-c12,2545.253,150.857135,81.230765,4576.494,3304.362,43.75163,10701.94853,4.39108119209875
-c13,2545.253,16332.387,137.685622,177.024371,7470.956,43.75163,26707.057623,1.75957703148744
-c14,2545.253,7465.49,3726.01,235.670225,3304.362,25.469348,17302.254573,2.71601165490486
-c15,5417.213,7465.49,7661.092,10086.008,157.113483,0.174571,30787.091054,1.52639056132117
-c1,3168.108182
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,1291.7259,2407.5018,1116.3819,1293.3931,972.3579,3.90263,7085.26323,0.999999985886199
-c1,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
-c2,1001.6715,182.333568,1116.3819,128.244736,972.3579,0.090100,3401.079704,2.08323933524495
-c3,1001.6715,182.333568,96.183552,686.8162,510.1917,0.090100,2477.28662,2.86009010293326
-c4,1291.7259,182.333568,96.183552,1293.3931,82.574848,3.90263,2950.113598,2.40169158049854
-c5,1001.6715,1366.6231,1116.3819,128.244736,82.574848,0.090100,3695.586184,1.91722305623754
-c6,1001.6715,182.333568,96.183552,686.8162,972.3579,0.090100,2939.45282,2.41040201113342
-c7,1001.6715,2407.5018,96.183552,128.244736,972.3579,0.090100,4606.049588,1.53825158431508
-c8,1291.7259,182.333568,1116.3819,686.8162,972.3579,0.090100,4249.705568,1.66723622375817
-c9,1001.6715,2407.5018,96.183552,686.8162,82.574848,0.090100,4274.838,1.65743428505515
-c10,1001.6715,1366.6231,96.183552,128.244736,510.1917,0.090100,3103.004688,2.28335555826413
-c11,1291.7259,182.333568,96.183552,686.8162,972.3579,0.090100,3229.50722,2.1939145906627
-c12,1001.6715,182.333568,96.183552,686.8162,510.1917,7.430588,2484.627108,2.85164036165541
-c13,1001.6715,2407.5018,96.183552,128.244736,972.3579,7.430588,4613.390076,1.53580403124351
-c14,1001.6715,1366.6231,653.4248,128.244736,510.1917,3.90263,3664.058466,1.93371997264085
-c15,1291.7259,1366.6231,1116.3819,1293.3931,82.574848,0.090100,5150.788948,1.37556851270217
-c1,1491.098304
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,5417.213,16332.387,7661.092,10086.008,7470.956,25.469348,46993.125348,0.999999997872029
-c1,2551.85474,553.269866,357.336004,478.667351,352.195669,4.712122,4298.035752,10.9336280492246
-c2,2551.85474,658.1146,7661.092,478.667351,7470.956,4.594964,18825.279655,2.49627766278048
-c3,2551.85474,553.269866,279.934025,4576.494,3304.362,4.614914,11270.529545,4.1695578493818
-c4,5417.213,553.269866,279.934025,10086.008,352.195669,25.469348,16714.089908,2.81158742866093
-c5,2551.85474,7465.49,7874.613,537.313205,246.75388,4.614914,18680.639739,2.51560576902143
-c6,2551.85474,553.269866,357.336004,4576.494,7619.68,4.712122,15663.346732,3.00019694718077
-c7,2551.85474,16805.478,357.336004,478.667351,7470.956,4.614914,27668.907009,1.69840916241734
-c8,5417.213,742.825278,7661.092,4807.606,7619.68,4.623863,26253.040141,1.79000698268118
-c9,2551.85474,16805.478,402.949446,4576.494,272.76268,4.623863,24614.162729,1.9091904800692
-c10,2551.85474,7465.49,402.949446,537.313205,3304.362,4.623863,14266.593254,3.29392758186552
-c11,5417.213,658.1146,357.336004,4576.494,7619.68,4.712122,18633.549726,2.52196311421182
-c12,2551.85474,553.269866,300.881147,4576.494,3304.362,43.75163,11330.613383,4.14744756923415
-c13,2551.85474,16805.478,357.336004,478.667351,7470.956,84.15043,27748.442525,1.69354100275384
-c14,2551.85474,7465.49,3726.01,537.313205,3304.362,25.5298076,17610.5597526,2.66846288484474
-c15,5417.213,7576.191,7874.613,10086.008,352.195669,4.712122,31310.932791,1.50085356803622
-c1,4298.035752
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c2,0,324.065467,0,229.498883,0,1.216143,554.780493,0
-c3,0,324.065467,175.390266,0,0,1.216143,500.671876,0
-c4,0,324.065467,175.390266,0,151.959867,0,651.4156,0
-c5,0,0,0,229.498883,151.959867,1.216143,382.674893,0
-c6,0,324.065467,175.390266,0,0,1.216143,500.671876,0
-c7,0,0,175.390266,229.498883,0,1.216143,406.105292,0
-c8,0,324.065467,0,0,0,1.216143,325.28161,0
-c9,0,0,175.390266,0,151.959867,1.216143,328.566276,0
-c10,0,0,175.390266,229.498883,0,1.216143,406.105292,0
-c11,0,324.065467,175.390266,0,0,1.216143,500.671876,0
-c12,0,324.065467,175.390266,0,0,0,499.455733,0
-c13,0,0,175.390266,229.498883,0,0,404.889149,0
-c14,0,0,0,229.498883,0,0,229.498883,0
-c15,0,0,0,0,151.959867,1.216143,153.17601,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c2,0,78.347264,0,72.144097,0,3.321408,153.812769,0
-c3,0,78.347264,44.260116,0,0,3.321408,125.928788,0
-c4,0,78.347264,44.260116,0,43.122319,0,165.729699,0
-c5,0,0,0,72.144097,43.122319,3.321408,118.587824,0
-c6,0,78.347264,44.260116,0,0,3.321408,125.928788,0
-c7,0,0,44.260116,72.144097,0,3.321408,119.725621,0
-c8,0,78.347264,0,0,0,3.321408,81.668672,0
-c9,0,0,44.260116,0,43.122319,3.321408,90.703843,0
-c10,0,0,44.260116,72.144097,0,3.321408,119.725621,0
-c11,0,78.347264,44.260116,0,0,3.321408,125.928788,0
-c12,0,78.347264,44.260116,0,0,0,122.60738,0
-c13,0,0,44.260116,72.144097,0,0,116.404213,0
-c14,0,0,0,72.144097,0,0,72.144097,0
-c15,0,0,0,0,43.122319,3.321408,46.443727,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c2,0,36.608647,0,29.286620,0,0.701660,66.596927,0
-c3,0,36.608647,19.907558,0,0,0.701660,57.217865,0
-c4,0,36.608647,19.907558,0,18.163317,0,74.679522,0
-c5,0,0,0,29.286620,18.163317,0.701660,48.151597,0
-c6,0,36.608647,19.907558,0,0,0.701660,57.217865,0
-c7,0,0,19.907558,29.286620,0,0.701660,49.895838,0
-c8,0,36.608647,0,0,0,0.701660,37.310307,0
-c9,0,0,19.907558,0,18.163317,0.701660,38.772535,0
-c10,0,0,19.907558,29.286620,0,0.701660,49.895838,0
-c11,0,36.608647,19.907558,0,0,0.701660,57.217865,0
-c12,0,36.608647,19.907558,0,0,0,56.516205,0
-c13,0,0,19.907558,29.286620,0,0,49.194178,0
-c14,0,0,0,29.286620,0,0,29.28662,0
-c15,0,0,0,0,18.163317,0.701660,18.864977,0
-c0,0
-
-Patch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0
-c3,0,0,0,0,0,0,0,0
-c4,0,0,0,0,0,0,0,0
-c5,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,0,0,0
-c7,0,0,0,0,0,0,0,0
-c8,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0
-c11,0,0,0,0,0,0,0,0
-c12,0,0,0,0,0,0,0,0
-c13,0,0,0,0,0,0,0,0
-c14,0,0,0,0,0,0,0,0
-c15,0,0,0,0,0,0,0,0
-c0,0
-
-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,6.60174,0,0,0,0,0,6.60174,0
-c2,6.60174,0,0,0,0,0,6.60174,0
-c3,6.60174,0,0,0,0,0,6.60174,0
-c4,0,0,0,0,0,0,0,0
-c5,6.60174,0,213.521,0,0,0,220.12274,0
-c6,6.60174,0,0,0,148.724,0,155.32574,0
-c7,6.60174,473.091,0,0,0,0,479.69274,0
-c8,0,0,0,231.112,148.724,0,379.836,0
-c9,6.60174,473.091,0,0,0,0,479.69274,0
-c10,6.60174,0,0,0,0,0,6.60174,0
-c11,0,0,0,0,148.724,0,148.724,0
-c12,6.60174,0,0,0,0,0,6.60174,0
-c13,6.60174,473.091,0,0,0,40.3988,520.09154,0
-c14,6.60174,0,0,0,0,0.0604596,6.6621996,0
-c15,0,110.701,213.521,0,0,0,324.222,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,5.44249,0,0,0,0,0,5.44249,0
-c2,5.44249,0,0,0,0,0,5.44249,0
-c3,5.44249,0,0,0,0,0,5.44249,0
-c4,0,0,0,0,0,0,0,0
-c5,5.44249,0,31.0982,0,0,0,36.54069,0
-c6,5.44249,0,0,0,20.7426,0,26.18509,0
-c7,5.44249,62.1589,0,0,0,0,67.60139,0
-c8,0,0,0,39.4494,20.7426,0,60.192,0
-c9,5.44249,62.1589,0,0,0,0,67.60139,0
-c10,5.44249,0,0,0,0,0,5.44249,0
-c11,0,0,0,0,20.7426,0,20.7426,0
-c12,5.44249,0,0,0,0,0,5.44249,0
-c13,5.44249,62.1589,0,0,0,7.36824,74.96963,0
-c14,5.44249,0,0,0,0,0.141077,5.583567,0
-c15,0,26.722,31.0982,0,0,0,57.8202,0
-c0,0
-
-Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,1291.7259,2407.5018,1116.3819,1293.3931,972.3579,3.90263,7085.26323,0.999999985886199
-c1,1007.11399,218.942215,116.09111,157.531356,100.738165,0.79176,1601.208596,4.42494675909504
-c2,1007.11399,218.942215,1116.3819,157.531356,972.3579,0.79176,3473.119121,2.04002879807851
-c3,1007.11399,218.942215,116.09111,686.8162,510.1917,0.79176,2539.946975,2.78953183699704
-c4,1291.7259,218.942215,116.09111,1293.3931,100.738165,3.90263,3024.79312,2.34239589772685
-c5,1007.11399,1366.6231,1147.4801,157.531356,100.738165,0.79176,3780.278471,1.87427013563335
-c6,1007.11399,218.942215,116.09111,686.8162,993.1005,0.79176,3022.855775,2.34389713667708
-c7,1007.11399,2469.6607,116.09111,157.531356,972.3579,0.79176,4723.546816,1.49998790231133
-c8,1291.7259,218.942215,1116.3819,726.2656,993.1005,0.79176,4347.207875,1.62984225064594
-c9,1007.11399,2469.6607,116.09111,686.8162,100.738165,0.79176,4381.211925,1.61719250051588
-c10,1007.11399,1366.6231,116.09111,157.531356,510.1917,0.79176,3158.343016,2.24334816382249
-c11,1291.7259,218.942215,116.09111,686.8162,993.1005,0.79176,3307.467685,2.14220173576082
-c12,1007.11399,218.942215,116.09111,686.8162,510.1917,7.430588,2546.585803,2.78225966053343
-c13,1007.11399,2469.6607,116.09111,157.531356,972.3579,14.798828,4737.553884,1.49555303304803
-c14,1007.11399,1366.6231,653.4248,157.531356,510.1917,4.043707,3698.928653,1.91549059285165
-c15,1291.7259,1393.3451,1147.4801,1293.3931,100.738165,0.79176,5227.474125,1.35538941466517
-c1,1601.208596
-
-Unpatch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0
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-c5,0,0,0,0,0,0,0,0
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-c7,0,0,0,0,0,0,0,0
-c8,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0
-c11,0,0,0,0,0,0,0,0
-c12,0,0,0,0,0,0,0,0
-c13,0,0,0,0,0,0,0,0
-c14,0,0,0,0,0,0,0,0
-c15,0,0,0,0,0,0,0,0
-c0,0
-
diff --git a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_promise_results2.csv b/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_promise_results2.csv
deleted file mode 100644
index b3853553e5d5a2c768c71ee52ff3748070d0a4b4..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_promise_results2.csv
+++ /dev/null
@@ -1,616 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,5417.213,16332.387,7661.092,10086.008,7470.956,25.469348,46993.125348,0.999999997872029
-c1,2545.253,111.955336,52.545506,104.439555,157.113483,0.057413,2971.364293,15.8153356951801
-c2,2545.253,111.955336,52.545506,177.024371,157.113483,0.057413,3043.949109,15.4382094185593
-c3,2545.253,150.857135,52.545506,77.507541,157.113483,0.077363,2983.354028,15.7517757972311
-c4,5417.213,150.857135,52.545506,10086.008,51.671694,0.057413,15758.352748,2.98210896794104
-c5,2545.253,111.955336,52.545506,235.670225,157.113483,0.077363,3102.614913,15.1462959958287
-c6,5417.213,340.412547,52.545506,235.670225,69.626370,25.469348,6140.936996,7.65243555069303
-c7,2545.253,111.955336,52.545506,177.024371,77.680494,25.469348,2989.928055,15.7171419886509
-c8,5417.213,97.584511,90.627243,235.670225,46.727966,25.469348,5913.292293,7.94703224951792
-c9,2545.253,111.955336,54.515960,70.091949,157.113483,0.057413,2938.987141,15.9895642595612
-c10,2545.253,16332.387,52.545506,235.670225,51.671694,25.469348,19242.996773,2.44208974611103
-c11,2545.253,150.857135,52.545506,177.024371,157.113483,43.75163,3126.545125,15.0303680152268
-c12,2545.253,150.857135,52.545506,177.024371,157.113483,0.086312,3082.879807,15.2432552566505
-c13,2545.253,97.584511,3726.01,177.024371,157.113483,0.057413,6703.042778,7.01071531292687
-c14,2545.253,168.307738,52.545506,177.024371,157.113483,0.174571,3100.418669,15.1570251792654
-c15,5417.213,340.412547,52.545506,177.024371,157.113483,43.75163,6188.060537,7.59416044940091
-c16,2545.253,111.955336,52.545506,10086.008,157.113483,43.75163,12996.626955,3.61579394016088
-c17,2545.253,111.955336,183.299064,70.091949,157.113483,0.057413,3067.770245,15.3183322293312
-c18,2545.253,111.955336,3726.01,70.091949,69.626370,43.75163,6566.688285,7.15628983634191
-c19,5417.213,97.584511,3726.01,10086.008,69.626370,43.75163,19440.193511,2.41731776382152
-c20,2545.253,168.307738,81.230765,77.507541,46.727966,25.469348,2944.496358,15.9596474366009
-c21,2545.253,168.307738,52.545506,177.024371,3304.362,0.057413,6247.550028,7.5218484662313
-c22,2545.253,16332.387,52.545506,4576.494,157.113483,0.057413,23663.850402,1.98586131804831
-c23,5417.213,150.857135,54.515960,104.439555,7470.956,0.051920,13198.03357,3.56061565859
-c24,5417.213,101.243926,60.283643,4576.494,157.113483,0.051920,10312.399972,4.55695328147661
-c25,5417.213,111.955336,54.515960,177.024371,157.113483,0.057413,5917.879563,7.94087207311975
-c26,2545.253,255.701869,81.230765,177.024371,46.727966,0.057413,3105.995384,15.1298112280192
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-c30,2545.253,340.412547,60.283643,77.507541,77.680494,0.086312,3101.223537,15.1530914402095
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-c43,2545.253,168.307738,81.230765,235.670225,69.626370,0.174571,3100.262669,15.1577878552397
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-c46,5417.213,111.955336,7661.092,116.520741,157.113483,0.057413,13463.951973,3.49029208461295
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-c49,2545.253,111.955336,90.627243,177.024371,157.113483,0.086312,3082.059745,15.247311120268
-c50,5417.213,111.955336,81.230765,10086.008,157.113483,0.057413,15853.577997,2.96419679270338
-c51,2545.253,340.412547,81.230765,235.670225,157.113483,0.057413,3359.737433,13.9871418187952
-c9,2938.987141
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,1291.7259,2407.5018,1116.3819,1293.3931,972.3579,3.90263,7085.26323,0.999999985886199
-c1,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
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-c3,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
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-c6,1291.7259,182.333568,96.183552,128.244736,82.574848,3.90263,1784.965234,3.96941223173357
-c7,1001.6715,182.333568,96.183552,128.244736,82.574848,3.90263,1494.910834,4.73958887372751
-c8,1291.7259,182.333568,96.183552,128.244736,82.574848,3.90263,1784.965234,3.96941223173357
-c9,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
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-c11,1001.6715,182.333568,96.183552,128.244736,82.574848,7.430588,1498.438792,4.72842987980854
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-c15,1291.7259,182.333568,96.183552,128.244736,82.574848,7.430588,1788.493192,3.96158222213785
-c16,1001.6715,182.333568,96.183552,1293.3931,82.574848,7.430588,2663.587156,2.66004547590462
-c17,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
-c18,1001.6715,182.333568,653.4248,128.244736,82.574848,7.430588,2055.68004,3.44667591622498
-c19,1291.7259,182.333568,653.4248,1293.3931,82.574848,7.430588,3510.882804,2.01808588430211
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-c21,1001.6715,182.333568,96.183552,128.244736,510.1917,0.090100,1918.715156,3.69271219783328
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-c32,1291.7259,182.333568,96.183552,128.244736,82.574848,0.090100,1781.152704,3.97790869715859
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-c35,1291.7259,182.333568,96.183552,128.244736,82.574848,0.090100,1781.152704,3.97790869715859
-c36,1001.6715,182.333568,1116.3819,128.244736,82.574848,0.090100,2511.296652,2.82135642645868
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-c44,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
-c45,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
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-c49,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
-c50,1291.7259,182.333568,96.183552,1293.3931,82.574848,0.090100,2946.301068,2.40479938268144
-c51,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
-c1,1491.098304
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,5417.213,16332.387,7661.092,10086.008,7470.956,25.469348,46993.125348,0.999999997872029
-c1,2551.85474,514.368067,272.195888,406.082535,352.195669,4.594964,4101.291863,11.4581272857311
-c2,2551.85474,514.368067,272.195888,478.667351,352.195669,4.594964,4173.876679,11.2588674357701
-c3,2551.85474,553.269866,272.195888,379.150521,352.195669,4.614914,4113.281598,11.4247281850036
-c4,5417.213,553.269866,272.195888,10086.008,246.75388,4.594964,16580.035598,2.83431991365788
-c5,2551.85474,514.368067,272.195888,537.313205,352.195669,4.614914,4232.542483,11.1028121812047
-c6,5417.213,742.825278,272.195888,537.313205,264.708556,25.469348,7259.725275,6.47312713919292
-c7,2551.85474,514.368067,272.195888,478.667351,272.76268,25.469348,4115.318074,11.4190746282744
-c8,5417.213,499.997242,310.277625,537.313205,241.810152,25.469348,7032.080572,6.68267722455389
-c9,2551.85474,514.368067,274.166342,371.734929,352.195669,4.594964,4068.914711,11.5493018484825
-c10,2551.85474,16805.478,272.195888,537.313205,246.75388,25.469348,20439.065061,2.29918173741469
-c11,2551.85474,553.269866,272.195888,478.667351,352.195669,43.75163,4251.935144,11.0521733402015
-c12,2551.85474,553.269866,272.195888,478.667351,352.195669,4.623863,4212.807377,11.1548238566706
-c13,2551.85474,499.997242,3726.01,478.667351,352.195669,4.594964,7613.319966,6.17248781617163
-c14,2551.85474,570.720469,272.195888,478.667351,352.195669,4.712122,4230.346239,11.10857636283
-c15,5417.213,742.825278,272.195888,478.667351,352.195669,43.75163,7306.848816,6.43138046074799
-c16,2551.85474,514.368067,272.195888,10086.008,352.195669,43.75163,13820.373994,3.40027882229336
-c17,2551.85474,514.368067,402.949446,371.734929,352.195669,4.594964,4197.697815,11.1949755079029
-c18,2551.85474,514.368067,3726.01,371.734929,264.708556,43.75163,7472.427922,6.28886958959589
-c19,5417.213,499.997242,3726.01,10243.514,264.708556,43.75163,20195.194428,2.32694591195175
-c20,2551.85474,570.720469,300.881147,379.150521,241.810152,25.469348,4069.886377,11.5465445077081
-c21,2551.85474,570.720469,272.195888,478.667351,3304.362,4.594964,7182.395412,6.54282060483667
-c22,2551.85474,16805.478,272.195888,4576.494,352.195669,4.594964,24562.813261,1.91318171324031
-c23,5417.213,553.269866,274.166342,406.082535,7470.956,4.589471,14126.277214,3.32664610098139
-c24,5417.213,503.656657,279.934025,4576.494,352.195669,4.589471,11134.082822,4.22065523287469
-c25,5417.213,514.368067,274.166342,478.667351,352.195669,4.594964,7041.205393,6.67401702658985
-c26,2551.85474,658.1146,300.881147,478.667351,241.810152,4.594964,4235.922954,11.093951601322
-c27,2551.85474,658.1146,300.881147,10086.008,241.810152,4.594964,13843.263603,3.39465651714892
-c28,2551.85474,742.825278,300.881147,406.082535,246.75388,4.614914,4253.012494,11.049373663811
-c29,5417.213,553.269866,402.949446,478.667351,241.810152,43.75163,7137.661445,6.58382651681196
-c30,2551.85474,742.825278,279.934025,379.150521,272.76268,4.623863,4231.151107,11.1064632410808
-c31,2551.85474,514.368067,274.166342,478.667351,352.195669,4.712122,4175.964291,11.2532390001407
-c32,5417.213,742.825278,300.881147,478.667351,246.75388,4.614914,7190.95557,6.53503199081743
-c33,5417.213,742.825278,7661.092,379.150521,352.195669,4.594964,14557.071432,3.22819910891402
-c34,2551.85474,514.368067,7661.092,418.163721,313.098433,4.594964,11463.171925,4.09948705694312
-c35,5417.213,658.1146,402.949446,379.150521,264.708556,4.623863,7126.759986,6.59389747668293
-c36,2551.85474,514.368067,7661.092,478.667351,246.75388,4.594964,11457.331002,4.10157696671582
-c37,2551.85474,503.656657,310.277625,537.313205,7470.956,4.594964,11378.653191,4.12993736131933
-c38,2551.85474,514.368067,310.277625,478.667351,352.195669,4.594964,4211.958416,11.1570722193694
-c39,2551.85474,553.269866,402.949446,478.667351,272.76268,4.594964,4264.099047,11.0206455638026
-c40,2551.85474,514.368067,279.934025,478.667351,352.195669,4.614914,4181.634766,11.2379791287114
-c41,2551.85474,514.368067,300.881147,537.313205,272.76268,4.66868,4181.848519,11.2374047052993
-c42,2551.85474,742.825278,402.949446,478.667351,352.195669,4.594964,4533.087448,10.3666926461046
-c43,2551.85474,570.720469,300.881147,537.313205,264.708556,4.712122,4230.190239,11.1089860223899
-c44,2551.85474,742.825278,279.934025,406.082535,352.195669,4.712122,4337.604369,10.8338890011412
-c45,2551.85474,553.269866,279.934025,537.313205,264.708556,4.623863,4191.704255,11.210982781251
-c46,5417.213,514.368067,7661.092,418.163721,352.195669,4.594964,14367.627421,3.27076445149444
-c47,2551.85474,742.825278,274.166342,537.313205,7470.956,4.712122,11581.827687,4.05748783458405
-c48,2551.85474,514.368067,300.881147,406.082535,3304.362,4.594964,7082.143453,6.63543812636976
-c49,2551.85474,514.368067,310.277625,478.667351,352.195669,4.623863,4211.987315,11.1569956692285
-c50,5417.213,514.368067,300.881147,10086.008,352.195669,4.594964,16675.260847,2.8181343306927
-c51,2551.85474,742.825278,300.881147,537.313205,352.195669,4.594964,4489.665003,10.4669556124797
-c9,4068.914711
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c2,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c3,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c4,0,324.065467,175.390266,0,151.959867,1.216143,652.631743,0
-c5,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c6,0,324.065467,175.390266,229.498883,151.959867,0,880.914483,0
-c7,0,324.065467,175.390266,229.498883,151.959867,0,880.914483,0
-c8,0,324.065467,175.390266,229.498883,151.959867,0,880.914483,0
-c9,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c10,0,0,175.390266,229.498883,151.959867,0,556.849016,0
-c11,0,324.065467,175.390266,229.498883,151.959867,0,880.914483,0
-c12,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c13,0,324.065467,0,229.498883,151.959867,1.216143,706.74036,0
-c14,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c15,0,324.065467,175.390266,229.498883,151.959867,0,880.914483,0
-c16,0,324.065467,175.390266,0,151.959867,0,651.4156,0
-c17,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c18,0,324.065467,0,229.498883,151.959867,0,705.524217,0
-c19,0,324.065467,0,0,151.959867,0,476.025334,0
-c20,0,324.065467,175.390266,229.498883,151.959867,0,880.914483,0
-c21,0,324.065467,175.390266,229.498883,0,1.216143,730.170759,0
-c22,0,0,175.390266,0,151.959867,1.216143,328.566276,0
-c23,0,324.065467,175.390266,229.498883,0,1.216143,730.170759,0
-c24,0,324.065467,175.390266,0,151.959867,1.216143,652.631743,0
-c25,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c26,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c27,0,324.065467,175.390266,0,151.959867,1.216143,652.631743,0
-c28,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c29,0,324.065467,175.390266,229.498883,151.959867,0,880.914483,0
-c30,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c31,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c32,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c33,0,324.065467,0,229.498883,151.959867,1.216143,706.74036,0
-c34,0,324.065467,0,229.498883,151.959867,1.216143,706.74036,0
-c35,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c36,0,324.065467,0,229.498883,151.959867,1.216143,706.74036,0
-c37,0,324.065467,175.390266,229.498883,0,1.216143,730.170759,0
-c38,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c39,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c40,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c41,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c42,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c43,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c44,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c45,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c46,0,324.065467,0,229.498883,151.959867,1.216143,706.74036,0
-c47,0,324.065467,175.390266,229.498883,0,1.216143,730.170759,0
-c48,0,324.065467,175.390266,229.498883,0,1.216143,730.170759,0
-c49,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c50,0,324.065467,175.390266,0,151.959867,1.216143,652.631743,0
-c51,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c2,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c3,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c4,0,78.347264,44.260116,0,43.122319,3.321408,169.051107,0
-c5,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c6,0,78.347264,44.260116,72.144097,43.122319,0,237.873796,0
-c7,0,78.347264,44.260116,72.144097,43.122319,0,237.873796,0
-c8,0,78.347264,44.260116,72.144097,43.122319,0,237.873796,0
-c9,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c10,0,0,44.260116,72.144097,43.122319,0,159.526532,0
-c11,0,78.347264,44.260116,72.144097,43.122319,0,237.873796,0
-c12,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c13,0,78.347264,0,72.144097,43.122319,3.321408,196.935088,0
-c14,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c15,0,78.347264,44.260116,72.144097,43.122319,0,237.873796,0
-c16,0,78.347264,44.260116,0,43.122319,0,165.729699,0
-c17,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c18,0,78.347264,0,72.144097,43.122319,0,193.61368,0
-c19,0,78.347264,0,0,43.122319,0,121.469583,0
-c20,0,78.347264,44.260116,72.144097,43.122319,0,237.873796,0
-c21,0,78.347264,44.260116,72.144097,0,3.321408,198.072885,0
-c22,0,0,44.260116,0,43.122319,3.321408,90.703843,0
-c23,0,78.347264,44.260116,72.144097,0,3.321408,198.072885,0
-c24,0,78.347264,44.260116,0,43.122319,3.321408,169.051107,0
-c25,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c26,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c27,0,78.347264,44.260116,0,43.122319,3.321408,169.051107,0
-c28,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c29,0,78.347264,44.260116,72.144097,43.122319,0,237.873796,0
-c30,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c31,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c32,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c33,0,78.347264,0,72.144097,43.122319,3.321408,196.935088,0
-c34,0,78.347264,0,72.144097,43.122319,3.321408,196.935088,0
-c35,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c36,0,78.347264,0,72.144097,43.122319,3.321408,196.935088,0
-c37,0,78.347264,44.260116,72.144097,0,3.321408,198.072885,0
-c38,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c39,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c40,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c41,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c42,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c43,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c44,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c45,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c46,0,78.347264,0,72.144097,43.122319,3.321408,196.935088,0
-c47,0,78.347264,44.260116,72.144097,0,3.321408,198.072885,0
-c48,0,78.347264,44.260116,72.144097,0,3.321408,198.072885,0
-c49,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c50,0,78.347264,44.260116,0,43.122319,3.321408,169.051107,0
-c51,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c2,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c3,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c4,0,36.608647,19.907558,0,18.163317,0.701660,75.381182,0
-c5,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c6,0,36.608647,19.907558,29.286620,18.163317,0,103.966142,0
-c7,0,36.608647,19.907558,29.286620,18.163317,0,103.966142,0
-c8,0,36.608647,19.907558,29.286620,18.163317,0,103.966142,0
-c9,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c10,0,0,19.907558,29.286620,18.163317,0,67.357495,0
-c11,0,36.608647,19.907558,29.286620,18.163317,0,103.966142,0
-c12,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c13,0,36.608647,0,29.286620,18.163317,0.701660,84.760244,0
-c14,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c15,0,36.608647,19.907558,29.286620,18.163317,0,103.966142,0
-c16,0,36.608647,19.907558,0,18.163317,0,74.679522,0
-c17,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c18,0,36.608647,0,29.286620,18.163317,0,84.058584,0
-c19,0,36.608647,0,0,18.163317,0,54.771964,0
-c20,0,36.608647,19.907558,29.286620,18.163317,0,103.966142,0
-c21,0,36.608647,19.907558,29.286620,0,0.701660,86.504485,0
-c22,0,0,19.907558,0,18.163317,0.701660,38.772535,0
-c23,0,36.608647,19.907558,29.286620,0,0.701660,86.504485,0
-c24,0,36.608647,19.907558,0,18.163317,0.701660,75.381182,0
-c25,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c26,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c27,0,36.608647,19.907558,0,18.163317,0.701660,75.381182,0
-c28,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c29,0,36.608647,19.907558,29.286620,18.163317,0,103.966142,0
-c30,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c31,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c32,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c33,0,36.608647,0,29.286620,18.163317,0.701660,84.760244,0
-c34,0,36.608647,0,29.286620,18.163317,0.701660,84.760244,0
-c35,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c36,0,36.608647,0,29.286620,18.163317,0.701660,84.760244,0
-c37,0,36.608647,19.907558,29.286620,0,0.701660,86.504485,0
-c38,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c39,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
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-Quantization Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
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-Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,1291.7259,2407.5018,1116.3819,1293.3931,972.3579,3.90263,7085.26323,0.999999985886199
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-c6,1291.7259,218.942215,116.09111,157.531356,100.738165,3.90263,1888.931376,3.75093714092993
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-c21,1007.11399,218.942215,116.09111,157.531356,510.1917,0.79176,2010.662131,3.52384558717063
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-c50,1291.7259,218.942215,116.09111,1293.3931,100.738165,0.79176,3021.68225,2.344807431529
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-c1,1601.208596
-
-Unpatch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
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-c39,0,0,0,0,0,0,0,0
-c40,0,0,0,0,0,0,0,0
-c41,0,0,0,0,0,0,0,0
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-c43,0,0,0,0,0,0,0,0
-c44,0,0,0,0,0,0,0,0
-c45,0,0,0,0,0,0,0,0
-c46,0,0,0,0,0,0,0,0
-c47,0,0,0,0,0,0,0,0
-c48,0,0,0,0,0,0,0,0
-c49,0,0,0,0,0,0,0,0
-c50,0,0,0,0,0,0,0,0
-c51,0,0,0,0,0,0,0,0
-c0,0
-
diff --git a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_results1.csv b/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_results1.csv
deleted file mode 100644
index ca505f7f4aef79e5d466ac7e797f2040a8af1225..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_results1.csv
+++ /dev/null
@@ -1,385 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,5417.213,16332.387,7661.092,10086.008,7470.956,25.469348,46993.125348,0.999999997872029
-c1,2545.253,255.701869,137.685622,4576.494,157.113483,0.174571,7672.422545,6.12493960804215
-c2,2545.253,255.701869,137.685622,4576.494,157.113483,0.174571,7672.422545,6.12493960804215
-c3,2545.253,255.701869,3726.01,4576.494,157.113483,0.174571,11260.746923,4.17318009649066
-c4,2545.253,255.701869,183.299064,4576.494,157.113483,0.174571,7718.035987,6.08874133500796
-c5,2545.253,255.701869,137.685622,4576.494,157.113483,0.174571,7672.422545,6.12493960804215
-c6,2545.253,255.701869,137.685622,4576.494,157.113483,0.174571,7672.422545,6.12493960804215
-c7,2545.253,255.701869,137.685622,4576.494,157.113483,0.174571,7672.422545,6.12493960804215
-c8,2545.253,255.701869,3726.01,4576.494,157.113483,0.174571,11260.746923,4.17318009649066
-c9,2545.253,255.701869,3726.01,4576.494,157.113483,0.174571,11260.746923,4.17318009649066
-c10,2545.253,255.701869,3726.01,4576.494,157.113483,0.174571,11260.746923,4.17318009649066
-c11,2545.253,255.701869,3726.01,4576.494,157.113483,0.174571,11260.746923,4.17318009649066
-c12,2545.253,255.701869,137.685622,4576.494,157.113483,0.174571,7672.422545,6.12493960804215
-c13,2545.253,255.701869,137.685622,4576.494,157.113483,0.174571,7672.422545,6.12493960804215
-c14,2545.253,255.701869,137.685622,177.024371,157.113483,0.174571,3272.952916,14.3580201482489
-c15,2545.253,255.701869,137.685622,4576.494,157.113483,0.174571,7672.422545,6.12493960804215
-c16,2545.253,255.701869,137.685622,4576.494,157.113483,0.174571,7672.422545,6.12493960804215
-c17,2545.253,255.701869,137.685622,4576.494,157.113483,0.174571,7672.422545,6.12493960804215
-c18,2545.253,255.701869,3726.01,4576.494,157.113483,0.174571,11260.746923,4.17318009649066
-c19,2545.253,255.701869,183.299064,4576.494,157.113483,0.174571,7718.035987,6.08874133500796
-c20,2545.253,255.701869,3726.01,4576.494,157.113483,0.174571,11260.746923,4.17318009649066
-c21,2545.253,255.701869,137.685622,4576.494,157.113483,0.174571,7672.422545,6.12493960804215
-c22,2545.253,255.701869,137.685622,4576.494,157.113483,0.174571,7672.422545,6.12493960804215
-c23,2545.253,255.701869,3726.01,4576.494,157.113483,0.174571,11260.746923,4.17318009649066
-c24,2545.253,255.701869,137.685622,4576.494,157.113483,0.174571,7672.422545,6.12493960804215
-c25,2545.253,255.701869,183.299064,4576.494,157.113483,0.174571,7718.035987,6.08874133500796
-c26,2545.253,255.701869,3726.01,4576.494,157.113483,0.174571,11260.746923,4.17318009649066
-c27,2545.253,255.701869,3726.01,4576.494,157.113483,0.174571,11260.746923,4.17318009649066
-c28,2545.253,255.701869,3726.01,4576.494,157.113483,0.174571,11260.746923,4.17318009649066
-c29,2545.253,255.701869,3726.01,4576.494,157.113483,0.174571,11260.746923,4.17318009649066
-c30,2545.253,340.412547,183.299064,4576.494,3304.362,0.174571,10949.995182,4.29161146993817
-c14,3272.952916
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,1291.7259,2407.5018,1116.3819,1293.3931,972.3579,3.90263,7085.26323,0.999999985886199
-c1,1001.6715,182.333568,96.183552,686.8162,82.574848,0.090100,2049.669768,3.45678264613099
-c2,1001.6715,182.333568,96.183552,686.8162,82.574848,0.090100,2049.669768,3.45678264613099
-c3,1001.6715,182.333568,653.4248,686.8162,82.574848,0.090100,2606.911016,2.7178767954588
-c4,1001.6715,182.333568,96.183552,686.8162,82.574848,0.090100,2049.669768,3.45678264613099
-c5,1001.6715,182.333568,96.183552,686.8162,82.574848,0.090100,2049.669768,3.45678264613099
-c6,1001.6715,182.333568,96.183552,686.8162,82.574848,0.090100,2049.669768,3.45678264613099
-c7,1001.6715,182.333568,96.183552,686.8162,82.574848,0.090100,2049.669768,3.45678264613099
-c8,1001.6715,182.333568,653.4248,686.8162,82.574848,0.090100,2606.911016,2.7178767954588
-c9,1001.6715,182.333568,653.4248,686.8162,82.574848,0.090100,2606.911016,2.7178767954588
-c10,1001.6715,182.333568,653.4248,686.8162,82.574848,0.090100,2606.911016,2.7178767954588
-c11,1001.6715,182.333568,653.4248,686.8162,82.574848,0.090100,2606.911016,2.7178767954588
-c12,1001.6715,182.333568,96.183552,686.8162,82.574848,0.090100,2049.669768,3.45678264613099
-c13,1001.6715,182.333568,96.183552,686.8162,82.574848,0.090100,2049.669768,3.45678264613099
-c14,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
-c15,1001.6715,182.333568,96.183552,686.8162,82.574848,0.090100,2049.669768,3.45678264613099
-c16,1001.6715,182.333568,96.183552,686.8162,82.574848,0.090100,2049.669768,3.45678264613099
-c17,1001.6715,182.333568,96.183552,686.8162,82.574848,0.090100,2049.669768,3.45678264613099
-c18,1001.6715,182.333568,653.4248,686.8162,82.574848,0.090100,2606.911016,2.7178767954588
-c19,1001.6715,182.333568,96.183552,686.8162,82.574848,0.090100,2049.669768,3.45678264613099
-c20,1001.6715,182.333568,653.4248,686.8162,82.574848,0.090100,2606.911016,2.7178767954588
-c21,1001.6715,182.333568,96.183552,686.8162,82.574848,0.090100,2049.669768,3.45678264613099
-c22,1001.6715,182.333568,96.183552,686.8162,82.574848,0.090100,2049.669768,3.45678264613099
-c23,1001.6715,182.333568,653.4248,686.8162,82.574848,0.090100,2606.911016,2.7178767954588
-c24,1001.6715,182.333568,96.183552,686.8162,82.574848,0.090100,2049.669768,3.45678264613099
-c25,1001.6715,182.333568,96.183552,686.8162,82.574848,0.090100,2049.669768,3.45678264613099
-c26,1001.6715,182.333568,653.4248,686.8162,82.574848,0.090100,2606.911016,2.7178767954588
-c27,1001.6715,182.333568,653.4248,686.8162,82.574848,0.090100,2606.911016,2.7178767954588
-c28,1001.6715,182.333568,653.4248,686.8162,82.574848,0.090100,2606.911016,2.7178767954588
-c29,1001.6715,182.333568,653.4248,686.8162,82.574848,0.090100,2606.911016,2.7178767954588
-c30,1001.6715,182.333568,96.183552,686.8162,510.1917,0.090100,2477.28662,2.86009010293326
-c14,1491.098304
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,5417.213,16332.387,7661.092,10086.008,7470.956,25.469348,46993.125348,0.999999997872029
-c1,2551.85474,658.1146,357.336004,4576.494,352.195669,4.712122,8500.707135,5.52814301785562
-c2,2551.85474,658.1146,357.336004,4576.494,352.195669,4.712122,8500.707135,5.52814301785562
-c3,2551.85474,658.1146,3726.01,4576.494,352.195669,4.712122,11869.381131,3.95918914671517
-c4,2551.85474,658.1146,402.949446,4576.494,352.195669,4.712122,8546.320577,5.4986382004678
-c5,2551.85474,658.1146,357.336004,4576.494,352.195669,4.712122,8500.707135,5.52814301785562
-c6,2551.85474,658.1146,357.336004,4576.494,352.195669,4.712122,8500.707135,5.52814301785562
-c7,2551.85474,658.1146,357.336004,4576.494,352.195669,4.712122,8500.707135,5.52814301785562
-c8,2551.85474,658.1146,3726.01,4576.494,352.195669,4.712122,11869.381131,3.95918914671517
-c9,2551.85474,658.1146,3726.01,4576.494,352.195669,4.712122,11869.381131,3.95918914671517
-c10,2551.85474,658.1146,3726.01,4576.494,352.195669,4.712122,11869.381131,3.95918914671517
-c11,2551.85474,658.1146,3726.01,4576.494,352.195669,4.712122,11869.381131,3.95918914671517
-c12,2551.85474,658.1146,357.336004,4576.494,352.195669,4.712122,8500.707135,5.52814301785562
-c13,2551.85474,658.1146,357.336004,4576.494,352.195669,4.712122,8500.707135,5.52814301785562
-c14,2551.85474,658.1146,357.336004,478.667351,352.195669,4.712122,4402.880486,10.6732682002382
-c15,2551.85474,658.1146,357.336004,4576.494,352.195669,4.712122,8500.707135,5.52814301785562
-c16,2551.85474,658.1146,357.336004,4576.494,352.195669,4.712122,8500.707135,5.52814301785562
-c17,2551.85474,658.1146,357.336004,4576.494,352.195669,4.712122,8500.707135,5.52814301785562
-c18,2551.85474,658.1146,3726.01,4576.494,352.195669,4.712122,11869.381131,3.95918914671517
-c19,2551.85474,658.1146,402.949446,4576.494,352.195669,4.712122,8546.320577,5.4986382004678
-c20,2551.85474,658.1146,3726.01,4576.494,352.195669,4.712122,11869.381131,3.95918914671517
-c21,2551.85474,658.1146,357.336004,4576.494,352.195669,4.712122,8500.707135,5.52814301785562
-c22,2551.85474,658.1146,357.336004,4576.494,352.195669,4.712122,8500.707135,5.52814301785562
-c23,2551.85474,658.1146,3726.01,4576.494,352.195669,4.712122,11869.381131,3.95918914671517
-c24,2551.85474,658.1146,357.336004,4576.494,352.195669,4.712122,8500.707135,5.52814301785562
-c25,2551.85474,658.1146,402.949446,4576.494,352.195669,4.712122,8546.320577,5.4986382004678
-c26,2551.85474,658.1146,3726.01,4576.494,352.195669,4.712122,11869.381131,3.95918914671517
-c27,2551.85474,658.1146,3726.01,4576.494,352.195669,4.712122,11869.381131,3.95918914671517
-c28,2551.85474,658.1146,3726.01,4576.494,352.195669,4.712122,11869.381131,3.95918914671517
-c29,2551.85474,658.1146,3726.01,4576.494,352.195669,4.712122,11869.381131,3.95918914671517
-c30,2551.85474,742.825278,402.949446,4576.494,3304.362,4.712122,11583.197586,4.05700797153778
-c14,4402.880486
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,0,324.065467,175.390266,0,151.959867,1.216143,652.631743,0
-c2,0,324.065467,175.390266,0,151.959867,1.216143,652.631743,0
-c3,0,324.065467,0,0,151.959867,1.216143,477.241477,0
-c4,0,324.065467,175.390266,0,151.959867,1.216143,652.631743,0
-c5,0,324.065467,175.390266,0,151.959867,1.216143,652.631743,0
-c6,0,324.065467,175.390266,0,151.959867,1.216143,652.631743,0
-c7,0,324.065467,175.390266,0,151.959867,1.216143,652.631743,0
-c8,0,324.065467,0,0,151.959867,1.216143,477.241477,0
-c9,0,324.065467,0,0,151.959867,1.216143,477.241477,0
-c10,0,324.065467,0,0,151.959867,1.216143,477.241477,0
-c11,0,324.065467,0,0,151.959867,1.216143,477.241477,0
-c12,0,324.065467,175.390266,0,151.959867,1.216143,652.631743,0
-c13,0,324.065467,175.390266,0,151.959867,1.216143,652.631743,0
-c14,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c15,0,324.065467,175.390266,0,151.959867,1.216143,652.631743,0
-c16,0,324.065467,175.390266,0,151.959867,1.216143,652.631743,0
-c17,0,324.065467,175.390266,0,151.959867,1.216143,652.631743,0
-c18,0,324.065467,0,0,151.959867,1.216143,477.241477,0
-c19,0,324.065467,175.390266,0,151.959867,1.216143,652.631743,0
-c20,0,324.065467,0,0,151.959867,1.216143,477.241477,0
-c21,0,324.065467,175.390266,0,151.959867,1.216143,652.631743,0
-c22,0,324.065467,175.390266,0,151.959867,1.216143,652.631743,0
-c23,0,324.065467,0,0,151.959867,1.216143,477.241477,0
-c24,0,324.065467,175.390266,0,151.959867,1.216143,652.631743,0
-c25,0,324.065467,175.390266,0,151.959867,1.216143,652.631743,0
-c26,0,324.065467,0,0,151.959867,1.216143,477.241477,0
-c27,0,324.065467,0,0,151.959867,1.216143,477.241477,0
-c28,0,324.065467,0,0,151.959867,1.216143,477.241477,0
-c29,0,324.065467,0,0,151.959867,1.216143,477.241477,0
-c30,0,324.065467,175.390266,0,0,1.216143,500.671876,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,0,78.347264,44.260116,0,43.122319,3.321408,169.051107,0
-c2,0,78.347264,44.260116,0,43.122319,3.321408,169.051107,0
-c3,0,78.347264,0,0,43.122319,3.321408,124.790991,0
-c4,0,78.347264,44.260116,0,43.122319,3.321408,169.051107,0
-c5,0,78.347264,44.260116,0,43.122319,3.321408,169.051107,0
-c6,0,78.347264,44.260116,0,43.122319,3.321408,169.051107,0
-c7,0,78.347264,44.260116,0,43.122319,3.321408,169.051107,0
-c8,0,78.347264,0,0,43.122319,3.321408,124.790991,0
-c9,0,78.347264,0,0,43.122319,3.321408,124.790991,0
-c10,0,78.347264,0,0,43.122319,3.321408,124.790991,0
-c11,0,78.347264,0,0,43.122319,3.321408,124.790991,0
-c12,0,78.347264,44.260116,0,43.122319,3.321408,169.051107,0
-c13,0,78.347264,44.260116,0,43.122319,3.321408,169.051107,0
-c14,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c15,0,78.347264,44.260116,0,43.122319,3.321408,169.051107,0
-c16,0,78.347264,44.260116,0,43.122319,3.321408,169.051107,0
-c17,0,78.347264,44.260116,0,43.122319,3.321408,169.051107,0
-c18,0,78.347264,0,0,43.122319,3.321408,124.790991,0
-c19,0,78.347264,44.260116,0,43.122319,3.321408,169.051107,0
-c20,0,78.347264,0,0,43.122319,3.321408,124.790991,0
-c21,0,78.347264,44.260116,0,43.122319,3.321408,169.051107,0
-c22,0,78.347264,44.260116,0,43.122319,3.321408,169.051107,0
-c23,0,78.347264,0,0,43.122319,3.321408,124.790991,0
-c24,0,78.347264,44.260116,0,43.122319,3.321408,169.051107,0
-c25,0,78.347264,44.260116,0,43.122319,3.321408,169.051107,0
-c26,0,78.347264,0,0,43.122319,3.321408,124.790991,0
-c27,0,78.347264,0,0,43.122319,3.321408,124.790991,0
-c28,0,78.347264,0,0,43.122319,3.321408,124.790991,0
-c29,0,78.347264,0,0,43.122319,3.321408,124.790991,0
-c30,0,78.347264,44.260116,0,0,3.321408,125.928788,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,0,36.608647,19.907558,0,18.163317,0.701660,75.381182,0
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diff --git a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_results2.csv b/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_results2.csv
deleted file mode 100644
index a2baf6209cb3a101a9d8f3b423c209e73f1d89cf..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_results2.csv
+++ /dev/null
@@ -1,385 +0,0 @@
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-c6,1001.6715,182.333568,96.183552,686.8162,82.574848,0.090100,2049.669768,3.45678264613099
-c7,1001.6715,182.333568,96.183552,128.244736,510.1917,0.090100,1918.715156,3.69271219783328
-c8,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
-c9,1001.6715,1366.6231,653.4248,128.244736,82.574848,0.090100,3232.629084,2.19179584997523
-c10,1001.6715,1366.6231,96.183552,128.244736,82.574848,0.090100,2675.387836,2.64831246888003
-c11,1001.6715,182.333568,96.183552,128.244736,510.1917,0.090100,1918.715156,3.69271219783328
-c12,1001.6715,1366.6231,96.183552,128.244736,82.574848,0.090100,2675.387836,2.64831246888003
-c13,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
-c14,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
-c15,1001.6715,182.333568,96.183552,686.8162,82.574848,0.090100,2049.669768,3.45678264613099
-c16,1001.6715,182.333568,96.183552,128.244736,510.1917,0.090100,1918.715156,3.69271219783328
-c17,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
-c18,1001.6715,1366.6231,653.4248,128.244736,82.574848,0.090100,3232.629084,2.19179584997523
-c19,1001.6715,182.333568,653.4248,686.8162,82.574848,0.090100,2606.911016,2.7178767954588
-c20,1001.6715,1366.6231,96.183552,128.244736,82.574848,0.090100,2675.387836,2.64831246888003
-c21,1001.6715,1366.6231,653.4248,686.8162,510.1917,0.090100,4218.8174,1.67944293157976
-c22,1001.6715,182.333568,96.183552,128.244736,510.1917,0.090100,1918.715156,3.69271219783328
-c23,1001.6715,1366.6231,96.183552,128.244736,82.574848,0.090100,2675.387836,2.64831246888003
-c24,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
-c25,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
-c26,1001.6715,182.333568,96.183552,128.244736,510.1917,0.090100,1918.715156,3.69271219783328
-c27,1001.6715,1366.6231,653.4248,128.244736,82.574848,0.090100,3232.629084,2.19179584997523
-c28,1001.6715,1366.6231,96.183552,128.244736,82.574848,0.090100,2675.387836,2.64831246888003
-c29,1001.6715,182.333568,96.183552,686.8162,82.574848,0.090100,2049.669768,3.45678264613099
-c30,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
-c4,1491.098304
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,5417.213,16332.387,7661.092,10086.008,7470.956,25.469348,46993.125348,0.999999997872029
-c1,2551.85474,7465.49,3726.01,4576.494,3304.362,4.712122,21628.922862,2.17269835537176
-c2,2551.85474,742.825278,300.881147,478.667351,3304.362,4.712122,7383.302638,6.36478375810573
-c3,2551.85474,7465.49,402.949446,406.082535,313.098433,4.712122,11144.187276,4.21682835746318
-c4,2551.85474,553.269866,300.881147,478.667351,264.708556,4.712122,4154.093782,11.3124851490778
-c5,2551.85474,553.269866,300.881147,537.313205,352.195669,4.712122,4300.226749,10.9280572858448
-c6,2551.85474,553.269866,300.881147,4576.494,272.76268,4.712122,8259.974555,5.68925781383042
-c7,2551.85474,742.825278,402.949446,537.313205,3304.362,4.712122,7544.016791,6.22919142772105
-c8,2551.85474,742.825278,310.277625,537.313205,264.708556,4.712122,4411.691526,10.6519515260426
-c9,2551.85474,7465.49,3726.01,478.667351,264.708556,4.712122,14491.442769,3.24281893616869
-c10,2551.85474,7465.49,300.881147,478.667351,272.76268,4.712122,11074.36804,4.2434136877086
-c11,2551.85474,742.825278,300.881147,478.667351,3304.362,4.712122,7383.302638,6.36478375810573
-c12,2551.85474,7465.49,402.949446,406.082535,313.098433,4.712122,11144.187276,4.21682835746318
-c13,2551.85474,553.269866,300.881147,478.667351,264.708556,4.712122,4154.093782,11.3124851490778
-c14,2551.85474,553.269866,300.881147,537.313205,352.195669,4.712122,4300.226749,10.9280572858448
-c15,2551.85474,553.269866,300.881147,4576.494,272.76268,4.712122,8259.974555,5.68925781383042
-c16,2551.85474,742.825278,402.949446,537.313205,3304.362,4.712122,7544.016791,6.22919142772105
-c17,2551.85474,742.825278,310.277625,537.313205,264.708556,4.712122,4411.691526,10.6519515260426
-c18,2551.85474,7465.49,3726.01,478.667351,264.708556,4.712122,14491.442769,3.24281893616869
-c19,2551.85474,553.269866,3726.01,4576.494,352.195669,4.712122,11764.536397,3.994473166026
-c20,2551.85474,7465.49,300.881147,478.667351,272.76268,4.712122,11074.36804,4.2434136877086
-c21,2551.85474,7465.49,3726.01,4576.494,3304.362,4.712122,21628.922862,2.17269835537176
-c22,2551.85474,742.825278,300.881147,478.667351,3304.362,4.712122,7383.302638,6.36478375810573
-c23,2551.85474,7465.49,300.881147,478.667351,272.76268,4.712122,11074.36804,4.2434136877086
-c24,2551.85474,553.269866,300.881147,478.667351,264.708556,4.712122,4154.093782,11.3124851490778
-c25,2551.85474,742.825278,310.277625,537.313205,264.708556,4.712122,4411.691526,10.6519515260426
-c26,2551.85474,742.825278,402.949446,537.313205,3304.362,4.712122,7544.016791,6.22919142772105
-c27,2551.85474,7465.49,3726.01,478.667351,264.708556,4.712122,14491.442769,3.24281893616869
-c28,2551.85474,7465.49,402.949446,406.082535,313.098433,4.712122,11144.187276,4.21682835746318
-c29,2551.85474,553.269866,300.881147,4576.494,272.76268,4.712122,8259.974555,5.68925781383042
-c30,2551.85474,553.269866,300.881147,537.313205,352.195669,4.712122,4300.226749,10.9280572858448
-c4,4154.093782
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,1.216143,1.216143,0
-c2,0,324.065467,175.390266,229.498883,0,1.216143,730.170759,0
-c3,0,0,175.390266,229.498883,151.959867,1.216143,558.065159,0
-c4,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c5,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c6,0,324.065467,175.390266,0,151.959867,1.216143,652.631743,0
-c7,0,324.065467,175.390266,229.498883,0,1.216143,730.170759,0
-c8,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c9,0,0,0,229.498883,151.959867,1.216143,382.674893,0
-c10,0,0,175.390266,229.498883,151.959867,1.216143,558.065159,0
-c11,0,324.065467,175.390266,229.498883,0,1.216143,730.170759,0
-c12,0,0,175.390266,229.498883,151.959867,1.216143,558.065159,0
-c13,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c14,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c15,0,324.065467,175.390266,0,151.959867,1.216143,652.631743,0
-c16,0,324.065467,175.390266,229.498883,0,1.216143,730.170759,0
-c17,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c18,0,0,0,229.498883,151.959867,1.216143,382.674893,0
-c19,0,324.065467,0,0,151.959867,1.216143,477.241477,0
-c20,0,0,175.390266,229.498883,151.959867,1.216143,558.065159,0
-c21,0,0,0,0,0,1.216143,1.216143,0
-c22,0,324.065467,175.390266,229.498883,0,1.216143,730.170759,0
-c23,0,0,175.390266,229.498883,151.959867,1.216143,558.065159,0
-c24,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c25,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c26,0,324.065467,175.390266,229.498883,0,1.216143,730.170759,0
-c27,0,0,0,229.498883,151.959867,1.216143,382.674893,0
-c28,0,0,175.390266,229.498883,151.959867,1.216143,558.065159,0
-c29,0,324.065467,175.390266,0,151.959867,1.216143,652.631743,0
-c30,0,324.065467,175.390266,229.498883,151.959867,1.216143,882.130626,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,3.321408,3.321408,0
-c2,0,78.347264,44.260116,72.144097,0,3.321408,198.072885,0
-c3,0,0,44.260116,72.144097,43.122319,3.321408,162.84794,0
-c4,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c5,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c6,0,78.347264,44.260116,0,43.122319,3.321408,169.051107,0
-c7,0,78.347264,44.260116,72.144097,0,3.321408,198.072885,0
-c8,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c9,0,0,0,72.144097,43.122319,3.321408,118.587824,0
-c10,0,0,44.260116,72.144097,43.122319,3.321408,162.84794,0
-c11,0,78.347264,44.260116,72.144097,0,3.321408,198.072885,0
-c12,0,0,44.260116,72.144097,43.122319,3.321408,162.84794,0
-c13,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c14,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c15,0,78.347264,44.260116,0,43.122319,3.321408,169.051107,0
-c16,0,78.347264,44.260116,72.144097,0,3.321408,198.072885,0
-c17,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c18,0,0,0,72.144097,43.122319,3.321408,118.587824,0
-c19,0,78.347264,0,0,43.122319,3.321408,124.790991,0
-c20,0,0,44.260116,72.144097,43.122319,3.321408,162.84794,0
-c21,0,0,0,0,0,3.321408,3.321408,0
-c22,0,78.347264,44.260116,72.144097,0,3.321408,198.072885,0
-c23,0,0,44.260116,72.144097,43.122319,3.321408,162.84794,0
-c24,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c25,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c26,0,78.347264,44.260116,72.144097,0,3.321408,198.072885,0
-c27,0,0,0,72.144097,43.122319,3.321408,118.587824,0
-c28,0,0,44.260116,72.144097,43.122319,3.321408,162.84794,0
-c29,0,78.347264,44.260116,0,43.122319,3.321408,169.051107,0
-c30,0,78.347264,44.260116,72.144097,43.122319,3.321408,241.195204,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0.701660,0.70166,0
-c2,0,36.608647,19.907558,29.286620,0,0.701660,86.504485,0
-c3,0,0,19.907558,29.286620,18.163317,0.701660,68.059155,0
-c4,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c5,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c6,0,36.608647,19.907558,0,18.163317,0.701660,75.381182,0
-c7,0,36.608647,19.907558,29.286620,0,0.701660,86.504485,0
-c8,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c9,0,0,0,29.286620,18.163317,0.701660,48.151597,0
-c10,0,0,19.907558,29.286620,18.163317,0.701660,68.059155,0
-c11,0,36.608647,19.907558,29.286620,0,0.701660,86.504485,0
-c12,0,0,19.907558,29.286620,18.163317,0.701660,68.059155,0
-c13,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c14,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c15,0,36.608647,19.907558,0,18.163317,0.701660,75.381182,0
-c16,0,36.608647,19.907558,29.286620,0,0.701660,86.504485,0
-c17,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c18,0,0,0,29.286620,18.163317,0.701660,48.151597,0
-c19,0,36.608647,0,0,18.163317,0.701660,55.473624,0
-c20,0,0,19.907558,29.286620,18.163317,0.701660,68.059155,0
-c21,0,0,0,0,0,0.701660,0.70166,0
-c22,0,36.608647,19.907558,29.286620,0,0.701660,86.504485,0
-c23,0,0,19.907558,29.286620,18.163317,0.701660,68.059155,0
-c24,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c25,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c26,0,36.608647,19.907558,29.286620,0,0.701660,86.504485,0
-c27,0,0,0,29.286620,18.163317,0.701660,48.151597,0
-c28,0,0,19.907558,29.286620,18.163317,0.701660,68.059155,0
-c29,0,36.608647,19.907558,0,18.163317,0.701660,75.381182,0
-c30,0,36.608647,19.907558,29.286620,18.163317,0.701660,104.667802,0
-c0,0
-
-Patch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0
-c3,0,0,0,0,0,0,0,0
-c4,0,0,0,0,0,0,0,0
-c5,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,0,0,0
-c7,0,0,0,0,0,0,0,0
-c8,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0
-c11,0,0,0,0,0,0,0,0
-c12,0,0,0,0,0,0,0,0
-c13,0,0,0,0,0,0,0,0
-c14,0,0,0,0,0,0,0,0
-c15,0,0,0,0,0,0,0,0
-c16,0,0,0,0,0,0,0,0
-c17,0,0,0,0,0,0,0,0
-c18,0,0,0,0,0,0,0,0
-c19,0,0,0,0,0,0,0,0
-c20,0,0,0,0,0,0,0,0
-c21,0,0,0,0,0,0,0,0
-c22,0,0,0,0,0,0,0,0
-c23,0,0,0,0,0,0,0,0
-c24,0,0,0,0,0,0,0,0
-c25,0,0,0,0,0,0,0,0
-c26,0,0,0,0,0,0,0,0
-c27,0,0,0,0,0,0,0,0
-c28,0,0,0,0,0,0,0,0
-c29,0,0,0,0,0,0,0,0
-c30,0,0,0,0,0,0,0,0
-c0,0
-
-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,6.60174,0,0,0,0,0,6.60174,0
-c2,6.60174,0,0,0,0,0,6.60174,0
-c3,6.60174,0,0,0,0,0,6.60174,0
-c4,6.60174,0,0,0,0,0,6.60174,0
-c5,6.60174,0,0,0,0,0,6.60174,0
-c6,6.60174,0,0,0,0,0,6.60174,0
-c7,6.60174,0,0,0,0,0,6.60174,0
-c8,6.60174,0,0,0,0,0,6.60174,0
-c9,6.60174,0,0,0,0,0,6.60174,0
-c10,6.60174,0,0,0,0,0,6.60174,0
-c11,6.60174,0,0,0,0,0,6.60174,0
-c12,6.60174,0,0,0,0,0,6.60174,0
-c13,6.60174,0,0,0,0,0,6.60174,0
-c14,6.60174,0,0,0,0,0,6.60174,0
-c15,6.60174,0,0,0,0,0,6.60174,0
-c16,6.60174,0,0,0,0,0,6.60174,0
-c17,6.60174,0,0,0,0,0,6.60174,0
-c18,6.60174,0,0,0,0,0,6.60174,0
-c19,6.60174,0,0,0,0,0,6.60174,0
-c20,6.60174,0,0,0,0,0,6.60174,0
-c21,6.60174,0,0,0,0,0,6.60174,0
-c22,6.60174,0,0,0,0,0,6.60174,0
-c23,6.60174,0,0,0,0,0,6.60174,0
-c24,6.60174,0,0,0,0,0,6.60174,0
-c25,6.60174,0,0,0,0,0,6.60174,0
-c26,6.60174,0,0,0,0,0,6.60174,0
-c27,6.60174,0,0,0,0,0,6.60174,0
-c28,6.60174,0,0,0,0,0,6.60174,0
-c29,6.60174,0,0,0,0,0,6.60174,0
-c30,6.60174,0,0,0,0,0,6.60174,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,5.44249,0,0,0,0,0,5.44249,0
-c2,5.44249,0,0,0,0,0,5.44249,0
-c3,5.44249,0,0,0,0,0,5.44249,0
-c4,5.44249,0,0,0,0,0,5.44249,0
-c5,5.44249,0,0,0,0,0,5.44249,0
-c6,5.44249,0,0,0,0,0,5.44249,0
-c7,5.44249,0,0,0,0,0,5.44249,0
-c8,5.44249,0,0,0,0,0,5.44249,0
-c9,5.44249,0,0,0,0,0,5.44249,0
-c10,5.44249,0,0,0,0,0,5.44249,0
-c11,5.44249,0,0,0,0,0,5.44249,0
-c12,5.44249,0,0,0,0,0,5.44249,0
-c13,5.44249,0,0,0,0,0,5.44249,0
-c14,5.44249,0,0,0,0,0,5.44249,0
-c15,5.44249,0,0,0,0,0,5.44249,0
-c16,5.44249,0,0,0,0,0,5.44249,0
-c17,5.44249,0,0,0,0,0,5.44249,0
-c18,5.44249,0,0,0,0,0,5.44249,0
-c19,5.44249,0,0,0,0,0,5.44249,0
-c20,5.44249,0,0,0,0,0,5.44249,0
-c21,5.44249,0,0,0,0,0,5.44249,0
-c22,5.44249,0,0,0,0,0,5.44249,0
-c23,5.44249,0,0,0,0,0,5.44249,0
-c24,5.44249,0,0,0,0,0,5.44249,0
-c25,5.44249,0,0,0,0,0,5.44249,0
-c26,5.44249,0,0,0,0,0,5.44249,0
-c27,5.44249,0,0,0,0,0,5.44249,0
-c28,5.44249,0,0,0,0,0,5.44249,0
-c29,5.44249,0,0,0,0,0,5.44249,0
-c30,5.44249,0,0,0,0,0,5.44249,0
-c0,0
-
-Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,1291.7259,2407.5018,1116.3819,1293.3931,972.3579,3.90263,7085.26323,0.999999985886199
-c1,1007.11399,1366.6231,653.4248,686.8162,510.1917,0.79176,4224.96155,1.67700060188712
-c2,1007.11399,218.942215,116.09111,157.531356,510.1917,0.79176,2010.662131,3.52384558717063
-c3,1007.11399,1366.6231,116.09111,157.531356,100.738165,0.79176,2748.889481,2.57750012185739
-c4,1007.11399,218.942215,116.09111,157.531356,100.738165,0.79176,1601.208596,4.42494675909504
-c5,1007.11399,218.942215,116.09111,157.531356,100.738165,0.79176,1601.208596,4.42494675909504
-c6,1007.11399,218.942215,116.09111,686.8162,100.738165,0.79176,2130.49344,3.32564408057299
-c7,1007.11399,218.942215,116.09111,157.531356,510.1917,0.79176,2010.662131,3.52384558717063
-c8,1007.11399,218.942215,116.09111,157.531356,100.738165,0.79176,1601.208596,4.42494675909504
-c9,1007.11399,1366.6231,653.4248,157.531356,100.738165,0.79176,3286.223171,2.15605047061941
-c10,1007.11399,1366.6231,116.09111,157.531356,100.738165,0.79176,2748.889481,2.57750012185739
-c11,1007.11399,218.942215,116.09111,157.531356,510.1917,0.79176,2010.662131,3.52384558717063
-c12,1007.11399,1366.6231,116.09111,157.531356,100.738165,0.79176,2748.889481,2.57750012185739
-c13,1007.11399,218.942215,116.09111,157.531356,100.738165,0.79176,1601.208596,4.42494675909504
-c14,1007.11399,218.942215,116.09111,157.531356,100.738165,0.79176,1601.208596,4.42494675909504
-c15,1007.11399,218.942215,116.09111,686.8162,100.738165,0.79176,2130.49344,3.32564408057299
-c16,1007.11399,218.942215,116.09111,157.531356,510.1917,0.79176,2010.662131,3.52384558717063
-c17,1007.11399,218.942215,116.09111,157.531356,100.738165,0.79176,1601.208596,4.42494675909504
-c18,1007.11399,1366.6231,653.4248,157.531356,100.738165,0.79176,3286.223171,2.15605047061941
-c19,1007.11399,218.942215,653.4248,686.8162,100.738165,0.79176,2667.82713,2.65581786943527
-c20,1007.11399,1366.6231,116.09111,157.531356,100.738165,0.79176,2748.889481,2.57750012185739
-c21,1007.11399,1366.6231,653.4248,686.8162,510.1917,0.79176,4224.96155,1.67700060188712
-c22,1007.11399,218.942215,116.09111,157.531356,510.1917,0.79176,2010.662131,3.52384558717063
-c23,1007.11399,1366.6231,116.09111,157.531356,100.738165,0.79176,2748.889481,2.57750012185739
-c24,1007.11399,218.942215,116.09111,157.531356,100.738165,0.79176,1601.208596,4.42494675909504
-c25,1007.11399,218.942215,116.09111,157.531356,100.738165,0.79176,1601.208596,4.42494675909504
-c26,1007.11399,218.942215,116.09111,157.531356,510.1917,0.79176,2010.662131,3.52384558717063
-c27,1007.11399,1366.6231,653.4248,157.531356,100.738165,0.79176,3286.223171,2.15605047061941
-c28,1007.11399,1366.6231,116.09111,157.531356,100.738165,0.79176,2748.889481,2.57750012185739
-c29,1007.11399,218.942215,116.09111,686.8162,100.738165,0.79176,2130.49344,3.32564408057299
-c30,1007.11399,218.942215,116.09111,157.531356,100.738165,0.79176,1601.208596,4.42494675909504
-c4,1601.208596
-
-Unpatch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0
-c3,0,0,0,0,0,0,0,0
-c4,0,0,0,0,0,0,0,0
-c5,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,0,0,0
-c7,0,0,0,0,0,0,0,0
-c8,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0
-c11,0,0,0,0,0,0,0,0
-c12,0,0,0,0,0,0,0,0
-c13,0,0,0,0,0,0,0,0
-c14,0,0,0,0,0,0,0,0
-c15,0,0,0,0,0,0,0,0
-c16,0,0,0,0,0,0,0,0
-c17,0,0,0,0,0,0,0,0
-c18,0,0,0,0,0,0,0,0
-c19,0,0,0,0,0,0,0,0
-c20,0,0,0,0,0,0,0,0
-c21,0,0,0,0,0,0,0,0
-c22,0,0,0,0,0,0,0,0
-c23,0,0,0,0,0,0,0,0
-c24,0,0,0,0,0,0,0,0
-c25,0,0,0,0,0,0,0,0
-c26,0,0,0,0,0,0,0,0
-c27,0,0,0,0,0,0,0,0
-c28,0,0,0,0,0,0,0,0
-c29,0,0,0,0,0,0,0,0
-c30,0,0,0,0,0,0,0,0
-c0,0
-
diff --git a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_results2_nodma.csv b/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_results2_nodma.csv
deleted file mode 100644
index bdc6580f750a3f05adaa9913814b04efb45265bb..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_results2_nodma.csv
+++ /dev/null
@@ -1,385 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,5417.213,16332.387,7661.092,10086.008,7470.956,25.469348,46993.125348,0.999999997872029
-c1,2545.253,7465.49,3726.01,4576.494,3304.362,0.174571,21617.783571,2.17381791136344
-c2,2545.253,340.412547,81.230765,177.024371,3304.362,0.174571,6448.457254,7.28749881843446
-c3,2545.253,7465.49,183.299064,104.439555,118.016247,0.174571,10416.672437,4.51133749103473
-c4,2545.253,150.857135,81.230765,177.024371,69.626370,0.174571,3024.166212,15.5392000636769
-c5,2545.253,150.857135,81.230765,235.670225,157.113483,0.174571,3170.299179,14.8229303331965
-c6,2545.253,150.857135,81.230765,4576.494,77.680494,0.174571,7431.689965,6.3233429996384
-c7,2545.253,340.412547,183.299064,235.670225,3304.362,0.174571,6609.171407,7.11028989007593
-c8,2545.253,340.412547,90.627243,235.670225,69.626370,0.174571,3281.763956,14.3194710363419
-c9,2545.253,7465.49,3726.01,177.024371,69.626370,0.174571,13983.578312,3.36059368807006
-c10,2545.253,7465.49,81.230765,177.024371,77.680494,0.174571,10346.853201,4.5417794164974
-c11,2545.253,340.412547,81.230765,177.024371,3304.362,0.174571,6448.457254,7.28749881843446
-c12,2545.253,7465.49,183.299064,104.439555,118.016247,0.174571,10416.672437,4.51133749103473
-c13,2545.253,150.857135,81.230765,177.024371,69.626370,0.174571,3024.166212,15.5392000636769
-c14,2545.253,150.857135,81.230765,235.670225,157.113483,0.174571,3170.299179,14.8229303331965
-c15,2545.253,150.857135,81.230765,4576.494,77.680494,0.174571,7431.689965,6.3233429996384
-c16,2545.253,340.412547,183.299064,235.670225,3304.362,0.174571,6609.171407,7.11028989007593
-c17,2545.253,340.412547,90.627243,235.670225,69.626370,0.174571,3281.763956,14.3194710363419
-c18,2545.253,7465.49,3726.01,177.024371,69.626370,0.174571,13983.578312,3.36059368807006
-c19,2545.253,150.857135,3726.01,4576.494,157.113483,0.174571,11155.902189,4.21240022820354
-c20,2545.253,7465.49,81.230765,177.024371,77.680494,0.174571,10346.853201,4.5417794164974
-c21,2545.253,7465.49,3726.01,4576.494,3304.362,0.174571,21617.783571,2.17381791136344
-c22,2545.253,340.412547,81.230765,177.024371,3304.362,0.174571,6448.457254,7.28749881843446
-c23,2545.253,7465.49,81.230765,177.024371,77.680494,0.174571,10346.853201,4.5417794164974
-c24,2545.253,150.857135,81.230765,177.024371,69.626370,0.174571,3024.166212,15.5392000636769
-c25,2545.253,340.412547,90.627243,235.670225,69.626370,0.174571,3281.763956,14.3194710363419
-c26,2545.253,340.412547,183.299064,235.670225,3304.362,0.174571,6609.171407,7.11028989007593
-c27,2545.253,7465.49,3726.01,177.024371,69.626370,0.174571,13983.578312,3.36059368807006
-c28,2545.253,7465.49,183.299064,104.439555,118.016247,0.174571,10416.672437,4.51133749103473
-c29,2545.253,150.857135,81.230765,4576.494,77.680494,0.174571,7431.689965,6.3233429996384
-c30,2545.253,150.857135,81.230765,235.670225,157.113483,0.174571,3170.299179,14.8229303331965
-c4,3024.166212
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,1291.7259,2407.5018,1116.3819,1293.3931,972.3579,3.90263,7085.26323,0.999999985886199
-c1,1001.6715,1366.6231,653.4248,686.8162,510.1917,0.090100,4218.8174,1.67944293157976
-c2,1001.6715,182.333568,96.183552,128.244736,510.1917,0.090100,1918.715156,3.69271219783328
-c3,1001.6715,1366.6231,96.183552,128.244736,82.574848,0.090100,2675.387836,2.64831246888003
-c4,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
-c5,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
-c6,1001.6715,182.333568,96.183552,686.8162,82.574848,0.090100,2049.669768,3.45678264613099
-c7,1001.6715,182.333568,96.183552,128.244736,510.1917,0.090100,1918.715156,3.69271219783328
-c8,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
-c9,1001.6715,1366.6231,653.4248,128.244736,82.574848,0.090100,3232.629084,2.19179584997523
-c10,1001.6715,1366.6231,96.183552,128.244736,82.574848,0.090100,2675.387836,2.64831246888003
-c11,1001.6715,182.333568,96.183552,128.244736,510.1917,0.090100,1918.715156,3.69271219783328
-c12,1001.6715,1366.6231,96.183552,128.244736,82.574848,0.090100,2675.387836,2.64831246888003
-c13,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
-c14,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
-c15,1001.6715,182.333568,96.183552,686.8162,82.574848,0.090100,2049.669768,3.45678264613099
-c16,1001.6715,182.333568,96.183552,128.244736,510.1917,0.090100,1918.715156,3.69271219783328
-c17,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
-c18,1001.6715,1366.6231,653.4248,128.244736,82.574848,0.090100,3232.629084,2.19179584997523
-c19,1001.6715,182.333568,653.4248,686.8162,82.574848,0.090100,2606.911016,2.7178767954588
-c20,1001.6715,1366.6231,96.183552,128.244736,82.574848,0.090100,2675.387836,2.64831246888003
-c21,1001.6715,1366.6231,653.4248,686.8162,510.1917,0.090100,4218.8174,1.67944293157976
-c22,1001.6715,182.333568,96.183552,128.244736,510.1917,0.090100,1918.715156,3.69271219783328
-c23,1001.6715,1366.6231,96.183552,128.244736,82.574848,0.090100,2675.387836,2.64831246888003
-c24,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
-c25,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
-c26,1001.6715,182.333568,96.183552,128.244736,510.1917,0.090100,1918.715156,3.69271219783328
-c27,1001.6715,1366.6231,653.4248,128.244736,82.574848,0.090100,3232.629084,2.19179584997523
-c28,1001.6715,1366.6231,96.183552,128.244736,82.574848,0.090100,2675.387836,2.64831246888003
-c29,1001.6715,182.333568,96.183552,686.8162,82.574848,0.090100,2049.669768,3.45678264613099
-c30,1001.6715,182.333568,96.183552,128.244736,82.574848,0.090100,1491.098304,4.7517073393635
-c4,1491.098304
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,5417.213,16332.387,7661.092,10086.008,7470.956,25.469348,46993.125348,0.999999997872029
-c1,2551.85474,7465.49,3726.01,4576.494,3304.362,16.043622,21640.254362,2.1715606639709
-c2,2551.85474,2522.2367884,673.629796,1223.341402,3492.309,20.472922,10483.8446484,4.48243239725311
-c3,2551.85474,7465.49,823.65976,1152.230657,792.0937522,4.712122,12790.0410312,3.6741965773171
-c4,2551.85474,2329.095086,673.100225,1223.479574,742.023533,4.712122,7524.26528,6.24554331548577
-c5,2551.85474,2338.258747,670.3014,1284.450006,831.495355,4.712122,7681.07237,6.11804217855531
-c6,2551.85474,2337.3387909,672.241926,4877.603,829.051566,4.712122,11272.8021449,4.16871726542178
-c7,2551.85474,2528.1343714,772.894637,1280.10994,3492.938,20.926922,10646.8586104,4.41380191343165
-c8,2551.85474,2526.3911951,679.672822,1281.002078,743.825003,4.712122,7787.4579601,6.03446272009798
-c9,2551.85474,7465.49,3726.01,1336.573655,739.8828856,4.712122,15824.5234026,2.96963920210798
-c10,2551.85474,7465.49,722.391922,1221.426035,749.227459,4.712122,12715.102278,3.69585111869085
-c11,2551.85474,2522.7929962,670.017717,1221.431058,3492.164,20.885922,10479.1464332,4.48444204870278
-c12,2551.85474,7465.49,821.940336,1148.492297,789.4342137,4.712122,12781.9237087,3.67652992235912
-c13,2551.85474,2337.0326585,667.777038,1223.584497,741.630288,4.712122,7526.5913435,6.24361315487417
-c14,2551.85474,2337.4752086,671.388474,1280.80088,830.174596,4.712122,7676.4060206,6.12176122650568
-c15,2551.85474,2337.069006,670.215899,4883.073,826.7645851,4.712122,11273.6893521,4.16838919926488
-c16,2551.85474,2523.8232109,770.740344,1280.660301,3492.395,20.932222,10640.4058179,4.41647862972455
-c17,2551.85474,2526.394765,681.279181,1284.204247,743.200746,4.712122,7791.645801,6.03121932709606
-c18,2551.85474,7465.49,3726.01,1336.035191,740.477526,4.712122,15824.579579,2.96962866004979
-c19,2551.85474,2336.5105736,3863.721,4576.494,902.097102,4.712122,14235.3895376,3.30114781149909
-c20,2551.85474,7465.49,718.9195146,1221.884731,748.256968,4.712122,12711.1180756,3.69700955484837
-c21,2551.85474,7465.49,3726.01,4576.494,3304.362,19.515522,21643.726262,2.17121232092945
-c22,2551.85474,2523.938098,669.424811,1219.711329,3491.85,20.798522,10477.5775,4.48511355792774
-c23,2551.85474,7465.49,716.178914,1222.588198,747.1898741,4.712122,12708.0138481,3.69791263528051
-c24,2551.85474,2337.0739621,669.25028,1220.715474,741.1878024,4.712122,7524.7943805,6.24510416460935
-c25,2551.85474,2526.9573985,679.479385,1280.319629,742.5067267,4.712122,7785.8300012,6.03572448116446
-c26,2551.85474,2524.6402554,769.500432,1279.196703,3491.59,20.709222,10637.4913524,4.41768865886116
-c27,2551.85474,7465.49,3726.01,1331.321972,739.463611,4.712122,15818.852445,2.9707037987944
-c28,2551.85474,7465.49,820.2617391,1147.969732,787.5597797,4.712122,12777.8481128,3.67770258069942
-c29,2551.85474,2336.5083139,668.332929,4875.324,821.0412029,4.712122,11257.7733078,4.17428239543715
-c30,2551.85474,2338.0065652,667.220353,1277.851397,826.24176,4.712122,7665.8869372,6.13016146989357
-c4,7524.26528
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,1.216143,1.216143,0
-c2,0,421.149947,200.145258,277.244900,0,1.216143,899.756248,0
-c3,0,0,200.145258,277.244900,184.944713,1.216143,663.551014,0
-c4,0,421.149947,200.145258,277.244900,184.944713,1.216143,1084.700961,0
-c5,0,421.149947,200.145258,277.244900,184.944713,1.216143,1084.700961,0
-c6,0,421.149947,200.145258,0,184.944713,1.216143,807.456061,0
-c7,0,421.149947,200.145258,277.244900,0,1.216143,899.756248,0
-c8,0,421.149947,200.145258,277.244900,184.944713,1.216143,1084.700961,0
-c9,0,0,0,277.244900,184.944713,1.216143,463.405756,0
-c10,0,0,200.145258,277.244900,184.944713,1.216143,663.551014,0
-c11,0,421.149947,200.145258,277.244900,0,1.216143,899.756248,0
-c12,0,0,200.145258,277.244900,184.944713,1.216143,663.551014,0
-c13,0,421.149947,200.145258,277.244900,184.944713,1.216143,1084.700961,0
-c14,0,421.149947,200.145258,277.244900,184.944713,1.216143,1084.700961,0
-c15,0,421.149947,200.145258,0,184.944713,1.216143,807.456061,0
-c16,0,421.149947,200.145258,277.244900,0,1.216143,899.756248,0
-c17,0,421.149947,200.145258,277.244900,184.944713,1.216143,1084.700961,0
-c18,0,0,0,277.244900,184.944713,1.216143,463.405756,0
-c19,0,421.149947,0,0,184.944713,1.216143,607.310803,0
-c20,0,0,200.145258,277.244900,184.944713,1.216143,663.551014,0
-c21,0,0,0,0,0,1.216143,1.216143,0
-c22,0,421.149947,200.145258,277.244900,0,1.216143,899.756248,0
-c23,0,0,200.145258,277.244900,184.944713,1.216143,663.551014,0
-c24,0,421.149947,200.145258,277.244900,184.944713,1.216143,1084.700961,0
-c25,0,421.149947,200.145258,277.244900,184.944713,1.216143,1084.700961,0
-c26,0,421.149947,200.145258,277.244900,0,1.216143,899.756248,0
-c27,0,0,0,277.244900,184.944713,1.216143,463.405756,0
-c28,0,0,200.145258,277.244900,184.944713,1.216143,663.551014,0
-c29,0,421.149947,200.145258,0,184.944713,1.216143,807.456061,0
-c30,0,421.149947,200.145258,277.244900,184.944713,1.216143,1084.700961,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,3.321408,3.321408,0
-c2,0,393.288704,122.995476,229.614817,0,3.321408,749.220405,0
-c3,0,0,122.995476,229.614817,148.102799,3.321408,504.0345,0
-c4,0,393.288704,122.995476,229.614817,148.102799,3.321408,897.323204,0
-c5,0,393.288704,122.995476,229.614817,148.102799,3.321408,897.323204,0
-c6,0,393.288704,122.995476,0,148.102799,3.321408,667.708387,0
-c7,0,393.288704,122.995476,229.614817,0,3.321408,749.220405,0
-c8,0,393.288704,122.995476,229.614817,148.102799,3.321408,897.323204,0
-c9,0,0,0,229.614817,148.102799,3.321408,381.039024,0
-c10,0,0,122.995476,229.614817,148.102799,3.321408,504.0345,0
-c11,0,393.288704,122.995476,229.614817,0,3.321408,749.220405,0
-c12,0,0,122.995476,229.614817,148.102799,3.321408,504.0345,0
-c13,0,393.288704,122.995476,229.614817,148.102799,3.321408,897.323204,0
-c14,0,393.288704,122.995476,229.614817,148.102799,3.321408,897.323204,0
-c15,0,393.288704,122.995476,0,148.102799,3.321408,667.708387,0
-c16,0,393.288704,122.995476,229.614817,0,3.321408,749.220405,0
-c17,0,393.288704,122.995476,229.614817,148.102799,3.321408,897.323204,0
-c18,0,0,0,229.614817,148.102799,3.321408,381.039024,0
-c19,0,393.288704,0,0,148.102799,3.321408,544.712911,0
-c20,0,0,122.995476,229.614817,148.102799,3.321408,504.0345,0
-c21,0,0,0,0,0,3.321408,3.321408,0
-c22,0,393.288704,122.995476,229.614817,0,3.321408,749.220405,0
-c23,0,0,122.995476,229.614817,148.102799,3.321408,504.0345,0
-c24,0,393.288704,122.995476,229.614817,148.102799,3.321408,897.323204,0
-c25,0,393.288704,122.995476,229.614817,148.102799,3.321408,897.323204,0
-c26,0,393.288704,122.995476,229.614817,0,3.321408,749.220405,0
-c27,0,0,0,229.614817,148.102799,3.321408,381.039024,0
-c28,0,0,122.995476,229.614817,148.102799,3.321408,504.0345,0
-c29,0,393.288704,122.995476,0,148.102799,3.321408,667.708387,0
-c30,0,393.288704,122.995476,229.614817,148.102799,3.321408,897.323204,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0.701660,0.70166,0
-c2,0,102.144647,36.291558,62.054620,0,0.701660,201.192485,0
-c3,0,0,36.291558,62.054620,40.008650,0.701660,139.056488,0
-c4,0,102.144647,36.291558,62.054620,40.008650,0.701660,241.201135,0
-c5,0,102.144647,36.291558,62.054620,40.008650,0.701660,241.201135,0
-c6,0,102.144647,36.291558,0,40.008650,0.701660,179.146515,0
-c7,0,102.144647,36.291558,62.054620,0,0.701660,201.192485,0
-c8,0,102.144647,36.291558,62.054620,40.008650,0.701660,241.201135,0
-c9,0,0,0,62.054620,40.008650,0.701660,102.76493,0
-c10,0,0,36.291558,62.054620,40.008650,0.701660,139.056488,0
-c11,0,102.144647,36.291558,62.054620,0,0.701660,201.192485,0
-c12,0,0,36.291558,62.054620,40.008650,0.701660,139.056488,0
-c13,0,102.144647,36.291558,62.054620,40.008650,0.701660,241.201135,0
-c14,0,102.144647,36.291558,62.054620,40.008650,0.701660,241.201135,0
-c15,0,102.144647,36.291558,0,40.008650,0.701660,179.146515,0
-c16,0,102.144647,36.291558,62.054620,0,0.701660,201.192485,0
-c17,0,102.144647,36.291558,62.054620,40.008650,0.701660,241.201135,0
-c18,0,0,0,62.054620,40.008650,0.701660,102.76493,0
-c19,0,102.144647,0,0,40.008650,0.701660,142.854957,0
-c20,0,0,36.291558,62.054620,40.008650,0.701660,139.056488,0
-c21,0,0,0,0,0,0.701660,0.70166,0
-c22,0,102.144647,36.291558,62.054620,0,0.701660,201.192485,0
-c23,0,0,36.291558,62.054620,40.008650,0.701660,139.056488,0
-c24,0,102.144647,36.291558,62.054620,40.008650,0.701660,241.201135,0
-c25,0,102.144647,36.291558,62.054620,40.008650,0.701660,241.201135,0
-c26,0,102.144647,36.291558,62.054620,0,0.701660,201.192485,0
-c27,0,0,0,62.054620,40.008650,0.701660,102.76493,0
-c28,0,0,36.291558,62.054620,40.008650,0.701660,139.056488,0
-c29,0,102.144647,36.291558,0,40.008650,0.701660,179.146515,0
-c30,0,102.144647,36.291558,62.054620,40.008650,0.701660,241.201135,0
-c0,0
-
-Patch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0
-c2,0,1196.1747904,238.515497,521.684414,0,0,1956.3747014,0
-c3,0,0,236.445362,523.058785,322.8415932,0,1082.3457402,0
-c4,0,1194.5813,238.150626,521.636186,320.960251,0,2275.328363,0
-c5,0,1197.451061,235.307601,523.633564,323.27086,0,2279.663086,0
-c6,0,1197.3483049,237.033027,0,324.10256,0,1758.4838919,0
-c7,0,1198.2328734,235.765639,519.301098,0,0,1953.2996104,0
-c8,0,1199.0089971,235.690245,520.781736,323.136621,0,2278.6175991,0
-c9,0,0,0,520.743567,319.2947036,0,840.0382706,0
-c10,0,0,234.667623,519.775147,320.329953,0,1074.772723,0
-c11,0,1194.9892982,234.868518,519.37237,0,0,1949.2301862,0
-c12,0,0,233.913838,519.200225,320.5362547,0,1073.6503177,0
-c13,0,1197.6845725,232.848339,521.412309,320.835906,0,2272.7811265,0
-c14,0,1196.5545226,236.270075,520.184738,321.883301,0,2274.8926366,0
-c15,0,1197.76962,235.32,0,321.0015791,0,1754.0911991,0
-c16,0,1196.1132129,233.776746,520.229259,0,0,1950.1192179,0
-c17,0,1197.961867,237.070404,523.800605,322.661664,0,2281.49454,0
-c18,0,0,0,520.870003,319.940244,0,840.810247,0
-c19,0,1197.7193876,0,0,320.189107,0,1517.9084946,0
-c20,0,0,232.0296156,520.170043,319.681662,0,1071.8813206,0
-c21,0,0,0,0,0,0,0,0
-c22,0,1198.27,235.041712,517.850241,0,0,1951.161953,0
-c23,0,0,230.770615,520.30101,318.1976681,0,1069.2692931,0
-c24,0,1197.9032761,234.173681,519.397086,320.5284204,0,2272.0024635,0
-c25,0,1198.5120005,234.910708,519.968287,321.7786447,0,2275.1696402,0
-c26,0,1194.4315574,232.473034,519.043561,0,0,1945.9481524,0
-c27,0,0,0,519.285984,318.598729,0,837.884713,0
-c28,0,0,230.8411411,518.47986,318.6336207,0,1067.9546218,0
-c29,0,1198.9265279,233.63753,0,317.9858969,0,1750.5499548,0
-c30,0,1198.0251792,233.028754,517.462755,318.097265,0,2266.6139532,0
-c0,0
-
-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,6.60174,0,0,0,0,11.3315,17.93324,0
-c2,6.60174,69.6338,0,0,187.947,15.7608,279.94334,0
-c3,6.60174,0,50.47,0,0,0,57.07174,0
-c4,6.60174,70.7919,0,0,0,0,77.39364,0
-c5,6.60174,73.1999,0,0,0,0,79.80164,0
-c6,6.60174,71.3667,0,301.109,75.9741,0,455.05154,0
-c7,6.60174,72.3373,0,0,188.576,16.2148,283.72984,0
-c8,6.60174,69.727,0,0,0,0,76.32874,0
-c9,6.60174,0,0,113.939,0,0,120.54074,0
-c10,6.60174,0,52.3655,0,0,0,58.96724,0
-c11,6.60174,71.6995,0,0,187.802,16.1738,282.27704,0
-c12,6.60174,0,50.6831,0,0,0,57.28484,0
-c13,6.60174,71.5133,0,0,0,0,78.11504,0
-c14,6.60174,72.0479,0,0,0,0,78.64964,0
-c15,6.60174,70.9476,0,306.579,76.848,0,460.97634,0
-c16,6.60174,71.5908,0,0,188.033,16.2201,282.44564,0
-c17,6.60174,69.6907,0,0,0,0,76.29244,0
-c18,6.60174,0,0,113.477,0,0,120.07874,0
-c19,6.60174,71.5704,137.711,0,73.7108,0,289.59394,0
-c20,6.60174,0,51.7873,0,0,0,58.38904,0
-c21,6.60174,0,0,0,0,14.8034,21.40514,0
-c22,6.60174,69.3249,0,0,187.488,16.0864,279.50104,0
-c23,6.60174,0,50.3426,0,0,0,56.94434,0
-c24,6.60174,71.2709,0,0,0,0,77.87264,0
-c25,6.60174,71.1902,0,0,0,0,77.79194,0
-c26,6.60174,71.5955,0,0,187.228,15.9971,281.42234,0
-c27,6.60174,0,0,110.437,0,0,117.03874,0
-c28,6.60174,0,51.6476,0,0,0,58.24934,0
-c29,6.60174,70.621,0,298.83,74.405,0,450.45774,0
-c30,6.60174,71.4056,0,0,0,0,78.00734,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,5.44249,0,0,0,0,0,5.44249,0
-c2,5.44249,0,0,0,84.5332,0,89.97569,0
-c3,5.44249,0,0,0,0,0,5.44249,0
-c4,5.44249,0,0,0,0,0,5.44249,0
-c5,5.44249,0,0,0,0,0,5.44249,0
-c6,5.44249,0,0,126.79,0,0,132.23249,0
-c7,5.44249,0,0,0,84.8673,0,90.30979,0
-c8,5.44249,0,0,0,0,0,5.44249,0
-c9,5.44249,0,0,0,0,0,5.44249,0
-c10,5.44249,0,0,0,0,0,5.44249,0
-c11,5.44249,0,0,0,84.4277,0,89.87019,0
-c12,5.44249,0,0,0,0,0,5.44249,0
-c13,5.44249,0,0,0,0,0,5.44249,0
-c14,5.44249,0,0,0,0,0,5.44249,0
-c15,5.44249,0,0,126.687,0,0,132.12949,0
-c16,5.44249,0,0,0,84.6577,0,90.10019,0
-c17,5.44249,0,0,0,0,0,5.44249,0
-c18,5.44249,0,0,0,0,0,5.44249,0
-c19,5.44249,0,63.5646,0,0,0,69.00709,0
-c20,5.44249,0,0,0,0,0,5.44249,0
-c21,5.44249,0,0,0,0,0,5.44249,0
-c22,5.44249,0,0,0,84.4342,0,89.87669,0
-c23,5.44249,0,0,0,0,0,5.44249,0
-c24,5.44249,0,0,0,0,0,5.44249,0
-c25,5.44249,0,0,0,0,0,5.44249,0
-c26,5.44249,0,0,0,84.637,0,90.07949,0
-c27,5.44249,0,0,0,0,0,5.44249,0
-c28,5.44249,0,0,0,0,0,5.44249,0
-c29,5.44249,0,0,126.691,0,0,132.13349,0
-c30,5.44249,0,0,0,0,0,5.44249,0
-c0,0
-
-Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,1291.7259,2407.5018,1116.3819,1293.3931,972.3579,3.90263,7085.26323,0.999999985886199
-c1,1007.11399,1366.6231,653.4248,686.8162,510.1917,0.79176,4224.96155,1.67700060188712
-c2,1007.11399,284.478215,132.47511,190.299356,594.7249,0.79176,2209.883331,3.20617057470485
-c3,1007.11399,1366.6231,132.47511,190.299356,122.583498,0.79176,2819.886814,2.51260545053191
-c4,1007.11399,284.478215,132.47511,190.299356,122.583498,0.79176,1737.741929,4.07728138685653
-c5,1007.11399,284.478215,132.47511,190.299356,122.583498,0.79176,1737.741929,4.07728138685653
-c6,1007.11399,284.478215,132.47511,813.6062,122.583498,0.79176,2361.048773,3.00089647064243
-c7,1007.11399,284.478215,132.47511,190.299356,595.059,0.79176,2210.217431,3.2056859248575
-c8,1007.11399,284.478215,132.47511,190.299356,122.583498,0.79176,1737.741929,4.07728138685653
-c9,1007.11399,1366.6231,653.4248,190.299356,122.583498,0.79176,3340.836504,2.12080507664361
-c10,1007.11399,1366.6231,132.47511,190.299356,122.583498,0.79176,2819.886814,2.51260545053191
-c11,1007.11399,284.478215,132.47511,190.299356,594.6194,0.79176,2209.777831,3.20632364483506
-c12,1007.11399,1366.6231,132.47511,190.299356,122.583498,0.79176,2819.886814,2.51260545053191
-c13,1007.11399,284.478215,132.47511,190.299356,122.583498,0.79176,1737.741929,4.07728138685653
-c14,1007.11399,284.478215,132.47511,190.299356,122.583498,0.79176,1737.741929,4.07728138685653
-c15,1007.11399,284.478215,132.47511,813.5032,122.583498,0.79176,2360.945773,3.0010273895
-c16,1007.11399,284.478215,132.47511,190.299356,594.8494,0.79176,2210.007831,3.20598995624147
-c17,1007.11399,284.478215,132.47511,190.299356,122.583498,0.79176,1737.741929,4.07728138685653
-c18,1007.11399,1366.6231,653.4248,190.299356,122.583498,0.79176,3340.836504,2.12080507664361
-c19,1007.11399,284.478215,716.9894,686.8162,122.583498,0.79176,2818.773063,2.51359822883343
-c20,1007.11399,1366.6231,132.47511,190.299356,122.583498,0.79176,2819.886814,2.51260545053191
-c21,1007.11399,1366.6231,653.4248,686.8162,510.1917,0.79176,4224.96155,1.67700060188712
-c22,1007.11399,284.478215,132.47511,190.299356,594.6259,0.79176,2209.784331,3.20631421355145
-c23,1007.11399,1366.6231,132.47511,190.299356,122.583498,0.79176,2819.886814,2.51260545053191
-c24,1007.11399,284.478215,132.47511,190.299356,122.583498,0.79176,1737.741929,4.07728138685653
-c25,1007.11399,284.478215,132.47511,190.299356,122.583498,0.79176,1737.741929,4.07728138685653
-c26,1007.11399,284.478215,132.47511,190.299356,594.8287,0.79176,2209.987131,3.20601998537068
-c27,1007.11399,1366.6231,653.4248,190.299356,122.583498,0.79176,3340.836504,2.12080507664361
-c28,1007.11399,1366.6231,132.47511,190.299356,122.583498,0.79176,2819.886814,2.51260545053191
-c29,1007.11399,284.478215,132.47511,813.5072,122.583498,0.79176,2360.949773,3.00102230505933
-c30,1007.11399,284.478215,132.47511,190.299356,122.583498,0.79176,1737.741929,4.07728138685653
-c4,1737.741929
-
-Unpatch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0
-c2,0,101.577,30.7428,17.7729,0,0,150.0927,0
-c3,0,0,30.3046,17.8726,18.1884,0,66.3656,0
-c4,0,98.4261,30.5781,17.9593,18.3894,0,165.3529,0
-c5,0,102.312,30.6223,18.2865,18.0635,0,169.2843,0
-c6,0,103.328,30.8374,0,18.2469,0,152.4123,0
-c7,0,102.713,30.6892,18.2789,0,0,151.6811,0
-c8,0,102.804,30.2146,17.6904,18.0145,0,168.7235,0
-c9,0,0,0,18.007,17.9143,0,35.9213,0
-c10,0,0,30.9873,17.7668,18.1695,0,66.9236,0
-c11,0,101.253,30.7777,18.1746,0,0,150.2053,0
-c12,0,0,30.9036,17.9928,17.8342,0,66.7306,0
-c13,0,102.539,30.5572,18.2881,18.1205,0,169.5048,0
-c14,0,103.577,30.7469,18.0862,18.1303,0,170.5404,0
-c15,0,103.056,30.5244,0,18.187,0,151.7674,0
-c16,0,101.268,30.5238,17.9011,0,0,149.6929,0
-c17,0,103.891,30.4408,17.8737,17.8652,0,170.0707,0
-c18,0,0,0,17.8041,17.8634,0,35.6675,0
-c19,0,101.925,0,0,18.0362,0,119.9612,0
-c20,0,0,30.7311,17.8306,17.8473,0,66.409,0
-c21,0,0,0,0,0,0,0,0
-c22,0,101.492,30.0116,17.977,0,0,149.4806,0
-c23,0,0,30.6942,18.4031,18.2642,0,67.3615,0
-c24,0,102.604,30.7051,17.4343,17.9855,0,168.7289,0
-c25,0,102.404,30.8007,17.8214,18.0542,0,169.0803,0
-c26,0,103.762,30.5876,17.6232,0,0,151.9728,0
-c27,0,0,0,17.7149,18.191,0,35.9059,0
-c28,0,0,31.3332,18.1906,17.8624,0,67.3862,0
-c29,0,101.665,30.3239,0,17.9223,0,149.9112,0
-c30,0,103.28,29.8201,17.8587,17.9835,0,168.9423,0
-c0,0
-
diff --git a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_tensors.txt b/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_tensors.txt
deleted file mode 100644
index e7f0a6e270ba81fa11ec07b261192e5b50a4d93d..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/alexnet_cifar10/alexnet_tensors.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-#Conv1,4
-Conv1,925.494,3271.78,759.668,1563.71,5.44249,6.60174,83.2352,403.353
-Add1,114.477,689.511,93.2842,353.575,309.757,1205.25,83.2903,330.731
-Tanh1,76.4029,470.583,71.349,309.397,100.913,396.573,83.2272,381.858
-Pool1,175.352,985.339,77.3703,318.571,210.001,844.263,20.9052,88.6981
-#Conv2,4
-Conv2,2080.05,13944.8,1196.88,6519.17,26.722,110.701,62.1589,473.091
-Add2,139.245,1083.98,57.2394,332.553,183.024,1140.1,62.1204,356.499
-Tanh2,55.9198,411.855,53.7113,299.392,76.8765,416.385,62.0292,344.851
-Pool2,132.287,891.752,58.7924,314.375,76.8368,405.93,15.6216,82.0579
-#Conv3,3
-Conv3,1014.71,6863.91,561.436,3134.15,20.4033,103.834,31.0982,213.521
-Add3,73.7075,582.264,64.5391,421.019,39.2865,257.541,31.1203,195.786
-Tanh3,27.9644,214.918,27.4497,170.841,39.0187,237.416,31.0592,188.341
-#Conv4,3
-Conv4,1226.86,9535.92,627.538,4183.57,39.4494,231.112,20.805,157.506
-Add4,47.9113,399.265,40.9697,274.189,75.4038,524.503,20.652,132.57
-Tanh4,18.6218,150.823,18.3085,118.735,26.1769,164.262,20.6587,128.708
-#Conv5,4
-Conv5,861.092,6591.9,430.284,2806.26,26.6141,161.682,20.7426,148.724
-Add5,47.955,388.123,41.049,260.675,79.1767,523.707,20.6328,125.982
-Tanh5,18.6201,147.003,18.3283,114.102,26.1523,156.042,20.633,123.091
-Pool3,44.6908,343.93,20.5304,123.325,26.4508,154.684,5.2822,29.1224
-#FC1,2
-Mul1,3.73053,25.3858,6.85001,40.5555,7.36824,40.3988,0.141077,0.0604596
-Add6,0.1721,0.083548,0.580578,3.19613,0.460408,1.00469,0.0886822,0.0103369
diff --git a/llvm/projects/soc_simulator/ddr_test.cpp b/llvm/projects/soc_simulator/ddr_test.cpp
deleted file mode 100644
index 53c0e3b6d94f6fd2e1a9ea2a177767b4c46c9b9c..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/ddr_test.cpp
+++ /dev/null
@@ -1,70 +0,0 @@
-#include <iostream>
-#include <fstream>
-#include <cstdlib>
-#include <cstring>
-#include <chrono>
-
-// Goes through a 16 MB array to clear out the cache
-char clearCache() {
- static const unsigned num_bytes = 16 * 1024 * 1024;
- static char *temp = (char *) std::malloc(num_bytes);
- for (unsigned i = 0; i < num_bytes; i++)
- temp[i] = rand();
- return temp[rand() % num_bytes];
-}
-
-int main() {
- srand(1);
- std::chrono::time_point<std::chrono::high_resolution_clock> start;
- std::chrono::time_point<std::chrono::high_resolution_clock> end;
- std::ofstream ofs;
- ofs.open("profile_data.txt");
-
- // 1 MB to 64 MB
- for (unsigned i = 20; i < 26; i++) {
- const unsigned num_bytes = 1 << i;
- char *src = (char *) std::malloc(num_bytes);
- char *dst = (char *) std::malloc(num_bytes);
-
- // Test writes
- clearCache();
- start = std::chrono::high_resolution_clock::now();
- for (unsigned j = 0; j < num_bytes; j++)
- src[j] = rand();
- end = std::chrono::high_resolution_clock::now();
- std::cout << num_bytes << "w = " << (static_cast<double>(num_bytes) / 1e9) / std::chrono::duration<double>(end - start).count() << "\n";
- ofs << num_bytes << "w\t" << std::to_string(std::chrono::duration<double>(start.time_since_epoch()).count()) << "\n";
- ofs << num_bytes << "w\t" << std::to_string(std::chrono::duration<double>(end.time_since_epoch()).count()) << "\n";
- ofs.flush();
-
- // Test reads
- clearCache();
- char sum;
- start = std::chrono::high_resolution_clock::now();
- for (unsigned j = 0; j < num_bytes; j++)
- sum += src[j];
- end = std::chrono::high_resolution_clock::now();
- std::cout << num_bytes << "r = " << (static_cast<double>(num_bytes) / 1e9) / std::chrono::duration<double>(end - start).count() << "\n";
- ofs << num_bytes << "r\t" << std::to_string(std::chrono::duration<double>(start.time_since_epoch()).count()) << "\n";
- ofs << num_bytes << "r\t" << std::to_string(std::chrono::duration<double>(end.time_since_epoch()).count()) << "\n";
- ofs.flush();
-
- // Test read-then-write
- clearCache();
- start = std::chrono::high_resolution_clock::now();
- std::memcpy(dst, src, num_bytes);
- end = std::chrono::high_resolution_clock::now();
- std::cout << num_bytes << "rw = " << (static_cast<double>(num_bytes) / 1e9) / std::chrono::duration<double>(end - start).count() << "\n";
- ofs << num_bytes << "rw\t" << std::to_string(std::chrono::duration<double>(start.time_since_epoch()).count()) << "\n";
- ofs << num_bytes << "rw\t" << std::to_string(std::chrono::duration<double>(end.time_since_epoch()).count()) << "\n";
- ofs.flush();
-
- // So the compiler doesn't optimize out everything
- std::cout << num_bytes << ": " << sum << "\n";
- std::cout << num_bytes << ": " << dst[rand() % num_bytes] << "\n";
-
- free(src);
- free(dst);
- }
- return 0;
-}
diff --git a/llvm/projects/soc_simulator/include/promise_timing_model.h b/llvm/projects/soc_simulator/include/promise_timing_model.h
deleted file mode 100644
index 6ff5592d19cca115c6afbc9cb606c731ee9f9fe8..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/include/promise_timing_model.h
+++ /dev/null
@@ -1,162 +0,0 @@
-#include <iostream>
-#include <fstream>
-#include <string>
-#include <boost/algorithm/string.hpp>
-
-#include <cmath>
-#include <cassert>
-#include <algorithm>
-#include <utility>
-#include <vector>
-
-// NOTE 1: This code uses seconds for all computations. Using clock cycles
-// would have been cleaner but it's not possible because we don't know anything
-// about Jetson's DRAM other than its bandwidth.
-
-// NOTE 2: All elements are assumed to be 1 byte long.
-
-// NOTE 3: PROMISE's frequency is fixed at 1 GHz and thus 1 cycle = 1 ns.
-
-#define NUM_ARGS (7)
-#define VOLTAGE_LEVELS (7)
-
-class Dram {
-private:
- const double energy_per_bit = 20e-12; // 20 pJ/bit
- double latency_;
- double bandwidth_;
-
-public:
- Dram(const double latency, const double bandwidth);
-
- // Calculates (time, energy) of accessing 'num_bytes' in memory
- std::pair<double, double> access(const unsigned num_bytes, const bool pipeline = false) const;
-};
-
-class Scratchpad {
-private:
- // Line size, latency, and energy
- const unsigned log_line_size = 6;
- const unsigned line_size = 1 << log_line_size; // 64 B
- const double line_latency = 1e-9; // 1 ns
- const double line_energy = 12e-12; // 12 pJ
-
- // Tag array. The tag is the address of the row being requested.
- unsigned num_lines_;
- std::vector<int> lines_;
-
- // DRAM
- Dram dram_;
-
- // Enable flag
- bool enable_;
-
-private:
- // Calculates the index within the scratchpad array
- unsigned getIndex(const unsigned address) const;
-
-public:
- Scratchpad(const bool enable,
- const unsigned size,
- const double dram_latency,
- const double dram_bandwidth);
-
- // Clears the scratchpad
- void clear();
-
- // Calculates (time, energy) of accessing 'num_bytes' starting from 'address'
- std::pair<double, double> access(const unsigned address,
- const unsigned num_bytes);
-};
-
-class Promise {
-private:
- // Compute energy in pJ/128-element-dot-product for swings 0 through 7
- const double compute_energy_per_dot[VOLTAGE_LEVELS + 1] = {
- 0.0, // This makes indexing simpler
- 30.54403e-12,
- 31.68943e-12,
- 35.04211e-12,
- 47.21840426e-12,
- 52.68045671e-12,
- 80.03489e-12,
- 106.5494e-12
- };
-
- // SRAM access energy per byte
- const double sram_energy_per_byte = 0.1875e-12; // 0.1875 pJ/B
-
- // Leakage energy (converted from pJ/clock to mJ/s)
- const double leakage_energy_per_s = 6e-3; // 6 pJ/ns ==> 6 mJ/s
-
- const unsigned num_banks_ = 256;
- const unsigned bank_x_ = 128;
- const unsigned bank_y_ = 128;
- const unsigned bank_size = bank_x_ * bank_y_;
- const unsigned vector_size_ = bank_x_;
-
- const double pipeline_latency_ = 14e-9; // 14 ns
- const double reduction_latency_ = 10e-9; // 10 ns
-
- const bool use_scratchpad_ = false;
- const unsigned scratchpad_size_ = 512 * 1024; // 512 KB
-
- const double dram_latency_ = 100e-9; // 100 ns
- const double dram_bandwidth_ = 30e9; // 30 GB/s (measured peak)
-
- // Scratchpad for array A
- Scratchpad scratch_;
-
- // DRAM
- Dram dram_;
-
- // uint version of min
- unsigned min(const unsigned x, const unsigned y) const;
-
- // Calculates energy of loading data into the SRAM
- double loadSRAM(const unsigned num_bytes) const;
-
- // Calculates (time, energy) of computing 'num_elements' elements
- std::pair<double, double> compute(const unsigned num_elements,
- const unsigned voltage_swing) const;
-
- // Calculates the number of banks required to fill up an entire column; i.e. all the rows
- unsigned banksPerColumnTile(const unsigned num_rows) const;
-
- // Calculates the number of fully filled column tiles
- unsigned activeColumnTiles(const unsigned num_rows, const unsigned remaining_columns) const;
-
- // Calculates the number of rows of A that can be operated on in parallel
- // based on the tiling of *B*
- unsigned numRowsA(const unsigned num_rows, const unsigned num_cols) const;
-
- // Calculates (time, energy) of A x B (GEMM)
- std::pair<double, double> run(const unsigned num_rows_a,
- const unsigned num_cols_a,
- const unsigned num_rows_b,
- const unsigned num_cols_b,
- const unsigned voltage_swing,
- const unsigned patch_factor);
-public:
- Promise();
-
- std::pair<double, double> fc_profile(const unsigned num_rows_a,
- const unsigned num_cols_a,
- const unsigned num_rows_b,
- const unsigned num_cols_b,
- const unsigned voltage_swing,
- const unsigned patch_factor);
-
- std::pair<double, double> conv_profile(const unsigned n,
- const unsigned c,
- const unsigned h,
- const unsigned w,
- const unsigned c_out,
- const unsigned c_in,
- const unsigned k_h,
- const unsigned k_w,
- const unsigned s_h,
- const unsigned s_w,
- const unsigned voltage_swing,
- const unsigned patch_factor);
-};
diff --git a/llvm/projects/soc_simulator/mobilenet_cifar10/mobilenet_layers.txt b/llvm/projects/soc_simulator/mobilenet_cifar10/mobilenet_layers.txt
deleted file mode 100644
index ec202b5be38d401551b82746655d45847567307c..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/mobilenet_cifar10/mobilenet_layers.txt
+++ /dev/null
@@ -1,83 +0,0 @@
-Conv1,2000,3,32,32,32,3,3,3,1,1
-NML1
-NML2
-NML3
-NML4
-NML5
-Conv3,2000,32,32,32,64,32,1,1,1,1
-NML6
-NML7
-NML8
-NML9
-NML10
-Conv5,2000,64,16,16,128,64,1,1,1,1
-NML11
-NML12
-NML13
-NML14
-NML15
-Conv7,2000,128,16,16,128,128,1,1,1,1
-NML16
-NML17
-NML18
-NML19
-NML20
-Conv9,2000,128,8,8,256,128,1,1,1,1
-NML21
-NML22
-NML23
-NML24
-NML25
-Conv11,2000,256,8,8,256,256,1,1,1,1
-NML26
-NML27
-NML28
-NML29
-NML30
-Conv13,2000,256,4,4,512,256,1,1,1,1
-NML31
-NML32
-NML33
-NML34
-NML35
-Conv15,2000,512,4,4,512,512,1,1,1,1
-NML36
-NML37
-NML38
-NML39
-NML40
-Conv17,2000,512,4,4,512,512,1,1,1,1
-NML41
-NML42
-NML43
-NML44
-NML45
-Conv19,2000,512,4,4,512,512,1,1,1,1
-NML46
-NML47
-NML48
-NML49
-NML50
-Conv21,2000,512,4,4,512,512,1,1,1,1
-NML51
-NML52
-NML53
-NML54
-NML55
-Conv23,2000,512,4,4,512,512,1,1,1,1
-NML56
-NML57
-NML58
-NML59
-NML60
-Conv25,2000,512,2,2,1024,512,1,1,1,1
-NML61
-NML62
-NML63
-NML64
-NML65
-Conv27,2000,1024,2,2,1024,1024,1,1,1,1
-NML66
-NML67
-NML68
-FC1,2000,1024,1024,10
diff --git a/llvm/projects/soc_simulator/mobilenet_shallow/HA_loss1.txt b/llvm/projects/soc_simulator/mobilenet_shallow/HA_loss1.txt
deleted file mode 100644
index 98ec2de71c8abc518c52ee3089721b0d96a06fdb..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/mobilenet_shallow/HA_loss1.txt
+++ /dev/null
@@ -1,25 +0,0 @@
-9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,9,8,8,9,8,8,8,8,8,8,8 8
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,7,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,7,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,8,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,9,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,7,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,9,8,8,9,8,8,7,8,8,8,8 8
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,9,8,8,9,8,8,8,8,8,8,8 8
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,7,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,7,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,9,8,8,9,8,8,7,8,8,8,8 8
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,8,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,9,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,7,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,9,8,8,9,8,8,8,8,8,8,8 8
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,7,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,7,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,8,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,9,8,8,9,8,8,7,8,8,8,8 8
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,9,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,7,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,8,9 9
diff --git a/llvm/projects/soc_simulator/mobilenet_shallow/HA_loss2.txt b/llvm/projects/soc_simulator/mobilenet_shallow/HA_loss2.txt
deleted file mode 100644
index 9fd37f52c3a00843b7ffaca9b3d8ecbc5f1139ec..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/mobilenet_shallow/HA_loss2.txt
+++ /dev/null
@@ -1,30 +0,0 @@
-9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9 9
-9,8,8,9,8,8,7,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,7,8,8,9,8,8,7,8,8,9,8,8,6,8,8,8,9 9
-9,8,8,9,8,8,7,8,8,9,8,8,8,8,8,9,8,8,6,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,7,8,8,8,8 8
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,5,8,8,9,8,8,7,8,8,9,8,8,7,8,8,9,8,8,7,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,8,8 8
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,8,8,8,9,8,8,6,8,8,9,8,8,8,8,8,8,8 8
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,8,8,8,9,8,8,8,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,8,8 8
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,8,8,8,9,8,8,6,8,8,9,8,8,5,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,8,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,5,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,8,9 9
-9,8,8,9,8,8,7,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,7,8,8,9,8,8,7,8,8,9,8,8,6,8,8,8,9 9
-9,8,8,9,8,8,7,8,8,9,8,8,8,8,8,9,8,8,6,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,7,8,8,8,8 8
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,5,8,8,9,8,8,7,8,8,9,8,8,7,8,8,9,8,8,7,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,8,8 8
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,8,8,8,9,8,8,6,8,8,9,8,8,8,8,8,8,8 8
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,8,8 8
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-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,8,8,8,8,9 9
-9,8,8,9,8,8,7,8,8,9,8,8,7,8,8,9,8,8,8,8,8,9,8,8,9,8,8,9,8,8,5,8,8,9,8,8,8,8,8,8,9 9
-9,8,8,9,8,8,7,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,7,8,8,9,8,8,7,8,8,9,8,8,6,8,8,8,9 9
-9,8,8,9,8,8,7,8,8,9,8,8,8,8,8,9,8,8,6,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,7,8,8,8,8 8
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,5,8,8,9,8,8,7,8,8,9,8,8,7,8,8,9,8,8,7,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,8,8 8
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,8,8,8,9,8,8,6,8,8,9,8,8,8,8,8,8,8 8
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,8,8 8
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,8,8,8,9,8,8,6,8,8,9,8,8,5,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,8,8,8,9,8,8,7,8,8,9,8,8,8,8,8,8,9 9
-9,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,5,8,8,9,8,8,8,8,8,9,8,8,8,8,8,9,8,8,7,8,8,8,9 9
diff --git a/llvm/projects/soc_simulator/mobilenet_shallow/HA_results_loss1.out b/llvm/projects/soc_simulator/mobilenet_shallow/HA_results_loss1.out
deleted file mode 100644
index ddc3ead56dc7ccf7afe7b065617716f129141aa9..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/mobilenet_shallow/HA_results_loss1.out
+++ /dev/null
@@ -1,319 +0,0 @@
-Compute Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,572.966,184.509,138.685,510.748,198.623,146.196,1046.81,410.676,295.687,405.855,99.5467,73.891,524.623,211.449,159.137,873.476,219.41,164.603,1372.91,218.702,165.077,504.488,71.2665,37.2098,954.913,148.915,79.6361,785.077,146.853,77.9379,1118.97,148.577,78.6669,256.743,121.755,15.0166,577.914,250.554,36.6268,125.046,5.547644,13535.292944,0.999999992611907
-c1,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,1118.97,108.421,56.8779,256.743,104.322,18.9772,243.935,192.276,31.8826,63.4788,25.29103,11378.11813,1.18958976083693
-c2,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,15.711348,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,5.547644,10019.043749,1.35095655313964
-c3,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,15.711348,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,5.547644,10019.043749,1.35095655313964
-c4,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,623.585,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,5.547644,10626.917401,1.27368006223125
-c5,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,623.585,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,5.547644,10626.917401,1.27368006223125
-c6,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,15.711348,108.421,56.8779,256.743,104.322,18.9772,243.935,192.276,31.8826,63.4788,5.547644,10255.116092,1.31985759016157
-c7,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1372.91,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,15.711348,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,5.547644,10349.083749,1.30787354141573
-c8,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,1118.97,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,25.29103,11142.045787,1.21479422013441
-c9,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,1118.97,108.421,56.8779,256.743,104.322,18.9772,243.935,192.276,31.8826,63.4788,25.29103,11378.11813,1.18958976083693
-c10,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,623.585,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,5.547644,10626.917401,1.27368006223125
-c11,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,15.711348,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,5.547644,10019.043749,1.35095655313964
-c12,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,15.711348,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,5.547644,10019.043749,1.35095655313964
-c13,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,623.585,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,5.547644,10626.917401,1.27368006223125
-c14,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,1118.97,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,25.29103,11142.045787,1.21479422013441
-c15,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,15.711348,108.421,56.8779,256.743,104.322,18.9772,243.935,192.276,31.8826,63.4788,5.547644,10255.116092,1.31985759016157
-c16,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1372.91,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,15.711348,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,5.547644,10349.083749,1.30787354141573
-c17,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,1118.97,108.421,56.8779,256.743,104.322,18.9772,243.935,192.276,31.8826,63.4788,25.29103,11378.11813,1.18958976083693
-c18,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,15.711348,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,5.547644,10019.043749,1.35095655313964
-c19,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,15.711348,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,5.547644,10019.043749,1.35095655313964
-c20,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,623.585,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,5.547644,10626.917401,1.27368006223125
-c21,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,15.711348,108.421,56.8779,256.743,104.322,18.9772,243.935,192.276,31.8826,63.4788,5.547644,10255.116092,1.31985759016157
-c22,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,1118.97,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,25.29103,11142.045787,1.21479422013441
-c23,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1372.91,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,15.711348,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,5.547644,10349.083749,1.30787354141573
-c24,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,623.585,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,5.547644,10626.917401,1.27368006223125
-c2,10019.043749
-
-Compute Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,166.211,52.3819,38.6469,138.263,52.1919,38.1309,261.935,97.7271,68.9954,92.893,23.3196,17.6047,116.17,46.1234,34.6677,197.299,46.2495,34.6984,292.058,45.663,34.4299,103.885,15.8328,8.79131,198.587,31.2316,17.2031,162.139,31.2579,17.184,233.12,31.2863,17.1913,53.1921,25.9908,4.43176,119.521,51.9366,8.7572,26.725,2.837158,3056.760228,0.999999967285626
-c1,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,233.12,27.6455,13.524,53.1921,26.0905,3.6779,63.1947,49.3667,6.98942,15.6197,6.340453,2803.076713,1.09050177070549
-c2,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2516.175454,1.21484378271644
-c3,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2516.175454,1.21484378271644
-c4,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,166.111,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2673.982838,1.14314874061474
-c5,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,166.111,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2673.982838,1.14314874061474
-c6,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,63.1947,49.3667,6.98942,15.6197,2.837158,2574.757034,1.18720332400873
-c7,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,292.058,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2526.995454,1.20964210766475
-c8,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,233.12,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,6.340453,2744.495133,1.11377866182652
-c9,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,233.12,27.6455,13.524,53.1921,26.0905,3.6779,63.1947,49.3667,6.98942,15.6197,6.340453,2803.076713,1.09050177070549
-c10,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,166.111,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2673.982838,1.14314874061474
-c11,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2516.175454,1.21484378271644
-c12,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2516.175454,1.21484378271644
-c13,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,166.111,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2673.982838,1.14314874061474
-c14,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,233.12,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,6.340453,2744.495133,1.11377866182652
-c15,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,63.1947,49.3667,6.98942,15.6197,2.837158,2574.757034,1.18720332400873
-c16,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,292.058,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2526.995454,1.20964210766475
-c17,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,233.12,27.6455,13.524,53.1921,26.0905,3.6779,63.1947,49.3667,6.98942,15.6197,6.340453,2803.076713,1.09050177070549
-c18,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2516.175454,1.21484378271644
-c19,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2516.175454,1.21484378271644
-c20,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,166.111,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2673.982838,1.14314874061474
-c21,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,63.1947,49.3667,6.98942,15.6197,2.837158,2574.757034,1.18720332400873
-c22,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,233.12,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,6.340453,2744.495133,1.11377866182652
-c23,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,292.058,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2526.995454,1.20964210766475
-c24,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,166.111,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2673.982838,1.14314874061474
-c2,2516.175454
-
-Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,572.966,184.509,138.685,510.748,198.623,146.196,1046.81,410.676,295.687,405.855,99.5467,73.891,524.623,211.449,159.137,873.476,219.41,164.603,1372.91,218.702,165.077,504.488,71.2665,37.2098,954.913,148.915,79.6361,785.077,146.853,77.9379,1118.97,148.577,78.6669,256.743,121.755,15.0166,577.914,250.554,36.6268,125.046,5.547644,13535.292944,0.999999992611907
-c1,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,1185.8437,378.575,56.8779,270.3063,138.7565,18.9772,243.935,192.276,31.8826,63.4788,25.29103,13597.77833,0.995404728329582
-c2,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,5.6044758,11965.4622918,1.1311968146986
-c3,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,5.6044758,11965.4622918,1.1311968146986
-c4,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,623.585,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,5.6044758,12528.1959148,1.0803864281825
-c5,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,623.585,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,5.6044758,12528.1959148,1.0803864281825
-c6,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,243.935,192.276,31.8826,63.4788,5.6044758,12182.9454528,1.11100331897069
-c7,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1507.228,683.9,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,5.6044758,12961.9392918,1.04423362390992
-c8,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,1185.8437,378.575,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,25.29103,13380.295169,1.01158402500721
-c9,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,1185.8437,378.575,56.8779,270.3063,138.7565,18.9772,243.935,192.276,31.8826,63.4788,25.29103,13597.77833,0.995404728329582
-c10,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,623.585,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,5.6044758,12528.1959148,1.0803864281825
-c11,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,5.6044758,11965.4622918,1.1311968146986
-c12,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,5.6044758,11965.4622918,1.1311968146986
-c13,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,623.585,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,5.6044758,12528.1959148,1.0803864281825
-c14,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,1185.8437,378.575,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,25.29103,13380.295169,1.01158402500721
-c15,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,243.935,192.276,31.8826,63.4788,5.6044758,12182.9454528,1.11100331897069
-c16,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1507.228,683.9,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,5.6044758,12961.9392918,1.04423362390992
-c17,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,1185.8437,378.575,56.8779,270.3063,138.7565,18.9772,243.935,192.276,31.8826,63.4788,25.29103,13597.77833,0.995404728329582
-c18,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,5.6044758,11965.4622918,1.1311968146986
-c19,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,5.6044758,11965.4622918,1.1311968146986
-c20,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,623.585,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,5.6044758,12528.1959148,1.0803864281825
-c21,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,243.935,192.276,31.8826,63.4788,5.6044758,12182.9454528,1.11100331897069
-c22,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,1185.8437,378.575,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,25.29103,13380.295169,1.01158402500721
-c23,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1507.228,683.9,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,5.6044758,12961.9392918,1.04423362390992
-c24,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,623.585,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,5.6044758,12528.1959148,1.0803864281825
-c2,11965.4622918
-
-Leakage Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,30.978009,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,30.978009,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,9.633508,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,9.633508,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,0,0,0,0,0,21.344501,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,30.978009,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,9.633508,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,9.633508,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,30.978009,0
-c12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,30.978009,0
-c13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,9.633508,0
-c14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,9.633508,0
-c15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,0,0,0,0,0,21.344501,0
-c16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,30.978009,0
-c17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,30.978009,0
-c19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,30.978009,0
-c20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,9.633508,0
-c21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,0,0,0,0,0,21.344501,0
-c22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,9.633508,0
-c23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,30.978009,0
-c24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,9.633508,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,32.751202,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,32.751202,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,8.955674,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,8.955674,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,0,0,0,0,0,23.795528,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,32.751202,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,8.955674,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,8.955674,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,32.751202,0
-c12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,32.751202,0
-c13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,8.955674,0
-c14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,8.955674,0
-c15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,0,0,0,0,0,23.795528,0
-c16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,32.751202,0
-c17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,32.751202,0
-c19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,32.751202,0
-c20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,8.955674,0
-c21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,0,0,0,0,0,23.795528,0
-c22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,8.955674,0
-c23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,32.751202,0
-c24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,8.955674,0
-c0,0
-
-Memory Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,8.255467,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,8.255467,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,2.377545,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,2.377545,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,0,0,0,0,0,5.877922,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,8.255467,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,2.377545,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,2.377545,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,8.255467,0
-c12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,8.255467,0
-c13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,2.377545,0
-c14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,2.377545,0
-c15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,0,0,0,0,0,5.877922,0
-c16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,8.255467,0
-c17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,8.255467,0
-c19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,8.255467,0
-c20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,2.377545,0
-c21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,0,0,0,0,0,5.877922,0
-c22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,2.377545,0
-c23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,8.255467,0
-c24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,2.377545,0
-c0,0
-
-Patch Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c0,0
-
-Quantization Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,66.8737,270.154,0,13.5633,34.4345,0,0,0,0,0,0,2219.6602,0
-c2,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c3,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c4,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c5,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c6,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c7,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,134.318,532.119,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,2549.1263318,0
-c8,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,66.8737,270.154,0,13.5633,34.4345,0,0,0,0,0,0,2219.6602,0
-c9,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,66.8737,270.154,0,13.5633,34.4345,0,0,0,0,0,0,2219.6602,0
-c10,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c11,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c12,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c13,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c14,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,66.8737,270.154,0,13.5633,34.4345,0,0,0,0,0,0,2219.6602,0
-c15,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c16,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,134.318,532.119,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,2549.1263318,0
-c17,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,66.8737,270.154,0,13.5633,34.4345,0,0,0,0,0,0,2219.6602,0
-c18,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c19,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c20,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c21,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c22,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,66.8737,270.154,0,13.5633,34.4345,0,0,0,0,0,0,2219.6602,0
-c23,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,134.318,532.119,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,2549.1263318,0
-c24,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,18.9605,73.8267,0,4.99161,10.5834,0,0,0,0,0,0,622.41277,0
-c2,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c3,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c4,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c5,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c6,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c7,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,37.6677,148.426,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,716.022858,0
-c8,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,18.9605,73.8267,0,4.99161,10.5834,0,0,0,0,0,0,622.41277,0
-c9,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,18.9605,73.8267,0,4.99161,10.5834,0,0,0,0,0,0,622.41277,0
-c10,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c11,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c12,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c13,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c14,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,18.9605,73.8267,0,4.99161,10.5834,0,0,0,0,0,0,622.41277,0
-c15,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c16,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,37.6677,148.426,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,716.022858,0
-c17,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,18.9605,73.8267,0,4.99161,10.5834,0,0,0,0,0,0,622.41277,0
-c18,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c19,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c20,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c21,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c22,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,18.9605,73.8267,0,4.99161,10.5834,0,0,0,0,0,0,622.41277,0
-c23,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,37.6677,148.426,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,716.022858,0
-c24,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c0,0
-
-Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,166.211,52.3819,38.6469,138.263,52.1919,38.1309,261.935,97.7271,68.9954,92.893,23.3196,17.6047,116.17,46.1234,34.6677,197.299,46.2495,34.6984,292.058,45.663,34.4299,103.885,15.8328,8.79131,198.587,31.2316,17.2031,162.139,31.2579,17.184,233.12,31.2863,17.1913,53.1921,25.9908,4.43176,119.521,51.9366,8.7572,26.725,2.837158,3056.760228,0.999999967285626
-c1,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,252.0805,101.4722,13.524,58.18371,36.6739,3.6779,63.1947,49.3667,6.98942,15.6197,6.340453,3425.489483,0.892357181049411
-c2,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,3.140746,3054.360079,1.00078577798928
-c3,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,3.140746,3054.360079,1.00078577798928
-c4,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,166.111,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,3.140746,3206.289541,0.953363722638182
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-c12,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,3.140746,3054.360079,1.00078577798928
-c13,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,166.111,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,3.140746,3206.289541,0.953363722638182
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-c15,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,63.1947,49.3667,6.98942,15.6197,3.140746,3110.564114,0.982702821000178
-c16,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,329.7257,188.9254,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,3.140746,3251.273779,0.940173095765213
-c17,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,252.0805,101.4722,13.524,58.18371,36.6739,3.6779,63.1947,49.3667,6.98942,15.6197,6.340453,3425.489483,0.892357181049411
-c18,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,3.140746,3054.360079,1.00078577798928
-c19,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,3.140746,3054.360079,1.00078577798928
-c20,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,166.111,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,3.140746,3206.289541,0.953363722638182
-c21,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,63.1947,49.3667,6.98942,15.6197,3.140746,3110.564114,0.982702821000178
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-c2,3054.360079
-
-Unpatch Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
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-c0,0
-
diff --git a/llvm/projects/soc_simulator/mobilenet_shallow/HA_results_loss2.out b/llvm/projects/soc_simulator/mobilenet_shallow/HA_results_loss2.out
deleted file mode 100644
index 25c65eae81141ec16294f4c133b32879d039277a..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/mobilenet_shallow/HA_results_loss2.out
+++ /dev/null
@@ -1,374 +0,0 @@
-Compute Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
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-c1,572.966,129.571,87.353,510.748,185.039,114.322,31.422697,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,15.711348,151.781,104.976,504.488,63.3415,35.9187,7.855674,109.567,57.0866,785.077,108.787,56.7435,15.711348,108.421,56.8779,256.743,104.322,18.9772,5.906058,192.276,31.8826,63.4788,5.547644,7728.157869,1.75142550117301
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-c5,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,15.711348,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,11.801625,108.421,56.8779,256.743,104.322,18.9772,243.935,192.276,31.8826,63.4788,25.29103,9243.791103,1.46425775385398
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-c7,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,623.585,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,25.29103,10646.660787,1.27131812383798
-c8,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,15.711348,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,11.801625,108.421,56.8779,256.743,104.322,18.9772,3.887477,192.276,31.8826,63.4788,5.547644,8984.000194,1.50659978863086
-c9,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,15.711348,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,15.711348,108.421,56.8779,256.743,104.322,18.9772,243.935,192.276,31.8826,63.4788,5.547644,9227.95744,1.4667701802191
-c10,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,7.768049,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,623.585,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,5.547644,9591.81545,1.41112940229548
-c11,572.966,129.571,87.353,510.748,185.039,114.322,31.422697,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,15.711348,151.781,104.976,504.488,63.3415,35.9187,7.855674,109.567,57.0866,785.077,108.787,56.7435,15.711348,108.421,56.8779,256.743,104.322,18.9772,5.906058,192.276,31.8826,63.4788,5.547644,7728.157869,1.75142550117301
-c12,572.966,129.571,87.353,510.748,185.039,114.322,31.422697,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,11.801625,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,15.711348,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,25.29103,8111.070457,1.66874308993882
-c13,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,7.768049,151.781,104.976,504.488,63.3415,35.9187,7.855674,109.567,57.0866,785.077,108.787,56.7435,15.711348,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,5.547644,8618.819472,1.57043465534099
-c14,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,15.711348,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,623.585,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,25.29103,9619.502135,1.40706791404992
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-c16,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,623.585,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,25.29103,10646.660787,1.27131812383798
-c17,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,7.768049,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,623.585,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,5.547644,9591.81545,1.41112940229548
-c18,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,15.711348,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,11.801625,108.421,56.8779,256.743,104.322,18.9772,3.887477,192.276,31.8826,63.4788,5.547644,8984.000194,1.50659978863086
-c19,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,15.711348,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,15.711348,108.421,56.8779,256.743,104.322,18.9772,243.935,192.276,31.8826,63.4788,5.547644,9227.95744,1.4667701802191
-c20,572.966,129.571,87.353,510.748,185.039,114.322,31.422697,331.516,220.807,405.855,92.5426,67.4597,15.711348,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,954.913,109.567,57.0866,785.077,108.787,56.7435,7.768049,108.421,56.8779,256.743,104.322,18.9772,243.935,192.276,31.8826,63.4788,5.547644,9238.670838,1.46506927617991
-c21,572.966,129.571,87.353,510.748,185.039,114.322,31.422697,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,15.711348,151.781,104.976,504.488,63.3415,35.9187,7.855674,109.567,57.0866,785.077,108.787,56.7435,15.711348,108.421,56.8779,256.743,104.322,18.9772,5.906058,192.276,31.8826,63.4788,5.547644,7728.157869,1.75142550117301
-c22,572.966,129.571,87.353,510.748,185.039,114.322,31.422697,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,11.801625,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,15.711348,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,25.29103,8111.070457,1.66874308993882
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-c24,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,15.711348,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,623.585,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,25.29103,9619.502135,1.40706791404992
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-c27,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,15.711348,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,11.801625,108.421,56.8779,256.743,104.322,18.9772,3.887477,192.276,31.8826,63.4788,5.547644,8984.000194,1.50659978863086
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-c29,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,7.768049,151.781,104.976,504.488,63.3415,35.9187,372.978,109.567,57.0866,785.077,108.787,56.7435,623.585,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,5.547644,9591.81545,1.41112940229548
-c1,7728.157869
-
-Compute Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,166.211,52.3819,38.6469,138.263,52.1919,38.1309,261.935,97.7271,68.9954,92.893,23.3196,17.6047,116.17,46.1234,34.6677,197.299,46.2495,34.6984,292.058,45.663,34.4299,103.885,15.8328,8.79131,198.587,31.2316,17.2031,162.139,31.2579,17.184,233.12,31.2863,17.1913,53.1921,25.9908,4.43176,119.521,51.9366,8.7572,26.725,2.837158,3056.760228,0.999999967285626
-c1,166.211,44.0763,27.7776,138.263,46.5303,27.6531,16.261248,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,8.303616,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,1922.094426,1.59032773188388
-c2,166.211,44.0763,27.7776,138.263,46.5303,27.6531,16.261248,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,8.303616,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,6.340453,2019.082613,1.51393511930881
-c3,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,8.303616,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2149.756178,1.42191012966541
-c4,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,8.303616,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,166.111,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,6.340453,2404.551749,1.27123905823501
-c5,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,8.303616,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,63.1947,49.3667,6.98942,15.6197,6.340453,2305.325945,1.32595570792677
-c6,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,166.111,27.6455,13.524,53.1921,26.0905,3.6779,63.1947,49.3667,6.98942,15.6197,2.837158,2639.079526,1.1582675254983
-c7,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,166.111,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,6.340453,2677.486133,1.14165301405679
-c8,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,8.303616,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2243.24107,1.36265340921859
-c9,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,8.303616,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,63.1947,49.3667,6.98942,15.6197,2.837158,2301.82265,1.32797376687671
-c10,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,8.303616,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,166.111,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2401.048454,1.27309388346505
-c11,166.211,44.0763,27.7776,138.263,46.5303,27.6531,16.261248,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,8.303616,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,1922.094426,1.59032773188388
-c12,166.211,44.0763,27.7776,138.263,46.5303,27.6531,16.261248,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,8.303616,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,6.340453,2019.082613,1.51393511930881
-c13,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,8.303616,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2149.756178,1.42191012966541
-c14,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,8.303616,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,166.111,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,6.340453,2404.551749,1.27123905823501
-c15,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,8.303616,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,63.1947,49.3667,6.98942,15.6197,6.340453,2305.325945,1.32595570792677
-c16,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,166.111,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,6.340453,2677.486133,1.14165301405679
-c17,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,8.303616,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,166.111,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2401.048454,1.27309388346505
-c18,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,8.303616,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2243.24107,1.36265340921859
-c19,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,8.303616,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,63.1947,49.3667,6.98942,15.6197,2.837158,2301.82265,1.32797376687671
-c20,166.211,44.0763,27.7776,138.263,46.5303,27.6531,16.261248,86.2346,54.8624,92.893,22.5995,15.858,8.303616,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,198.587,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,63.1947,49.3667,6.98942,15.6197,2.837158,2270.151198,1.34650066305845
-c21,166.211,44.0763,27.7776,138.263,46.5303,27.6531,16.261248,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,8.303616,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,1922.094426,1.59032773188388
-c22,166.211,44.0763,27.7776,138.263,46.5303,27.6531,16.261248,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,8.303616,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,6.340453,2019.082613,1.51393511930881
-c23,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,8.303616,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2149.756178,1.42191012966541
-c24,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,8.303616,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,166.111,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,6.340453,2404.551749,1.27123905823501
-c25,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,8.303616,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,63.1947,49.3667,6.98942,15.6197,6.340453,2305.325945,1.32595570792677
-c26,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,166.111,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,6.340453,2677.486133,1.14165301405679
-c27,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,8.303616,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2243.24107,1.36265340921859
-c28,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,8.303616,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,63.1947,49.3667,6.98942,15.6197,2.837158,2301.82265,1.32797376687671
-c29,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,8.303616,40.4994,26.741,103.885,15.2944,7.99054,97.6367,27.6042,13.5937,162.139,27.6361,13.5283,166.111,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2401.048454,1.27309388346505
-c1,1922.094426
-
-Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,572.966,184.509,138.685,510.748,198.623,146.196,1046.81,410.676,295.687,405.855,99.5467,73.891,524.623,211.449,159.137,873.476,219.41,164.603,1372.91,218.702,165.077,504.488,71.2665,37.2098,954.913,148.915,79.6361,785.077,146.853,77.9379,1118.97,148.577,78.6669,256.743,121.755,15.0166,577.914,250.554,36.6268,125.046,5.547644,13535.292944,0.999999992611907
-c1,572.966,281.424,87.353,651.192,404.181,114.322,152.598914,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,91.934874,151.781,104.976,536.2213,121.4188,35.9187,38.09458,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,24.49524,192.276,31.8826,63.4788,5.6044758,9902.2150608,1.36689545967273
-c2,572.966,281.424,87.353,651.192,404.181,114.322,152.598914,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,88.025151,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,25.29103,10254.831911,1.31989416593867
-c3,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,83.991575,151.781,104.976,536.2213,121.4188,35.9187,38.09458,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,5.6044758,10671.7004468,1.26833515283173
-c4,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,91.934874,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,623.585,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,25.29103,11596.947343,1.16714273394159
-c5,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,91.934874,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,56.941654,108.421,56.8779,270.3063,138.7565,18.9772,243.935,192.276,31.8826,63.4788,25.29103,11247.787158,1.20337383998555
-c6,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,38.09458,109.567,57.0866,852.5958,379.656,56.7435,623.585,108.421,56.8779,270.3063,138.7565,18.9772,243.935,192.276,31.8826,63.4788,5.6044758,12410.7956558,1.09060637289712
-c7,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,623.585,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,25.29103,12547.882469,1.07869139431058
-c8,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,91.934874,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,56.941654,108.421,56.8779,270.3063,138.7565,18.9772,22.476659,192.276,31.8826,63.4788,5.6044758,11006.6422628,1.22973859764412
-c9,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,91.934874,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,243.935,192.276,31.8826,63.4788,5.6044758,11232.0103268,1.2050641363103
-c10,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,83.991575,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,623.585,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,5.6044758,11569.3174898,1.16993010511988
-c11,572.966,281.424,87.353,651.192,404.181,114.322,152.598914,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,91.934874,151.781,104.976,536.2213,121.4188,35.9187,38.09458,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,24.49524,192.276,31.8826,63.4788,5.6044758,9902.2150608,1.36689545967273
-c12,572.966,281.424,87.353,651.192,404.181,114.322,152.598914,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,88.025151,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,25.29103,10254.831911,1.31989416593867
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-c14,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,91.934874,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,623.585,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,25.29103,11596.947343,1.16714273394159
-c15,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,91.934874,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,56.941654,108.421,56.8779,270.3063,138.7565,18.9772,243.935,192.276,31.8826,63.4788,25.29103,11247.787158,1.20337383998555
-c16,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,623.585,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,25.29103,12547.882469,1.07869139431058
-c17,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,83.991575,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,623.585,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,5.6044758,11569.3174898,1.16993010511988
-c18,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,91.934874,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,56.941654,108.421,56.8779,270.3063,138.7565,18.9772,22.476659,192.276,31.8826,63.4788,5.6044758,11006.6422628,1.22973859764412
-c19,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,91.934874,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,243.935,192.276,31.8826,63.4788,5.6044758,11232.0103268,1.2050641363103
-c20,572.966,281.424,87.353,651.192,404.181,114.322,152.598914,331.516,220.807,475.2483,216.7906,67.4597,76.301921,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,1021.9148,364.726,57.0866,852.5958,379.656,56.7435,52.908078,108.421,56.8779,270.3063,138.7565,18.9772,243.935,192.276,31.8826,63.4788,5.6044758,11670.4277888,1.1597940600781
-c21,572.966,281.424,87.353,651.192,404.181,114.322,152.598914,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,91.934874,151.781,104.976,536.2213,121.4188,35.9187,38.09458,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,24.49524,192.276,31.8826,63.4788,5.6044758,9902.2150608,1.36689545967273
-c22,572.966,281.424,87.353,651.192,404.181,114.322,152.598914,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,88.025151,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,25.29103,10254.831911,1.31989416593867
-c23,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,83.991575,151.781,104.976,536.2213,121.4188,35.9187,38.09458,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,5.6044758,10671.7004468,1.26833515283173
-c24,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,91.934874,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,623.585,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,25.29103,11596.947343,1.16714273394159
-c25,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,91.934874,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,56.941654,108.421,56.8779,270.3063,138.7565,18.9772,243.935,192.276,31.8826,63.4788,25.29103,11247.787158,1.20337383998555
-c26,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,623.585,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,25.29103,12547.882469,1.07869139431058
-c27,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,91.934874,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,56.941654,108.421,56.8779,270.3063,138.7565,18.9772,22.476659,192.276,31.8826,63.4788,5.6044758,11006.6422628,1.22973859764412
-c28,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,91.934874,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,243.935,192.276,31.8826,63.4788,5.6044758,11232.0103268,1.2050641363103
-c29,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,83.991575,151.781,104.976,536.2213,121.4188,35.9187,372.978,109.567,57.0866,852.5958,379.656,56.7435,623.585,108.421,56.8779,270.3063,138.7565,18.9772,26.451839,192.276,31.8826,63.4788,5.6044758,11569.3174898,1.16993010511988
-c1,9902.2150608
-
-Leakage Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,50.323486,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,12.490088,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,122.707169,0
-c2,0,0,0,0,0,0,50.323486,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,110.217081,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,12.490088,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,72.383683,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,38.549094,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,0,0,0,0,0,50.260087,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12.490088,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12.490088,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,9.633508,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,59.893595,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,0,0,0,0,0,50.260087,0
-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,38.549094,0
-c11,0,0,0,0,0,0,50.323486,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,12.490088,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,122.707169,0
-c12,0,0,0,0,0,0,50.323486,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,110.217081,0
-c13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,12.490088,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,72.383683,0
-c14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,38.549094,0
-c15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,0,0,0,0,0,50.260087,0
-c16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,9.633508,0
-c17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,38.549094,0
-c18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,59.893595,0
-c19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,0,0,0,0,0,50.260087,0
-c20,0,0,0,0,0,0,50.323486,0,0,0,0,0,25.140123,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,0,0,0,0,0,96.80811,0
-c21,0,0,0,0,0,0,50.323486,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,12.490088,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,122.707169,0
-c22,0,0,0,0,0,0,50.323486,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,110.217081,0
-c23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,12.490088,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,72.383683,0
-c24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,38.549094,0
-c25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,0,0,0,0,0,50.260087,0
-c26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,9.633508,0
-c27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,59.893595,0
-c28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,0,0,0,0,0,50.260087,0
-c29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,38.549094,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,70.852731,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,17.748818,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,168.660691,0
-c2,0,0,0,0,0,0,70.852731,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,150.911873,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,17.748818,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,97.80796,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,56.263614,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,0,0,0,0,0,71.103468,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,17.748818,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,17.748818,0
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-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,80.059142,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,0,0,0,0,0,71.103468,0
-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,56.263614,0
-c11,0,0,0,0,0,0,70.852731,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,17.748818,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,168.660691,0
-c12,0,0,0,0,0,0,70.852731,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,150.911873,0
-c13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,17.748818,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,97.80796,0
-c14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,56.263614,0
-c15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,0,0,0,0,0,71.103468,0
-c16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,8.955674,0
-c17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,56.263614,0
-c18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,80.059142,0
-c19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,0,0,0,0,0,71.103468,0
-c20,0,0,0,0,0,0,70.852731,0,0,0,0,0,35.450450,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,0,0,0,0,0,130.098709,0
-c21,0,0,0,0,0,0,70.852731,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,17.748818,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,168.660691,0
-c22,0,0,0,0,0,0,70.852731,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,150.911873,0
-c23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,17.748818,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,97.80796,0
-c24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,56.263614,0
-c25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,0,0,0,0,0,71.103468,0
-c26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,8.955674,0
-c27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,80.059142,0
-c28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,0,0,0,0,0,71.103468,0
-c29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,56.263614,0
-c0,0
-
-Memory Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,16.574126,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,4.157930,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,39.762784,0
-c2,0,0,0,0,0,0,16.574126,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,35.604854,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,4.157930,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,23.188658,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,13.152806,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,0,0,0,0,0,16.653183,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.157930,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.15793,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,2.377545,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,19.030728,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,0,0,0,0,0,16.653183,0
-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,13.152806,0
-c11,0,0,0,0,0,0,16.574126,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,4.157930,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,39.762784,0
-c12,0,0,0,0,0,0,16.574126,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,35.604854,0
-c13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,4.157930,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,23.188658,0
-c14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,13.152806,0
-c15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,0,0,0,0,0,16.653183,0
-c16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,2.377545,0
-c17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,13.152806,0
-c18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,19.030728,0
-c19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,0,0,0,0,0,16.653183,0
-c20,0,0,0,0,0,0,16.574126,0,0,0,0,0,8.307830,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,0,0,0,0,0,30.759878,0
-c21,0,0,0,0,0,0,16.574126,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,4.157930,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,39.762784,0
-c22,0,0,0,0,0,0,16.574126,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,35.604854,0
-c23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,4.157930,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,23.188658,0
-c24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,13.152806,0
-c25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,0,0,0,0,0,16.653183,0
-c26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,2.377545,0
-c27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,19.030728,0
-c28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,0,0,0,0,0,16.653183,0
-c29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,13.152806,0
-c0,0
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-Patch Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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-c13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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-c15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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-c17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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-c20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c0,0
-
-Quantization Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c2,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0,1882.6325,0
-c3,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c4,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0,1882.6325,0
-c5,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0,1882.6325,0
-c6,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c7,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0,1882.6325,0
-c8,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c9,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c10,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c11,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c12,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0,1882.6325,0
-c13,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c14,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0,1882.6325,0
-c15,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0,1882.6325,0
-c16,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0,1882.6325,0
-c17,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c18,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c19,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c20,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,67.0018,255.159,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,2204.8501318,0
-c21,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c22,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0,1882.6325,0
-c23,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c24,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0,1882.6325,0
-c25,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0,1882.6325,0
-c26,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0,1882.6325,0
-c27,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c28,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c29,0,151.853,0,140.444,219.142,0,0,0,0,69.3933,124.248,0,0,0,0,141.551,559.805,0,0,0,0,31.7333,58.0773,0,0,0,0,67.5188,270.869,0,0,0,0,13.5633,34.4345,0,0,0,0,0,0.0568318,1882.6893318,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c2,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0,529.62557,0
-c3,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c4,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0,529.62557,0
-c5,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0,529.62557,0
-c6,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c7,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0,529.62557,0
-c8,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c9,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c10,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c11,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c12,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0,529.62557,0
-c13,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c14,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0,529.62557,0
-c15,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0,529.62557,0
-c16,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0,529.62557,0
-c17,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c18,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c19,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c20,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,18.9755,69.3636,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,618.268258,0
-c21,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c22,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0,529.62557,0
-c23,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c24,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0,529.62557,0
-c25,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0,529.62557,0
-c26,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0,529.62557,0
-c27,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c28,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c29,0,56.5113,0,38.0284,59.1021,0,0,0,0,19.2796,34.054,0,0,0,0,37.842,149.921,0,0,0,0,9.74596,16.9199,0,0,0,0,19.097,73.5493,0,0,0,0,4.99161,10.5834,0,0,0,0,0,0.303588,529.929158,0
-c0,0
-
-Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,166.211,52.3819,38.6469,138.263,52.1919,38.1309,261.935,97.7271,68.9954,92.893,23.3196,17.6047,116.17,46.1234,34.6677,197.299,46.2495,34.6984,292.058,45.663,34.4299,103.885,15.8328,8.79131,198.587,31.2316,17.2031,162.139,31.2579,17.184,233.12,31.2863,17.1913,53.1921,25.9908,4.43176,119.521,51.9366,8.7572,26.725,2.837158,3056.760228,0.999999967285626
-c1,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,32.835374,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,19.078877,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,3.140746,2491.786368,1.22673442016622
-c2,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,32.835374,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,19.078877,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,6.340453,2584.313037,1.18281340764627
-c3,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,19.078877,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,3.140746,2702.873994,1.13092956671033
-c4,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,19.078877,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,166.111,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,6.340453,2947.330125,1.03712851789453
-c5,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,19.078877,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,63.1947,49.3667,6.98942,15.6197,6.340453,2851.604698,1.07194385075516
-c6,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,166.111,27.6455,13.524,58.18371,36.6739,3.6779,63.1947,49.3667,6.98942,15.6197,3.140746,3173.166614,0.963315357656308
-c7,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,166.111,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,6.340453,3209.489248,0.95241326471603
-c8,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,19.078877,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,3.140746,2792.200956,1.09474932739228
-c9,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,19.078877,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,63.1947,49.3667,6.98942,15.6197,3.140746,2848.404991,1.07314800049274
-c10,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,19.078877,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,166.111,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,3.140746,2944.130418,1.03825567831025
-c11,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,32.835374,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,19.078877,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,3.140746,2491.786368,1.22673442016622
-c12,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,32.835374,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,19.078877,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,6.340453,2584.313037,1.18281340764627
-c13,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,19.078877,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,3.140746,2702.873994,1.13092956671033
-c14,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,19.078877,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,166.111,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,6.340453,2947.330125,1.03712851789453
-c15,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,19.078877,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,63.1947,49.3667,6.98942,15.6197,6.340453,2851.604698,1.07194385075516
-c16,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,166.111,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,6.340453,3209.489248,0.95241326471603
-c17,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,19.078877,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,166.111,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,3.140746,2944.130418,1.03825567831025
-c18,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,19.078877,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,3.140746,2792.200956,1.09474932739228
-c19,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,19.078877,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,63.1947,49.3667,6.98942,15.6197,3.140746,2848.404991,1.07314800049274
-c20,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,32.835374,86.2346,54.8624,112.1726,56.6535,15.858,16.611446,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,217.5625,96.9678,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,63.1947,49.3667,6.98942,15.6197,3.140746,2919.179334,1.04712995453366
-c21,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,32.835374,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,19.078877,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,3.140746,2491.786368,1.22673442016622
-c22,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,32.835374,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,19.078877,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,6.340453,2584.313037,1.18281340764627
-c23,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,19.078877,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,3.140746,2702.873994,1.13092956671033
-c24,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,19.078877,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,166.111,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,6.340453,2947.330125,1.03712851789453
-c25,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,19.078877,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,63.1947,49.3667,6.98942,15.6197,6.340453,2851.604698,1.07194385075516
-c26,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,166.111,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,6.340453,3209.489248,0.95241326471603
-c27,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,19.078877,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,3.140746,2792.200956,1.09474932739228
-c28,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,19.078877,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,63.1947,49.3667,6.98942,15.6197,3.140746,2848.404991,1.07314800049274
-c29,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,19.078877,40.4994,26.741,113.63096,32.2143,7.99054,97.6367,27.6042,13.5937,181.236,101.1854,13.5283,166.111,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,3.140746,2944.130418,1.03825567831025
-c1,2491.786368
-
-Unpatch Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
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-c0,0
-
diff --git a/llvm/projects/soc_simulator/mobilenet_shallow/HS_loss1.txt b/llvm/projects/soc_simulator/mobilenet_shallow/HS_loss1.txt
deleted file mode 100644
index 4d905b811accaa167503d0c189e3bb475c3ad9e6..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/mobilenet_shallow/HS_loss1.txt
+++ /dev/null
@@ -1,64 +0,0 @@
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diff --git a/llvm/projects/soc_simulator/mobilenet_shallow/HS_loss2.txt b/llvm/projects/soc_simulator/mobilenet_shallow/HS_loss2.txt
deleted file mode 100644
index b8677344889c875db8f5485832c7c2dce383cff7..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/mobilenet_shallow/HS_loss2.txt
+++ /dev/null
@@ -1,76 +0,0 @@
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-8,8,8,9,8,8,8,8,8,9,8,8,9,8,8,9,8,8,5,8,8,9,8,8,9,8,8,9,8,8,5,8,8,9,8,8,7,8,8,8,5
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diff --git a/llvm/projects/soc_simulator/mobilenet_shallow/HS_results_loss1.out b/llvm/projects/soc_simulator/mobilenet_shallow/HS_results_loss1.out
deleted file mode 100644
index 4b0b840cee7d4daa6bac3bf282a7ff61dcde2cb1..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/mobilenet_shallow/HS_results_loss1.out
+++ /dev/null
@@ -1,748 +0,0 @@
-Compute Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,572.966,184.509,138.685,510.748,198.623,146.196,1046.81,410.676,295.687,405.855,99.5467,73.891,524.623,211.449,159.137,873.476,219.41,164.603,1372.91,218.702,165.077,504.488,71.2665,37.2098,954.913,148.915,79.6361,785.077,146.853,77.9379,1118.97,148.577,78.6669,256.743,121.755,15.0166,577.914,250.554,36.6268,125.046,5.547644,13535.292944,0.999999992611907
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-c41,507.609,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1372.91,151.781,104.976,504.488,63.3415,35.9187,5.900812,109.567,57.0866,785.077,108.787,56.7435,11.801625,108.421,56.8779,256.743,104.322,18.9772,2.585881,192.276,31.8826,63.4788,0.039278,9901.954696,1.36693140120855
-c42,507.609,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1372.91,151.781,104.976,504.488,63.3415,35.9187,5.900812,109.567,57.0866,785.077,108.787,56.7435,5.167169,108.421,56.8779,256.743,104.322,18.9772,3.484413,192.276,31.8826,63.4788,0.039278,9896.218772,1.36772368508302
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-c44,572.966,129.571,87.353,510.748,185.039,114.322,1046.81,331.516,220.807,405.855,92.5426,67.4597,524.623,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,7.855674,109.567,57.0866,785.077,108.787,56.7435,5.167169,108.421,56.8779,256.743,104.322,18.9772,3.484413,192.276,31.8826,63.4788,5.547644,9572.861,1.413923465786
-c45,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,5.900812,109.567,57.0866,785.077,108.787,56.7435,15.711348,108.421,56.8779,256.743,104.322,18.9772,3.484413,192.276,31.8826,63.4788,0.017407,9642.05808,1.40377631946627
-c46,507.609,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,5.900812,109.567,57.0866,785.077,108.787,56.7435,11.801625,108.421,56.8779,256.743,104.322,18.9772,2.585881,192.276,31.8826,63.4788,0.039278,9571.914696,1.41406324987935
-c47,507.609,129.571,87.353,510.748,185.039,114.322,1046.81,331.516,220.807,405.855,92.5426,67.4597,524.623,155.327,109.967,873.476,157.738,111.071,1372.91,151.781,104.976,504.488,63.3415,35.9187,3.884025,109.567,57.0866,785.077,108.787,56.7435,11.801625,108.421,56.8779,256.743,104.322,18.9772,577.914,192.276,31.8826,63.4788,0.012918,10409.101668,1.3003324634226
-c48,507.609,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,2.583585,109.567,57.0866,785.077,108.787,56.7435,7.768049,108.421,56.8779,256.743,104.322,18.9772,3.484413,192.276,31.8826,63.4788,0.039278,9565.462425,1.4150170897251
-c49,507.609,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,5.900812,109.567,57.0866,785.077,108.787,56.7435,6.962637,108.421,56.8779,256.743,104.322,18.9772,3.484413,192.276,31.8826,63.4788,0.039278,9567.97424,1.41464561494633
-c50,507.609,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,2.583585,109.567,57.0866,785.077,108.787,56.7435,6.962637,108.421,56.8779,256.743,104.322,18.9772,7.862657,192.276,31.8826,63.4788,0.039278,9569.035257,1.41448875869171
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-c52,507.609,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,524.623,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,2.583585,109.567,57.0866,785.077,108.787,56.7435,1118.97,108.421,56.8779,256.743,104.322,18.9772,5.906058,192.276,31.8826,63.4788,0.012918,10494.182661,1.28979009154499
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-c54,572.966,129.571,87.353,510.748,185.039,114.322,1046.81,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,7.855674,109.567,57.0866,785.077,108.787,56.7435,5.167169,108.421,56.8779,256.743,104.322,18.9772,3.484413,192.276,31.8826,63.4788,0.039278,9752.229634,1.38791776990351
-c55,507.609,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1372.91,151.781,104.976,504.488,63.3415,35.9187,5.900812,109.567,57.0866,785.077,108.787,56.7435,11.801625,108.421,56.8779,256.743,104.322,18.9772,2.585881,192.276,31.8826,63.4788,0.019420,9901.934838,1.36693414254385
-c56,507.609,129.571,87.353,510.748,185.039,114.322,1046.81,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,5.900812,109.567,57.0866,785.077,108.787,56.7435,11.801625,108.421,56.8779,256.743,104.322,18.9772,3.484413,192.276,31.8826,63.4788,0.012918,9691.525868,1.39661112075555
-c57,507.609,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,5.900812,109.567,57.0866,785.077,108.787,56.7435,7.768049,108.421,56.8779,256.743,104.322,18.9772,3.484413,192.276,31.8826,63.4788,0.012918,9568.753292,1.41453043980799
-c58,507.609,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,5.900812,109.567,57.0866,785.077,108.787,56.7435,7.768049,108.421,56.8779,256.743,104.322,18.9772,3.484413,192.276,31.8826,63.4788,0.017407,9568.757781,1.41452977620806
-c59,507.609,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1372.91,151.781,104.976,504.488,63.3415,35.9187,5.900812,109.567,57.0866,785.077,108.787,56.7435,11.801625,108.421,56.8779,256.743,104.322,18.9772,2.585881,192.276,31.8826,63.4788,25.29103,9927.206448,1.3634543492728
-c60,572.966,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,3.884025,109.567,57.0866,785.077,108.787,56.7435,11.801625,108.421,56.8779,256.743,104.322,18.9772,3.887477,192.276,31.8826,63.4788,0.017407,9636.534634,1.40458093262968
-c61,507.609,129.571,87.353,510.748,185.039,114.322,928.071,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1042.87,151.781,104.976,504.488,63.3415,35.9187,3.481319,109.567,57.0866,785.077,108.787,56.7435,11.801625,108.421,56.8779,256.743,104.322,18.9772,3.484413,192.276,31.8826,63.4788,0.039278,9570.393735,1.41428797783639
-c62,507.609,129.571,87.353,510.748,185.039,114.322,1046.81,331.516,220.807,405.855,92.5426,67.4597,709.5,155.327,109.967,873.476,157.738,111.071,1372.91,151.781,104.976,504.488,63.3415,35.9187,5.900812,109.567,57.0866,785.077,108.787,56.7435,11.801625,108.421,56.8779,256.743,104.322,18.9772,3.484413,192.276,31.8826,63.4788,0.019420,10021.57237,1.35061568277019
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-c7,9385.492525
-
-Compute Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,166.211,52.3819,38.6469,138.263,52.1919,38.1309,261.935,97.7271,68.9954,92.893,23.3196,17.6047,116.17,46.1234,34.6677,197.299,46.2495,34.6984,292.058,45.663,34.4299,103.885,15.8328,8.79131,198.587,31.2316,17.2031,162.139,31.2579,17.184,233.12,31.2863,17.1913,53.1921,25.9908,4.43176,119.521,51.9366,8.7572,26.725,2.837158,3056.760228,0.999999967285626
-c1,166.211,44.0763,27.7776,138.263,46.5303,27.6531,261.935,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,0.036040,2437.901444,1.25384892409749
-c2,191.843,44.0763,27.7776,138.263,46.5303,27.6531,261.935,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,292.058,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,0.036040,2474.353444,1.23537731114143
-c3,166.211,44.0763,27.7776,138.263,46.5303,27.6531,261.935,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,0.036040,2437.901444,1.25384892409749
-c4,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,116.17,40.5929,27.7158,197.299,40.5301,27.3118,292.058,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,63.1947,49.3667,6.98942,15.6197,0.036040,2419.263024,1.26350879227472
-c5,191.843,44.0763,27.7776,138.263,46.5303,27.6531,261.935,86.2346,54.8624,92.893,22.5995,15.858,116.17,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,0.036040,2393.505444,1.2771059735636
-c6,191.843,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,292.058,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,0.036040,2456.341444,1.24443615565865
-c7,191.843,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,116.17,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,0.036040,2375.493444,1.286789532946
-c8,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,0.036040,2419.889444,1.26318171652879
-c9,191.843,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,0.036040,2445.521444,1.24994205653173
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-c47,191.843,44.0763,27.7776,138.263,46.5303,27.6531,261.935,86.2346,54.8624,92.893,22.5995,15.858,116.17,40.5929,27.7158,197.299,40.5301,27.3118,292.058,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,119.521,49.3667,6.98942,15.6197,0.036040,2519.233324,1.2133691935329
-c48,191.843,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,0.036040,2445.521444,1.24994205653173
-c49,191.843,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,0.036040,2445.521444,1.24994205653173
-c50,191.843,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,0.036040,2445.521444,1.24994205653173
-c51,191.843,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,0.036040,2445.521444,1.24994205653173
-c52,191.843,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,116.17,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,233.12,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,0.036040,2600.309828,1.17553688315572
-c53,191.843,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,0.036040,2445.521444,1.24994205653173
-c54,166.211,44.0763,27.7776,138.263,46.5303,27.6531,261.935,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,0.036040,2437.901444,1.25384892409749
-c55,191.843,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,292.058,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,0.036040,2456.341444,1.24443615565865
-c56,191.843,44.0763,27.7776,138.263,46.5303,27.6531,261.935,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,0.036040,2463.533444,1.24080316886483
-c57,191.843,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,0.036040,2445.521444,1.24994205653173
-c58,191.843,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,0.036040,2445.521444,1.24994205653173
-c59,191.843,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,292.058,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,6.340453,2462.645857,1.24125037921559
-c60,166.211,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,0.036040,2419.889444,1.26318171652879
-c61,191.843,44.0763,27.7776,138.263,46.5303,27.6531,243.923,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,0.036040,2445.521444,1.24994205653173
-c62,191.843,44.0763,27.7776,138.263,46.5303,27.6531,261.935,86.2346,54.8624,92.893,22.5995,15.858,186.198,40.5929,27.7158,197.299,40.5301,27.3118,292.058,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,0.036040,2474.353444,1.23537731114143
-c63,191.843,44.0763,27.7776,138.263,46.5303,27.6531,261.935,86.2346,54.8624,92.893,22.5995,15.858,116.17,40.5929,27.7158,197.299,40.5301,27.3118,281.238,40.4994,26.741,103.885,15.2944,7.99054,4.151808,27.6042,13.5937,162.139,27.6361,13.5283,8.303616,27.6455,13.524,53.1921,26.0905,3.6779,4.613120,49.3667,6.98942,15.6197,2.837158,2396.306562,1.27561312434393
-c13,2360.681444
-
-Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,572.966,184.509,138.685,510.748,198.623,146.196,1046.81,410.676,295.687,405.855,99.5467,73.891,524.623,211.449,159.137,873.476,219.41,164.603,1372.91,218.702,165.077,504.488,71.2665,37.2098,954.913,148.915,79.6361,785.077,146.853,77.9379,1118.97,148.577,78.6669,256.743,121.755,15.0166,577.914,250.554,36.6268,125.046,5.547644,13535.292944,0.999999992611907
-c1,572.966,281.424,87.353,651.192,404.181,114.322,1336.459,1267.11,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,34.122931,109.567,57.0866,852.5958,379.656,56.7435,56.941654,108.421,56.8779,270.3063,138.7565,18.9772,21.175063,192.276,31.8826,63.4788,1.016415,12956.814663,1.04464663511683
-c2,523.5897,129.571,87.353,651.192,404.181,114.322,1336.459,1267.11,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1507.228,683.9,104.976,536.2213,121.4188,35.9187,36.139718,109.567,57.0866,852.5958,379.656,56.7435,56.941654,108.421,56.8779,270.3063,138.7565,18.9772,21.175063,192.276,31.8826,63.4788,1.016415,13754.07915,0.984092987831228
-c3,572.966,281.424,87.353,651.192,404.181,114.322,1336.459,1267.11,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,36.139718,109.567,57.0866,852.5958,379.656,56.7435,52.908078,108.421,56.8779,270.3063,138.7565,18.9772,22.073595,192.276,31.8826,63.4788,1.016415,12955.696406,1.04473680266681
-c4,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,662.986,676.028,109.967,1015.027,717.543,111.071,1507.228,683.9,104.976,536.2213,121.4188,35.9187,33.720225,109.567,57.0866,852.5958,379.656,56.7435,50.307198,108.421,56.8779,270.3063,138.7565,18.9772,243.935,192.276,31.8826,63.4788,1.016415,13299.219438,1.01775092179849
-c5,523.5897,129.571,87.353,651.192,404.181,114.322,1336.459,1267.11,220.807,475.2483,216.7906,67.4597,662.986,676.028,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,32.822491,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,24.49524,192.276,31.8826,63.4788,1.016415,13235.701823,1.02263506860028
-c6,523.5897,129.571,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1507.228,683.9,104.976,536.2213,121.4188,35.9187,36.139718,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,21.175063,192.276,31.8826,63.4788,1.020904,12414.011362,1.0903238639204
-c7,523.5897,129.571,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,662.986,676.028,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,36.139718,109.567,57.0866,852.5958,379.656,56.7435,52.102666,108.421,56.8779,270.3063,138.7565,18.9772,24.49524,192.276,31.8826,63.4788,1.016415,11886.288339,1.13873165820118
-c8,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,36.139718,109.567,57.0866,852.5958,379.656,56.7435,52.102666,108.421,56.8779,270.3063,138.7565,18.9772,22.073595,192.276,31.8826,63.4788,1.016415,11610.908994,1.16573929176609
-c9,523.5897,129.571,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,32.822491,109.567,57.0866,852.5958,379.656,56.7435,50.307198,108.421,56.8779,270.3063,138.7565,18.9772,22.073595,192.276,31.8826,63.4788,1.042775,11404.593359,1.18682818398218
-c10,523.5897,129.571,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,662.986,676.028,109.967,1015.027,717.543,111.071,1507.228,683.9,104.976,536.2213,121.4188,35.9187,33.720225,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,22.476659,192.276,31.8826,63.4788,1.016415,12887.075976,1.05029976265968
-c11,523.5897,129.571,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,33.720225,109.567,57.0866,852.5958,379.656,56.7435,50.307198,108.421,56.8779,270.3063,138.7565,18.9772,22.073595,192.276,31.8826,63.4788,1.042775,11405.491093,1.1867347679254
-c12,523.5897,129.571,87.353,651.192,404.181,114.322,1336.459,1267.11,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,33.720225,109.567,57.0866,852.5958,379.656,56.7435,56.941654,108.421,56.8779,270.3063,138.7565,18.9772,24.49524,192.276,31.8826,63.4788,1.016415,12758.502834,1.06088410325399
-c13,572.966,281.424,87.353,651.192,404.181,114.322,928.071,331.516,220.807,475.2483,216.7906,67.4597,662.986,676.028,109.967,1015.027,717.543,111.071,1507.228,683.9,104.976,536.2213,121.4188,35.9187,33.720225,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,22.476659,192.276,31.8826,63.4788,1.016415,13088.305276,1.03415167626052
-c14,572.966,281.424,87.353,651.192,404.181,114.322,1336.459,1267.11,220.807,475.2483,216.7906,67.4597,709.5,155.327,109.967,1015.027,717.543,111.071,1507.228,683.9,104.976,536.2213,121.4188,35.9187,32.822491,109.567,57.0866,852.5958,379.656,56.7435,60.851377,108.421,56.8779,270.3063,138.7565,18.9772,22.073595,192.276,31.8826,63.4788,1.020904,13956.803967,0.969798879386962
-c15,523.5897,129.571,87.353,651.192,404.181,114.322,1336.459,1267.11,220.807,475.2483,216.7906,67.4597,662.986,676.028,109.967,1015.027,717.543,111.071,1042.87,151.781,104.976,536.2213,121.4188,35.9187,32.822491,109.567,57.0866,852.5958,379.656,56.7435,56.941654,108.421,56.8779,270.3063,138.7565,18.9772,24.49524,192.276,31.8826,63.4788,1.016415,13231.7921,1.0229372362725
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-c9,11404.593359
-
-Leakage Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
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-Memory Energy
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-Memory Time
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-c0,0
-
-Quantization Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
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-
-Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,166.211,52.3819,38.6469,138.263,52.1919,38.1309,261.935,97.7271,68.9954,92.893,23.3196,17.6047,116.17,46.1234,34.6677,197.299,46.2495,34.6984,292.058,45.663,34.4299,103.885,15.8328,8.79131,198.587,31.2316,17.2031,162.139,31.2579,17.184,233.12,31.2863,17.1913,53.1921,25.9908,4.43176,119.521,51.9366,8.7572,26.725,2.837158,3056.760228,0.999999967285626
-c1,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,337.163,337.6786,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3306.772394,0.924393871530734
-c2,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,337.163,337.6786,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,329.7257,188.9254,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3480.343124,0.87829275196796
-c3,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,337.163,337.6786,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3306.772394,0.924393871530734
-c4,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,154.0927,182.6029,27.7158,235.141,190.4511,27.3118,329.7257,188.9254,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,63.1947,49.3667,6.98942,15.6197,0.196023,3325.110829,0.9192957147205
-c5,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,337.163,337.6786,54.8624,112.1726,56.6535,15.858,154.0927,182.6029,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3393.334124,0.900813190277717
-c6,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,329.7257,188.9254,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3135.659124,0.974838147144271
-c7,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,154.0927,182.6029,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3048.650124,1.0026601949729
-c8,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2962.088394,1.03196114302181
-c9,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2938.745424,1.0401581909819
-c10,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,154.0927,182.6029,27.7158,235.141,190.4511,27.3118,329.7257,188.9254,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3245.563824,0.941827152254235
-c11,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2938.745424,1.0401581909819
-c12,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,337.163,337.6786,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3283.429424,0.930965688666933
-c13,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,154.0927,182.6029,27.7158,235.141,190.4511,27.3118,329.7257,188.9254,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3268.906794,0.935101649303812
-c14,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,337.163,337.6786,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,329.7257,188.9254,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3503.686094,0.872441211554462
-c15,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,337.163,337.6786,54.8624,112.1726,56.6535,15.858,154.0927,182.6029,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3393.334124,0.900813190277717
-c16,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,337.163,337.6786,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3306.772394,0.924393871530734
-c17,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,337.163,337.6786,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3306.772394,0.924393871530734
-c18,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2962.088394,1.03196114302181
-c19,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2962.088394,1.03196114302181
-c20,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,337.163,337.6786,54.8624,112.1726,56.6535,15.858,154.0927,182.6029,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3393.334124,0.900813190277717
-c21,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,337.163,337.6786,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3306.772394,0.924393871530734
-c22,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,337.163,337.6786,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3283.429424,0.930965688666933
-c23,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2938.745424,1.0401581909819
-c24,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,154.0927,182.6029,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3048.650124,1.0026601949729
-c25,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2938.745424,1.0401581909819
-c26,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,337.163,337.6786,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,329.7257,188.9254,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3503.686094,0.872441211554462
-c27,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,329.7257,188.9254,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3135.659124,0.974838147144271
-c28,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2938.745424,1.0401581909819
-c29,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2938.745424,1.0401581909819
-c30,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2938.745424,1.0401581909819
-c31,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2938.745424,1.0401581909819
-c32,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,337.163,337.6786,54.8624,112.1726,56.6535,15.858,154.0927,182.6029,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3393.334124,0.900813190277717
-c33,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2938.745424,1.0401581909819
-c34,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2938.745424,1.0401581909819
-c35,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2962.088394,1.03196114302181
-c36,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,337.163,337.6786,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,63.1947,49.3667,6.98942,15.6197,0.196023,3339.633459,0.915298093038477
-c37,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,337.163,337.6786,54.8624,112.1726,56.6535,15.858,154.0927,182.6029,27.7158,235.141,190.4511,27.3118,329.7257,188.9254,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,3.140746,3616.535517,0.845217786223727
-c38,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,154.0927,182.6029,27.7158,235.141,190.4511,27.3118,329.7257,188.9254,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,129.10426,84.3721,6.98942,15.6197,0.196023,3402.682819,0.898338252715695
-c39,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,337.163,337.6786,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3283.429424,0.930965688666933
-c40,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2938.745424,1.0401581909819
-c41,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,329.7257,188.9254,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3135.659124,0.974838147144271
-c42,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,329.7257,188.9254,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3135.659124,0.974838147144271
-c43,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,154.0927,182.6029,27.7158,235.141,190.4511,27.3118,329.7257,188.9254,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3245.563824,0.941827152254235
-c44,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,337.163,337.6786,54.8624,112.1726,56.6535,15.858,154.0927,182.6029,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,3.140746,3419.621817,0.893888360231843
-c45,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2962.088394,1.03196114302181
-c46,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2938.745424,1.0401581909819
-c47,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,337.163,337.6786,54.8624,112.1726,56.6535,15.858,154.0927,182.6029,27.7158,235.141,190.4511,27.3118,329.7257,188.9254,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,129.10426,84.3721,6.98942,15.6197,0.196023,3747.366819,0.815708814768453
-c48,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2938.745424,1.0401581909819
-c49,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2938.745424,1.0401581909819
-c50,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2938.745424,1.0401581909819
-c51,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2938.745424,1.0401581909819
-c52,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,154.0927,182.6029,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,252.0805,101.4722,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3360.375786,0.909648304743251
-c53,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2938.745424,1.0401581909819
-c54,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,337.163,337.6786,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3306.772394,0.924393871530734
-c55,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,329.7257,188.9254,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3135.659124,0.974838147144271
-c56,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,337.163,337.6786,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3283.429424,0.930965688666933
-c57,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2938.745424,1.0401581909819
-c58,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2938.745424,1.0401581909819
-c59,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,329.7257,188.9254,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,6.340453,3141.803554,0.972931654754515
-c60,166.211,100.5876,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2962.088394,1.03196114302181
-c61,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,243.923,86.2346,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,2938.745424,1.0401581909819
-c62,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,337.163,337.6786,54.8624,112.1726,56.6535,15.858,186.198,40.5929,27.7158,235.141,190.4511,27.3118,329.7257,188.9254,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,0.196023,3480.343124,0.87829275196796
-c63,199.37933,44.0763,27.7776,176.2914,105.6324,27.6531,337.163,337.6786,54.8624,112.1726,56.6535,15.858,154.0927,182.6029,27.7158,235.141,190.4511,27.3118,281.238,40.4994,26.741,113.63096,32.2143,7.99054,8.309738,27.6042,13.5937,181.236,101.1854,13.5283,14.181538,27.6455,13.524,58.18371,36.6739,3.6779,6.990665,49.3667,6.98942,15.6197,3.140746,3396.278847,0.900032145681113
-c9,2938.745424
-
-Unpatch Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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diff --git a/llvm/projects/soc_simulator/mobilenet_shallow/confs/HA_loss1.txt b/llvm/projects/soc_simulator/mobilenet_shallow/confs/HA_loss1.txt
deleted file mode 100644
index 6e5e15bc0bd2bc835293154ef3c5911662a51062..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/mobilenet_shallow/confs/HA_loss1.txt
+++ /dev/null
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diff --git a/llvm/projects/soc_simulator/mobilenet_shallow/confs/HA_loss2.txt b/llvm/projects/soc_simulator/mobilenet_shallow/confs/HA_loss2.txt
deleted file mode 100644
index 36bb418742d308ee60d0d997a3a436ca8410870a..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/mobilenet_shallow/confs/HA_loss2.txt
+++ /dev/null
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diff --git a/llvm/projects/soc_simulator/mobilenet_shallow/confs/HS_loss1.txt b/llvm/projects/soc_simulator/mobilenet_shallow/confs/HS_loss1.txt
deleted file mode 100644
index 474eb6a9cc983edec9a18cc27a2ed3875d992bc7..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/mobilenet_shallow/confs/HS_loss1.txt
+++ /dev/null
@@ -1,64 +0,0 @@
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diff --git a/llvm/projects/soc_simulator/mobilenet_shallow/confs/HS_loss2.txt b/llvm/projects/soc_simulator/mobilenet_shallow/confs/HS_loss2.txt
deleted file mode 100644
index 574b3850329006a5bd6a1d0b4cb56e72521363ff..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/mobilenet_shallow/confs/HS_loss2.txt
+++ /dev/null
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-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,5,8,8,8,8,8,7,8,8,8,8,8,8,8,8,8,8,8,3,8,8,8,8 8
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,5,8,8,8,8,8,7,8,8,8,8,8,6,8,8,8,8,8,8,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,4,8,8,8,8,8,7,8,8,8,8,8,8,8,8,8,8,8,3,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,5,8,8,8,8,8,8,8,8,8,8,8,4,8,8,8,8,8,6,8,8,8,8 8
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,5,8,8,8,8,8,7,8,8,8,8,8,4,8,8,8,8,8,8,8,8,8,5
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,3,8,8,8,8,8,7,8,8,8,8,8,4,8,8,8,8,8,7,8,8,8,8 8
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,5,8,8,8,8,8,7,8,8,8,8,8,8,8,8,8,8,8,3,8,8,8,7
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,3,8,8,8,8,8,7,8,8,8,8,8,6,8,8,8,8,8,5,8,8,8,8 8
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,7,8,8,8,8,8,7,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,5,8,8,8,8,8,7,8,8,8,8,8,7,8,8,8,8,8,8,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,5,8,8,8,8,8,8,8,8,8,8,8,4,8,8,8,8,8,7,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,6,8,8,8,8,8,8,8,8,8,8,8,5,8,8,8,8,8,6,8,8,8,8 8
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,6,8,8,8,8,8,8,8,8,8,8,8,4,8,8,8,3
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,5,8,8,8,8,8,7,8,8,8,8,8,7,8,8,8,8,8,5,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,5,8,8,8,8,8,8,8,8,8,8,8,4,8,8,8,8,8,5,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,7,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,7
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,4,8,8,8,8,8,6,8,8,8,8,8,7,8,8,8,8,8,3,8,8,8,8 8
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,5,8,8,8,8,8,8,8,8,8,8,8,5,8,8,8,8,8,7,8,8,8,5
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,3,8,8,8,4
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,5,8,8,8,8,8,7,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,5,8,8,8,8,8,8,8,8,8,8,8,4,8,8,8,8,8,8,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,4,8,8,8,8,8,8,8,8,8,8,8,6,8,8,8,8,8,7,8,8,8,3
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,5,8,8,8,8,8,7,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,5
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,5,8,8,8,8,8,7,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,5,8,8,8,8,8,8,8,8,8,8,8,4,8,8,8,8,8,8,8,8,8,8 8
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,3,8,8,8,8,8,7,8,8,8,8,8,6,8,8,8,8,8,7,8,8,8,8 8
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,5,8,8,8,8,8,7,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,3,8,8,8,8,8,7,8,8,8,8,8,8,8,8,8,8,8,3,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,4,8,8,8,8,8,8,8,8,8,8,8,6,8,8,8,8,8,5,8,8,8,5
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,3,8,8,8,8,8,4,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8 8
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,4,8,8,8,8,8,7,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8 8
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,4,8,8,8,8,8,8,8,8,8,8,8,4,8,8,8,8,8,8,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,3,8,8,8,8,8,7,8,8,8,8,8,6,8,8,8,8,8,7,8,8,8,8 8
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,3,8,8,8,8,8,7,8,8,8,8,8,8,8,8,8,8,8,7,8,8,8,8 8
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,6,8,8,8,8,8,8,8,8,8,8,8,3,8,8,8,4
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,3,8,8,8,8,8,7,8,8,8,8,8,8,8,8,8,8,8,3,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,5,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,3,8,8,8,8,8,7,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8 8
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,5,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,6,8,8,8,8,8,8,8,8,8,8,8,5,8,8,8,4
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,7,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,6,8,8,8,8,8,8,8,8,8,8,8,3,8,8,8,8 8
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,6,8,8,8,8,8,8,8,8,8,8,8,4,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,4
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,3,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,7,8,8,8,7
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,7,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,6,8,8,8,8,8,8,8,8,8,8,8,4,8,8,8,7
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,7,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,3,8,8,8,8,8,4,8,8,8,8,8,3,8,8,8,8 8
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,6,8,8,8,8,8,3,8,8,8,8,8,3,8,8,8,3
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,4,8,8,8,8,8,3,8,8,8,8,8,3,8,8,8,3
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,3,8,8,8,8,8,4,8,8,8,8,8,3,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,7,8,8,8,8,8,4,8,8,8,8,8,3,8,8,8,3
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,5,8,8,8,8,8,3,8,8,8,8,8,3,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,6,8,8,8,8,8,3,8,8,8,8,8,6,8,8,8,3
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,7,8,8,8,8,8,4,8,8,8,8,8,3,8,8,8,3
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,7,8,8,8,8,8,4,8,8,8,8,8,3,8,8,8,4
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,6,8,8,8,8,8,7,8,8,8,8,8,3,8,8,8,3
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,4,8,8,8,8,8,4,8,8,8,8,8,3,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,7,8,8,8,8,8,3,8,8,8,8,8,3,8,8,8,7
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,7,8,8,8,8,8,3,8,8,8,8,8,3,8,8,8,5
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,7,8,8,8,8,8,4,8,8,8,8,8,3,8,8,8,4
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,7,8,8,8,8,8,3,8,8,8,8,8,3,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,5,8,8,8,8,8,6,8,8,8,8,8,3,8,8,8,6
-8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,7,8,8,8,8,8,4,8,8,8,8,8,3,8,8,8,6
diff --git a/llvm/projects/soc_simulator/mobilenet_shallow/info/mobilenet_shallow_layers.txt b/llvm/projects/soc_simulator/mobilenet_shallow/info/mobilenet_shallow_layers.txt
deleted file mode 100644
index 4ab1093cab28b2f3dfa284a4669c6a2885ff667d..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/mobilenet_shallow/info/mobilenet_shallow_layers.txt
+++ /dev/null
@@ -1,41 +0,0 @@
-Conv1,4500,3,32,32,32,3,3,3,1,1
-NML1
-NML2
-NML3
-NML4
-NML5
-Conv3,4500,32,32,32,64,32,1,1,1,1
-NML6
-NML7
-NML8
-NML9
-NML10
-Conv5,4500,64,16,16,128,64,1,1,1,1
-NML11
-NML12
-NML13
-NML14
-NML15
-Conv7,4500,128,16,16,128,128,1,1,1,1
-NML16
-NML17
-NML18
-NML19
-NML20
-Conv9,4500,128,8,8,256,128,1,1,1,1
-NML21
-NML22
-NML23
-NML24
-NML25
-Conv11,4500,256,8,8,256,256,1,1,1,1
-NML26
-NML27
-NML28
-NML29
-NML30
-Conv13,4500,256,4,4,512,256,1,1,1,1
-NML31
-NML32
-NML33
-FC1,4500,1024,1024,10
diff --git a/llvm/projects/soc_simulator/mobilenet_shallow/info/mobilenet_shallow_tensors.txt b/llvm/projects/soc_simulator/mobilenet_shallow/info/mobilenet_shallow_tensors.txt
deleted file mode 100644
index 76a5b9f8f5db9a4e924d492d32bf213977b9fe84..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/mobilenet_shallow/info/mobilenet_shallow_tensors.txt
+++ /dev/null
@@ -1,83 +0,0 @@
-#Conv1,1
-Conv1,165.401,567.689,190.845,489.961,7.45501,15.3385,38.3605,99.4665
-#NML1,1
-BatchNorm1,52.2864,184.046,44.208,126.823,57.7603,152.189,37.5847,103.341
-#NML2,1
-Relu1,38.6453,138.524,27.6261,85.2148,53.911,151.346,37.5139,107.477
-#NML3,1
-Conv2,137.297,505.587,146.888,454.401,57.2503,165.991,37.8788,118.31
-#NML4,1
-BatchNorm2,52.1549,197.914,46.4921,157.667,58.2651,183.446,38.5037,124.791
-#NML5,1
-Relu2,38.1169,146.069,27.6025,98.9958,53.3869,174.312,37.5912,124.95
-#Conv3,1
-Conv3,258.954,1032.03,243.635,792.454,56.4655,189.556,75.237,260.038
-#NML6,1
-BatchNorm3,97.6062,409.331,86.3211,303.765,250.71,842.032,74.8439,261.343
-#NML7,1
-Relu3,68.9786,295.295,54.9261,205.544,110.52,385.151,74.8142,272.084
-#NML8,1
-Conv4,99.7425,435.587,87.5137,315.332,233.217,831.941,19.1707,65.0227
-#NML9,1
-BatchNorm4,23.3301,99.2457,22.6355,87.6934,34.3529,118.103,20.3281,69.1027
-#NML10,1
-Relu4,17.5398,73.9864,15.9996,64.6513,31.5324,110.256,20.234,70.3149
-#Conv5,1
-Conv5,118.361,532.734,185.585,673.394,31.7649,112.807,37.938,132.277
-#NML11,1
-BatchNorm5,46.095,210.096,40.6227,149.173,142.468,500.126,37.5912,130.645
-#NML12,1
-Relu5,34.7036,158.225,27.7104,106.31,53.5821,188.448,37.6388,132.982
-#NML13,1
-Conv6,211.862,941.502,323.448,1119.27,115.938,414.557,37.786,129.099
-#NML14,1
-BatchNorm6,46.239,219.088,40.6142,145.624,149.523,514.012,37.5237,127.816
-#NML15,1
-Relu6,34.7264,164.507,27.2599,102.738,48.5245,167.544,37.4762,130.429
-#Conv7,1
-Conv7,292.43,1371.64,282.611,981.699,64.6579,227.877,37.7122,126.901
-#NML16,1
-BatchNorm7,45.4864,217.634,40.5067,143.829,148.787,503.668,37.3823,125.643
-#NML17,1
-Relu7,34.3235,164.29,26.7374,100.069,46.8449,159.109,37.4335,128.935
-#NML18,1
-Conv8,107.339,520.234,39.6826,147.475,64.2402,223.151,9.65537,29.9378
-#NML19,1
-BatchNorm8,15.8015,71.2826,15.3482,60.7097,16.8328,55.515,10.2509,31.976
-#NML20,1
-Relu8,8.79447,36.7775,7.99351,34.5499,15.1718,49.6238,10.0626,31.6603
-#Conv9,1
-Conv9,199.034,954.881,98.1388,359.856,15.2921,50.2916,18.9832,64.2926
-#NML21,1
-BatchNorm9,31.138,148.359,27.6217,104.698,70.2457,248.698,18.8196,63.4597
-#NML22,1
-Relu9,17.2023,79.3874,13.5987,54.9348,24.3837,83.7626,18.7985,64.38
-#NML23,1
-Conv10,164.266,793.877,160.813,577.662,59.6641,211.628,18.9793,63.4051
-#NML24,1
-BatchNorm10,31.2193,145.953,27.6333,103.328,74.0125,258.461,18.8411,62.7266
-#NML25,1
-Relu10,17.2051,77.3085,13.5078,53.7829,24.1874,81.846,18.7678,63.1334
-#Conv11,1
-Conv11,233.349,1117.62,166.85,596.404,35.2647,122.36,18.9644,63.7352
-#NML26,1
-BatchNorm11,31.252,148.499,27.646,103.913,74.0691,259.032,18.7909,62.9738
-#NML27,1
-Relu11,17.1975,78.9983,13.5936,54.7397,24.3747,82.9966,18.8176,63.5157
-#NML28,1
-Conv12,54.8812,263.615,24.574,94.3137,35.5476,124.286,4.91026,13.2274
-#NML29,1
-BatchNorm12,25.9899,121.522,26.0654,100.201,10.5018,32.8201,4.89614,13.1409
-#NML30,1
-Relu12,4.36155,14.8101,3.67647,18.1925,7.42945,22.2844,4.76369,12.6395
-#Conv13,1
-Conv13,119.767,577.124,63.594,236.007,8.73277,27.0192,9.60037,30.5066
-#NML31,1
-BatchNorm13,51.9324,249.047,49.3767,184.971,35.341,123.97,9.4716,29.8925
-#NML32,1
-Relu13,8.7646,36.7714,6.99017,30.535,13.4667,44.1842,9.50975,29.6265
-#NML33,1
-Pool1,26.8288,124.626,15.5609,61.4365,33.2992,116.519,2.61816,4.58362
-#FC1,2
-Mul1,2.40501,4.96909,5.46799,21.0966,6.83807,19.3828,0.310624,0.0619082
-Add1,0.43324,0.179815,0.940664,3.68353,1.92657,2.71477,0.208831,0.0318638
diff --git a/llvm/projects/soc_simulator/mobilenet_shallow/mobilenet_shallow_layers.txt b/llvm/projects/soc_simulator/mobilenet_shallow/mobilenet_shallow_layers.txt
deleted file mode 100644
index ba85aa142542d34722b19b9a16314100ecdd62da..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/mobilenet_shallow/mobilenet_shallow_layers.txt
+++ /dev/null
@@ -1,41 +0,0 @@
-Conv1,2000,3,32,32,32,3,3,3,1,1
-NML1
-NML2
-NML3
-NML4
-NML5
-Conv3,2000,32,32,32,64,32,1,1,1,1
-NML6
-NML7
-NML8
-NML9
-NML10
-Conv5,2000,64,16,16,128,64,1,1,1,1
-NML11
-NML12
-NML13
-NML14
-NML15
-Conv7,2000,128,16,16,128,128,1,1,1,1
-NML16
-NML17
-NML18
-NML19
-NML20
-Conv9,2000,128,8,8,256,128,1,1,1,1
-NML21
-NML22
-NML23
-NML24
-NML25
-Conv11,2000,256,8,8,256,256,1,1,1,1
-NML26
-NML27
-NML28
-NML29
-NML30
-Conv13,2000,256,4,4,512,256,1,1,1,1
-NML31
-NML32
-NML33
-FC1,2000,1024,1024,10
diff --git a/llvm/projects/soc_simulator/mobilenet_shallow/mobilenet_shallow_tensors.txt b/llvm/projects/soc_simulator/mobilenet_shallow/mobilenet_shallow_tensors.txt
deleted file mode 100644
index 80f75053287bfc0751dccd29ae41a62138b59419..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/mobilenet_shallow/mobilenet_shallow_tensors.txt
+++ /dev/null
@@ -1,83 +0,0 @@
-#Conv1,1
-Conv1,166.211,572.966,191.843,507.609,7.53633,15.9807,38.0736,101.279
-#NML1,1
-BatchNorm1,52.3819,184.509,44.0763,129.571,56.5113,151.853,37.5819,105.16
-#NML2,1
-Relu1,38.6469,138.685,27.7776,87.353,55.2939,157.83,37.5253,110.145
-#NML3,1
-Conv2,138.263,510.748,3084.84,10928.7,59.002,175.657,38.0284,140.444
-#NML4,1
-BatchNorm2,52.1919,198.623,46.5303,185.039,59.1021,219.142,38.507,144.452
-#NML5,1
-Relu2,38.1309,146.196,27.6531,114.322,54.9847,208.258,37.5913,143.1
-#Conv3,1
-Conv3,261.935,1046.81,243.923,928.071,56.2446,214.924,75.228,289.649
-#NML6,1
-BatchNorm3,97.7271,410.676,86.2346,331.516,251.444,935.594,74.8545,283.758
-#NML7,1
-Relu3,68.9954,295.687,54.8624,220.807,110.125,417.751,74.8592,291.745
-#NML8,1
-Conv4,92.893,405.855,1583.56,6051.66,231.224,880.293,19.2796,69.3933
-#NML9,1
-BatchNorm4,23.3196,99.5467,22.5995,92.5426,34.054,124.248,20.2424,73.0214
-#NML10,1
-Relu4,17.6047,73.891,15.858,67.4597,30.9943,114.518,20.2169,73.2485
-#Conv5,1
-Conv5,116.17,524.623,186.198,709.5,31.8313,117.965,37.9227,138.363
-#NML11,1
-BatchNorm5,46.1234,211.449,40.5929,155.327,142.01,520.701,37.5993,135.493
-#NML12,1
-Relu5,34.6677,159.137,27.7158,109.967,53.3662,195.421,37.6391,138.68
-#NML13,1
-Conv6,197.299,873.476,3302.47,12220,115.346,427.277,37.842,141.551
-#NML14,1
-BatchNorm6,46.2495,219.41,40.5301,157.738,149.921,559.805,37.4612,137.044
-#NML15,1
-Relu6,34.6984,164.603,27.3118,111.071,48.6817,179.964,37.5311,140.008
-#Conv7,1
-Conv7,292.058,1372.91,281.238,1042.87,64.7019,243.41,37.6677,134.318
-#NML16,1
-BatchNorm7,45.663,218.702,40.4994,151.781,148.426,532.119,37.3944,132.668
-#NML17,1
-Relu7,34.4299,165.077,26.741,104.976,46.7743,167.757,37.3853,135.277
-#NML18,1
-Conv8,103.885,504.488,794.673,2993.53,64.3356,233.98,9.74596,31.7333
-#NML19,1
-BatchNorm8,15.8328,71.2665,15.2944,63.3415,16.9199,58.0773,10.2545,33.5749
-#NML20,1
-Relu8,8.79131,37.2098,7.99054,35.9187,15.5217,53.4902,10.0192,32.9858
-#Conv9,1
-Conv9,198.587,954.913,97.6367,372.978,15.3789,53.2605,18.9755,67.0018
-#NML21,1
-BatchNorm9,31.2316,148.915,27.6042,109.567,69.3636,255.159,18.8095,66.0347
-#NML22,1
-Relu9,17.2031,79.6361,13.5937,57.0866,24.73,88.498,18.8081,66.8284
-#NML23,1
-Conv10,162.139,785.077,1672.38,6225.71,59.6133,219.941,19.097,67.5188
-#NML24,1
-BatchNorm10,31.2579,146.853,27.6361,108.787,73.5493,270.869,18.7658,66.0488
-#NML25,1
-Relu10,17.184,77.9379,13.5283,56.7435,24.4533,86.8627,18.8086,66.1414
-#Conv11,1
-Conv11,233.12,1118.97,166.111,623.585,35.1905,128.418,18.9605,66.8737
-#NML26,1
-BatchNorm11,31.2863,148.577,27.6455,108.421,73.8267,270.154,18.8306,66.0084
-#NML27,1
-Relu11,17.1913,78.6669,13.524,56.8779,24.5173,87.138,18.7795,66.7656
-#NML28,1
-Conv12,53.1921,256.743,396.977,1509.34,35.3114,127.967,4.99161,13.5633
-#NML29,1
-BatchNorm12,25.9908,121.755,26.0905,104.322,10.5834,34.4345,4.90361,13.3056
-#NML30,1
-Relu12,4.43176,15.0166,3.6779,18.9772,7.84663,24.5065,4.76517,12.9712
-#Conv13,1
-Conv13,119.521,577.914,63.1947,243.935,8.92772,28.6239,9.58326,31.2502
-#NML31,1
-BatchNorm13,51.9366,250.554,49.3667,192.276,35.0054,127.493,9.47022,30.7329
-#NML32,1
-Relu13,8.7572,36.6268,6.98942,31.8826,13.5881,46.2214,9.4503,31.2116
-#NML33,1
-Pool1,26.725,125.046,15.6197,63.4788,33.2369,120.141,2.60648,5.14839
-#FC1,2
-Mul1,2.40563,5.37123,5.40178,21.3448,6.87904,20.2727,0.303588,0.0568318
-Add1,0.431528,0.176414,0.938673,3.94623,2.02678,2.85623,0.208607,0.0415936
diff --git a/llvm/projects/soc_simulator/mobilenet_shallow/result_test_conf.out b/llvm/projects/soc_simulator/mobilenet_shallow/result_test_conf.out
deleted file mode 100644
index 7907f1be58e22b0ac7dd4873471aac1b5205bf31..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/mobilenet_shallow/result_test_conf.out
+++ /dev/null
@@ -1,66 +0,0 @@
-Compute Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,572.966,184.509,138.685,510.748,198.623,146.196,1046.81,410.676,295.687,405.855,99.5467,73.891,524.623,211.449,159.137,873.476,219.41,164.603,1372.91,218.702,165.077,504.488,71.2665,37.2098,954.913,148.915,79.6361,785.077,146.853,77.9379,1118.97,148.577,78.6669,256.743,121.755,15.0166,577.914,250.554,36.6268,125.046,5.547644,13535.292944,0.999999992611907
-c1,572.966,184.509,138.685,510.748,198.623,146.196,31.422697,410.676,295.687,405.855,99.5467,73.891,524.623,211.449,159.137,873.476,219.41,164.603,15.711348,218.702,165.077,504.488,71.2665,37.2098,7.855674,148.915,79.6361,785.077,146.853,77.9379,15.711348,148.577,78.6669,256.743,121.755,15.0166,5.906058,250.554,36.6268,125.046,5.547644,8540.383069,1.58485780744951
-c1,8540.383069
-
-Compute Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,166.211,52.3819,38.6469,138.263,52.1919,38.1309,261.935,97.7271,68.9954,92.893,23.3196,17.6047,116.17,46.1234,34.6677,197.299,46.2495,34.6984,292.058,45.663,34.4299,103.885,15.8328,8.79131,198.587,31.2316,17.2031,162.139,31.2579,17.184,233.12,31.2863,17.1913,53.1921,25.9908,4.43176,119.521,51.9366,8.7572,26.725,2.837158,3056.760228,0.999999967285626
-c1,166.211,52.3819,38.6469,138.263,52.1919,38.1309,16.261248,97.7271,68.9954,92.893,23.3196,17.6047,116.17,46.1234,34.6677,197.299,46.2495,34.6984,8.303616,45.663,34.4299,103.885,15.8328,8.79131,4.151808,31.2316,17.2031,162.139,31.2579,17.184,8.303616,31.2863,17.1913,53.1921,25.9908,4.43176,4.613120,51.9366,8.7572,26.725,2.837158,1993.172636,1.53361531230578
-c1,1993.172636
-
-Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,572.966,184.509,138.685,510.748,198.623,146.196,1046.81,410.676,295.687,405.855,99.5467,73.891,524.623,211.449,159.137,873.476,219.41,164.603,1372.91,218.702,165.077,504.488,71.2665,37.2098,954.913,148.915,79.6361,785.077,146.853,77.9379,1118.97,148.577,78.6669,256.743,121.755,15.0166,577.914,250.554,36.6268,125.046,5.547644,13535.292944,0.999999992611907
-c1,572.966,184.509,138.685,510.748,198.623,146.196,152.598914,410.676,295.687,405.855,99.5467,73.891,524.623,211.449,159.137,873.476,219.41,164.603,91.934874,218.702,165.077,504.488,71.2665,37.2098,38.09458,148.915,79.6361,785.077,146.853,77.9379,60.851377,148.577,78.6669,256.743,121.755,15.0166,24.49524,250.554,36.6268,125.046,5.547644,8831.750929,1.53257184215853
-c1,8831.750929
-
-Leakage Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,50.323486,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,12.490088,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,122.707169,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,70.852731,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,17.748818,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,168.660691,0
-c0,0
-
-Memory Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,16.574126,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,4.157930,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,39.762784,0
-c0,0
-
-Patch Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c0,0
-
-Quantization Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c0,0
-
-Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,166.211,52.3819,38.6469,138.263,52.1919,38.1309,261.935,97.7271,68.9954,92.893,23.3196,17.6047,116.17,46.1234,34.6677,197.299,46.2495,34.6984,292.058,45.663,34.4299,103.885,15.8328,8.79131,198.587,31.2316,17.2031,162.139,31.2579,17.184,233.12,31.2863,17.1913,53.1921,25.9908,4.43176,119.521,51.9366,8.7572,26.725,2.837158,3056.760228,0.999999967285626
-c1,166.211,52.3819,38.6469,138.263,52.1919,38.1309,32.835374,97.7271,68.9954,92.893,23.3196,17.6047,116.17,46.1234,34.6677,197.299,46.2495,34.6984,19.078877,45.663,34.4299,103.885,15.8328,8.79131,8.309738,31.2316,17.2031,162.139,31.2579,17.184,14.181538,31.2863,17.1913,53.1921,25.9908,4.43176,6.990665,51.9366,8.7572,26.725,2.837158,2032.93542,1.50361887916642
-c1,2032.93542
-
-Unpatch Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c0,0
-
diff --git a/llvm/projects/soc_simulator/mobilenet_shallow/results/HA_loss1_results.txt b/llvm/projects/soc_simulator/mobilenet_shallow/results/HA_loss1_results.txt
deleted file mode 100644
index d1d1c5e872107045ebcbe8e4f248ddb20d584f2a..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/mobilenet_shallow/results/HA_loss1_results.txt
+++ /dev/null
@@ -1,319 +0,0 @@
-Compute Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,567.689,184.046,138.524,505.587,197.914,146.069,1032.03,409.331,295.295,435.587,99.2457,73.9864,532.734,210.096,158.225,941.502,219.088,164.507,1371.64,217.634,164.29,520.234,71.2826,36.7775,954.881,148.359,79.3874,793.877,145.953,77.3085,1117.62,148.499,78.9983,263.615,121.522,14.8101,577.124,249.047,36.7714,124.626,5.148905,13630.861805,0.999999992663707
-c1,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,8898.261135,1.5318567802196
-c2,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,8898.261135,1.5318567802196
-c3,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,9478.953787,1.43801330479033
-c4,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,9478.953787,1.43801330479033
-c5,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,24.78013,9126.405478,1.49356301213025
-c6,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,8898.261135,1.5318567802196
-c7,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,9478.953787,1.43801330479033
-c8,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,24.78013,9707.09813,1.40421591314215
-c9,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,9478.953787,1.43801330479033
-c10,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,8898.261135,1.5318567802196
-c11,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,8898.261135,1.5318567802196
-c12,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,9478.953787,1.43801330479033
-c13,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,9478.953787,1.43801330479033
-c14,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,24.78013,9126.405478,1.49356301213025
-c15,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,8898.261135,1.5318567802196
-c16,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,24.78013,9707.09813,1.40421591314215
-c17,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,8898.261135,1.5318567802196
-c18,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,8898.261135,1.5318567802196
-c19,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,9478.953787,1.43801330479033
-c20,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,24.78013,9126.405478,1.49356301213025
-c21,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,9478.953787,1.43801330479033
-c22,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,8898.261135,1.5318567802196
-c23,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,9478.953787,1.43801330479033
-
-c1,8898.261135
-
-Compute Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,165.401,52.2864,38.6453,137.297,52.1549,38.1169,258.954,97.6062,68.9786,99.7425,23.3301,17.5398,118.361,46.095,34.7036,211.862,46.239,34.7264,292.43,45.4864,34.3235,107.339,15.8015,8.79447,199.034,31.138,17.2023,164.266,31.2193,17.2051,233.349,31.252,17.1975,54.8812,25.9899,4.36155,119.767,51.9324,8.7646,26.8288,2.83825,3083.44147,0.999999967568706
-c1,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2580.93424,1.1946996954599
-c2,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2580.93424,1.1946996954599
-c3,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2739.480624,1.12555691412122
-c4,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2739.480624,1.12555691412122
-c5,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,6.408654,2639.91512,1.16800776276444
-c6,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2580.93424,1.1946996954599
-c7,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2739.480624,1.12555691412122
-c8,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,6.408654,2798.461504,1.10183447419563
-c9,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2739.480624,1.12555691412122
-c10,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2580.93424,1.1946996954599
-c11,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2580.93424,1.1946996954599
-c12,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2739.480624,1.12555691412122
-c13,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2739.480624,1.12555691412122
-c14,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,6.408654,2639.91512,1.16800776276444
-c15,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2580.93424,1.1946996954599
-c16,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,6.408654,2798.461504,1.10183447419563
-c17,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2580.93424,1.1946996954599
-c18,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2580.93424,1.1946996954599
-c19,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2739.480624,1.12555691412122
-c20,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,6.408654,2639.91512,1.16800776276444
-c21,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2739.480624,1.12555691412122
-c22,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2580.93424,1.1946996954599
-c23,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2739.480624,1.12555691412122
-
-c1,2580.93424
-
-Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,567.689,184.046,138.524,505.587,197.914,146.069,1032.03,409.331,295.295,435.587,99.2457,73.9864,532.734,210.096,158.225,941.502,219.088,164.507,1371.64,217.634,164.29,520.234,71.2826,36.7775,954.881,148.359,79.3874,793.877,145.953,77.3085,1117.62,148.499,78.9983,263.615,121.522,14.8101,577.124,249.047,36.7714,124.626,5.148905,13630.861805,0.999999992663707
-c1,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,8977.328846,1.51836497102776
-c2,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,8977.328846,1.51836497102776
-c3,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,9512.881469,1.43288463186795
-c4,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,9512.881469,1.43288463186795
-c5,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,24.78013,9186.884007,1.48373068020025
-c6,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,8977.328846,1.51836497102776
-c7,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,9512.881469,1.43288463186795
-c8,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,24.78013,9722.43663,1.40200056668304
-c9,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,9512.881469,1.43288463186795
-c10,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,8977.328846,1.51836497102776
-c11,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,8977.328846,1.51836497102776
-c12,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,9512.881469,1.43288463186795
-c13,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,9512.881469,1.43288463186795
-c14,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,24.78013,9186.884007,1.48373068020025
-c15,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,8977.328846,1.51836497102776
-c16,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,24.78013,9722.43663,1.40200056668304
-c17,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,8977.328846,1.51836497102776
-c18,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,8977.328846,1.51836497102776
-c19,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,9512.881469,1.43288463186795
-c20,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,24.78013,9186.884007,1.48373068020025
-c21,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,9512.881469,1.43288463186795
-c22,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,8977.328846,1.51836497102776
-c23,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,9512.881469,1.43288463186795
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-c1,8977.328846
-
-Leakage Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,30.978009,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,30.978009,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,9.633508,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,9.633508,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,0,0,0,0,0,21.344501,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,30.978009,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,9.633508,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,9.633508,0
-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,30.978009,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,30.978009,0
-c12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,9.633508,0
-c13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,9.633508,0
-c14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,0,0,0,0,0,21.344501,0
-c15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,30.978009,0
-c16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,30.978009,0
-c18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,30.978009,0
-c19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,9.633508,0
-c20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,0,0,0,0,0,21.344501,0
-c21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,9.633508,0
-c22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,30.978009,0
-c23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,9.633508,0
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-c0,0
-
-Memory Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,32.751202,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,32.751202,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,8.955674,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,8.955674,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,0,0,0,0,0,23.795528,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,32.751202,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,8.955674,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,8.955674,0
-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,32.751202,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,32.751202,0
-c12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,8.955674,0
-c13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,8.955674,0
-c14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,0,0,0,0,0,23.795528,0
-c15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,32.751202,0
-c16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,32.751202,0
-c18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,32.751202,0
-c19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,8.955674,0
-c20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,0,0,0,0,0,23.795528,0
-c21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,8.955674,0
-c22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,32.751202,0
-c23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,8.955674,0
-
-c0,0
-
-Memory Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,8.255467,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,8.255467,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,2.377545,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,2.377545,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,0,0,0,0,0,5.877922,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,8.255467,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,2.377545,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,2.377545,0
-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,8.255467,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,8.255467,0
-c12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,2.377545,0
-c13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,2.377545,0
-c14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,0,0,0,0,0,5.877922,0
-c15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,8.255467,0
-c16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,8.255467,0
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-c20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,0,0,0,0,0,5.877922,0
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-c0,0
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-Patch Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
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-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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-c15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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-c0,0
-
-Quantization Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
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-c1,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c2,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c3,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c4,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c5,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c6,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c7,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c8,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c9,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c10,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c11,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c12,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c13,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c14,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c15,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c16,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c17,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c18,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c19,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c20,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c21,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c22,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c23,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-
-c0,0
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-Quantization Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c2,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c3,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c4,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c5,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c6,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c7,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c8,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c9,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
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-c12,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c13,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c14,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c15,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c16,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c17,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c18,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c19,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c20,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c21,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c22,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
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-c0,0
-
-Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,165.401,52.2864,38.6453,137.297,52.1549,38.1169,258.954,97.6062,68.9786,99.7425,23.3301,17.5398,118.361,46.095,34.7036,211.862,46.239,34.7264,292.43,45.4864,34.3235,107.339,15.8015,8.79447,199.034,31.138,17.2023,164.266,31.2193,17.2051,233.349,31.252,17.1975,54.8812,25.9899,4.36155,119.767,51.9324,8.7646,26.8288,2.83825,3083.44147,0.999999967568706
-c1,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,14.181538,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2596.644717,1.18747140533545
-c2,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,14.181538,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2596.644717,1.18747140533545
-c3,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2749.313179,1.12153150881428
-c4,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2749.313179,1.12153150881428
-c5,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,14.181538,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,6.408654,2653.248052,1.16213836526211
-c6,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,14.181538,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2596.644717,1.18747140533545
-c7,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2749.313179,1.12153150881428
-c8,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,6.408654,2805.916514,1.0989070219034
-c9,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2749.313179,1.12153150881428
-c10,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,14.181538,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2596.644717,1.18747140533545
-c11,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,14.181538,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2596.644717,1.18747140533545
-c12,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2749.313179,1.12153150881428
-c13,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2749.313179,1.12153150881428
-c14,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,14.181538,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,6.408654,2653.248052,1.16213836526211
-c15,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,14.181538,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2596.644717,1.18747140533545
-c16,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,6.408654,2805.916514,1.0989070219034
-c17,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,14.181538,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2596.644717,1.18747140533545
-c18,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,14.181538,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2596.644717,1.18747140533545
-c19,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2749.313179,1.12153150881428
-c20,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,14.181538,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,6.408654,2653.248052,1.16213836526211
-c21,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2749.313179,1.12153150881428
-c22,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,14.181538,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2596.644717,1.18747140533545
-c23,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2749.313179,1.12153150881428
-
-c1,2596.644717
-
-Unpatch Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-
-c0,0
-
diff --git a/llvm/projects/soc_simulator/mobilenet_shallow/results/HA_loss2_results.txt b/llvm/projects/soc_simulator/mobilenet_shallow/results/HA_loss2_results.txt
deleted file mode 100644
index 1127c71bb854e69baaaf74a21f82b84266251ef4..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/mobilenet_shallow/results/HA_loss2_results.txt
+++ /dev/null
@@ -1,385 +0,0 @@
-Compute Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,567.689,184.046,138.524,505.587,197.914,146.069,1032.03,409.331,295.295,435.587,99.2457,73.9864,532.734,210.096,158.225,941.502,219.088,164.507,1371.64,217.634,164.29,520.234,71.2826,36.7775,954.881,148.359,79.3874,793.877,145.953,77.3085,1117.62,148.499,78.9983,263.615,121.522,14.8101,577.124,249.047,36.7714,124.626,5.148905,13630.861805,0.999999992663707
-c1,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,15.711348,143.829,100.069,147.475,60.7097,34.5499,7.855674,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,5.906058,184.971,30.535,61.4365,5.148905,7558.685333,1.80333762078388
-c2,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,11.801625,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,7928.36376,1.71925280495388
-c3,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,7.768049,143.829,100.069,147.475,60.7097,34.5499,7.855674,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,7572.329858,1.80008820014496
-c4,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,15.711348,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,8512.966135,1.60118828487283
-c5,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,15.711348,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,11.801625,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,24.78013,8156.508103,1.67116386887057
-c6,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,7.855674,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,24.78013,9355.097804,1.45705175347997
-c7,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,9478.953787,1.43801330479033
-c8,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,15.711348,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,11.801625,103.913,54.7397,94.3137,100.201,18.1925,3.887477,184.971,30.535,61.4365,24.78013,7924.38858,1.72011524868818
-c9,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,15.711348,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,24.78013,8160.417826,1.67036320058689
-c10,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,7.768049,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,8505.022836,1.60268372085199
-c11,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,15.711348,143.829,100.069,147.475,60.7097,34.5499,7.855674,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,5.906058,184.971,30.535,61.4365,24.78013,7578.316558,1.79866617088515
-c12,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,11.801625,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,7928.36376,1.71925280495388
-c13,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,7.768049,143.829,100.069,147.475,60.7097,34.5499,7.855674,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,7572.329858,1.80008820014496
-c14,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,15.711348,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,8512.966135,1.60118828487283
-c15,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,15.711348,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,11.801625,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,24.78013,8156.508103,1.67116386887057
-c16,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,9478.953787,1.43801330479033
-c17,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,7.768049,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,8505.022836,1.60268372085199
-c18,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,15.711348,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,11.801625,103.913,54.7397,94.3137,100.201,18.1925,3.887477,184.971,30.535,61.4365,24.78013,7924.38858,1.72011524868818
-c19,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,15.711348,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,24.78013,8160.417826,1.67036320058689
-c20,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,15.711348,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,7.768049,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,24.78013,8460.779527,1.61106451248314
-c21,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,15.711348,143.829,100.069,147.475,60.7097,34.5499,7.855674,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,5.906058,184.971,30.535,61.4365,24.78013,7578.316558,1.79866617088515
-c22,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,11.801625,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,7928.36376,1.71925280495388
-c23,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,7.768049,143.829,100.069,147.475,60.7097,34.5499,7.855674,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,7572.329858,1.80008820014496
-c24,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,15.711348,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,8512.966135,1.60118828487283
-c25,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,15.711348,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,11.801625,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,24.78013,8156.508103,1.67116386887057
-c26,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,9478.953787,1.43801330479033
-c27,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,15.711348,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,11.801625,103.913,54.7397,94.3137,100.201,18.1925,3.887477,184.971,30.535,61.4365,24.78013,7924.38858,1.72011524868818
-c28,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,15.711348,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,24.78013,8160.417826,1.67036320058689
-c29,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,7.768049,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,7.862657,184.971,30.535,61.4365,24.78013,8505.022836,1.60268372085199
-
-c1,7558.685333
-
-Compute Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,165.401,52.2864,38.6453,137.297,52.1549,38.1169,258.954,97.6062,68.9786,99.7425,23.3301,17.5398,118.361,46.095,34.7036,211.862,46.239,34.7264,292.43,45.4864,34.3235,107.339,15.8015,8.79447,199.034,31.138,17.2023,164.266,31.2193,17.2051,233.349,31.252,17.1975,54.8812,25.9899,4.36155,119.767,51.9324,8.7646,26.8288,2.83825,3083.44147,0.999999967568706
-c1,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,2.83825,2209.06946,1.3958100395897
-c2,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2306.626856,1.33677509576455
-c3,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2212.639864,1.39355770489916
-c4,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2465.17324,1.25080107754208
-c5,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,6.408654,2365.607736,1.30344574577238
-c6,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,6.408654,2704.474512,1.14012587003719
-c7,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2739.480624,1.12555691412122
-c8,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2306.626856,1.33677509576455
-c9,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,6.408654,2365.607736,1.30344574577238
-c10,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2465.17324,1.25080107754208
-c11,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2212.639864,1.39355770489916
-c12,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2306.626856,1.33677509576455
-c13,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2212.639864,1.39355770489916
-c14,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2465.17324,1.25080107754208
-c15,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,6.408654,2365.607736,1.30344574577238
-c16,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2739.480624,1.12555691412122
-c17,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2465.17324,1.25080107754208
-c18,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2306.626856,1.33677509576455
-c19,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,6.408654,2365.607736,1.30344574577238
-c20,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,8.303616,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,6.408654,2462.633736,1.25209092189213
-c21,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2212.639864,1.39355770489916
-c22,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2306.626856,1.33677509576455
-c23,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2212.639864,1.39355770489916
-c24,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2465.17324,1.25080107754208
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-c26,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2739.480624,1.12555691412122
-c27,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2306.626856,1.33677509576455
-c28,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,6.408654,2365.607736,1.30344574577238
-c29,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2465.17324,1.25080107754208
-
-c1,2209.06946
-
-Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,567.689,184.046,138.524,505.587,197.914,146.069,1032.03,409.331,295.295,435.587,99.2457,73.9864,532.734,210.096,158.225,941.502,219.088,164.507,1371.64,217.634,164.29,520.234,71.2826,36.7775,954.881,148.359,79.3874,793.877,145.953,77.3085,1117.62,148.499,78.9983,263.615,121.522,14.8101,577.124,249.047,36.7714,124.626,5.148905,13630.861805,0.999999992663707
-c1,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,91.934874,143.829,100.069,147.475,60.7097,34.5499,38.09458,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,24.49524,184.971,30.535,61.4365,5.2108132,7744.2773842,1.76012053194244
-c2,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,88.025151,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,8083.654997,1.68622506050001
-c3,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,83.991575,143.829,100.069,147.475,60.7097,34.5499,38.09458,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,7757.860001,1.75703887767233
-c4,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,91.934874,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,8623.117343,1.5807347974908
-c5,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,91.934874,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,56.941654,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,24.78013,8293.210158,1.64361705310089
-c6,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,38.09458,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,24.78013,9400.67521,1.44998751212055
-c7,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,9512.881469,1.43288463186795
-c8,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,91.934874,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,56.941654,103.913,54.7397,94.3137,100.201,18.1925,22.476659,184.971,30.535,61.4365,24.78013,8079.679817,1.68705467853003
-c9,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,91.934874,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,24.78013,8297.119881,1.64284255696121
-c10,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,83.991575,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,8615.174044,1.5821922548708
-c11,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,91.934874,143.829,100.069,147.475,60.7097,34.5499,38.09458,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,24.49524,184.971,30.535,61.4365,24.78013,7763.846701,1.75568402550709
-c12,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,88.025151,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,8083.654997,1.68622506050001
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-c14,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,91.934874,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,8623.117343,1.5807347974908
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-c16,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,9512.881469,1.43288463186795
-c17,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,83.991575,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,8615.174044,1.5821922548708
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-c20,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,76.301921,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,52.908078,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,24.78013,8581.848629,1.58833629389647
-c21,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,91.934874,143.829,100.069,147.475,60.7097,34.5499,38.09458,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,24.49524,184.971,30.535,61.4365,24.78013,7763.846701,1.75568402550709
-c22,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,88.025151,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,8083.654997,1.68622506050001
-c23,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,83.991575,143.829,100.069,147.475,60.7097,34.5499,38.09458,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,7757.860001,1.75703887767233
-c24,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,91.934874,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,8623.117343,1.5807347974908
-c25,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,91.934874,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,56.941654,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,24.78013,8293.210158,1.64361705310089
-c26,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,9512.881469,1.43288463186795
-c27,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,91.934874,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,56.941654,103.913,54.7397,94.3137,100.201,18.1925,22.476659,184.971,30.535,61.4365,24.78013,8079.679817,1.68705467853003
-c28,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,91.934874,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,24.78013,8297.119881,1.64284255696121
-c29,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,83.991575,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,26.451839,184.971,30.535,61.4365,24.78013,8615.174044,1.5821922548708
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-c1,7744.2773842
-
-Leakage Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,12.490088,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,72.383683,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,59.893595,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,12.490088,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,72.383683,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,38.549094,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,0,0,0,0,0,50.260087,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12.490088,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12.490088,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,9.633508,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,59.893595,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,0,0,0,0,0,50.260087,0
-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,38.549094,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,12.490088,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,72.383683,0
-c12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,59.893595,0
-c13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,12.490088,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,72.383683,0
-c14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,38.549094,0
-c15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,0,0,0,0,0,50.260087,0
-c16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,9.633508,0
-c17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,38.549094,0
-c18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,59.893595,0
-c19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,0,0,0,0,0,50.260087,0
-c20,0,0,0,0,0,0,0,0,0,0,0,0,25.140123,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,0,0,0,0,0,46.484624,0
-c21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,12.490088,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,72.383683,0
-c22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,59.893595,0
-c23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,12.490088,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,72.383683,0
-c24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,38.549094,0
-c25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,0,0,0,0,0,50.260087,0
-c26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,9.633508,0
-c27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,9.633508,0,0,0,0,59.893595,0
-c28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,21.344501,0,0,0,0,0,0,0,0,0,0,50.260087,0
-c29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28.915586,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.633508,0,0,0,0,38.549094,0
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-c0,0
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-Memory Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,17.748818,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,97.80796,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,80.059142,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,17.748818,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,97.80796,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,56.263614,0
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-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,17.748818,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,97.80796,0
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-c13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,17.748818,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,97.80796,0
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-c16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,8.955674,0
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-c20,0,0,0,0,0,0,0,0,0,0,0,0,35.450450,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,0,0,0,0,0,59.245978,0
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-c22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,80.059142,0
-c23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,17.748818,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,97.80796,0
-c24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,56.263614,0
-c25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,0,0,0,0,0,71.103468,0
-c26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,8.955674,0
-c27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,8.955674,0,0,0,0,80.059142,0
-c28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,23.795528,0,0,0,0,0,0,0,0,0,0,71.103468,0
-c29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47.307940,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.955674,0,0,0,0,56.263614,0
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-c0,0
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-Memory Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,4.157930,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,23.188658,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,19.030728,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,4.157930,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,23.188658,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,13.152806,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,0,0,0,0,0,16.653183,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.157930,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.15793,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,2.377545,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,19.030728,0
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-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,13.152806,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,4.157930,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,23.188658,0
-c12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,19.030728,0
-c13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,4.157930,0,0,0,0,0,5.877922,0,0,0,0,0,2.377545,0,0,0,0,23.188658,0
-c14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,13.152806,0
-c15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,5.877922,0,0,0,0,0,0,0,0,0,0,16.653183,0
-c16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,2.377545,0
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-c24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.775261,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,13.152806,0
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-c26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.377545,0,0,0,0,2.377545,0
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-c0,0
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-Patch Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
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-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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-c20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-
-c0,0
-
-Quantization Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0619082,15.4004082,0
-c2,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c3,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c4,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c5,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c6,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c7,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c8,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c9,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c10,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c11,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c12,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c13,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c14,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c15,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c16,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c17,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c18,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c19,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c20,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c21,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c22,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c23,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c24,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c25,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c26,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c27,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c28,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c29,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-
-c0,0
-
-Quantization Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.310624,7.765634,0
-c2,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c3,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c4,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c5,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c6,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c7,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c8,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c9,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c10,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c11,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c12,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c13,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c14,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c15,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c16,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c17,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c18,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c19,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c20,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c21,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c22,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c23,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c24,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c25,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c26,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c27,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c28,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c29,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-
-c0,0
-
-Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,165.401,52.2864,38.6453,137.297,52.1549,38.1169,258.954,97.6062,68.9786,99.7425,23.3301,17.5398,118.361,46.095,34.7036,211.862,46.239,34.7264,292.43,45.4864,34.3235,107.339,15.8015,8.79447,199.034,31.138,17.2023,164.266,31.2193,17.2051,233.349,31.252,17.1975,54.8812,25.9899,4.36155,119.767,51.9324,8.7646,26.8288,2.83825,3083.44147,0.999999967568706
-c1,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,19.078877,40.5067,26.7374,39.6826,15.3482,7.99351,8.309738,27.6217,13.5987,160.813,27.6333,13.5078,14.181538,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,3.148874,2240.023752,1.37652171303755
-c2,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,19.078877,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,14.181538,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2333.112594,1.32159988582189
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-c15,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,19.078877,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,14.181538,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,6.408654,2389.715929,1.29029618271854
-c16,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2749.313179,1.12153150881428
-c17,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,19.078877,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2485.781056,1.2404315893044
-c18,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,19.078877,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,14.181538,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2333.112594,1.32159988582189
-c19,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,19.078877,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,14.181538,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,6.408654,2389.715929,1.29029618271854
-c20,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,16.611446,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,14.181538,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,6.408654,2484.274498,1.24118383389758
-c21,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,19.078877,40.5067,26.7374,39.6826,15.3482,7.99351,8.309738,27.6217,13.5987,160.813,27.6333,13.5078,14.181538,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2243.283532,1.37452144972455
-c22,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,19.078877,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,14.181538,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2333.112594,1.32159988582189
-c23,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,19.078877,40.5067,26.7374,39.6826,15.3482,7.99351,8.309738,27.6217,13.5987,160.813,27.6333,13.5078,14.181538,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2243.283532,1.37452144972455
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-c26,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2749.313179,1.12153150881428
-c27,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,19.078877,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,14.181538,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2333.112594,1.32159988582189
-c28,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,19.078877,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,14.181538,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,6.408654,2389.715929,1.29029618271854
-c29,198.30001,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,19.078877,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,6.990665,49.3767,6.99017,15.5609,6.408654,2485.781056,1.2404315893044
-
-c1,2240.023752
-
-Unpatch Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
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diff --git a/llvm/projects/soc_simulator/mobilenet_shallow/results/HS_loss1_results.txt b/llvm/projects/soc_simulator/mobilenet_shallow/results/HS_loss1_results.txt
deleted file mode 100644
index 85b21baa2464f296262fc489ac6162bbd7d6810a..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/mobilenet_shallow/results/HS_loss1_results.txt
+++ /dev/null
@@ -1,759 +0,0 @@
-Compute Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
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-
-c9,8501.325445
-
-Compute Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
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-c37,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2486.947248,1.23984991981423
-c38,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,0.036040,2539.555514,1.21416575915947
-c39,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c40,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c41,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c42,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c43,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c44,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2486.947248,1.23984991981423
-c45,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c46,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c47,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,0.036040,2539.555514,1.21416575915947
-c48,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c49,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c50,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c51,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c52,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2639.121018,1.16835921207615
-c53,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c54,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c55,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c56,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c57,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c58,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c59,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2486.947248,1.23984991981423
-c60,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c61,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c62,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c63,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2486.947248,1.23984991981423
-
-c1,2480.574634
-
-Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,567.689,184.046,138.524,505.587,197.914,146.069,1032.03,409.331,295.295,435.587,99.2457,73.9864,532.734,210.096,158.225,941.502,219.088,164.507,1371.64,217.634,164.29,520.234,71.2826,36.7775,954.881,148.359,79.3874,793.877,145.953,77.3085,1117.62,148.499,78.9983,263.615,121.522,14.8101,577.124,249.047,36.7714,124.626,5.148905,13630.861805,0.999999992663707
-c1,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,34.122931,104.698,54.9348,577.662,103.328,53.7829,56.941654,103.913,54.7397,94.3137,100.201,18.1925,21.175063,184.971,30.535,61.4365,1.016415,8618.645563,1.58155496095141
-c2,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,36.139718,104.698,54.9348,577.662,103.328,53.7829,56.941654,103.913,54.7397,94.3137,100.201,18.1925,21.175063,184.971,30.535,61.4365,1.016415,8620.66235,1.5811849592835
-c3,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,36.139718,104.698,54.9348,577.662,103.328,53.7829,52.908078,103.913,54.7397,94.3137,100.201,18.1925,22.073595,184.971,30.535,61.4365,1.016415,8617.527306,1.58176019208357
-c4,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,33.720225,104.698,54.9348,577.662,103.328,53.7829,50.307198,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,1.016415,8826.440338,1.54432150771853
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-c9,8611.635559
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-Leakage Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
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-c0,0
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-Memory Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
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-c0,0
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-Memory Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
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-c59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c60,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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-
-c0,0
-
-Quantization Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c2,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c3,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c4,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c5,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c6,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c7,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c8,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c9,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c10,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c11,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c12,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c13,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c14,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c15,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c16,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c17,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c18,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c19,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c20,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c21,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c22,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c23,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c24,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c25,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c26,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c27,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c28,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c29,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c30,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c31,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c32,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c33,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c34,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c35,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c36,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c37,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c38,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c39,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c40,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c41,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c42,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c43,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c44,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c45,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c46,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c47,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c48,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c49,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c50,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c51,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c52,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c53,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c54,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c55,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c56,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c57,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c58,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c59,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c60,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c61,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c62,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-c63,15.3385,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.3385,0
-
-c0,0
-
-Quantization Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c2,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c3,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c4,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c5,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c6,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
-c7,7.45501,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.45501,0
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-c0,0
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-Time
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-
-c1,2500.603024
-
-Unpatch Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
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diff --git a/llvm/projects/soc_simulator/mobilenet_shallow/results/HS_loss2_results.txt b/llvm/projects/soc_simulator/mobilenet_shallow/results/HS_loss2_results.txt
deleted file mode 100644
index 965e1ff061ce5ca3247947e16033a1aef2a56db0..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/mobilenet_shallow/results/HS_loss2_results.txt
+++ /dev/null
@@ -1,880 +0,0 @@
-Compute Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
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-c62,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,7.855674,104.698,54.9348,577.662,103.328,53.7829,6.962637,103.913,54.7397,94.3137,100.201,18.1925,2.585881,184.971,30.535,61.4365,0.012918,8507.46811,1.60222306666721
-c63,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,3.884025,104.698,54.9348,577.662,103.328,53.7829,5.167169,103.913,54.7397,94.3137,100.201,18.1925,2.585881,184.971,30.535,61.4365,0.029504,8501.717579,1.6033068045378
-c64,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,5.900812,104.698,54.9348,577.662,103.328,53.7829,5.167169,103.913,54.7397,94.3137,100.201,18.1925,5.906058,184.971,30.535,61.4365,0.012918,8507.037957,1.60230408206342
-c65,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,7.855674,104.698,54.9348,577.662,103.328,53.7829,6.962637,103.913,54.7397,94.3137,100.201,18.1925,2.585881,184.971,30.535,61.4365,0.012918,8507.46811,1.60222306666721
-c66,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,7.855674,104.698,54.9348,577.662,103.328,53.7829,6.962637,103.913,54.7397,94.3137,100.201,18.1925,2.585881,184.971,30.535,61.4365,0.017407,8507.472599,1.60222222124818
-c67,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,5.900812,104.698,54.9348,577.662,103.328,53.7829,15.711348,103.913,54.7397,94.3137,100.201,18.1925,2.585881,184.971,30.535,61.4365,0.012918,8514.261959,1.60094459279551
-c68,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,3.481319,104.698,54.9348,577.662,103.328,53.7829,6.962637,103.913,54.7397,94.3137,100.201,18.1925,2.585881,184.971,30.535,61.4365,0.029504,8503.110341,1.60304419183775
-c69,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,7.855674,104.698,54.9348,577.662,103.328,53.7829,5.167169,103.913,54.7397,94.3137,100.201,18.1925,2.585881,184.971,30.535,61.4365,0.039278,8505.699002,1.60255631448271
-c70,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,7.855674,104.698,54.9348,577.662,103.328,53.7829,5.167169,103.913,54.7397,94.3137,100.201,18.1925,2.585881,184.971,30.535,61.4365,0.019420,8505.679144,1.6025600559315
-c71,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,7.855674,104.698,54.9348,577.662,103.328,53.7829,6.962637,103.913,54.7397,94.3137,100.201,18.1925,2.585881,184.971,30.535,61.4365,0.017407,8507.472599,1.60222222124818
-c72,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,7.855674,104.698,54.9348,577.662,103.328,53.7829,5.167169,103.913,54.7397,94.3137,100.201,18.1925,2.585881,184.971,30.535,61.4365,0.029504,8505.689228,1.60255815600135
-c73,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,3.884025,104.698,54.9348,577.662,103.328,53.7829,11.801625,103.913,54.7397,94.3137,100.201,18.1925,2.585881,184.971,30.535,61.4365,0.029504,8508.352035,1.60205661316344
-c74,489.961,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,981.699,143.829,100.069,147.475,60.7097,34.5499,7.855674,104.698,54.9348,577.662,103.328,53.7829,6.962637,103.913,54.7397,94.3137,100.201,18.1925,2.585881,184.971,30.535,61.4365,0.029504,8507.484696,1.60221994300935
-
-c8,7536.883696
-
-Compute Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,165.401,52.2864,38.6453,137.297,52.1549,38.1169,258.954,97.6062,68.9786,99.7425,23.3301,17.5398,118.361,46.095,34.7036,211.862,46.239,34.7264,292.43,45.4864,34.3235,107.339,15.8015,8.79447,199.034,31.138,17.2023,164.266,31.2193,17.2051,233.349,31.252,17.1975,54.8812,25.9899,4.36155,119.767,51.9324,8.7646,26.8288,2.83825,3083.44147,0.999999967568706
-c1,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2364.813634,1.30388344150237
-c2,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,0.036040,2265.24813,1.36119363395331
-c3,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2300.254242,1.34047849131285
-c4,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,0.036040,2265.24813,1.36119363395331
-c5,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,0.036040,2265.24813,1.36119363395331
-c6,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,0.036040,2265.24813,1.36119363395331
-c7,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2206.26725,1.39758287679868
-c8,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2206.26725,1.39758287679868
-c9,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2458.800626,1.25404284999386
-c10,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2371.186248,1.30037922687973
-c11,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,0.036040,2265.24813,1.36119363395331
-c12,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2364.813634,1.30388344150237
-c13,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,98.1388,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2306.626856,1.33677509576455
-c14,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,0.036040,2265.24813,1.36119363395331
-c15,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2212.639864,1.39355770489916
-c16,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2364.813634,1.30388344150237
-c17,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2212.639864,1.39355770489916
-c18,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,0.036040,2423.794514,1.27215460096736
-c19,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,8.303616,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,0.036040,2265.24813,1.36119363395331
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-c57,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,166.85,27.646,13.5936,24.574,26.0654,3.67647,63.594,49.3767,6.99017,15.5609,0.036040,2698.101898,1.14281871933887
-c58,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,6.408654,2486.947248,1.23984991981423
-c59,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c60,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c61,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c62,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c63,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c64,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c65,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c66,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c67,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c68,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c69,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c70,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c71,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c72,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c73,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-c74,190.845,44.208,27.6261,146.888,46.4921,27.6025,243.635,86.3211,54.9261,87.5137,22.6355,15.9996,185.585,40.6227,27.7104,323.448,40.6142,27.2599,282.611,40.5067,26.7374,39.6826,15.3482,7.99351,4.151808,27.6217,13.5987,160.813,27.6333,13.5078,8.303616,27.646,13.5936,24.574,26.0654,3.67647,4.613120,49.3767,6.99017,15.5609,0.036040,2480.574634,1.24303510300932
-
-c7,2206.26725
-
-Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
-c0,567.689,184.046,138.524,505.587,197.914,146.069,1032.03,409.331,295.295,435.587,99.2457,73.9864,532.734,210.096,158.225,941.502,219.088,164.507,1371.64,217.634,164.29,520.234,71.2826,36.7775,954.881,148.359,79.3874,793.877,145.953,77.3085,1117.62,148.499,78.9983,263.615,121.522,14.8101,577.124,249.047,36.7714,124.626,5.148905,13630.861805,0.999999992663707
-c1,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,83.991575,143.829,100.069,147.475,60.7097,34.5499,34.122931,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,22.476659,184.971,30.535,61.4365,1.033001,8261.718666,1.64988208762273
-c2,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,91.934874,143.829,100.069,147.475,60.7097,34.5499,38.09458,104.698,54.9348,577.662,103.328,53.7829,56.941654,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,1.033001,7947.701609,1.7150696269293
-c3,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,83.991575,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,56.941654,103.913,54.7397,94.3137,100.201,18.1925,21.175063,184.971,30.535,61.4365,1.033001,8046.687793,1.69397172926985
-c4,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,83.991575,143.829,100.069,147.475,60.7097,34.5499,38.09458,104.698,54.9348,577.662,103.328,53.7829,52.102666,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,1.033001,7934.919322,1.71783241644618
-c5,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,83.991575,143.829,100.069,147.475,60.7097,34.5499,34.122931,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,1.033001,7939.696384,1.71679885150129
-c6,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,83.991575,143.829,100.069,147.475,60.7097,34.5499,38.09458,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,1.033001,7943.668033,1.71594049207242
-c7,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,83.186163,143.829,100.069,147.475,60.7097,34.5499,38.09458,104.698,54.9348,577.662,103.328,53.7829,60.851377,103.913,54.7397,94.3137,100.201,18.1925,21.175063,184.971,30.535,61.4365,1.033001,7728.030684,1.76382084724884
-c8,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,83.991575,143.829,100.069,147.475,60.7097,34.5499,38.09458,104.698,54.9348,577.662,103.328,53.7829,52.102666,103.913,54.7397,94.3137,100.201,18.1925,24.49524,184.971,30.535,61.4365,1.042775,7723.417336,1.76487441187168
-c9,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,83.991575,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,22.073595,184.971,30.535,61.4365,1.042775,8587.058445,1.58737264146602
-c10,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,83.991575,143.829,100.069,147.475,60.7097,34.5499,38.09458,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,21.175063,184.971,30.535,61.4365,24.78013,8288.135848,1.64462333756594
-c11,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,83.991575,143.829,100.069,147.475,60.7097,34.5499,38.09458,104.698,54.9348,577.662,103.328,53.7829,56.941654,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,1.033001,7939.75831,1.71678546136015
-c12,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,83.186163,143.829,100.069,147.475,60.7097,34.5499,38.09458,104.698,54.9348,577.662,103.328,53.7829,596.404,103.913,54.7397,94.3137,100.201,18.1925,21.175063,184.971,30.535,61.4365,1.033001,8263.583307,1.64950979903627
-c13,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,83.991575,143.829,100.069,147.475,60.7097,34.5499,359.856,104.698,54.9348,577.662,103.328,53.7829,52.102666,103.913,54.7397,94.3137,100.201,18.1925,24.49524,184.971,30.535,61.4365,24.78013,8068.916111,1.68930516175365
-c14,505.2995,126.823,85.2148,454.401,157.667,98.9958,792.454,303.765,205.544,315.332,87.6934,64.6513,673.394,149.173,106.31,1119.27,145.624,102.738,83.991575,143.829,100.069,147.475,60.7097,34.5499,38.09458,104.698,54.9348,577.662,103.328,53.7829,52.102666,103.913,54.7397,94.3137,100.201,18.1925,236.007,184.971,30.535,61.4365,1.022917,7934.909238,1.71783459953629
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-c8,7723.417336
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-Leakage Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
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-
-c0,0
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-Memory Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
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-
-c0,0
-
-Memory Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
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-c0,0
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-Patch Energy
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-c0,0
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-Quantization Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
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-
-c0,0
-
-Quantization Time
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
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-c7,2237.070901
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-Unpatch Energy
-Configuration,Conv1,NML1,NML2,NML3,NML4,NML5,Conv3,NML6,NML7,NML8,NML9,NML10,Conv5,NML11,NML12,NML13,NML14,NML15,Conv7,NML16,NML17,NML18,NML19,NML20,Conv9,NML21,NML22,NML23,NML24,NML25,Conv11,NML26,NML27,NML28,NML29,NML30,Conv13,NML31,NML32,NML33,FC1,Total,Improvement
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diff --git a/llvm/projects/soc_simulator/mobilenet_shallow/test_conf.txt b/llvm/projects/soc_simulator/mobilenet_shallow/test_conf.txt
deleted file mode 100644
index 0142d9b480bac8f9f4fe69826d9afecf6ff22f30..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/mobilenet_shallow/test_conf.txt
+++ /dev/null
@@ -1,2 +0,0 @@
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diff --git a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_confs1.txt b/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_confs1.txt
deleted file mode 100644
index 8ea283e0a2031578fd063967089b6f6cba54a549..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_confs1.txt
+++ /dev/null
@@ -1,9 +0,0 @@
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diff --git a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_confs2.txt b/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_confs2.txt
deleted file mode 100644
index 834a35f35200d56d029fda983afeb6aac188c6ec..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_confs2.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-9 9,9 9 9,9 9,9 9
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diff --git a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_fp16.csv b/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_fp16.csv
deleted file mode 100644
index 2beaa67647d3eac8f2d103e02829ffe3687264ec..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_fp16.csv
+++ /dev/null
@@ -1,27 +0,0 @@
-Add1,32.6334,133.401,91.0356,42.3651,4087.88,2789.67,1298.21,0.30589,1.78751,1.34011,0.538346,42.1225,33.8662,11.5884
-Add1_f2h,150.837,630.018,462.198,167.819,4177.07,3064.48,1112.6,3.34,12.5306,8.92834,3.71182,20.7606,19.9311,3.16457
-Add1_h2f,36.4014,148.497,98.4511,50.0455,4079.39,2704.57,1374.82,0.232494,1.69279,1.38382,0.450555,35.7399,31.9227,8.46863
-ClipRelu1,32.7163,162.53,121.022,41.5078,4967.92,3699.21,1268.71,0.509468,2.88278,2.1792,0.771439,50.356,41.5967,12.18
-ClipRelu1_f2h,146.28,768.006,611.086,156.92,5250.53,4177.78,1072.75,3.35138,15.912,12.4068,3.55674,20.529,19.3936,3.22652
-ClipRelu1_h2f,36.4137,177.541,128.57,48.9711,4875.78,3530.89,1344.88,0.247154,3.76564,2.8014,1.02577,101.77,75.6996,27.7919
-ClipRelu2,33.9773,143.994,89.1194,54.8744,4237.92,2622.89,1615.02,0.358201,1.94994,1.31654,0.703475,32.9941,25.2563,10.9937
-ClipRelu2_f2h,45.9326,186.794,119.276,67.5184,4066.86,2596.9,1469.96,0.535982,2.01803,1.37983,0.80847,31.9019,27.4409,8.03545
-ClipRelu2_h2f,36.3771,153.809,93.3831,60.4256,4228.16,2567.08,1661.09,0.272555,1.57442,1.09211,0.576984,27.6439,21.9052,9.42432
-ClipRelu3,32.7232,158.115,116.963,41.1516,4831.86,3574.3,1257.56,0.305072,2.35216,1.8683,0.53915,53.4613,44.534,10.9329
-ClipRelu3_f2h,164.164,848.865,673.112,175.753,5171.2,4100.61,1070.59,3.58947,15.7511,11.9108,3.87994,20.1933,19.4671,3.41194
-ClipRelu3_h2f,36.4919,173.755,124.9,48.8553,4761.5,3422.69,1338.8,0.203696,1.48147,1.21164,0.365454,33.735,29.09,7.55146
-ClipRelu4,32.7295,118.22,75.8625,42.3577,3612.06,2317.88,1294.18,0.276555,1.37079,0.949542,0.473891,31.4617,23.0321,10.4641
-ClipRelu4_f2h,163.003,595.319,411.376,183.943,3652.48,2524,1128.48,3.34937,10.2919,6.68183,3.72234,18.2987,17.2686,3.28064
-ClipRelu4_h2f,36.6193,132.943,83.2401,49.7026,3630.37,2273.09,1357.27,0.290084,1.45682,1.02302,0.49639,23.3497,18.4425,7.63415
-Conv1,2915.2,13346.4,10579.6,2766.84,4578.36,3629.24,949.121,19.1516,21.5507,9.90059,14.4656,23.7835,21.855,2.90162
-Conv1_f2h,135.389,213.981,45.5551,168.426,1580.59,336.366,1244.22,2.76286,4.12087,1.96844,2.56877,13.6602,9.16935,15.2463
-Conv1_h2f,36.5344,219.647,187.103,32.5449,6012.68,5121.79,890.894,0.35844,3.81895,3.22822,0.598408,117.302,99.2913,18.2
-Conv2,1441.62,6486.13,4951.37,1534.75,4499.3,3434.69,1064.61,7.26356,12.662,11.4595,6.58994,20.9029,19.7516,2.06059
-Conv2_f2h,46.0741,220.445,153.984,66.4611,4784.9,3342.35,1442.55,0.634155,4.11911,2.88656,1.31165,81.2524,58.8475,24.3049
-Conv2_h2f,36.4601,170.807,136.465,34.3419,4684.78,3742.87,941.904,0.250998,1.42323,1.12292,0.309893,24.1214,18.7186,5.77896
-Conv3,2555.58,13947.5,11383.1,2564.39,5457.74,4454.29,1003.45,9.97427,18.0324,15.5434,10.2457,19.44,19.0993,1.46803
-Conv3_f2h,49.4496,206.48,121.717,84.7629,4175.81,2461.68,1714.14,0.747212,2.66778,1.58538,1.30637,32.2439,28.9397,7.96794
-Conv3_h2f,36.6494,221.625,188.876,32.7499,6047.14,5153.55,893.593,0.293397,2.27039,1.92317,0.360122,33.9749,28.4655,6.08276
-Conv4,1291.92,4870.81,3421.82,1448.98,3770.3,2648.72,1121.58,7.14123,18.8651,16.1964,7.55982,20.3334,19.1008,2.49458
-Conv4_f2h,51.784,240.28,166.251,74.0293,4640.36,3210.75,1429.61,0.781657,3.06736,2.07678,1.09645,37.4368,31.5405,8.47733
-Conv4_h2f,36.7521,150.132,114.253,35.879,4084.99,3108.74,976.248,0.253787,1.30348,1.00169,0.314015,22.3553,16.931,5.92274
diff --git a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_fp32.csv b/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_fp32.csv
deleted file mode 100644
index e790a0bb8ea6443dcfca9f588a5f502aa4aff1f0..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_fp32.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-Add1,36.9674,189.809,150.644,39.1653,5134.5,4075.05,1059.46,0.417224,2.39475,1.88794,0.518749,28.8232,22.4334,7.04925
-ClipRelu1,35.8466,224.605,189.527,35.0779,6266.3,5287.65,978.65,0.364512,4.0914,3.39016,0.708936,123.31,102.442,21.0743
-ClipRelu2,35.6596,186.419,140.926,45.4927,5227.72,3951.98,1275.75,0.363001,2.20865,1.6516,0.571887,31.3186,23.0189,9.04323
-ClipRelu3,35.7018,224.339,189.337,35.0012,6283.64,5303.27,980.364,0.36817,2.76771,2.30071,0.481815,34.9818,28.6265,7.17676
-ClipRelu4,35.7814,168.038,128.569,39.4683,4696.23,3593.19,1103.04,0.344254,1.9021,1.47145,0.444171,29.0004,22.4544,7.22865
-Conv1,3221.85,16081.5,13019.4,3062.09,4991.49,4041.07,950.42,16.1668,28.1619,19.2301,13.2614,19.7491,18.2005,2.65225
-Conv2,1531.77,7672.48,6141.04,1531.44,5008.96,4009.17,999.786,6.88078,12.1701,8.68228,6.44409,18.5451,17.5047,2.29932
-Conv3,2958.33,17316.3,14500,2816.36,5853.44,4901.43,952.008,7.26292,17.3729,14.9615,7.27995,12.8672,12.488,1.42891
-Conv4,1236.26,5658.48,4357.46,1301.02,4577.33,3524.93,1052.4,10.0982,13.1562,7.31823,8.97215,28.8055,27.1761,2.57428
diff --git a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_layers.txt b/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_layers.txt
deleted file mode 100644
index 10199705ce8b6061351a9018c085549a2d330230..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_layers.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-Conv1,2000,1,240,300,1,1,9,9,1,1
-Conv2,2000,1,240,300,1,1,5,5,1,1
-Conv3,2000,1,240,300,1,1,9,9,1,1
-Conv4,2000,1,240,300,1,1,3,3,1,1
diff --git a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_ops.txt b/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_ops.txt
deleted file mode 100644
index 0807a77c985bd73c6c538a1259b2ffc44eeda73a..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_ops.txt
+++ /dev/null
@@ -1,13 +0,0 @@
-#Conv1,2
-Conv1
-ClipRelu1
-#Conv2,3
-Conv2
-Add1
-ClipRelu2
-#Conv3,2
-Conv3
-ClipRelu3
-#Conv4,2
-Conv4
-ClipRelu4
diff --git a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_promise_confs1.txt b/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_promise_confs1.txt
deleted file mode 100644
index 39ea99038c7ed190c73ff101597f7b46ca3ecf46..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_promise_confs1.txt
+++ /dev/null
@@ -1,16 +0,0 @@
-9 9,9 9 9,9 9,9 9
-8 8,8 8 8,8 8,9 9
-9 9,8 8 8,9 9,8 8
-9 9,8 8 8,8 8,8 8
-8 8,9 9 9,9 9,9 9
-8 8,8 8 8,9 9,9 9
-8 8,9 9 9,8 8,9 9
-9 9,8 8 8,9 9,9 9
-9 9,8 8 8,8 8,9 9
-9 9,8 8 8,8 8,8 8
-8 8,8 8 8,8 8,8 8
-8 8,9 9 9,9 9,9 9
-8 8,9 9 9,8 8,8 8
-9 9,9 9 9,8 8,9 9
-9 9,9 9 9,9 9,9 9
-9 9,9 9 9,9 9,8 8
diff --git a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_promise_confs2.txt b/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_promise_confs2.txt
deleted file mode 100644
index 4a23019eb2d256d728fcd7c6d66a76d5a25c6206..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_promise_confs2.txt
+++ /dev/null
@@ -1,34 +0,0 @@
-9 9,9 9 9,9 9,9 9
-5,9 9 9,9 9,8 8
-5,8 8 8,9 9,9 9
-5,9 9 9,9 9,9 9
-5,8 8 8,9 9,8 8
-7,9 9 9,7,9 9
-7,8 8 8,7,8 8
-7,9 9 9,7,8 8
-7,8 8 8,7,9 9
-7,9 9 9,9 9,9 9
-7,8 8 8,9 9,8 8
-7,8 8 8,9 9,9 9
-9 9,9 9 9,9 9,9 9
-6,9 9 9,9 9,9 9
-7,9 9 9,8 8,9 9
-7,9 9 9,9 9,8 8
-8 8,8 8 8,9 9,8 8
-8 8,8 8 8,7,9 9
-7,8 8 8,8 8,9 9
-6,8 8 8,8 8,9 9
-8 8,8 8 8,9 9,9 9
-8 8,8 8 8,8 8,9 9
-8 8,9 9 9,8 8,9 9
-6,9 9 9,8 8,8 8
-8 8,9 9 9,9 9,9 9
-9 9,9 9 9,7,8 8
-7,8 8 8,8 8,8 8
-8 8,9 9 9,7,9 9
-6,8 8 8,9 9,9 9
-6,8 8 8,8 8,8 8
-8 8,9 9 9,9 9,8 8
-8 8,8 8 8,7,8 8
-9 9,8 8 8,9 9,8 8
-9 9,9 9 9,8 8,8 8
diff --git a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_promise_results1.csv b/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_promise_results1.csv
deleted file mode 100644
index 40a0db161373ec37f4f22800a62a9e942b25d337..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_promise_results1.csv
+++ /dev/null
@@ -1,220 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,16306.105,8048.708,17540.639,5826.518,47721.97,0.999999997904529
-c1,13508.93,6763.525,14105.615,5826.518,40204.588,1.186978209584
-c2,16306.105,6763.525,17540.639,4989.03,45599.299,1.04655051594861
-c3,16306.105,6763.525,14105.615,4989.03,42164.275,1.13181051700329
-c4,13508.93,8048.708,17540.639,5826.518,44924.795,1.06226349822573
-c5,13508.93,6763.525,17540.639,5826.518,43639.612,1.09354707119406
-c6,13508.93,8048.708,14105.615,5826.518,41489.771,1.15021049127938
-c7,16306.105,6763.525,17540.639,5826.518,46436.787,1.02767596511861
-c8,16306.105,6763.525,14105.615,5826.518,43001.763,1.10976775275524
-c9,16306.105,6763.525,14105.615,4989.03,42164.275,1.13181051700329
-c10,13508.93,6763.525,14105.615,4989.03,39367.1,1.21222975222399
-c11,13508.93,8048.708,17540.639,5826.518,44924.795,1.06226349822573
-c12,13508.93,8048.708,14105.615,4989.03,40652.283,1.17390626948576
-c13,16306.105,8048.708,14105.615,5826.518,44286.946,1.07756289838192
-c14,16306.105,8048.708,17540.639,5826.518,47721.97,0.999999997904529
-c15,16306.105,8048.708,17540.639,4989.03,46884.482,1.01786279516139
-c10,39367.1
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,3257.6966,1604.397,2994.0318,1272.0414,9128.1668,0.999999989044898
-c1,2947.9163,1508.2307,2588.3032,1272.0414,8316.4916,1.0975982576884
-c2,3257.6966,1508.2307,2994.0318,1324.6495,9084.6086,1.00479471394294
-c3,3257.6966,1508.2307,2588.3032,1324.6495,8678.88,1.05176781967526
-c4,2947.9163,1604.397,2994.0318,1272.0414,8818.3865,1.0351288976149
-c5,2947.9163,1508.2307,2994.0318,1272.0414,8722.2202,1.04654164719962
-c6,2947.9163,1604.397,2588.3032,1272.0414,8412.6579,1.08505145460567
-c7,3257.6966,1508.2307,2994.0318,1272.0414,9032.0005,1.01064727564345
-c8,3257.6966,1508.2307,2588.3032,1272.0414,8626.2719,1.05818212085128
-c9,3257.6966,1508.2307,2588.3032,1324.6495,8678.88,1.05176781967526
-c10,2947.9163,1508.2307,2588.3032,1324.6495,8369.0997,1.09069876308561
-c11,2947.9163,1604.397,2994.0318,1272.0414,8818.3865,1.0351288976149
-c12,2947.9163,1604.397,2588.3032,1324.6495,8465.266,1.07830831212736
-c13,3257.6966,1604.397,2588.3032,1272.0414,8722.4382,1.04651549097229
-c14,3257.6966,1604.397,2994.0318,1272.0414,9128.1668,0.999999989044898
-c15,3257.6966,1604.397,2994.0318,1324.6495,9180.7749,0.994269743022784
-c1,8316.4916
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,16306.105,8048.708,17540.639,5826.518,47721.97,0.999999997904529
-c1,13722.911,6763.525,14105.615,5976.65,40568.701,1.17632481952941
-c2,16306.105,6983.97,17762.264,5229.31,46281.649,1.03112077741413
-c3,16306.105,6983.97,14105.615,4989.03,42384.72,1.1259239152083
-c4,13722.911,8219.515,17540.639,5826.518,45309.583,1.05324231067577
-c5,13722.911,6763.525,17762.264,5826.518,44075.218,1.08273928200936
-c6,13722.911,8219.515,14312.095,5976.65,42231.171,1.1300176802343
-c7,16306.105,6983.97,17762.264,5826.518,46878.857,1.01798492864708
-c8,16306.105,6983.97,14105.615,5976.65,43372.34,1.10028580173381
-c9,16306.105,6983.97,14105.615,4989.03,42384.72,1.1259239152083
-c10,13722.911,6763.525,14105.615,4989.03,39581.081,1.20567626436055
-c11,13722.911,8219.515,17540.639,5826.518,45309.583,1.05324231067577
-c12,13722.911,8219.515,14312.095,4989.03,41243.551,1.15707713635745
-c13,16306.105,8048.708,14312.095,5976.65,44643.558,1.06895534386181
-c14,16306.105,8048.708,17540.639,5826.518,47721.97,0.999999997904529
-c15,16306.105,8048.708,17540.639,5229.31,47124.762,1.0126729106607
-c10,39581.081
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,0,0,0
-c2,0,0,0,0,0,0
-c3,0,0,0,0,0,0
-c4,0,0,0,0,0,0
-c5,0,0,0,0,0,0
-c6,0,0,0,0,0,0
-c7,0,0,0,0,0,0
-c8,0,0,0,0,0,0
-c9,0,0,0,0,0,0
-c10,0,0,0,0,0,0
-c11,0,0,0,0,0,0
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-c13,0,0,0,0,0,0
-c14,0,0,0,0,0,0
-c15,0,0,0,0,0,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,0,0,0
-c2,0,0,0,0,0,0
-c3,0,0,0,0,0,0
-c4,0,0,0,0,0,0
-c5,0,0,0,0,0,0
-c6,0,0,0,0,0,0
-c7,0,0,0,0,0,0
-c8,0,0,0,0,0,0
-c9,0,0,0,0,0,0
-c10,0,0,0,0,0,0
-c11,0,0,0,0,0,0
-c12,0,0,0,0,0,0
-c13,0,0,0,0,0,0
-c14,0,0,0,0,0,0
-c15,0,0,0,0,0,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,0,0,0
-c2,0,0,0,0,0,0
-c3,0,0,0,0,0,0
-c4,0,0,0,0,0,0
-c5,0,0,0,0,0,0
-c6,0,0,0,0,0,0
-c7,0,0,0,0,0,0
-c8,0,0,0,0,0,0
-c9,0,0,0,0,0,0
-c10,0,0,0,0,0,0
-c11,0,0,0,0,0,0
-c12,0,0,0,0,0,0
-c13,0,0,0,0,0,0
-c14,0,0,0,0,0,0
-c15,0,0,0,0,0,0
-c0,0
-
-Patch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,0,0,0
-c2,0,0,0,0,0,0
-c3,0,0,0,0,0,0
-c4,0,0,0,0,0,0
-c5,0,0,0,0,0,0
-c6,0,0,0,0,0,0
-c7,0,0,0,0,0,0
-c8,0,0,0,0,0,0
-c9,0,0,0,0,0,0
-c10,0,0,0,0,0,0
-c11,0,0,0,0,0,0
-c12,0,0,0,0,0,0
-c13,0,0,0,0,0,0
-c14,0,0,0,0,0,0
-c15,0,0,0,0,0,0
-c0,0
-
-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,213.981,0,0,150.132,364.113,0
-c2,0,220.445,221.625,240.28,682.35,0
-c3,0,220.445,0,0,220.445,0
-c4,213.981,170.807,0,0,384.788,0
-c5,213.981,0,221.625,0,435.606,0
-c6,213.981,170.807,206.48,150.132,741.4,0
-c7,0,220.445,221.625,0,442.07,0
-c8,0,220.445,0,150.132,370.577,0
-c9,0,220.445,0,0,220.445,0
-c10,213.981,0,0,0,213.981,0
-c11,213.981,170.807,0,0,384.788,0
-c12,213.981,170.807,206.48,0,591.268,0
-c13,0,0,206.48,150.132,356.612,0
-c14,0,0,0,0,0,0
-c15,0,0,0,240.28,240.28,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,135.389,0,0,36.7521,172.1411,0
-c2,0,46.0741,36.6494,51.784,134.5075,0
-c3,0,46.0741,0,0,46.0741,0
-c4,135.389,36.4601,0,0,171.8491,0
-c5,135.389,0,36.6494,0,172.0384,0
-c6,135.389,36.4601,49.4496,36.7521,258.0508,0
-c7,0,46.0741,36.6494,0,82.7235,0
-c8,0,46.0741,0,36.7521,82.8262,0
-c9,0,46.0741,0,0,46.0741,0
-c10,135.389,0,0,0,135.389,0
-c11,135.389,36.4601,0,0,171.8491,0
-c12,135.389,36.4601,49.4496,0,221.2987,0
-c13,0,0,49.4496,36.7521,86.2017,0
-c14,0,0,0,0,0,0
-c15,0,0,0,51.784,51.784,0
-c0,0
-
-Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,3257.6966,1604.397,2994.0318,1272.0414,9128.1668,0.999999989044898
-c1,3083.3053,1508.2307,2588.3032,1308.7935,8488.6327,1.07534004769296
-c2,3257.6966,1554.3048,3030.6812,1376.4335,9219.1161,0.990134694256267
-c3,3257.6966,1554.3048,2588.3032,1324.6495,8724.9541,1.04621372109896
-c4,3083.3053,1640.8571,2994.0318,1272.0414,8990.2356,1.01534232300495
-c5,3083.3053,1508.2307,3030.6812,1272.0414,8894.2586,1.02629877406197
-c6,3083.3053,1640.8571,2637.7528,1308.7935,8670.7087,1.05275900858301
-c7,3257.6966,1554.3048,3030.6812,1272.0414,9114.724,1.00147483345108
-c8,3257.6966,1554.3048,2588.3032,1308.7935,8709.0981,1.04811848372544
-c9,3257.6966,1554.3048,2588.3032,1324.6495,8724.9541,1.04621372109896
-c10,3083.3053,1508.2307,2588.3032,1324.6495,8504.4887,1.07333515448924
-c11,3083.3053,1640.8571,2994.0318,1272.0414,8990.2356,1.01534232300495
-c12,3083.3053,1640.8571,2637.7528,1324.6495,8686.5647,1.05083735747876
-c13,3257.6966,1604.397,2637.7528,1308.7935,8808.6399,1.03627424891924
-c14,3257.6966,1604.397,2994.0318,1272.0414,9128.1668,0.999999989044898
-c15,3257.6966,1604.397,2994.0318,1376.4335,9232.5589,0.988693037325838
-c1,8488.6327
-
-Unpatch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,0,0,0
-c2,0,0,0,0,0,0
-c3,0,0,0,0,0,0
-c4,0,0,0,0,0,0
-c5,0,0,0,0,0,0
-c6,0,0,0,0,0,0
-c7,0,0,0,0,0,0
-c8,0,0,0,0,0,0
-c9,0,0,0,0,0,0
-c10,0,0,0,0,0,0
-c11,0,0,0,0,0,0
-c12,0,0,0,0,0,0
-c13,0,0,0,0,0,0
-c14,0,0,0,0,0,0
-c15,0,0,0,0,0,0
-c0,0
-
diff --git a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_promise_results2.csv b/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_promise_results2.csv
deleted file mode 100644
index 3762ea7ee951b3a472bd4148816e772b22b8dd67..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_promise_results2.csv
+++ /dev/null
@@ -1,418 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,16306.105,8048.708,17540.639,5826.518,47721.97,0.999999997904529
-c1,7.585986,8048.708,17540.639,4989.03,30585.962986,1.56025722864498
-c2,7.585986,6763.525,17540.639,5826.518,30138.267986,1.58343438527472
-c3,7.585986,8048.708,17540.639,5826.518,31423.450986,1.51867374049381
-c4,7.585986,6763.525,17540.639,4989.03,29300.779986,1.62869281500125
-c5,15.343114,8048.708,15.343114,5826.518,13905.912228,3.4317755552011
-c6,15.343114,6763.525,15.343114,4989.03,11783.241228,4.04998664387874
-c7,15.343114,8048.708,15.343114,4989.03,13068.424228,3.65170037352953
-c8,15.343114,6763.525,15.343114,5826.518,12620.729228,3.78123710284519
-c9,15.343114,8048.708,17540.639,5826.518,31431.208114,1.51829893636554
-c10,15.343114,6763.525,17540.639,4989.03,29308.537114,1.62826174679248
-c11,15.343114,6763.525,17540.639,5826.518,30146.025114,1.58302693841832
-c12,16306.105,8048.708,17540.639,5826.518,47721.97,0.999999997904529
-c13,11.525024,8048.708,17540.639,5826.518,31427.390024,1.51848339336191
-c14,15.343114,8048.708,14105.615,5826.518,27996.184114,1.70458836944414
-c15,15.343114,8048.708,17540.639,4989.03,30593.720114,1.5598616208225
-c16,13508.93,6763.525,17540.639,4989.03,42802.124,1.11494396606359
-c17,13508.93,6763.525,15.343114,5826.518,26114.316114,1.82742560092062
-c18,15.343114,6763.525,14105.615,5826.518,26711.001114,1.78660356523767
-c19,11.525024,6763.525,14105.615,5826.518,26707.183024,1.78685898016386
-c20,13508.93,6763.525,17540.639,5826.518,43639.612,1.09354707119406
-c21,13508.93,6763.525,14105.615,5826.518,40204.588,1.186978209584
-c22,13508.93,8048.708,14105.615,5826.518,41489.771,1.15021049127938
-c23,11.525024,8048.708,14105.615,4989.03,27154.878024,1.75739952807309
-c24,13508.93,8048.708,17540.639,5826.518,44924.795,1.06226349822573
-c25,16306.105,8048.708,15.343114,4989.03,29359.186114,1.62545275104538
-c26,15.343114,6763.525,14105.615,4989.03,25873.513114,1.84443332473991
-c27,13508.93,8048.708,15.343114,5826.518,27399.499114,1.74170957021054
-c28,11.525024,6763.525,17540.639,5826.518,30142.207024,1.58322745921292
-c29,11.525024,6763.525,14105.615,4989.03,25869.695024,1.84470554334933
-c30,13508.93,8048.708,17540.639,4989.03,44087.307,1.08244238850324
-c31,13508.93,6763.525,15.343114,4989.03,25276.828114,1.88797303189996
-c32,16306.105,6763.525,17540.639,4989.03,45599.299,1.04655051594861
-c33,16306.105,8048.708,14105.615,4989.03,43449.458,1.09833291568716
-c6,11783.241228
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,3257.6966,1604.397,2994.0318,1272.0414,9128.1668,0.999999989044898
-c1,7.919790,1604.397,2994.0318,1324.6495,5930.99809,1.53906079677964
-c2,7.919790,1508.2307,2994.0318,1272.0414,5782.22369,1.57866024068224
-c3,7.919790,1604.397,2994.0318,1272.0414,5878.38999,1.55283447682867
-c4,7.919790,1508.2307,2994.0318,1324.6495,5834.83179,1.56442670021809
-c5,7.919790,1604.397,7.919790,1272.0414,2892.27798,3.15604742957496
-c6,7.919790,1508.2307,7.919790,1324.6495,2848.71978,3.20430480514637
-c7,7.919790,1604.397,7.919790,1324.6495,2944.88608,3.09966709816948
-c8,7.919790,1508.2307,7.919790,1272.0414,2796.11168,3.2645929484264
-c9,7.919790,1604.397,2994.0318,1272.0414,5878.38999,1.55283447682867
-c10,7.919790,1508.2307,2994.0318,1324.6495,5834.83179,1.56442670021809
-c11,7.919790,1508.2307,2994.0318,1272.0414,5782.22369,1.57866024068224
-c12,3257.6966,1604.397,2994.0318,1272.0414,9128.1668,0.999999989044898
-c13,7.919790,1604.397,2994.0318,1272.0414,5878.38999,1.55283447682867
-c14,7.919790,1604.397,2588.3032,1272.0414,5472.66139,1.66795750416494
-c15,7.919790,1604.397,2994.0318,1324.6495,5930.99809,1.53906079677964
-c16,2947.9163,1508.2307,2994.0318,1324.6495,8774.8283,1.04026727177935
-c17,2947.9163,1508.2307,7.919790,1272.0414,5736.10819,1.59135189548522
-c18,7.919790,1508.2307,2588.3032,1272.0414,5376.49509,1.69779130779805
-c19,7.919790,1508.2307,2588.3032,1272.0414,5376.49509,1.69779130779805
-c20,2947.9163,1508.2307,2994.0318,1272.0414,8722.2202,1.04654164719962
-c21,2947.9163,1508.2307,2588.3032,1272.0414,8316.4916,1.0975982576884
-c22,2947.9163,1604.397,2588.3032,1272.0414,8412.6579,1.08505145460567
-c23,7.919790,1604.397,2588.3032,1324.6495,5525.26949,1.65207627452618
-c24,2947.9163,1604.397,2994.0318,1272.0414,8818.3865,1.0351288976149
-c25,3257.6966,1604.397,7.919790,1324.6495,6194.66289,1.47355341440455
-c26,7.919790,1508.2307,2588.3032,1324.6495,5429.10319,1.68133968215587
-c27,2947.9163,1604.397,7.919790,1272.0414,5832.27449,1.56511266044488
-c28,7.919790,1508.2307,2994.0318,1272.0414,5782.22369,1.57866024068224
-c29,7.919790,1508.2307,2588.3032,1324.6495,5429.10319,1.68133968215587
-c30,2947.9163,1604.397,2994.0318,1324.6495,8870.9946,1.0289902213559
-c31,2947.9163,1508.2307,7.919790,1324.6495,5788.71629,1.57688962198405
-c32,3257.6966,1508.2307,2994.0318,1324.6495,9084.6086,1.00479471394294
-c33,3257.6966,1604.397,2588.3032,1324.6495,8775.0463,1.04024142823906
-c8,2796.11168
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,16306.105,8048.708,17540.639,5826.518,47721.97,0.999999997904529
-c1,82.701434,8048.708,17540.639,5229.31,30901.358434,1.54433242627481
-c2,82.701434,6763.525,17762.264,5826.518,30435.008434,1.5679959460727
-c3,82.701434,8048.708,17540.639,5826.518,31498.566434,1.51505211986356
-c4,82.701434,6763.525,17762.264,5229.31,29837.800434,1.59937961732873
-c5,90.458562,8048.708,90.458562,5826.518,14056.143124,3.39509702195675
-c6,90.458562,6763.525,90.458562,4989.03,11933.472124,3.99900122145707
-c7,90.458562,8048.708,90.458562,4989.03,13218.655124,3.61019855585273
-c8,90.458562,6763.525,90.458562,5826.518,12770.960124,3.73675660740982
-c9,90.458562,8048.708,17540.639,5826.518,31506.323562,1.5146791009945
-c10,90.458562,6763.525,17762.264,5229.31,29845.557562,1.59896392422785
-c11,90.458562,6763.525,17762.264,5826.518,30442.765562,1.56759640467123
-c12,16306.105,8048.708,17540.639,5826.518,47721.97,0.999999997904529
-c13,86.640472,8048.708,17540.639,5826.518,31502.505472,1.51486267944402
-c14,90.458562,8048.708,14312.095,5976.65,28427.911562,1.67870122038513
-c15,90.458562,8048.708,17540.639,5229.31,30909.115562,1.54394485179885
-c16,13722.911,6763.525,17762.264,5229.31,43478.01,1.09761164069466
-c17,13722.911,6763.525,90.458562,5826.518,26403.412562,1.80741673854463
-c18,90.458562,6763.525,14105.615,5976.65,26936.248562,1.77166355266549
-c19,86.640472,6763.525,14105.615,5976.65,26932.430472,1.77191471346866
-c20,13722.911,6763.525,17762.264,5826.518,44075.218,1.08273928200936
-c21,13722.911,6763.525,14105.615,5976.65,40568.701,1.17632481952941
-c22,13722.911,8219.515,14312.095,5976.65,42231.171,1.1300176802343
-c23,86.640472,8048.708,14312.095,4989.03,27436.473472,1.73936238105695
-c24,13722.911,8219.515,17540.639,5826.518,45309.583,1.05324231067577
-c25,16306.105,8048.708,90.458562,4989.03,29434.301562,1.6213046447645
-c26,90.458562,6763.525,14105.615,4989.03,25948.628562,1.83909410480276
-c27,13722.911,8219.515,90.458562,5826.518,27859.402562,1.71295740181437
-c28,86.640472,6763.525,17762.264,5826.518,30438.947472,1.56779303512774
-c29,86.640472,6763.525,14105.615,4989.03,25944.810472,1.83936474955428
-c30,13722.911,8219.515,17540.639,5229.31,44712.375,1.06731011030546
-c31,13722.911,6763.525,90.458562,4989.03,25565.924562,1.86662405647044
-c32,16306.105,6983.97,17762.264,5229.31,46281.649,1.03112077741413
-c33,16306.105,8048.708,14312.095,4989.03,43655.938,1.09313811767568
-c6,11933.472124
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,28.951489,0,0,0,28.951489,0
-c2,28.951489,0,0,0,28.951489,0
-c3,28.951489,0,0,0,28.951489,0
-c4,28.951489,0,0,0,28.951489,0
-c5,28.951489,0,28.951489,0,57.902978,0
-c6,28.951489,0,28.951489,0,57.902978,0
-c7,28.951489,0,28.951489,0,57.902978,0
-c8,28.951489,0,28.951489,0,57.902978,0
-c9,28.951489,0,0,0,28.951489,0
-c10,28.951489,0,0,0,28.951489,0
-c11,28.951489,0,0,0,28.951489,0
-c12,0,0,0,0,0,0
-c13,28.951489,0,0,0,28.951489,0
-c14,28.951489,0,0,0,28.951489,0
-c15,28.951489,0,0,0,28.951489,0
-c16,0,0,0,0,0,0
-c17,0,0,28.951489,0,28.951489,0
-c18,28.951489,0,0,0,28.951489,0
-c19,28.951489,0,0,0,28.951489,0
-c20,0,0,0,0,0,0
-c21,0,0,0,0,0,0
-c22,0,0,0,0,0,0
-c23,28.951489,0,0,0,28.951489,0
-c24,0,0,0,0,0,0
-c25,0,0,28.951489,0,28.951489,0
-c26,28.951489,0,0,0,28.951489,0
-c27,0,0,28.951489,0,28.951489,0
-c28,28.951489,0,0,0,28.951489,0
-c29,28.951489,0,0,0,28.951489,0
-c30,0,0,0,0,0,0
-c31,0,0,28.951489,0,28.951489,0
-c32,0,0,0,0,0,0
-c33,0,0,0,0,0,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,46.163959,0,0,0,46.163959,0
-c2,46.163959,0,0,0,46.163959,0
-c3,46.163959,0,0,0,46.163959,0
-c4,46.163959,0,0,0,46.163959,0
-c5,46.163959,0,46.163959,0,92.327918,0
-c6,46.163959,0,46.163959,0,92.327918,0
-c7,46.163959,0,46.163959,0,92.327918,0
-c8,46.163959,0,46.163959,0,92.327918,0
-c9,46.163959,0,0,0,46.163959,0
-c10,46.163959,0,0,0,46.163959,0
-c11,46.163959,0,0,0,46.163959,0
-c12,0,0,0,0,0,0
-c13,46.163959,0,0,0,46.163959,0
-c14,46.163959,0,0,0,46.163959,0
-c15,46.163959,0,0,0,46.163959,0
-c16,0,0,0,0,0,0
-c17,0,0,46.163959,0,46.163959,0
-c18,46.163959,0,0,0,46.163959,0
-c19,46.163959,0,0,0,46.163959,0
-c20,0,0,0,0,0,0
-c21,0,0,0,0,0,0
-c22,0,0,0,0,0,0
-c23,46.163959,0,0,0,46.163959,0
-c24,0,0,0,0,0,0
-c25,0,0,46.163959,0,46.163959,0
-c26,46.163959,0,0,0,46.163959,0
-c27,0,0,46.163959,0,46.163959,0
-c28,46.163959,0,0,0,46.163959,0
-c29,46.163959,0,0,0,46.163959,0
-c30,0,0,0,0,0,0
-c31,0,0,46.163959,0,46.163959,0
-c32,0,0,0,0,0,0
-c33,0,0,0,0,0,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,10.930367,0,0,0,10.930367,0
-c2,10.930367,0,0,0,10.930367,0
-c3,10.930367,0,0,0,10.930367,0
-c4,10.930367,0,0,0,10.930367,0
-c5,10.930367,0,10.930367,0,21.860734,0
-c6,10.930367,0,10.930367,0,21.860734,0
-c7,10.930367,0,10.930367,0,21.860734,0
-c8,10.930367,0,10.930367,0,21.860734,0
-c9,10.930367,0,0,0,10.930367,0
-c10,10.930367,0,0,0,10.930367,0
-c11,10.930367,0,0,0,10.930367,0
-c12,0,0,0,0,0,0
-c13,10.930367,0,0,0,10.930367,0
-c14,10.930367,0,0,0,10.930367,0
-c15,10.930367,0,0,0,10.930367,0
-c16,0,0,0,0,0,0
-c17,0,0,10.930367,0,10.930367,0
-c18,10.930367,0,0,0,10.930367,0
-c19,10.930367,0,0,0,10.930367,0
-c20,0,0,0,0,0,0
-c21,0,0,0,0,0,0
-c22,0,0,0,0,0,0
-c23,10.930367,0,0,0,10.930367,0
-c24,0,0,0,0,0,0
-c25,0,0,10.930367,0,10.930367,0
-c26,10.930367,0,0,0,10.930367,0
-c27,0,0,10.930367,0,10.930367,0
-c28,10.930367,0,0,0,10.930367,0
-c29,10.930367,0,0,0,10.930367,0
-c30,0,0,0,0,0,0
-c31,0,0,10.930367,0,10.930367,0
-c32,0,0,0,0,0,0
-c33,0,0,0,0,0,0
-c0,0
-
-Patch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,0,0,0
-c2,0,0,0,0,0,0
-c3,0,0,0,0,0,0
-c4,0,0,0,0,0,0
-c5,0,0,0,0,0,0
-c6,0,0,0,0,0,0
-c7,0,0,0,0,0,0
-c8,0,0,0,0,0,0
-c9,0,0,0,0,0,0
-c10,0,0,0,0,0,0
-c11,0,0,0,0,0,0
-c12,0,0,0,0,0,0
-c13,0,0,0,0,0,0
-c14,0,0,0,0,0,0
-c15,0,0,0,0,0,0
-c16,0,0,0,0,0,0
-c17,0,0,0,0,0,0
-c18,0,0,0,0,0,0
-c19,0,0,0,0,0,0
-c20,0,0,0,0,0,0
-c21,0,0,0,0,0,0
-c22,0,0,0,0,0,0
-c23,0,0,0,0,0,0
-c24,0,0,0,0,0,0
-c25,0,0,0,0,0,0
-c26,0,0,0,0,0,0
-c27,0,0,0,0,0,0
-c28,0,0,0,0,0,0
-c29,0,0,0,0,0,0
-c30,0,0,0,0,0,0
-c31,0,0,0,0,0,0
-c32,0,0,0,0,0,0
-c33,0,0,0,0,0,0
-c0,0
-
-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,240.28,240.28,0
-c2,0,0,221.625,0,221.625,0
-c3,0,0,0,0,0,0
-c4,0,0,221.625,240.28,461.905,0
-c5,0,0,0,0,0,0
-c6,0,0,0,0,0,0
-c7,0,0,0,0,0,0
-c8,0,0,0,0,0,0
-c9,0,0,0,0,0,0
-c10,0,0,221.625,240.28,461.905,0
-c11,0,0,221.625,0,221.625,0
-c12,0,0,0,0,0,0
-c13,0,0,0,0,0,0
-c14,0,0,206.48,150.132,356.612,0
-c15,0,0,0,240.28,240.28,0
-c16,213.981,0,221.625,240.28,675.886,0
-c17,213.981,0,0,0,213.981,0
-c18,0,0,0,150.132,150.132,0
-c19,0,0,0,150.132,150.132,0
-c20,213.981,0,221.625,0,435.606,0
-c21,213.981,0,0,150.132,364.113,0
-c22,213.981,170.807,206.48,150.132,741.4,0
-c23,0,0,206.48,0,206.48,0
-c24,213.981,170.807,0,0,384.788,0
-c25,0,0,0,0,0,0
-c26,0,0,0,0,0,0
-c27,213.981,170.807,0,0,384.788,0
-c28,0,0,221.625,0,221.625,0
-c29,0,0,0,0,0,0
-c30,213.981,170.807,0,240.28,625.068,0
-c31,213.981,0,0,0,213.981,0
-c32,0,220.445,221.625,240.28,682.35,0
-c33,0,0,206.48,0,206.48,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,51.784,51.784,0
-c2,0,0,36.6494,0,36.6494,0
-c3,0,0,0,0,0,0
-c4,0,0,36.6494,51.784,88.4334,0
-c5,0,0,0,0,0,0
-c6,0,0,0,0,0,0
-c7,0,0,0,0,0,0
-c8,0,0,0,0,0,0
-c9,0,0,0,0,0,0
-c10,0,0,36.6494,51.784,88.4334,0
-c11,0,0,36.6494,0,36.6494,0
-c12,0,0,0,0,0,0
-c13,0,0,0,0,0,0
-c14,0,0,49.4496,36.7521,86.2017,0
-c15,0,0,0,51.784,51.784,0
-c16,135.389,0,36.6494,51.784,223.8224,0
-c17,135.389,0,0,0,135.389,0
-c18,0,0,0,36.7521,36.7521,0
-c19,0,0,0,36.7521,36.7521,0
-c20,135.389,0,36.6494,0,172.0384,0
-c21,135.389,0,0,36.7521,172.1411,0
-c22,135.389,36.4601,49.4496,36.7521,258.0508,0
-c23,0,0,49.4496,0,49.4496,0
-c24,135.389,36.4601,0,0,171.8491,0
-c25,0,0,0,0,0,0
-c26,0,0,0,0,0,0
-c27,135.389,36.4601,0,0,171.8491,0
-c28,0,0,36.6494,0,36.6494,0
-c29,0,0,0,0,0,0
-c30,135.389,36.4601,0,51.784,223.6331,0
-c31,135.389,0,0,0,135.389,0
-c32,0,46.0741,36.6494,51.784,134.5075,0
-c33,0,0,49.4496,0,49.4496,0
-c0,0
-
-Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,3257.6966,1604.397,2994.0318,1272.0414,9128.1668,0.999999989044898
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diff --git a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_results1.csv b/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_results1.csv
deleted file mode 100644
index 1e8d11387a221361bf025c7ff0716709f5cd82b0..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_results1.csv
+++ /dev/null
@@ -1,143 +0,0 @@
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-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,16306.105,8048.708,17540.639,5826.518,47721.97,0.999999997904529
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-Compute Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,3257.6966,1604.397,2994.0318,1272.0414,9128.1668,0.999999989044898
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-Unpatch Energy
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diff --git a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_results2.csv b/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_results2.csv
deleted file mode 100644
index 870bb0106874567d837a62a781422af940cd1631..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_results2.csv
+++ /dev/null
@@ -1,154 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,16306.105,8048.708,17540.639,5826.518,47721.97,0.999999997904529
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-c9,15.343114,6763.525,14105.615,5826.518,26711.001114,1.78660356523767
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-Compute Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,3257.6966,1604.397,2994.0318,1272.0414,9128.1668,0.999999989044898
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-Energy
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-c9,90.458562,6763.525,14105.615,5976.65,26936.248562,1.77166355266549
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-Leakage Energy
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-c0,0,0,0,0,0,0
-c1,0,0,0,0,0,0
-c2,0,0,0,0,0,0
-c3,0,0,36.6494,0,36.6494,0
-c4,0,0,0,0,0,0
-c5,0,0,0,0,0,0
-c6,0,0,0,0,0,0
-c7,0,0,0,0,0,0
-c8,0,0,0,0,0,0
-c9,0,0,0,36.7521,36.7521,0
-c0,0
-
-Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,3257.6966,1604.397,2994.0318,1272.0414,9128.1668,0.999999989044898
-c1,18.850157,1508.2307,2588.3032,1324.6495,5440.033557,1.67796145677413
-c2,18.850157,1508.2307,2588.3032,1324.6495,5440.033557,1.67796145677413
-c3,18.850157,1508.2307,3030.6812,1272.0414,5829.803457,1.56577605244341
-c4,18.850157,1604.397,2994.0318,1272.0414,5889.320357,1.5499524718765
-c5,18.850157,1508.2307,2588.3032,1324.6495,5440.033557,1.67796145677413
-c6,18.850157,1508.2307,2588.3032,1324.6495,5440.033557,1.67796145677413
-c7,18.850157,1604.397,2994.0318,1272.0414,5889.320357,1.5499524718765
-c8,18.850157,1604.397,2994.0318,1272.0414,5889.320357,1.5499524718765
-c9,18.850157,1508.2307,2588.3032,1308.7935,5424.177557,1.68286648727664
-c9,5424.177557
-
-Unpatch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,0,0,0
-c2,0,0,0,0,0,0
-c3,0,0,0,0,0,0
-c4,0,0,0,0,0,0
-c5,0,0,0,0,0,0
-c6,0,0,0,0,0,0
-c7,0,0,0,0,0,0
-c8,0,0,0,0,0,0
-c9,0,0,0,0,0,0
-c0,0
-
diff --git a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_tensors.txt b/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_tensors.txt
deleted file mode 100644
index 71dce21c4710dee6c0180112e14825189281bce6..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_tensors.txt
+++ /dev/null
@@ -1,13 +0,0 @@
-#Conv1,2
-Conv1,3221.85,16081.5,2915.2,13346.4,135.389,213.981,36.5344,219.647
-ClipRelu1,35.8466,224.605,32.7163,162.53,146.28,768.006,36.4137,177.541
-#Conv2,3
-Conv2,1531.77,7672.48,1441.62,6486.13,46.0741,220.445,36.4601,170.807
-Add1,36.9674,189.809,32.6334,133.401,150.837,630.018,36.4014,148.497
-ClipRelu2,35.6596,186.419,33.9773,143.994,45.9326,186.794,36.3771,153.809
-#Conv3,2
-Conv3,2958.33,17316.3,2555.58,13947.5,49.4496,206.48,36.6494,221.625
-ClipRelu3,35.7018,224.339,32.7232,158.115,164.164,848.865,36.4919,173.755
-#Conv4,2
-Conv4,1236.26,5658.48,1291.92,4870.81,51.784,240.28,36.7521,150.132
-ClipRelu4,35.7814,168.038,32.7295,118.22,163.003,595.319,36.6193,132.943
diff --git a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_confs1.txt b/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_confs1.txt
deleted file mode 100644
index 3c5d575879b4b851938894d007277b2e65fcacc4..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_confs1.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-9 9,9 9 9,9 9
-8 8,8 8 8,8 8
-8 8,8 8 8,9 9
-9 9,8 8 8,8 8
-8 8,9 9 9,9 9
-8 8,8 8 8,8 8
-9 9,9 9 9,8 8
diff --git a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_confs2.txt b/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_confs2.txt
deleted file mode 100644
index 6ebcf19aec794b6fd5bcb2db1348a9bda7df8ee9..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_confs2.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-9 9,9 9 9,9 9
-8 8,8 8 8,9 9
-8 8,8 8 8,8 8
-9 9,9 9 9,9 9
-9 9,9 9 9,8 8
-8 8,9 9 9,8 8
-9 9,8 8 8,8 8
diff --git a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_fp16.csv b/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_fp16.csv
deleted file mode 100644
index a506b2186159108b62d813ff72c9f5e080517b35..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_fp16.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-Add1,32.6705,133.276,90.8676,42.4081,4079.4,2781.35,1298.05,0.274066,1.81083,1.3749,0.524813,44.8557,36.2428,11.5976
-Add1_f2h,152.652,636.419,466.532,169.887,4169.31,3056.39,1112.92,3.38804,12.9973,9.35441,3.72695,20.7109,19.0044,3.41117
-Add1_h2f,36.4091,148.094,97.9338,50.1598,4067.47,2689.79,1377.67,0.253791,1.95641,1.60863,0.461607,43.3346,38.2765,8.27312
-ClipRelu1,32.6587,162.45,120.933,41.5169,4974.22,3702.98,1271.23,0.330531,2.05041,1.57201,0.558148,43.869,36.143,11.1024
-ClipRelu1_f2h,146.743,769.608,612.403,157.206,5244.94,4173.62,1071.31,2.99355,13.4267,10.3296,3.13177,19.476,18.332,2.95025
-ClipRelu1_h2f,36.4146,177.696,128.645,49.0517,4879.8,3532.76,1347.03,0.279016,1.96727,1.57977,0.479455,37.5859,32.5144,8.48889
-ClipRelu2,34.0918,144.473,89.3027,55.1698,4237.69,2619.43,1618.26,0.352182,2.37489,1.66636,0.820247,49.3303,37.5143,16.197
-ClipRelu2_f2h,45.6929,185.575,118.34,67.2347,4061.46,2589.99,1471.47,0.516908,2.23797,1.56401,0.785261,33.2568,27.0842,8.72852
-ClipRelu2_h2f,36.3378,153.377,92.9806,60.3967,4220.92,2558.82,1662.1,0.172363,1.47966,1.20127,0.437181,40.4136,33.6048,10.527
-ClipRelu3,32.8262,117.875,75.0274,42.8476,3590.86,2285.58,1305.28,0.384277,1.99531,1.51246,0.60442,42.3563,36.5341,9.89428
-ClipRelu3_f2h,163.696,595.184,407.98,187.204,3636.27,2492.66,1143.61,4.28169,13.549,8.69333,4.92486,18.018,17.0336,3.84045
-ClipRelu3_h2f,36.522,132.298,82.3713,49.9263,3622.73,2255.58,1367.15,0.257881,2.4465,1.66724,0.87317,76.2326,50.7005,27.8681
-Conv1,2918.68,13346.8,10575.4,2771.38,4573.05,3623.51,949.542,19.0663,24.1442,11.5013,15.9164,22.9092,21.5007,2.65872
-Conv1_f2h,135.408,213.93,45.3082,168.622,1580.02,334.561,1245.45,3.68648,5.50091,2.08695,4.00579,14.7526,11.0198,13.018
-Conv1_h2f,36.4759,219.457,186.947,32.5105,6016.51,5125.23,891.287,0.249261,1.92932,1.63679,0.307051,34.8454,29.3924,6.0193
-Conv2,1440.02,6478.39,4945.38,1533.01,4498.87,3434.29,1064.58,5.09304,12.7265,13.2649,4.32559,18.6424,17.5201,1.96949
-Conv2_f2h,45.9656,219.948,153.574,66.3745,4785.2,3341.18,1444.02,0.569206,2.71891,1.96107,0.854423,37.0203,30.7544,8.97458
-Conv2_h2f,36.459,170.667,136.354,34.3128,4681.07,3739.93,941.134,0.267004,2.1182,1.68416,0.440793,47.758,38.0809,9.90734
-Conv3,1223.57,4572.94,3095.04,1477.9,3737.52,2529.64,1207.88,8.78168,18.7734,14.433,9.07594,21.7904,19.8837,3.12811
-Conv3_f2h,49.5704,206.506,121.53,84.976,4166.12,2451.84,1714.27,0.66723,2.51126,1.59248,1.12596,33.159,28.3058,8.69624
-Conv3_h2f,36.6344,149.378,113.062,36.3157,4077.55,3086.25,991.301,0.271434,1.29255,0.954645,0.352811,21.212,15.9903,5.95645
diff --git a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_fp32.csv b/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_fp32.csv
deleted file mode 100644
index 4bd84e896cea5e41ca36173932788c4f289d729e..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_fp32.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-Add1,36.8972,189.335,150.257,39.0772,5131.39,4072.31,1059.08,0.22475,1.5892,1.25644,0.353501,27.5054,21.4014,6.89302
-ClipRelu1,35.8282,224.935,189.773,35.1614,6278.15,5296.76,981.388,0.266349,2.10296,1.76652,0.359258,34.6377,29.1787,6.49807
-ClipRelu2,35.6699,186.473,140.964,45.5092,5227.75,3951.91,1275.84,0.283793,1.78712,1.33686,0.468235,30.0455,22.8704,8.03787
-ClipRelu3,35.6563,165.27,125.599,39.6716,4635.07,3522.47,1112.6,0.291613,1.72066,1.28598,0.444424,26.11,19.6517,6.98899
-Conv1,3222.23,16082.5,13014.3,3068.26,4991.2,4038.98,952.22,15.3047,25.6007,16.0853,15.2341,18.3593,17.1448,3.32665
-Conv2,1532.25,7670.01,6138.24,1531.76,5005.79,4006.1,999.684,6.87238,10.0722,7.16904,6.53694,18.9873,18.0292,2.42076
-Conv3,1164.4,5092.98,3767.29,1325.69,4374.05,3235.53,1138.53,8.17306,13.3788,8.398,7.54984,21.8335,20.1398,2.64523
diff --git a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_layers.txt b/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_layers.txt
deleted file mode 100644
index b7d5c3cdcc00c398df81022401291a084d3f8ab8..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_layers.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-Conv1,2000,1,240,300,1,1,9,9,1,1
-Conv2,2000,1,240,300,1,1,5,5,1,1
-Conv3,2000,1,240,300,1,1,3,3,1,1
diff --git a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_ops.txt b/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_ops.txt
deleted file mode 100644
index 7c5a3a1c50a80a638bf569b41f5d3ef2add5f224..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_ops.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-#Conv1,2
-Conv1
-ClipRelu1
-#Conv2,3
-Conv2
-Add1
-ClipRelu2
-#Conv3,2
-Conv3
-ClipRelu3
diff --git a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_promise_confs1.txt b/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_promise_confs1.txt
deleted file mode 100644
index 0d24c059750ac51d2e5a76925dfc5de8f4e3f059..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_promise_confs1.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-9 9,9 9 9,9 9
-9 9,9 9 9,8 8
-9 9,8 8 8,9 9
-9 9,9 9 9,9 9
-9 9,8 8 8,8 8
-9 9,9 9 9,8 8
-8 8,8 8 8,9 9
-8 8,8 8 8,8 8
-8 8,9 9 9,8 8
-8 8,9 9 9,9 9
diff --git a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_promise_confs2.txt b/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_promise_confs2.txt
deleted file mode 100644
index ba2839b706b3718fd6bcf065409164737a878b08..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_promise_confs2.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-9 9,9 9 9,9 9
-8 8,9 9 9,8 8
-9 9,8 8 8,9 9
-9 9,9 9 9,9 9
-9 9,8 8 8,8 8
-8 8,9 9 9,9 9
-9 9,9 9 9,8 8
-8 8,8 8 8,9 9
-8 8,8 8 8,8 8
-8 8,9 9 9,8 8
diff --git a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_promise_results1.csv b/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_promise_results1.csv
deleted file mode 100644
index d74e5b5acea78b727e36599c2cecb813971fb82c..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_promise_results1.csv
+++ /dev/null
@@ -1,154 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,16307.435,8045.818,5258.25,29611.503,0.999999996622934
-c1,16307.435,8045.818,4690.815,29044.068,1.0195370324173
-c2,16307.435,6756.139,5258.25,28321.824,1.04553657615577
-c3,16307.435,8045.818,5258.25,29611.503,0.999999996622934
-c4,16307.435,6756.139,4690.815,27754.389,1.06691244016609
-c5,16307.435,8045.818,4690.815,29044.068,1.0195370324173
-c6,13509.25,6756.139,5258.25,25523.639,1.16015991622449
-c7,13509.25,6756.139,4690.815,24956.204,1.18653874128237
-c8,13509.25,8045.818,4690.815,26245.883,1.1282342029482
-c9,13509.25,8045.818,5258.25,26813.318,1.10435802423125
-c7,24956.204
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,3258.0582,1604.8171,1200.0563,6062.9316,0.999999983506329
-c1,3258.0582,1604.8171,1256.3962,6119.2715,0.990793021836128
-c2,3258.0582,1506.7823,1200.0563,5964.8968,1.01643527149648
-c3,3258.0582,1604.8171,1200.0563,6062.9316,0.999999983506329
-c4,3258.0582,1506.7823,1256.3962,6021.2367,1.00692462385801
-c5,3258.0582,1604.8171,1256.3962,6119.2715,0.990793021836128
-c6,2951.3387,1506.7823,1200.0563,5658.1773,1.07153437783693
-c7,2951.3387,1506.7823,1256.3962,5714.5172,1.06097003153705
-c8,2951.3387,1604.8171,1256.3962,5812.552,1.04307565690465
-c9,2951.3387,1604.8171,1200.0563,5756.2121,1.05328493622942
-c6,5658.1773
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,16307.435,8045.818,5258.25,29611.503,0.999999996622934
-c1,16307.435,8045.818,4897.321,29250.574,1.0123392073867
-c2,16307.435,6976.087,5407.628,28691.15,1.032077936813
-c3,16307.435,8045.818,5258.25,29611.503,0.999999996622934
-c4,16307.435,6976.087,4690.815,27974.337,1.05852384970366
-c5,16307.435,8045.818,4897.321,29250.574,1.0123392073867
-c6,13723.18,6756.139,5407.628,25886.947,1.14387775760549
-c7,13723.18,6756.139,4690.815,25170.134,1.17645392282594
-c8,13723.18,8216.485,4897.321,26836.986,1.10338407187982
-c9,13723.18,8216.485,5258.25,27197.915,1.08874165137754
-c7,25170.134
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,0,0,0,0,0
-c2,0,0,0,0,0
-c3,0,0,0,0,0
-c4,0,0,0,0,0
-c5,0,0,0,0,0
-c6,0,0,0,0,0
-c7,0,0,0,0,0
-c8,0,0,0,0,0
-c9,0,0,0,0,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,0,0,0,0,0
-c2,0,0,0,0,0
-c3,0,0,0,0,0
-c4,0,0,0,0,0
-c5,0,0,0,0,0
-c6,0,0,0,0,0
-c7,0,0,0,0,0
-c8,0,0,0,0,0
-c9,0,0,0,0,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,0,0,0,0,0
-c2,0,0,0,0,0
-c3,0,0,0,0,0
-c4,0,0,0,0,0
-c5,0,0,0,0,0
-c6,0,0,0,0,0
-c7,0,0,0,0,0
-c8,0,0,0,0,0
-c9,0,0,0,0,0
-c0,0
-
-Patch Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,0,0,0,0,0
-c2,0,0,0,0,0
-c3,0,0,0,0,0
-c4,0,0,0,0,0
-c5,0,0,0,0,0
-c6,0,0,0,0,0
-c7,0,0,0,0,0
-c8,0,0,0,0,0
-c9,0,0,0,0,0
-c0,0
-
-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,0,0,206.506,206.506,0
-c2,0,219.948,149.378,369.326,0
-c3,0,0,0,0,0
-c4,0,219.948,0,219.948,0
-c5,0,0,206.506,206.506,0
-c6,213.93,0,149.378,363.308,0
-c7,213.93,0,0,213.93,0
-c8,213.93,170.667,206.506,591.103,0
-c9,213.93,170.667,0,384.597,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,0,0,49.5704,49.5704,0
-c2,0,45.9656,36.6344,82.6,0
-c3,0,0,0,0,0
-c4,0,45.9656,0,45.9656,0
-c5,0,0,49.5704,49.5704,0
-c6,135.408,0,36.6344,172.0424,0
-c7,135.408,0,0,135.408,0
-c8,135.408,36.459,49.5704,221.4374,0
-c9,135.408,36.459,0,171.867,0
-c0,0
-
-Time
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,3258.0582,1604.8171,1200.0563,6062.9316,0.999999983506329
-c1,3258.0582,1604.8171,1305.9666,6168.8419,0.982831396881295
-c2,3258.0582,1552.7479,1236.6907,6047.4968,1.00255224603753
-c3,3258.0582,1604.8171,1200.0563,6062.9316,0.999999983506329
-c4,3258.0582,1552.7479,1256.3962,6067.2023,0.999296084139207
-c5,3258.0582,1604.8171,1305.9666,6168.8419,0.982831396881295
-c6,3086.7467,1506.7823,1236.6907,5830.2197,1.0399147558725
-c7,3086.7467,1506.7823,1256.3962,5849.9252,1.03641179828399
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diff --git a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_promise_results2.csv b/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_promise_results2.csv
deleted file mode 100644
index 2f278d260729ab866e55bc325c2b7db70ab87d4b..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_promise_results2.csv
+++ /dev/null
@@ -1,154 +0,0 @@
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-c0,16307.435,8045.818,5258.25,29611.503,0.999999996622934
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diff --git a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_results1.csv b/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_results1.csv
deleted file mode 100644
index cdda603e1802f9a53b8b54138515783f87671e43..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_results1.csv
+++ /dev/null
@@ -1,121 +0,0 @@
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-c0,16307.435,8045.818,5258.25,29611.503,0.999999996622934
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-Compute Time
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-c0,3258.0582,1604.8171,1200.0563,6062.9316,0.999999983506329
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diff --git a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_results2.csv b/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_results2.csv
deleted file mode 100644
index 3772196ed94a052d40cee85ef52b9566646aa63d..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_results2.csv
+++ /dev/null
@@ -1,121 +0,0 @@
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-c0,0
-
diff --git a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_tensors.txt b/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_tensors.txt
deleted file mode 100644
index 1b0a2e35250a19f38f2c4eb020d2d79038f3b038..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEO/pipeline_GEO_tensors.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-#Conv1,2
-Conv1,3222.23,16082.5,2918.68,13346.8,135.408,213.93,36.4759,219.457
-ClipRelu1,35.8282,224.935,32.6587,162.45,146.743,769.608,36.4146,177.696
-#Conv2,3
-Conv2,1532.25,7670.01,1440.02,6478.39,45.9656,219.948,36.459,170.667
-Add1,36.8972,189.335,32.6705,133.276,152.652,636.419,36.4091,148.094
-ClipRelu2,35.6699,186.473,34.0918,144.473,45.6929,185.575,36.3378,153.377
-#Conv3,2
-Conv3,1164.4,5092.98,1223.57,4572.94,49.5704,206.506,36.6344,149.378
-ClipRelu3,35.6563,165.27,32.8262,117.875,163.696,595.184,36.522,132.298
diff --git a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_confs1.txt b/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_confs1.txt
deleted file mode 100644
index da21e938c3aab5ec3f870ef5bdce0fb0e6457808..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_confs1.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-9 9,9 9 9,9 9,9 9
-8 8,8 8 8,8 8,7
-8 8,8 8 8,8 8,7
-8 8,8 8 8,9 9,7
-9 9,9 9 9,9 9,7
-8 8,8 8 8,8 8,7
-8 8,8 8 8,8 8,6
-8 8,8 8 8,9 9,6
-9 9,9 9 9,9 9,6
diff --git a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_confs2.txt b/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_confs2.txt
deleted file mode 100644
index e004da0e84fe4a8f72bf37eccf5c2ce353263551..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_confs2.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-9 9,9 9 9,9 9,9 9
-8 8,8 8 8,8 8,6
-8 8,8 8 8,9 9,6
-9 9,9 9 9,9 9,6
-8 8,8 8 8,8 8,6
-8 8,8 8 8,8 8,5
-9 9,9 9 9,9 9,5
diff --git a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_fp16.csv b/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_fp16.csv
deleted file mode 100644
index 8f1731ca860ca20f23d2c879d4a19031ac96772d..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_fp16.csv
+++ /dev/null
@@ -1,27 +0,0 @@
-Add1,32.585,132.874,90.5805,42.2932,4077.85,2779.91,1297.94,0.267716,1.60873,1.24676,0.493805,44.9777,37.3317,11.3155
-Add1_f2h,152.052,635.102,465.63,169.472,4177.23,3062.65,1114.59,3.70789,13.4672,9.54204,4.05265,24.3579,22.8349,4.12229
-Add1_h2f,36.3936,148.401,98.2724,50.1289,4077.66,2700.25,1377.41,0.24462,1.89046,1.57426,0.469717,42.8092,38.0548,9.49759
-ClipRelu1,32.7405,163.037,121.339,41.6976,4979.72,3706.16,1273.55,0.438457,2.75189,2.11249,0.739841,56.149,47.98,12.1784
-ClipRelu1_f2h,146.657,769.821,612.394,157.427,5249.64,4176.19,1073.45,3.76174,16.813,12.8582,4.0054,23.1617,22.2292,4.05231
-ClipRelu1_h2f,36.3842,177.909,128.755,49.154,4889.74,3538.77,1350.97,0.206959,1.67567,1.40286,0.463424,36.4341,32.4012,10.492
-ClipRelu2,34.0553,144.51,89.3398,55.1703,4243.37,2623.36,1620,0.391793,2.26213,1.54527,0.812253,42.5212,33.0461,13.097
-ClipRelu2_f2h,45.6549,185.567,118.399,67.1677,4064.67,2593.46,1471.21,0.523371,2.17308,1.52455,0.833304,31.5794,27.8107,8.31535
-ClipRelu2_h2f,36.3736,153.724,93.2158,60.5083,4226.21,2562.7,1663.51,0.252782,1.79709,1.32467,0.589658,34.7854,29.0642,9.549
-ClipRelu3,32.7854,118.216,75.162,43.0543,3605.75,2292.56,1313.18,0.506606,3.97908,2.57351,1.46189,107.077,70.5545,38.4207
-ClipRelu3_f2h,165.958,602.927,413.07,189.857,3633.17,2489.15,1144.03,3.02065,10.2904,6.9028,3.47812,25.9071,19.545,7.72569
-ClipRelu3_h2f,36.5443,132.298,82.3714,49.9267,3621.12,2254.57,1366.55,0.600315,1.56585,1.17732,0.515657,68.5219,46.0293,24.9645
-ClipRelu4,32.7184,153.056,112.078,40.9786,4678,3425.53,1252.46,0.154794,1.68025,1.41169,0.366783,47.2146,40.8675,9.29592
-ClipRelu4_f2h,165.584,825.553,648.957,176.596,4986.3,3919.79,1066.5,4.67005,19.877,14.903,5.01446,23.5566,23.11,3.43511
-ClipRelu4_h2f,36.6609,169.727,120.701,49.0256,4629.64,3292.37,1337.27,0.289045,1.92183,1.53364,0.497796,37.7378,32.9471,8.48898
-Conv1,2915.93,13352.1,10576.6,2775.47,4579.12,3627.28,951.839,15.8558,18.3107,9.62974,12.2137,20.0909,18.348,2.75611
-Conv1_f2h,135.06,214.749,45.2804,169.468,1590.14,335.188,1254.95,3.27252,4.99991,2.01686,3.41797,16.3964,10.1737,14.2329
-Conv1_h2f,36.5275,220.001,187.373,32.6287,6022.94,5129.68,893.265,0.32198,2.10465,1.77041,0.358425,31.9138,27.186,5.94593
-Conv2,1440.78,6485.81,4949.68,1536.13,4501.67,3435.49,1066.18,5.5314,12.769,13.1432,5.45702,18.9795,18.0565,2.37007
-Conv2_f2h,45.7961,219.712,153.373,66.339,4797.66,3349.09,1448.57,0.526669,2.73821,2.03676,0.853528,32.5454,29.6105,7.99933
-Conv2_h2f,36.4586,170.747,136.394,34.3529,4683.3,3741.06,942.241,0.257013,1.47757,1.17259,0.319922,23.0558,18.3627,5.39354
-Conv3,1209.29,4559.4,3094.84,1464.56,3770.47,2559.36,1211.1,9.26546,17.9525,14.4448,9.49139,24.2389,22.3724,2.90811
-Conv3_f2h,49.3758,206.121,121.426,84.6944,4174.75,2459.42,1715.33,0.792172,3.13741,2.04183,1.32468,38.1041,33.4828,8.37125
-Conv3_h2f,36.5783,149.412,113.105,36.3067,4084.72,3092.15,992.574,0.207318,1.1695,0.884863,0.308639,23.5563,18.2546,6.207
-Conv4,2636.43,13346.5,10809.1,2537.42,5062.38,4099.94,962.445,9.2555,18.7556,14.6325,10.0121,15.2368,15.0861,1.65147
-Conv4_f2h,52.0572,187.688,112.685,75.0037,3605.8,2164.97,1440.83,1.39581,4.67122,2.79345,2.01277,38.9395,32.0697,10.8245
-Conv4_h2f,36.6862,213.45,180.703,32.7466,5818.23,4925.62,892.61,0.176256,1.64194,1.38908,0.264839,31.3851,26.7179,5.1348
diff --git a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_fp32.csv b/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_fp32.csv
deleted file mode 100644
index 32c71387d272af1306da6391e268ab0d1e7690c9..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_fp32.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-Add1,36.9761,189.903,150.681,39.2229,5135.79,4075.05,1060.74,0.327484,2.16644,1.67428,0.505853,27.874,21.2818,7.37459
-ClipRelu1,35.896,225.112,189.944,35.1678,6271.16,5291.45,979.702,0.3556,2.8365,2.39001,0.458028,39.0597,32.8052,6.86919
-ClipRelu2,35.8218,187.167,141.441,45.7264,5226.02,3949.26,1276.76,0.560645,3.09466,2.31204,0.794514,103.938,77.8175,26.3982
-ClipRelu3,35.7282,165.939,126.097,39.8416,4644.45,3529.32,1115.12,0.435428,2.30666,1.73936,0.576096,26.442,19.9043,7.12496
-ClipRelu4,35.8348,218.266,183.176,35.0905,6090.92,5111.69,979.23,0.294607,2.10359,1.7505,0.370725,32.8505,27.2303,6.44718
-Conv1,3230.39,16104.1,13023.8,3080.3,4985.31,4031.77,953.548,17.2527,24.4108,13.9864,14.0096,20.57,18.9428,2.87602
-Conv2,1531.88,7675.32,6141.51,1533.8,5010.48,4009.22,1001.26,7.36237,11.0004,7.1979,6.76237,19.5752,18.4653,2.44314
-Conv3,1163.22,5096.2,3770.21,1325.99,4381.36,3241.41,1139.94,10.0955,15.8531,9.13125,10.5922,27.2638,25.85,3.25685
-Conv4,3022.09,16672.3,13850.5,2821.84,5516.86,4583.12,933.739,7.95461,13.8577,12.9997,6.94323,13.4733,12.9753,1.37836
diff --git a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_layers.txt b/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_layers.txt
deleted file mode 100644
index aa19e9d56f9b6f6d681a08bf5f51a8e6aecd36d8..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_layers.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-Conv1,2000,1,240,300,1,1,9,9,1,1
-Conv2,2000,1,240,300,1,1,5,5,1,1
-Conv3,2000,1,240,300,1,1,3,3,1,1
-Conv4,2000,1,240,300,1,1,9,9,1,1
diff --git a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_ops.txt b/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_ops.txt
deleted file mode 100644
index 0807a77c985bd73c6c538a1259b2ffc44eeda73a..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_ops.txt
+++ /dev/null
@@ -1,13 +0,0 @@
-#Conv1,2
-Conv1
-ClipRelu1
-#Conv2,3
-Conv2
-Add1
-ClipRelu2
-#Conv3,2
-Conv3
-ClipRelu3
-#Conv4,2
-Conv4
-ClipRelu4
diff --git a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_promise_confs1.txt b/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_promise_confs1.txt
deleted file mode 100644
index 680259f083a6ec52920bb2ce22dbd0f1a50200b3..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_promise_confs1.txt
+++ /dev/null
@@ -1,46 +0,0 @@
-9 9,9 9 9,9 9,9 9
-8 8,8 8 8,9 9,3
-8 8,8 8 8,8 8,3
-8 8,9 9 9,9 9,3
-8 8,8 8 8,8 8,3
-8 8,9 9 9,8 8,3
-9 9,9 9 9,8 8,3
-9 9,9 9 9,9 9,3
-9 9,8 8 8,8 8,3
-9 9,8 8 8,9 9,3
-8 8,9 9 9,9 9,8 8
-8 8,9 9 9,9 9,6
-8 8,9 9 9,8 8,6
-8 8,9 9 9,9 9,7
-8 8,9 9 9,9 9,4
-8 8,8 8 8,9 9,6
-8 8,9 9 9,8 8,5
-8 8,8 8 8,8 8,8 8
-8 8,9 9 9,8 8,8 8
-9 9,9 9 9,8 8,6
-8 8,8 8 8,8 8,5
-8 8,8 8 8,8 8,4
-9 9,8 8 8,8 8,5
-8 8,8 8 8,8 8,6
-8 8,8 8 8,9 9,5
-8 8,9 9 9,9 9,9 9
-8 8,9 9 9,8 8,9 9
-8 8,8 8 8,8 8,7
-8 8,8 8 8,9 9,4
-8 8,8 8 8,8 8,9 9
-9 9,8 8 8,8 8,6
-9 9,9 9 9,8 8,5
-8 8,9 9 9,8 8,7
-9 9,9 9 9,9 9,9 9
-9 9,8 8 8,8 8,7
-8 8,8 8 8,9 9,8 8
-8 8,9 9 9,9 9,8 8
-8 8,9 9 9,8 8,4
-8 8,8 8 8,9 9,7
-9 9,9 9 9,9 9,6
-8 8,8 8 8,9 9,9 9
-8 8,9 9 9,9 9,5
-9 9,8 8 8,9 9,9 9
-9 9,8 8 8,9 9,7
-9 9,8 8 8,8 8,4
-9 9,9 9 9,8 8,4
diff --git a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_promise_confs2.txt b/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_promise_confs2.txt
deleted file mode 100644
index 1e0ff4012e24dd4b11fa809d00f55bf49a2d0656..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_promise_confs2.txt
+++ /dev/null
@@ -1,44 +0,0 @@
-9 9,9 9 9,9 9,9 9
-8 8,8 8 8,8 8,5
-9 9,9 9 9,9 9,9 9
-9 9,8 8 8,8 8,5
-8 8,8 8 8,8 8,4
-8 8,8 8 8,8 8,7
-8 8,8 8 8,8 8,6
-8 8,9 9 9,8 8,9 9
-8 8,9 9 9,8 8,8 8
-9 9,8 8 8,8 8,8 8
-8 8,8 8 8,9 9,4
-8 8,9 9 9,8 8,4
-9 9,9 9 9,8 8,4
-8 8,9 9 9,9 9,4
-8 8,8 8 8,9 9,9 9
-8 8,8 8 8,9 9,2
-9 9,8 8 8,9 9,9 9
-8 8,8 8 8,8 8,9 9
-8 8,8 8 8,8 8,5
-8 8,8 8 8,9 9,5
-8 8,8 8 8,8 8,8 8
-8 8,8 8 8,8 8,3
-9 9,8 8 8,8 8,2
-8 8,9 9 9,9 9,6
-9 9,8 8 8,8 8,4
-8 8,9 9 9,8 8,3
-9 9,8 8 8,9 9,7
-9 9,8 8 8,9 9,4
-9 9,9 9 9,8 8,8 8
-8 8,8 8 8,8 8,2
-8 8,8 8 8,9 9,6
-8 8,8 8 8,9 9,3
-9 9,9 9 9,9 9,5
-9 9,9 9 9,9 9,2
-8 8,8 8 8,9 9,7
-8 8,9 9 9,9 9,3
-8 8,9 9 9,9 9,2
-9 9,9 9 9,9 9,7
-8 8,9 9 9,8 8,2
-8 8,9 9 9,9 9,5
-9 9,8 8 8,9 9,6
-8 8,9 9 9,8 8,5
-8 8,9 9 9,8 8,6
-8 8,9 9 9,8 8,7
diff --git a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_promise_results1.csv b/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_promise_results1.csv
deleted file mode 100644
index 282361a378d03d391107aeedc3c70df4d54b21fc..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_promise_results1.csv
+++ /dev/null
@@ -1,550 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,16329.212,8052.39,5262.139,16890.566,46534.307,0.999999997851048
-c1,13515.137,6763.194,5262.139,5.046064,25545.516064,1.82162328219378
-c2,13515.137,6763.194,4677.616,5.046064,24960.993064,1.86428106823546
-c3,13515.137,8052.39,5262.139,5.046064,26834.712064,1.73410866923432
-c4,13515.137,6763.194,4677.616,5.046064,24960.993064,1.86428106823546
-c5,13515.137,8052.39,4677.616,5.046064,26250.189064,1.77272273008295
-c6,16329.212,8052.39,4677.616,5.046064,29064.264064,1.60108326628957
-c7,16329.212,8052.39,5262.139,5.046064,29648.787064,1.56951806300201
-c8,16329.212,6763.194,4677.616,5.046064,27775.068064,1.67539848058103
-c9,16329.212,6763.194,5262.139,5.046064,28359.591064,1.64086663770637
-c10,13515.137,8052.39,5262.139,13499.556,40329.222,1.15386076340907
-c11,13515.137,8052.39,5262.139,11.525024,26841.191024,1.73369008793322
-c12,13515.137,8052.39,4677.616,11.525024,26256.668024,1.7722853021654
-c13,13515.137,8052.39,5262.139,15.343114,26845.009114,1.73344351007828
-c14,13515.137,8052.39,5262.139,6.799450,26836.46545,1.73399536959516
-c15,13515.137,6763.194,5262.139,11.525024,25551.995024,1.82116139167122
-c16,13515.137,8052.39,4677.616,7.585986,26252.728986,1.77255122115345
-c17,13515.137,6763.194,4677.616,13499.556,38455.503,1.21008186732057
-c18,13515.137,8052.39,4677.616,13499.556,39744.699,1.17083052718343
-c19,16329.212,8052.39,4677.616,11.525024,29070.743024,1.60072643487337
-c20,13515.137,6763.194,4677.616,7.585986,24963.532986,1.86409138641106
-c21,13515.137,6763.194,4677.616,6.799450,24962.74645,1.86415012093291
-c22,16329.212,6763.194,4677.616,7.585986,27777.607986,1.67524528591263
-c23,13515.137,6763.194,4677.616,11.525024,24967.472024,1.86379729469163
-c24,13515.137,6763.194,5262.139,7.585986,25548.055986,1.82144218109417
-c25,13515.137,8052.39,5262.139,16890.566,43720.232,1.06436550687937
-c26,13515.137,8052.39,4677.616,16890.566,43135.709,1.07878850193748
-c27,13515.137,6763.194,4677.616,15.343114,24971.290114,1.86351232159846
-c28,13515.137,6763.194,5262.139,6.799450,25547.26945,1.8214982587053
-c29,13515.137,6763.194,4677.616,16890.566,41846.513,1.11202352484657
-c30,16329.212,6763.194,4677.616,11.525024,27781.547024,1.67500775936988
-c31,16329.212,8052.39,4677.616,7.585986,29066.803986,1.60094336007216
-c32,13515.137,8052.39,4677.616,15.343114,26260.486114,1.77202762434732
-c33,16329.212,8052.39,5262.139,16890.566,46534.307,0.999999997851048
-c34,16329.212,6763.194,4677.616,15.343114,27785.365114,1.67477759034649
-c35,13515.137,6763.194,5262.139,13499.556,39040.026,1.19196403406093
-c36,13515.137,8052.39,5262.139,13499.556,40329.222,1.15386076340907
-c37,13515.137,8052.39,4677.616,6.799450,26251.94245,1.77260432866519
-c38,13515.137,6763.194,5262.139,15.343114,25555.813114,1.82088930648889
-c39,16329.212,8052.39,5262.139,11.525024,29655.266024,1.56917516118123
-c40,13515.137,6763.194,5262.139,16890.566,42431.036,1.09670447099924
-c41,13515.137,8052.39,5262.139,7.585986,26837.251986,1.73394455031688
-c42,16329.212,6763.194,5262.139,16890.566,45245.111,1.02849359563182
-c43,16329.212,6763.194,5262.139,15.343114,28369.888114,1.64027107364618
-c44,16329.212,6763.194,4677.616,6.799450,27776.82145,1.67529272261174
-c45,16329.212,8052.39,4677.616,6.799450,29066.01745,1.60098668212632
-c2,24960.993064
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,3266.286,1604.6779,1198.9482,3057.9248,9127.8369,0.999999989044502
-c1,2948.6705,1507.4203,1198.9482,7.919790,5662.95879,1.61184940192988
-c2,2948.6705,1507.4203,1242.0754,7.919790,5706.08599,1.5996668742865
-c3,2948.6705,1604.6779,1198.9482,7.919790,5760.21639,1.584634347658
-c4,2948.6705,1507.4203,1242.0754,7.919790,5706.08599,1.5996668742865
-c5,2948.6705,1604.6779,1242.0754,7.919790,5803.34359,1.57285823269929
-c6,3266.286,1604.6779,1242.0754,7.919790,6120.95909,1.49124289456339
-c7,3266.286,1604.6779,1198.9482,7.919790,6077.83189,1.50182448527999
-c8,3266.286,1507.4203,1242.0754,7.919790,6023.70149,1.51532023351774
-c9,3266.286,1507.4203,1198.9482,7.919790,5980.57429,1.5262475315519
-c10,2948.6705,1604.6779,1198.9482,2669.1484,8421.445,1.08388011696472
-c11,2948.6705,1604.6779,1198.9482,7.919790,5760.21639,1.584634347658
-c12,2948.6705,1604.6779,1242.0754,7.919790,5803.34359,1.57285823269929
-c13,2948.6705,1604.6779,1198.9482,7.919790,5760.21639,1.584634347658
-c14,2948.6705,1604.6779,1198.9482,7.919790,5760.21639,1.584634347658
-c15,2948.6705,1507.4203,1198.9482,7.919790,5662.95879,1.61184940192988
-c16,2948.6705,1604.6779,1242.0754,7.919790,5803.34359,1.57285823269929
-c17,2948.6705,1507.4203,1242.0754,2669.1484,8367.3146,1.09089202776131
-c18,2948.6705,1604.6779,1242.0754,2669.1484,8464.5722,1.07835772162995
-c19,3266.286,1604.6779,1242.0754,7.919790,6120.95909,1.49124289456339
-c20,2948.6705,1507.4203,1242.0754,7.919790,5706.08599,1.5996668742865
-c21,2948.6705,1507.4203,1242.0754,7.919790,5706.08599,1.5996668742865
-c22,3266.286,1507.4203,1242.0754,7.919790,6023.70149,1.51532023351774
-c23,2948.6705,1507.4203,1242.0754,7.919790,5706.08599,1.5996668742865
-c24,2948.6705,1507.4203,1198.9482,7.919790,5662.95879,1.61184940192988
-c25,2948.6705,1604.6779,1198.9482,3057.9248,8810.2214,1.03605078487527
-c26,2948.6705,1604.6779,1242.0754,3057.9248,8853.3486,1.03100388443979
-c27,2948.6705,1507.4203,1242.0754,7.919790,5706.08599,1.5996668742865
-c28,2948.6705,1507.4203,1198.9482,7.919790,5662.95879,1.61184940192988
-c29,2948.6705,1507.4203,1242.0754,3057.9248,8756.091,1.04245567979529
-c30,3266.286,1507.4203,1242.0754,7.919790,6023.70149,1.51532023351774
-c31,3266.286,1604.6779,1242.0754,7.919790,6120.95909,1.49124289456339
-c32,2948.6705,1604.6779,1242.0754,7.919790,5803.34359,1.57285823269929
-c33,3266.286,1604.6779,1198.9482,3057.9248,9127.8369,0.999999989044502
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-c40,2948.6705,1507.4203,1198.9482,3057.9248,8712.9638,1.04761559955505
-c41,2948.6705,1604.6779,1198.9482,7.919790,5760.21639,1.584634347658
-c42,3266.286,1507.4203,1198.9482,3057.9248,9030.5793,1.01076979623257
-c43,3266.286,1507.4203,1198.9482,7.919790,5980.57429,1.5262475315519
-c44,3266.286,1507.4203,1242.0754,7.919790,6023.70149,1.51532023351774
-c45,3266.286,1604.6779,1242.0754,7.919790,6120.95909,1.49124289456339
-c1,5662.95879
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,16329.212,8052.39,5262.139,16890.566,46534.307,0.999999997851048
-c1,13729.886,6763.194,5411.551,80.161512,25984.792512,1.79082849322069
-c2,13729.886,6763.194,4677.616,80.161512,25250.857512,1.84288025836736
-c3,13729.886,8223.137,5262.139,80.161512,27295.323512,1.70484540361126
-c4,13729.886,6763.194,4677.616,80.161512,25250.857512,1.84288025836736
-c5,13729.886,8223.137,4883.737,80.161512,26916.921512,1.72881236832277
-c6,16329.212,8052.39,4883.737,80.161512,29345.500512,1.58573907514023
-c7,16329.212,8052.39,5262.139,80.161512,29723.902512,1.56555172473259
-c8,16329.212,6982.906,4677.616,80.161512,28069.895512,1.65780121320103
-c9,16329.212,6982.906,5411.551,80.161512,28803.830512,1.61555966728305
-c10,13729.886,8223.137,5262.139,13687.244,40902.406,1.13769118829418
-c11,13729.886,8223.137,5262.139,86.640472,27301.802472,1.70444082866984
-c12,13729.886,8223.137,4883.737,86.640472,26923.400472,1.72839633966577
-c13,13729.886,8223.137,5262.139,90.458562,27305.620562,1.70420250013799
-c14,13729.886,8223.137,5262.139,81.914898,27297.076898,1.7047358954737
-c15,13729.886,6763.194,5411.551,86.640472,25991.271472,1.79038208542789
-c16,13729.886,8223.137,4883.737,82.701434,26919.461434,1.72864925032865
-c17,13729.886,6763.194,4677.616,13499.556,38670.252,1.20336187309211
-c18,13729.886,8223.137,4883.737,13499.556,40336.316,1.1536578324266
-c19,16329.212,8052.39,4883.737,86.640472,29351.979472,1.58538904968409
-c20,13729.886,6763.194,4677.616,82.701434,25253.397434,1.84269490619424
-c21,13729.886,6763.194,4677.616,81.914898,25252.610898,1.84275230009624
-c22,16329.212,6982.906,4677.616,82.701434,28072.435434,1.65765121959724
-c23,13729.886,6763.194,4677.616,86.640472,25257.336472,1.84240752651597
-c24,13729.886,6763.194,5411.551,82.701434,25987.332434,1.79065346314855
-c25,13729.886,8223.137,5262.139,16890.566,44105.728,1.05506266429825
-c26,13729.886,8223.137,4883.737,17104.016,43940.776,1.05902332935808
-c27,13729.886,6763.194,4677.616,90.458562,25261.154562,1.84212905635707
-c28,13729.886,6763.194,5411.551,81.914898,25986.545898,1.79070766094045
-c29,13729.886,6763.194,4677.616,17104.016,42274.712,1.10075987956876
-c30,16329.212,6982.906,4677.616,86.640472,28076.374472,1.65741865569808
-c31,16329.212,8052.39,4883.737,82.701434,29348.040434,1.58560183757718
-c32,13729.886,8223.137,4883.737,90.458562,26927.218562,1.72815126523519
-c33,16329.212,8052.39,5262.139,16890.566,46534.307,0.999999997851048
-c34,16329.212,6982.906,4677.616,90.458562,28080.192562,1.6571932949368
-c35,13729.886,6763.194,5411.551,13687.244,39591.875,1.17534991415448
-c36,13729.886,8223.137,5262.139,13687.244,40902.406,1.13769118829418
-c37,13729.886,8223.137,4883.737,81.914898,26918.674898,1.72869975968198
-c38,13729.886,6763.194,5411.551,90.458562,25995.089562,1.79011911884361
-c39,16329.212,8052.39,5262.139,86.640472,29730.381472,1.56521055363198
-c40,13729.886,6763.194,5411.551,16890.566,42795.197,1.08737218551098
-c41,13729.886,8223.137,5262.139,82.701434,27297.863434,1.70468677675235
-c42,16329.212,6982.906,5411.551,16890.566,45614.235,1.02017071859219
-c43,16329.212,6982.906,5411.551,90.458562,28814.127562,1.6149823290111
-c44,16329.212,6982.906,4677.616,81.914898,28071.648898,1.65769766511812
-c45,16329.212,8052.39,4883.737,81.914898,29347.253898,1.5856443333053
-c2,25250.857512
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,28.951489,28.951489,0
-c2,0,0,0,28.951489,28.951489,0
-c3,0,0,0,28.951489,28.951489,0
-c4,0,0,0,28.951489,28.951489,0
-c5,0,0,0,28.951489,28.951489,0
-c6,0,0,0,28.951489,28.951489,0
-c7,0,0,0,28.951489,28.951489,0
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-c12,0,0,0,28.951489,28.951489,0
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-c22,0,0,0,28.951489,28.951489,0
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-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
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-c7,0,0,0,46.163959,46.163959,0
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-c13,0,0,0,46.163959,46.163959,0
-c14,0,0,0,46.163959,46.163959,0
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-c16,0,0,0,46.163959,46.163959,0
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-c20,0,0,0,46.163959,46.163959,0
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-c22,0,0,0,46.163959,46.163959,0
-c23,0,0,0,46.163959,46.163959,0
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-Memory Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
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-c2,0,0,0,10.930367,10.930367,0
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-c4,0,0,0,10.930367,10.930367,0
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-c7,0,0,0,10.930367,10.930367,0
-c8,0,0,0,10.930367,10.930367,0
-c9,0,0,0,10.930367,10.930367,0
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-c12,0,0,0,10.930367,10.930367,0
-c13,0,0,0,10.930367,10.930367,0
-c14,0,0,0,10.930367,10.930367,0
-c15,0,0,0,10.930367,10.930367,0
-c16,0,0,0,10.930367,10.930367,0
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-c19,0,0,0,10.930367,10.930367,0
-c20,0,0,0,10.930367,10.930367,0
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-c22,0,0,0,10.930367,10.930367,0
-c23,0,0,0,10.930367,10.930367,0
-c24,0,0,0,10.930367,10.930367,0
-c25,0,0,0,0,0,0
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-c27,0,0,0,10.930367,10.930367,0
-c28,0,0,0,10.930367,10.930367,0
-c29,0,0,0,0,0,0
-c30,0,0,0,10.930367,10.930367,0
-c31,0,0,0,10.930367,10.930367,0
-c32,0,0,0,10.930367,10.930367,0
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-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
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-c1,214.749,0,149.412,0,364.161,0
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-c4,214.749,0,0,0,214.749,0
-c5,214.749,170.747,206.121,0,591.617,0
-c6,0,0,206.121,0,206.121,0
-c7,0,0,0,0,0,0
-c8,0,219.712,0,0,219.712,0
-c9,0,219.712,149.412,0,369.124,0
-c10,214.749,170.747,0,187.688,573.184,0
-c11,214.749,170.747,0,0,385.496,0
-c12,214.749,170.747,206.121,0,591.617,0
-c13,214.749,170.747,0,0,385.496,0
-c14,214.749,170.747,0,0,385.496,0
-c15,214.749,0,149.412,0,364.161,0
-c16,214.749,170.747,206.121,0,591.617,0
-c17,214.749,0,0,0,214.749,0
-c18,214.749,170.747,206.121,0,591.617,0
-c19,0,0,206.121,0,206.121,0
-c20,214.749,0,0,0,214.749,0
-c21,214.749,0,0,0,214.749,0
-c22,0,219.712,0,0,219.712,0
-c23,214.749,0,0,0,214.749,0
-c24,214.749,0,149.412,0,364.161,0
-c25,214.749,170.747,0,0,385.496,0
-c26,214.749,170.747,206.121,213.45,805.067,0
-c27,214.749,0,0,0,214.749,0
-c28,214.749,0,149.412,0,364.161,0
-c29,214.749,0,0,213.45,428.199,0
-c30,0,219.712,0,0,219.712,0
-c31,0,0,206.121,0,206.121,0
-c32,214.749,170.747,206.121,0,591.617,0
-c33,0,0,0,0,0,0
-c34,0,219.712,0,0,219.712,0
-c35,214.749,0,149.412,187.688,551.849,0
-c36,214.749,170.747,0,187.688,573.184,0
-c37,214.749,170.747,206.121,0,591.617,0
-c38,214.749,0,149.412,0,364.161,0
-c39,0,0,0,0,0,0
-c40,214.749,0,149.412,0,364.161,0
-c41,214.749,170.747,0,0,385.496,0
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-c43,0,219.712,149.412,0,369.124,0
-c44,0,219.712,0,0,219.712,0
-c45,0,0,206.121,0,206.121,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,135.06,0,36.5783,0,171.6383,0
-c2,135.06,0,0,0,135.06,0
-c3,135.06,36.4586,0,0,171.5186,0
-c4,135.06,0,0,0,135.06,0
-c5,135.06,36.4586,49.3758,0,220.8944,0
-c6,0,0,49.3758,0,49.3758,0
-c7,0,0,0,0,0,0
-c8,0,45.7961,0,0,45.7961,0
-c9,0,45.7961,36.5783,0,82.3744,0
-c10,135.06,36.4586,0,52.0572,223.5758,0
-c11,135.06,36.4586,0,0,171.5186,0
-c12,135.06,36.4586,49.3758,0,220.8944,0
-c13,135.06,36.4586,0,0,171.5186,0
-c14,135.06,36.4586,0,0,171.5186,0
-c15,135.06,0,36.5783,0,171.6383,0
-c16,135.06,36.4586,49.3758,0,220.8944,0
-c17,135.06,0,0,0,135.06,0
-c18,135.06,36.4586,49.3758,0,220.8944,0
-c19,0,0,49.3758,0,49.3758,0
-c20,135.06,0,0,0,135.06,0
-c21,135.06,0,0,0,135.06,0
-c22,0,45.7961,0,0,45.7961,0
-c23,135.06,0,0,0,135.06,0
-c24,135.06,0,36.5783,0,171.6383,0
-c25,135.06,36.4586,0,0,171.5186,0
-c26,135.06,36.4586,49.3758,36.6862,257.5806,0
-c27,135.06,0,0,0,135.06,0
-c28,135.06,0,36.5783,0,171.6383,0
-c29,135.06,0,0,36.6862,171.7462,0
-c30,0,45.7961,0,0,45.7961,0
-c31,0,0,49.3758,0,49.3758,0
-c32,135.06,36.4586,49.3758,0,220.8944,0
-c33,0,0,0,0,0,0
-c34,0,45.7961,0,0,45.7961,0
-c35,135.06,0,36.5783,52.0572,223.6955,0
-c36,135.06,36.4586,0,52.0572,223.5758,0
-c37,135.06,36.4586,49.3758,0,220.8944,0
-c38,135.06,0,36.5783,0,171.6383,0
-c39,0,0,0,0,0,0
-c40,135.06,0,36.5783,0,171.6383,0
-c41,135.06,36.4586,0,0,171.5186,0
-c42,0,45.7961,36.5783,0,82.3744,0
-c43,0,45.7961,36.5783,0,82.3744,0
-c44,0,45.7961,0,0,45.7961,0
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-c0,0
-
-Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,3266.286,1604.6779,1198.9482,3057.9248,9127.8369,0.999999989044502
-c1,3083.7305,1507.4203,1235.5265,18.850157,5845.527457,1.5615078042134
-c2,3083.7305,1507.4203,1242.0754,18.850157,5852.076357,1.5597603631924
-c3,3083.7305,1641.1365,1198.9482,18.850157,5942.665357,1.53598363664374
-c4,3083.7305,1507.4203,1242.0754,18.850157,5852.076357,1.5597603631924
-c5,3083.7305,1641.1365,1291.4512,18.850157,6035.168357,1.51244111329037
-c6,3266.286,1604.6779,1291.4512,18.850157,6181.265257,1.47669390858025
-c7,3266.286,1604.6779,1198.9482,18.850157,6088.762257,1.49912845416049
-c8,3266.286,1553.2164,1242.0754,18.850157,6080.427957,1.50118327434065
-c9,3266.286,1553.2164,1235.5265,18.850157,6073.879057,1.50280186089649
-c10,3083.7305,1641.1365,1198.9482,2721.2056,8645.0208,1.05584902634533
-c11,3083.7305,1641.1365,1198.9482,18.850157,5942.665357,1.53598363664374
-c12,3083.7305,1641.1365,1291.4512,18.850157,6035.168357,1.51244111329037
-c13,3083.7305,1641.1365,1198.9482,18.850157,5942.665357,1.53598363664374
-c14,3083.7305,1641.1365,1198.9482,18.850157,5942.665357,1.53598363664374
-c15,3083.7305,1507.4203,1235.5265,18.850157,5845.527457,1.5615078042134
-c16,3083.7305,1641.1365,1291.4512,18.850157,6035.168357,1.51244111329037
-c17,3083.7305,1507.4203,1242.0754,2669.1484,8502.3746,1.07356323639794
-c18,3083.7305,1641.1365,1291.4512,2669.1484,8685.4666,1.05093223142517
-c19,3266.286,1604.6779,1291.4512,18.850157,6181.265257,1.47669390858025
-c20,3083.7305,1507.4203,1242.0754,18.850157,5852.076357,1.5597603631924
-c21,3083.7305,1507.4203,1242.0754,18.850157,5852.076357,1.5597603631924
-c22,3266.286,1553.2164,1242.0754,18.850157,6080.427957,1.50118327434065
-c23,3083.7305,1507.4203,1242.0754,18.850157,5852.076357,1.5597603631924
-c24,3083.7305,1507.4203,1235.5265,18.850157,5845.527457,1.5615078042134
-c25,3083.7305,1641.1365,1198.9482,3057.9248,8981.74,1.01626597946204
-c26,3083.7305,1641.1365,1291.4512,3094.611,9110.9292,1.00185574922637
-c27,3083.7305,1507.4203,1242.0754,18.850157,5852.076357,1.5597603631924
-c28,3083.7305,1507.4203,1235.5265,18.850157,5845.527457,1.5615078042134
-c29,3083.7305,1507.4203,1242.0754,3094.611,8927.8372,1.02240179712952
-c30,3266.286,1553.2164,1242.0754,18.850157,6080.427957,1.50118327434065
-c31,3266.286,1604.6779,1291.4512,18.850157,6181.265257,1.47669390858025
-c32,3083.7305,1641.1365,1291.4512,18.850157,6035.168357,1.51244111329037
-c33,3266.286,1604.6779,1198.9482,3057.9248,9127.8369,0.999999989044502
-c34,3266.286,1553.2164,1242.0754,18.850157,6080.427957,1.50118327434065
-c35,3083.7305,1507.4203,1235.5265,2721.2056,8547.8829,1.06784766473757
-c36,3083.7305,1641.1365,1198.9482,2721.2056,8645.0208,1.05584902634533
-c37,3083.7305,1641.1365,1291.4512,18.850157,6035.168357,1.51244111329037
-c38,3083.7305,1507.4203,1235.5265,18.850157,5845.527457,1.5615078042134
-c39,3266.286,1604.6779,1198.9482,18.850157,6088.762257,1.49912845416049
-c40,3083.7305,1507.4203,1235.5265,3057.9248,8884.6021,1.02737710642802
-c41,3083.7305,1641.1365,1198.9482,18.850157,5942.665357,1.53598363664374
-c42,3266.286,1553.2164,1235.5265,3057.9248,9112.9537,1.00163318067079
-c43,3266.286,1553.2164,1235.5265,18.850157,6073.879057,1.50280186089649
-c44,3266.286,1553.2164,1242.0754,18.850157,6080.427957,1.50118327434065
-c45,3266.286,1604.6779,1291.4512,18.850157,6181.265257,1.47669390858025
-c1,5845.527457
-
-Unpatch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,0,0,0
-c2,0,0,0,0,0,0
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-
diff --git a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_promise_results2.csv b/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_promise_results2.csv
deleted file mode 100644
index 76a1c5a4797a1520feee8c0c017c483778f867c7..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_promise_results2.csv
+++ /dev/null
@@ -1,528 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,16329.212,8052.39,5262.139,16890.566,46534.307,0.999999997851048
-c1,13515.137,6763.194,4677.616,7.585986,24963.532986,1.86409138641106
-c2,16329.212,8052.39,5262.139,16890.566,46534.307,0.999999997851048
-c3,16329.212,6763.194,4677.616,7.585986,27777.607986,1.67524528591263
-c4,13515.137,6763.194,4677.616,6.799450,24962.74645,1.86415012093291
-c5,13515.137,6763.194,4677.616,15.343114,24971.290114,1.86351232159846
-c6,13515.137,6763.194,4677.616,11.525024,24967.472024,1.86379729469163
-c7,13515.137,8052.39,4677.616,16890.566,43135.709,1.07878850193748
-c8,13515.137,8052.39,4677.616,13499.556,39744.699,1.17083052718343
-c9,16329.212,6763.194,4677.616,13499.556,41269.578,1.127569244523
-c10,13515.137,6763.194,5262.139,6.799450,25547.26945,1.8214982587053
-c11,13515.137,8052.39,4677.616,6.799450,26251.94245,1.77260432866519
-c12,16329.212,8052.39,4677.616,6.799450,29066.01745,1.60098668212632
-c13,13515.137,8052.39,5262.139,6.799450,26836.46545,1.73399536959516
-c14,13515.137,6763.194,5262.139,16890.566,42431.036,1.09670447099924
-c15,13515.137,6763.194,5262.139,4.563278,25545.033278,1.82165770979483
-c16,16329.212,6763.194,5262.139,16890.566,45245.111,1.02849359563182
-c17,13515.137,6763.194,4677.616,16890.566,41846.513,1.11202352484657
-c18,13515.137,6763.194,4677.616,7.585986,24963.532986,1.86409138641106
-c19,13515.137,6763.194,5262.139,7.585986,25548.055986,1.82144218109417
-c20,13515.137,6763.194,4677.616,13499.556,38455.503,1.21008186732057
-c21,13515.137,6763.194,4677.616,5.046064,24960.993064,1.86428106823546
-c22,16329.212,6763.194,4677.616,4.563278,27774.585278,1.67542760284945
-c23,13515.137,8052.39,5262.139,11.525024,26841.191024,1.73369008793322
-c24,16329.212,6763.194,4677.616,6.799450,27776.82145,1.67529272261174
-c25,13515.137,8052.39,4677.616,5.046064,26250.189064,1.77272273008295
-c26,16329.212,6763.194,5262.139,15.343114,28369.888114,1.64027107364618
-c27,16329.212,6763.194,5262.139,6.799450,28361.34445,1.64076519425804
-c28,16329.212,8052.39,4677.616,13499.556,42558.774,1.09341276820283
-c29,13515.137,6763.194,4677.616,4.563278,24960.510278,1.86431712714556
-c30,13515.137,6763.194,5262.139,11.525024,25551.995024,1.82116139167122
-c31,13515.137,6763.194,5262.139,5.046064,25545.516064,1.82162328219378
-c32,16329.212,8052.39,5262.139,7.585986,29651.326986,1.56938361864995
-c33,16329.212,8052.39,5262.139,4.563278,29648.304278,1.56954362066419
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-c43,13515.137,8052.39,4677.616,15.343114,26260.486114,1.77202762434732
-c29,24960.510278
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,3266.286,1604.6779,1198.9482,3057.9248,9127.8369,0.999999989044502
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-c15,2948.6705,1507.4203,1198.9482,7.919790,5662.95879,1.61184940192988
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-c21,2948.6705,1507.4203,1242.0754,7.919790,5706.08599,1.5996668742865
-c22,3266.286,1507.4203,1242.0754,7.919790,6023.70149,1.51532023351774
-c23,2948.6705,1604.6779,1198.9482,7.919790,5760.21639,1.584634347658
-c24,3266.286,1507.4203,1242.0754,7.919790,6023.70149,1.51532023351774
-c25,2948.6705,1604.6779,1242.0754,7.919790,5803.34359,1.57285823269929
-c26,3266.286,1507.4203,1198.9482,7.919790,5980.57429,1.5262475315519
-c27,3266.286,1507.4203,1198.9482,7.919790,5980.57429,1.5262475315519
-c28,3266.286,1604.6779,1242.0754,2669.1484,8782.1877,1.03935797182565
-c29,2948.6705,1507.4203,1242.0754,7.919790,5706.08599,1.5996668742865
-c30,2948.6705,1507.4203,1198.9482,7.919790,5662.95879,1.61184940192988
-c31,2948.6705,1507.4203,1198.9482,7.919790,5662.95879,1.61184940192988
-c32,3266.286,1604.6779,1198.9482,7.919790,6077.83189,1.50182448527999
-c33,3266.286,1604.6779,1198.9482,7.919790,6077.83189,1.50182448527999
-c34,2948.6705,1507.4203,1198.9482,7.919790,5662.95879,1.61184940192988
-c35,2948.6705,1604.6779,1198.9482,7.919790,5760.21639,1.584634347658
-c36,2948.6705,1604.6779,1198.9482,7.919790,5760.21639,1.584634347658
-c37,3266.286,1604.6779,1198.9482,7.919790,6077.83189,1.50182448527999
-c38,2948.6705,1604.6779,1242.0754,7.919790,5803.34359,1.57285823269929
-c39,2948.6705,1604.6779,1198.9482,7.919790,5760.21639,1.584634347658
-c40,3266.286,1507.4203,1198.9482,7.919790,5980.57429,1.5262475315519
-c41,2948.6705,1604.6779,1242.0754,7.919790,5803.34359,1.57285823269929
-c42,2948.6705,1604.6779,1242.0754,7.919790,5803.34359,1.57285823269929
-c43,2948.6705,1604.6779,1242.0754,7.919790,5803.34359,1.57285823269929
-c10,5662.95879
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,16329.212,8052.39,5262.139,16890.566,46534.307,0.999999997851048
-c1,13729.886,6763.194,4677.616,82.701434,25253.397434,1.84269490619424
-c2,16329.212,8052.39,5262.139,16890.566,46534.307,0.999999997851048
-c3,16329.212,6982.906,4677.616,82.701434,28072.435434,1.65765121959724
-c4,13729.886,6763.194,4677.616,81.914898,25252.610898,1.84275230009624
-c5,13729.886,6763.194,4677.616,90.458562,25261.154562,1.84212905635707
-c6,13729.886,6763.194,4677.616,86.640472,25257.336472,1.84240752651597
-c7,13729.886,8223.137,4883.737,17104.016,43940.776,1.05902332935808
-c8,13729.886,8223.137,4883.737,13499.556,40336.316,1.1536578324266
-c9,16329.212,6982.906,4677.616,13499.556,41489.29,1.12159805308407
-c10,13729.886,6763.194,5411.551,81.914898,25986.545898,1.79070766094045
-c11,13729.886,8223.137,4883.737,81.914898,26918.674898,1.72869975968198
-c12,16329.212,8052.39,4883.737,81.914898,29347.253898,1.5856443333053
-c13,13729.886,8223.137,5262.139,81.914898,27297.076898,1.7047358954737
-c14,13729.886,6763.194,5411.551,16890.566,42795.197,1.08737218551098
-c15,13729.886,6763.194,5411.551,79.678726,25984.309726,1.79086176664341
-c16,16329.212,6982.906,5411.551,16890.566,45614.235,1.02017071859219
-c17,13729.886,6763.194,4677.616,17104.016,42274.712,1.10075987956876
-c18,13729.886,6763.194,4677.616,82.701434,25253.397434,1.84269490619424
-c19,13729.886,6763.194,5411.551,82.701434,25987.332434,1.79065346314855
-c20,13729.886,6763.194,4677.616,13499.556,38670.252,1.20336187309211
-c21,13729.886,6763.194,4677.616,80.161512,25250.857512,1.84288025836736
-c22,16329.212,6982.906,4677.616,79.678726,28069.412726,1.65782972691529
-c23,13729.886,8223.137,5262.139,86.640472,27301.802472,1.70444082866984
-c24,16329.212,6982.906,4677.616,81.914898,28071.648898,1.65769766511812
-c25,13729.886,8223.137,4883.737,80.161512,26916.921512,1.72881236832277
-c26,16329.212,6982.906,5411.551,90.458562,28814.127562,1.6149823290111
-c27,16329.212,6982.906,5411.551,81.914898,28805.583898,1.61546132872123
-c28,16329.212,8052.39,4883.737,13499.556,42764.895,1.08814266681084
-c29,13729.886,6763.194,4677.616,79.678726,25250.374726,1.84291549415275
-c30,13729.886,6763.194,5411.551,86.640472,25991.271472,1.79038208542789
-c31,13729.886,6763.194,5411.551,80.161512,25984.792512,1.79082849322069
-c32,16329.212,8052.39,5262.139,82.701434,29726.442434,1.56541795900319
-c33,16329.212,8052.39,5262.139,79.678726,29723.419726,1.5655771533831
-c34,13729.886,6763.194,5411.551,90.458562,25995.089562,1.79011911884361
-c35,13729.886,8223.137,5262.139,80.161512,27295.323512,1.70484540361126
-c36,13729.886,8223.137,5262.139,79.678726,27294.840726,1.70487555859543
-c37,16329.212,8052.39,5262.139,90.458562,29734.199562,1.56500956908117
-c38,13729.886,8223.137,4883.737,79.678726,26916.438726,1.72884337712053
-c39,13729.886,8223.137,5262.139,82.701434,27297.863434,1.70468677675235
-c40,16329.212,6982.906,5411.551,86.640472,28810.309472,1.615196354753
-c41,13729.886,8223.137,4883.737,82.701434,26919.461434,1.72864925032865
-c42,13729.886,8223.137,4883.737,86.640472,26923.400472,1.72839633966577
-c43,13729.886,8223.137,4883.737,90.458562,26927.218562,1.72815126523519
-c29,25250.374726
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,28.951489,28.951489,0
-c2,0,0,0,0,0,0
-c3,0,0,0,28.951489,28.951489,0
-c4,0,0,0,28.951489,28.951489,0
-c5,0,0,0,28.951489,28.951489,0
-c6,0,0,0,28.951489,28.951489,0
-c7,0,0,0,0,0,0
-c8,0,0,0,0,0,0
-c9,0,0,0,0,0,0
-c10,0,0,0,28.951489,28.951489,0
-c11,0,0,0,28.951489,28.951489,0
-c12,0,0,0,28.951489,28.951489,0
-c13,0,0,0,28.951489,28.951489,0
-c14,0,0,0,0,0,0
-c15,0,0,0,28.951489,28.951489,0
-c16,0,0,0,0,0,0
-c17,0,0,0,0,0,0
-c18,0,0,0,28.951489,28.951489,0
-c19,0,0,0,28.951489,28.951489,0
-c20,0,0,0,0,0,0
-c21,0,0,0,28.951489,28.951489,0
-c22,0,0,0,28.951489,28.951489,0
-c23,0,0,0,28.951489,28.951489,0
-c24,0,0,0,28.951489,28.951489,0
-c25,0,0,0,28.951489,28.951489,0
-c26,0,0,0,28.951489,28.951489,0
-c27,0,0,0,28.951489,28.951489,0
-c28,0,0,0,0,0,0
-c29,0,0,0,28.951489,28.951489,0
-c30,0,0,0,28.951489,28.951489,0
-c31,0,0,0,28.951489,28.951489,0
-c32,0,0,0,28.951489,28.951489,0
-c33,0,0,0,28.951489,28.951489,0
-c34,0,0,0,28.951489,28.951489,0
-c35,0,0,0,28.951489,28.951489,0
-c36,0,0,0,28.951489,28.951489,0
-c37,0,0,0,28.951489,28.951489,0
-c38,0,0,0,28.951489,28.951489,0
-c39,0,0,0,28.951489,28.951489,0
-c40,0,0,0,28.951489,28.951489,0
-c41,0,0,0,28.951489,28.951489,0
-c42,0,0,0,28.951489,28.951489,0
-c43,0,0,0,28.951489,28.951489,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,46.163959,46.163959,0
-c2,0,0,0,0,0,0
-c3,0,0,0,46.163959,46.163959,0
-c4,0,0,0,46.163959,46.163959,0
-c5,0,0,0,46.163959,46.163959,0
-c6,0,0,0,46.163959,46.163959,0
-c7,0,0,0,0,0,0
-c8,0,0,0,0,0,0
-c9,0,0,0,0,0,0
-c10,0,0,0,46.163959,46.163959,0
-c11,0,0,0,46.163959,46.163959,0
-c12,0,0,0,46.163959,46.163959,0
-c13,0,0,0,46.163959,46.163959,0
-c14,0,0,0,0,0,0
-c15,0,0,0,46.163959,46.163959,0
-c16,0,0,0,0,0,0
-c17,0,0,0,0,0,0
-c18,0,0,0,46.163959,46.163959,0
-c19,0,0,0,46.163959,46.163959,0
-c20,0,0,0,0,0,0
-c21,0,0,0,46.163959,46.163959,0
-c22,0,0,0,46.163959,46.163959,0
-c23,0,0,0,46.163959,46.163959,0
-c24,0,0,0,46.163959,46.163959,0
-c25,0,0,0,46.163959,46.163959,0
-c26,0,0,0,46.163959,46.163959,0
-c27,0,0,0,46.163959,46.163959,0
-c28,0,0,0,0,0,0
-c29,0,0,0,46.163959,46.163959,0
-c30,0,0,0,46.163959,46.163959,0
-c31,0,0,0,46.163959,46.163959,0
-c32,0,0,0,46.163959,46.163959,0
-c33,0,0,0,46.163959,46.163959,0
-c34,0,0,0,46.163959,46.163959,0
-c35,0,0,0,46.163959,46.163959,0
-c36,0,0,0,46.163959,46.163959,0
-c37,0,0,0,46.163959,46.163959,0
-c38,0,0,0,46.163959,46.163959,0
-c39,0,0,0,46.163959,46.163959,0
-c40,0,0,0,46.163959,46.163959,0
-c41,0,0,0,46.163959,46.163959,0
-c42,0,0,0,46.163959,46.163959,0
-c43,0,0,0,46.163959,46.163959,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,10.930367,10.930367,0
-c2,0,0,0,0,0,0
-c3,0,0,0,10.930367,10.930367,0
-c4,0,0,0,10.930367,10.930367,0
-c5,0,0,0,10.930367,10.930367,0
-c6,0,0,0,10.930367,10.930367,0
-c7,0,0,0,0,0,0
-c8,0,0,0,0,0,0
-c9,0,0,0,0,0,0
-c10,0,0,0,10.930367,10.930367,0
-c11,0,0,0,10.930367,10.930367,0
-c12,0,0,0,10.930367,10.930367,0
-c13,0,0,0,10.930367,10.930367,0
-c14,0,0,0,0,0,0
-c15,0,0,0,10.930367,10.930367,0
-c16,0,0,0,0,0,0
-c17,0,0,0,0,0,0
-c18,0,0,0,10.930367,10.930367,0
-c19,0,0,0,10.930367,10.930367,0
-c20,0,0,0,0,0,0
-c21,0,0,0,10.930367,10.930367,0
-c22,0,0,0,10.930367,10.930367,0
-c23,0,0,0,10.930367,10.930367,0
-c24,0,0,0,10.930367,10.930367,0
-c25,0,0,0,10.930367,10.930367,0
-c26,0,0,0,10.930367,10.930367,0
-c27,0,0,0,10.930367,10.930367,0
-c28,0,0,0,0,0,0
-c29,0,0,0,10.930367,10.930367,0
-c30,0,0,0,10.930367,10.930367,0
-c31,0,0,0,10.930367,10.930367,0
-c32,0,0,0,10.930367,10.930367,0
-c33,0,0,0,10.930367,10.930367,0
-c34,0,0,0,10.930367,10.930367,0
-c35,0,0,0,10.930367,10.930367,0
-c36,0,0,0,10.930367,10.930367,0
-c37,0,0,0,10.930367,10.930367,0
-c38,0,0,0,10.930367,10.930367,0
-c39,0,0,0,10.930367,10.930367,0
-c40,0,0,0,10.930367,10.930367,0
-c41,0,0,0,10.930367,10.930367,0
-c42,0,0,0,10.930367,10.930367,0
-c43,0,0,0,10.930367,10.930367,0
-c0,0
-
-Patch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,0,0,0
-c2,0,0,0,0,0,0
-c3,0,0,0,0,0,0
-c4,0,0,0,0,0,0
-c5,0,0,0,0,0,0
-c6,0,0,0,0,0,0
-c7,0,0,0,0,0,0
-c8,0,0,0,0,0,0
-c9,0,0,0,0,0,0
-c10,0,0,0,0,0,0
-c11,0,0,0,0,0,0
-c12,0,0,0,0,0,0
-c13,0,0,0,0,0,0
-c14,0,0,0,0,0,0
-c15,0,0,0,0,0,0
-c16,0,0,0,0,0,0
-c17,0,0,0,0,0,0
-c18,0,0,0,0,0,0
-c19,0,0,0,0,0,0
-c20,0,0,0,0,0,0
-c21,0,0,0,0,0,0
-c22,0,0,0,0,0,0
-c23,0,0,0,0,0,0
-c24,0,0,0,0,0,0
-c25,0,0,0,0,0,0
-c26,0,0,0,0,0,0
-c27,0,0,0,0,0,0
-c28,0,0,0,0,0,0
-c29,0,0,0,0,0,0
-c30,0,0,0,0,0,0
-c31,0,0,0,0,0,0
-c32,0,0,0,0,0,0
-c33,0,0,0,0,0,0
-c34,0,0,0,0,0,0
-c35,0,0,0,0,0,0
-c36,0,0,0,0,0,0
-c37,0,0,0,0,0,0
-c38,0,0,0,0,0,0
-c39,0,0,0,0,0,0
-c40,0,0,0,0,0,0
-c41,0,0,0,0,0,0
-c42,0,0,0,0,0,0
-c43,0,0,0,0,0,0
-c0,0
-
-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,214.749,0,0,0,214.749,0
-c2,0,0,0,0,0,0
-c3,0,219.712,0,0,219.712,0
-c4,214.749,0,0,0,214.749,0
-c5,214.749,0,0,0,214.749,0
-c6,214.749,0,0,0,214.749,0
-c7,214.749,170.747,206.121,213.45,805.067,0
-c8,214.749,170.747,206.121,0,591.617,0
-c9,0,219.712,0,0,219.712,0
-c10,214.749,0,149.412,0,364.161,0
-c11,214.749,170.747,206.121,0,591.617,0
-c12,0,0,206.121,0,206.121,0
-c13,214.749,170.747,0,0,385.496,0
-c14,214.749,0,149.412,0,364.161,0
-c15,214.749,0,149.412,0,364.161,0
-c16,0,219.712,149.412,0,369.124,0
-c17,214.749,0,0,213.45,428.199,0
-c18,214.749,0,0,0,214.749,0
-c19,214.749,0,149.412,0,364.161,0
-c20,214.749,0,0,0,214.749,0
-c21,214.749,0,0,0,214.749,0
-c22,0,219.712,0,0,219.712,0
-c23,214.749,170.747,0,0,385.496,0
-c24,0,219.712,0,0,219.712,0
-c25,214.749,170.747,206.121,0,591.617,0
-c26,0,219.712,149.412,0,369.124,0
-c27,0,219.712,149.412,0,369.124,0
-c28,0,0,206.121,0,206.121,0
-c29,214.749,0,0,0,214.749,0
-c30,214.749,0,149.412,0,364.161,0
-c31,214.749,0,149.412,0,364.161,0
-c32,0,0,0,0,0,0
-c33,0,0,0,0,0,0
-c34,214.749,0,149.412,0,364.161,0
-c35,214.749,170.747,0,0,385.496,0
-c36,214.749,170.747,0,0,385.496,0
-c37,0,0,0,0,0,0
-c38,214.749,170.747,206.121,0,591.617,0
-c39,214.749,170.747,0,0,385.496,0
-c40,0,219.712,149.412,0,369.124,0
-c41,214.749,170.747,206.121,0,591.617,0
-c42,214.749,170.747,206.121,0,591.617,0
-c43,214.749,170.747,206.121,0,591.617,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,135.06,0,0,0,135.06,0
-c2,0,0,0,0,0,0
-c3,0,45.7961,0,0,45.7961,0
-c4,135.06,0,0,0,135.06,0
-c5,135.06,0,0,0,135.06,0
-c6,135.06,0,0,0,135.06,0
-c7,135.06,36.4586,49.3758,36.6862,257.5806,0
-c8,135.06,36.4586,49.3758,0,220.8944,0
-c9,0,45.7961,0,0,45.7961,0
-c10,135.06,0,36.5783,0,171.6383,0
-c11,135.06,36.4586,49.3758,0,220.8944,0
-c12,0,0,49.3758,0,49.3758,0
-c13,135.06,36.4586,0,0,171.5186,0
-c14,135.06,0,36.5783,0,171.6383,0
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diff --git a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_results1.csv b/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_results1.csv
deleted file mode 100644
index 8a52300d986765ae762a169faa4b5ebc99509663..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_results1.csv
+++ /dev/null
@@ -1,143 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,16329.212,8052.39,5262.139,16890.566,46534.307,0.999999997851048
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-c8,16329.212,8052.39,5262.139,11.525024,29655.266024,1.56917516118123
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-Compute Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
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-Quantization Time
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-Time
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-c0,3266.286,1604.6779,1198.9482,3057.9248,9127.8369,0.999999989044502
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-Unpatch Energy
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diff --git a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_results2.csv b/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_results2.csv
deleted file mode 100644
index 070c3ca75d9394b53a9d5d2779218edec113762e..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_results2.csv
+++ /dev/null
@@ -1,121 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,16329.212,8052.39,5262.139,16890.566,46534.307,0.999999997851048
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-c6,16329.212,8052.39,5262.139,7.585986,29651.326986,1.56938361864995
-c5,24963.532986
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,3266.286,1604.6779,1198.9482,3057.9248,9127.8369,0.999999989044502
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-Energy
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-c5,25253.397434
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-Leakage Energy
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-c2,0,0,0,28.951489,28.951489,0
-c3,0,0,0,28.951489,28.951489,0
-c4,0,0,0,28.951489,28.951489,0
-c5,0,0,0,28.951489,28.951489,0
-c6,0,0,0,28.951489,28.951489,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,46.163959,46.163959,0
-c2,0,0,0,46.163959,46.163959,0
-c3,0,0,0,46.163959,46.163959,0
-c4,0,0,0,46.163959,46.163959,0
-c5,0,0,0,46.163959,46.163959,0
-c6,0,0,0,46.163959,46.163959,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,10.930367,10.930367,0
-c2,0,0,0,10.930367,10.930367,0
-c3,0,0,0,10.930367,10.930367,0
-c4,0,0,0,10.930367,10.930367,0
-c5,0,0,0,10.930367,10.930367,0
-c6,0,0,0,10.930367,10.930367,0
-c0,0
-
-Patch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,0,0,0
-c2,0,0,0,0,0,0
-c3,0,0,0,0,0,0
-c4,0,0,0,0,0,0
-c5,0,0,0,0,0,0
-c6,0,0,0,0,0,0
-c0,0
-
-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,214.749,0,0,0,214.749,0
-c2,214.749,0,149.412,0,364.161,0
-c3,0,0,0,0,0,0
-c4,214.749,0,0,0,214.749,0
-c5,214.749,0,0,0,214.749,0
-c6,0,0,0,0,0,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,135.06,0,0,0,135.06,0
-c2,135.06,0,36.5783,0,171.6383,0
-c3,0,0,0,0,0,0
-c4,135.06,0,0,0,135.06,0
-c5,135.06,0,0,0,135.06,0
-c6,0,0,0,0,0,0
-c0,0
-
-Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,3266.286,1604.6779,1198.9482,3057.9248,9127.8369,0.999999989044502
-c1,3083.7305,1507.4203,1242.0754,18.850157,5852.076357,1.5597603631924
-c2,3083.7305,1507.4203,1235.5265,18.850157,5845.527457,1.5615078042134
-c3,3266.286,1604.6779,1198.9482,18.850157,6088.762257,1.49912845416049
-c4,3083.7305,1507.4203,1242.0754,18.850157,5852.076357,1.5597603631924
-c5,3083.7305,1507.4203,1242.0754,18.850157,5852.076357,1.5597603631924
-c6,3266.286,1604.6779,1198.9482,18.850157,6088.762257,1.49912845416049
-c2,5845.527457
-
-Unpatch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,0,0,0
-c2,0,0,0,0,0,0
-c3,0,0,0,0,0,0
-c4,0,0,0,0,0,0
-c5,0,0,0,0,0,0
-c6,0,0,0,0,0,0
-c0,0
-
diff --git a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_tensors.txt b/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_tensors.txt
deleted file mode 100644
index 3fa0edb09a67a17457cfd7a9ed1d5557586b15cb..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GEOM/pipeline_GEOM_tensors.txt
+++ /dev/null
@@ -1,13 +0,0 @@
-#Conv1,2
-Conv1,3230.39,16104.1,2915.93,13352.1,135.06,214.749,36.5275,220.001
-ClipRelu1,35.896,225.112,32.7405,163.037,146.657,769.821,36.3842,177.909
-#Conv2,3
-Conv2,1531.88,7675.32,1440.78,6485.81,45.7961,219.712,36.4586,170.747
-Add1,36.9761,189.903,32.585,132.874,152.052,635.102,36.3936,148.401
-ClipRelu2,35.8218,187.167,34.0553,144.51,45.6549,185.567,36.3736,153.724
-#Conv3,2
-Conv3,1163.22,5096.2,1209.29,4559.4,49.3758,206.121,36.5783,149.412
-ClipRelu3,35.7282,165.939,32.7854,118.216,165.958,602.927,36.5443,132.298
-#Conv4,2
-Conv4,3022.09,16672.3,2636.43,13346.5,52.0572,187.688,36.6862,213.45
-ClipRelu4,35.8348,218.266,32.7184,153.056,165.584,825.553,36.6609,169.727
diff --git a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_confs1.txt b/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_confs1.txt
deleted file mode 100644
index 24c151f89f008f3d34a44852bf82df6fa6943607..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_confs1.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-9 9,9 9,9 9
-8 8,8 8,7
-8 8,9 9,7
-8 8,9 9,7
-8 8,8 8,7
-9 9,8 8,7
diff --git a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_confs2.txt b/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_confs2.txt
deleted file mode 100644
index 66478e91d8eccacfd04be6fe0b4a9051b95b8cd3..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_confs2.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-9 9,9 9,9 9
-7,8 8,6
-7,9 9,7
-7,8 8,7
-7,9 9,7
diff --git a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_fp16.csv b/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_fp16.csv
deleted file mode 100644
index 119769d485e8f3635444942e1577dfb1f30ffb63..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_fp16.csv
+++ /dev/null
@@ -1,18 +0,0 @@
-ClipRelu1,32.7258,163.65,121.949,41.7015,5000.72,3726.44,1274.27,0.1971,1.70103,1.4139,0.398118,49.4173,42.3377,10.335
-ClipRelu1_f2h,146.88,774.188,616.345,157.843,5271.44,4196.76,1074.68,4.2071,19.2749,14.9998,4.3002,23.6111,21.7033,3.0557
-ClipRelu1_h2f,36.6148,179.907,130.314,49.5932,4913.52,3559.06,1354.45,0.222917,1.8488,1.52567,0.447701,41.4702,36.6395,8.40931
-ClipRelu2,32.7748,123.92,80.2193,43.7008,3780.97,2447.6,1333.37,0.445617,2.16331,1.5542,0.692356,42.4077,34.4301,11.1092
-ClipRelu2_f2h,146.27,552.927,385.072,167.855,3780.54,2632.94,1147.6,3.76973,12.337,8.22917,4.16647,18.9891,17.2608,3.32655
-ClipRelu2_h2f,36.484,139.599,88.2673,51.3316,3826.26,2419.31,1406.95,0.289736,1.58681,1.08099,0.562266,24.5146,18.7651,8.30503
-ClipRelu3,32.4827,162.777,121.496,41.2813,5011.23,3740.36,1270.88,0.281157,2.02032,1.60136,0.508272,48.9996,40.6658,11.6879
-ClipRelu3_f2h,146.047,772.606,615.731,156.875,5290.56,4216.4,1074.16,3.56391,16.2321,12.5509,3.7181,21.601,20.3207,3.37736
-ClipRelu3_h2f,36.3734,179.038,130.004,49.0347,4922.22,3574.13,1348.09,0.280951,1.95425,1.54761,0.494932,35.4942,30.786,8.04821
-Conv1,2906.31,13461.6,10671.8,2789.79,4631.98,3672.06,959.919,17.7692,22.4048,12.9946,13.4788,22.4664,20.6187,2.76273
-Conv1_f2h,56.1955,96.0964,23.101,72.9955,1710.16,411.082,1299.08,0.937944,1.71283,1.24906,1.05611,23.7767,21.1557,14.9728
-Conv1_h2f,36.7417,222.237,189.427,32.8092,6050.45,5157.21,893.24,0.714378,1.89711,1.60827,0.301805,104.891,89.2244,15.8479
-Conv2,1133.43,4702.49,3426.56,1275.92,4149.01,3023.27,1125.73,5.59071,13.8692,13.5082,4.35632,21.9851,20.0789,2.81015
-Conv2_f2h,48.4445,232.944,162.751,70.1925,4808.55,3359.62,1448.93,0.734068,3.74504,2.72272,1.13338,37.893,33.4323,7.75873
-Conv2_h2f,36.5806,152.865,116.681,36.1843,4178.89,3189.72,989.176,0.326615,1.46034,1.11218,0.355733,22.9956,17.5344,5.81516
-Conv3,2473.6,13742.6,11357,2385.59,5555.74,4591.32,964.423,7.28207,12.9199,10.5659,5.71242,14.463,13.641,1.27044
-Conv3_f2h,48.5975,185.44,112.819,72.6212,3816.02,2321.67,1494.35,0.678009,2.51404,1.7155,1.06,34.9156,31.6881,7.7767
-Conv3_h2f,36.5346,221.214,188.635,32.5786,6056.57,5164.61,891.963,0.637827,2.10279,1.77584,0.340253,107.972,91.7763,16.4052
diff --git a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_fp32.csv b/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_fp32.csv
deleted file mode 100644
index 3b793e99df3c860b148a514488630229c6122385..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_fp32.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-ClipRelu1,35.9208,226.08,190.765,35.3154,6293.88,5310.73,983.149,0.271835,2.25039,1.86576,0.397483,44.304,36.3115,8.44462
-ClipRelu2,35.7686,170.519,130.589,39.9305,4767.29,3650.94,1116.35,0.291639,1.73338,1.30825,0.434816,28.4654,21.5327,7.40692
-ClipRelu3,35.5024,222.945,188.105,34.8406,6279.77,5298.41,981.364,0.35378,2.38839,1.9942,0.407469,36.9255,30.7992,6.8009
-Conv1,3133.21,16042.6,13049.2,2993.34,5120.24,4164.87,955.365,12.2858,15.5634,11.1935,8.89478,18.1658,16.3007,2.63801
-Conv2,1158.82,5542.63,4326.76,1215.87,4783.12,3733.88,1049.24,6.91453,11.4784,7.18899,6.17588,21.3002,20.0687,1.95484
-Conv3,2954.1,17122.9,14368.5,2754.37,5796.34,4863.95,932.392,6.76714,14.0092,11.3429,6.20296,12.2512,11.6083,1.38573
diff --git a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_layers.txt b/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_layers.txt
deleted file mode 100644
index deefedb4efd481a87ecb2d58d8e5d503ff1daa7e..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_layers.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-Conv1,2000,1,240,300,1,1,9,9,1,1
-Conv2,2000,1,240,300,1,1,3,3,1,1
-Conv3,2000,1,240,300,1,1,9,9,1,1
diff --git a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_ops.txt b/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_ops.txt
deleted file mode 100644
index 6dbd74c42edb6ae286efef7aaad8239709c29748..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_ops.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-#Conv1,2
-Conv1
-ClipRelu1
-#Conv2,2
-Conv2
-ClipRelu2
-#Conv3,2
-Conv3
-ClipRelu3
diff --git a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_promise_confs1.txt b/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_promise_confs1.txt
deleted file mode 100644
index e061e9d839507ea88117b1ebacf5a705d477171f..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_promise_confs1.txt
+++ /dev/null
@@ -1,14 +0,0 @@
-9 9,9 9,9 9
-8 8,9 9,9 9
-8 8,8 8,8 8
-8 8,8 8,9 9
-8 8,9 9,8 8
-8 8,8 8,7
-9 9,8 8,8 8
-9 9,9 9,8 8
-9 9,8 8,9 9
-8 8,9 9,9 9
-8 8,9 9,7
-9 9,9 9,9 9
-9 9,8 8,7
-9 9,9 9,7
diff --git a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_promise_confs2.txt b/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_promise_confs2.txt
deleted file mode 100644
index 4f35063f25b6b4b6f63252258c2545953e6b2a75..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_promise_confs2.txt
+++ /dev/null
@@ -1,52 +0,0 @@
-9 9,9 9,9 9
-7,9 9,3
-5,9 9,4
-4,9 9,5
-4,8 8,6
-4,9 9,6
-4,9 9,6
-5,8 8,5
-6,8 8,4
-6,9 9,4
-6,9 9,4
-5,9 9,5
-5,9 9,5
-4,9 9,7
-7,8 8,4
-5,8 8,6
-7,8 8,4
-5,9 9,6
-7,9 9,4
-6,8 8,5
-7,9 9,4
-6,9 9,5
-6,9 9,5
-5,8 8,7
-5,8 8,7
-5,9 9,7
-5,9 9,7
-7,8 8,5
-6,9 9,6
-6,9 9,6
-6,8 8,6
-9 9,8 8,3
-8 8,9 9,4
-7,9 9,9 9
-5,8 8,8 8
-3,9 9,8 8
-7,9 9,7
-7,8 8,7
-9 9,9 9,5
-8 8,8 8,4
-8 8,9 9,2
-8 8,9 9,8 8
-9 9,8 8,4
-8 8,9 9,7
-4,9 9,9 9
-8 8,9 9,6
-8 8,9 9,5
-8 8,8 8,3
-8 8,8 8,8 8
-8 8,8 8,5
-7,8 8,8 8
-9 9,9 9,2
diff --git a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_promise_results1.csv b/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_promise_results1.csv
deleted file mode 100644
index 408ee686dfca8f052cbf009b197115c6e2755081..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_promise_results1.csv
+++ /dev/null
@@ -1,198 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,16268.68,5713.149,17345.845,39327.674,0.999999997457261
-c1,13625.25,5713.149,17345.845,36684.244,1.07205899875691
-c2,13625.25,4826.41,13905.377,32357.037,1.21542877607913
-c3,13625.25,4826.41,17345.845,35797.505,1.09861494230222
-c4,13625.25,5713.149,13905.377,33243.776,1.18300862939574
-c5,13625.25,4826.41,15.343114,18467.003114,2.12961862540779
-c6,16268.68,4826.41,13905.377,35000.467,1.12363283288868
-c7,16268.68,5713.149,13905.377,35887.206,1.09586892583427
-c8,16268.68,4826.41,17345.845,38440.935,1.02306756840574
-c9,13625.25,5713.149,17345.845,36684.244,1.07205899875691
-c10,13625.25,5713.149,15.343114,19353.742114,2.03204494330566
-c11,16268.68,5713.149,17345.845,39327.674,0.999999997457261
-c12,16268.68,4826.41,15.343114,21110.433114,1.86294964207171
-c13,16268.68,5713.149,15.343114,21997.172114,1.78785134822785
-c5,18467.003114
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,3169.1308,1194.5886,2989.6024,7353.3218,0.999999986400704
-c1,2939.0358,1194.5886,2989.6024,7123.2268,1.03230205961851
-c2,2939.0358,1166.2048,2506.0827,6611.3233,1.11223144824529
-c3,2939.0358,1166.2048,2989.6024,7094.843,1.03643191207428
-c4,2939.0358,1194.5886,2506.0827,6639.7071,1.10747681765244
-c5,2939.0358,1166.2048,7.919790,4113.16039,1.78775465189786
-c6,3169.1308,1166.2048,2506.0827,6841.4183,1.0748241621942
-c7,3169.1308,1194.5886,2506.0827,6869.8021,1.07038333651004
-c8,3169.1308,1166.2048,2989.6024,7324.938,1.00387494059506
-c9,2939.0358,1194.5886,2989.6024,7123.2268,1.03230205961851
-c10,2939.0358,1194.5886,7.919790,4141.54419,1.77550239357696
-c11,3169.1308,1194.5886,2989.6024,7353.3218,0.999999986400704
-c12,3169.1308,1166.2048,7.919790,4343.25539,1.69304380479814
-c13,3169.1308,1194.5886,7.919790,4371.63919,1.6820513569865
-c5,4113.16039
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,16268.68,5713.149,17345.845,39327.674,0.999999997457261
-c1,13721.3464,5866.014,17345.845,36933.2054,1.06483240400024
-c2,13721.3464,4826.41,13905.377,32453.1334,1.2118297914129
-c3,13721.3464,4826.41,17567.059,36114.8154,1.08896234012327
-c4,13721.3464,5866.014,14090.817,33678.1774,1.1677494721916
-c5,13721.3464,4826.41,90.458562,18638.214962,2.11005581109439
-c6,16268.68,5059.354,13905.377,35233.411,1.11620398854881
-c7,16268.68,5713.149,14090.817,36072.646,1.09023535148978
-c8,16268.68,5059.354,17567.059,38895.093,1.01112173453057
-c9,13721.3464,5866.014,17345.845,36933.2054,1.06483240400024
-c10,13721.3464,5866.014,90.458562,19677.818962,1.99857890125365
-c11,16268.68,5713.149,17345.845,39327.674,0.999999997457261
-c12,16268.68,5059.354,90.458562,21418.492562,1.83615507499152
-c13,16268.68,5713.149,90.458562,22072.287562,1.78176701039046
-c5,18638.214962
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,0,0,0,0,0
-c2,0,0,0,0,0
-c3,0,0,0,0,0
-c4,0,0,0,0,0
-c5,0,0,28.951489,28.951489,0
-c6,0,0,0,0,0
-c7,0,0,0,0,0
-c8,0,0,0,0,0
-c9,0,0,0,0,0
-c10,0,0,28.951489,28.951489,0
-c11,0,0,0,0,0
-c12,0,0,28.951489,28.951489,0
-c13,0,0,28.951489,28.951489,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,0,0,0,0,0
-c2,0,0,0,0,0
-c3,0,0,0,0,0
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diff --git a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_promise_results2.csv b/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_promise_results2.csv
deleted file mode 100644
index 4bbd96279b68828be34786e140f3ba91321ce530..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_promise_results2.csv
+++ /dev/null
@@ -1,616 +0,0 @@
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-c50,90.458562,4826.41,13905.377,18822.245562,2.08942517838868
-c51,16268.68,5713.149,79.678726,22061.507726,1.78263762885923
-c7,4991.812868
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,28.951489,0,28.951489,57.902978,0
-c2,28.951489,0,28.951489,57.902978,0
-c3,28.951489,0,28.951489,57.902978,0
-c4,28.951489,0,28.951489,57.902978,0
-c5,28.951489,0,28.951489,57.902978,0
-c6,28.951489,0,28.951489,57.902978,0
-c7,28.951489,0,28.951489,57.902978,0
-c8,28.951489,0,28.951489,57.902978,0
-c9,28.951489,0,28.951489,57.902978,0
-c10,28.951489,0,28.951489,57.902978,0
-c11,28.951489,0,28.951489,57.902978,0
-c12,28.951489,0,28.951489,57.902978,0
-c13,28.951489,0,28.951489,57.902978,0
-c14,28.951489,0,28.951489,57.902978,0
-c15,28.951489,0,28.951489,57.902978,0
-c16,28.951489,0,28.951489,57.902978,0
-c17,28.951489,0,28.951489,57.902978,0
-c18,28.951489,0,28.951489,57.902978,0
-c19,28.951489,0,28.951489,57.902978,0
-c20,28.951489,0,28.951489,57.902978,0
-c21,28.951489,0,28.951489,57.902978,0
-c22,28.951489,0,28.951489,57.902978,0
-c23,28.951489,0,28.951489,57.902978,0
-c24,28.951489,0,28.951489,57.902978,0
-c25,28.951489,0,28.951489,57.902978,0
-c26,28.951489,0,28.951489,57.902978,0
-c27,28.951489,0,28.951489,57.902978,0
-c28,28.951489,0,28.951489,57.902978,0
-c29,28.951489,0,28.951489,57.902978,0
-c30,28.951489,0,28.951489,57.902978,0
-c31,0,0,28.951489,28.951489,0
-c32,0,0,28.951489,28.951489,0
-c33,28.951489,0,0,28.951489,0
-c34,28.951489,0,0,28.951489,0
-c35,28.951489,0,0,28.951489,0
-c36,28.951489,0,28.951489,57.902978,0
-c37,28.951489,0,28.951489,57.902978,0
-c38,0,0,28.951489,28.951489,0
-c39,0,0,28.951489,28.951489,0
-c40,0,0,28.951489,28.951489,0
-c41,0,0,0,0,0
-c42,0,0,28.951489,28.951489,0
-c43,0,0,28.951489,28.951489,0
-c44,28.951489,0,0,28.951489,0
-c45,0,0,28.951489,28.951489,0
-c46,0,0,28.951489,28.951489,0
-c47,0,0,28.951489,28.951489,0
-c48,0,0,0,0,0
-c49,0,0,28.951489,28.951489,0
-c50,28.951489,0,0,28.951489,0
-c51,0,0,28.951489,28.951489,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,46.163959,0,46.163959,92.327918,0
-c2,46.163959,0,46.163959,92.327918,0
-c3,46.163959,0,46.163959,92.327918,0
-c4,46.163959,0,46.163959,92.327918,0
-c5,46.163959,0,46.163959,92.327918,0
-c6,46.163959,0,46.163959,92.327918,0
-c7,46.163959,0,46.163959,92.327918,0
-c8,46.163959,0,46.163959,92.327918,0
-c9,46.163959,0,46.163959,92.327918,0
-c10,46.163959,0,46.163959,92.327918,0
-c11,46.163959,0,46.163959,92.327918,0
-c12,46.163959,0,46.163959,92.327918,0
-c13,46.163959,0,46.163959,92.327918,0
-c14,46.163959,0,46.163959,92.327918,0
-c15,46.163959,0,46.163959,92.327918,0
-c16,46.163959,0,46.163959,92.327918,0
-c17,46.163959,0,46.163959,92.327918,0
-c18,46.163959,0,46.163959,92.327918,0
-c19,46.163959,0,46.163959,92.327918,0
-c20,46.163959,0,46.163959,92.327918,0
-c21,46.163959,0,46.163959,92.327918,0
-c22,46.163959,0,46.163959,92.327918,0
-c23,46.163959,0,46.163959,92.327918,0
-c24,46.163959,0,46.163959,92.327918,0
-c25,46.163959,0,46.163959,92.327918,0
-c26,46.163959,0,46.163959,92.327918,0
-c27,46.163959,0,46.163959,92.327918,0
-c28,46.163959,0,46.163959,92.327918,0
-c29,46.163959,0,46.163959,92.327918,0
-c30,46.163959,0,46.163959,92.327918,0
-c31,0,0,46.163959,46.163959,0
-c32,0,0,46.163959,46.163959,0
-c33,46.163959,0,0,46.163959,0
-c34,46.163959,0,0,46.163959,0
-c35,46.163959,0,0,46.163959,0
-c36,46.163959,0,46.163959,92.327918,0
-c37,46.163959,0,46.163959,92.327918,0
-c38,0,0,46.163959,46.163959,0
-c39,0,0,46.163959,46.163959,0
-c40,0,0,46.163959,46.163959,0
-c41,0,0,0,0,0
-c42,0,0,46.163959,46.163959,0
-c43,0,0,46.163959,46.163959,0
-c44,46.163959,0,0,46.163959,0
-c45,0,0,46.163959,46.163959,0
-c46,0,0,46.163959,46.163959,0
-c47,0,0,46.163959,46.163959,0
-c48,0,0,0,0,0
-c49,0,0,46.163959,46.163959,0
-c50,46.163959,0,0,46.163959,0
-c51,0,0,46.163959,46.163959,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,10.930367,0,10.930367,21.860734,0
-c2,10.930367,0,10.930367,21.860734,0
-c3,10.930367,0,10.930367,21.860734,0
-c4,10.930367,0,10.930367,21.860734,0
-c5,10.930367,0,10.930367,21.860734,0
-c6,10.930367,0,10.930367,21.860734,0
-c7,10.930367,0,10.930367,21.860734,0
-c8,10.930367,0,10.930367,21.860734,0
-c9,10.930367,0,10.930367,21.860734,0
-c10,10.930367,0,10.930367,21.860734,0
-c11,10.930367,0,10.930367,21.860734,0
-c12,10.930367,0,10.930367,21.860734,0
-c13,10.930367,0,10.930367,21.860734,0
-c14,10.930367,0,10.930367,21.860734,0
-c15,10.930367,0,10.930367,21.860734,0
-c16,10.930367,0,10.930367,21.860734,0
-c17,10.930367,0,10.930367,21.860734,0
-c18,10.930367,0,10.930367,21.860734,0
-c19,10.930367,0,10.930367,21.860734,0
-c20,10.930367,0,10.930367,21.860734,0
-c21,10.930367,0,10.930367,21.860734,0
-c22,10.930367,0,10.930367,21.860734,0
-c23,10.930367,0,10.930367,21.860734,0
-c24,10.930367,0,10.930367,21.860734,0
-c25,10.930367,0,10.930367,21.860734,0
-c26,10.930367,0,10.930367,21.860734,0
-c27,10.930367,0,10.930367,21.860734,0
-c28,10.930367,0,10.930367,21.860734,0
-c29,10.930367,0,10.930367,21.860734,0
-c30,10.930367,0,10.930367,21.860734,0
-c31,0,0,10.930367,10.930367,0
-c32,0,0,10.930367,10.930367,0
-c33,10.930367,0,0,10.930367,0
-c34,10.930367,0,0,10.930367,0
-c35,10.930367,0,0,10.930367,0
-c36,10.930367,0,10.930367,21.860734,0
-c37,10.930367,0,10.930367,21.860734,0
-c38,0,0,10.930367,10.930367,0
-c39,0,0,10.930367,10.930367,0
-c40,0,0,10.930367,10.930367,0
-c41,0,0,0,0,0
-c42,0,0,10.930367,10.930367,0
-c43,0,0,10.930367,10.930367,0
-c44,10.930367,0,0,10.930367,0
-c45,0,0,10.930367,10.930367,0
-c46,0,0,10.930367,10.930367,0
-c47,0,0,10.930367,10.930367,0
-c48,0,0,0,0,0
-c49,0,0,10.930367,10.930367,0
-c50,10.930367,0,0,10.930367,0
-c51,0,0,10.930367,10.930367,0
-c0,0
-
-Patch Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,0,0,0,0,0
-c2,0,0,0,0,0
-c3,0,0,0,0,0
-c4,0,0,0,0,0
-c5,0,0,0,0,0
-c6,0,0,0,0,0
-c7,0,0,0,0,0
-c8,0,0,0,0,0
-c9,0,0,0,0,0
-c10,0,0,0,0,0
-c11,0,0,0,0,0
-c12,0,0,0,0,0
-c13,0,0,0,0,0
-c14,0,0,0,0,0
-c15,0,0,0,0,0
-c16,0,0,0,0,0
-c17,0,0,0,0,0
-c18,0,0,0,0,0
-c19,0,0,0,0,0
-c20,0,0,0,0,0
-c21,0,0,0,0,0
-c22,0,0,0,0,0
-c23,0,0,0,0,0
-c24,0,0,0,0,0
-c25,0,0,0,0,0
-c26,0,0,0,0,0
-c27,0,0,0,0,0
-c28,0,0,0,0,0
-c29,0,0,0,0,0
-c30,0,0,0,0,0
-c31,0,0,0,0,0
-c32,0,0,0,0,0
-c33,0,0,0,0,0
-c34,0,0,0,0,0
-c35,0,0,0,0,0
-c36,0,0,0,0,0
-c37,0,0,0,0,0
-c38,0,0,0,0,0
-c39,0,0,0,0,0
-c40,0,0,0,0,0
-c41,0,0,0,0,0
-c42,0,0,0,0,0
-c43,0,0,0,0,0
-c44,0,0,0,0,0
-c45,0,0,0,0,0
-c46,0,0,0,0,0
-c47,0,0,0,0,0
-c48,0,0,0,0,0
-c49,0,0,0,0,0
-c50,0,0,0,0,0
-c51,0,0,0,0,0
-c0,0
-
-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,0,0,0,0,0
-c2,0,0,0,0,0
-c3,0,0,0,0,0
-c4,0,0,0,0,0
-c5,0,0,0,0,0
-c6,0,0,0,0,0
-c7,0,0,0,0,0
-c8,0,0,0,0,0
-c9,0,0,0,0,0
-c10,0,0,0,0,0
-c11,0,0,0,0,0
-c12,0,0,0,0,0
-c13,0,0,0,0,0
-c14,0,0,0,0,0
-c15,0,0,0,0,0
-c16,0,0,0,0,0
-c17,0,0,0,0,0
-c18,0,0,0,0,0
-c19,0,0,0,0,0
-c20,0,0,0,0,0
-c21,0,0,0,0,0
-c22,0,0,0,0,0
-c23,0,0,0,0,0
-c24,0,0,0,0,0
-c25,0,0,0,0,0
-c26,0,0,0,0,0
-c27,0,0,0,0,0
-c28,0,0,0,0,0
-c29,0,0,0,0,0
-c30,0,0,0,0,0
-c31,0,232.944,0,232.944,0
-c32,96.0964,152.865,0,248.9614,0
-c33,0,0,0,0,0
-c34,0,0,0,0,0
-c35,0,0,185.44,185.44,0
-c36,0,0,0,0,0
-c37,0,0,0,0,0
-c38,0,0,0,0,0
-c39,96.0964,0,0,96.0964,0
-c40,96.0964,152.865,0,248.9614,0
-c41,96.0964,152.865,185.44,434.4014,0
-c42,0,232.944,0,232.944,0
-c43,96.0964,152.865,0,248.9614,0
-c44,0,0,0,0,0
-c45,96.0964,152.865,0,248.9614,0
-c46,96.0964,152.865,0,248.9614,0
-c47,96.0964,0,0,96.0964,0
-c48,96.0964,0,0,96.0964,0
-c49,96.0964,0,0,96.0964,0
-c50,0,0,0,0,0
-c51,0,0,0,0,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,0,0,0,0,0
-c2,0,0,0,0,0
-c3,0,0,0,0,0
-c4,0,0,0,0,0
-c5,0,0,0,0,0
-c6,0,0,0,0,0
-c7,0,0,0,0,0
-c8,0,0,0,0,0
-c9,0,0,0,0,0
-c10,0,0,0,0,0
-c11,0,0,0,0,0
-c12,0,0,0,0,0
-c13,0,0,0,0,0
-c14,0,0,0,0,0
-c15,0,0,0,0,0
-c16,0,0,0,0,0
-c17,0,0,0,0,0
-c18,0,0,0,0,0
-c19,0,0,0,0,0
-c20,0,0,0,0,0
-c21,0,0,0,0,0
-c22,0,0,0,0,0
-c23,0,0,0,0,0
-c24,0,0,0,0,0
-c25,0,0,0,0,0
-c26,0,0,0,0,0
-c27,0,0,0,0,0
-c28,0,0,0,0,0
-c29,0,0,0,0,0
-c30,0,0,0,0,0
-c31,0,48.4445,0,48.4445,0
-c32,56.1955,36.5806,0,92.7761,0
-c33,0,0,0,0,0
-c34,0,0,0,0,0
-c35,0,0,48.5975,48.5975,0
-c36,0,0,0,0,0
-c37,0,0,0,0,0
-c38,0,0,0,0,0
-c39,56.1955,0,0,56.1955,0
-c40,56.1955,36.5806,0,92.7761,0
-c41,56.1955,36.5806,48.5975,141.3736,0
-c42,0,48.4445,0,48.4445,0
-c43,56.1955,36.5806,0,92.7761,0
-c44,0,0,0,0,0
-c45,56.1955,36.5806,0,92.7761,0
-c46,56.1955,36.5806,0,92.7761,0
-c47,56.1955,0,0,56.1955,0
-c48,56.1955,0,0,56.1955,0
-c49,56.1955,0,0,56.1955,0
-c50,0,0,0,0,0
-c51,0,0,0,0,0
-c0,0
-
-Time
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,3169.1308,1194.5886,2989.6024,7353.3218,0.999999986400704
-c1,18.850157,1194.5886,18.850157,1232.288914,5.96720551466346
-c2,18.850157,1194.5886,18.850157,1232.288914,5.96720551466346
-c3,18.850157,1194.5886,18.850157,1232.288914,5.96720551466346
-c4,18.850157,1166.2048,18.850157,1203.905114,6.10789098218824
-c5,18.850157,1194.5886,18.850157,1232.288914,5.96720551466346
-c6,18.850157,1194.5886,18.850157,1232.288914,5.96720551466346
-c7,18.850157,1166.2048,18.850157,1203.905114,6.10789098218824
-c8,18.850157,1166.2048,18.850157,1203.905114,6.10789098218824
-c9,18.850157,1194.5886,18.850157,1232.288914,5.96720551466346
-c10,18.850157,1194.5886,18.850157,1232.288914,5.96720551466346
-c11,18.850157,1194.5886,18.850157,1232.288914,5.96720551466346
-c12,18.850157,1194.5886,18.850157,1232.288914,5.96720551466346
-c13,18.850157,1194.5886,18.850157,1232.288914,5.96720551466346
-c14,18.850157,1166.2048,18.850157,1203.905114,6.10789098218824
-c15,18.850157,1166.2048,18.850157,1203.905114,6.10789098218824
-c16,18.850157,1166.2048,18.850157,1203.905114,6.10789098218824
-c17,18.850157,1194.5886,18.850157,1232.288914,5.96720551466346
-c18,18.850157,1194.5886,18.850157,1232.288914,5.96720551466346
-c19,18.850157,1166.2048,18.850157,1203.905114,6.10789098218824
-c20,18.850157,1194.5886,18.850157,1232.288914,5.96720551466346
-c21,18.850157,1194.5886,18.850157,1232.288914,5.96720551466346
-c22,18.850157,1194.5886,18.850157,1232.288914,5.96720551466346
-c23,18.850157,1166.2048,18.850157,1203.905114,6.10789098218824
-c24,18.850157,1166.2048,18.850157,1203.905114,6.10789098218824
-c25,18.850157,1194.5886,18.850157,1232.288914,5.96720551466346
-c26,18.850157,1194.5886,18.850157,1232.288914,5.96720551466346
-c27,18.850157,1166.2048,18.850157,1203.905114,6.10789098218824
-c28,18.850157,1194.5886,18.850157,1232.288914,5.96720551466346
-c29,18.850157,1194.5886,18.850157,1232.288914,5.96720551466346
-c30,18.850157,1166.2048,18.850157,1203.905114,6.10789098218824
-c31,3169.1308,1214.6493,18.850157,4402.630257,1.67021103379904
-c32,2995.2313,1231.1692,18.850157,4245.250657,1.73212896502641
-c33,18.850157,1194.5886,2989.6024,4203.041157,1.74952405897832
-c34,18.850157,1166.2048,2506.0827,3691.137657,1.99215588365807
-c35,18.850157,1194.5886,2554.6802,3768.118957,1.95145686449047
-c36,18.850157,1194.5886,18.850157,1232.288914,5.96720551466346
-c37,18.850157,1166.2048,18.850157,1203.905114,6.10789098218824
-c38,3169.1308,1194.5886,18.850157,4382.569557,1.67785622945091
-c39,2995.2313,1166.2048,18.850157,4180.286257,1.75904738862845
-c40,2995.2313,1231.1692,18.850157,4245.250657,1.73212896502641
-c41,2995.2313,1231.1692,2554.6802,6781.0807,1.08438787515996
-c42,3169.1308,1214.6493,18.850157,4402.630257,1.67021103379904
-c43,2995.2313,1231.1692,18.850157,4245.250657,1.73212896502641
-c44,18.850157,1194.5886,2989.6024,4203.041157,1.74952405897832
-c45,2995.2313,1231.1692,18.850157,4245.250657,1.73212896502641
-c46,2995.2313,1231.1692,18.850157,4245.250657,1.73212896502641
-c47,2995.2313,1166.2048,18.850157,4180.286257,1.75904738862845
-c48,2995.2313,1166.2048,2506.0827,6667.5188,1.10285728623881
-c49,2995.2313,1166.2048,18.850157,4180.286257,1.75904738862845
-c50,18.850157,1166.2048,2506.0827,3691.137657,1.99215588365807
-c51,3169.1308,1194.5886,18.850157,4382.569557,1.67785622945091
-c4,1203.905114
-
-Unpatch Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,0,0,0,0,0
-c2,0,0,0,0,0
-c3,0,0,0,0,0
-c4,0,0,0,0,0
-c5,0,0,0,0,0
-c6,0,0,0,0,0
-c7,0,0,0,0,0
-c8,0,0,0,0,0
-c9,0,0,0,0,0
-c10,0,0,0,0,0
-c11,0,0,0,0,0
-c12,0,0,0,0,0
-c13,0,0,0,0,0
-c14,0,0,0,0,0
-c15,0,0,0,0,0
-c16,0,0,0,0,0
-c17,0,0,0,0,0
-c18,0,0,0,0,0
-c19,0,0,0,0,0
-c20,0,0,0,0,0
-c21,0,0,0,0,0
-c22,0,0,0,0,0
-c23,0,0,0,0,0
-c24,0,0,0,0,0
-c25,0,0,0,0,0
-c26,0,0,0,0,0
-c27,0,0,0,0,0
-c28,0,0,0,0,0
-c29,0,0,0,0,0
-c30,0,0,0,0,0
-c31,0,0,0,0,0
-c32,0,0,0,0,0
-c33,0,0,0,0,0
-c34,0,0,0,0,0
-c35,0,0,0,0,0
-c36,0,0,0,0,0
-c37,0,0,0,0,0
-c38,0,0,0,0,0
-c39,0,0,0,0,0
-c40,0,0,0,0,0
-c41,0,0,0,0,0
-c42,0,0,0,0,0
-c43,0,0,0,0,0
-c44,0,0,0,0,0
-c45,0,0,0,0,0
-c46,0,0,0,0,0
-c47,0,0,0,0,0
-c48,0,0,0,0,0
-c49,0,0,0,0,0
-c50,0,0,0,0,0
-c51,0,0,0,0,0
-c0,0
-
diff --git a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_results1.csv b/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_results1.csv
deleted file mode 100644
index 8be42209fe3813932175240022e7550b115fb013..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_results1.csv
+++ /dev/null
@@ -1,110 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,16268.68,5713.149,17345.845,39327.674,0.999999997457261
-c1,13625.25,4826.41,15.343114,18467.003114,2.12961862540779
-c2,13625.25,5713.149,15.343114,19353.742114,2.03204494330566
-c3,13625.25,5713.149,15.343114,19353.742114,2.03204494330566
-c4,13625.25,4826.41,15.343114,18467.003114,2.12961862540779
-c5,16268.68,4826.41,15.343114,21110.433114,1.86294964207171
-c1,18467.003114
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,3169.1308,1194.5886,2989.6024,7353.3218,0.999999986400704
-c1,2939.0358,1166.2048,7.919790,4113.16039,1.78775465189786
-c2,2939.0358,1194.5886,7.919790,4141.54419,1.77550239357696
-c3,2939.0358,1194.5886,7.919790,4141.54419,1.77550239357696
-c4,2939.0358,1166.2048,7.919790,4113.16039,1.78775465189786
-c5,3169.1308,1166.2048,7.919790,4343.25539,1.69304380479814
-c1,4113.16039
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,16268.68,5713.149,17345.845,39327.674,0.999999997457261
-c1,13721.3464,4826.41,90.458562,18638.214962,2.11005581109439
-c2,13721.3464,5866.014,90.458562,19677.818962,1.99857890125365
-c3,13721.3464,5866.014,90.458562,19677.818962,1.99857890125365
-c4,13721.3464,4826.41,90.458562,18638.214962,2.11005581109439
-c5,16268.68,5059.354,90.458562,21418.492562,1.83615507499152
-c1,18638.214962
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,0,0,28.951489,28.951489,0
-c2,0,0,28.951489,28.951489,0
-c3,0,0,28.951489,28.951489,0
-c4,0,0,28.951489,28.951489,0
-c5,0,0,28.951489,28.951489,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,0,0,46.163959,46.163959,0
-c2,0,0,46.163959,46.163959,0
-c3,0,0,46.163959,46.163959,0
-c4,0,0,46.163959,46.163959,0
-c5,0,0,46.163959,46.163959,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,0,0,10.930367,10.930367,0
-c2,0,0,10.930367,10.930367,0
-c3,0,0,10.930367,10.930367,0
-c4,0,0,10.930367,10.930367,0
-c5,0,0,10.930367,10.930367,0
-c0,0
-
-Patch Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,0,0,0,0,0
-c2,0,0,0,0,0
-c3,0,0,0,0,0
-c4,0,0,0,0,0
-c5,0,0,0,0,0
-c0,0
-
-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,96.0964,0,0,96.0964,0
-c2,96.0964,152.865,0,248.9614,0
-c3,96.0964,152.865,0,248.9614,0
-c4,96.0964,0,0,96.0964,0
-c5,0,232.944,0,232.944,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,56.1955,0,0,56.1955,0
-c2,56.1955,36.5806,0,92.7761,0
-c3,56.1955,36.5806,0,92.7761,0
-c4,56.1955,0,0,56.1955,0
-c5,0,48.4445,0,48.4445,0
-c0,0
-
-Time
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,3169.1308,1194.5886,2989.6024,7353.3218,0.999999986400704
-c1,2995.2313,1166.2048,18.850157,4180.286257,1.75904738862845
-c2,2995.2313,1231.1692,18.850157,4245.250657,1.73212896502641
-c3,2995.2313,1231.1692,18.850157,4245.250657,1.73212896502641
-c4,2995.2313,1166.2048,18.850157,4180.286257,1.75904738862845
-c5,3169.1308,1214.6493,18.850157,4402.630257,1.67021103379904
-c1,4180.286257
-
-Unpatch Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,0,0,0,0,0
-c2,0,0,0,0,0
-c3,0,0,0,0,0
-c4,0,0,0,0,0
-c5,0,0,0,0,0
-c0,0
-
diff --git a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_results2.csv b/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_results2.csv
deleted file mode 100644
index 3962cd8a7becc7eb4f032529b362789713a3e8d4..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_results2.csv
+++ /dev/null
@@ -1,99 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,16268.68,5713.149,17345.845,39327.674,0.999999997457261
-c1,15.343114,4826.41,11.525024,4853.278138,8.10332152236272
-c2,15.343114,5713.149,15.343114,5743.835228,6.84693619405936
-c3,15.343114,4826.41,15.343114,4857.096228,8.09695162380975
-c4,15.343114,5713.149,15.343114,5743.835228,6.84693619405936
-c1,4853.278138
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,3169.1308,1194.5886,2989.6024,7353.3218,0.999999986400704
-c1,7.919790,1166.2048,7.919790,1182.04438,6.2208503355136
-c2,7.919790,1194.5886,7.919790,1210.42818,6.07497521455794
-c3,7.919790,1166.2048,7.919790,1182.04438,6.2208503355136
-c4,7.919790,1194.5886,7.919790,1210.42818,6.07497521455794
-c1,1182.04438
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,16268.68,5713.149,17345.845,39327.674,0.999999997457261
-c1,90.458562,4826.41,86.640472,5003.509034,7.86001842841844
-c2,90.458562,5713.149,90.458562,5894.066124,6.67241807359781
-c3,90.458562,4826.41,90.458562,5007.327124,7.85402515967069
-c4,90.458562,5713.149,90.458562,5894.066124,6.67241807359781
-c1,5003.509034
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,28.951489,0,28.951489,57.902978,0
-c2,28.951489,0,28.951489,57.902978,0
-c3,28.951489,0,28.951489,57.902978,0
-c4,28.951489,0,28.951489,57.902978,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,46.163959,0,46.163959,92.327918,0
-c2,46.163959,0,46.163959,92.327918,0
-c3,46.163959,0,46.163959,92.327918,0
-c4,46.163959,0,46.163959,92.327918,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,10.930367,0,10.930367,21.860734,0
-c2,10.930367,0,10.930367,21.860734,0
-c3,10.930367,0,10.930367,21.860734,0
-c4,10.930367,0,10.930367,21.860734,0
-c0,0
-
-Patch Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,0,0,0,0,0
-c2,0,0,0,0,0
-c3,0,0,0,0,0
-c4,0,0,0,0,0
-c0,0
-
-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,0,0,0,0,0
-c2,0,0,0,0,0
-c3,0,0,0,0,0
-c4,0,0,0,0,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,0,0,0,0,0
-c2,0,0,0,0,0
-c3,0,0,0,0,0
-c4,0,0,0,0,0
-c0,0
-
-Time
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,3169.1308,1194.5886,2989.6024,7353.3218,0.999999986400704
-c1,18.850157,1166.2048,18.850157,1203.905114,6.10789098218824
-c2,18.850157,1194.5886,18.850157,1232.288914,5.96720551466346
-c3,18.850157,1166.2048,18.850157,1203.905114,6.10789098218824
-c4,18.850157,1194.5886,18.850157,1232.288914,5.96720551466346
-c1,1203.905114
-
-Unpatch Energy
-Configuration,Conv1,Conv2,Conv3,Total,Improvement
-c0,0,0,0,0,0
-c1,0,0,0,0,0
-c2,0,0,0,0,0
-c3,0,0,0,0,0
-c4,0,0,0,0,0
-c0,0
-
diff --git a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_tensors.txt b/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_tensors.txt
deleted file mode 100644
index 9f9f7622734cbfa5fe8a93b9ba57d47c4a474881..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSM/pipeline_GSM_tensors.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-#Conv1,2
-Conv1,3133.21,16042.6,2906.31,13461.6,56.1955,96.0964,36.7417,222.237
-ClipRelu1,35.9208,226.08,32.7258,163.65,146.88,774.188,36.6148,179.907
-#Conv2,2
-Conv2,1158.82,5542.63,1133.43,4702.49,48.4445,232.944,36.5806,152.865
-ClipRelu2,35.7686,170.519,32.7748,123.92,146.27,552.927,36.484,139.599
-#Conv3,2
-Conv3,2954.1,17122.9,2473.6,13742.6,48.5975,185.44,36.5346,221.214
-ClipRelu3,35.5024,222.945,32.4827,162.777,146.047,772.606,36.3734,179.038
diff --git a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_confs1.txt b/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_confs1.txt
deleted file mode 100644
index 8e8d9f3412cb2b8697b1255f266a911b0de7fb5b..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_confs1.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-9 9,9 9,9 9,9 9 9
-8 8,8 8,8 8,8 8 8
-8 8,8 8,8 8,8 8 8
-8 8,8 8,8 8,8 8 8
-8 8,9 9,8 8,8 8 8
-8 8,8 8,8 8,8 8 8
-8 8,8 8,8 8,8 8 8
-8 8,8 8,8 8,8 8 8
-9 9,9 9,9 9,9 9 9
diff --git a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_confs2.txt b/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_confs2.txt
deleted file mode 100644
index f959aacca24ebf7e053b7942c15670ba9f4a6250..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_confs2.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-9 9,9 9,9 9,9 9 9
-7,8 8,8 8,8 8 8
-7,8 8,8 8,8 8 8
-7,8 8,8 8,8 8 8
-7,8 8,8 8,8 8 8
-7,9 9,8 8,8 8 8
-7,8 8,8 8,8 8 8
-7,8 8,8 8,8 8 8
-7,9 9,9 9,9 9 9
-7,9 9,9 9,8 8 8
diff --git a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_fp16.csv b/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_fp16.csv
deleted file mode 100644
index adf98595058033c6e719a7ab8ea4e0e0863b70fa..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_fp16.csv
+++ /dev/null
@@ -1,27 +0,0 @@
-Add1,32.7422,134.826,92.2852,42.5406,4117.81,2818.53,1299.28,0.480545,2.45071,1.88399,0.67206,44.7915,39.1725,10.2198
-Add1_f2h,146.433,615.386,452.33,163.055,4202.81,3089.28,1113.53,3.30273,12.1106,8.61188,3.58125,21.0664,19.9283,2.66236
-Add1_h2f,36.3565,149.573,99.4412,50.1316,4114.05,2735.15,1378.89,0.1795,1.48908,1.19317,0.397784,33.5047,28.1316,8.81403
-ClipRelu1,32.8093,164.115,122.311,41.8039,5002.18,3728.01,1274.17,0.383646,2.27289,1.77877,0.560485,50.4256,41.0598,12.0637
-ClipRelu1_f2h,146.021,769.892,612.882,157.01,5272.86,4197.58,1075.28,3.20992,14.4719,11.2309,3.26632,21.7023,19.7576,3.00022
-ClipRelu1_h2f,36.6263,179.956,130.385,49.5713,4913.3,3559.88,1353.42,0.306986,2.11226,1.60989,0.59845,41.1508,34.7001,9.69627
-ClipRelu2,32.6475,123.226,79.6998,43.5266,3774.46,2441.22,1333.23,0.223447,1.71043,1.36201,0.456888,46.0505,38.219,11.3045
-ClipRelu2_f2h,145.654,550.465,383.19,167.275,3779.64,2631.15,1148.48,3.23396,10.0407,6.6131,3.49054,19.7919,17.7688,3.39794
-ClipRelu2_h2f,36.5405,139.548,88.1952,51.353,3819.03,2413.65,1405.38,0.321831,1.60076,1.17453,0.528099,32.0447,26.5949,8.35867
-ClipRelu3,32.6239,163.406,121.952,41.4536,5008.81,3738.16,1270.65,0.252253,1.7972,1.46955,0.42257,41.824,36.9792,8.17033
-ClipRelu3_f2h,145.606,770.056,613.619,156.437,5289.04,4214.63,1074.41,3.44964,15.7416,12.2167,3.55151,21.0011,19.3086,3.1549
-ClipRelu3_h2f,36.4944,179.388,130.181,49.207,4916.63,3567.99,1348.65,0.558737,2.22449,1.73305,0.576189,92.4995,69.5845,24.4802
-ClipRelu4,33.7585,143.672,89.0642,54.6075,4255.92,2638.32,1617.6,0.301993,1.86943,1.33236,0.632059,45.4988,34.9092,13.4074
-ClipRelu4_f2h,49.1495,200.587,128.424,72.1624,4081.34,2613.1,1468.24,0.679613,2.60138,1.81128,1.01555,34.1158,30.7982,8.29963
-ClipRelu4_h2f,36.4394,154.389,93.9278,60.4616,4236.9,2577.66,1659.24,0.260819,1.49794,1.07238,0.537429,29.9756,24.2888,9.27329
-Conv1,2912.47,13477.7,10682,2795.64,4627.7,3667.8,959.897,16.874,19.5344,10.4216,12.2431,21.5177,19.3465,3.10583
-Conv1_f2h,56.6917,96.9595,23.2424,73.7171,1710.36,409.948,1300.41,0.996916,1.96889,1.07875,1.27633,23.3814,16.9444,15.6866
-Conv1_h2f,36.8048,222.547,189.687,32.8596,6047.08,5154.23,892.85,0.767615,5.8828,4.95376,0.934317,115.286,97.0952,18.4143
-Conv2,1136.75,4708.62,3430.35,1278.27,4142.27,3017.77,1124.5,5.26944,10.5517,10.949,4.42046,20.5193,19.0805,2.22352
-Conv2_f2h,49.0656,235.394,164.481,70.9135,4797.65,3352.36,1445.29,0.628751,3.06546,2.23285,0.944601,35.6073,30.5252,8.25377
-Conv2_h2f,36.5672,152.421,116.314,36.1071,4168.22,3180.81,987.413,0.17204,1.2502,0.951453,0.307672,25.1303,19.1077,6.35024
-Conv3,2476.66,13751.7,11364.3,2387.45,5552.57,4588.58,963.982,5.39738,10.1281,10.6951,4.05957,13.5026,12.5865,1.43885
-Conv3_f2h,49.3364,187.77,114.095,73.6744,3806.12,2312.79,1493.33,0.690044,2.50089,1.74511,1.02682,36.6426,32.9583,7.7257
-Conv3_h2f,36.496,221.431,188.804,32.6269,6067.25,5173.27,893.979,0.29224,2.18121,1.83368,0.35668,31.4194,26.5476,5.39186
-Conv4,1437.6,6507.38,4976.29,1531.09,4526.62,3461.58,1065.04,4.87199,11.4425,12.0961,4.02899,17.7563,16.7398,1.63006
-Conv4_f2h,49.0482,236.319,165.433,70.8861,4818.2,3372.97,1445.23,0.679402,3.35184,2.39746,1.04878,34.0698,29.9535,6.64119
-Conv4_h2f,36.5705,171.537,137.169,34.3684,4691.32,3751.38,939.935,0.503451,2.74819,2.18383,0.570163,87.6813,69.6122,18.1999
diff --git a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_fp32.csv b/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_fp32.csv
deleted file mode 100644
index 07f6d29eb8878988dd7385f39fc7dfc58c2ca13b..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_fp32.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-Add1,36.8449,189.882,150.771,39.1102,5153.49,4092.03,1061.46,0.403419,2.58036,2.0136,0.575255,34.4887,26.8533,8.13957
-ClipRelu1,35.9733,226.348,190.976,35.3723,6292.16,5308.86,983.295,0.377922,2.50327,2.09712,0.419746,29.9011,24.7858,5.90174
-ClipRelu2,35.7905,170.526,130.612,39.9134,4764.52,3649.33,1115.19,0.263273,1.68219,1.29396,0.400556,27.1081,20.8148,6.87275
-ClipRelu3,35.7453,224.772,189.644,35.1276,6288.11,5305.4,982.715,0.26882,2.28079,1.92129,0.371073,37.9406,31.6551,6.78118
-ClipRelu4,35.5248,186.169,140.805,45.3642,5240.54,3963.56,1276.97,0.290282,1.78105,1.34507,0.450777,27.7985,20.973,7.55094
-Conv1,3133.72,16043.1,13048.7,2994.38,5119.56,4164.02,955.538,12.8997,20.1128,14.6161,10.0908,16.3762,15.0127,2.44283
-Conv2,1160.86,5545.29,4327.83,1217.46,4776.99,3728.23,1048.76,7.23604,11.931,7.55606,6.20326,22.2041,20.7609,2.04701
-Conv3,2955.11,17131.1,14376.3,2754.85,5797.15,4864.91,932.236,7.81069,12.3346,8.71001,7.15089,12.9919,12.528,1.32979
-Conv4,1532.34,7694.78,6158.49,1536.29,5021.69,4019.1,1002.58,7.70784,12.3714,7.83712,6.94529,19.857,18.8424,2.02751
diff --git a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_layers.txt b/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_layers.txt
deleted file mode 100644
index cb7b918f537fcbb77d6c71844162161ca21ca01a..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_layers.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-Conv1,2000,1,240,300,1,1,9,9,1,1
-Conv2,2000,1,240,300,1,1,3,3,1,1
-Conv3,2000,1,240,300,1,1,9,9,1,1
-Conv4,2000,1,240,300,1,1,5,5,1,1
diff --git a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_ops.txt b/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_ops.txt
deleted file mode 100644
index d80034c986694bf89a332c0a382f4f3281728537..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_ops.txt
+++ /dev/null
@@ -1,13 +0,0 @@
-#Conv1,2
-Conv1
-ClipRelu1
-#Conv2,2
-Conv2
-ClipRelu2
-#Conv3,2
-Conv3
-ClipRelu3
-#Conv4,3
-Conv4
-Add1
-ClipRelu4
diff --git a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_promise_confs1.txt b/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_promise_confs1.txt
deleted file mode 100644
index 8e7edaca0182fd1a9b2f6e510e33db9c51b923f5..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_promise_confs1.txt
+++ /dev/null
@@ -1,14 +0,0 @@
-9 9,9 9,9 9,9 9 9
-8 8,8 8,9 9,9 9 9
-8 8,9 9,8 8,8 8 8
-8 8,9 9,9 9,8 8 8
-8 8,8 8,8 8,8 8 8
-8 8,8 8,9 9,9 9 9
-8 8,9 9,8 8,9 9 9
-8 8,9 9,9 9,9 9 9
-9 9,8 8,8 8,8 8 8
-8 8,8 8,8 8,9 9 9
-9 9,9 9,8 8,8 8 8
-9 9,9 9,8 8,9 9 9
-9 9,9 9,9 9,9 9 9
-8 8,9 9,9 9,9 9 9
diff --git a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_promise_confs2.txt b/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_promise_confs2.txt
deleted file mode 100644
index 4c0531b59728b06aa987b947d9ed062aa5f80b72..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_promise_confs2.txt
+++ /dev/null
@@ -1,52 +0,0 @@
-9 9,9 9,9 9,9 9 9
-7,9 9,6,9 9 9
-7,8 8,6,9 9 9
-7,9 9,5,8 8 8
-5,9 9,7,9 9 9
-7,8 8,5,8 8 8
-5,9 9,7,8 8 8
-5,8 8,7,8 8 8
-6,9 9,6,8 8 8
-6,9 9,6,9 9 9
-6,8 8,6,8 8 8
-6,8 8,6,9 9 9
-9 9,8 8,4,8 8 8
-8 8,8 8,4,9 9 9
-9 9,8 8,4,9 9 9
-5,9 9,9 9,8 8 8
-5,9 9,8 8,8 8 8
-5,8 8,8 8,8 8 8
-5,8 8,8 8,9 9 9
-5,9 9,8 8,9 9 9
-5,9 9,9 9,8 8 8
-5,8 8,9 9,9 9 9
-5,8 8,9 9,8 8 8
-5,8 8,8 8,9 9 9
-5,9 9,9 9,9 9 9
-7,9 9,6,9 9 9
-8 8,9 9,6,8 8 8
-7,8 8,6,8 8 8
-7,8 8,8 8,9 9 9
-6,8 8,8 8,8 8 8
-7,9 9,6,8 8 8
-9 9,8 8,6,8 8 8
-9 9,9 9,6,8 8 8
-7,9 9,7,9 9 9
-6,8 8,8 8,9 9 9
-8 8,8 8,8 8,8 8 8
-8 8,8 8,8 8,9 9 9
-7,8 8,8 8,8 8 8
-8 8,8 8,6,8 8 8
-7,8 8,9 9,9 9 9
-7,8 8,9 9,8 8 8
-7,8 8,6,9 9 9
-6,8 8,9 9,9 9 9
-8 8,8 8,5,9 9 9
-8 8,8 8,5,8 8 8
-8 8,9 9,6,8 8 8
-7,8 8,7,8 8 8
-7,9 9,8 8,9 9 9
-6,9 9,8 8,9 9 9
-8 8,9 9,8 8,9 9 9
-7,9 9,7,8 8 8
-6,8 8,7,9 9 9
diff --git a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_promise_results1.csv b/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_promise_results1.csv
deleted file mode 100644
index 62918ed77910a43edb399dfc9a5e48e8fc956b37..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_promise_results1.csv
+++ /dev/null
@@ -1,198 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,16269.448,5715.816,17355.872,8070.831,47411.967,0.999999997890828
-c1,13641.815,4831.846,17355.872,8070.831,43900.364,1.07999029101447
-c2,13641.815,5715.816,13915.106,6785.878,40058.615,1.18356480576384
-c3,13641.815,5715.816,17355.872,6785.878,43499.381,1.08994578315966
-c4,13641.815,4831.846,13915.106,6785.878,39174.645,1.21027176835866
-c5,13641.815,4831.846,17355.872,8070.831,43900.364,1.07999029101447
-c6,13641.815,5715.816,13915.106,8070.831,41343.568,1.14677975750235
-c7,13641.815,5715.816,17355.872,8070.831,44784.334,1.05867303718601
-c8,16269.448,4831.846,13915.106,6785.878,41802.278,1.13419577006259
-c9,13641.815,4831.846,13915.106,8070.831,40459.598,1.1718348482557
-c10,16269.448,5715.816,13915.106,6785.878,42686.248,1.11070822830175
-c11,16269.448,5715.816,13915.106,8070.831,43971.201,1.07825044151455
-c12,16269.448,5715.816,17355.872,8070.831,47411.967,0.999999997890828
-c13,13641.815,5715.816,17355.872,8070.831,44784.334,1.05867303718601
-c4,39174.645
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,3169.6933,1196.6505,2990.8553,1604.7097,8961.9088,0.999999988841663
-c1,2945.2793,1169.3975,2990.8553,1604.7097,8710.2418,1.02889321592779
-c2,2945.2793,1196.6505,2509.2839,1504.1007,8155.3144,1.09890413177812
-c3,2945.2793,1196.6505,2990.8553,1504.1007,8636.8858,1.03763195482298
-c4,2945.2793,1169.3975,2509.2839,1504.1007,8128.0614,1.10258870457612
-c5,2945.2793,1169.3975,2990.8553,1604.7097,8710.2418,1.02889321592779
-c6,2945.2793,1196.6505,2509.2839,1604.7097,8255.9234,1.08551257772677
-c7,2945.2793,1196.6505,2990.8553,1604.7097,8737.4948,1.02568400926906
-c8,3169.6933,1169.3975,2509.2839,1504.1007,8352.4754,1.07296439241276
-c9,2945.2793,1169.3975,2509.2839,1604.7097,8228.6704,1.08910774832945
-c10,3169.6933,1196.6505,2509.2839,1504.1007,8379.7284,1.06947484038415
-c11,3169.6933,1196.6505,2509.2839,1604.7097,8480.3374,1.05678680830804
-c12,3169.6933,1196.6505,2990.8553,1604.7097,8961.9088,0.999999988841663
-c13,2945.2793,1196.6505,2990.8553,1604.7097,8737.4948,1.02568400926906
-c4,8128.0614
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,16269.448,5715.816,17355.872,8070.831,47411.967,0.999999997890828
-c1,13738.7745,4831.846,17577.303,8070.831,44218.7545,1.07221398315908
-c2,13738.7745,5868.237,14102.876,6785.878,40495.7655,1.17078826137812
-c3,13738.7745,5868.237,17355.872,7022.197,43985.0805,1.07791019939611
-c4,13738.7745,4831.846,13915.106,6785.878,39271.6045,1.20728367182634
-c5,13738.7745,4831.846,17577.303,8070.831,44218.7545,1.07221398315908
-c6,13738.7745,5868.237,14102.876,8242.368,41952.2555,1.1301410692206
-c7,13738.7745,5868.237,17355.872,8070.831,45033.7145,1.05281048701232
-c8,16269.448,5067.24,13915.106,6785.878,42037.672,1.1278447314403
-c9,13738.7745,4831.846,13915.106,8242.368,40728.0945,1.16410962667524
-c10,16269.448,5715.816,14102.876,6785.878,42874.018,1.10584379773819
-c11,16269.448,5715.816,14102.876,8242.368,44330.508,1.06951102146289
-c12,16269.448,5715.816,17355.872,8070.831,47411.967,0.999999997890828
-c13,13738.7745,5868.237,17355.872,8070.831,45033.7145,1.05281048701232
-c4,39271.6045
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,0,0,0
-c2,0,0,0,0,0,0
-c3,0,0,0,0,0,0
-c4,0,0,0,0,0,0
-c5,0,0,0,0,0,0
-c6,0,0,0,0,0,0
-c7,0,0,0,0,0,0
-c8,0,0,0,0,0,0
-c9,0,0,0,0,0,0
-c10,0,0,0,0,0,0
-c11,0,0,0,0,0,0
-c12,0,0,0,0,0,0
-c13,0,0,0,0,0,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,0,0,0
-c2,0,0,0,0,0,0
-c3,0,0,0,0,0,0
-c4,0,0,0,0,0,0
-c5,0,0,0,0,0,0
-c6,0,0,0,0,0,0
-c7,0,0,0,0,0,0
-c8,0,0,0,0,0,0
-c9,0,0,0,0,0,0
-c10,0,0,0,0,0,0
-c11,0,0,0,0,0,0
-c12,0,0,0,0,0,0
-c13,0,0,0,0,0,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,0,0,0
-c2,0,0,0,0,0,0
-c3,0,0,0,0,0,0
-c4,0,0,0,0,0,0
-c5,0,0,0,0,0,0
-c6,0,0,0,0,0,0
-c7,0,0,0,0,0,0
-c8,0,0,0,0,0,0
-c9,0,0,0,0,0,0
-c10,0,0,0,0,0,0
-c11,0,0,0,0,0,0
-c12,0,0,0,0,0,0
-c13,0,0,0,0,0,0
-c0,0
-
-Patch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,0,0,0
-c2,0,0,0,0,0,0
-c3,0,0,0,0,0,0
-c4,0,0,0,0,0,0
-c5,0,0,0,0,0,0
-c6,0,0,0,0,0,0
-c7,0,0,0,0,0,0
-c8,0,0,0,0,0,0
-c9,0,0,0,0,0,0
-c10,0,0,0,0,0,0
-c11,0,0,0,0,0,0
-c12,0,0,0,0,0,0
-c13,0,0,0,0,0,0
-c0,0
-
-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,96.9595,0,221.431,0,318.3905,0
-c2,96.9595,152.421,187.77,0,437.1505,0
-c3,96.9595,152.421,0,236.319,485.6995,0
-c4,96.9595,0,0,0,96.9595,0
-c5,96.9595,0,221.431,0,318.3905,0
-c6,96.9595,152.421,187.77,171.537,608.6875,0
-c7,96.9595,152.421,0,0,249.3805,0
-c8,0,235.394,0,0,235.394,0
-c9,96.9595,0,0,171.537,268.4965,0
-c10,0,0,187.77,0,187.77,0
-c11,0,0,187.77,171.537,359.307,0
-c12,0,0,0,0,0,0
-c13,96.9595,152.421,0,0,249.3805,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,56.6917,0,36.496,0,93.1877,0
-c2,56.6917,36.5672,49.3364,0,142.5953,0
-c3,56.6917,36.5672,0,49.0482,142.3071,0
-c4,56.6917,0,0,0,56.6917,0
-c5,56.6917,0,36.496,0,93.1877,0
-c6,56.6917,36.5672,49.3364,36.5705,179.1658,0
-c7,56.6917,36.5672,0,0,93.2589,0
-c8,0,49.0656,0,0,49.0656,0
-c9,56.6917,0,0,36.5705,93.2622,0
-c10,0,0,49.3364,0,49.3364,0
-c11,0,0,49.3364,36.5705,85.9069,0
-c12,0,0,0,0,0,0
-c13,56.6917,36.5672,0,0,93.2589,0
-c0,0
-
-Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,3169.6933,1196.6505,2990.8553,1604.7097,8961.9088,0.999999988841663
-c1,3001.971,1169.3975,3027.3513,1604.7097,8803.4295,1.01800198413582
-c2,3001.971,1233.2177,2558.6203,1504.1007,8297.9097,1.08002003106855
-c3,3001.971,1233.2177,2990.8553,1553.1489,8779.1929,1.02081236851724
-c4,3001.971,1169.3975,2509.2839,1504.1007,8184.7531,1.09495162297624
-c5,3001.971,1169.3975,3027.3513,1604.7097,8803.4295,1.01800198413582
-c6,3001.971,1233.2177,2558.6203,1641.2802,8435.0892,1.06245571104979
-c7,3001.971,1233.2177,2990.8553,1604.7097,8830.7537,1.01485207299064
-c8,3169.6933,1218.4631,2509.2839,1504.1007,8401.541,1.06669820373788
-c9,3001.971,1169.3975,2509.2839,1641.2802,8321.9326,1.07690234024603
-c10,3169.6933,1196.6505,2558.6203,1504.1007,8429.0648,1.06321506671517
-c11,3169.6933,1196.6505,2558.6203,1641.2802,8566.2443,1.04618878256614
-c12,3169.6933,1196.6505,2990.8553,1604.7097,8961.9088,0.999999988841663
-c13,3001.971,1233.2177,2990.8553,1604.7097,8830.7537,1.01485207299064
-c4,8184.7531
-
-Unpatch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,0,0,0
-c2,0,0,0,0,0,0
-c3,0,0,0,0,0,0
-c4,0,0,0,0,0,0
-c5,0,0,0,0,0,0
-c6,0,0,0,0,0,0
-c7,0,0,0,0,0,0
-c8,0,0,0,0,0,0
-c9,0,0,0,0,0,0
-c10,0,0,0,0,0,0
-c11,0,0,0,0,0,0
-c12,0,0,0,0,0,0
-c13,0,0,0,0,0,0
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-
diff --git a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_promise_results2.csv b/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_promise_results2.csv
deleted file mode 100644
index 2923a28122195bfcd79bed384207073372a8eab9..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_promise_results2.csv
+++ /dev/null
@@ -1,616 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,16269.448,5715.816,17355.872,8070.831,47411.967,0.999999997890828
-c1,15.343114,5715.816,11.525024,8070.831,13813.515138,3.43228831931014
-c2,15.343114,4831.846,11.525024,8070.831,12929.545138,3.66694776399838
-c3,15.343114,5715.816,7.585986,6785.878,12524.6231,3.78550046918777
-c4,7.585986,5715.816,15.343114,8070.831,13809.5761,3.43326734385955
-c5,15.343114,4831.846,7.585986,6785.878,11640.6531,4.07296447934726
-c6,7.585986,5715.816,15.343114,6785.878,12524.6231,3.78550046918777
-c7,7.585986,4831.846,15.343114,6785.878,11640.6531,4.07296447934726
-c8,11.525024,5715.816,11.525024,6785.878,12524.744048,3.78546391365375
-c9,11.525024,5715.816,11.525024,8070.831,13809.697048,3.43323727463976
-c10,11.525024,4831.846,11.525024,6785.878,11640.774048,4.07292216112155
-c11,11.525024,4831.846,11.525024,8070.831,12925.727048,3.66803093219681
-c12,16269.448,4831.846,6.799450,6785.878,27893.97145,1.6997209205227
-c13,13641.815,4831.846,6.799450,8070.831,26551.29145,1.78567460308725
-c14,16269.448,4831.846,6.799450,8070.831,29178.92445,1.62487026959326
-c15,7.585986,5715.816,17355.872,6785.878,29865.151986,1.58753475835221
-c16,7.585986,5715.816,13915.106,6785.878,26424.385986,1.79425046416195
-c17,7.585986,4831.846,13915.106,6785.878,25540.415986,1.85635061074784
-c18,7.585986,4831.846,13915.106,8070.831,26825.368986,1.7674301832717
-c19,7.585986,5715.816,13915.106,8070.831,27709.338986,1.71104647616639
-c20,7.585986,5715.816,17355.872,6785.878,29865.151986,1.58753475835221
-c21,7.585986,4831.846,17355.872,8070.831,30266.134986,1.56650219346741
-c22,7.585986,4831.846,17355.872,6785.878,28981.181986,1.635956975782
-c23,7.585986,4831.846,13915.106,8070.831,26825.368986,1.7674301832717
-c24,7.585986,5715.816,17355.872,8070.831,31150.104986,1.52204838054651
-c25,15.343114,5715.816,11.525024,8070.831,13813.515138,3.43228831931014
-c26,13641.815,5715.816,11.525024,6785.878,26155.034024,1.81272816449872
-c27,15.343114,4831.846,11.525024,6785.878,11644.592138,4.0715867100335
-c28,15.343114,4831.846,13915.106,8070.831,26833.126114,1.76691924086218
-c29,11.525024,4831.846,13915.106,6785.878,25544.355024,1.85606435432987
-c30,15.343114,5715.816,11.525024,6785.878,12528.562138,3.7843102903058
-c31,16269.448,4831.846,11.525024,6785.878,27898.697024,1.69943301614661
-c32,16269.448,5715.816,11.525024,6785.878,28782.667024,1.64724022258751
-c33,15.343114,5715.816,15.343114,8070.831,13817.333228,3.43133988842279
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-c37,15.343114,4831.846,13915.106,6785.878,25548.173114,1.85578697165005
-c38,13641.815,4831.846,11.525024,6785.878,25271.064024,1.87613654760872
-c39,15.343114,4831.846,17355.872,8070.831,30273.892114,1.56610080609571
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-c43,13641.815,4831.846,7.585986,8070.831,26552.077986,1.78562170713857
-c44,13641.815,4831.846,7.585986,6785.878,25267.124986,1.87642902936631
-c45,13641.815,5715.816,11.525024,6785.878,26155.034024,1.81272816449872
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-c49,13641.815,5715.816,13915.106,8070.831,41343.568,1.14677975750235
-c50,15.343114,5715.816,15.343114,6785.878,12532.380228,3.78315736987902
-c51,11.525024,4831.846,15.343114,8070.831,12929.545138,3.66694776399838
-c5,11640.6531
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,3169.6933,1196.6505,2990.8553,1604.7097,8961.9088,0.999999988841663
-c1,7.919790,1196.6505,7.919790,1604.7097,2817.19978,3.18114055861737
-c2,7.919790,1169.3975,7.919790,1604.7097,2789.94678,3.21221485048489
-c3,7.919790,1196.6505,7.919790,1504.1007,2716.59078,3.29895416567106
-c4,7.919790,1196.6505,7.919790,1604.7097,2817.19978,3.18114055861737
-c5,7.919790,1169.3975,7.919790,1504.1007,2689.33778,3.33238484708362
-c6,7.919790,1196.6505,7.919790,1504.1007,2716.59078,3.29895416567106
-c7,7.919790,1169.3975,7.919790,1504.1007,2689.33778,3.33238484708362
-c8,7.919790,1196.6505,7.919790,1504.1007,2716.59078,3.29895416567106
-c9,7.919790,1196.6505,7.919790,1604.7097,2817.19978,3.18114055861737
-c10,7.919790,1169.3975,7.919790,1504.1007,2689.33778,3.33238484708362
-c11,7.919790,1169.3975,7.919790,1604.7097,2789.94678,3.21221485048489
-c12,3169.6933,1169.3975,7.919790,1504.1007,5851.11129,1.53165923576786
-c13,2945.2793,1169.3975,7.919790,1604.7097,5727.30629,1.56476852987081
-c14,3169.6933,1169.3975,7.919790,1604.7097,5951.72029,1.50576778019641
-c15,7.919790,1196.6505,2990.8553,1504.1007,5699.52629,1.57239535125655
-c16,7.919790,1196.6505,2509.2839,1504.1007,5217.95489,1.71751362692379
-c17,7.919790,1169.3975,2509.2839,1504.1007,5190.70189,1.72653117386922
-c18,7.919790,1169.3975,2509.2839,1604.7097,5291.31089,1.6937029059409
-c19,7.919790,1196.6505,2509.2839,1604.7097,5318.56389,1.68502415630427
-c20,7.919790,1196.6505,2990.8553,1504.1007,5699.52629,1.57239535125655
-c21,7.919790,1169.3975,2990.8553,1604.7097,5772.88229,1.55241492803043
-c22,7.919790,1169.3975,2990.8553,1504.1007,5672.27329,1.57995008065011
-c23,7.919790,1169.3975,2509.2839,1604.7097,5291.31089,1.6937029059409
-c24,7.919790,1196.6505,2990.8553,1604.7097,5800.13529,1.54512062174466
-c25,7.919790,1196.6505,7.919790,1604.7097,2817.19978,3.18114055861737
-c26,2945.2793,1196.6505,7.919790,1504.1007,5653.95029,1.58507029277277
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-c28,7.919790,1169.3975,2509.2839,1604.7097,5291.31089,1.6937029059409
-c29,7.919790,1169.3975,2509.2839,1504.1007,5190.70189,1.72653117386922
-c30,7.919790,1196.6505,7.919790,1504.1007,2716.59078,3.29895416567106
-c31,3169.6933,1169.3975,7.919790,1504.1007,5851.11129,1.53165923576786
-c32,3169.6933,1196.6505,7.919790,1504.1007,5878.36429,1.5245582283476
-c33,7.919790,1196.6505,7.919790,1604.7097,2817.19978,3.18114055861737
-c34,7.919790,1169.3975,2509.2839,1604.7097,5291.31089,1.6937029059409
-c35,2945.2793,1169.3975,2509.2839,1504.1007,8128.0614,1.10258870457612
-c36,2945.2793,1169.3975,2509.2839,1604.7097,8228.6704,1.08910774832945
-c37,7.919790,1169.3975,2509.2839,1504.1007,5190.70189,1.72653117386922
-c38,2945.2793,1169.3975,7.919790,1504.1007,5626.69729,1.59274760642495
-c39,7.919790,1169.3975,2990.8553,1604.7097,5772.88229,1.55241492803043
-c40,7.919790,1169.3975,2990.8553,1504.1007,5672.27329,1.57995008065011
-c41,7.919790,1169.3975,7.919790,1604.7097,2789.94678,3.21221485048489
-c42,7.919790,1169.3975,2990.8553,1604.7097,5772.88229,1.55241492803043
-c43,2945.2793,1169.3975,7.919790,1604.7097,5727.30629,1.56476852987081
-c44,2945.2793,1169.3975,7.919790,1504.1007,5626.69729,1.59274760642495
-c45,2945.2793,1196.6505,7.919790,1504.1007,5653.95029,1.58507029277277
-c46,7.919790,1169.3975,7.919790,1504.1007,2689.33778,3.33238484708362
-c47,7.919790,1196.6505,2509.2839,1604.7097,5318.56389,1.68502415630427
-c48,7.919790,1196.6505,2509.2839,1604.7097,5318.56389,1.68502415630427
-c49,2945.2793,1196.6505,2509.2839,1604.7097,8255.9234,1.08551257772677
-c50,7.919790,1196.6505,7.919790,1504.1007,2716.59078,3.29895416567106
-c51,7.919790,1169.3975,7.919790,1604.7097,2789.94678,3.21221485048489
-c5,2689.33778
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,16269.448,5715.816,17355.872,8070.831,47411.967,0.999999997890828
-c1,90.458562,5715.816,86.640472,8070.831,13963.746034,3.39536157024208
-c2,90.458562,4831.846,86.640472,8070.831,13079.776034,3.62483015873305
-c3,90.458562,5715.816,82.701434,6785.878,12674.853996,3.74063217145533
-c4,82.701434,5715.816,90.458562,8070.831,13959.806996,3.39631963922949
-c5,90.458562,4831.846,82.701434,6785.878,11790.883996,4.02106971911328
-c6,82.701434,5715.816,90.458562,6785.878,12674.853996,3.74063217145533
-c7,82.701434,4831.846,90.458562,6785.878,11790.883996,4.02106971911328
-c8,86.640472,5715.816,86.640472,6785.878,12674.974944,3.74059647734325
-c9,86.640472,5715.816,86.640472,8070.831,13959.927944,3.3962902137148
-c10,86.640472,4831.846,86.640472,6785.878,11791.004944,4.02102847238846
-c11,86.640472,4831.846,86.640472,8070.831,13075.957944,3.62588858425982
-c12,16269.448,5067.24,81.914898,6785.878,28204.480898,1.68100831223812
-c13,13738.7745,4831.846,81.914898,8070.831,26723.366398,1.77417643108507
-c14,16269.448,5067.24,81.914898,8070.831,29489.433898,1.60776117314478
-c15,82.701434,5715.816,17355.872,7022.197,30176.586434,1.57115076440408
-c16,82.701434,5715.816,14102.876,6785.878,26687.271434,1.77657603324478
-c17,82.701434,4831.846,13915.106,6785.878,25615.531434,1.8509070146395
-c18,82.701434,4831.846,13915.106,8242.368,27072.021434,1.75132717519654
-c19,82.701434,5715.816,14102.876,8242.368,28143.761434,1.68463504577106
-c20,82.701434,5715.816,17355.872,7022.197,30176.586434,1.57115076440408
-c21,82.701434,4831.846,17577.303,8070.831,30562.681434,1.5513025893116
-c22,82.701434,4831.846,17577.303,7022.197,29514.047434,1.60642036458679
-c23,82.701434,4831.846,13915.106,8242.368,27072.021434,1.75132717519654
-c24,82.701434,5715.816,17355.872,8070.831,31225.220434,1.51838693816029
-c25,90.458562,5715.816,86.640472,8070.831,13963.746034,3.39536157024208
-c26,13738.7745,5868.237,86.640472,6785.878,26479.529972,1.79051391286337
-c27,90.458562,4831.846,86.640472,6785.878,11794.823034,4.0197268294197
-c28,90.458562,4831.846,13915.106,8242.368,27079.778562,1.75082549941707
-c29,86.640472,4831.846,13915.106,6785.878,25619.470472,1.85062243447831
-c30,90.458562,5715.816,86.640472,6785.878,12678.793034,3.7394700346406
-c31,16269.448,5067.24,86.640472,6785.878,28209.206472,1.68072671164953
-c32,16269.448,5715.816,86.640472,6785.878,28857.782472,1.6429525339207
-c33,90.458562,5715.816,90.458562,8070.831,13967.564124,3.39443343446623
-c34,86.640472,4831.846,13915.106,8242.368,27075.960472,1.75107239035612
-c35,13738.7745,4831.846,13915.106,6785.878,39271.6045,1.20728367182634
-c36,13738.7745,4831.846,13915.106,8242.368,40728.0945,1.16410962667524
-c37,90.458562,4831.846,13915.106,6785.878,25623.288562,1.85034667584701
-c38,13738.7745,4831.846,86.640472,6785.878,25443.138972,1.86344801503587
-c39,90.458562,4831.846,17577.303,8070.831,30570.438562,1.55090895241011
-c40,90.458562,4831.846,17577.303,7022.197,29521.804562,1.60599826273588
-c41,90.458562,4831.846,86.640472,8070.831,13079.776034,3.62483015873305
-c42,86.640472,4831.846,17577.303,8070.831,30566.620472,1.55110267712849
-c43,13738.7745,4831.846,82.701434,8070.831,26724.152934,1.77412421413991
-c44,13738.7745,4831.846,82.701434,6785.878,25439.199934,1.86373655369009
-c45,13738.7745,5868.237,86.640472,6785.878,26479.529972,1.79051391286337
-c46,90.458562,4831.846,90.458562,6785.878,11798.641124,4.01842602888524
-c47,90.458562,5715.816,14102.876,8242.368,28151.518562,1.6841708459586
-c48,86.640472,5715.816,14102.876,8242.368,28147.700472,1.68439929502317
-c49,13738.7745,5868.237,14102.876,8242.368,41952.2555,1.1301410692206
-c50,90.458562,5715.816,90.458562,6785.878,12682.611124,3.73834427016731
-c51,86.640472,4831.846,90.458562,8070.831,13079.776034,3.62483015873305
-c5,11790.883996
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,28.951489,0,28.951489,0,57.902978,0
-c2,28.951489,0,28.951489,0,57.902978,0
-c3,28.951489,0,28.951489,0,57.902978,0
-c4,28.951489,0,28.951489,0,57.902978,0
-c5,28.951489,0,28.951489,0,57.902978,0
-c6,28.951489,0,28.951489,0,57.902978,0
-c7,28.951489,0,28.951489,0,57.902978,0
-c8,28.951489,0,28.951489,0,57.902978,0
-c9,28.951489,0,28.951489,0,57.902978,0
-c10,28.951489,0,28.951489,0,57.902978,0
-c11,28.951489,0,28.951489,0,57.902978,0
-c12,0,0,28.951489,0,28.951489,0
-c13,0,0,28.951489,0,28.951489,0
-c14,0,0,28.951489,0,28.951489,0
-c15,28.951489,0,0,0,28.951489,0
-c16,28.951489,0,0,0,28.951489,0
-c17,28.951489,0,0,0,28.951489,0
-c18,28.951489,0,0,0,28.951489,0
-c19,28.951489,0,0,0,28.951489,0
-c20,28.951489,0,0,0,28.951489,0
-c21,28.951489,0,0,0,28.951489,0
-c22,28.951489,0,0,0,28.951489,0
-c23,28.951489,0,0,0,28.951489,0
-c24,28.951489,0,0,0,28.951489,0
-c25,28.951489,0,28.951489,0,57.902978,0
-c26,0,0,28.951489,0,28.951489,0
-c27,28.951489,0,28.951489,0,57.902978,0
-c28,28.951489,0,0,0,28.951489,0
-c29,28.951489,0,0,0,28.951489,0
-c30,28.951489,0,28.951489,0,57.902978,0
-c31,0,0,28.951489,0,28.951489,0
-c32,0,0,28.951489,0,28.951489,0
-c33,28.951489,0,28.951489,0,57.902978,0
-c34,28.951489,0,0,0,28.951489,0
-c35,0,0,0,0,0,0
-c36,0,0,0,0,0,0
-c37,28.951489,0,0,0,28.951489,0
-c38,0,0,28.951489,0,28.951489,0
-c39,28.951489,0,0,0,28.951489,0
-c40,28.951489,0,0,0,28.951489,0
-c41,28.951489,0,28.951489,0,57.902978,0
-c42,28.951489,0,0,0,28.951489,0
-c43,0,0,28.951489,0,28.951489,0
-c44,0,0,28.951489,0,28.951489,0
-c45,0,0,28.951489,0,28.951489,0
-c46,28.951489,0,28.951489,0,57.902978,0
-c47,28.951489,0,0,0,28.951489,0
-c48,28.951489,0,0,0,28.951489,0
-c49,0,0,0,0,0,0
-c50,28.951489,0,28.951489,0,57.902978,0
-c51,28.951489,0,28.951489,0,57.902978,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,46.163959,0,46.163959,0,92.327918,0
-c2,46.163959,0,46.163959,0,92.327918,0
-c3,46.163959,0,46.163959,0,92.327918,0
-c4,46.163959,0,46.163959,0,92.327918,0
-c5,46.163959,0,46.163959,0,92.327918,0
-c6,46.163959,0,46.163959,0,92.327918,0
-c7,46.163959,0,46.163959,0,92.327918,0
-c8,46.163959,0,46.163959,0,92.327918,0
-c9,46.163959,0,46.163959,0,92.327918,0
-c10,46.163959,0,46.163959,0,92.327918,0
-c11,46.163959,0,46.163959,0,92.327918,0
-c12,0,0,46.163959,0,46.163959,0
-c13,0,0,46.163959,0,46.163959,0
-c14,0,0,46.163959,0,46.163959,0
-c15,46.163959,0,0,0,46.163959,0
-c16,46.163959,0,0,0,46.163959,0
-c17,46.163959,0,0,0,46.163959,0
-c18,46.163959,0,0,0,46.163959,0
-c19,46.163959,0,0,0,46.163959,0
-c20,46.163959,0,0,0,46.163959,0
-c21,46.163959,0,0,0,46.163959,0
-c22,46.163959,0,0,0,46.163959,0
-c23,46.163959,0,0,0,46.163959,0
-c24,46.163959,0,0,0,46.163959,0
-c25,46.163959,0,46.163959,0,92.327918,0
-c26,0,0,46.163959,0,46.163959,0
-c27,46.163959,0,46.163959,0,92.327918,0
-c28,46.163959,0,0,0,46.163959,0
-c29,46.163959,0,0,0,46.163959,0
-c30,46.163959,0,46.163959,0,92.327918,0
-c31,0,0,46.163959,0,46.163959,0
-c32,0,0,46.163959,0,46.163959,0
-c33,46.163959,0,46.163959,0,92.327918,0
-c34,46.163959,0,0,0,46.163959,0
-c35,0,0,0,0,0,0
-c36,0,0,0,0,0,0
-c37,46.163959,0,0,0,46.163959,0
-c38,0,0,46.163959,0,46.163959,0
-c39,46.163959,0,0,0,46.163959,0
-c40,46.163959,0,0,0,46.163959,0
-c41,46.163959,0,46.163959,0,92.327918,0
-c42,46.163959,0,0,0,46.163959,0
-c43,0,0,46.163959,0,46.163959,0
-c44,0,0,46.163959,0,46.163959,0
-c45,0,0,46.163959,0,46.163959,0
-c46,46.163959,0,46.163959,0,92.327918,0
-c47,46.163959,0,0,0,46.163959,0
-c48,46.163959,0,0,0,46.163959,0
-c49,0,0,0,0,0,0
-c50,46.163959,0,46.163959,0,92.327918,0
-c51,46.163959,0,46.163959,0,92.327918,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,10.930367,0,10.930367,0,21.860734,0
-c2,10.930367,0,10.930367,0,21.860734,0
-c3,10.930367,0,10.930367,0,21.860734,0
-c4,10.930367,0,10.930367,0,21.860734,0
-c5,10.930367,0,10.930367,0,21.860734,0
-c6,10.930367,0,10.930367,0,21.860734,0
-c7,10.930367,0,10.930367,0,21.860734,0
-c8,10.930367,0,10.930367,0,21.860734,0
-c9,10.930367,0,10.930367,0,21.860734,0
-c10,10.930367,0,10.930367,0,21.860734,0
-c11,10.930367,0,10.930367,0,21.860734,0
-c12,0,0,10.930367,0,10.930367,0
-c13,0,0,10.930367,0,10.930367,0
-c14,0,0,10.930367,0,10.930367,0
-c15,10.930367,0,0,0,10.930367,0
-c16,10.930367,0,0,0,10.930367,0
-c17,10.930367,0,0,0,10.930367,0
-c18,10.930367,0,0,0,10.930367,0
-c19,10.930367,0,0,0,10.930367,0
-c20,10.930367,0,0,0,10.930367,0
-c21,10.930367,0,0,0,10.930367,0
-c22,10.930367,0,0,0,10.930367,0
-c23,10.930367,0,0,0,10.930367,0
-c24,10.930367,0,0,0,10.930367,0
-c25,10.930367,0,10.930367,0,21.860734,0
-c26,0,0,10.930367,0,10.930367,0
-c27,10.930367,0,10.930367,0,21.860734,0
-c28,10.930367,0,0,0,10.930367,0
-c29,10.930367,0,0,0,10.930367,0
-c30,10.930367,0,10.930367,0,21.860734,0
-c31,0,0,10.930367,0,10.930367,0
-c32,0,0,10.930367,0,10.930367,0
-c33,10.930367,0,10.930367,0,21.860734,0
-c34,10.930367,0,0,0,10.930367,0
-c35,0,0,0,0,0,0
-c36,0,0,0,0,0,0
-c37,10.930367,0,0,0,10.930367,0
-c38,0,0,10.930367,0,10.930367,0
-c39,10.930367,0,0,0,10.930367,0
-c40,10.930367,0,0,0,10.930367,0
-c41,10.930367,0,10.930367,0,21.860734,0
-c42,10.930367,0,0,0,10.930367,0
-c43,0,0,10.930367,0,10.930367,0
-c44,0,0,10.930367,0,10.930367,0
-c45,0,0,10.930367,0,10.930367,0
-c46,10.930367,0,10.930367,0,21.860734,0
-c47,10.930367,0,0,0,10.930367,0
-c48,10.930367,0,0,0,10.930367,0
-c49,0,0,0,0,0,0
-c50,10.930367,0,10.930367,0,21.860734,0
-c51,10.930367,0,10.930367,0,21.860734,0
-c0,0
-
-Patch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,0,0,0
-c2,0,0,0,0,0,0
-c3,0,0,0,0,0,0
-c4,0,0,0,0,0,0
-c5,0,0,0,0,0,0
-c6,0,0,0,0,0,0
-c7,0,0,0,0,0,0
-c8,0,0,0,0,0,0
-c9,0,0,0,0,0,0
-c10,0,0,0,0,0,0
-c11,0,0,0,0,0,0
-c12,0,0,0,0,0,0
-c13,0,0,0,0,0,0
-c14,0,0,0,0,0,0
-c15,0,0,0,0,0,0
-c16,0,0,0,0,0,0
-c17,0,0,0,0,0,0
-c18,0,0,0,0,0,0
-c19,0,0,0,0,0,0
-c20,0,0,0,0,0,0
-c21,0,0,0,0,0,0
-c22,0,0,0,0,0,0
-c23,0,0,0,0,0,0
-c24,0,0,0,0,0,0
-c25,0,0,0,0,0,0
-c26,0,0,0,0,0,0
-c27,0,0,0,0,0,0
-c28,0,0,0,0,0,0
-c29,0,0,0,0,0,0
-c30,0,0,0,0,0,0
-c31,0,0,0,0,0,0
-c32,0,0,0,0,0,0
-c33,0,0,0,0,0,0
-c34,0,0,0,0,0,0
-c35,0,0,0,0,0,0
-c36,0,0,0,0,0,0
-c37,0,0,0,0,0,0
-c38,0,0,0,0,0,0
-c39,0,0,0,0,0,0
-c40,0,0,0,0,0,0
-c41,0,0,0,0,0,0
-c42,0,0,0,0,0,0
-c43,0,0,0,0,0,0
-c44,0,0,0,0,0,0
-c45,0,0,0,0,0,0
-c46,0,0,0,0,0,0
-c47,0,0,0,0,0,0
-c48,0,0,0,0,0,0
-c49,0,0,0,0,0,0
-c50,0,0,0,0,0,0
-c51,0,0,0,0,0,0
-c0,0
-
-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,0,0,0
-c2,0,0,0,0,0,0
-c3,0,0,0,0,0,0
-c4,0,0,0,0,0,0
-c5,0,0,0,0,0,0
-c6,0,0,0,0,0,0
-c7,0,0,0,0,0,0
-c8,0,0,0,0,0,0
-c9,0,0,0,0,0,0
-c10,0,0,0,0,0,0
-c11,0,0,0,0,0,0
-c12,0,235.394,0,0,235.394,0
-c13,96.9595,0,0,0,96.9595,0
-c14,0,235.394,0,0,235.394,0
-c15,0,0,0,236.319,236.319,0
-c16,0,0,187.77,0,187.77,0
-c17,0,0,0,0,0,0
-c18,0,0,0,171.537,171.537,0
-c19,0,0,187.77,171.537,359.307,0
-c20,0,0,0,236.319,236.319,0
-c21,0,0,221.431,0,221.431,0
-c22,0,0,221.431,236.319,457.75,0
-c23,0,0,0,171.537,171.537,0
-c24,0,0,0,0,0,0
-c25,0,0,0,0,0,0
-c26,96.9595,152.421,0,0,249.3805,0
-c27,0,0,0,0,0,0
-c28,0,0,0,171.537,171.537,0
-c29,0,0,0,0,0,0
-c30,0,0,0,0,0,0
-c31,0,235.394,0,0,235.394,0
-c32,0,0,0,0,0,0
-c33,0,0,0,0,0,0
-c34,0,0,0,171.537,171.537,0
-c35,96.9595,0,0,0,96.9595,0
-c36,96.9595,0,0,171.537,268.4965,0
-c37,0,0,0,0,0,0
-c38,96.9595,0,0,0,96.9595,0
-c39,0,0,221.431,0,221.431,0
-c40,0,0,221.431,236.319,457.75,0
-c41,0,0,0,0,0,0
-c42,0,0,221.431,0,221.431,0
-c43,96.9595,0,0,0,96.9595,0
-c44,96.9595,0,0,0,96.9595,0
-c45,96.9595,152.421,0,0,249.3805,0
-c46,0,0,0,0,0,0
-c47,0,0,187.77,171.537,359.307,0
-c48,0,0,187.77,171.537,359.307,0
-c49,96.9595,152.421,187.77,171.537,608.6875,0
-c50,0,0,0,0,0,0
-c51,0,0,0,0,0,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,0,0,0,0,0,0
-c1,0,0,0,0,0,0
-c2,0,0,0,0,0,0
-c3,0,0,0,0,0,0
-c4,0,0,0,0,0,0
-c5,0,0,0,0,0,0
-c6,0,0,0,0,0,0
-c7,0,0,0,0,0,0
-c8,0,0,0,0,0,0
-c9,0,0,0,0,0,0
-c10,0,0,0,0,0,0
-c11,0,0,0,0,0,0
-c12,0,49.0656,0,0,49.0656,0
-c13,56.6917,0,0,0,56.6917,0
-c14,0,49.0656,0,0,49.0656,0
-c15,0,0,0,49.0482,49.0482,0
-c16,0,0,49.3364,0,49.3364,0
-c17,0,0,0,0,0,0
-c18,0,0,0,36.5705,36.5705,0
-c19,0,0,49.3364,36.5705,85.9069,0
-c20,0,0,0,49.0482,49.0482,0
-c21,0,0,36.496,0,36.496,0
-c22,0,0,36.496,49.0482,85.5442,0
-c23,0,0,0,36.5705,36.5705,0
-c24,0,0,0,0,0,0
-c25,0,0,0,0,0,0
-c26,56.6917,36.5672,0,0,93.2589,0
-c27,0,0,0,0,0,0
-c28,0,0,0,36.5705,36.5705,0
-c29,0,0,0,0,0,0
-c30,0,0,0,0,0,0
-c31,0,49.0656,0,0,49.0656,0
-c32,0,0,0,0,0,0
-c33,0,0,0,0,0,0
-c34,0,0,0,36.5705,36.5705,0
-c35,56.6917,0,0,0,56.6917,0
-c36,56.6917,0,0,36.5705,93.2622,0
-c37,0,0,0,0,0,0
-c38,56.6917,0,0,0,56.6917,0
-c39,0,0,36.496,0,36.496,0
-c40,0,0,36.496,49.0482,85.5442,0
-c41,0,0,0,0,0,0
-c42,0,0,36.496,0,36.496,0
-c43,56.6917,0,0,0,56.6917,0
-c44,56.6917,0,0,0,56.6917,0
-c45,56.6917,36.5672,0,0,93.2589,0
-c46,0,0,0,0,0,0
-c47,0,0,49.3364,36.5705,85.9069,0
-c48,0,0,49.3364,36.5705,85.9069,0
-c49,56.6917,36.5672,49.3364,36.5705,179.1658,0
-c50,0,0,0,0,0,0
-c51,0,0,0,0,0,0
-c0,0
-
-Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Total,Improvement
-c0,3169.6933,1196.6505,2990.8553,1604.7097,8961.9088,0.999999988841663
-c1,18.850157,1196.6505,18.850157,1604.7097,2839.060514,3.15664581298721
-c2,18.850157,1169.3975,18.850157,1604.7097,2811.807514,3.18724110261976
-c3,18.850157,1196.6505,18.850157,1504.1007,2738.451514,3.27261900636963
-c4,18.850157,1196.6505,18.850157,1604.7097,2839.060514,3.15664581298721
-c5,18.850157,1169.3975,18.850157,1504.1007,2711.198514,3.30551541068322
-c6,18.850157,1196.6505,18.850157,1504.1007,2738.451514,3.27261900636963
-c7,18.850157,1169.3975,18.850157,1504.1007,2711.198514,3.30551541068322
-c8,18.850157,1196.6505,18.850157,1504.1007,2738.451514,3.27261900636963
-c9,18.850157,1196.6505,18.850157,1604.7097,2839.060514,3.15664581298721
-c10,18.850157,1169.3975,18.850157,1504.1007,2711.198514,3.30551541068322
-c11,18.850157,1169.3975,18.850157,1604.7097,2811.807514,3.18724110261976
-c12,3169.6933,1218.4631,18.850157,1504.1007,5911.107257,1.51611335385192
-c13,3001.971,1169.3975,18.850157,1604.7097,5794.928357,1.54650896322532
-c14,3169.6933,1218.4631,18.850157,1604.7097,6011.716257,1.49074045876513
-c15,18.850157,1196.6505,2990.8553,1553.1489,5759.504857,1.55602067658747
-c16,18.850157,1196.6505,2558.6203,1504.1007,5278.221657,1.69790304625126
-c17,18.850157,1169.3975,2509.2839,1504.1007,5201.632257,1.72290315518737
-c18,18.850157,1169.3975,2509.2839,1641.2802,5338.811757,1.67863356867494
-c19,18.850157,1196.6505,2558.6203,1641.2802,5415.401157,1.65489284629015
-c20,18.850157,1196.6505,2990.8553,1553.1489,5759.504857,1.55602067658747
-c21,18.850157,1169.3975,3027.3513,1604.7097,5820.308657,1.53976518672169
-c22,18.850157,1169.3975,3027.3513,1553.1489,5768.747857,1.55352753609651
-c23,18.850157,1169.3975,2509.2839,1641.2802,5338.811757,1.67863356867494
-c24,18.850157,1196.6505,2990.8553,1604.7097,5811.065657,1.54221431571386
-c25,18.850157,1196.6505,18.850157,1604.7097,2839.060514,3.15664581298721
-c26,3001.971,1233.2177,18.850157,1504.1007,5758.139557,1.55638962127382
-c27,18.850157,1169.3975,18.850157,1504.1007,2711.198514,3.30551541068322
-c28,18.850157,1169.3975,2509.2839,1641.2802,5338.811757,1.67863356867494
-c29,18.850157,1169.3975,2509.2839,1504.1007,5201.632257,1.72290315518737
-c30,18.850157,1196.6505,18.850157,1504.1007,2738.451514,3.27261900636963
-c31,3169.6933,1218.4631,18.850157,1504.1007,5911.107257,1.51611335385192
-c32,3169.6933,1196.6505,18.850157,1504.1007,5889.294657,1.52172869074823
-c33,18.850157,1196.6505,18.850157,1604.7097,2839.060514,3.15664581298721
-c34,18.850157,1169.3975,2509.2839,1641.2802,5338.811757,1.67863356867494
-c35,3001.971,1169.3975,2509.2839,1504.1007,8184.7531,1.09495162297624
-c36,3001.971,1169.3975,2509.2839,1641.2802,8321.9326,1.07690234024603
-c37,18.850157,1169.3975,2509.2839,1504.1007,5201.632257,1.72290315518737
-c38,3001.971,1169.3975,18.850157,1504.1007,5694.319357,1.5738331626237
-c39,18.850157,1169.3975,3027.3513,1604.7097,5820.308657,1.53976518672169
-c40,18.850157,1169.3975,3027.3513,1553.1489,5768.747857,1.55352753609651
-c41,18.850157,1169.3975,18.850157,1604.7097,2811.807514,3.18724110261976
-c42,18.850157,1169.3975,3027.3513,1604.7097,5820.308657,1.53976518672169
-c43,3001.971,1169.3975,18.850157,1604.7097,5794.928357,1.54650896322532
-c44,3001.971,1169.3975,18.850157,1504.1007,5694.319357,1.5738331626237
-c45,3001.971,1233.2177,18.850157,1504.1007,5758.139557,1.55638962127382
-c46,18.850157,1169.3975,18.850157,1504.1007,2711.198514,3.30551541068322
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-c48,18.850157,1196.6505,2558.6203,1641.2802,5415.401157,1.65489284629015
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diff --git a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_results1.csv b/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_results1.csv
deleted file mode 100644
index b45df9c4a9dbeb4c276364c549f61ea91945386f..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_results1.csv
+++ /dev/null
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diff --git a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_results2.csv b/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_results2.csv
deleted file mode 100644
index 6d54eb0f35f60d2549ef8b12a44a85e28ec638e2..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_results2.csv
+++ /dev/null
@@ -1,154 +0,0 @@
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diff --git a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_tensors.txt b/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_tensors.txt
deleted file mode 100644
index 38f513ebc98833ddfdd95ef72d04b05aa60ed8a4..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/pipeline_GSME/pipeline_GSME_tensors.txt
+++ /dev/null
@@ -1,13 +0,0 @@
-#Conv1,2
-Conv1,3133.72,16043.1,2912.47,13477.7,56.6917,96.9595,36.8048,222.547
-ClipRelu1,35.9733,226.348,32.8093,164.115,146.021,769.892,36.6263,179.956
-#Conv2,2
-Conv2,1160.86,5545.29,1136.75,4708.62,49.0656,235.394,36.5672,152.421
-ClipRelu2,35.7905,170.526,32.6475,123.226,145.654,550.465,36.5405,139.548
-#Conv3,2
-Conv3,2955.11,17131.1,2476.66,13751.7,49.3364,187.77,36.496,221.431
-ClipRelu3,35.7453,224.772,32.6239,163.406,145.606,770.056,36.4944,179.388
-#Conv4,3
-Conv4,1532.34,7694.78,1437.6,6507.38,49.0482,236.319,36.5705,171.537
-Add1,36.8449,189.882,32.7422,134.826,146.433,615.386,36.3565,149.573
-ClipRelu4,35.5248,186.169,33.7585,143.672,49.1495,200.587,36.4394,154.389
diff --git a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_confs1.txt b/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_confs1.txt
deleted file mode 100644
index 7361a062e7d4cc86d5c955dd4c7662d2e88175a6..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_confs1.txt
+++ /dev/null
@@ -1,34 +0,0 @@
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-9 9 9,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8,8 8
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-9 9 9,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,7,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8,8 8
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-9 9 9,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,7,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8,8 8
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-9 9 9,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,7,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,7,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8,8 8
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diff --git a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_confs2.txt b/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_confs2.txt
deleted file mode 100644
index 3d0ad476552644463569166477024d81f4fc4dd0..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_confs2.txt
+++ /dev/null
@@ -1,46 +0,0 @@
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-9 9 9,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8,8 8
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-9 9 9,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,7,8 8,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,7,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8,8 8
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-9 9 9,8 8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,7,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8,8 8
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-9 9 9,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,7,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,7,7,8,8,8 8 8,8 8,8,8,7,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,7,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,7,8,8,8,8 8
-9 9 9,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8,8 8
-9 9 9,8 8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,7,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8,8 8
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-9 9 9,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,7,8,8,8 8 8,8 8,8,8,7,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8,8 8
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-9 9 9,8 8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,7,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,7,8,8,8 8 8,8 8,8,8,7,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,7,8,8,8,8 8
-9 9 9,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,7,8 8,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,7,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8,8 8
-9 9 9,8 8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8 8,8,8,7,7,8,8,8 8 8,8 8,8,8,7,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,7,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,7,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,7,8 8,8 8,8,8,8 8 8,8 8,8,8,8 8 8,8 8,8,8,8,8 8
diff --git a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_fp16.csv b/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_fp16.csv
deleted file mode 100644
index 8aa9f4f5f1ab4418335e9f3eb0a9ed81035e44c2..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_fp16.csv
+++ /dev/null
@@ -1,220 +0,0 @@
-Add1,18.4067,51.7458,26.6786,25.0672,5671.44,2933.87,2737.57,13.0333,37.8611,20.1787,17.9155,4176.73,2237.06,1963.86
-Add10,25.4775,107.72,67.1668,40.5535,8455.96,5272.51,3183.45,18.0181,76.2046,47.5202,28.6861,5980.97,3729.6,2251.5
-Add10_f2h,60.3765,243.339,153.879,89.4597,8062.26,5098.54,2963.73,42.7111,172.129,108.851,63.281,5701.54,3605.88,2095.77
-Add10_h2f,20.7649,83.3467,51.6117,31.735,8027.75,4971.09,3056.66,14.6838,58.9536,36.5095,22.4455,5678,3516.32,2161.83
-Add11,10.3212,47.2214,28.8656,18.3558,9155.38,5597.11,3558.27,7.30548,33.4486,20.446,13.0032,6482.41,3963.32,2519.21
-Add11_f2h,15.502,62.8822,38.5801,24.3021,8113.12,4977.95,3135.17,10.9812,44.5638,27.3406,17.2241,5739.52,3521.8,2217.83
-Add11_h2f,11.2293,44.7914,27.2866,17.5048,7976.14,4859.07,3117.07,7.94857,31.74,19.3364,12.4042,5644.45,3438.81,2205.75
-Add12,9.45661,44.0115,27.6225,16.389,9310.33,5843.69,3466.64,6.69292,31.1762,19.5662,11.6103,6590.69,4136.82,2453.95
-Add12_f2h,40.1768,172.415,108.883,63.5318,8582.39,5419.84,3162.55,28.4316,122.029,77.0664,44.9633,6069.24,3832.87,2236.42
-Add12_h2f,10.4892,42.8418,26.8148,16.027,8169.01,5112.99,3056.02,7.41765,30.3184,18.9771,11.3417,5780.72,3618.28,2162.52
-Add13,9.59742,41.731,25.0231,16.7079,8695.68,5214.21,3481.47,6.79463,29.5817,17.7385,11.8435,6156.17,3691.56,2464.67
-Add13_f2h,41.9408,167.231,101.045,66.1867,7973.97,4817.94,3156.03,29.6717,118.333,71.503,46.8306,5639.03,3407.28,2231.81
-Add13_h2f,10.4872,39.7994,23.8049,15.9945,7589.81,4539.64,3050.17,7.41618,28.1695,16.8494,11.3204,5371.29,3212.81,2158.55
-Add14,11.4693,49.588,29.3241,20.2638,8647.95,5114.03,3533.91,8.11133,35.0867,20.7497,14.3374,6118.69,3618.52,2500.24
-Add14_f2h,27.8587,110.353,65.5177,44.8356,7922.71,4703.85,3218.86,19.7031,78.0565,46.3446,31.7133,5603.04,3326.8,2276.35
-Add14_h2f,10.4665,40.3814,23.8416,16.5399,7716.46,4555.89,3160.57,7.40173,28.5808,16.8747,11.7065,5461.05,3224.35,2236.77
-Add15,9.39457,42.3399,26.0019,16.338,9014.62,5536.14,3478.47,6.64916,29.9924,18.4192,11.5736,6380.26,3918.39,2461.94
-Add15_f2h,40.147,165.995,102.472,63.5229,8269.94,5105.32,3164.62,28.4279,117.537,72.5574,44.9805,5848.22,3610.39,2237.87
-Add15_h2f,10.4569,41.0957,25.1843,15.9113,7859.73,4816.65,3043.07,7.39464,29.0899,17.8276,11.2626,5562.97,3409.29,2153.74
-Add16,9.36312,41.8676,26.0997,15.7679,8940.62,5573.43,3367.19,6.62684,29.8234,18.5944,11.2294,6357.34,3963.59,2393.83
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-Add16_h2f,10.4433,40.5802,25.225,15.3552,7771.59,4830.88,2940.71,7.3846,28.7184,17.8523,10.8664,5499.86,3418.89,2081.03
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-Add17_h2f,10.4534,40.67,24.8815,15.7885,7780.74,4760.17,3020.57,7.392,28.7841,17.611,11.1736,5506.18,3368.83,2137.45
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-Add18_h2f,10.4893,41.2948,25.8578,15.437,7874.36,4930.71,2943.64,7.42007,29.2336,18.3062,10.9279,5572.7,3489.61,2083.18
-Add19,9.34031,41.5447,26.3195,15.2252,8899.53,5638.08,3261.45,6.61153,29.4292,18.6449,10.7846,6300.46,3991.71,2308.83
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-Add1_h2f,20.7976,56.1939,29.1321,27.0618,5404.42,2801.87,2602.56,14.707,41.1458,21.9739,19.3755,3957.28,2113.49,1863.36
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-Add21_f2h,8.53298,33.0218,20.3905,12.6312,7739.9,4779.34,2960.57,6.03492,23.3902,14.4432,8.94732,5481.37,3384.74,2096.7
-Add21_h2f,5.65061,21.0508,12.9633,8.08745,7451.76,4588.85,2862.92,3.99628,14.9378,9.19932,5.73878,5287.67,3256.31,2031.46
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-Add22_h2f,5.28191,19.8187,12.4203,7.39838,7501.83,4701.31,2800.52,3.73584,14.076,8.82147,5.25475,5324.53,3336.79,1987.84
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-Add23_h2f,5.29007,18.9716,11.6231,7.34846,7171.96,4393.94,2778.02,3.74105,13.4814,8.25981,5.22185,5095.23,3121.71,1973.61
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-Add24_f2h,14.405,57.1891,34.8867,22.3024,7940.5,4843.94,3096.56,10.1872,40.4599,24.6826,15.7779,5617.28,3426.88,2190.47
-Add24_h2f,5.33388,19.3044,11.7551,7.54925,7310.5,4451.56,2858.94,3.83415,13.6997,8.34285,5.35713,5195.18,3163.71,2031.57
-Add25,8.75476,40.2434,24.9644,15.279,9195.92,5704.85,3491.07,6.19722,28.5635,17.7193,10.8446,6522.5,4046.65,2475.93
-Add25_f2h,20.1161,82.2092,51.1235,31.0857,8171.72,5081.58,3090.13,14.2438,58.2576,36.2306,22.0276,5781.64,3595.38,2186.32
-Add25_h2f,5.27612,19.5688,12.1329,7.43597,7418.53,4599.54,2818.99,3.73158,13.8926,8.61378,5.27904,5266.09,3265.1,2001.08
-Add26,8.7674,40.3179,25.3121,15.0058,9197.87,5774.46,3423.41,6.20711,28.5804,17.9435,10.6371,6512.63,4088.68,2423.99
-Add26_f2h,20.1714,82.6833,52.0254,30.6579,8196.8,5157.5,3039.3,14.2777,58.546,36.8384,21.7078,5797.29,3647.74,2149.57
-Add26_h2f,5.2755,19.4721,12.2095,7.26253,7382.4,4628.99,2753.42,3.73117,13.8207,8.66606,5.15474,5238.94,3285.02,1953.97
-Add27,11.1843,50.4854,31.4916,18.9938,9027.49,5631.16,3396.33,7.9091,35.73,22.288,13.4425,6388.21,3984.94,2403.36
-Add27_f2h,17.215,69.9894,43.7366,26.2528,8132.49,5082.37,3050.12,12.1926,49.5781,30.9806,18.5981,5752.34,3595.04,2157.37
-Add27_h2f,5.28841,19.6402,12.2402,7.40007,7427.35,4628.85,2798.5,3.74078,13.9399,8.68767,5.25242,5269.55,3284.09,1985.53
-Add28,8.73735,40.0918,25.2493,14.8425,9179.93,5781.38,3398.55,6.18426,28.4037,17.8884,10.5155,6499.5,4093.29,2406.26
-Add28_f2h,20.0577,82.5983,52.1453,30.453,8234.96,5198.73,3036.23,14.212,58.5442,36.9609,21.5836,5824.23,3676.89,2147.37
-Add28_h2f,5.33532,19.8316,12.4781,7.35346,7421.97,4670.26,2751.71,3.83712,14.3924,9.0537,5.33892,5266.13,3313.81,1952.39
-Add29,8.73956,39.8315,25.3262,14.5053,9114.93,5795.69,3319.24,6.18539,28.2229,17.9451,10.2782,6452.34,4102.78,2349.66
-Add29_f2h,19.9302,82.4415,52.5543,29.8873,8271.76,5273.03,2998.74,14.1093,58.388,37.2219,21.1665,5850.58,3729.7,2120.93
-Add29_h2f,5.31975,19.701,12.5142,7.18678,7475.14,4748.32,2726.81,3.80674,13.9904,8.88687,5.10378,5314.32,3375.78,1938.64
-Add2_f2h,27.9959,99.3789,62.5849,36.794,7108.69,4477.52,2631.18,19.8106,70.7329,44.6346,26.1164,5062.88,3195.89,1868.28
-Add2_h2f,21.8283,78.8418,48.6398,30.202,7222.01,4455.29,2766.73,15.4364,56.1344,34.7155,21.4366,5140.28,3178.69,1963.21
-Add3,19.3839,84.2165,55.3964,28.8201,8699.77,5723.1,2976.67,13.7121,59.6058,39.2138,20.3932,6162.34,4054.84,2107.63
-Add30,11.2081,50.3589,31.7726,18.5862,8986.61,5669.86,3316.75,7.92595,35.6402,22.487,13.1536,6359.89,4012.74,2347.24
-Add30_f2h,17.4758,71.5971,45.271,26.3262,8195.3,5182.19,3013.12,12.3884,50.7593,32.0942,18.6656,5796.6,3665.49,2131.17
-Add30_h2f,5.27643,19.4931,12.2911,7.20202,7388.83,4658.89,2729.94,3.73199,13.8463,8.73137,5.11513,5246.89,3308.6,1938.37
-Add31,0.396442,1.22564,0.764432,0.461209,6165.78,3846.09,2319.7,0.284326,1.58002,0.98564,0.594413,8102.09,5055.82,3046.44
-Add31_f2h,0.809076,1.36364,0.850185,0.513452,2660.4,1659.12,1001.27,0.636169,1.498,0.93363,0.564389,2743.85,1710.87,1033.03
-Add31_h2f,0.129454,0.0344863,0.0215079,0.0129784,514.231,320.98,193.251,0.0934957,0.085008,0.0529757,0.0320343,1260.83,787.256,473.598
-Add3_f2h,27.9995,113.419,75.4189,38,8101.81,5387.42,2714.39,19.8133,80.4002,53.4775,26.9247,5739.24,3817.49,1921.9
-Add3_h2f,21.828,88.7986,57.8825,30.9161,8136.52,5303.75,2832.77,15.4361,62.9262,41.0275,21.9002,5765.62,3759.19,2006.56
-Add4,25.2021,109.339,69.1431,40.1956,8679.65,5488.99,3190.66,17.8374,77.4612,49.0014,28.4636,6145.82,3888.12,2258.01
-Add4_f2h,54.8381,227.205,145.608,81.5971,8290.89,5313.68,2977.21,38.8148,160.96,103.191,57.7775,5871.03,3764.37,2106.96
-Add4_h2f,20.8082,85.9608,53.9681,31.9927,8261.17,5186.46,3074.71,14.7156,60.8827,38.2401,22.6459,5849.43,3673.87,2175.88
-Add5,18.3902,84.3374,55.4563,28.881,9158.85,6020.58,3138.27,13.0145,59.9538,39.4588,20.5013,6495.82,4272.67,2223.85
-Add5_f2h,52.5577,223.355,147.844,75.5108,8633.1,5736.35,2896.75,41.0843,172.923,114.184,58.7452,6111.46,4062.06,2049.8
-Add5_h2f,21.3908,91.48,59.7227,31.7573,8543.72,5576.36,2967.36,15.1322,64.8702,42.3847,22.4926,6049.21,3950.45,2099.43
-Add6,17.4475,76.3058,49.8737,26.4321,8757.37,5725.3,3032.07,12.3416,53.9826,35.2918,18.696,6200.62,4055.79,2145.43
-Add6_f2h,78.6158,330.238,219.427,110.811,8421.49,5598.56,2822.92,55.6768,233.593,155.218,78.4009,5961.87,3965.63,1996.89
-Add6_h2f,20.8524,85.6316,55.5811,30.0505,8212.43,5330.35,2882.08,14.7456,60.6346,39.379,21.2623,5814.33,3775.95,2039.03
-Add7,24.9686,108.541,68.418,40.1235,8695.27,5481.14,3214.12,17.6568,76.8331,48.4614,28.383,6155.36,3882.66,2273.62
-Add7_f2h,62.7404,259.957,165.983,93.9749,8308.17,5308.25,2999.92,44.4591,183.937,117.441,66.5205,5881.22,3760,2121.98
-Add7_h2f,20.8101,86.1797,53.9434,32.2363,8283.86,5185.4,3098.46,14.7162,61.0198,38.2202,22.8086,5865.81,3674.38,2192.31
-Add8,17.6899,77.5743,50.2264,27.348,8775.77,5682.64,3093.13,12.5138,54.8845,35.5378,19.3485,6210.13,4022.02,2188.31
-Add8_f2h,83.3626,352.496,231.707,120.789,8457.12,5559.21,2897.92,58.9719,249.424,163.976,85.4559,5981.72,3932.59,2049.31
-Add8_h2f,20.8989,85.9449,55.2763,30.6686,8225.31,5290.3,2935.01,14.7816,60.8117,39.1174,21.6967,5818.86,3743.18,2075.91
-Add9,17.6956,75.1977,48.6944,26.5033,8500.24,5504.64,2995.6,12.5184,53.2155,34.46,18.7562,6013.36,3894.4,2119.04
-Add9_f2h,82.8254,337.55,222.322,115.228,8151.08,5368.6,2782.49,58.5965,238.83,157.31,81.5231,5764.4,3796.86,1967.62
-Add9_h2f,20.8517,82.5853,53.0859,29.4994,7921.12,5091.73,2829.38,14.7458,58.4972,37.6036,20.8945,5610.17,3606.42,2003.84
-Conv1,174.622,421.265,201.391,219.874,4799.44,2288.31,2511.12,123.525,321.252,166.184,159.301,3644.22,1880.24,1813.82
-Conv10,90.3128,381.213,233.648,147.565,8444.02,5175.72,3268.3,63.8945,269.653,165.267,104.388,5971.16,3660.1,2311.1
-Conv10_f2h,32.0547,135.935,84.4552,51.4794,8482.08,5270.04,3212.04,22.6804,96.1837,59.7582,36.4267,5998.4,3727.01,2271.46
-Conv10_h2f,10.5675,40.812,24.9802,15.8318,7723.92,4727.67,2996.25,7.47381,28.8877,17.6823,11.206,5466.01,3345.8,2120.33
-Conv11,140.577,589.922,354.18,235.742,8393.31,5039.32,3353.99,99.4117,417.168,250.461,166.709,5935.19,3563.56,2371.67
-Conv11_f2h,15.1273,59.0802,34.3177,24.7625,7812.41,4538.14,3274.27,10.6992,41.7989,24.28,17.5198,5526.88,3210.68,2316.31
-Conv11_h2f,10.5792,42.4989,26.5104,15.9885,8034.13,5011.61,3022.52,7.48174,30.082,18.7653,11.3169,5685.66,3546.75,2138.95
-Conv12,167.515,695.778,426.345,269.434,8307.72,5090.78,3216.94,118.467,492.044,301.501,190.545,5874.73,3599.97,2274.79
-Conv12_f2h,14.881,59.0727,35.667,23.4057,7952.24,4801.46,3150.78,10.5393,41.8586,25.2741,16.585,5631.84,3400.55,2231.35
-Conv12_h2f,10.5947,41.783,26.4937,15.2892,7887.33,5001.18,2886.15,7.493,29.5751,18.7533,10.8221,5581.66,3539.24,2042.45
-Conv13,163.856,684.766,420.14,264.626,8358.58,5128.51,3230.07,115.875,484.236,297.105,187.133,5910.6,3626.58,2284.04
-Conv13_f2h,14.6929,58.143,34.969,23.174,7914.08,4759.78,3154.29,10.3926,41.147,24.7477,16.3996,5598.75,3367.36,2231.44
-Conv13_h2f,10.5613,42.4051,27.0983,15.3068,8030.86,5132,2898.86,7.46856,30.0103,19.1779,10.8326,5683.46,3632,2051.49
-Conv14,166.656,693.932,433.67,260.262,8328.27,5204.82,3123.45,117.858,490.722,306.673,184.051,5889.14,3680.52,2208.65
-Conv14_f2h,14.7899,59.1467,36.4903,22.6564,7995.87,4932.92,3062.95,10.4854,41.9732,25.8964,16.0771,5656.55,3489.75,2166.83
-Conv14_h2f,10.5466,41.836,27.0179,14.8181,7933.62,5123.59,2810.03,7.45794,29.6061,19.1205,10.4858,5614.19,3625.83,1988.41
-Conv15,38.244,164.048,100.733,63.3146,8579.14,5268.08,3311.06,27.046,116.03,71.2479,44.7823,6067.29,3725.73,2341.58
-Conv15_f2h,14.5914,58.2375,35.5473,22.6902,7982.55,4872.46,3110.09,10.3186,41.2036,25.1511,16.0533,5647.33,3447.24,2200.2
-Conv15_h2f,5.36591,19.9204,12.363,7.55743,7424.2,4607.58,2816.61,3.79436,14.1705,8.79504,5.37569,5280.79,3277.55,2003.32
-Conv16,65.7442,285.597,177.243,108.354,8688.58,5392.23,3296.35,46.4938,201.969,125.343,76.6265,6144,3813.07,2330.95
-Conv16_f2h,8.07266,31.1377,19.0284,12.1093,7714.42,4714.32,3000.1,5.70882,22.0551,13.478,8.57725,5463.71,3338.92,2124.82
-Conv16_h2f,5.37147,20.4791,12.9421,7.53703,7625.01,4818.74,2806.27,3.79835,14.5391,9.18856,5.35066,5413.07,3421,1992.12
-Conv17,42.064,181.248,112.411,68.8372,8619.77,5346.16,3273.62,29.7779,128.287,79.5627,48.7247,6095.56,3780.63,2314.96
-Conv17_f2h,16.9562,69.8631,43.5974,26.2657,8239.64,5141.83,3097.8,12.0049,49.482,30.8794,18.6029,5827.98,3636.93,2191.09
-Conv17_h2f,5.36951,19.8329,12.2666,7.56627,7386.95,4568.8,2818.14,3.7977,14.0692,8.70208,5.36729,5238.68,3240.19,1998.54
-Conv18,67.3709,286.812,175.784,111.027,8515.35,5219.14,3296.21,47.6566,202.876,124.339,78.5384,6021.72,3690.82,2330.93
-Conv18_f2h,8.03381,30.3417,18.3174,12.0243,7553.07,4559.83,2993.24,5.68152,21.5474,13.0086,8.539,5363.11,3237.82,2125.33
-Conv18_h2f,5.36257,20.0592,12.5343,7.52491,7480.48,4674.3,2806.19,3.79235,14.2331,8.89391,5.33933,5306.51,3315.91,1990.66
-Conv19,80.3214,341.92,212.062,129.858,8514.78,5281.11,3233.67,56.8122,241.827,149.981,91.8466,6021.13,3734.55,2286.62
-Conv19_f2h,8.10378,31.2805,19.2244,12.0561,7714.35,4741.22,2973.13,5.74732,22.2537,13.6761,8.5778,5464.6,3358.57,2106.07
-Conv19_h2f,5.41437,20.2452,12.8106,7.43458,7545.76,4774.79,2770.98,3.86967,14.3806,9.10008,5.28076,5364.57,3394.74,1969.92
-Conv1_f2h,6.3224,13.0911,5.82586,7.26522,4088.68,1806.81,2281.87,4.50384,10.2562,5.13352,5.33075,3166.3,1578.37,1655.62
-Conv1_h2f,20.9136,52.8942,27.7814,25.1128,5059.28,2657.42,2401.86,14.789,39.2661,21.4514,18.0694,3756.14,2052.16,1728.3
-Conv2,307.782,1024.12,595.423,428.699,6653.72,3868.33,2785.39,217.646,734.491,430.209,305.079,4771.02,2794.33,1981.87
-Conv20,78.1278,333.871,207.881,125.989,8547.41,5322.05,3225.36,55.2511,236.106,147.009,89.0988,6044.29,3763.53,2280.79
-Conv20_f2h,8.0013,30.8855,19.0615,11.824,7719.03,4763.93,2955.1,5.65835,21.9342,13.5377,8.39683,5481.37,3383.09,2098.36
-Conv20_h2f,5.3629,19.8115,12.5793,7.23219,7388.9,4691.57,2697.33,3.79279,14.0609,8.92816,5.1329,5243.67,3329.52,1914.21
-Conv21,80.2786,343.147,215.866,127.282,8549.99,5378.74,3171.25,56.7803,242.685,152.666,90.0207,6046.01,3803.58,2242.47
-Conv21_f2h,8.01787,30.88,19.2978,11.5821,7702.66,4813.67,2888.99,5.67027,21.8781,13.6724,8.20592,5456.47,3410.01,2046.52
-Conv21_h2f,5.37001,20.3721,13.0519,7.3202,7587.08,4860.82,2726.26,3.79825,14.4575,9.26289,5.19478,5382.54,3448.52,1934.07
-Conv2_f2h,27.627,79.905,40.5987,39.3063,5780.5,2936.19,2844.31,19.5433,58.0274,30.2178,28.055,4192.93,2182.46,2028.31
-Conv2_h2f,20.9352,73.9105,47.1414,26.7692,7060.33,4503.14,2557.2,14.8049,52.7684,33.7394,19.0438,5039.88,3222.35,1818.95
-Conv3,309.083,1232.59,785.441,447.15,7978.42,5084.33,2894.09,218.568,873.993,557.401,316.681,5658.77,3609.32,2050.03
-Conv3_f2h,27.8734,103.065,61.5928,41.4719,7402.94,4425.27,2977.67,19.7311,73.258,43.8773,29.4084,5262.47,3153.68,2110.77
-Conv3_h2f,20.9195,84.5406,56.8816,27.659,8083.02,5438.55,2644.47,14.7928,59.8999,40.3131,19.5884,5727.3,3854.57,1872.89
-Conv4,322.719,1401.3,895.195,506.101,8698.14,5558.46,3139.68,228.36,990.931,633.099,357.9,6156.32,3935.77,2220.97
-Conv4_f2h,27.8507,115.914,70.2976,45.6165,8325.55,5049.3,3276.25,19.7057,82.0999,49.8158,32.291,5894.33,3576.74,2318.09
-Conv4_h2f,20.9916,91.4069,61.7262,29.6807,8709.15,5881.22,2827.93,14.8444,64.6849,43.6858,21,6162.81,4162.15,2000.74
-Conv5,342.079,1466.82,953.788,513.027,8625.68,5618.29,3007.38,243.025,1037.96,674.59,363.661,6110.16,3984.53,2127.34
-Conv5_f2h,29.7996,126.901,80.1563,46.7446,8541.6,5399.26,3142.34,21.1314,89.8283,56.7306,33.108,6046.59,3824.36,2222.92
-Conv5_h2f,21.0449,89.4069,60.7654,28.6415,8496.02,5774.26,2721.76,14.8823,63.3142,43.0491,20.2693,6015.41,4089.96,1925.85
-Conv6,366.167,1544.8,970.645,574.154,8437.56,5301.57,3135.99,258.931,1092.95,686.959,406.064,5969.24,3751.88,2217.78
-Conv6_f2h,29.7189,123.016,74.1021,48.9137,8298.6,5003.1,3295.51,21.0821,87.1348,52.4765,34.6703,5873.05,3542.91,2330.96
-Conv6_h2f,21.0334,89.908,60.4888,29.4191,8548.78,5751.46,2797.32,14.8743,63.6171,42.8066,20.812,6048.14,4069.61,1978.68
-Conv7,370.721,1526.79,975.829,550.957,8238.3,5265.69,2972.61,262.168,1079.73,690.151,389.613,5826.45,3724.58,2102.05
-Conv7_f2h,28.6144,117.921,73.4724,44.4486,8244.58,5137.45,3107.13,20.2404,83.4145,51.9759,31.4423,5831.8,3634.58,2197.47
-Conv7_h2f,20.9627,85.9711,57.9984,27.9727,8202.44,5533.61,2668.84,14.8231,60.8081,41.0246,19.7842,5801.7,3914.18,1887.59
-Conv8,85.7697,364.625,220.731,143.895,8503.51,5147.94,3355.57,60.6542,257.845,156.091,101.757,6013.43,3640.68,2372.84
-Conv8_f2h,28.2226,114.351,68.3001,46.0511,8103.03,4839.74,3263.29,19.9598,80.8973,48.3224,32.5765,5731.14,3423.29,2307.96
-Conv8_h2f,10.5944,42.4202,26.1786,16.2416,8007.38,4941.51,3065.88,7.49241,30.028,18.5325,11.496,5666.85,3497.34,2169.6
-Conv9,138.631,604.548,373.126,231.422,8721.95,5383.25,3338.7,98.0322,427.509,263.864,163.649,6167.67,3806.87,2360.86
-Conv9_f2h,14.972,60.9,36.4674,24.4325,8135.05,4871.29,3263.76,10.589,43.0913,25.8049,17.2872,5754.87,3446.19,2308.78
-Conv9_h2f,10.6041,44.2087,28.2382,15.9704,8337.89,5325.84,3012.05,7.49957,31.2942,19.9899,11.3047,5900.93,3769.39,2131.62
-Mul1,1.17541,5.23179,3.26342,1.96837,9516.94,5936.74,3580.2,0.876982,4.143,2.58393,1.55914,7779.43,4853.1,2926.46
-Mul1_f2h,0.866649,1.15065,0.717796,0.432853,2581.39,1610.48,970.91,0.63788,1.21901,0.760487,0.45855,2684.43,1674.92,1009.55
-Mul1_h2f,0.182164,0.0453634,0.0282781,0.0170852,423.822,264.119,159.703,0.132397,0.122776,0.0765148,0.0462633,1112.34,692.957,419.399
-Pool1,5.19545,23.3959,14.6038,8.79204,9005.65,5621.39,3384.26,3.67464,16.5981,10.3611,6.23727,6386.83,3986.88,2400.04
-Pool1_f2h,7.76594,29.7619,18.5909,11.171,7665.15,4788.08,2877.06,5.49184,21.0853,13.1714,7.9142,5430.35,3392.22,2038.2
-Pool1_h2f,0.247834,0.0992326,0.0620025,0.0372301,788.32,492.524,295.796,0.176061,0.193642,0.121002,0.0726439,1528.12,954.657,573.489
-Relu1,15.7218,47.6272,24.3858,23.2413,6068.37,3109.12,2959.26,11.12,34.6311,18.1982,16.591,4417.52,2323.69,2113.76
-Relu10,7.86875,36.0799,21.9123,14.1676,9171.18,5569.92,3601.25,5.56493,25.5542,15.5208,10.0338,6495.21,3945.03,2550.3
-Relu10_f2h,14.7797,59.1119,36.0231,23.0889,7999.45,4875.03,3124.43,10.4543,41.8299,25.4918,16.3388,5659.12,3448.94,2210.27
-Relu10_h2f,10.4938,41.2344,25,16.2344,7859.14,4764.95,3094.19,7.42133,29.1837,17.6947,11.4895,5561.82,3372.3,2189.62
-Relu11,8.59732,38.8844,23.5484,15.3361,9045.57,5477.91,3567.66,6.08155,27.547,16.6833,10.8639,6405.63,3879.35,2526.35
-Relu11_f2h,14.7683,58.4599,35.5476,22.9123,7916.54,4813.8,3102.74,10.4441,41.3655,25.1536,16.2123,5600.65,3405.66,2195.05
-Relu11_h2f,11.0992,43.4074,26.2047,17.2027,7820.83,4721.3,3099.53,7.85061,30.7308,18.5528,12.1783,5534.58,3341.23,2193.41
-Relu12,7.85466,36.5025,22.6357,13.8668,9292.64,5762.53,3530.1,5.55462,26.0339,16.1441,9.89001,6622.72,4106.96,2515.83
-Relu12_f2h,14.3058,56.635,35.2456,21.3894,7924.65,4931.75,2992.9,10.1179,40.145,24.9837,15.1618,5617.17,3495.78,2121.45
-Relu12_h2f,10.4801,41.3643,25.6069,15.7575,7893.64,4886.59,3007.04,7.41178,29.2778,18.1254,11.1528,5586.04,3458.19,2127.9
-Relu13,8.56125,39.5474,24.3571,15.1903,9237.04,5689.16,3547.87,6.05455,28.1545,17.3375,10.8174,6571.95,4047.17,2524.85
-Relu13_f2h,14.7184,58.0328,35.8934,22.1394,7898.2,4885.07,3013.13,10.4134,41.0859,25.4118,15.6744,5593.87,3459.87,2134.05
-Relu13_h2f,11.0576,43.5721,26.7531,16.819,7881.1,4838.94,3042.16,7.82003,30.8389,18.9355,11.9037,5577.37,3424.54,2152.88
-Relu14,3.97634,19.8199,12.1465,7.6734,9965.36,6107.18,3858.18,2.81232,14.0894,8.63491,5.45462,7079.91,4338.99,2741
-Relu14_f2h,7.82028,30.3544,18.6345,11.72,7763.83,4766.17,2997.66,5.53103,21.5062,13.2029,8.30349,5500.08,3376.57,2123.59
-Relu14_h2f,5.27702,19.5391,11.9656,7.57355,7405.25,4534.9,2870.36,3.73208,13.8726,8.4959,5.37685,5256.59,3219.25,2037.4
-Relu15,4.46626,21.8376,13.2267,8.61093,9781.82,5924.68,3857.13,3.15993,15.5046,9.39167,6.11315,6943.47,4205.89,2737.67
-Relu15_f2h,7.89618,29.6604,18.0015,11.6589,7507.67,4556.56,2951.11,5.60608,21.1257,12.8217,8.30428,5317.97,3227.59,2090.44
-Relu15_h2f,5.52563,20.1041,12.1597,7.94437,7274.55,4399.92,2874.63,3.90829,14.2691,8.63063,5.63864,5160.14,3121.08,2039.12
-Relu16,3.96712,20.0653,12.3763,7.689,10117.4,6240.44,3876.96,2.80579,14.2471,8.7882,5.45913,7183.49,4431.09,2752.52
-Relu16_f2h,7.75796,29.607,18.2965,11.3105,7632.82,4716.92,2915.9,5.48702,20.9737,12.9613,8.01269,5405.95,3340.76,2065.26
-Relu16_h2f,5.27422,19.6466,12.1051,7.54152,7449.65,4590.04,2859.6,3.73078,13.9436,8.59179,5.35198,5285.08,3256.59,2028.56
-Relu17,4.46968,21.963,13.6141,8.34894,9828.48,6092.31,3736.18,3.16255,15.5965,9.66821,5.92856,6975.6,4324.14,2651.58
-Relu17_f2h,8.1633,31.6672,19.672,11.9952,7758.66,4819.83,2938.83,5.77354,22.4306,13.9347,8.4963,5494.62,3413.55,2081.16
-Relu17_h2f,5.53729,20.6582,12.7873,7.87092,7461.37,4618.48,2842.9,3.91618,14.6649,9.07828,5.5869,5295.73,3278.22,2017.6
-Relu18,3.96121,19.9626,12.504,7.45868,10080,6313.84,3766.17,2.80174,14.1804,8.88221,5.29831,7159.1,4484.31,2674.84
-Relu18_f2h,7.75018,29.9637,18.8008,11.1629,7732.03,4851.48,2880.55,5.4848,21.2416,13.3283,7.91355,5476.67,3436.38,2040.34
-Relu18_h2f,5.25633,19.6445,12.2951,7.34942,7474.65,4678.23,2796.42,3.71689,13.9391,8.72404,5.21518,5303.65,3319.39,1984.31
-Relu19,4.48848,22.1017,13.8543,8.24737,9855.95,6178.04,3677.91,3.18658,15.7342,9.86359,5.87074,6998.62,4387.13,2611.56
-Relu19_f2h,8.14713,31.7315,19.936,11.7955,7788.29,4893.2,2895.1,5.76233,22.481,14.1242,8.35708,5515.4,3465.21,2050.25
-Relu19_h2f,5.53758,20.7902,13.0137,7.77652,7509.53,4700.58,2808.94,3.91635,14.7505,9.2334,5.51724,5327.62,3334.91,1992.78
-Relu1_f2h,27.4697,75.5402,38.6841,36.8561,5510.99,2824.48,2686.52,19.4328,55.1294,29.0815,26.3358,4027.76,2127.42,1921.08
-Relu1_h2f,20.8432,59.4527,30.4349,29.0178,5705.97,2921.25,2784.72,14.7403,43.2523,22.743,20.713,4151.39,2183.21,1987.71
-Relu2,15.6922,62.2018,37.6932,24.5087,7928.92,4804.97,3123.95,11.0981,44.251,26.8823,17.3847,5640.39,3426.77,2215.66
-Relu2_f2h,27.4183,99.9831,61.0632,38.9199,7289.67,4451.68,2837.98,19.3974,71.2089,43.604,27.6298,5186.27,3175.2,2012.89
-Relu2_h2f,20.833,77.2117,46.7116,30.5001,7413.36,4485.02,2928.34,14.733,54.8865,33.2729,21.6286,5269.9,3194.8,2076.54
-Relu3,16.7972,74.5864,45.9807,28.6058,8880.69,5474.72,3405.97,11.8791,52.8166,32.5723,20.2471,6287.66,3877.63,2410.37
-Relu3_f2h,27.5363,113.932,70.807,43.1255,8274.93,5142.95,3131.98,19.4862,80.7269,50.1877,30.5436,5858.58,3642.6,2216.3
-Relu3_h2f,21.969,91.4346,55.9909,35.4437,8323.89,5097.22,3226.67,15.5355,64.7301,39.6523,25.081,5892.34,3609.53,2283.11
-Relu4,15.7301,72.5141,46.4394,26.0747,9222.54,5906.65,3315.89,11.1258,51.3576,32.9074,18.4553,6532.12,4185.96,2346.8
-Relu4_f2h,27.4947,117.455,75.7723,41.6831,8542.99,5511.04,3031.95,19.4508,83.1966,53.701,29.5038,6047.59,3903.27,2144.91
-Relu4_h2f,20.8274,89.0752,56.8414,32.2339,8553.76,5458.43,3095.33,14.7283,63.0717,40.27,22.8082,6056.29,3866.87,2190.05
-Relu5,17.3272,77.2477,47.3925,29.8552,8915.61,5469.7,3445.92,12.2651,54.7406,33.6028,21.1449,6311.35,3874.11,2438.06
-Relu5_f2h,30.181,124.467,77.0351,47.4321,8235.63,5094.97,3140.67,21.369,88.3475,54.7363,33.6223,5829.28,3608.44,2221.57
-Relu5_h2f,21.9931,91.2,55.5979,35.6021,8293.67,5056.11,3237.57,15.5589,64.5961,39.4043,25.2004,5871.35,3581.64,2290.5
-Relu6,15.8935,70.7734,44.6559,26.1175,8909.94,5622.41,3287.53,11.2415,50.1636,31.6568,18.5089,6317.03,3987.22,2330.08
-Relu6_f2h,29.4192,120.534,76.6245,43.9095,8205.47,5217.71,2987.77,20.8359,85.3161,54.229,31.0903,5806.09,3692.5,2113.8
-Relu6_h2f,20.9166,86.1995,54.1674,32.0321,8242.75,5179.8,3062.95,14.7909,60.9747,38.322,22.6552,5830.79,3664.73,2166.31
-Relu7,16.8139,72.987,44.3367,28.6503,8682.5,5274.35,3408.15,11.8908,51.6385,31.3713,20.2688,6142.58,3731.84,2410.93
-Relu7_f2h,28.3017,113.854,69.72,44.1342,8047.34,4928.22,3119.11,20.0174,80.5279,49.3132,31.2168,5691.5,3485.8,2205.85
-Relu7_h2f,22.041,89.2722,53.8809,35.3913,8101.07,4889.65,3211.41,15.6007,63.1996,38.1463,25.0552,5730.02,3458.88,2271.31
-Relu8,8.6595,40.1854,24.2306,15.9548,9283.68,5598.03,3685.64,6.13232,28.4788,17.1721,11.3072,6572.26,3963.36,2609.04
-Relu8_f2h,15.0619,61.1893,37.0436,24.1457,8125.34,4919.08,3206.27,10.652,43.2952,26.2123,17.0839,5748.51,3480.4,2268.24
-Relu8_h2f,10.7582,43.1574,25.9549,17.2025,8022.66,4824.84,3197.82,7.60841,30.5473,18.3724,12.1756,5677.22,3414.52,2262.83
-Relu9,8.57887,38.5238,22.5326,15.9912,8978.24,5251.35,3726.89,6.06691,27.4339,16.0488,11.3857,6387.85,3736.81,2651.17
-Relu9_f2h,14.4553,55.9886,32.856,23.1325,7749.62,4547.83,3201.79,10.2233,39.6978,23.2972,16.4014,5494.46,3224.62,2269.95
-Relu9_h2f,11.1054,43.0539,25.1182,17.9357,7754,4523.73,3230.27,7.85503,30.4771,17.782,12.6957,5487.43,3201.59,2285.95
-Softmax1,2.22516,6.67368,4.16081,2.51286,5989.43,3734.33,2255.1,1.57533,4.88219,3.04381,1.83848,4370.27,2724.81,1645.55
diff --git a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_fp32.csv b/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_fp32.csv
deleted file mode 100644
index 1c825c847259023cf9ab4d78b89dda08710ec175..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_fp32.csv
+++ /dev/null
@@ -1,74 +0,0 @@
-Add1,21.6934,58.7548,31.0822,27.6726,5431.3,2876.93,2554.37,15.3418,45.0353,25.8605,19.9268,4170.35,2397.52,1841.24
-Add10,32.0305,157.253,106.44,50.8121,9819.95,6647.01,3172.94,22.6511,111.473,75.5251,35.9672,6961.24,4716.53,2245.89
-Add11,13.2849,62.5018,41.4729,21.0289,9426.61,6255.38,3171.23,9.39853,44.3375,29.446,14.8999,6690.48,4443.87,2247.88
-Add12,13.3116,62.903,42.3375,20.5655,9473.31,6376.28,3097.03,9.4262,44.5564,30.0016,14.5585,6714.71,4521.5,2193.76
-Add13,13.2261,60.7273,40.0247,20.7027,9184.29,6053.35,3130.94,9.35469,43.0083,28.3589,14.6532,6503.87,4288.67,2215.77
-Add14,18.2347,85.0395,55.6313,29.4083,9327.04,6101.58,3225.47,12.8946,60.2002,39.3989,20.8069,6602.17,4320.89,2281.89
-Add15,13.201,61.5741,40.8812,20.6929,9329.61,6194.38,3135.23,9.33612,43.6009,28.9576,14.646,6605.87,4387.49,2218.81
-Add16,13.1048,61.3472,41.1577,20.1895,9363.36,6282.02,3081.34,9.26961,43.4341,29.1451,14.2906,6627.73,4447.56,2180.42
-Add17,18.074,85.9011,57.2285,28.6726,9505.77,6332.9,3172.88,12.7825,60.7891,40.5063,20.2854,6725.93,4481.8,2244.41
-Add18,13.0014,61.4375,41.4414,19.9961,9451.79,6375.57,3076.22,9.19633,43.4954,29.3433,14.1535,6689.95,4513.32,2176.85
-Add19,12.9281,61.0404,41.4361,19.6043,9443.71,6410.78,3032.93,9.14237,43.1927,29.3243,13.8701,6682.35,4536.91,2145.7
-Add2,21.6463,74.901,45.7092,29.1918,6924.08,4226.11,2697.96,15.3072,54.6989,34.0441,20.8568,5058.68,3149.19,1928.09
-Add20,18.0244,86.3285,58.1531,28.1754,9582.35,6454.96,3127.39,12.7469,61.1622,41.2057,19.9583,6788.91,4573.83,2215.28
-Add21,11.443,53.5959,35.9335,17.6624,9368.34,6281.11,3087.23,8.0933,37.9341,25.4348,12.5,6629.85,4445.42,2184.56
-Add22,11.2802,53.8629,36.7609,17.102,9550.59,6518.28,3032.31,7.97721,38.1207,26.0183,12.103,6758.92,4613.25,2145.78
-Add23,11.4043,53.4182,36.0306,17.3876,9367.74,6318.51,3049.23,8.06561,37.8152,25.5087,12.3072,6629.92,4472.24,2157.8
-Add24,14.1964,67.2674,45.1882,22.0792,9476.37,6365.9,3110.47,10.0397,47.6016,31.9802,15.6223,6704.74,4504.4,2200.47
-Add25,11.4019,54.4409,37.1263,17.3146,9550.12,6512.81,3037.31,8.06306,38.5289,26.2759,12.2533,6758.64,4609.3,2149.39
-Add26,11.389,54.7724,37.8032,16.9691,9618.53,6638.66,2979.88,8.05392,38.7645,26.7556,12.0092,6806.88,4698.25,2108.68
-Add27,14.534,71.0934,48.8721,22.2213,9782.97,6725.16,3057.81,10.2779,50.3048,34.583,15.7223,6921.71,4758.46,2163.32
-Add28,11.3928,55.6158,38.6494,16.9664,9763.99,6785.38,2978.61,8.05735,39.362,27.355,12.0073,6909.78,4802.07,2107.78
-Add29,11.3734,55.2916,38.7026,16.589,9723.57,6806.29,2917.28,8.04323,39.1303,27.3912,11.7396,6881.02,4816.78,2064.32
-Add3,21.6875,87.1673,56.8165,30.3508,8044.5,5244.29,2800.21,15.3377,62.5303,41.0116,21.5851,5774.2,3788.18,1992.12
-Add30,14.737,73.1106,50.9526,22.158,9922.47,6915.21,3007.26,10.4237,51.7335,36.0556,15.6783,7019.51,4892.23,2127.34
-Add31,0.224655,0.0762958,0.0528715,0.0234243,659.266,456.971,202.295,0.159906,0.176819,0.122519,0.0543026,1530.64,1061.12,469.546
-Add4,31.9722,131.604,84.4071,47.197,8233.73,5281.05,2952.68,22.61,94.3563,60.9261,33.5436,5903.71,3812.26,2098.54
-Add5,21.6558,98.7438,66.2685,32.4754,9121.5,6121.82,2999.68,15.3155,70.2815,47.2673,23.0367,6492.68,4366.94,2127.81
-Add6,21.6444,102.842,70.2169,32.6251,9505.17,6490.1,3015.08,15.3068,73.0591,49.9573,23.1196,6753.39,4618.34,2136.68
-Add7,31.9182,154.555,104.005,50.5501,9684.68,6517.16,3167.52,22.5717,109.752,73.9651,35.8127,6876.69,4634.46,2243.88
-Add8,21.6322,107.553,73.8345,33.7187,9945.81,6827.96,3117.85,15.2981,76.4125,52.5414,23.892,7066.74,4859.42,2209.25
-Add9,21.8129,104.865,72.0378,32.8272,9617.72,6607.25,3010.47,15.4261,74.3584,51.1311,23.241,6820.84,4690.62,2131.47
-Conv1,96.2953,255.037,132.22,122.817,5159.09,2638.34,2520.75,68.2526,204.018,117.903,90.0101,4055.42,2323.39,1823.93
-Conv10,70.4671,340.841,226.917,113.924,9673.98,6440.5,3233.48,49.8536,241.418,160.812,80.6306,6848.59,4561.91,2287.37
-Conv11,129.334,614.475,398.927,215.548,9503.33,6169.85,3333.48,91.4697,434.917,282.486,152.474,6725.99,4368.85,2357.8
-Conv12,129.068,617.019,406.726,210.293,9562.46,6303.54,3258.92,91.2758,436.559,287.85,148.736,6765.83,4461.37,2304.88
-Conv13,129.135,621.204,409.93,211.274,9622.23,6349.82,3272.41,91.3223,439.472,290.061,149.429,6807.38,4493.24,2314.43
-Conv14,129.174,625.346,417.91,207.436,9683.85,6471.74,3212.11,91.3536,442.335,295.64,146.707,6849.89,4578.44,2271.64
-Conv15,61.1393,299.396,199.643,99.7523,9796.81,6532.83,3263.98,43.2444,211.769,141.223,70.5503,6929.6,4621.32,2308.42
-Conv16,84.5931,417.41,280.311,137.099,9869.83,6628.22,3241.61,59.8259,295.223,198.268,96.9599,6980.4,4688.15,2292.36
-Conv17,36.7136,180.084,122.113,57.9714,9812.69,6654.16,3158.53,25.9732,127.397,86.3877,41.0109,6940.16,4706.54,2233.71
-Conv18,84.6165,415.961,279.353,136.608,9832.95,6603.82,3229.13,59.8443,294.189,197.58,96.6131,6953.97,4670.58,2283.48
-Conv19,84.5493,420.587,286.448,134.139,9950.05,6776.78,3173.27,59.7919,297.44,202.583,94.8602,7036.73,4792.81,2243.99
-Conv2,167.13,533.192,304.506,228.686,6388.94,3650.52,2738.43,118.195,395.752,235.038,163.802,4746.52,2820.79,1962.43
-Conv20,84.5033,424.692,291.118,133.574,10052.5,6890.93,3161.61,59.759,300.336,205.879,94.46,7109.04,4873.38,2235.73
-Conv21,84.5829,428.664,296.983,131.681,10137.1,7023.19,3113.87,59.8165,303.148,210.028,93.1228,7168.75,4966.83,2201.97
-Conv3,167.964,647.325,407.112,240.213,7707.18,4847.1,2860.08,118.79,467.662,297.418,171.175,5566.56,3540.11,2037.55
-Conv4,167.592,744.048,484.83,259.218,8881.63,5787.69,3093.94,118.526,530.921,347.314,183.963,6338.14,4146.67,2195.7
-Conv5,177.005,835.874,561.072,274.802,9500.55,6383.92,3116.64,125.364,591.914,397.787,194.351,6756.17,4549.8,2208.86
-Conv6,218.988,1050.1,698.053,352.047,9847.98,6588.16,3259.83,159.09,748.601,496.158,252.938,7006.45,4702.25,2308.95
-Conv7,264.136,1280.25,861.007,419.244,9708.54,6531.25,3177.29,186.823,907.588,611.208,296.66,6890.93,4643.37,2249.66
-Conv8,100.507,490.584,323.717,166.867,9761,6440.68,3320.32,71.0869,347.741,229.7,118.109,6916.34,4568.28,2349.42
-Conv9,129.091,627.118,413.499,213.62,9716.34,6406.64,3309.7,91.291,444.149,293.072,151.141,6880.99,4540.5,2341.49
-Mul1,0.500934,0.486919,0.337278,0.14964,1850.52,1282.1,568.424,0.361084,0.684926,0.474355,0.210576,2540.71,1760.3,780.419
-Pool1,7.47447,35.1354,24.3713,10.764,9455.2,6558.7,2896.5,5.30974,24.9294,17.2922,7.63735,6710.44,4654.93,2055.55
-Relu1,19.1763,53.4017,28.007,25.3948,5567.29,2919.23,2648.06,13.5622,40.6992,23.0638,18.2718,4241.1,2402.95,1904.65
-Relu10,9.40207,43.1235,28.5091,14.6144,9172.92,6064.26,3108.66,6.64869,30.5546,20.2073,10.3494,6498.77,4297.98,2201.24
-Relu11,9.41748,43.1252,28.5286,14.5966,9164.63,6062.75,3101.88,6.66047,30.6133,20.2564,10.3587,6504.87,4304.29,2200.96
-Relu12,9.36322,43.724,29.3495,14.3744,9339.53,6269.12,3070.41,6.6214,30.9587,20.7838,10.1759,6612.24,4439.05,2173.4
-Relu13,9.40974,43.6635,29.1918,14.4716,9280.16,6204.35,3075.81,6.6551,30.9301,20.6807,10.25,6572.25,4394.33,2178.05
-Relu14,4.80981,20.8303,13.9278,6.90257,8662.5,5792.07,2870.43,3.40296,14.8309,9.91719,4.91395,6164.1,4121.99,2042.24
-Relu15,4.87483,20.4251,13.6704,6.7547,8463.01,5664.4,2798.61,3.50721,14.5206,9.71904,4.80188,6020.04,4029.63,1990.55
-Relu16,4.86403,20.6177,14.0222,6.5955,8564.49,5824.72,2739.77,3.49763,14.6509,9.9643,4.68671,6092.52,4143.6,1948.97
-Relu17,4.81198,21.0221,14.3894,6.63266,8734.67,5978.78,2755.88,3.40391,14.9788,10.2531,4.72591,6219.34,4257.18,1962.25
-Relu18,4.8106,21.1957,14.6867,6.509,8812.82,6106.5,2706.32,3.40271,15.0581,10.4344,4.62392,6259.54,4337.51,1922.11
-Relu19,4.81593,21.5459,14.9597,6.58625,8949.06,6213.41,2735.65,3.40702,15.3081,10.6291,4.67908,6356.15,4413.28,1942.92
-Relu2,19.0626,66.8226,40.3526,26.4701,7014.02,4236.15,2777.88,13.4814,48.633,29.9128,18.8899,5105.81,3141.06,1982.51
-Relu3,19.0941,78.945,49.9544,28.9906,8264.08,5228.56,3035.53,13.5031,56.5704,36.0347,20.6026,5917.81,3768.6,2156.24
-Relu4,19.1928,87.579,58.1748,29.4042,9138.5,6070.31,3068.19,13.5876,62.3198,41.4872,20.8544,6504.83,4330.45,2176.66
-Relu5,19.1354,92.4142,61.363,31.0513,9656.77,6411.83,3244.94,13.5331,65.6453,43.6566,22.0048,6856.9,4559.75,2298.84
-Relu6,19.1447,95.3407,64.7883,30.5524,9960.4,6768.59,3191.81,13.5399,67.727,46.0989,21.6475,7074.5,4815.39,2261.14
-Relu7,19.0951,93.1857,62.2371,30.9487,9757.72,6516.69,3241.03,13.5037,66.0793,44.1769,21.9139,6916.8,4623.74,2294.28
-Relu8,9.48155,43.6732,28.8388,14.8344,9253.12,6110.32,3142.8,6.73217,30.9767,20.4743,10.5085,6568.69,4341.77,2228.2
-Relu9,9.39785,42.293,27.4628,14.8302,8999.41,5843.66,3155.75,6.64609,29.965,19.467,10.5012,6374.39,4141.04,2234.03
-Softmax1,2.04054,7.19751,4.98957,2.20793,7058.19,4893.15,2165.04,1.44402,5.29548,3.67114,1.62439,5192.94,3600.29,1592.7
diff --git a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_layers.txt b/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_layers.txt
deleted file mode 100644
index 6837e87207b24eec8c1913275aa742824a67f74f..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_layers.txt
+++ /dev/null
@@ -1,41 +0,0 @@
-Conv1,2000,3,32,32,16,3,3,3,1,1
-Conv2,2000,16,32,32,16,16,3,3,1,1
-Conv3,2000,16,32,32,16,16,3,3,1,1
-NML1
-NML2
-Conv4,2000,16,32,32,16,16,3,3,1,1
-Conv5,2000,16,32,32,16,16,3,3,1,1
-NML3
-NML4
-Conv6,2000,16,32,32,16,16,3,3,1,1
-Conv7,2000,16,32,32,16,16,3,3,1,1
-NML5
-NML6
-Conv8,2000,16,32,32,32,16,3,3,2,2
-Conv9,2000,32,16,16,32,32,3,3,1,1
-Conv10,2000,16,32,32,32,16,1,1,2,2
-NML7
-NML8
-Conv11,2000,32,16,16,32,32,3,3,1,1
-Conv12,2000,32,16,16,32,32,3,3,1,1
-NML9
-NML10
-Conv13,2000,32,16,16,32,32,3,3,1,1
-Conv14,2000,32,16,16,32,32,3,3,1,1
-NML11
-NML12
-Conv15,2000,32,16,16,64,32,3,3,2,2
-Conv16,2000,64,8,8,64,64,3,3,1,1
-Conv17,2000,32,16,16,64,32,1,1,2,2
-NML13
-NML14
-Conv18,2000,64,8,8,64,64,3,3,1,1
-Conv19,2000,64,8,8,64,64,3,3,1,1
-NML15
-NML16
-Conv20,2000,64,8,8,64,64,3,3,1,1
-Conv21,2000,64,8,8,64,64,3,3,1,1
-NML17
-NML18
-NML19
-FC1,2000,64,64,10
diff --git a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_ops.txt b/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_ops.txt
deleted file mode 100644
index 86795a48547725b624c69f8768a4f7e53103d623..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_ops.txt
+++ /dev/null
@@ -1,114 +0,0 @@
-#Conv1,3
-Conv1
-Add1
-Relu1
-#Conv2,3
-Conv2
-Add2
-Relu2
-#Conv3,2
-Conv3
-Add3
-#NML1,1
-Add4
-#NML2,1
-Relu3
-#Conv4,3
-Conv4
-Add5
-Relu4
-#Conv5,2
-Conv5
-Add6
-#NML3,1
-Add7
-#NML4,1
-Relu5
-#Conv6,3
-Conv6
-Add8
-Relu6
-#Conv7,2
-Conv7
-Add9
-#NML5,1
-Add10
-#NML6,1
-Relu7
-#Conv8,3
-Conv8
-Add11
-Relu8
-#Conv9,2
-Conv9
-Add12
-#Conv10,2
-Conv10
-Add13
-#NML7,1
-Add14
-#NML8,1
-Relu9
-#Conv11,3
-Conv11
-Add15
-Relu10
-#Conv12,2
-Conv12
-Add16
-#NML9,1
-Add17
-#NML10,1
-Relu11
-#Conv13,3
-Conv13
-Add18
-Relu12
-#Conv14,2
-Conv14
-Add19
-#NML11,1
-Add20
-#NML12,1
-Relu13
-#Conv15,3
-Conv15
-Add21
-Relu14
-#Conv16,2
-Conv16
-Add22
-#Conv17,2
-Conv17
-Add23
-#NML13,1
-Add24
-#NML14,1
-Relu15
-#Conv18,3
-Conv18
-Add25
-Relu16
-#Conv19,2
-Conv19
-Add26
-#NML15,1
-Add27
-#NML16,1
-Relu17
-#Conv20,3
-Conv20
-Add28
-Relu18
-#Conv21,2
-Conv21
-Add29
-#NML17,1
-Add30
-#NML18,1
-Relu19
-#NML19,1
-Pool1
-#FC1,2
-Mul1
-Add31
diff --git a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_promise_confs1.txt b/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_promise_confs1.txt
deleted file mode 100644
index 0f382344773bc67d7a63545f5441e02e17c8025d..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_promise_confs1.txt
+++ /dev/null
@@ -1,13 +0,0 @@
-9 9 9,9 9 9,9 9,9,9,9 9 9,9 9,9,9,9 9 9,9 9,9,9,9 9 9,9 9,9 9,9,9,9 9 9,9 9,9,9,9 9 9,9 9,9,9,9 9 9,9 9,9 9,9,9,9 9 9,9 9,9,9,9 9 9,9 9,9,9,9,9 9
-9 9 9,9 9 9,9 9,8,8,9 9 9,7,8,8,9 9 9,7,8,8,7,8 8,9 9,8,8,8 8 8,8 8,8,8,9 9 9,8 8,8,8,7,9 9,8 8,8,8,8 8 8,8 8,8,8,9 9 9,8 8,8,8,8,9 9
-8 8 8,8 8 8,7,8,8,8 8 8,7,8,8,7,9 9,8,8,9 9 9,7,9 9,8,8,7,8 8,8,8,9 9 9,9 9,8,8,9 9 9,7,8 8,8,8,9 9 9,9 9,8,8,9 9 9,8 8,8,8,8,7
-8 8 8,8 8 8,7,8,8,8 8 8,8 8,8,8,9 9 9,7,8,8,9 9 9,9 9,9 9,8,8,8 8 8,8 8,8,8,9 9 9,9 9,8,8,7,7,9 9,8,8,9 9 9,8 8,8,8,7,9 9,8,8,8,9 9
-8 8 8,9 9 9,9 9,8,8,8 8 8,9 9,8,8,8 8 8,8 8,8,8,9 9 9,8 8,9 9,8,8,9 9 9,8 8,8,8,8 8 8,8 8,8,8,9 9 9,8 8,9 9,8,8,9 9 9,9 9,8,8,8 8 8,7,8,8,8,9 9
-9 9 9,8 8 8,7,8,8,7,8 8,8,8,8 8 8,9 9,8,8,7,8 8,8 8,8,8,8 8 8,8 8,8,8,9 9 9,8 8,8,8,9 9 9,8 8,9 9,8,8,9 9 9,9 9,8,8,9 9 9,9 9,8,8,8,7
-8 8 8,8 8 8,7,8,8,7,8 8,8,8,7,8 8,8,8,9 9 9,9 9,8 8,8,8,9 9 9,8 8,8,8,8 8 8,7,8,8,8 8 8,9 9,7,8,8,9 9 9,9 9,8,8,9 9 9,9 9,8,8,8,8 8
-8 8 8,8 8 8,7,8,8,9 9 9,7,8,8,7,8 8,8,8,9 9 9,7,7,8,8,7,8 8,8,8,9 9 9,8 8,8,8,9 9 9,9 9,8 8,8,8,9 9 9,9 9,8,8,9 9 9,8 8,8,8,8,7
-8 8 8,8 8 8,7,8,8,9 9 9,8 8,8,8,8 8 8,9 9,8,8,9 9 9,8 8,7,8,8,9 9 9,8 8,8,8,9 9 9,9 9,8,8,8 8 8,8 8,7,8,8,8 8 8,7,8,8,8 8 8,9 9,8,8,8,9 9
-8 8 8,9 9 9,7,8,8,8 8 8,8 8,8,8,8 8 8,9 9,8,8,9 9 9,8 8,8 8,8,8,8 8 8,9 9,8,8,7,9 9,8,8,9 9 9,8 8,7,8,8,9 9 9,9 9,8,8,8 8 8,9 9,8,8,8,9 9
-9 9 9,8 8 8,9 9,8,8,7,7,8,8,7,9 9,8,8,8 8 8,7,9 9,8,8,8 8 8,9 9,8,8,8 8 8,9 9,8,8,8 8 8,9 9,7,8,8,9 9 9,9 9,8,8,8 8 8,9 9,8,8,8,9 9
-8 8 8,8 8 8,7,8,8,8 8 8,7,8,8,7,9 9,8,8,9 9 9,7,9 9,8,8,7,9 9,8,8,9 9 9,8 8,8,8,8 8 8,9 9,8 8,8,8,9 9 9,9 9,8,8,9 9 9,8 8,8,8,8,9 9
-8 8 8,8 8 8,7,8,8,7,7,8,8,7,7,8,8,8 8 8,9 9,8 8,8,8,9 9 9,9 9,8,8,8 8 8,8 8,8,8,9 9 9,9 9,9 9,8,8,9 9 9,9 9,8,8,8 8 8,9 9,8,8,8,7
diff --git a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_promise_confs2.txt b/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_promise_confs2.txt
deleted file mode 100644
index 25db61b421ddaf79a95c0b645fbcd381a7f07dc0..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_promise_confs2.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-9 9 9,9 9 9,9 9,9,9,9 9 9,9 9,9,9,9 9 9,9 9,9,9,9 9 9,9 9,9 9,9,9,9 9 9,9 9,9,9,9 9 9,9 9,9,9,9 9 9,9 9,9 9,9,9,9 9 9,9 9,9,9,9 9 9,9 9,9,9,9,9 9
-9 9 9,9 9 9,9 9,8,8,9 9 9,7,8,8,9 9 9,7,8,8,7,8 8,9 9,8,8,8 8 8,8 8,8,8,9 9 9,8 8,8,8,7,9 9,8 8,8,8,8 8 8,8 8,8,8,9 9 9,8 8,8,8,8,9 9
-9 9 9,9 9 9,7,8,8,9 9 9,7,8,8,9 9 9,9 9,8,8,7,8 8,9 9,8,8,7,7,8,8,9 9 9,8 8,8,8,9 9 9,9 9,8 8,8,8,8 8 8,9 9,8,8,8 8 8,8 8,8,8,8,7
-8 8 8,9 9 9,7,8,8,7,9 9,8,8,7,7,8,8,9 9 9,9 9,8 8,8,8,9 9 9,7,8,8,8 8 8,7,8,8,8 8 8,7,9 9,8,8,9 9 9,9 9,8,8,9 9 9,9 9,8,8,8,8 8
-8 8 8,8 8 8,8 8,8,8,7,9 9,8,8,7,7,8,8,7,9 9,8 8,8,8,9 9 9,8 8,8,8,8 8 8,8 8,8,8,8 8 8,7,9 9,8,8,9 9 9,9 9,8,8,8 8 8,9 9,8,8,8,9 9
-8 8 8,8 8 8,8 8,8,8,7,7,8,8,7,7,8,8,8 8 8,9 9,9 9,8,8,9 9 9,8 8,8,8,9 9 9,8 8,8,8,7,7,9 9,8,8,9 9 9,9 9,8,8,8 8 8,9 9,8,8,8,9 9
-8 8 8,8 8 8,7,8,8,8 8 8,7,8,8,7,9 9,8,8,9 9 9,7,9 9,8,8,7,8 8,8,8,9 9 9,9 9,8,8,9 9 9,7,8 8,8,8,9 9 9,9 9,8,8,9 9 9,8 8,8,8,8,7
-8 8 8,8 8 8,8 8,8,8,7,7,8,8,7,7,8,8,8 8 8,9 9,8 8,8,8,9 9 9,7,8,8,9 9 9,8 8,8,8,8 8 8,7,9 9,8,8,9 9 9,9 9,8,8,8 8 8,9 9,8,8,8,9 9
-8 8 8,8 8 8,7,8,8,7,7,8,8,7,7,8,8,8 8 8,9 9,8 8,8,8,9 9 9,7,8,8,8 8 8,8 8,8,8,7,7,9 9,8,8,9 9 9,9 9,8,8,8 8 8,9 9,8,8,8,8 8
-8 8 8,8 8 8,7,8,8,8 8 8,8 8,8,8,9 9 9,7,8,8,9 9 9,9 9,9 9,8,8,8 8 8,8 8,8,8,9 9 9,9 9,8,8,7,7,9 9,8,8,9 9 9,8 8,8,8,7,9 9,8,8,8,9 9
-8 8 8,9 9 9,9 9,8,8,8 8 8,9 9,8,8,8 8 8,8 8,8,8,9 9 9,8 8,9 9,8,8,9 9 9,8 8,8,8,8 8 8,8 8,8,8,9 9 9,8 8,9 9,8,8,9 9 9,9 9,8,8,8 8 8,7,8,8,8,9 9
-9 9 9,9 9 9,7,8,8,7,8 8,8,8,9 9 9,9 9,8,8,8 8 8,8 8,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,9 9 9,8 8,9 9,8,8,9 9 9,8 8,8,8,9 9 9,7,8,8,8,7
-8 8 8,9 9 9,8 8,8,8,7,9 9,8,8,8 8 8,9 9,8,8,9 9 9,7,7,8,8,8 8 8,7,8,8,9 9 9,8 8,8,8,9 9 9,7,9 9,8,8,8 8 8,9 9,8,8,8 8 8,9 9,8,8,8,9 9
-8 8 8,9 9 9,7,8,8,7,9 9,8,8,9 9 9,9 9,8,8,8 8 8,7,7,8,8,9 9 9,7,8,8,9 9 9,8 8,8,8,7,8 8,9 9,8,8,8 8 8,9 9,8,8,8 8 8,8 8,8,8,8,8 8
-9 9 9,8 8 8,7,8,8,7,8 8,8,8,8 8 8,9 9,8,8,7,8 8,8 8,8,8,8 8 8,8 8,8,8,9 9 9,8 8,8,8,9 9 9,8 8,9 9,8,8,9 9 9,9 9,8,8,9 9 9,9 9,8,8,8,7
-8 8 8,8 8 8,7,8,8,7,8 8,8,8,7,8 8,8,8,9 9 9,9 9,8 8,8,8,9 9 9,8 8,8,8,8 8 8,7,8,8,8 8 8,9 9,7,8,8,9 9 9,9 9,8,8,9 9 9,9 9,8,8,8,8 8
-8 8 8,9 9 9,8 8,8,8,7,7,8,8,7,7,8,8,8 8 8,9 9,8 8,8,8,9 9 9,7,8,8,8 8 8,8 8,8,8,9 9 9,8 8,7,8,8,8 8 8,9 9,8,8,8 8 8,8 8,8,8,8,9 9
-8 8 8,8 8 8,7,8,8,9 9 9,7,8,8,7,8 8,8,8,9 9 9,7,7,8,8,7,8 8,8,8,9 9 9,8 8,8,8,9 9 9,9 9,8 8,8,8,9 9 9,9 9,8,8,9 9 9,8 8,8,8,8,7
-8 8 8,8 8 8,7,8,8,9 9 9,7,8,8,9 9 9,7,8,8,8 8 8,9 9,8 8,8,8,9 9 9,7,8,8,8 8 8,8 8,8,8,7,7,9 9,8,8,9 9 9,9 9,8,8,8 8 8,9 9,8,8,8,9 9
-8 8 8,8 8 8,7,8,8,9 9 9,8 8,8,8,8 8 8,9 9,8,8,9 9 9,8 8,7,8,8,9 9 9,8 8,8,8,9 9 9,9 9,8,8,8 8 8,8 8,7,8,8,8 8 8,7,8,8,8 8 8,9 9,8,8,8,9 9
-9 9 9,9 9 9,7,8,8,8 8 8,7,8,8,8 8 8,9 9,8,8,8 8 8,8 8,9 9,8,8,8 8 8,7,8,8,9 9 9,9 9,8,8,7,8 8,8 8,8,8,8 8 8,9 9,8,8,8 8 8,9 9,8,8,8,7
-9 9 9,9 9 9,7,8,8,8 8 8,9 9,8,8,9 9 9,9 9,8,8,9 9 9,9 9,8 8,8,8,8 8 8,7,8,8,8 8 8,8 8,8,8,7,8 8,9 9,8,8,9 9 9,9 9,8,8,8 8 8,9 9,8,8,8,9 9
-8 8 8,8 8 8,7,8,8,7,7,8,8,7,7,8,8,7,7,9 9,8,8,9 9 9,9 9,8,8,8 8 8,8 8,8,8,8 8 8,8 8,9 9,8,8,9 9 9,9 9,8,8,9 9 9,9 9,8,8,8,7
-8 8 8,8 8 8,8 8,8,8,9 9 9,7,8,8,7,7,8,8,8 8 8,9 9,8 8,8,8,9 9 9,7,8,8,8 8 8,8 8,8,8,8 8 8,8 8,9 9,8,8,9 9 9,9 9,8,8,8 8 8,9 9,8,8,8,9 9
-8 8 8,8 8 8,7,8,8,7,7,8,8,8 8 8,7,8,8,9 9 9,9 9,9 9,8,8,9 9 9,7,8,8,8 8 8,9 9,8,8,8 8 8,7,9 9,8,8,9 9 9,9 9,8,8,8 8 8,9 9,8,8,8,9 9
-8 8 8,9 9 9,7,8,8,8 8 8,8 8,8,8,8 8 8,9 9,8,8,9 9 9,8 8,8 8,8,8,8 8 8,9 9,8,8,7,9 9,8,8,9 9 9,8 8,7,8,8,9 9 9,9 9,8,8,8 8 8,9 9,8,8,8,9 9
-9 9 9,8 8 8,9 9,8,8,7,7,8,8,7,9 9,8,8,8 8 8,7,9 9,8,8,8 8 8,9 9,8,8,8 8 8,9 9,8,8,8 8 8,9 9,7,8,8,9 9 9,9 9,8,8,8 8 8,9 9,8,8,8,9 9
-9 9 9,8 8 8,7,8,8,7,7,8,8,7,7,8,8,7,7,8 8,8,8,9 9 9,9 9,8,8,9 9 9,8 8,8,8,9 9 9,8 8,9 9,8,8,9 9 9,9 9,8,8,8 8 8,9 9,8,8,8,9 9
-8 8 8,8 8 8,7,8,8,8 8 8,7,8,8,7,9 9,8,8,9 9 9,7,9 9,8,8,7,9 9,8,8,9 9 9,8 8,8,8,8 8 8,9 9,8 8,8,8,9 9 9,9 9,8,8,9 9 9,8 8,8,8,8,9 9
-8 8 8,8 8 8,8 8,8,8,9 9 9,7,8,8,9 9 9,7,8,8,9 9 9,8 8,7,8,8,7,8 8,8,8,8 8 8,8 8,8,8,8 8 8,9 9,8 8,8,8,8 8 8,9 9,8,8,9 9 9,9 9,8,8,8,9 9
-8 8 8,8 8 8,7,8,8,7,7,8,8,7,7,8,8,8 8 8,9 9,8 8,8,8,9 9 9,9 9,8,8,8 8 8,8 8,8,8,9 9 9,9 9,9 9,8,8,9 9 9,9 9,8,8,8 8 8,9 9,8,8,8,7
-9 9 9,8 8 8,9 9,8,8,8 8 8,7,8,8,8 8 8,7,8,8,9 9 9,7,7,8,8,8 8 8,9 9,8,8,8 8 8,9 9,8,8,8 8 8,9 9,7,8,8,9 9 9,9 9,8,8,8 8 8,9 9,8,8,8,9 9
diff --git a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_promise_results1.csv b/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_promise_results1.csv
deleted file mode 100644
index 43efb1d48e791b041d332cb015d671f111dae293..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_promise_results1.csv
+++ /dev/null
@@ -1,187 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,367.1935,674.9156,734.4923,131.604,78.945,930.3708,938.716,154.555,92.4142,1252.9937,1385.115,157.253,93.1857,596.759,690.021,401.5683,85.0395,42.293,719.1726,678.3662,85.9011,43.1252,726.3655,686.3864,86.3285,43.6635,373.8222,471.2729,233.5022,67.2674,20.4251,491.0196,475.3594,71.0934,21.0221,501.5035,483.9556,73.1106,21.5459,35.1354,0.5632148,15217.3421148,0.99999999342855
-c1,367.1935,674.9156,734.4923,109.339,74.5864,930.3708,17.457054,108.541,77.2477,1252.9937,17.457054,107.72,72.987,8.728527,648.5595,401.5683,49.588,38.5238,668.3418,737.6456,49.8165,38.8844,726.3655,735.4767,49.6507,39.5474,6.546395,471.2729,221.6533,49.7509,21.8376,347.1207,382.2379,50.4854,21.963,501.5035,382.9785,50.3589,22.1017,23.3959,0.5632148,11291.7676448,1.34764923072455
-c2,520.638,1160.9424,17.457054,109.339,74.5864,1558.1515,17.457054,108.541,77.2477,17.457054,1385.115,107.72,72.987,596.759,13.092790,401.5683,49.588,38.5238,13.092790,737.6456,49.8165,38.8844,726.3655,686.3864,49.6507,39.5474,373.8222,10.910659,221.6533,49.7509,21.8376,491.0196,475.3594,50.4854,21.963,501.5035,382.9785,50.3589,22.1017,23.3959,0.005455,11365.706356,1.33888220443761
-c3,520.638,1160.9424,17.457054,109.339,74.5864,1558.1515,1543.1258,108.541,77.2477,1252.9937,17.457054,107.72,72.987,596.759,690.021,401.5683,49.588,38.5238,668.3418,737.6456,49.8165,38.8844,726.3655,686.3864,49.6507,39.5474,6.546395,10.910659,233.5022,49.7509,21.8376,491.0196,382.2379,50.4854,21.963,10.910659,483.9556,50.3589,22.1017,23.3959,0.5632148,13253.8246358,1.1481472267923
-c4,520.638,674.9156,734.4923,109.339,74.5864,1558.1515,938.716,108.541,77.2477,1693.1477,1601.9877,107.72,72.987,596.759,648.5595,401.5683,49.588,38.5238,719.1726,737.6456,49.8165,38.8844,763.2659,735.4767,49.6507,39.5474,373.8222,326.6018,233.5022,49.7509,21.8376,491.0196,475.3594,50.4854,21.963,393.9254,10.910659,50.3589,22.1017,23.3959,0.5632148,15686.5261738,0.970089989918059
-c5,367.1935,1160.9424,17.457054,109.339,74.5864,17.457054,1543.1258,108.541,77.2477,1693.1477,1385.115,107.72,72.987,8.728527,648.5595,422.944,49.588,38.5238,668.3418,737.6456,49.8165,38.8844,726.3655,735.4767,49.6507,39.5474,373.8222,326.6018,233.5022,49.7509,21.8376,491.0196,475.3594,50.4854,21.963,501.5035,483.9556,50.3589,22.1017,23.3959,0.005455,14074.59519,1.08119216227922
-c6,520.638,1160.9424,17.457054,109.339,74.5864,17.457054,1543.1258,108.541,77.2477,17.457054,1601.9877,107.72,72.987,596.759,690.021,422.944,49.588,38.5238,719.1726,737.6456,49.8165,38.8844,763.2659,13.092790,49.6507,39.5474,226.8183,471.2729,2.182132,49.7509,21.8376,491.0196,475.3594,50.4854,21.963,501.5035,483.9556,50.3589,22.1017,23.3959,6.45743,12536.860114,1.21380807116336
-c7,520.638,1160.9424,17.457054,109.339,74.5864,930.3708,17.457054,108.541,77.2477,17.457054,1601.9877,107.72,72.987,596.759,13.092790,4.364263,49.588,38.5238,13.092790,737.6456,49.8165,38.8844,726.3655,735.4767,49.6507,39.5474,373.8222,471.2729,221.6533,49.7509,21.8376,491.0196,475.3594,50.4854,21.963,501.5035,382.9785,50.3589,22.1017,23.3959,0.005455,11067.04686,1.37501378369501
-c8,520.638,1160.9424,17.457054,109.339,74.5864,930.3708,1543.1258,108.541,77.2477,1693.1477,1385.115,107.72,72.987,596.759,648.5595,4.364263,49.588,38.5238,719.1726,737.6456,49.8165,38.8844,726.3655,686.3864,49.6507,39.5474,226.8183,326.6018,2.182132,49.7509,21.8376,347.1207,10.910659,50.4854,21.963,393.9254,483.9556,50.3589,22.1017,23.3959,0.5632148,14218.4527228,1.07025302291669
-c9,520.638,674.9156,17.457054,109.339,74.5864,1558.1515,1543.1258,108.541,77.2477,1693.1477,1385.115,107.72,72.987,596.759,648.5595,422.944,49.588,38.5238,668.3418,678.3662,49.8165,38.8844,13.092790,686.3864,49.6507,39.5474,373.8222,326.6018,2.182132,49.7509,21.8376,491.0196,475.3594,50.4854,21.963,393.9254,483.9556,50.3589,22.1017,23.3959,0.5632148,14710.7549908,1.03443650722707
-c10,367.1935,1160.9424,734.4923,109.339,74.5864,17.457054,17.457054,108.541,77.2477,17.457054,1385.115,107.72,72.987,452.0318,13.092790,401.5683,49.588,38.5238,668.3418,678.3662,49.8165,38.8844,763.2659,686.3864,49.6507,39.5474,226.8183,471.2729,2.182132,49.7509,21.8376,491.0196,475.3594,50.4854,21.963,393.9254,483.9556,50.3589,22.1017,23.3959,0.5632148,10964.5893988,1.38786245636149
-c11,520.638,1160.9424,17.457054,109.339,74.5864,1558.1515,17.457054,108.541,77.2477,17.457054,1385.115,107.72,72.987,596.759,13.092790,401.5683,49.588,38.5238,13.092790,678.3662,49.8165,38.8844,726.3655,735.4767,49.6507,39.5474,226.8183,471.2729,221.6533,49.7509,21.8376,491.0196,475.3594,50.4854,21.963,501.5035,382.9785,50.3589,22.1017,23.3959,0.5632148,11669.4333568,1.30403435360716
-c12,520.638,1160.9424,17.457054,109.339,74.5864,17.457054,17.457054,108.541,77.2477,17.457054,17.457054,107.72,72.987,452.0318,690.021,422.944,49.588,38.5238,719.1726,678.3662,49.8165,38.8844,763.2659,735.4767,49.6507,39.5474,373.8222,471.2729,233.5022,49.7509,21.8376,491.0196,475.3594,50.4854,21.963,393.9254,483.9556,50.3589,22.1017,23.3959,0.005455,10209.331925,1.49053259092139
-c12,10209.331925
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,137.165,207.8389,189.6515,31.9722,19.0941,208.4406,198.6494,31.9182,19.1354,259.7649,285.9489,32.0305,19.0951,123.27345,142.4026,83.6932,18.2347,9.39785,151.93707,142.1728,18.074,9.41748,151.49962,142.1021,18.0244,9.40974,77.39211,95.8733,48.1179,14.1964,4.87483,100.88243,95.9383,14.534,4.81198,100.7067,95.9563,14.737,4.81593,7.47447,0.725589,3341.380949,0.999999970072255
-c1,137.165,207.8389,189.6515,25.2021,16.7972,208.4406,9.024416,24.9686,17.3272,259.7649,9.024416,25.4775,16.8139,4.555456,148.08761,83.6932,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,151.49962,175.99631,11.2352,8.56125,3.459840,95.8733,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,100.7067,89.01816,11.2081,4.48848,5.19545,0.725589,2667.980987,1.25240053810022
-c2,208.7505,342.9757,9.024416,25.2021,16.7972,356.8393,9.024416,24.9686,17.3272,9.024416,285.9489,25.4775,16.8139,123.27345,6.804352,83.6932,11.4693,8.57887,6.804352,176.87812,11.2953,8.59732,151.49962,142.1021,11.2352,8.56125,77.39211,5.766400,50.93214,11.2873,4.46626,100.88243,95.9383,11.1843,4.46968,100.7067,89.01816,11.2081,4.48848,5.19545,0.003604,2671.905996,1.25056077157885
-c3,208.7505,342.9757,9.024416,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,259.7649,9.024416,25.4775,16.8139,123.27345,142.4026,83.6932,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,151.49962,142.1021,11.2352,8.56125,3.459840,5.766400,48.1179,11.2873,4.46626,100.88243,89.0888,11.1843,4.46968,5.766400,95.9563,11.2081,4.48848,5.19545,0.725589,3111.982111,1.07371466880149
-c4,208.7505,207.8389,189.6515,25.2021,16.7972,356.8393,198.6494,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,123.27345,148.08761,83.6932,11.4693,8.57887,151.93707,176.87812,11.2953,8.59732,181.04838,175.99631,11.2352,8.56125,77.39211,74.50821,48.1179,11.2873,4.46626,100.88243,95.9383,11.1843,4.46968,90.82636,5.766400,11.2081,4.48848,5.19545,0.725589,3723.591349,0.897354340497627
-c5,137.165,342.9757,9.024416,25.2021,16.7972,9.024416,359.5265,24.9686,17.3272,399.7504,285.9489,25.4775,16.8139,4.555456,148.08761,99.91022,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,151.49962,175.99631,11.2352,8.56125,77.39211,74.50821,48.1179,11.2873,4.46626,100.88243,95.9383,11.1843,4.46968,100.7067,95.9563,11.2081,4.48848,5.19545,0.003604,3290.311852,1.01552101981363
-c6,208.7505,342.9757,9.024416,25.2021,16.7972,9.024416,359.5265,24.9686,17.3272,9.024416,388.4166,25.4775,16.8139,123.27345,142.4026,99.91022,11.4693,8.57887,151.93707,176.87812,11.2953,8.59732,181.04838,6.804352,11.2352,8.56125,51.58494,95.8733,1.153280,11.2873,4.46626,100.88243,95.9383,11.1843,4.46968,100.7067,95.9563,11.2081,4.48848,5.19545,1.571852,2991.287152,1.11703780596996
-c7,208.7505,342.9757,9.024416,25.2021,16.7972,208.4406,9.024416,24.9686,17.3272,9.024416,388.4166,25.4775,16.8139,123.27345,6.804352,2.306560,11.4693,8.57887,6.804352,176.87812,11.2953,8.59732,151.49962,175.99631,11.2352,8.56125,77.39211,95.8733,50.93214,11.2873,4.46626,100.88243,95.9383,11.1843,4.46968,100.7067,89.01816,11.2081,4.48848,5.19545,0.003604,2668.589466,1.25211497173335
-c8,208.7505,342.9757,9.024416,25.2021,16.7972,208.4406,359.5265,24.9686,17.3272,399.7504,285.9489,25.4775,16.8139,123.27345,148.08761,2.306560,11.4693,8.57887,151.93707,176.87812,11.2953,8.59732,151.49962,142.1021,11.2352,8.56125,51.58494,74.50821,1.153280,11.2873,4.46626,80.09278,5.766400,11.1843,4.46968,90.82636,95.9563,11.2081,4.48848,5.19545,0.725589,3349.738715,0.997504920096285
-c9,208.7505,207.8389,9.024416,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,285.9489,25.4775,16.8139,123.27345,148.08761,99.91022,11.4693,8.57887,157.84032,142.1728,11.2953,8.59732,6.804352,142.1021,11.2352,8.56125,77.39211,74.50821,1.153280,11.2873,4.46626,100.88243,95.9383,11.1843,4.46968,90.82636,95.9563,11.2081,4.48848,5.19545,0.725589,3423.875657,0.975906015914467
-c10,137.165,342.9757,189.6515,25.2021,16.7972,9.024416,9.024416,24.9686,17.3272,9.024416,285.9489,25.4775,16.8139,104.7504,6.804352,83.6932,11.4693,8.57887,157.84032,142.1728,11.2953,8.59732,181.04838,142.1021,11.2352,8.56125,51.58494,95.8733,1.153280,11.2873,4.46626,100.88243,95.9383,11.1843,4.46968,90.82636,95.9563,11.2081,4.48848,5.19545,0.725589,2572.789709,1.2987384112419
-c11,208.7505,342.9757,9.024416,25.2021,16.7972,356.8393,9.024416,24.9686,17.3272,9.024416,285.9489,25.4775,16.8139,123.27345,6.804352,83.6932,11.4693,8.57887,6.804352,142.1728,11.2953,8.59732,151.49962,175.99631,11.2352,8.56125,51.58494,95.8733,50.93214,11.2873,4.46626,100.88243,95.9383,11.1843,4.46968,100.7067,89.01816,11.2081,4.48848,5.19545,0.725589,2736.116601,1.22121287727924
-c12,208.7505,342.9757,9.024416,25.2021,16.7972,9.024416,9.024416,24.9686,17.3272,9.024416,9.024416,25.4775,16.8139,104.7504,142.4026,99.91022,11.4693,8.57887,151.93707,142.1728,11.2953,8.59732,181.04838,175.99631,11.2352,8.56125,77.39211,95.8733,48.1179,11.2873,4.46626,100.88243,95.9383,11.1843,4.46968,90.82636,95.9563,11.2081,4.48848,5.19545,0.003604,2438.679674,1.37015978261031
-c12,2438.679674
-
-Energy
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,367.1935,674.9156,734.4923,131.604,78.945,930.3708,938.716,154.555,92.4142,1252.9937,1385.115,157.253,93.1857,596.759,690.021,401.5683,85.0395,42.293,719.1726,678.3662,85.9011,43.1252,726.3655,686.3864,86.3285,43.6635,373.8222,471.2729,233.5022,67.2674,20.4251,491.0196,475.3594,71.0934,21.0221,501.5035,483.9556,73.1106,21.5459,35.1354,0.5632148,15217.3421148,0.99999999342855
-c1,367.1935,674.9156,734.4923,336.544,74.5864,1021.7777,67.644319,108.541,77.2477,1342.9017,67.644319,107.72,72.987,29.480448,648.5595,442.3803,159.941,38.5238,668.3418,737.6456,49.8165,38.8844,768.7706,794.6234,49.6507,39.5474,18.903715,471.2729,291.5164,49.7509,21.8376,347.1207,382.2379,50.4854,21.963,521.315,413.8585,50.3589,22.1017,23.3959,0.6085782,12207.0880792,1.24659885235606
-c2,533.7291,1160.9424,67.644319,109.339,74.5864,1558.1515,67.644319,108.541,77.2477,67.644319,1385.115,351.059,72.987,639.1792,42.156294,401.5683,159.941,38.5238,42.156294,737.6456,49.8165,38.8844,768.7706,686.3864,175.4817,39.5474,393.7426,29.560412,221.6533,49.7509,21.8376,511.0788,475.3594,120.4748,21.963,521.315,413.8585,50.3589,22.1017,23.3959,0.08824,12331.227597,1.23404923570604
-c3,533.7291,1160.9424,67.644319,109.339,74.5864,1558.1515,1543.1258,108.541,77.2477,1342.9017,67.644319,107.72,72.987,639.1792,690.021,401.5683,159.941,38.5238,668.3418,737.6456,49.8165,38.8844,768.7706,686.3864,175.4817,39.5474,18.903715,29.560412,233.5022,106.94,21.8376,511.0788,413.5184,50.4854,21.963,29.560412,483.9556,121.956,22.1017,23.3959,0.6085782,14008.0356552,1.08632947407713
-c4,533.7291,748.8261,734.4923,336.544,74.5864,1558.1515,1028.1229,368.498,77.2477,1693.1477,1601.9877,107.72,72.987,639.1792,709.4595,442.3803,159.941,38.5238,761.6715,796.7183,49.8165,38.8844,763.2659,735.4767,49.6507,39.5474,393.7426,357.7395,253.3351,106.94,21.8376,511.0788,475.3594,120.4748,21.963,393.9254,29.560412,50.3589,22.1017,23.3959,0.6085782,16942.9772902,0.898150411485638
-c5,367.1935,1240.8474,67.644319,109.339,74.5864,67.644319,1543.1258,108.541,77.2477,1693.1477,1471.0861,351.059,72.987,29.480448,648.5595,422.944,49.588,38.5238,668.3418,737.6456,49.8165,38.8844,768.7706,794.6234,49.6507,39.5474,393.7426,357.7395,253.3351,106.94,21.8376,511.0788,475.3594,120.4748,21.963,521.315,483.9556,121.956,22.1017,23.3959,0.08824,15016.108626,1.0134011675376
-c6,533.7291,1160.9424,67.644319,109.339,74.5864,67.644319,1543.1258,108.541,77.2477,67.644319,1601.9877,107.72,72.987,639.1792,690.021,558.879,49.588,38.5238,761.6715,796.7183,49.8165,38.8844,763.2659,42.156294,49.6507,39.5474,226.8183,491.752,10.570936,49.7509,21.8376,511.0788,475.3594,120.4748,21.963,521.315,483.9556,121.956,22.1017,23.3959,6.45743,13219.828417,1.15109981156195
-c7,533.7291,1160.9424,67.644319,109.339,74.5864,1021.7777,67.644319,108.541,77.2477,67.644319,1601.9877,107.72,72.987,639.1792,42.156294,21.193653,49.588,38.5238,42.156294,737.6456,49.8165,38.8844,768.7706,794.6234,49.6507,39.5474,393.7426,471.2729,291.5164,49.7509,21.8376,511.0788,475.3594,120.4748,21.963,521.315,413.8585,50.3589,22.1017,23.3959,0.08824,11771.641438,1.29271198631703
-c8,533.7291,1160.9424,67.644319,109.339,74.5864,1021.7777,1670.0268,108.541,77.2477,1693.1477,1471.0861,351.059,72.987,639.1792,709.4595,21.193653,49.588,38.5238,761.6715,796.7183,49.8165,38.8844,768.7706,686.3864,175.4817,39.5474,226.8183,326.6018,10.570936,49.7509,21.8376,347.1207,29.560412,50.4854,21.963,393.9254,504.3277,121.956,22.1017,23.3959,0.6085782,15338.3594982,0.992110141725051
-c9,533.7291,748.8261,67.644319,109.339,74.5864,1558.1515,1543.1258,108.541,77.2477,1693.1477,1471.0861,351.059,72.987,639.1792,709.4595,422.944,49.588,38.5238,668.3418,720.1492,175.6035,38.8844,42.156294,686.3864,175.4817,39.5474,393.7426,357.7395,10.570936,49.7509,21.8376,511.0788,475.3594,120.4748,21.963,393.9254,504.3277,121.956,22.1017,23.3959,0.6085782,15844.5487272,0.960414984406291
-c10,367.1935,1240.8474,819.0329,336.544,74.5864,67.644319,67.644319,108.541,77.2477,67.644319,1385.115,351.059,72.987,452.0318,42.156294,401.5683,159.941,38.5238,668.3418,720.1492,175.6035,38.8844,763.2659,728.2224,175.4817,39.5474,226.8183,491.752,10.570936,49.7509,21.8376,511.0788,475.3594,120.4748,21.963,393.9254,504.3277,121.956,22.1017,23.3959,0.6085782,12435.7253652,1.22367948354795
-c11,533.7291,1160.9424,67.644319,109.339,74.5864,1558.1515,67.644319,108.541,77.2477,67.644319,1385.115,351.059,72.987,639.1792,42.156294,401.5683,159.941,38.5238,42.156294,678.3662,175.6035,38.8844,768.7706,794.6234,49.6507,39.5474,226.8183,491.752,291.5164,49.7509,21.8376,511.0788,475.3594,120.4748,21.963,521.315,413.8585,50.3589,22.1017,23.3959,0.6085782,12745.7919232,1.19391106391045
-c12,533.7291,1160.9424,67.644319,109.339,74.5864,67.644319,67.644319,108.541,77.2477,67.644319,67.644319,107.72,72.987,452.0318,734.2297,558.879,49.588,38.5238,761.6715,678.3662,175.6035,38.8844,763.2659,735.4767,49.6507,39.5474,393.7426,471.2729,233.5022,106.94,21.8376,511.0788,475.3594,120.4748,21.963,393.9254,504.3277,121.956,22.1017,23.3959,0.08824,11080.999035,1.37328249279752
-c12,11080.999035
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,23.842121,0,0,0,23.842121,0,0,10.859436,0,0,0,0,0,0,0,0,0,0,0,0,7.351726,0,0,0,0,0,0,0,0,0,0,0,0,0,0,65.895404,0
-c2,0,0,23.842121,0,0,0,23.842121,0,0,23.842121,0,0,0,0,15.774626,0,0,0,15.774626,0,0,0,0,0,0,0,0,11.797401,0,0,0,0,0,0,0,0,0,0,0,0,0.023423,114.896439,0
-c3,0,0,23.842121,0,0,0,0,0,0,0,23.842121,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.351726,11.797401,0,0,0,0,0,0,0,11.797401,0,0,0,0,0,78.63077,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.797401,0,0,0,0,11.797401,0
-c5,0,0,23.842121,0,0,23.842121,0,0,0,0,0,0,0,10.859436,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.023423,58.567101,0
-c6,0,0,23.842121,0,0,23.842121,0,0,0,23.842121,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0,0,0,0,3.468407,0,0,0,0,0,0,0,0,0,0,0,0,90.769396,0
-c7,0,0,23.842121,0,0,0,23.842121,0,0,23.842121,0,0,0,0,15.774626,6.991873,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.023423,110.090911,0
-c8,0,0,23.842121,0,0,0,0,0,0,0,0,0,0,0,0,6.991873,0,0,0,0,0,0,0,0,0,0,0,0,3.468407,0,0,0,11.797401,0,0,0,0,0,0,0,0,46.099802,0
-c9,0,0,23.842121,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,3.468407,0,0,0,0,0,0,0,0,0,0,0,0,43.085154,0
-c10,0,0,0,0,0,23.842121,23.842121,0,0,23.842121,0,0,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,3.468407,0,0,0,0,0,0,0,0,0,0,0,0,90.769396,0
-c11,0,0,23.842121,0,0,0,23.842121,0,0,23.842121,0,0,0,0,15.774626,0,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,103.075615,0
-c12,0,0,23.842121,0,0,23.842121,23.842121,0,0,23.842121,23.842121,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.023423,119.234028,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,26.345144,0,0,0,26.345144,0,0,9.892485,0,0,0,0,0,0,0,0,0,0,0,0,5.005594,0,0,0,0,0,0,0,0,0,0,0,0,0,0,67.588367,0
-c2,0,0,26.345144,0,0,0,26.345144,0,0,26.345144,0,0,0,0,13.288878,0,0,0,13.288878,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0.059362,112.524902,0
-c3,0,0,26.345144,0,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.005594,6.852352,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,71.400586,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,6.852352,0
-c5,0,0,26.345144,0,0,26.345144,0,0,0,0,0,0,0,9.892485,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.059362,62.642135,0
-c6,0,0,26.345144,0,0,26.345144,0,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,4.920397,0,0,0,0,0,0,0,0,0,0,0,0,97.244707,0
-c7,0,0,26.345144,0,0,0,26.345144,0,0,26.345144,0,0,0,0,13.288878,9.837517,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.059362,115.510067,0
-c8,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,0,0,0,0,0,0,4.920397,0,0,0,6.852352,0,0,0,0,0,0,0,0,47.95541,0
-c9,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,4.920397,0,0,0,0,0,0,0,0,0,0,0,0,44.554419,0
-c10,0,0,0,0,0,26.345144,26.345144,0,0,26.345144,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,4.920397,0,0,0,0,0,0,0,0,0,0,0,0,97.244707,0
-c11,0,0,26.345144,0,0,0,26.345144,0,0,26.345144,0,0,0,0,13.288878,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,105.613188,0
-c12,0,0,26.345144,0,0,26.345144,26.345144,0,0,26.345144,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.059362,131.785082,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,6.518272,0,0,0,6.518272,0,0,2.573634,0,0,0,0,0,0,0,0,0,0,0,0,1.429432,0,0,0,0,0,0,0,0,0,0,0,0,0,0,17.03961,0
-c2,0,0,6.518272,0,0,0,6.518272,0,0,6.518272,0,0,0,0,3.533916,0,0,0,3.533916,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,0,0,0,0,0.013655,28.708076,0
-c3,0,0,6.518272,0,0,0,0,0,0,0,6.518272,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.429432,2.071773,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,18.609522,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,2.071773,0
-c5,0,0,6.518272,0,0,6.518272,0,0,0,0,0,0,0,2.573634,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.013655,15.623833,0
-c6,0,0,6.518272,0,0,6.518272,0,0,0,6.518272,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0,0,0,0,1.153683,0,0,0,0,0,0,0,0,0,0,0,0,24.242415,0
-c7,0,0,6.518272,0,0,0,6.518272,0,0,6.518272,0,0,0,0,3.533916,2.308683,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.013655,28.944986,0
-c8,0,0,6.518272,0,0,0,0,0,0,0,0,0,0,0,0,2.308683,0,0,0,0,0,0,0,0,0,0,0,0,1.153683,0,0,0,2.071773,0,0,0,0,0,0,0,0,12.052411,0
-c9,0,0,6.518272,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,1.153683,0,0,0,0,0,0,0,0,0,0,0,0,11.205871,0
-c10,0,0,0,0,0,6.518272,6.518272,0,0,6.518272,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,1.153683,0,0,0,0,0,0,0,0,0,0,0,0,24.242415,0
-c11,0,0,6.518272,0,0,0,6.518272,0,0,6.518272,0,0,0,0,3.533916,0,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,26.622648,0
-c12,0,0,6.518272,0,0,6.518272,6.518272,0,0,6.518272,6.518272,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.013655,32.605015,0
-c0,0
-
-Patch Energy
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c0,0
-
-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,227.205,0,91.4069,0,0,0,89.908,0,0,0,0,0,40.812,110.353,0,0,0,0,0,42.4051,59.1467,0,0,0,0,69.8631,0,0,0,0,0,0,19.8115,30.88,0,0,0,0.0453634,781.8366634,0
-c2,13.0911,0,0,0,0,0,0,0,0,0,0,243.339,0,42.4202,0,0,110.353,0,0,0,0,0,42.4051,0,125.831,0,19.9204,0,0,0,0,20.0592,0,69.9894,0,19.8115,30.88,0,0,0,0,738.0999,0
-c3,13.0911,0,0,0,0,0,0,0,0,89.908,0,0,0,42.4202,0,0,110.353,0,0,0,0,0,42.4051,0,125.831,0,0,0,0,57.1891,0,20.0592,31.2805,0,0,0,0,71.5971,0,0,0.0453634,604.1796634,0
-c4,13.0911,73.9105,0,227.205,0,0,89.4069,259.957,0,0,0,0,0,42.4202,60.9,40.812,110.353,0,42.4989,59.0727,0,0,0,0,0,0,19.9204,31.1377,19.8329,57.1891,0,20.0592,0,69.9894,0,0,0,0,0,0,0.0453634,1237.8013634,0
-c5,0,79.905,0,0,0,0,0,0,0,0,85.9711,243.339,0,0,0,0,0,0,0,0,0,0,42.4051,59.1467,0,0,19.9204,31.1377,19.8329,57.1891,0,20.0592,0,69.9894,0,19.8115,0,71.5971,0,0,0,820.3042,0
-c6,13.0911,0,0,0,0,0,0,0,0,0,0,0,0,42.4202,0,135.935,0,0,42.4989,59.0727,0,0,0,0,0,0,0,20.4791,0,0,0,20.0592,0,69.9894,0,19.8115,0,71.5971,0,0,0,494.9542,0
-c7,13.0911,0,0,0,0,91.4069,0,0,0,0,0,0,0,42.4202,0,0,0,0,0,0,0,0,42.4051,59.1467,0,0,19.9204,0,69.8631,0,0,20.0592,0,69.9894,0,19.8115,30.88,0,0,0,0,478.9936,0
-c8,13.0911,0,0,0,0,91.4069,126.901,0,0,0,85.9711,243.339,0,42.4202,60.9,0,0,0,42.4989,59.0727,0,0,42.4051,0,125.831,0,0,0,0,0,0,0,0,0,0,0,20.3721,71.5971,0,0,0.0453634,1025.8515634,0
-c9,13.0911,73.9105,0,0,0,0,0,0,0,0,85.9711,243.339,0,42.4202,60.9,0,0,0,0,41.783,125.787,0,0,0,125.831,0,19.9204,31.1377,0,0,0,20.0592,0,69.9894,0,0,20.3721,71.5971,0,0,0.0453634,1046.1541634,0
-c10,0,79.905,84.5406,227.205,0,0,0,0,0,0,0,243.339,0,0,0,0,110.353,0,0,41.783,125.787,0,0,41.836,125.831,0,0,20.4791,0,0,0,20.0592,0,69.9894,0,0,20.3721,71.5971,0,0,0.0453634,1283.1218634,0
-c11,13.0911,0,0,0,0,0,0,0,0,0,0,243.339,0,42.4202,0,0,110.353,0,0,0,125.787,0,42.4051,59.1467,0,0,0,20.4791,69.8631,0,0,20.0592,0,69.9894,0,19.8115,30.88,0,0,0,0.0453634,867.6697634,0
-c12,13.0911,0,0,0,0,0,0,0,0,0,0,0,0,0,44.2087,135.935,0,0,42.4989,0,125.787,0,0,0,0,0,19.9204,0,0,57.1891,0,20.0592,0,69.9894,0,0,20.3721,71.5971,0,0,0,620.648,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,54.8381,0,20.9916,0,0,0,21.0334,0,0,0,0,0,10.5675,27.8587,0,0,0,0,0,10.5613,14.7899,0,0,0,0,16.9562,0,0,0,0,0,0,5.3629,8.01787,0,0,0,0.182164,191.159634,0
-c2,6.3224,0,0,0,0,0,0,0,0,0,0,60.3765,0,10.5944,0,0,27.8587,0,0,0,0,0,10.5613,0,31.0414,0,5.36591,0,0,0,0,5.36257,0,17.215,0,5.3629,8.01787,0,0,0,0,188.07895,0
-c3,6.3224,0,0,0,0,0,0,0,0,21.0334,0,0,0,10.5944,0,0,27.8587,0,0,0,0,0,10.5613,0,31.0414,0,0,0,0,14.405,0,5.36257,8.10378,0,0,0,0,17.4758,0,0,0.182164,152.940914,0
-c4,6.3224,20.9352,0,54.8381,0,0,21.0449,62.7404,0,0,0,0,0,10.5944,14.972,10.5675,27.8587,0,10.5792,14.881,0,0,0,0,0,0,5.36591,8.07266,5.36951,14.405,0,5.36257,0,17.215,0,0,0,0,0,0,0.182164,311.306614,0
-c5,0,27.627,0,0,0,0,0,0,0,0,20.9627,60.3765,0,0,0,0,0,0,0,0,0,0,10.5613,14.7899,0,0,5.36591,8.07266,5.36951,14.405,0,5.36257,0,17.215,0,5.3629,0,17.4758,0,0,0,212.94675,0
-c6,6.3224,0,0,0,0,0,0,0,0,0,0,0,0,10.5944,0,32.0547,0,0,10.5792,14.881,0,0,0,0,0,0,0,5.37147,0,0,0,5.36257,0,17.215,0,5.3629,0,17.4758,0,0,0,125.21944,0
-c7,6.3224,0,0,0,0,20.9916,0,0,0,0,0,0,0,10.5944,0,0,0,0,0,0,0,0,10.5613,14.7899,0,0,5.36591,0,16.9562,0,0,5.36257,0,17.215,0,5.3629,8.01787,0,0,0,0,121.54005,0
-c8,6.3224,0,0,0,0,20.9916,29.7996,0,0,0,20.9627,60.3765,0,10.5944,14.972,0,0,0,10.5792,14.881,0,0,10.5613,0,31.0414,0,0,0,0,0,0,0,0,0,0,0,5.37001,17.4758,0,0,0.182164,254.110074,0
-c9,6.3224,20.9352,0,0,0,0,0,0,0,0,20.9627,60.3765,0,10.5944,14.972,0,0,0,0,10.5947,31.2262,0,0,0,31.0414,0,5.36591,8.07266,0,0,0,5.36257,0,17.215,0,0,5.37001,17.4758,0,0,0.182164,266.069614,0
-c10,0,27.627,20.9195,54.8381,0,0,0,0,0,0,0,60.3765,0,0,0,0,27.8587,0,0,10.5947,31.2262,0,0,10.5466,31.0414,0,0,5.37147,0,0,0,5.36257,0,17.215,0,0,5.37001,17.4758,0,0,0.182164,326.005714,0
-c11,6.3224,0,0,0,0,0,0,0,0,0,0,60.3765,0,10.5944,0,0,27.8587,0,0,0,31.2262,0,10.5613,14.7899,0,0,0,5.37147,16.9562,0,0,5.36257,0,17.215,0,5.3629,8.01787,0,0,0,0.182164,220.197574,0
-c12,6.3224,0,0,0,0,0,0,0,0,0,0,0,0,0,10.6041,32.0547,0,0,10.5792,0,31.2262,0,0,0,0,0,5.36591,0,0,14.405,0,5.36257,0,17.215,0,0,5.37001,17.4758,0,0,0,155.98089,0
-c0,0
-
-Time
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,137.165,207.8389,189.6515,31.9722,19.0941,208.4406,198.6494,31.9182,19.1354,259.7649,285.9489,32.0305,19.0951,123.27345,142.4026,83.6932,18.2347,9.39785,151.93707,142.1728,18.074,9.41748,151.49962,142.1021,18.0244,9.40974,77.39211,95.8733,48.1179,14.1964,4.87483,100.88243,95.9383,14.534,4.81198,100.7067,95.9563,14.737,4.81593,7.47447,0.725589,3341.380949,0.999999970072255
-c1,137.165,207.8389,189.6515,80.0402,16.7972,229.4322,15.542688,24.9686,17.3272,280.7983,15.542688,25.4775,16.8139,7.12909,148.08761,94.2607,39.328,8.57887,157.84032,176.87812,11.2953,8.59732,162.06092,190.78621,11.2352,8.56125,4.889272,95.8733,67.88834,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,106.0696,97.03603,11.2081,4.48848,5.19545,0.907753,2876.180231,1.16174250723641
-c2,215.0729,342.9757,15.542688,25.2021,16.7972,356.8393,15.542688,24.9686,17.3272,15.542688,285.9489,85.854,16.8139,133.86785,10.338268,83.6932,39.328,8.57887,10.338268,176.87812,11.2953,8.59732,162.06092,142.1021,42.2766,8.56125,82.75802,7.838173,50.93214,11.2873,4.46626,106.245,95.9383,28.3993,4.46968,106.0696,97.03603,11.2081,4.48848,5.19545,0.017259,2888.693022,1.15671025196563
-c3,215.0729,342.9757,15.542688,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,280.7983,15.542688,25.4775,16.8139,133.86785,142.4026,83.6932,39.328,8.57887,157.84032,176.87812,11.2953,8.59732,162.06092,142.1021,42.2766,8.56125,4.889272,7.838173,48.1179,25.6923,4.46626,106.245,97.19258,11.1843,4.46968,7.838173,95.9563,28.6839,4.48848,5.19545,0.907753,3283.532547,1.01761770270591
-c4,215.0729,228.7741,189.6515,80.0402,16.7972,356.8393,219.6943,87.709,17.3272,399.7504,388.4166,25.4775,16.8139,133.86785,163.05961,94.2607,39.328,8.57887,162.51627,191.75912,11.2953,8.59732,181.04838,175.99631,11.2352,8.56125,82.75802,82.58087,53.48741,25.6923,4.46626,106.245,95.9383,28.3993,4.46968,90.82636,7.838173,11.2081,4.48848,5.19545,0.907753,4036.969736,0.827695297399294
-c5,137.165,370.6027,15.542688,25.2021,16.7972,15.542688,359.5265,24.9686,17.3272,399.7504,306.9116,85.854,16.8139,7.12909,148.08761,99.91022,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,162.06092,190.78621,11.2352,8.56125,82.75802,82.58087,53.48741,25.6923,4.46626,106.245,95.9383,28.3993,4.46968,106.0696,95.9563,28.6839,4.48848,5.19545,0.017259,3518.882435,0.949557399477104
-c6,215.0729,342.9757,15.542688,25.2021,16.7972,15.542688,359.5265,24.9686,17.3272,15.542688,388.4166,25.4775,16.8139,133.86785,142.4026,131.96492,11.4693,8.57887,162.51627,191.75912,11.2953,8.59732,181.04838,10.338268,11.2352,8.56125,51.58494,101.24477,2.306963,11.2873,4.46626,106.245,95.9383,28.3993,4.46968,106.0696,95.9563,28.6839,4.48848,5.19545,1.571852,3140.749007,1.06388025122823
-c7,215.0729,342.9757,15.542688,25.2021,16.7972,229.4322,15.542688,24.9686,17.3272,15.542688,388.4166,25.4775,16.8139,133.86785,10.338268,4.615243,11.4693,8.57887,10.338268,176.87812,11.2953,8.59732,162.06092,190.78621,11.2352,8.56125,82.75802,95.8733,67.88834,11.2873,4.46626,106.245,95.9383,28.3993,4.46968,106.0696,97.03603,11.2081,4.48848,5.19545,0.017259,2819.074502,1.18527581591117
-c8,215.0729,342.9757,15.542688,25.2021,16.7972,229.4322,389.3261,24.9686,17.3272,399.7504,306.9116,85.854,16.8139,133.86785,163.05961,4.615243,11.4693,8.57887,162.51627,191.75912,11.2953,8.59732,162.06092,142.1021,42.2766,8.56125,51.58494,74.50821,2.306963,11.2873,4.46626,80.09278,7.838173,11.1843,4.46968,90.82636,101.32631,28.6839,4.48848,5.19545,0.907753,3615.9012,0.924079689066734
-c9,215.0729,228.7741,15.542688,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,306.9116,85.854,16.8139,133.86785,163.05961,99.91022,11.4693,8.57887,157.84032,152.7675,42.5215,8.59732,10.338268,142.1021,42.2766,8.56125,82.75802,82.58087,2.306963,11.2873,4.46626,106.245,95.9383,28.3993,4.46968,90.82636,101.32631,28.6839,4.48848,5.19545,0.907753,3701.151142,0.902795030660355
-c10,137.165,370.6027,210.571,80.0402,16.7972,15.542688,15.542688,24.9686,17.3272,15.542688,285.9489,85.854,16.8139,104.7504,10.338268,83.6932,39.328,8.57887,157.84032,152.7675,42.5215,8.59732,181.04838,152.6487,42.2766,8.56125,51.58494,101.24477,2.306963,11.2873,4.46626,106.245,95.9383,28.3993,4.46968,90.82636,101.32631,28.6839,4.48848,5.19545,0.907753,2923.037838,1.14311925464992
-c11,215.0729,342.9757,15.542688,25.2021,16.7972,356.8393,15.542688,24.9686,17.3272,15.542688,285.9489,85.854,16.8139,133.86785,10.338268,83.6932,39.328,8.57887,10.338268,142.1728,42.5215,8.59732,162.06092,190.78621,11.2352,8.56125,51.58494,101.24477,67.88834,11.2873,4.46626,106.245,95.9383,28.3993,4.46968,106.0696,97.03603,11.2081,4.48848,5.19545,0.907753,2982.936823,1.12016480242549
-c12,215.0729,342.9757,15.542688,25.2021,16.7972,15.542688,15.542688,24.9686,17.3272,15.542688,15.542688,25.4775,16.8139,104.7504,153.0067,131.96492,11.4693,8.57887,162.51627,142.1728,42.5215,8.59732,181.04838,175.99631,11.2352,8.56125,82.75802,95.8733,48.1179,25.6923,4.46626,106.245,95.9383,28.3993,4.46968,90.82636,101.32631,28.6839,4.48848,5.19545,0.017259,2627.265579,1.27180930946877
-c12,2627.265579
-
-Unpatch Energy
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c0,0
-
diff --git a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_promise_results2.csv b/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_promise_results2.csv
deleted file mode 100644
index 79f9be2d437ef747e412badb8800b6261fbc6f41..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_promise_results2.csv
+++ /dev/null
@@ -1,396 +0,0 @@
-Compute Energy
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-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
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-c13,208.7505,207.8389,9.024416,25.2021,16.7972,9.024416,198.6494,24.9686,17.3272,259.7649,285.9489,25.4775,16.8139,104.7504,6.804352,2.306560,11.4693,8.57887,151.93707,6.804352,11.2953,8.59732,151.49962,175.99631,11.2352,8.56125,3.459840,74.50821,48.1179,11.2873,4.46626,80.09278,95.9383,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,2501.256808,1.33588074792036
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-c16,208.7505,207.8389,328.4669,25.2021,16.7972,9.024416,9.024416,24.9686,17.3272,9.024416,9.024416,25.4775,16.8139,104.7504,142.4026,99.91022,11.4693,8.57887,151.93707,6.804352,11.2953,8.59732,181.04838,175.99631,11.2352,8.56125,77.39211,74.50821,1.153280,11.2873,4.46626,80.09278,95.9383,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,0.725589,2392.281395,1.39673401979812
-c17,208.7505,342.9757,9.024416,25.2021,16.7972,208.4406,9.024416,24.9686,17.3272,9.024416,388.4166,25.4775,16.8139,123.27345,6.804352,2.306560,11.4693,8.57887,6.804352,176.87812,11.2953,8.59732,151.49962,175.99631,11.2352,8.56125,77.39211,95.8733,50.93214,11.2873,4.46626,100.88243,95.9383,11.1843,4.46968,100.7067,89.01816,11.2081,4.48848,5.19545,0.003604,2668.589466,1.25211497173335
-c18,208.7505,342.9757,9.024416,25.2021,16.7972,208.4406,9.024416,24.9686,17.3272,259.7649,9.024416,25.4775,16.8139,104.7504,142.4026,99.91022,11.4693,8.57887,151.93707,6.804352,11.2953,8.59732,181.04838,175.99631,11.2352,8.56125,3.459840,5.766400,48.1179,11.2873,4.46626,100.88243,95.9383,11.1843,4.46968,90.82636,95.9563,11.2081,4.48848,5.19545,0.725589,2590.150709,1.29003335921202
-c19,208.7505,342.9757,9.024416,25.2021,16.7972,208.4406,359.5265,24.9686,17.3272,399.7504,285.9489,25.4775,16.8139,123.27345,148.08761,2.306560,11.4693,8.57887,151.93707,176.87812,11.2953,8.59732,151.49962,142.1021,11.2352,8.56125,51.58494,74.50821,1.153280,11.2873,4.46626,80.09278,5.766400,11.1843,4.46968,90.82636,95.9563,11.2081,4.48848,5.19545,0.725589,3349.738715,0.997504920096285
-c20,137.165,207.8389,9.024416,25.2021,16.7972,356.8393,9.024416,24.9686,17.3272,399.7504,285.9489,25.4775,16.8139,104.7504,148.08761,83.6932,11.4693,8.57887,157.84032,6.804352,11.2953,8.59732,151.49962,142.1021,11.2352,8.56125,3.459840,74.50821,50.93214,11.2873,4.46626,80.09278,95.9383,11.1843,4.46968,90.82636,95.9563,11.2081,4.48848,5.19545,0.003604,2930.709778,1.14012682527288
-c21,137.165,207.8389,9.024416,25.2021,16.7972,356.8393,198.6494,24.9686,17.3272,259.7649,285.9489,25.4775,16.8139,123.27345,142.4026,99.91022,11.4693,8.57887,157.84032,6.804352,11.2953,8.59732,181.04838,175.99631,11.2352,8.56125,3.459840,74.50821,48.1179,11.2873,4.46626,100.88243,95.9383,11.1843,4.46968,90.82636,95.9563,11.2081,4.48848,5.19545,0.725589,3091.544687,1.08081272606839
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-c27,2081.109117
-
-Energy
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,367.1935,674.9156,734.4923,131.604,78.945,930.3708,938.716,154.555,92.4142,1252.9937,1385.115,157.253,93.1857,596.759,690.021,401.5683,85.0395,42.293,719.1726,678.3662,85.9011,43.1252,726.3655,686.3864,86.3285,43.6635,373.8222,471.2729,233.5022,67.2674,20.4251,491.0196,475.3594,71.0934,21.0221,501.5035,483.9556,73.1106,21.5459,35.1354,0.5632148,15217.3421148,0.99999999342855
-c1,367.1935,674.9156,734.4923,336.544,74.5864,1021.7777,67.644319,108.541,77.2477,1342.9017,67.644319,107.72,72.987,29.480448,648.5595,442.3803,159.941,38.5238,668.3418,737.6456,49.8165,38.8844,768.7706,794.6234,49.6507,39.5474,18.903715,471.2729,291.5164,49.7509,21.8376,347.1207,382.2379,50.4854,21.963,521.315,413.8585,50.3589,22.1017,23.3959,0.6085782,12207.0880792,1.24659885235606
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-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
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-c11,0,0,23.842121,0,0,23.842121,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.797401,0,0,0,0.023423,75.279692,0
-c12,0,0,0,0,0,23.842121,0,0,0,0,0,0,0,0,15.774626,6.991873,0,0,0,15.774626,0,0,0,0,0,0,0,11.797401,0,0,0,0,0,0,0,0,0,0,0,0,0,74.180647,0
-c13,0,0,23.842121,0,0,23.842121,0,0,0,0,0,0,0,0,15.774626,6.991873,0,0,0,15.774626,0,0,0,0,0,0,7.351726,0,0,0,0,0,0,0,0,0,0,0,0,0,0,93.577093,0
-c14,0,0,23.842121,0,0,23.842121,0,0,0,0,0,0,0,10.859436,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.023423,58.567101,0
-c15,0,0,23.842121,0,0,23.842121,0,0,0,23.842121,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0,0,0,0,3.468407,0,0,0,0,0,0,0,0,0,0,0,0,90.769396,0
-c16,0,0,0,0,0,23.842121,23.842121,0,0,23.842121,23.842121,0,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,0,0,0,3.468407,0,0,0,0,0,0,0,0,0,0,0,0,114.611517,0
-c17,0,0,23.842121,0,0,0,23.842121,0,0,23.842121,0,0,0,0,15.774626,6.991873,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.023423,110.090911,0
-c18,0,0,23.842121,0,0,0,23.842121,0,0,0,23.842121,0,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,0,7.351726,11.797401,0,0,0,0,0,0,0,0,0,0,0,0,0,106.450116,0
-c19,0,0,23.842121,0,0,0,0,0,0,0,0,0,0,0,0,6.991873,0,0,0,0,0,0,0,0,0,0,0,0,3.468407,0,0,0,11.797401,0,0,0,0,0,0,0,0,46.099802,0
-c20,0,0,23.842121,0,0,0,23.842121,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,0,7.351726,0,0,0,0,0,0,0,0,0,0,0,0,0,0.023423,70.834017,0
-c21,0,0,23.842121,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,0,7.351726,0,0,0,0,0,0,0,0,0,0,0,0,0,0,46.968473,0
-c22,0,0,23.842121,0,0,23.842121,23.842121,0,0,23.842121,23.842121,0,0,10.859436,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.023423,145.86809,0
-c23,0,0,0,0,0,0,23.842121,0,0,23.842121,23.842121,0,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,87.300989,0
-c24,0,0,23.842121,0,0,23.842121,23.842121,0,0,0,23.842121,0,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,0,0,11.797401,0,0,0,0,0,0,0,0,0,0,0,0,0,122.940511,0
-c25,0,0,23.842121,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,3.468407,0,0,0,0,0,0,0,0,0,0,0,0,43.085154,0
-c26,0,0,0,0,0,23.842121,23.842121,0,0,23.842121,0,0,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,3.468407,0,0,0,0,0,0,0,0,0,0,0,0,90.769396,0
-c27,0,0,23.842121,0,0,23.842121,23.842121,0,0,23.842121,23.842121,0,0,10.859436,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,145.844667,0
-c28,0,0,23.842121,0,0,0,23.842121,0,0,23.842121,0,0,0,0,15.774626,0,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,103.075615,0
-c29,0,0,0,0,0,0,23.842121,0,0,0,23.842121,0,0,0,0,6.991873,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,70.450741,0
-c30,0,0,23.842121,0,0,23.842121,23.842121,0,0,23.842121,23.842121,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.023423,119.234028,0
-c31,0,0,0,0,0,0,23.842121,0,0,0,23.842121,0,0,0,15.774626,6.991873,0,0,0,0,0,0,0,0,0,0,0,0,3.468407,0,0,0,0,0,0,0,0,0,0,0,0,73.919148,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,26.345144,0,0,0,26.345144,0,0,9.892485,0,0,0,0,0,0,0,0,0,0,0,0,5.005594,0,0,0,0,0,0,0,0,0,0,0,0,0,0,67.588367,0
-c2,0,0,26.345144,0,0,0,26.345144,0,0,0,0,0,0,9.892485,0,0,0,0,13.288878,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.059362,89.219891,0
-c3,0,0,26.345144,0,0,26.345144,0,0,0,26.345144,26.345144,0,0,0,0,0,0,0,0,13.288878,0,0,0,13.288878,0,0,0,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0,138.810684,0
-c4,0,0,0,0,0,26.345144,0,0,0,26.345144,26.345144,0,0,9.892485,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0,95.780269,0
-c5,0,0,0,0,0,26.345144,26.345144,0,0,26.345144,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.005594,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0,117.238522,0
-c6,0,0,26.345144,0,0,0,26.345144,0,0,26.345144,0,0,0,0,13.288878,0,0,0,13.288878,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0.059362,112.524902,0
-c7,0,0,0,0,0,26.345144,26.345144,0,0,26.345144,26.345144,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0,125.521806,0
-c8,0,0,26.345144,0,0,26.345144,26.345144,0,0,26.345144,26.345144,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,5.005594,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0,156.872544,0
-c9,0,0,26.345144,0,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.005594,6.852352,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,71.400586,0
-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,6.852352,0
-c11,0,0,26.345144,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0,0,0,0.059362,72.89088,0
-c12,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,13.288878,9.837517,0,0,0,13.288878,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0,69.612769,0
-c13,0,0,26.345144,0,0,26.345144,0,0,0,0,0,0,0,0,13.288878,9.837517,0,0,0,13.288878,0,0,0,0,0,0,5.005594,0,0,0,0,0,0,0,0,0,0,0,0,0,0,94.111155,0
-c14,0,0,26.345144,0,0,26.345144,0,0,0,0,0,0,0,9.892485,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.059362,62.642135,0
-c15,0,0,26.345144,0,0,26.345144,0,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,4.920397,0,0,0,0,0,0,0,0,0,0,0,0,97.244707,0
-c16,0,0,0,0,0,26.345144,26.345144,0,0,26.345144,26.345144,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,4.920397,0,0,0,0,0,0,0,0,0,0,0,0,123.589851,0
-c17,0,0,26.345144,0,0,0,26.345144,0,0,26.345144,0,0,0,0,13.288878,9.837517,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.059362,115.510067,0
-c18,0,0,26.345144,0,0,0,26.345144,0,0,0,26.345144,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,5.005594,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0,104.182256,0
-c19,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,0,0,0,0,0,0,4.920397,0,0,0,6.852352,0,0,0,0,0,0,0,0,47.95541,0
-c20,0,0,26.345144,0,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,5.005594,0,0,0,0,0,0,0,0,0,0,0,0,0,0.059362,71.044122,0
-c21,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,5.005594,0,0,0,0,0,0,0,0,0,0,0,0,0,0,44.639616,0
-c22,0,0,26.345144,0,0,26.345144,26.345144,0,0,26.345144,26.345144,0,0,9.892485,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.059362,154.966445,0
-c23,0,0,0,0,0,0,26.345144,0,0,26.345144,26.345144,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,92.32431,0
-c24,0,0,26.345144,0,0,26.345144,26.345144,0,0,0,26.345144,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0,125.521806,0
-c25,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,4.920397,0,0,0,0,0,0,0,0,0,0,0,0,44.554419,0
-c26,0,0,0,0,0,26.345144,26.345144,0,0,26.345144,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,4.920397,0,0,0,0,0,0,0,0,0,0,0,0,97.244707,0
-c27,0,0,26.345144,0,0,26.345144,26.345144,0,0,26.345144,26.345144,0,0,9.892485,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,154.907083,0
-c28,0,0,26.345144,0,0,0,26.345144,0,0,26.345144,0,0,0,0,13.288878,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,105.613188,0
-c29,0,0,0,0,0,0,26.345144,0,0,0,26.345144,0,0,0,0,9.837517,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,75.816683,0
-c30,0,0,26.345144,0,0,26.345144,26.345144,0,0,26.345144,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.059362,131.785082,0
-c31,0,0,0,0,0,0,26.345144,0,0,0,26.345144,0,0,0,13.288878,9.837517,0,0,0,0,0,0,0,0,0,0,0,0,4.920397,0,0,0,0,0,0,0,0,0,0,0,0,80.73708,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,6.518272,0,0,0,6.518272,0,0,2.573634,0,0,0,0,0,0,0,0,0,0,0,0,1.429432,0,0,0,0,0,0,0,0,0,0,0,0,0,0,17.03961,0
-c2,0,0,6.518272,0,0,0,6.518272,0,0,0,0,0,0,2.573634,0,0,0,0,3.533916,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.013655,22.691665,0
-c3,0,0,6.518272,0,0,6.518272,0,0,0,6.518272,6.518272,0,0,0,0,0,0,0,0,3.533916,0,0,0,3.533916,0,0,0,2.071773,0,0,0,0,0,0,0,0,0,0,0,0,0,35.212693,0
-c4,0,0,0,0,0,6.518272,0,0,0,6.518272,6.518272,0,0,2.573634,0,0,0,0,0,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,0,0,0,0,0,24.200223,0
-c5,0,0,0,0,0,6.518272,6.518272,0,0,6.518272,6.518272,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.429432,2.071773,0,0,0,0,0,0,0,0,0,0,0,0,0,29.574293,0
-c6,0,0,6.518272,0,0,0,6.518272,0,0,6.518272,0,0,0,0,3.533916,0,0,0,3.533916,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,0,0,0,0,0.013655,28.708076,0
-c7,0,0,0,0,0,6.518272,6.518272,0,0,6.518272,6.518272,0,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,0,0,0,0,0,31.678777,0
-c8,0,0,6.518272,0,0,6.518272,6.518272,0,0,6.518272,6.518272,0,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,1.429432,2.071773,0,0,0,0,0,0,0,0,0,0,0,0,0,39.626481,0
-c9,0,0,6.518272,0,0,0,0,0,0,0,6.518272,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.429432,2.071773,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,18.609522,0
-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,2.071773,0
-c11,0,0,6.518272,0,0,6.518272,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.071773,0,0,0,0.013655,18.655888,0
-c12,0,0,0,0,0,6.518272,0,0,0,0,0,0,0,0,3.533916,2.308683,0,0,0,3.533916,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,0,0,0,0,0,17.96656,0
-c13,0,0,6.518272,0,0,6.518272,0,0,0,0,0,0,0,0,3.533916,2.308683,0,0,0,3.533916,0,0,0,0,0,0,1.429432,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.842491,0
-c14,0,0,6.518272,0,0,6.518272,0,0,0,0,0,0,0,2.573634,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.013655,15.623833,0
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-c23,0,0,0,0,0,0,6.518272,0,0,6.518272,6.518272,0,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.088732,0
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-c27,0,0,6.518272,0,0,6.518272,6.518272,0,0,6.518272,6.518272,0,0,2.573634,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,38.69891,0
-c28,0,0,6.518272,0,0,0,6.518272,0,0,6.518272,0,0,0,0,3.533916,0,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,26.622648,0
-c29,0,0,0,0,0,0,6.518272,0,0,0,6.518272,0,0,0,0,2.308683,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18.879143,0
-c30,0,0,6.518272,0,0,6.518272,6.518272,0,0,6.518272,6.518272,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.013655,32.605015,0
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-Patch Energy
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
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-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
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-c1,0,0,0,227.205,0,91.4069,0,0,0,89.908,0,0,0,0,0,40.812,110.353,0,0,0,0,0,42.4051,59.1467,0,0,0,0,69.8631,0,0,0,0,0,0,19.8115,30.88,0,0,0,0.0453634,781.8366634,0
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-c3,13.0911,73.9105,0,0,0,0,0,259.957,0,0,0,0,0,42.4202,0,135.935,0,0,42.4989,0,0,0,0,0,0,0,0,0,0,57.1891,0,20.0592,0,69.9894,0,19.8115,0,71.5971,0,0,0,806.459,0
-c4,13.0911,0,0,0,0,0,0,259.957,0,0,0,0,0,0,0,135.935,0,0,42.4989,59.0727,0,0,0,0,0,0,0,0,0,57.1891,0,20.0592,0,69.9894,0,0,20.3721,71.5971,0,0,0.0453634,749.8069634,0
-c5,13.0911,0,0,0,0,0,0,0,0,0,0,0,0,0,44.2087,0,110.353,0,42.4989,59.0727,0,0,42.4051,59.1467,0,0,0,0,0,57.1891,0,20.0592,0,69.9894,0,0,20.3721,71.5971,0,0,0.0453634,610.0284634,0
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-c13,13.0911,73.9105,0,0,0,0,0,259.957,0,89.908,0,243.339,0,0,0,0,0,0,42.4989,0,0,0,42.4051,59.1467,0,0,0,0,19.8329,57.1891,0,0,20.2452,69.9894,0,0,0,0,0,0,0,991.5129,0
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-c20,0,0,0,0,0,0,0,0,0,0,85.9711,243.339,0,0,0,40.812,110.353,0,0,0,0,0,42.4051,0,125.831,0,0,0,0,0,0,0,20.2452,69.9894,0,0,20.3721,71.5971,0,0,0,830.915,0
-c21,0,0,0,0,0,0,89.4069,259.957,0,89.908,0,243.339,0,42.4202,0,135.935,0,0,0,0,0,0,0,0,0,0,0,0,19.8329,57.1891,0,20.0592,0,69.9894,0,0,20.3721,71.5971,0,0,0.0453634,1120.0512634,0
-c22,13.0911,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,110.353,0,42.4989,0,125.787,0,0,0,0,0,0,0,19.8329,57.1891,0,20.0592,0,69.9894,0,19.8115,0,71.5971,0,0,0,550.2092,0
-c23,13.0911,0,0,0,0,91.4069,0,0,0,0,0,0,0,0,44.2087,135.935,0,0,42.4989,0,0,0,0,0,0,0,0,0,19.8329,57.1891,0,20.0592,0,69.9894,0,0,20.3721,71.5971,0,0,0.0453634,586.2257634,0
-c24,13.0911,0,0,0,0,0,0,0,0,0,0,0,0,42.4202,0,0,110.353,0,42.4989,0,0,0,0,41.836,125.831,0,0,0,0,57.1891,0,20.0592,0,69.9894,0,0,20.3721,71.5971,0,0,0.0453634,615.2824634,0
-c25,13.0911,73.9105,0,0,0,0,0,0,0,0,85.9711,243.339,0,42.4202,60.9,0,0,0,0,41.783,125.787,0,0,0,125.831,0,19.9204,31.1377,0,0,0,20.0592,0,69.9894,0,0,20.3721,71.5971,0,0,0.0453634,1046.1541634,0
-c26,0,79.905,84.5406,227.205,0,0,0,0,0,0,0,243.339,0,0,0,0,110.353,0,0,41.783,125.787,0,0,41.836,125.831,0,0,20.4791,0,0,0,20.0592,0,69.9894,0,0,20.3721,71.5971,0,0,0.0453634,1283.1218634,0
-c27,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,42.4989,0,125.787,0,42.4051,59.1467,0,0,19.9204,31.1377,19.8329,57.1891,0,20.0592,0,69.9894,0,0,20.3721,71.5971,0,0,0.0453634,659.8859634,0
-c28,13.0911,0,0,0,0,0,0,0,0,0,0,243.339,0,42.4202,0,0,110.353,0,0,0,125.787,0,42.4051,59.1467,0,0,0,20.4791,69.8631,0,0,20.0592,0,69.9894,0,19.8115,30.88,0,0,0,0.0453634,867.6697634,0
-c29,13.0911,0,0,0,0,91.4069,0,0,0,89.908,0,0,0,42.4202,60.9,0,0,0,0,0,0,0,0,0,0,0,0,20.4791,69.8631,0,0,0,20.2452,69.9894,0,19.8115,0,71.5971,0,0,0.0453634,569.7569634,0
-c30,13.0911,0,0,0,0,0,0,0,0,0,0,0,0,0,44.2087,135.935,0,0,42.4989,0,125.787,0,0,0,0,0,19.9204,0,0,57.1891,0,20.0592,0,69.9894,0,0,20.3721,71.5971,0,0,0,620.648,0
-c31,0,79.905,84.5406,227.205,0,0,0,0,0,0,0,0,0,42.4202,0,0,0,0,0,41.783,125.787,0,0,41.836,125.831,0,0,20.4791,0,0,0,20.0592,0,69.9894,0,0,20.3721,71.5971,0,0,0.0453634,971.8500634,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,54.8381,0,20.9916,0,0,0,21.0334,0,0,0,0,0,10.5675,27.8587,0,0,0,0,0,10.5613,14.7899,0,0,0,0,16.9562,0,0,0,0,0,0,5.3629,8.01787,0,0,0,0.182164,191.159634,0
-c2,0,0,0,0,0,20.9916,0,0,0,21.0334,0,60.3765,0,0,0,10.5675,27.8587,0,0,0,0,0,10.5613,14.7899,0,0,5.36591,0,16.9562,0,0,0,5.41437,17.215,0,0,0,0,0,0,0,211.13038,0
-c3,6.3224,20.9352,0,0,0,0,0,62.7404,0,0,0,0,0,10.5944,0,32.0547,0,0,10.5792,0,0,0,0,0,0,0,0,0,0,14.405,0,5.36257,0,17.215,0,5.3629,0,17.4758,0,0,0,203.04757,0
-c4,6.3224,0,0,0,0,0,0,62.7404,0,0,0,0,0,0,0,32.0547,0,0,10.5792,14.881,0,0,0,0,0,0,0,0,0,14.405,0,5.36257,0,17.215,0,0,5.37001,17.4758,0,0,0.182164,186.588244,0
-c5,6.3224,0,0,0,0,0,0,0,0,0,0,0,0,0,10.6041,0,27.8587,0,10.5792,14.881,0,0,10.5613,14.7899,0,0,0,0,0,14.405,0,5.36257,0,17.215,0,0,5.37001,17.4758,0,0,0.182164,155.607144,0
-c6,6.3224,0,0,0,0,0,0,0,0,0,0,60.3765,0,10.5944,0,0,27.8587,0,0,0,0,0,10.5613,0,31.0414,0,5.36591,0,0,0,0,5.36257,0,17.215,0,5.3629,8.01787,0,0,0,0,188.07895,0
-c7,6.3224,0,0,0,0,0,0,0,0,0,0,0,0,0,10.6041,32.0547,0,0,10.5792,0,0,0,10.5613,14.7899,0,0,0,0,0,14.405,0,5.36257,0,17.215,0,0,5.37001,17.4758,0,0,0.182164,144.922144,0
-c8,6.3224,0,0,0,0,0,0,0,0,0,0,0,0,0,10.6041,32.0547,0,0,10.5792,0,0,0,0,0,0,0,0,0,0,14.405,0,5.36257,0,17.215,0,0,5.37001,17.4758,0,0,0,119.38878,0
-c9,6.3224,0,0,0,0,0,0,0,0,21.0334,0,0,0,10.5944,0,0,27.8587,0,0,0,0,0,10.5613,0,31.0414,0,0,0,0,14.405,0,5.36257,8.10378,0,0,0,0,17.4758,0,0,0.182164,152.940914,0
-c10,6.3224,20.9352,0,54.8381,0,0,21.0449,62.7404,0,0,0,0,0,10.5944,14.972,10.5675,27.8587,0,10.5792,14.881,0,0,0,0,0,0,5.36591,8.07266,5.36951,14.405,0,5.36257,0,17.215,0,0,0,0,0,0,0.182164,311.306614,0
-c11,0,0,0,0,0,0,0,0,0,21.0334,0,60.3765,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.36591,8.07266,5.36951,14.405,0,5.36257,8.10378,0,0,5.3629,0,0,0,0,0,133.45223,0
-c12,6.3224,20.9352,27.8734,0,0,0,0,62.7404,0,0,20.9627,60.3765,0,10.5944,0,0,0,0,0,0,0,0,10.5613,14.7899,0,0,5.36591,0,0,14.405,0,0,5.41437,17.215,0,0,5.37001,17.4758,0,0,0.182164,300.584454,0
-c13,6.3224,20.9352,0,0,0,0,0,62.7404,0,21.0334,0,60.3765,0,0,0,0,0,0,10.5792,0,0,0,10.5613,14.7899,0,0,0,0,5.36951,14.405,0,0,5.41437,17.215,0,0,0,0,0,0,0,249.74218,0
-c14,0,27.627,0,0,0,0,0,0,0,0,20.9627,60.3765,0,0,0,0,0,0,0,0,0,0,10.5613,14.7899,0,0,5.36591,8.07266,5.36951,14.405,0,5.36257,0,17.215,0,5.3629,0,17.4758,0,0,0,212.94675,0
-c15,6.3224,0,0,0,0,0,0,0,0,0,0,0,0,10.5944,0,32.0547,0,0,10.5792,14.881,0,0,0,0,0,0,0,5.37147,0,0,0,5.36257,0,17.215,0,5.3629,0,17.4758,0,0,0,125.21944,0
-c16,6.3224,20.9352,27.8734,0,0,0,0,0,0,0,0,0,0,0,10.6041,32.0547,0,0,10.5792,0,0,0,0,0,0,0,5.36591,8.07266,0,0,0,0,5.41437,17.215,0,0,0,0,0,0,0.182164,144.619104,0
-c17,6.3224,0,0,0,0,20.9916,0,0,0,0,0,0,0,10.5944,0,0,0,0,0,0,0,0,10.5613,14.7899,0,0,5.36591,0,16.9562,0,0,5.36257,0,17.215,0,5.3629,8.01787,0,0,0,0,121.54005,0
-c18,6.3224,0,0,0,0,20.9916,0,0,0,21.0334,0,0,0,0,10.6041,32.0547,0,0,10.5792,0,0,0,0,0,0,0,0,0,0,14.405,0,5.36257,0,17.215,0,0,5.37001,17.4758,0,0,0.182164,161.595944,0
-c19,6.3224,0,0,0,0,20.9916,29.7996,0,0,0,20.9627,60.3765,0,10.5944,14.972,0,0,0,10.5792,14.881,0,0,10.5613,0,31.0414,0,0,0,0,0,0,0,0,0,0,0,5.37001,17.4758,0,0,0.182164,254.110074,0
-c20,0,0,0,0,0,0,0,0,0,0,20.9627,60.3765,0,0,0,10.5675,27.8587,0,0,0,0,0,10.5613,0,31.0414,0,0,0,0,0,0,0,5.41437,17.215,0,0,5.37001,17.4758,0,0,0,206.84328,0
-c21,0,0,0,0,0,0,21.0449,62.7404,0,21.0334,0,60.3765,0,10.5944,0,32.0547,0,0,0,0,0,0,0,0,0,0,0,0,5.36951,14.405,0,5.36257,0,17.215,0,0,5.37001,17.4758,0,0,0.182164,273.224354,0
-c22,6.3224,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.8587,0,10.5792,0,31.2262,0,0,0,0,0,0,0,5.36951,14.405,0,5.36257,0,17.215,0,5.3629,0,17.4758,0,0,0,141.17728,0
-c23,6.3224,0,0,0,0,20.9916,0,0,0,0,0,0,0,0,10.6041,32.0547,0,0,10.5792,0,0,0,0,0,0,0,0,0,5.36951,14.405,0,5.36257,0,17.215,0,0,5.37001,17.4758,0,0,0.182164,145.932054,0
-c24,6.3224,0,0,0,0,0,0,0,0,0,0,0,0,10.5944,0,0,27.8587,0,10.5792,0,0,0,0,10.5466,31.0414,0,0,0,0,14.405,0,5.36257,0,17.215,0,0,5.37001,17.4758,0,0,0.182164,156.953244,0
-c25,6.3224,20.9352,0,0,0,0,0,0,0,0,20.9627,60.3765,0,10.5944,14.972,0,0,0,0,10.5947,31.2262,0,0,0,31.0414,0,5.36591,8.07266,0,0,0,5.36257,0,17.215,0,0,5.37001,17.4758,0,0,0.182164,266.069614,0
-c26,0,27.627,20.9195,54.8381,0,0,0,0,0,0,0,60.3765,0,0,0,0,27.8587,0,0,10.5947,31.2262,0,0,10.5466,31.0414,0,0,5.37147,0,0,0,5.36257,0,17.215,0,0,5.37001,17.4758,0,0,0.182164,326.005714,0
-c27,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.5792,0,31.2262,0,10.5613,14.7899,0,0,5.36591,8.07266,5.36951,14.405,0,5.36257,0,17.215,0,0,5.37001,17.4758,0,0,0.182164,173.602224,0
-c28,6.3224,0,0,0,0,0,0,0,0,0,0,60.3765,0,10.5944,0,0,27.8587,0,0,0,31.2262,0,10.5613,14.7899,0,0,0,5.37147,16.9562,0,0,5.36257,0,17.215,0,5.3629,8.01787,0,0,0,0.182164,220.197574,0
-c29,6.3224,0,0,0,0,20.9916,0,0,0,21.0334,0,0,0,10.5944,14.972,0,0,0,0,0,0,0,0,0,0,0,0,5.37147,16.9562,0,0,0,5.41437,17.215,0,5.3629,0,17.4758,0,0,0.182164,141.891704,0
-c30,6.3224,0,0,0,0,0,0,0,0,0,0,0,0,0,10.6041,32.0547,0,0,10.5792,0,31.2262,0,0,0,0,0,5.36591,0,0,14.405,0,5.36257,0,17.215,0,0,5.37001,17.4758,0,0,0,155.98089,0
-c31,0,27.627,20.9195,54.8381,0,0,0,0,0,0,0,0,0,10.5944,0,0,0,0,0,10.5947,31.2262,0,0,10.5466,31.0414,0,0,5.37147,0,0,0,5.36257,0,17.215,0,0,5.37001,17.4758,0,0,0.182164,248.364914,0
-c0,0
-
-Time
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,137.165,207.8389,189.6515,31.9722,19.0941,208.4406,198.6494,31.9182,19.1354,259.7649,285.9489,32.0305,19.0951,123.27345,142.4026,83.6932,18.2347,9.39785,151.93707,142.1728,18.074,9.41748,151.49962,142.1021,18.0244,9.40974,77.39211,95.8733,48.1179,14.1964,4.87483,100.88243,95.9383,14.534,4.81198,100.7067,95.9563,14.737,4.81593,7.47447,0.725589,3341.380949,0.999999970072255
-c1,137.165,207.8389,189.6515,80.0402,16.7972,229.4322,15.542688,24.9686,17.3272,280.7983,15.542688,25.4775,16.8139,7.12909,148.08761,94.2607,39.328,8.57887,157.84032,176.87812,11.2953,8.59732,162.06092,190.78621,11.2352,8.56125,4.889272,95.8733,67.88834,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,106.0696,97.03603,11.2081,4.48848,5.19545,0.907753,2876.180231,1.16174250723641
-c2,137.165,207.8389,15.542688,25.2021,16.7972,229.4322,15.542688,24.9686,17.3272,280.7983,285.9489,85.854,16.8139,7.12909,148.08761,94.2607,39.328,8.57887,10.338268,10.338268,11.2953,8.59732,162.06092,190.78621,11.2352,8.56125,82.75802,95.8733,67.88834,11.2873,4.46626,80.09278,101.35267,28.3993,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,0.017259,2747.170141,1.21629919366908
-c3,215.0729,228.7741,15.542688,25.2021,16.7972,15.542688,198.6494,87.709,17.3272,15.542688,15.542688,25.4775,16.8139,133.86785,142.4026,131.96492,11.4693,8.57887,162.51627,10.338268,11.2953,8.59732,181.04838,10.338268,11.2352,8.56125,51.58494,7.838173,48.1179,25.6923,4.46626,106.245,95.9383,28.3993,4.46968,106.0696,95.9563,28.6839,4.48848,5.19545,1.571852,2340.925283,1.42737610231637
-c4,215.0729,342.9757,328.4669,25.2021,16.7972,15.542688,198.6494,87.709,17.3272,15.542688,15.542688,25.4775,16.8139,7.12909,142.4026,131.96492,11.4693,8.57887,162.51627,191.75912,11.2953,8.59732,181.04838,175.99631,11.2352,8.56125,51.58494,7.838173,48.1179,25.6923,4.46626,106.245,95.9383,28.3993,4.46968,90.82636,101.32631,28.6839,4.48848,5.19545,0.907753,2977.8539,1.1220768207575
-c5,215.0729,342.9757,328.4669,25.2021,16.7972,15.542688,15.542688,24.9686,17.3272,15.542688,15.542688,25.4775,16.8139,104.7504,153.0067,83.6932,39.328,8.57887,162.51627,191.75912,11.2953,8.59732,162.06092,190.78621,11.2352,8.56125,4.889272,7.838173,48.1179,25.6923,4.46626,106.245,95.9383,28.3993,4.46968,90.82636,101.32631,28.6839,4.48848,5.19545,0.907753,2768.92595,1.20674257407488
-c6,215.0729,342.9757,15.542688,25.2021,16.7972,356.8393,15.542688,24.9686,17.3272,15.542688,285.9489,85.854,16.8139,133.86785,10.338268,83.6932,39.328,8.57887,10.338268,176.87812,11.2953,8.59732,162.06092,142.1021,42.2766,8.56125,82.75802,7.838173,50.93214,11.2873,4.46626,106.245,95.9383,28.3993,4.46968,106.0696,97.03603,11.2081,4.48848,5.19545,0.017259,2888.693022,1.15671025196563
-c7,215.0729,342.9757,328.4669,25.2021,16.7972,15.542688,15.542688,24.9686,17.3272,15.542688,15.542688,25.4775,16.8139,104.7504,153.0067,131.96492,11.4693,8.57887,162.51627,10.338268,11.2953,8.59732,162.06092,190.78621,11.2352,8.56125,51.58494,7.838173,48.1179,25.6923,4.46626,106.245,95.9383,28.3993,4.46968,90.82636,101.32631,28.6839,4.48848,5.19545,0.907753,2654.613786,1.2587069504239
-c8,215.0729,342.9757,15.542688,25.2021,16.7972,15.542688,15.542688,24.9686,17.3272,15.542688,15.542688,25.4775,16.8139,104.7504,153.0067,131.96492,11.4693,8.57887,162.51627,10.338268,11.2953,8.59732,181.04838,175.99631,11.2352,8.56125,4.889272,7.838173,48.1179,25.6923,4.46626,106.245,95.9383,28.3993,4.46968,90.82636,101.32631,28.6839,4.48848,5.19545,1.571852,2299.855565,1.45286549927906
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-Unpatch Energy
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
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diff --git a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_results1.csv b/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_results1.csv
deleted file mode 100644
index 0631464ab20d0003a9923616f97d3d2fde356614..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_results1.csv
+++ /dev/null
@@ -1,418 +0,0 @@
-Compute Energy
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-c31,367.1935,1160.9424,1316.8065,109.339,74.5864,1558.1515,1543.1258,108.541,77.2477,1693.1477,1601.9877,107.72,72.987,452.0318,648.5595,422.944,49.588,38.5238,668.3418,737.6456,49.8165,38.8844,763.2659,13.092790,49.6507,39.5474,226.8183,326.6018,221.6533,49.7509,21.8376,347.1207,382.2379,50.4854,21.963,393.9254,382.9785,50.3589,22.1017,23.3959,6.45743,16291.35512,0.934074661641317
-c32,367.1935,1160.9424,1316.8065,109.339,74.5864,1558.1515,17.457054,108.541,77.2477,1693.1477,1601.9877,107.72,72.987,452.0318,648.5595,422.944,49.588,38.5238,668.3418,13.092790,49.8165,38.8844,763.2659,13.092790,49.6507,39.5474,226.8183,326.6018,221.6533,49.7509,21.8376,347.1207,382.2379,50.4854,21.963,393.9254,382.9785,50.3589,22.1017,23.3959,6.45743,14041.133564,1.08376876674963
-c33,367.1935,1160.9424,1316.8065,109.339,74.5864,1558.1515,1543.1258,108.541,77.2477,1693.1477,1601.9877,107.72,72.987,452.0318,648.5595,422.944,49.588,38.5238,668.3418,737.6456,49.8165,38.8844,763.2659,735.4767,49.6507,39.5474,226.8183,326.6018,221.6533,49.7509,21.8376,347.1207,382.2379,50.4854,21.963,393.9254,382.9785,50.3589,22.1017,23.3959,6.45743,17013.73903,0.894414919526276
-c22,13178.986828
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,137.165,207.8389,189.6515,31.9722,19.0941,208.4406,198.6494,31.9182,19.1354,259.7649,285.9489,32.0305,19.0951,123.27345,142.4026,83.6932,18.2347,9.39785,151.93707,142.1728,18.074,9.41748,151.49962,142.1021,18.0244,9.40974,77.39211,95.8733,48.1179,14.1964,4.87483,100.88243,95.9383,14.534,4.81198,100.7067,95.9563,14.737,4.81593,7.47447,0.725589,3341.380949,0.999999970072255
-c1,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,6.804352,11.2352,8.56125,51.58494,74.50821,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3954.706354,0.844912508138335
-c2,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,6.804352,6.804352,11.2953,8.59732,181.04838,175.99631,11.2352,8.56125,51.58494,5.766400,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3734.046766,0.894841727731009
-c3,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,175.99631,11.2352,8.56125,51.58494,5.766400,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,4055.156502,0.823983208774732
-c4,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,175.99631,11.2352,8.56125,51.58494,5.766400,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,4055.156502,0.823983208774732
-c5,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,6.804352,11.2352,8.56125,51.58494,74.50821,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3954.706354,0.844912508138335
-c6,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,175.99631,11.2352,8.56125,51.58494,5.766400,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,4055.156502,0.823983208774732
-c7,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,6.804352,11.2352,8.56125,51.58494,74.50821,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3954.706354,0.844912508138335
-c8,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,2.306560,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,6.804352,11.2352,8.56125,51.58494,74.50821,50.93214,11.2873,4.46626,80.09278,5.766400,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3773.780294,0.885420082820008
-c9,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,6.804352,11.2352,8.56125,51.58494,74.50821,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3954.706354,0.844912508138335
-c10,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,9.024416,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,157.84032,6.804352,11.2953,8.59732,181.04838,6.804352,11.2352,8.56125,51.58494,74.50821,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3434.130502,0.9729918096458
-c11,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,2.306560,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,6.804352,11.2352,8.56125,51.58494,74.50821,50.93214,11.2873,4.46626,80.09278,5.766400,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3773.780294,0.885420082820008
-c12,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,6.804352,11.2352,8.56125,51.58494,74.50821,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3954.706354,0.844912508138335
-c13,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,6.804352,6.804352,11.2953,8.59732,181.04838,175.99631,11.2352,8.56125,51.58494,5.766400,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3734.046766,0.894841727731009
-c14,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,175.99631,11.2352,8.56125,51.58494,5.766400,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,4055.156502,0.823983208774732
-c15,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,175.99631,11.2352,8.56125,51.58494,5.766400,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,4055.156502,0.823983208774732
-c16,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,6.804352,11.2352,8.56125,51.58494,74.50821,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3954.706354,0.844912508138335
-c17,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,175.99631,11.2352,8.56125,51.58494,5.766400,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,4055.156502,0.823983208774732
-c18,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,6.804352,11.2352,8.56125,51.58494,74.50821,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3954.706354,0.844912508138335
-c19,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,2.306560,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,6.804352,11.2352,8.56125,51.58494,74.50821,50.93214,11.2873,4.46626,80.09278,5.766400,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3773.780294,0.885420082820008
-c20,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,6.804352,11.2352,8.56125,51.58494,74.50821,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3954.706354,0.844912508138335
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-c22,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,9.024416,24.9686,17.3272,399.7504,9.024416,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,157.84032,6.804352,11.2953,8.59732,181.04838,175.99631,11.2352,8.56125,51.58494,74.50821,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3223.930276,1.03643086521792
-c23,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,6.804352,11.2352,8.56125,51.58494,74.50821,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3954.706354,0.844912508138335
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-c22,3223.930276
-
-Energy
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
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-c29,367.1935,1240.8474,1316.8065,109.339,74.5864,1558.1515,1543.1258,108.541,77.2477,1693.1477,1601.9877,107.72,72.987,452.0318,648.5595,422.944,49.588,38.5238,668.3418,737.6456,49.8165,38.8844,763.2659,42.156294,49.6507,39.5474,226.8183,326.6018,221.6533,49.7509,21.8376,347.1207,382.2379,50.4854,21.963,393.9254,382.9785,50.3589,22.1017,23.3959,6.45743,16400.323624,0.927868398873808
-c30,367.1935,1240.8474,1316.8065,109.339,74.5864,1558.1515,1543.1258,108.541,77.2477,1693.1477,1601.9877,107.72,72.987,452.0318,648.5595,21.193653,49.588,38.5238,668.3418,737.6456,49.8165,38.8844,763.2659,42.156294,49.6507,39.5474,226.8183,326.6018,221.6533,49.7509,21.8376,347.1207,29.560412,50.4854,21.963,393.9254,382.9785,50.3589,22.1017,23.3959,6.45743,15645.895789,0.972609189193103
-c31,367.1935,1240.8474,1316.8065,109.339,74.5864,1558.1515,1543.1258,108.541,77.2477,1693.1477,1601.9877,107.72,72.987,452.0318,648.5595,422.944,49.588,38.5238,668.3418,737.6456,49.8165,38.8844,763.2659,42.156294,49.6507,39.5474,226.8183,326.6018,221.6533,49.7509,21.8376,347.1207,382.2379,50.4854,21.963,393.9254,382.9785,50.3589,22.1017,23.3959,6.45743,16400.323624,0.927868398873808
-c32,367.1935,1240.8474,1316.8065,109.339,74.5864,1558.1515,67.644319,108.541,77.2477,1693.1477,1601.9877,107.72,72.987,452.0318,648.5595,422.944,49.588,38.5238,668.3418,42.156294,49.8165,38.8844,763.2659,42.156294,49.6507,39.5474,226.8183,326.6018,221.6533,49.7509,21.8376,347.1207,382.2379,50.4854,21.963,393.9254,382.9785,50.3589,22.1017,23.3959,6.45743,14229.352837,1.06943317677018
-c33,367.1935,1240.8474,1316.8065,109.339,74.5864,1558.1515,1543.1258,108.541,77.2477,1693.1477,1601.9877,107.72,72.987,452.0318,648.5595,422.944,49.588,38.5238,668.3418,737.6456,49.8165,38.8844,763.2659,735.4767,49.6507,39.5474,226.8183,326.6018,221.6533,49.7509,21.8376,347.1207,382.2379,50.4854,21.963,393.9254,382.9785,50.3589,22.1017,23.3959,6.45743,17093.64403,0.890233937191484
-c22,13388.329862
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,15.774626,0,0,0,0,0,0,0,11.797401,0,0,0,0,0,0,0,0,0,0,0,0,0,43.346653,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.797401,0,0,0,0,0,0,0,0,0,0,0,0,0,11.797401,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.797401,0,0,0,0,0,0,0,0,0,0,0,0,0,11.797401,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.797401,0,0,0,0,0,0,0,0,0,0,0,0,0,11.797401,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.991873,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,0,0,0,11.797401,0,0,0,0,0,0,0,0,34.5639,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0
-c10,0,0,0,0,0,0,23.842121,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,55.391373,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.991873,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,0,0,0,11.797401,0,0,0,0,0,0,0,0,34.5639,0
-c12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0
-c13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,15.774626,0,0,0,0,0,0,0,11.797401,0,0,0,0,0,0,0,0,0,0,0,0,0,43.346653,0
-c14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.797401,0,0,0,0,0,0,0,0,0,0,0,0,0,11.797401,0
-c15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.797401,0,0,0,0,0,0,0,0,0,0,0,0,0,11.797401,0
-c16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0
-c17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.797401,0,0,0,0,0,0,0,0,0,0,0,0,0,11.797401,0
-c18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0
-c19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.991873,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,0,0,0,11.797401,0,0,0,0,0,0,0,0,34.5639,0
-c20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0
-c21,0,0,0,0,0,0,23.842121,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,55.391373,0
-c22,0,0,0,0,0,0,23.842121,0,0,0,23.842121,0,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,63.458868,0
-c23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0
-c24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,15.774626,0,0,0,0,0,0,0,11.797401,0,0,0,0,0,0,0,0,0,0,0,0,0,43.346653,0
-c25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.797401,0,0,0,0,0,0,0,0,0,0,0,0,0,11.797401,0
-c26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.797401,0,0,0,0,0,0,0,0,0,0,0,0,0,11.797401,0
-c27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0
-c28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.797401,0,0,0,0,0,0,0,0,0,0,0,0,0,11.797401,0
-c29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0
-c30,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.991873,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,0,0,0,11.797401,0,0,0,0,0,0,0,0,34.5639,0
-c31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0
-c32,0,0,0,0,0,0,23.842121,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,55.391373,0
-c33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,13.288878,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0,33.430108,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,29.978747,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0
-c10,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,52.9229,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,29.978747,0
-c12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0
-c13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,13.288878,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0,33.430108,0
-c14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0
-c15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0
-c16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0
-c17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0
-c18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0
-c19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,29.978747,0
-c20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0
-c21,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,52.9229,0
-c22,0,0,0,0,0,0,26.345144,0,0,0,26.345144,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,65.979166,0
-c23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0
-c24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,13.288878,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0,33.430108,0
-c25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0
-c26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0
-c27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0
-c28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0
-c29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0
-c30,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,29.978747,0
-c31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0
-c32,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,52.9229,0
-c33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,3.533916,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,0,0,0,0,0,9.139605,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,0,0,0,0,0,2.071773,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,0,0,0,0,0,2.071773,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,0,0,0,0,0,2.071773,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.308683,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,7.914372,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0
-c10,0,0,0,0,0,0,6.518272,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.586104,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.308683,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,7.914372,0
-c12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0
-c13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,3.533916,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,0,0,0,0,0,9.139605,0
-c14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,0,0,0,0,0,2.071773,0
-c15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,0,0,0,0,0,2.071773,0
-c16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0
-c17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,0,0,0,0,0,2.071773,0
-c18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0
-c19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.308683,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,7.914372,0
-c20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0
-c21,0,0,0,0,0,0,6.518272,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.586104,0
-c22,0,0,0,0,0,0,6.518272,0,0,0,6.518272,0,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.57046,0
-c23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0
-c24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,3.533916,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,0,0,0,0,0,9.139605,0
-c25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,0,0,0,0,0,2.071773,0
-c26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,0,0,0,0,0,2.071773,0
-c27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0
-c28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,0,0,0,0,0,2.071773,0
-c29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0
-c30,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.308683,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,7.914372,0
-c31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0
-c32,0,0,0,0,0,0,6.518272,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.586104,0
-c33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c0,0
-
-Patch Energy
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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-c20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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-c26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c30,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c0,0
-
-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c2,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c3,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c4,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c5,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c6,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c7,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c8,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c9,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c10,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c11,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c12,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c13,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c14,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c15,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c16,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c17,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c18,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c19,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c20,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c21,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c22,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c23,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c24,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c25,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c26,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c27,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c28,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c29,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c30,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c31,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c32,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c33,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c2,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c3,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c4,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c5,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c6,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c7,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c8,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c9,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c10,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c11,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c12,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c13,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c14,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c15,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c16,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c17,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c18,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c19,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c20,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c21,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c22,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c23,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c24,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c25,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c26,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c27,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c28,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c29,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c30,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c31,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c32,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c33,0,27.627,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.627,0
-c0,0
-
-Time
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,137.165,207.8389,189.6515,31.9722,19.0941,208.4406,198.6494,31.9182,19.1354,259.7649,285.9489,32.0305,19.0951,123.27345,142.4026,83.6932,18.2347,9.39785,151.93707,142.1728,18.074,9.41748,151.49962,142.1021,18.0244,9.40974,77.39211,95.8733,48.1179,14.1964,4.87483,100.88243,95.9383,14.534,4.81198,100.7067,95.9563,14.737,4.81593,7.47447,0.725589,3341.380949,0.999999970072255
-c1,137.165,370.6027,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,10.338268,11.2352,8.56125,51.58494,74.50821,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3985.86727,0.838307108296983
-c2,137.165,370.6027,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,10.338268,10.338268,11.2953,8.59732,181.04838,175.99631,11.2352,8.56125,51.58494,7.838173,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3770.813371,0.886116742368029
-c3,137.165,370.6027,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,175.99631,11.2352,8.56125,51.58494,7.838173,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,4084.855275,0.817992472744522
-c4,137.165,370.6027,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,175.99631,11.2352,8.56125,51.58494,7.838173,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,4084.855275,0.817992472744522
-c5,137.165,370.6027,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,10.338268,11.2352,8.56125,51.58494,74.50821,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3985.86727,0.838307108296983
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-Unpatch Energy
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
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diff --git a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_results2.csv b/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_results2.csv
deleted file mode 100644
index abf09ee84725703f377f9ee103c0c119469018aa..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_results2.csv
+++ /dev/null
@@ -1,550 +0,0 @@
-Compute Energy
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-c27,12056.745877
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,137.165,207.8389,189.6515,31.9722,19.0941,208.4406,198.6494,31.9182,19.1354,259.7649,285.9489,32.0305,19.0951,123.27345,142.4026,83.6932,18.2347,9.39785,151.93707,142.1728,18.074,9.41748,151.49962,142.1021,18.0244,9.40974,77.39211,95.8733,48.1179,14.1964,4.87483,100.88243,95.9383,14.534,4.81198,100.7067,95.9563,14.737,4.81593,7.47447,0.725589,3341.380949,0.999999970072255
-c1,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,9.024416,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,2.306560,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,6.804352,11.2352,8.56125,51.58494,74.50821,50.93214,11.2873,4.46626,80.09278,5.766400,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3423.27821,0.976076335727433
-c2,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,9.024416,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,2.306560,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,6.804352,11.2352,8.56125,51.58494,74.50821,50.93214,11.2873,4.46626,80.09278,5.766400,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3423.27821,0.976076335727433
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-c5,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,9.024416,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,6.804352,99.91022,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,6.804352,11.2352,8.56125,51.58494,74.50821,1.153280,11.2873,4.46626,80.09278,5.766400,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3329.819752,1.00347198873028
-c6,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,9.024416,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,157.84032,6.804352,11.2953,8.59732,181.04838,6.804352,11.2352,8.56125,51.58494,74.50821,50.93214,11.2873,4.46626,80.09278,5.766400,11.1843,4.46968,90.82636,5.766400,11.2081,4.48848,5.19545,1.571852,3267.556342,1.02259318494123
-c7,137.165,342.9757,9.024416,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,6.804352,11.2352,8.56125,51.58494,74.50821,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3635.26387,0.91915772185314
-c8,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,359.5265,24.9686,17.3272,399.7504,9.024416,25.4775,16.8139,104.7504,148.08761,99.91022,11.4693,8.57887,6.804352,6.804352,11.2953,8.59732,181.04838,175.99631,11.2352,8.56125,3.459840,74.50821,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3375.271292,0.989959194664961
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-c45,137.165,342.9757,328.4669,25.2021,16.7972,356.8393,9.024416,24.9686,17.3272,399.7504,388.4166,25.4775,16.8139,104.7504,148.08761,2.306560,11.4693,8.57887,157.84032,176.87812,11.2953,8.59732,181.04838,175.99631,11.2352,8.56125,3.459840,74.50821,50.93214,11.2873,4.46626,80.09278,89.0888,11.1843,4.46968,90.82636,89.01816,11.2081,4.48848,5.19545,1.571852,3627.667468,0.921082454873936
-c27,2966.434488
-
-Energy
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
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-c1,367.1935,1240.8474,1316.8065,109.339,74.5864,1558.1515,67.644319,108.541,77.2477,1693.1477,1601.9877,107.72,72.987,452.0318,648.5595,21.193653,49.588,38.5238,668.3418,737.6456,49.8165,38.8844,763.2659,42.156294,49.6507,39.5474,226.8183,326.6018,221.6533,49.7509,21.8376,347.1207,29.560412,50.4854,21.963,393.9254,382.9785,50.3589,22.1017,23.3959,6.45743,14170.414308,1.07388123428549
-c2,367.1935,1240.8474,1316.8065,109.339,74.5864,1558.1515,67.644319,108.541,77.2477,1693.1477,1601.9877,107.72,72.987,452.0318,648.5595,21.193653,49.588,38.5238,668.3418,737.6456,49.8165,38.8844,763.2659,42.156294,49.6507,39.5474,226.8183,326.6018,221.6533,49.7509,21.8376,347.1207,29.560412,50.4854,21.963,393.9254,382.9785,50.3589,22.1017,23.3959,6.45743,14170.414308,1.07388123428549
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-c7,367.1935,1240.8474,67.644319,109.339,74.5864,1558.1515,1543.1258,108.541,77.2477,1693.1477,1601.9877,107.72,72.987,452.0318,648.5595,422.944,49.588,38.5238,668.3418,737.6456,49.8165,38.8844,763.2659,42.156294,49.6507,39.5474,226.8183,326.6018,221.6533,49.7509,21.8376,347.1207,382.2379,50.4854,21.963,393.9254,382.9785,50.3589,22.1017,23.3959,6.45743,15151.161443,1.00436801968035
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-c12,367.1935,1240.8474,67.644319,109.339,74.5864,1558.1515,1543.1258,108.541,77.2477,1693.1477,1601.9877,107.72,72.987,452.0318,648.5595,422.944,49.588,38.5238,668.3418,737.6456,49.8165,38.8844,763.2659,42.156294,49.6507,39.5474,226.8183,29.560412,221.6533,49.7509,21.8376,347.1207,382.2379,50.4854,21.963,393.9254,382.9785,50.3589,22.1017,23.3959,6.45743,14854.120055,1.02445260681951
-c13,367.1935,1240.8474,1316.8065,109.339,74.5864,1558.1515,67.644319,108.541,77.2477,1693.1477,1601.9877,107.72,72.987,452.0318,648.5595,21.193653,49.588,38.5238,668.3418,737.6456,49.8165,38.8844,763.2659,735.4767,49.6507,39.5474,18.903715,326.6018,221.6533,49.7509,21.8376,347.1207,382.2379,50.4854,21.963,393.9254,382.9785,50.3589,22.1017,23.3959,6.45743,15008.497617,1.01391507676104
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-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
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-c41,0,0,23.842121,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0,0,0,15.774626,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,55.391373,0
-c42,0,0,0,0,0,0,0,0,0,0,23.842121,0,0,0,0,0,0,0,15.774626,15.774626,0,0,0,0,0,0,7.351726,0,0,0,0,0,0,0,0,0,0,0,0,0,0,62.743099,0
-c43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.991873,0,0,0,0,0,0,0,15.774626,0,0,0,0,3.468407,0,0,0,11.797401,0,0,0,0,0,0,0,0,38.032307,0
-c44,0,0,23.842121,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.774626,0,0,0,11.797401,0,0,0,0,0,0,0,0,0,0,0,0,0,51.414148,0
-c45,0,0,0,0,0,0,23.842121,0,0,0,0,0,0,0,0,6.991873,0,0,0,0,0,0,0,0,0,0,7.351726,0,0,0,0,0,0,0,0,0,0,0,0,0,0,38.18572,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,56.323891,0
-c2,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,56.323891,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.837517,0
-c4,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,13.288878,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,59.775252,0
-c5,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,4.920397,0,0,0,6.852352,0,0,0,0,0,0,0,0,64.695649,0
-c6,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,13.288878,0,0,0,0,0,0,0,0,6.852352,0,0,0,6.852352,0,0,0,0,66.627604,0
-c7,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,39.634022,0
-c8,0,0,0,0,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,13.288878,13.288878,0,0,0,0,0,0,5.005594,0,0,0,0,0,0,0,0,0,0,0,0,0,0,57.928494,0
-c9,0,0,0,0,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,33.197496,0
-c10,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,52.9229,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,0,13.288878,0,0,0,0,4.920397,0,0,0,6.852352,0,0,0,0,0,0,0,0,34.899144,0
-c12,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0,46.486374,0
-c13,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,0,0,0,0,5.005594,0,0,0,0,0,0,0,0,0,0,0,0,0,0,41.188255,0
-c14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,29.978747,0
-c15,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,56.323891,0
-c16,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,56.323891,0
-c17,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,56.323891,0
-c18,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,13.288878,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,59.775252,0
-c19,0,0,0,0,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,13.288878,13.288878,0,0,0,0,0,0,5.005594,0,0,0,0,0,0,0,0,0,0,0,0,0,0,57.928494,0
-c20,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,0,0,0,0,5.005594,0,0,0,0,0,0,0,0,0,0,0,0,0,0,41.188255,0
-c21,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,13.288878,0,0,0,0,0,0,0,0,6.852352,0,0,0,6.852352,0,0,0,0,66.627604,0
-c22,0,0,0,0,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,33.197496,0
-c23,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,52.9229,0
-c24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,0,13.288878,0,0,0,0,4.920397,0,0,0,6.852352,0,0,0,0,0,0,0,0,34.899144,0
-c25,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0,46.486374,0
-c26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,29.978747,0
-c27,0,0,0,0,0,0,26.345144,0,0,0,26.345144,0,0,0,0,0,0,0,0,13.288878,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,86.120396,0
-c28,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,4.920397,0,0,0,6.852352,0,0,0,0,0,0,0,0,64.695649,0
-c29,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,39.634022,0
-c30,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,56.323891,0
-c31,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,56.323891,0
-c32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.837517,0
-c33,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,13.288878,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,59.775252,0
-c34,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,56.323891,0
-c35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,29.978747,0
-c36,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,0,0,0,0,56.323891,0
-c37,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,39.634022,0
-c38,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,13.288878,0,0,0,0,0,0,0,0,6.852352,0,0,0,6.852352,0,0,0,0,66.627604,0
-c39,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,13.288878,0,0,0,0,0,0,0,0,13.288878,0,0,0,0,4.920397,0,0,0,6.852352,0,0,0,0,0,0,0,0,64.695649,0
-c40,0,0,0,0,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.852352,0,0,0,0,33.197496,0
-c41,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,13.288878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,52.9229,0
-c42,0,0,0,0,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,13.288878,13.288878,0,0,0,0,0,0,5.005594,0,0,0,0,0,0,0,0,0,0,0,0,0,0,57.928494,0
-c43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,0,13.288878,0,0,0,0,4.920397,0,0,0,6.852352,0,0,0,0,0,0,0,0,34.899144,0
-c44,0,0,26.345144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.288878,0,0,0,6.852352,0,0,0,0,0,0,0,0,0,0,0,0,0,46.486374,0
-c45,0,0,0,0,0,0,26.345144,0,0,0,0,0,0,0,0,9.837517,0,0,0,0,0,0,0,0,0,0,5.005594,0,0,0,0,0,0,0,0,0,0,0,0,0,0,41.188255,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,6.518272,0,0,0,0,0,0,0,0,2.308683,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,14.432644,0
-c2,0,0,0,0,0,0,6.518272,0,0,0,0,0,0,0,0,2.308683,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,14.432644,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.308683,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.308683,0
-c4,0,0,0,0,0,0,6.518272,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,3.533916,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,15.657877,0
-c5,0,0,0,0,0,0,6.518272,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,3.533916,0,0,0,0,1.153683,0,0,0,2.071773,0,0,0,0,0,0,0,0,16.81156,0
-c6,0,0,0,0,0,0,6.518272,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0,0,0,3.533916,0,0,0,0,0,0,0,0,2.071773,0,0,0,2.071773,0,0,0,0,17.72965,0
-c7,0,0,6.518272,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.052188,0
-c8,0,0,0,0,0,0,0,0,0,0,6.518272,0,0,0,0,0,0,0,3.533916,3.533916,0,0,0,0,0,0,1.429432,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.015536,0
-c9,0,0,0,0,0,0,0,0,0,0,6.518272,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,8.590045,0
-c10,0,0,6.518272,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0,0,0,3.533916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.586104,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.308683,0,0,0,0,0,0,0,3.533916,0,0,0,0,1.153683,0,0,0,2.071773,0,0,0,0,0,0,0,0,9.068055,0
-c12,0,0,6.518272,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,0,0,0,2.071773,0,0,0,0,0,0,0,0,0,0,0,0,0,12.123961,0
-c13,0,0,0,0,0,0,6.518272,0,0,0,0,0,0,0,0,2.308683,0,0,0,0,0,0,0,0,0,0,1.429432,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.256387,0
-c14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.308683,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,7.914372,0
-c15,0,0,0,0,0,0,6.518272,0,0,0,0,0,0,0,0,2.308683,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,14.432644,0
-c16,0,0,0,0,0,0,6.518272,0,0,0,0,0,0,0,0,2.308683,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,14.432644,0
-c17,0,0,0,0,0,0,6.518272,0,0,0,0,0,0,0,0,2.308683,0,0,0,0,0,0,0,3.533916,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,14.432644,0
-c18,0,0,0,0,0,0,6.518272,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.533916,3.533916,0,0,0,0,0,0,0,0,2.071773,0,0,0,0,0,0,0,0,15.657877,0
-c19,0,0,0,0,0,0,0,0,0,0,6.518272,0,0,0,0,0,0,0,3.533916,3.533916,0,0,0,0,0,0,1.429432,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.015536,0
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-Patch Energy
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
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-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
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-c26,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
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-c40,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c41,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c42,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c43,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
-c44,0,79.905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79.905,0
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-c0,0
-
-Quantization Time
-Configuration,Conv1,Conv2,Conv3,NML1,NML2,Conv4,Conv5,NML3,NML4,Conv6,Conv7,NML5,NML6,Conv8,Conv9,Conv10,NML7,NML8,Conv11,Conv12,NML9,NML10,Conv13,Conv14,NML11,NML12,Conv15,Conv16,Conv17,NML13,NML14,Conv18,Conv19,NML15,NML16,Conv20,Conv21,NML17,NML18,NML19,FC1,Total,Improvement
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diff --git a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_tensors.txt b/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_tensors.txt
deleted file mode 100644
index a68f03802ad3eb3236f0eaf1a2dbe4eb524d712a..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/resnet18_cifar10/resnet18_tensors.txt
+++ /dev/null
@@ -1,114 +0,0 @@
-#Conv1,3
-Conv1,96.2953,255.037,174.622,421.265,6.3224,13.0911,20.9136,52.8942
-Add1,21.6934,58.7548,18.4067,51.7458,27.7102,72.282,20.7976,56.1939
-Relu1,19.1763,53.4017,15.7218,47.6272,27.4697,75.5402,20.8432,59.4527
-#Conv2,3
-Conv2,167.13,533.192,307.782,1024.12,27.627,79.905,20.9352,73.9105
-Add2,21.6463,74.901,19.5015,74.6206,27.9959,99.3789,21.8283,78.8418
-Relu2,19.0626,66.8226,15.6922,62.2018,27.4183,99.9831,20.833,77.2117
-#Conv3,2
-Conv3,167.964,647.325,309.083,1232.59,27.8734,103.065,20.9195,84.5406
-Add3,21.6875,87.1673,19.3839,84.2165,27.9995,113.419,21.828,88.7986
-#NML1,1
-Add4,31.9722,131.604,25.2021,109.339,54.8381,227.205,20.8082,85.9608
-#NML2,1
-Relu3,19.0941,78.945,16.7972,74.5864,27.5363,113.932,21.969,91.4346
-#Conv4,3
-Conv4,167.592,744.048,322.719,1401.3,27.8507,115.914,20.9916,91.4069
-Add5,21.6558,98.7438,18.3902,84.3374,52.5577,223.355,21.3908,91.48
-Relu4,19.1928,87.579,15.7301,72.5141,27.4947,117.455,20.8274,89.0752
-#Conv5,2
-Conv5,177.005,835.874,342.079,1466.82,29.7996,126.901,21.0449,89.4069
-Add6,21.6444,102.842,17.4475,76.3058,78.6158,330.238,20.8524,85.6316
-#NML3,1
-Add7,31.9182,154.555,24.9686,108.541,62.7404,259.957,20.8101,86.1797
-#NML4,1
-Relu5,19.1354,92.4142,17.3272,77.2477,30.181,124.467,21.9931,91.2
-#Conv6,3
-Conv6,218.988,1050.1,366.167,1544.8,29.7189,123.016,21.0334,89.908
-Add8,21.6322,107.553,17.6899,77.5743,83.3626,352.496,20.8989,85.9449
-Relu6,19.1447,95.3407,15.8935,70.7734,29.4192,120.534,20.9166,86.1995
-#Conv7,2
-Conv7,264.136,1280.25,370.721,1526.79,28.6144,117.921,20.9627,85.9711
-Add9,21.8129,104.865,17.6956,75.1977,82.8254,337.55,20.8517,82.5853
-#NML5,1
-Add10,32.0305,157.253,25.4775,107.72,60.3765,243.339,20.7649,83.3467
-#NML6,1
-Relu7,19.0951,93.1857,16.8139,72.987,28.3017,113.854,22.041,89.2722
-#Conv8,3
-Conv8,100.507,490.584,85.7697,364.625,28.2226,114.351,10.5944,42.4202
-Add11,13.2849,62.5018,10.3212,47.2214,15.502,62.8822,11.2293,44.7914
-Relu8,9.48155,43.6732,8.6595,40.1854,15.0619,61.1893,10.7582,43.1574
-#Conv9,2
-Conv9,129.091,627.118,138.631,604.548,14.972,60.9,10.6041,44.2087
-Add12,13.3116,62.903,9.45661,44.0115,40.1768,172.415,10.4892,42.8418
-#Conv10,2
-Conv10,70.4671,340.841,90.3128,381.213,32.0547,135.935,10.5675,40.812
-Add13,13.2261,60.7273,9.59742,41.731,41.9408,167.231,10.4872,39.7994
-#NML7,1
-Add14,18.2347,85.0395,11.4693,49.588,27.8587,110.353,10.4665,40.3814
-#NML8,1
-Relu9,9.39785,42.293,8.57887,38.5238,14.4553,55.9886,11.1054,43.0539
-#Conv11,3
-Conv11,129.334,614.475,140.577,589.922,15.1273,59.0802,10.5792,42.4989
-Add15,13.201,61.5741,9.39457,42.3399,40.147,165.995,10.4569,41.0957
-Relu10,9.40207,43.1235,7.86875,36.0799,14.7797,59.1119,10.4938,41.2344
-#Conv12,2
-Conv12,129.068,617.019,167.515,695.778,14.881,59.0727,10.5947,41.783
-Add16,13.1048,61.3472,9.36312,41.8676,40.662,165.398,10.4433,40.5802
-#NML9,1
-Add17,18.074,85.9011,11.2953,49.8165,31.2262,125.787,10.4534,40.67
-#NML10,1
-Relu11,9.41748,43.1252,8.59732,38.8844,14.7683,58.4599,11.0992,43.4074
-#Conv13,3
-Conv13,129.135,621.204,163.856,684.766,14.6929,58.143,10.5613,42.4051
-Add18,13.0014,61.4375,9.33772,41.9974,40.52,167.742,10.4893,41.2948
-Relu12,9.36322,43.724,7.85466,36.5025,14.3058,56.635,10.4801,41.3643
-#Conv14,2
-Conv14,129.174,625.346,166.656,693.932,14.7899,59.1467,10.5466,41.836
-Add19,12.9281,61.0404,9.34031,41.5447,40.5583,165.866,10.4607,40.9009
-#NML11,1
-Add20,18.0244,86.3285,11.2352,49.6507,31.0414,125.831,10.4493,41.12
-#NML12,1
-Relu13,9.40974,43.6635,8.56125,39.5474,14.7184,58.0328,11.0576,43.5721
-#Conv15,3
-Conv15,61.1393,299.396,38.244,164.048,14.5914,58.2375,5.36591,19.9204
-Add21,11.443,53.5959,9.3646,42.9504,8.53298,33.0218,5.65061,21.0508
-Relu14,4.80981,20.8303,3.97634,19.8199,7.82028,30.3544,5.27702,19.5391
-#Conv16,2
-Conv16,84.5931,417.41,65.7442,285.597,8.07266,31.1377,5.37147,20.4791
-Add22,11.2802,53.8629,8.76401,41.0048,20.0626,83.8331,5.28191,19.8187
-#Conv17,2
-Conv17,36.7136,180.084,42.064,181.248,16.9562,69.8631,5.36951,19.8329
-Add23,11.4043,53.4182,8.86814,40.4053,20.3149,81.1815,5.29007,18.9716
-#NML13,1
-Add24,14.1964,67.2674,11.2873,49.7509,14.405,57.1891,5.33388,19.3044
-#NML14,1
-Relu15,4.87483,20.4251,4.46626,21.8376,7.89618,29.6604,5.52563,20.1041
-#Conv18,3
-Conv18,84.6165,415.961,67.3709,286.812,8.03381,30.3417,5.36257,20.0592
-Add25,11.4019,54.4409,8.75476,40.2434,20.1161,82.2092,5.27612,19.5688
-Relu16,4.86403,20.6177,3.96712,20.0653,7.75796,29.607,5.27422,19.6466
-#Conv19,2
-Conv19,84.5493,420.587,80.3214,341.92,8.10378,31.2805,5.41437,20.2452
-Add26,11.389,54.7724,8.7674,40.3179,20.1714,82.6833,5.2755,19.4721
-#NML15,1
-Add27,14.534,71.0934,11.1843,50.4854,17.215,69.9894,5.28841,19.6402
-#NML16,1
-Relu17,4.81198,21.0221,4.46968,21.963,8.1633,31.6672,5.53729,20.6582
-#Conv20,3
-Conv20,84.5033,424.692,78.1278,333.871,8.0013,30.8855,5.3629,19.8115
-Add28,11.3928,55.6158,8.73735,40.0918,20.0577,82.5983,5.33532,19.8316
-Relu18,4.8106,21.1957,3.96121,19.9626,7.75018,29.9637,5.25633,19.6445
-#Conv21,2
-Conv21,84.5829,428.664,80.2786,343.147,8.01787,30.88,5.37001,20.3721
-Add29,11.3734,55.2916,8.73956,39.8315,19.9302,82.4415,5.31975,19.701
-#NML17,1
-Add30,14.737,73.1106,11.2081,50.3589,17.4758,71.5971,5.27643,19.4931
-#NML18,1
-Relu19,4.81593,21.5459,4.48848,22.1017,8.14713,31.7315,5.53758,20.7902
-#NML19,1
-Pool1,7.47447,35.1354,5.19545,23.3959,7.76594,29.7619,0.247834,0.0992326
-#FC1,2
-Mul1,0.500934,0.486919,1.17541,5.23179,0.866649,1.15065,0.182164,0.0453634
-Add31,0.224655,0.0762958,0.396442,1.22564,0.809076,1.36364,0.129454,0.0344863
diff --git a/llvm/projects/soc_simulator/src/Makefile b/llvm/projects/soc_simulator/src/Makefile
deleted file mode 100644
index c31b4d295f0bffb35da63e965fc9cb1d8163af99..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/src/Makefile
+++ /dev/null
@@ -1,16 +0,0 @@
-all: clean timing quant patch gemm
-
-timing:
- g++ -std=c++11 -O3 promise_timing_model.cpp -o ptm
-
-quant:
- nvcc -std=c++11 -O3 -arch=sm_62 quantization.cu -o quantize
-
-patch:
- nvcc -std=c++11 -O3 -arch=sm_62 patch.cu -o patch
-
-gemm:
- nvcc -std=c++11 -O3 -arch=sm_62 gemm.cu fp16_emu.cpp -o gemm -lcublas
-
-clean:
- rm -rf ptm quantize patch gemm
diff --git a/llvm/projects/soc_simulator/src/copy_tensor_data.pl b/llvm/projects/soc_simulator/src/copy_tensor_data.pl
deleted file mode 100755
index 98729936071d49bf95017c429e202f107babdae9..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/src/copy_tensor_data.pl
+++ /dev/null
@@ -1,130 +0,0 @@
-#!/usr/bin/perl
-
-use strict;
-use warnings;
-
-my %tensors;
-
-########################################
-if (($#ARGV + 1) != 4) {
- print "Usage: copy_tensor_data.pl <tensor info> <FP16 data> <FP32 data> <output tensor file>\n";
- exit;
-}
-
-# Input tensor file
-my $tensor_filename = $ARGV[0];
-
-# FP16 data file
-my $fp16_data_filename = $ARGV[1];
-
-# FP32 data file
-my $fp32_data_filename = $ARGV[2];
-
-# Output tensor file
-my $output_filename = $ARGV[3];
-########################################
-
-########################################
-print "Reading FP16 data\n";
-open(my $fp16_data_file, '<', $fp16_data_filename) or die "Couldn't open FP16 data file $fp16_data_filename: $!";
-while (my $line = <$fp16_data_file>) {
- chomp $line;
- my @tokens = split /,/, $line;
-
- # The format of each line is:
- # 0 1 2
- # Op name, time, energy
- my $op_name = $tokens[0];
- my $op_time = $tokens[1];
- my $op_energy = $tokens[2];
-
- if (is_f2h($op_name)) {
- # Remove _f2h
- $op_name = substr($op_name, 0, -4);
- $tensors{$op_name}{"f2h_time"} = $op_time;
- $tensors{$op_name}{"f2h_energy"} = $op_energy;
- } elsif (is_h2f($op_name)) {
- # Remove _h2f
- $op_name = substr($op_name, 0, -4);
- $tensors{$op_name}{"h2f_time"} = $op_time;
- $tensors{$op_name}{"h2f_energy"} = $op_energy;
- } else {
- $tensors{$op_name}{"fp16_time"} = $op_time;
- $tensors{$op_name}{"fp16_energy"} = $op_energy;
- }
-}
-########################################
-
-########################################
-print "Reading FP32 data\n";
-open(my $fp32_data_file, '<', $fp32_data_filename) or die "Couldn't open FP32 data file $fp32_data_filename: $!";
-while (my $line = <$fp32_data_file>) {
- chomp $line;
- my @tokens = split /,/, $line;
-
- # The format of each line is:
- # 0 1 2
- # Op name, time, energy
- my $op_name = $tokens[0];
- my $op_time = $tokens[1];
- my $op_energy = $tokens[2];
-
- $tensors{$op_name}{"Name"} = $op_name;
- $tensors{$op_name}{"fp32_time"} = $op_time;
- $tensors{$op_name}{"fp32_energy"} = $op_energy;
-}
-########################################
-
-########################################
-print "Generating output tensor file\n";
-open(my $tensor_file, '<', $tensor_filename) or die "Couldn't open tensor info file $tensor_filename: $!";
-open(my $output_file, '>', $output_filename) or die "Couldn't open results file $output_filename: $!";
-while (my $line = <$tensor_file>) {
- print $output_file $line;
- chomp $line;
-
- # Layer lines look like this: #layer_name, num_tensor_ops_in_layer
- my @tokens = split /,/, $line;
- my $layer_name = substr($tokens[0], 1);
- my $num_ops = $tokens[1];
-
- # Tensor lines look like this: name
- for (my $i = 0; $i < $num_ops; $i++) {
- my $op_name = <$tensor_file>;
- chomp $op_name;
-
- # Format of each output line:
- # Name, FP32 time, FP32 energy, FP16 time, FP16 energy, f2h time, f2h energy, h2f time, h2f energy
- print $output_file $tensors{$op_name}{"Name"} . ",";
- print $output_file $tensors{$op_name}{"fp32_time"} . ",";
- print $output_file $tensors{$op_name}{"fp32_energy"} . ",";
- print $output_file $tensors{$op_name}{"fp16_time"} . ",";
- print $output_file $tensors{$op_name}{"fp16_energy"} . ",";
- print $output_file $tensors{$op_name}{"f2h_time"} . ",";
- print $output_file $tensors{$op_name}{"f2h_energy"} . ",";
- print $output_file $tensors{$op_name}{"h2f_time"} . ",";
- print $output_file $tensors{$op_name}{"h2f_energy"} . "\n";
- }
-}
-
-print "Done!\n";
-########################################
-
-sub is_f2h {
- my ($name) = @_;
- if ($name =~ /f2h/i) {
- return 1;
- } else {
- return;
- }
-}
-
-sub is_h2f {
- my ($name) = @_;
- if ($name =~ /h2f/i) {
- return 1;
- } else {
- return;
- }
-}
-
diff --git a/llvm/projects/soc_simulator/src/driver.pl b/llvm/projects/soc_simulator/src/driver.pl
deleted file mode 100755
index fe53ca9d850e58e72a1e8ebe8d7e048d24ddc017..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/src/driver.pl
+++ /dev/null
@@ -1,506 +0,0 @@
-#!/usr/bin/perl
-
-use strict;
-use warnings;
-
-my $fp16_swing = 8;
-my $iterations = 10;
-
-my $PROMISE = "PROMISE";
-my $fp16 = "FP16";
-my $fp32 = "FP32";
-
-my @layers;
-my %tensors;
-my %results;
-
-########################################
-if (($#ARGV + 1) != 6) {
- print "Usage: driver.pl <layer info> <tensor info> <configurations> <results file> <smart DMA> <detailed results?>\n";
- exit;
-}
-
-my $layer_filename = $ARGV[0];
-my $tensor_filename = $ARGV[1];
-my $config_filename = $ARGV[2];
-my $results_filename = $ARGV[3];
-my $smart_dma = $ARGV[4];
-my $detailed_results = $ARGV[5];
-########################################
-
-########################################
-print "Reading layer info\n";
-open(my $layer_file, '<', $layer_filename) or die "Couldn't open layer info file $layer_filename: $!";
-while (my $line = <$layer_file>) {
- chomp $line;
- add_layer($line);
-}
-########################################
-
-########################################
-print "Reading tensor info\n";
-open(my $tensor_file, '<', $tensor_filename) or die "Couldn't open tensor info file $tensor_filename: $!";
-while (my $line = <$tensor_file>) {
- chomp $line;
- # Layer lines look like this: #layer_name, num_tensor_ops_in_layer
- my @tokens = split /,/, $line;
- my $layer_name = substr($tokens[0], 1);
- my $num_ops = $tokens[1];
-
- # Tensor lines look like this: name, FP32 time, FP32 energy, FP16 time, FP16 energy, f2h time, f2h energy, h2f time, h2f energy
- for (my $i = 0; $i < $num_ops; $i++) {
- my $op = <$tensor_file>;
- chomp $op;
- my @values = split /,/, $op;
- $tensors{$layer_name}{$i} = [@values];
- }
-}
-########################################
-
-########################################
-print "Running simulations\n";
-my $conf_count = 0;
-open(my $config_file, '<', $config_filename) or die "Couldn't open config file $config_filename: $!";
-open(my $results_file, '>', $results_filename) or die "Couldn't open results file $results_filename: $!";
-while (my $line = <$config_file>) {
- chomp $line;
-
- # Each line has a bunch of comma separated voltage swing levels
- my @levels = split /,/, $line;
- my $layer_count = 0;
- my $prev = $fp32;
- my $curr;
- foreach my $level (@levels) {
- my %layer = %{$layers[$layer_count]};
-
- if (is_promise($level)) {
- # The voltage level corresponds to PROMISE
- print "Running layer $layer{\"Name\"} on PROMISE\n";
- $curr = $PROMISE;
-
- # Quantization
- my ($qtime, $qenergy) = quantize($curr, $prev, 0, %layer);
-
- # Patching
- my ($ptime, $penergy) = patch(%layer);
-
- # Compute
- my ($ttime, $tenergy, $ctime, $cenergy, $mtime, $menergy, $lenergy) = promise($level, %layer);
-
- # Unpatching
- my ($utime, $uenergy) = unpatch(%layer);
-
- # Layer info
- $results{"Time"}{$conf_count}{$layer{"Name"}} = $mtime + $ctime;
- $results{"Quantization Time"}{$conf_count}{$layer{"Name"}} = 0.0;
- $results{"Memory Time"}{$conf_count}{$layer{"Name"}} = $mtime;
- $results{"Compute Time"}{$conf_count}{$layer{"Name"}} = $ctime;
-
- $results{"Energy"}{$conf_count}{$layer{"Name"}} = $qenergy + $penergy + $menergy + $cenergy + $uenergy + $lenergy;
- $results{"Quantization Energy"}{$conf_count}{$layer{"Name"}} = $qenergy;
- $results{"Patch Energy"}{$conf_count}{$layer{"Name"}} = $penergy;
- $results{"Memory Energy"}{$conf_count}{$layer{"Name"}} = $menergy;
- $results{"Compute Energy"}{$conf_count}{$layer{"Name"}} = $cenergy;
- $results{"Unpatch Energy"}{$conf_count}{$layer{"Name"}} = $uenergy;
- $results{"Leakage Energy"}{$conf_count}{$layer{"Name"}} = $lenergy;
-
- # Aggregate info
- $results{"Time"}{$conf_count}{"Total"} += ($mtime + $ctime);
- $results{"Quantization Time"}{$conf_count}{"Total"} += 0.0;
- $results{"Memory Time"}{$conf_count}{"Total"} += $mtime;
- $results{"Compute Time"}{$conf_count}{"Total"} += $ctime;
-
- $results{"Energy"}{$conf_count}{"Total"} += ($qenergy + $penergy + $menergy + $cenergy + $uenergy + $lenergy);
- $results{"Quantization Energy"}{$conf_count}{"Total"} += $qenergy;
- $results{"Patch Energy"}{$conf_count}{"Total"} += $penergy;
- $results{"Memory Energy"}{$conf_count}{"Total"} += $menergy;
- $results{"Compute Energy"}{$conf_count}{"Total"} += $cenergy;
- $results{"Unpatch Energy"}{$conf_count}{"Total"} += $uenergy;
- $results{"Leakage Energy"}{$conf_count}{"Total"} += $lenergy;
- } else {
- # The voltage level corresponds to GPU (FP16 or FP32)
- print "Running layer $layer{\"Name\"} on the GPU\n";
- my @sublevels = split / /, $level;
- my $tensor_count = 0;
-
- my $total_qtime;
- my $total_ctime;
- my $total_qenergy;
- my $total_cenergy;
-
- foreach my $sublevel (@sublevels) {
- if ($sublevel == $fp16_swing) {
- $curr = $fp16;
- } else {
- $curr = $fp32;
- }
-
- # Quantization
- my ($qtime, $qenergy) = quantize($curr, $prev, $tensor_count, %layer);
-
- # Compute
- my ($ctime, $cenergy) = gpu($curr, $layer{"Name"}, $tensor_count);
-
- # Update total
- $total_qtime += $qtime;
- $total_ctime += $ctime;
- $total_qenergy += $qenergy;
- $total_cenergy += $cenergy;
-
- $prev = $curr;
- $tensor_count++;
- }
-
- # Layer info
- $results{"Time"}{$conf_count}{$layer{"Name"}} = $total_qtime + $total_ctime;
- $results{"Quantization Time"}{$conf_count}{$layer{"Name"}} = $total_qtime;
- $results{"Memory Time"}{$conf_count}{$layer{"Name"}} = 0.0;
- $results{"Compute Time"}{$conf_count}{$layer{"Name"}} = $total_ctime;
-
- $results{"Energy"}{$conf_count}{$layer{"Name"}} = $total_qenergy + $total_cenergy;
- $results{"Quantization Energy"}{$conf_count}{$layer{"Name"}} = $total_qenergy;
- $results{"Patch Energy"}{$conf_count}{$layer{"Name"}} = 0.0;
- $results{"Memory Energy"}{$conf_count}{$layer{"Name"}} = 0.0;
- $results{"Compute Energy"}{$conf_count}{$layer{"Name"}} = $total_cenergy;
- $results{"Unpatch Energy"}{$conf_count}{$layer{"Name"}} = 0.0;
- $results{"Leakage Energy"}{$conf_count}{$layer{"Name"}} = 0.0;
-
- # Aggregate info
- $results{"Time"}{$conf_count}{"Total"} += ($total_qtime + $total_ctime);
- $results{"Quantization Time"}{$conf_count}{"Total"} += $total_qtime;
- $results{"Memory Time"}{$conf_count}{"Total"} += 0.0;
- $results{"Compute Time"}{$conf_count}{"Total"} += $total_ctime;
-
- $results{"Energy"}{$conf_count}{"Total"} += ($total_qenergy + $total_cenergy);
- $results{"Quantization Energy"}{$conf_count}{"Total"} += $total_qenergy;
- $results{"Patch Energy"}{$conf_count}{"Total"} += 0.0;
- $results{"Memory Energy"}{$conf_count}{"Total"} += 0.0;
- $results{"Compute Energy"}{$conf_count}{"Total"} += $total_cenergy;
- $results{"Unpatch Energy"}{$conf_count}{"Total"} += 0.0;
- $results{"Leakage Energy"}{$conf_count}{"Total"} += 0.0;
- }
-
- $prev = $curr;
- $layer_count++;
- }
-
- print "\n";
- $conf_count++;
-}
-
-
-#foreach my $config (sort keys %{$results{$attribute}}) {
-#foreach my $layer (sort keys %{$results{$attribute}{$config}}) {
-
-########################################
-print "Printing results\n";
-if ($detailed_results) {
- my @attributes_to_print = ("Time", "Energy");
- #foreach my $attribute (@attributes_to_print) {
- foreach my $attribute (sort keys %results) {
- print $results_file $attribute . "\n";
-
- # Print header
- print $results_file "Configuration,";
- foreach my $layer (@layers) {
- print $results_file ${$layer}{"Name"} . ",";
- }
- print $results_file "Total,Improvement\n";
-
- my $baseline = $results{$attribute}{0}{"Total"};
- my $best_config = undef;
- my $best_result = undef;
- for (my $config = 0; $config < $conf_count; $config++) {
- print $results_file "c$config";
-
- # This is *really* important. It ensures that each configuration's layer
- # data is printed in the right order.
- foreach my $layer (@layers) {
- my $layer_name = ${$layer}{"Name"};
- print $results_file "," . $results{$attribute}{$config}{$layer_name};
- }
-
- my $val = $results{$attribute}{$config}{"Total"};
- print $results_file "," . $val;
- print $results_file "," . ($baseline / ($val + 0.0001));
- print $results_file "\n";
-
- if ((!defined $best_result) or ($val < $best_result)) {
- $best_result = $val;
- $best_config = $config;
- }
- #$best_result = $val if !defined $best_result or $val < $best_result;
- #$best_config = $config if !defined $best_config or $val < $best_result;
- }
-
- print $results_file "\n";
- print $results_file "c$best_config";
- print $results_file "," . $results{$attribute}{$best_config}{"Total"};
- print $results_file "\n\n";
- }
-} else {
- my @attributes_to_print = ("Time", "Energy");
- foreach my $attribute (@attributes_to_print) {
- print $results_file $attribute . "\n";
-
- # Print header
- print $results_file "Configuration,";
- print $results_file "Total,Improvement\n";
-
- my $baseline = $results{$attribute}{0}{"Total"};
- my $best_config = undef;
- my $best_result = undef;
- for (my $config = 0; $config < $conf_count; $config++) {
- print $results_file "c$config";
-
- my $val = $results{$attribute}{$config}{"Total"};
- print $results_file "," . $val;
- print $results_file "," . ($baseline / ($val + 0.0001));
- print $results_file "\n";
-
- if ((!defined $best_result) or ($val < $best_result)) {
- $best_result = $val;
- $best_config = $config;
- }
- }
-
- print $results_file "\n";
- print $results_file "c$best_config";
- print $results_file "," . $results{$attribute}{$best_config}{"Total"};
- print $results_file "\n\n";
- }
-}
-
-# Cleanup
-`rm -f blah profile_data.txt`;
-print "Done!\n";
-########################################
-
-sub add_layer {
- my ($line) = @_;
- my @tokens = split /,/, $line;
-
- # The format of each line is:
- # 0 1 2 3 4 5 6 7 8 9 10
- # Name, N, C, H, W, Co, C, Kh, Kw, Sh, Sw
- # OR
- # 0 1 2 3 4
- # Name, RA, CA, RB, CB
- my %layer;
- my $layer_name = $tokens[0];
- $layer{"Name"} = $layer_name;
- if (is_conv($layer_name)) {
- $layer{"N"} = $tokens[1];
- $layer{"C"} = $tokens[2];
- $layer{"H"} = $tokens[3];
- $layer{"W"} = $tokens[4];
- $layer{"Co"} = $tokens[5];
- $layer{"Kh"} = $tokens[7];
- $layer{"Kw"} = $tokens[8];
- $layer{"Sh"} = $tokens[9];
- $layer{"Sw"} = $tokens[10];
- } elsif (is_fc($layer_name)) {
- $layer{"RA"} = $tokens[1];
- $layer{"CA"} = $tokens[2];
- $layer{"RB"} = $tokens[3];
- $layer{"CB"} = $tokens[4];
- } elsif (not is_nml($layer_name)) {
- die "Illegal layer name\n";
- }
-
- push @layers, \%layer;
-}
-
-sub quantize {
- my ($curr, $prev, $tensor, %layer) = @_;
- my $size;
- my $te;
-
- # No quantization needed if on same device/knob
- if ($curr eq $prev) {
- return (0.0, 0.0);
- }
-
- # No quantization needed with smart DMA
- if ($smart_dma and (($curr eq $PROMISE) or ($prev eq $PROMISE))) {
- return (0.0, 0.0);
- }
-
- my $layer_name = $layer{"Name"};
- if (is_conv($layer_name)) {
- # Input + Kernel
- $size = ($layer{"N"} * $layer{"C"} * $layer{"H"} * $layer{"W"}) + ($layer{"Co"} * $layer{"C"} * $layer{"Kh"} * $layer{"Kw"});
- } elsif (is_fc($layer_name)) {
- # Matrix A + matrix B
- $size = ($layer{"RA"} * $layer{"CA"}) + ($layer{"RB"} * $layer{"CB"});
- } elsif (not is_nml($layer_name)) {
- die "This should never, ever happen\n";
- }
-
- if ($curr eq $PROMISE) {
- # We are offloading to PROMISE
- my $type;
- if ($prev eq $fp32) {
- $type = "f2c";
- } else {
- $type = "h2c";
- }
- $te = `~/awesome_profiler/pp "./quantize $size $type" $iterations blah`;
- chomp $te;
- } elsif ($prev eq $PROMISE) {
- # We are coming back from PROMISE
- my $type;
- if ($curr eq $fp32) {
- $type = "c2f";
- } else {
- $type = "c2h";
- }
- $te = `~/awesome_profiler/pp "./quantize $size $type" $iterations blah`;
- chomp $te;
- } else {
- # We are converting between FP16 and FP32
- # Tensor lines look like this: name, FP32 time, FP32 energy, FP16 time, FP16 energy, f2h time, f2h energy, h2f time, h2f energy
- my @info = @{$tensors{$layer{"Name"}}{$tensor}};
- if ($curr eq $fp32) {
- $te = $info[7] . "," . $info[8]; # h2f
- } else {
- $te = $info[5] . "," . $info[6]; # f2h
- }
- }
-
- my @temp = split /,/, $te;
- print "Quantization: ($temp[0], $temp[1])\n";
- return ($temp[0], $temp[1]);
-}
-
-sub patch {
- # No patching needed with smart DMA
- if ($smart_dma) {
- return (0.0, 0.0);
- }
-
- my (%layer) = @_;
- if (is_conv($layer{"Name"})) {
- my $te_input = `~/awesome_profiler/pp "./patch $layer{"N"} $layer{"C"} $layer{"H"} $layer{"W"} $layer{"Co"} $layer{"Kh"} $layer{"Kw"} patch" $iterations blah`;
- my $te_kernel = `~/awesome_profiler/pp "./patch $layer{"N"} $layer{"C"} $layer{"H"} $layer{"W"} $layer{"Co"} $layer{"Kh"} $layer{"Kw"} kernel" $iterations blah`;
- chomp $te_input;
- chomp $te_kernel;
- my @input = split /,/, $te_input;
- my @kernel = split /,/, $te_kernel;
- print "Patch: ($input[0] + $kernel[0], $input[1] + $kernel[1])\n";
- return ($input[0] + $kernel[0], $input[1] + $kernel[1]);
- } else {
- return (0.0, 0.0);
- }
-}
-
-sub unpatch {
- # No unpatching needed with smart DMA
- if ($smart_dma) {
- return (0.0, 0.0);
- }
-
- my (%layer) = @_;
- if (is_conv($layer{"Name"})) {
- my $te = `~/awesome_profiler/pp "./patch $layer{"N"} $layer{"C"} $layer{"H"} $layer{"W"} $layer{"Co"} $layer{"Kh"} $layer{"Kw"} unpatch" $iterations blah`;
- chomp $te;
- my @temp = split /,/, $te;
- print "Unpatch: ($temp[0], $temp[1])\n";
- return ($temp[0], $temp[1]);
- } else {
- return (0.0, 0.0);
- }
-}
-
-sub promise {
- my ($swing, %layer) = @_;
- my $rows_a;
- my $cols_a;
- my $rows_b;
- my $cols_b;
- my $patch_factor = 1;
- my $layer_name = $layer{"Name"};
-
- if (is_conv($layer_name)) {
- $rows_a = ($layer{"N"} * $layer{"H"} * $layer{"W"}) / ($layer{"Sh"} * $layer{"Sw"});
- $cols_a = $layer{"C"} * $layer{"Kh"} * $layer{"Kw"};
- $rows_b = $cols_a;
- $cols_b = $layer{"Co"};
-
- if ($smart_dma) {
- $patch_factor = $layer{"Kh"} * $layer{"Kw"};
- }
- } elsif (is_fc($layer_name)) {
- $rows_a = $layer{"RA"};
- $cols_a = $layer{"CA"};
- $rows_b = $cols_a;
- $cols_b = $layer{"CB"};
- } else {
- # It's either an NML or something else, either way, this is bad
- die "PROMISE can't run whatever this is!\n";
- }
-
- #print "[$rows_a x $cols_a] x [$rows_b x $cols_b] : $swing\n";
- my $te = `./ptm $rows_a $cols_a $rows_b $cols_b $patch_factor $swing`;
- chomp $te;
- my @temp = split /,/, $te;
- print "PROMISE: ($temp[0], $temp[1])\n";
- return ($temp[0], $temp[1], $temp[2], $temp[3], $temp[4], $temp[5], $temp[6]);
-}
-
-sub gpu {
- my ($curr, $layer_name, $tensor) = @_;
- my @info = @{$tensors{$layer_name}{$tensor}};
- my $time;
- my $energy;
-
- # Tensor lines look like this: name, FP32 time, FP32 energy, FP16 time, FP16 energy, f2h time, f2h energy, h2f time, h2f energy
- if ($curr eq $fp32) {
- $time = $info[1];
- $energy = $info[2];
- } else {
- $time = $info[3];
- $energy = $info[4];
- }
-
- print "GPU: ($time, $energy)\n";
- return ($time, $energy);
-}
-
-sub is_promise {
- my ($level) = @_;
- my @sublevels = split / /, $level;
- if ($sublevels[0] < $fp16_swing) {
- return 1;
- } else {
- return;
- }
-}
-
-sub is_conv {
- my ($name) = @_;
- if ($name =~ /conv/i) {
- return 1;
- } else {
- return;
- }
-}
-
-sub is_fc {
- my ($name) = @_;
- if ($name =~ /fc/i) {
- return 1;
- } else {
- return;
- }
-}
-
-sub is_nml {
- my ($name) = @_;
- if ($name =~ /nml/i) {
- return 1;
- } else {
- return;
- }
-}
-
diff --git a/llvm/projects/soc_simulator/src/driver.py b/llvm/projects/soc_simulator/src/driver.py
deleted file mode 100644
index dbf2651bd3a9512c46d9e0a549c61290ad913ab0..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/src/driver.py
+++ /dev/null
@@ -1,306 +0,0 @@
-from collections import defaultdict
-import os
-import subprocess
-import sys
-
-class Driver:
- fp16_swing = 8
-
- class ApproxTypes:
- FP16 = 0
- FP32 = 1
- PROMISE = 2
-
- results_time_key = "Time"
- results_energy_key = "Energy"
-
- def __get_str(self, appr):
- if appr == Driver.ApproxTypes.FP16:
- return "FP16"
- elif appr == Driver.ApproxTypes.FP32:
- return "FP32"
- elif appr == Driver.ApproxTypes.PROMISE:
- return "PROMISE"
-
- def driver(self):
- self.__parse_tensor_layer_file()
- self.__parse_tensor_table()
- self.__run_simulations()
- self.__display_results()
-
-
- def __init__(self, layer_filename, table_filename, config_filename, results_filename):
- self.__layer_filename = layer_filename
- self.__table_filename = table_filename
- self.__config_filename = config_filename
- self.__results_filename = results_filename
-
- # NOTE: Use an OrderedDict if we want to search by operation name
- # Using a list bc we care about the order the data is read in
- # since it corresponds to the data in the configuration file
- self.__tensor_layers = []
-
- # [layer_name][operation_name][cols]
- # Operation names need to be stored in order of insertion
- self.__tensor_table = defaultdict(lambda: list(defaultdict(str)))
-
- # [Time/Energy][number corresponding to order the layer config was read in] = time/energy
- self.__aggregate_results = defaultdict(lambda: defaultdict(float))
- self.__config_count = 0
-
-
- @staticmethod
- def is_conv(operation_name):
- return operation_name.startswith("Conv")
-
-
- @staticmethod
- def is_nml(operation_name):
- return operation_name.startswith("NML")
-
-
- @staticmethod
- def is_fc(operation_name):
- return operation_name.startswith("FC")
-
-
- def __parse_tensor_layer_file(self):
- if not os.path.isfile(self.__layer_filename):
- print("ERROR: %s was not found." % self.__layer_filename)
- exit(1)
-
- layer_file = open(self.__layer_filename, "r")
- for line in layer_file:
- layer_data = line.strip().split(',')
- layer_name = layer_data[0]
-
- tensor_layer = defaultdict(str)
- tensor_layer["Name"] = layer_name
-
- if Driver.is_conv(layer_name):
- tensor_layer["N"] = float(layer_data[1])
- tensor_layer["Cin"] = float(layer_data[2])
- tensor_layer["H"] = float(layer_data[3])
- tensor_layer["W"] = float(layer_data[4])
- tensor_layer["Cout"] = float(layer_data[5])
- tensor_layer["Kh"] = float(layer_data[7])
- tensor_layer["Kw"] = float(layer_data[8])
- tensor_layer["Sh"] = float(layer_data[9])
- tensor_layer["Sw"] = float(layer_data[10])
-
- elif Driver.is_fc(layer_name):
- tensor_layer["RA"] = float(layer_data[1])
- tensor_layer["CA"] = float(layer_data[2])
- tensor_layer["RB"] = float(layer_data[3])
- tensor_layer["CB"] = float(layer_data[4])
-
- elif not Driver.is_nml(layer_name): # TODO should we store data for NMLs?
- print("ERROR: Invalid layer name %s" % layer_name)
- exit(1)
-
- self.__tensor_layers.append(tensor_layer)
- layer_file.close()
-
-
- def __parse_tensor_table(self):
- if not os.path.isfile(self.__table_filename):
- print("ERROR: %s was not found." % self.__table_filename)
- exit(1)
- table_file = open(self.__table_filename, "r")
- line = table_file.readline().strip()
-
- while line:
- # Line here MUST be a header or there's a bug
- # Get the description of the layer
- assert(line.startswith("**"))
-
- header_contents = line.split(' ')[1:]
- layer_name = header_contents[0]
- num_ops = int(header_contents[1])
- col_names = header_contents[2:]
-
- layer_operations = []
-
- # Go through all operations in the layer
- for op_count in range(num_ops):
- operation_data = defaultdict(str)
-
- line = table_file.readline().strip()
- op_data = line.split(' ')
- op_name = op_data[0]
- operation_data["Name"] = op_name
-
- # Number of data items (#s) needs to match up with the # of cols
- assert(len(op_data) - 1 == len(col_names))
-
- # Go through all data items (each col element) per operation
- for i in range(len(col_names)):
- operation_data[col_names[i]] = float(op_data[i + 1])
-
- layer_operations.append(operation_data)
-
- self.__tensor_table[layer_name] = layer_operations
- line = table_file.readline().strip()
- table_file.close()
-
-
- @staticmethod
- def is_promise(config_layer):
- return float(config_layer.split(' ')[0]) < Driver.fp16_swing
-
-
- def __quantize(self, curr_layer, prev_layer, h2f_f2h_operation_ind, layer_data):
- print(self.__get_str(curr_layer), self.__get_str(prev_layer), h2f_f2h_operation_ind)
- if curr_layer == prev_layer or curr_layer == Driver.ApproxTypes.PROMISE \
- or prev_layer == Driver.ApproxTypes.PROMISE: # No quantization needed
- return 0.0, 0.0
-
- layer_name = layer_data["Name"]
-
- # NOTE: Ignoring logic where curr == promise or prev == promise bc
- # smartDMA is always true so we'd return near the beginning of the method
-
- # Get h2f/f2h data using the first tensor operation in the layer
- # (which is why order matters in the tensor table)
- tensor_op_row = self.__tensor_table[layer_name][h2f_f2h_operation_ind]
- if curr_layer == Driver.ApproxTypes.FP32:
- time = tensor_op_row["h2f_time"]
- energy = tensor_op_row["h2f_energy"]
- elif curr_layer == Driver.ApproxTypes.FP16:
- time = tensor_op_row["f2h_time"]
- energy = tensor_op_row["f2h_energy"]
-
- print("Quantization: (%f, %f)" % (time, energy))
- return (time, energy)
-
-
- def __run_promise_simulation(self, swing, layer_data):
- layer_name = layer_data["Name"]
- patch_factor = 1
-
- if Driver.is_conv(layer_name):
- rows_a = layer_data["N"] * layer_data["H"] * layer_data["W"] \
- / (layer_data["Sh"] * layer_data["Sw"])
- cols_a = layer_data["Cin"] * layer_data["Kh"] * layer_data["Kw"]
- rows_b = cols_a
- cols_b = layer_data["Cout"]
- patch_factor = layer_data["Kh"] * layer_data["Kw"]
- elif Driver.is_fc(layer_name):
- rows_a = layer_data["RA"]
- cols_a = layer_data["CA"]
- rows_b = cols_a
- cols_b = layer_data["CB"]
- else:
- print("PROMISE can't run whatever this layer is.")
- exit(1)
- # Run promise simulator
- # TODO need to print time and energy in the ptm runner so we can pipe it
- output = subprocess.Popen(["./ptm_new", str(rows_a), str(cols_a), str(rows_b), \
- str(cols_b), str(patch_factor), str(swing)], \
- stdout = subprocess.PIPE, stderr = subprocess.PIPE).communicate()[0]
- total_time_energy = output.strip().split(',')
-
- assert(len(total_time_energy) == 2)
- print("PROMISE: (%s, %s)" % (total_time_energy[0], total_time_energy[1]))
- return float(total_time_energy[0]), float(total_time_energy[1])
-
-
- def __run_gpu_simulation(self, curr_layer, layer_name, tensor_ind):
- tensor_info = self.__tensor_table[layer_name][tensor_ind]
- if curr_layer == Driver.ApproxTypes.FP32:
- conversion_time = tensor_info["fp32_time"]
- conversion_energy = tensor_info["fp32_energy"]
- else:
- conversion_time = tensor_info["fp16_time"]
- conversion_energy = tensor_info["fp16_energy"]
- print("GPU: (%f, %f)" % (conversion_time, conversion_energy))
- return (conversion_time, conversion_energy)
-
-
- def __run_simulations(self):
- if not os.path.isfile(self.__config_filename):
- print("ERROR: %s was not found" % self.__config_filename)
- exit(1)
-
- config_file = open(self.__config_filename, "r")
-
- # each line = indepedent configuration
- # layers are separated by commas
- # tensor ops are separated by spaces
- for config in config_file:
- config_layers = config.strip().split(',')
- prev_layer = Driver.ApproxTypes.FP32
- curr_layer = None
-
- for layer_ind, config_layer in enumerate(config_layers): # level
- layer_data = self.__tensor_layers[layer_ind] # layer
- layer_name = layer_data["Name"]
- if Driver.is_promise(config_layer):
- print("Running layer %s on PROMISE" % layer_name)
- curr_layer = Driver.ApproxTypes.PROMISE
- quant_time, quant_energy = self.__quantize(curr_layer, prev_layer, 0, layer_data)
- # Compute
- time, energy = self.__run_promise_simulation(config_layer, layer_data)
- self.__aggregate_results[Driver.results_time_key][self.__config_count] += time
- self.__aggregate_results[Driver.results_energy_key][self.__config_count] += energy
- else:
- print("Running layer %s on the GPU" % layer_name)
- tensor_ops = config_layer.split(' ')
-
- total_time = 0
- total_energy = 0
- for tensor_ind, tensor_op in enumerate(tensor_ops): # sublevle
- tensor_op = int(tensor_op)
- if tensor_op == Driver.fp16_swing:
- curr_layer = Driver.ApproxTypes.FP16
- else:
- curr_layer = Driver.ApproxTypes.FP32
-
- quant_time, quant_energy = self.__quantize(curr_layer, prev_layer, tensor_ind, layer_data)
- conv_time, conv_energy = self.__run_gpu_simulation(curr_layer, layer_name, tensor_ind)
- total_time += quant_time + conv_time
- total_energy += quant_energy + conv_energy
- prev_layer = curr_layer
-
- self.__aggregate_results[Driver.results_time_key][self.__config_count] += total_time
- self.__aggregate_results[Driver.results_energy_key][self.__config_count] += total_energy
- prev_layer = curr_layer
-
- self.__config_count += 1
- print("\n")
- config_file.close()
-
-
- def __display_results(self):
- results_file = open(self.__results_filename, "w")
- attributes_to_print = [Driver.results_time_key, Driver.results_energy_key]
-
- for attribute in attributes_to_print:
- results_file.write("%s\n" % attribute)
- results_file.write("Configuration,Total,Improvement\n")
-
- baseline_val = self.__aggregate_results[attribute][0]
- print(baseline_val)
- best_config = None
- best_result = None
-
- for config_ind in range(self.__config_count):
- results_file.write("c%d" % config_ind)
- time_or_energy_val = self.__aggregate_results[attribute][config_ind]
-
- # Using repr to keep all decimal digits when writing to file
- results_file.write(",%s" % repr(time_or_energy_val))
- results_file.write(",%s\n" % repr(baseline_val / (time_or_energy_val + 0.0001)))
-
- if not best_result or time_or_energy_val < best_result:
- best_result = time_or_energy_val
- best_config = config_ind
- results_file.write("\nc%d,%s\n\n" % (best_config, repr(self.__aggregate_results[attribute][best_config])))
- results_file.close()
-
-
-if __name__ == "__main__":
- if len(sys.argv) != 5:
- print("Usage: python driver.py <layer info> <tensor info> <configurations> <results file>")
- exit(1)
- Driver(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4]).driver()
diff --git a/llvm/projects/soc_simulator/src/driver_new_config_fp16_repl.py b/llvm/projects/soc_simulator/src/driver_new_config_fp16_repl.py
deleted file mode 100644
index d6c3d63112c83cd9b545914a9a33f4c5b5dae6ce..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/src/driver_new_config_fp16_repl.py
+++ /dev/null
@@ -1,515 +0,0 @@
-from collections import defaultdict
-import os
-import subprocess
-import sys
-
-class Driver:
- class PrecisionTypes:
- FP16 = 0
- FP32 = 1
- PROMISE = 2
-
- class ApproxTypes:
- PERF = 3
- SAMP = 4
- REDUCE = 5
-
- results_time_key = "Time"
- results_energy_key = "Energy"
-
-
- def __init__(self, layer_filename, table_filename, config_filename, results_filename):
- self.__layer_filename = layer_filename
- self.__table_filename = table_filename
- self.__config_filename = config_filename
- self.__results_filename = results_filename
-
- # NOTE: Use an OrderedDict if we want to search by operation name
- # Using a list bc we care about the order the data is read in
- # since it corresponds to the data in the configuration file
- self.__tensor_layers = []
-
- # [layer_name][operation_name][cols]
- # Operation names need to be stored in order of insertion
- self.__tensor_table = defaultdict(lambda: list(defaultdict(str)))
-
- self.__conf_results = [] # indexed
- #self.__conf_results = {} # {conf name: (first line, [[layer value if promise], [tensor vals if gpu]])}
-
-
- @staticmethod
- def is_conv(operation_name):
- return operation_name.startswith("Conv")
-
-
- @staticmethod
- def is_nml(operation_name):
- return operation_name.startswith("NML")
-
-
- @staticmethod
- def is_fc(operation_name):
- return operation_name.startswith("FC")
-
-
- # FOR DEBUGGING ONLY
- def __get_str(self, appr):
- if appr == Driver.PrecisionTypes.FP16:
- return "FP16"
- elif appr == Driver.PrecisionTypes.FP32:
- return "FP32"
- elif appr == Driver.PrecisionTypes.PROMISE:
- return "PROMISE"
- elif appr == Driver.ApproxTypes.PERF:
- return "PERF"
- elif appr == Driver.ApproxTypes.SAMP:
- return "SAMP"
- elif appr == Driver.ApproxTypes.REDUCE:
- return "REDUCE"
-
- def driver(self):
- self.__parse_tensor_layer_file()
- self.__parse_tensor_table()
- self.__run_simulations()
- self.__write_output()
-
-
- def __parse_tensor_layer_file(self):
- if not os.path.isfile(self.__layer_filename):
- print("ERROR: %s was not found." % self.__layer_filename)
- exit(1)
- layer_file = open(self.__layer_filename, "r")
- for line in layer_file:
- layer_data = line.strip().split(',')
- layer_name = layer_data[0]
-
- tensor_layer = defaultdict(str)
- tensor_layer["Name"] = layer_name
-
- if Driver.is_conv(layer_name):
- tensor_layer["N"] = float(layer_data[1])
- tensor_layer["Cin"] = float(layer_data[2])
- tensor_layer["H"] = float(layer_data[3])
- tensor_layer["W"] = float(layer_data[4])
- tensor_layer["Cout"] = float(layer_data[5])
- tensor_layer["Kh"] = float(layer_data[7])
- tensor_layer["Kw"] = float(layer_data[8])
- tensor_layer["Sh"] = float(layer_data[9])
- tensor_layer["Sw"] = float(layer_data[10])
-
- elif Driver.is_fc(layer_name):
- tensor_layer["RA"] = float(layer_data[1])
- tensor_layer["CA"] = float(layer_data[2])
- tensor_layer["RB"] = float(layer_data[3])
- tensor_layer["CB"] = float(layer_data[4])
-
- elif not Driver.is_nml(layer_name): # TODO should we store data for NMLs?
- print("ERROR: Invalid layer name %s" % layer_name)
- exit(1)
-
- self.__tensor_layers.append(tensor_layer)
- layer_file.close()
-
-
- def __parse_tensor_table(self):
- if not os.path.isfile(self.__table_filename):
- print("ERROR: %s was not found." % self.__table_filename)
- exit(1)
- table_file = open(self.__table_filename, "r")
- line = table_file.readline().strip()
-
- while line:
- # Line here MUST be a header or there's a bug
- # Get the description of the layer
- assert(line.startswith("**"))
- header_contents = line.split(' ')[1:]
- layer_name = header_contents[0]
- num_ops = int(header_contents[1])
- col_names = header_contents[2:]
-
- layer_operations = []
-
- # Go through all operations in the layer
- for op_count in range(num_ops):
- operation_data = defaultdict(str)
-
- line = table_file.readline().strip()
- op_data = line.split(' ')
- op_name = op_data[0]
- operation_data["Name"] = op_name
-
- # Number of data items (#s) needs to match up with the # of cols
- assert(len(op_data) - 1 == len(col_names))
-
- # Go through all data items (each col element) per operation
- for i in range(len(col_names)):
- operation_data[col_names[i]] = float(op_data[i + 1])
-
- layer_operations.append(operation_data)
-
- self.__tensor_table[layer_name] = layer_operations
- line = table_file.readline().strip()
- table_file.close()
-
-
- @staticmethod
- def is_promise(layer_hardware):
- return layer_hardware == "promise"
-
- @staticmethod
- def is_gpu(layer_hardware):
- return layer_hardware == "gpu"
-
- def __run_simulations(self):
- config_file = open(self.__config_filename, "r")
- line = config_file.readline().strip()
-
- while line:
- assert(line == "+++++")
- print("CONFIGURATION")
-
- curr_conf_results = []
-
- prev_layer = Driver.PrecisionTypes.FP32
- curr_layer = None
-
- line = config_file.readline().strip()
- first_line = line
- conf_name = line.split(' ')[0]
- print("CONF NAME: %s" % conf_name)
- assert(conf_name.startswith("conf"))
- line = config_file.readline().strip()
-
- while line != "-----":
- layer_as_lst = line.split(' ')
- layer_results = []
- # Skip softmax
- if line.find("softmax") != -1:
- layer_results.append((0, 0, ' '.join(layer_as_lst[2:])))
- curr_conf_results.append((layer_as_lst[1], layer_results))
- line = config_file.readline().strip()
- continue
- layer_ind = int(layer_as_lst[0]) - 1
- layer_table_data = self.__tensor_layers[layer_ind]
- layer_name = layer_table_data["Name"]
-
- if Driver.is_promise(layer_as_lst[1]):
- print("Running layer %s on PROMISE" % layer_name)
- curr_layer = Driver.PrecisionTypes.PROMISE
-
- total_time = 0
- total_energy = 0
-
- # To support multiple sets of <param> <number> in the future
- for i in range(2, len(layer_as_lst), 2):
- param_name = layer_as_lst[i] # Use when there's more than 1 type of param
- param_val = int(layer_as_lst[i + 1])
- time, energy = self.__run_promise_simulation(param_val, layer_table_data)
- total_time += time
- total_energy += energy
- print("Curr promise: ", time, energy)
- print("Total promise: ", total_time, total_energy)
- layer_results.append((total_time, total_energy, ' '.join(layer_as_lst[2:])))
-
- elif Driver.is_gpu(layer_as_lst[1]):
- print("Running layer %s on the GPU" % layer_name)
-
- tensor_count = 0
-
- # 3 elements per tensor operation
- for i in range(2, len(layer_as_lst), 3):
- op_type = layer_as_lst[i]
- precision_type = layer_as_lst[i + 1]
- op_number = layer_as_lst[i + 2]
- #print(' '.join(layer_as_lst[i : i + 3]))
-
- approx_type = None
- if line.find("fp16") != -1:
- curr_layer = Driver.PrecisionTypes.FP16
- elif line.find("fp32") != -1:
- curr_layer = Driver.PrecisionTypes.FP32
- if precision_type == "perf" or precision_type == "samp" or precision_type == "reduce": # Handle approx type
- if precision_type == "perf":
- approx_type = Driver.ApproxTypes.PERF
- elif precision_type == "samp":
- approx_type = Driver.ApproxTypes.SAMP
- elif precision_type == "reduce":
- approx_type = Driver.ApproxTypes.REDUCE
- curr_layer = Driver.PrecisionTypes.FP16
- quant_time, quant_energy = self.__quantize(precision_type, op_number, curr_layer, prev_layer, tensor_count, layer_table_data)
- if quant_time != 0:
- assert i == 2 #and layer_ind == 0
- conv_time, conv_energy = self.__run_gpu_simulation(curr_layer, layer_name, \
- tensor_count, approx_type, op_number)
- print(quant_time, conv_time)
- layer_results.append((quant_time + conv_time, quant_energy + conv_energy, ' '.join(layer_as_lst[i : i + 3])))
- prev_layer = curr_layer
- tensor_count += 1
- line = config_file.readline().strip()
- prev_layer = curr_layer
- curr_conf_results.append((layer_as_lst[1], layer_results))
-
- if not self.__conf_results: # we're appending the baseline
- # need to find the fp16 baseline
- self.fp16_baseline = []
-
- prev_layer = Driver.PrecisionTypes.FP32
- curr_layer = None
-
- has_quantized = False
- for layer_ind, (hardware, layer) in enumerate(curr_conf_results):
- if layer[0][2].find("softmax") != -1: continue
- fp16_layer = []
- layer_table_data = self.__tensor_layers[layer_ind]
- layer_name = layer_table_data["Name"]
-
- for tensor_ind, (op_time, op_energy, tensor_op) in enumerate(layer):
- curr_layer = Driver.PrecisionTypes.FP16 # always
-
- quant_time, quant_energy = self.__quantize("fp16", "1", curr_layer, prev_layer, tensor_ind, layer_table_data)
- if quant_time != 0:
- assert not has_quantized
- has_quantized = True
- tensor_info = self.__tensor_table[layer_name][tensor_ind]
- fp16_time = tensor_info["fp16_time"] + quant_time
- fp16_energy = tensor_info["fp16_energy"] + quant_energy
- fp16_layer.append((fp16_time, fp16_energy, tensor_op.replace("fp32", "fp16")))
- prev_layer = curr_layer
-
- prev_layer = curr_layer
- self.fp16_baseline.append((hardware, fp16_layer))
- self.__conf_results.append( (first_line, curr_conf_results) )
- line = config_file.readline().strip()
- config_file.close()
-
-
- def __quantize(self, precision_type, op_number, curr_layer, prev_layer, h2f_f2h_operation_ind, layer_data):
- if curr_layer == prev_layer or curr_layer == Driver.PrecisionTypes.PROMISE \
- or prev_layer == Driver.PrecisionTypes.PROMISE:
- return 0.0, 0.0
- layer_name = layer_data["Name"]
- print("QUANTIZATION")
- print(precision_type, op_number, self.__get_str(curr_layer), self.__get_str(prev_layer), h2f_f2h_operation_ind, layer_data)
- # NOTE: Ignoring logic where curr == promise or prev == promise bc
- # smartDMA is always true so we'd return near the beginning of the method
-
- # Get h2f/f2h data using the first tensor operation in the layer
- # (which is why order matters in the tensor table)
- tensor_op_row = self.__tensor_table[layer_name][h2f_f2h_operation_ind]
- time_key = None
- energy_key = None
-
- if op_number == "1":
- lookup_key = "_" #lookup_key = precision_type
- else:
- lookup_key = "_" + precision_type + str(op_number) + "_"
-
- if curr_layer == Driver.PrecisionTypes.FP32:
- time_key = "h2f%stime" % lookup_key
- energy_key = "h2f%senergy" % lookup_key
- elif curr_layer == Driver.PrecisionTypes.FP16:
- time_key = "f2h%stime" % lookup_key
- energy_key = "f2h%senergy" % lookup_key
- print(tensor_op_row)
- time = tensor_op_row[time_key]
- energy = tensor_op_row[energy_key]
- print(time_key, energy_key)
- return (time, energy)
-
-
- def __run_promise_simulation(self, swing, layer_data):
- layer_name = layer_data["Name"]
- patch_factor = 1
-
- if Driver.is_conv(layer_name):
- rows_a = layer_data["N"] * layer_data["H"] * layer_data["W"] \
- / (layer_data["Sh"] * layer_data["Sw"])
- cols_a = layer_data["Cin"] * layer_data["Kh"] * layer_data["Kw"]
- rows_b = cols_a
- cols_b = layer_data["Cout"]
- patch_factor = layer_data["Kh"] * layer_data["Kw"]
- elif Driver.is_fc(layer_name):
- rows_a = layer_data["RA"]
- cols_a = layer_data["CA"]
- rows_b = layer_data["RB"]
- cols_b = layer_data["CB"]
- else:
- print("PROMISE can't run whatever this layer is.")
- exit(1)
- # Run promise simulator
- # TODO need to print time and energy in the ptm runner so we can pipe it
- output = subprocess.Popen(["./ptm_new", str(rows_a), str(cols_a), str(rows_b), \
- str(cols_b), str(patch_factor), str(swing)], \
- stdout = subprocess.PIPE, stderr = subprocess.PIPE).communicate()[0]
- total_time_energy = output.strip().split(',')
-
- assert(len(total_time_energy) == 2)
- return float(total_time_energy[0]), float(total_time_energy[1])
-
-
- def __run_gpu_simulation(self, curr_layer, layer_name, tensor_ind, \
- approx_type = None, knob_number = None):
- tensor_info = self.__tensor_table[layer_name][tensor_ind]
- time_key = None
- energy_key = None
-
- if approx_type == Driver.ApproxTypes.PERF or approx_type == Driver.ApproxTypes.SAMP or approx_type == Driver.ApproxTypes.REDUCE: # fp16_perf2_energy
- approx_type_str = None
- if approx_type == Driver.ApproxTypes.PERF:
- approx_type_str = "perf"
- elif approx_type == Driver.ApproxTypes.SAMP:
- approx_type_str = "samp"
- elif approx_type == Driver.ApproxTypes.REDUCE:
- approx_type_str = "reduce"
-
- if curr_layer == Driver.PrecisionTypes.FP32:
- time_key = "fp32_%s%s_time" % (approx_type_str, knob_number)
- energy_key = "fp32_%s%s_energy" % (approx_type_str, knob_number)
-
- elif curr_layer == Driver.PrecisionTypes.FP16:
- time_key = "fp16_%s%s_time" % (approx_type_str, knob_number)
- energy_key = "fp16_%s%s_energy" % (approx_type_str, knob_number)
-
- else: # None for now
- if curr_layer == Driver.PrecisionTypes.FP32:
- time_key = "fp32_time"
- energy_key = "fp32_energy"
-
- elif curr_layer == Driver.PrecisionTypes.FP16:
- time_key = "fp16_time"
- energy_key = "fp16_energy"
- #print(time_key, energy_key)
- conversion_time = tensor_info[time_key]
- conversion_energy = tensor_info[energy_key]
- #print("GPU: (%f, %f)\n" % (conversion_time, conversion_energy))
- return conversion_time, conversion_energy
-
-
- def __write_output(self):
- config_file = open(self.__config_filename, "r")
- results_file = open(self.__results_filename, "w")
-
- def write_conf_to_file(conf_name, final_conf, time_speedup, energy_speedup):
- # conf = [layer value if promise], [tensor vals if gpu]]
- conf_str = ["+++++"]
-
- # process the first line
- first_line, layers = final_conf
- first_line_lst = first_line.split(' ')
- assert first_line_lst[0] == conf_name
-
- new_header = [conf_name]
- new_header.append(repr(time_speedup))
- new_header.append(repr(energy_speedup))
- new_header.append(repr(abs(float(first_line_lst[-2]))))
- new_header.append(repr(abs(float(first_line_lst[-1]))))
- conf_str.append(' '.join(new_header))
-
- for ind, (hardware, layer) in enumerate(layers):
- layer_lst = [str(ind + 1)]
- layer_lst.append(hardware)
- for op_time, op_energy, tensor_op in layer:
- layer_lst.append(tensor_op)
- conf_str.append(' '.join(layer_lst))
- conf_str.append("-----\n")
- results_file.write('\n'.join(conf_str))
-
- fp32_baseline_conf = None
- baseline_total_time = baseline_total_energy = 0
-
- def get_baseline_times_energies(conf):
- curr_time = curr_energy = 0
- for hardware, layer in conf[1]:
- for op_time, op_energy, tensor_op in layer:
- curr_time += op_time
- curr_energy += op_energy
- return curr_time, curr_energy
-
- def get_final_times_energies_conf(curr_conf, curr_conf_name):
- final_time = final_energy = 0
-
- final_conf = [] # List (conf) of lists (layers) of tuples (operation data)
-
- #for hardware, layer in self.fp16_baseline:
- #print(hardware, layer)
- for layer_ind, (hardware, layer) in enumerate(curr_conf[1]):
- final_conf_layer = []
-
- for tensor_ind, (op_time, op_energy, tensor_op) in enumerate(layer):
- if tensor_op.find("softmax") != -1:
- final_conf_layer.append((None, None, tensor_op))
- continue
- # layer name, operation name, val name
- if tensor_op.find("promise") != -1: # compute sum of entire fp16 baseline layer
- baseline_time = 0
- baseline_energy = 0
- baseline_op = []
-
- if tensor_op.find("fp32") != -1:
- assert False
- baseline_layer = fp32_baseline_conf[layer_ind][1]
- else:
- baseline_layer = self.fp16_baseline[layer_ind][1]
-
- for op_time, op_energy, tensor_op in baseline_layer:
- baseline_time += op_time
- baseline_energy += op_energy
- baseline_op.append(tensor_op)
- else: # look at the individual tensor operation as before
- if tensor_op.find("fp32") != -1:
- assert False
- baseline_layer = fp32_baseline_conf[1][layer_ind]
- else:
- baseline_layer = self.fp16_baseline[layer_ind][1]
- baseline_time = baseline_layer[tensor_ind][0]
- baseline_energy = baseline_layer[tensor_ind][1]
- baseline_op = baseline_layer[tensor_ind][2]
-
- final_tensor_op = tensor_op
- if op_time > baseline_time:
- print("**************** BIGGER ******************")
- print(curr_conf_name)
- print(baseline_time, baseline_energy, baseline_op, layer_ind)
- print(op_time, tensor_op, layer_ind)
- final_time += baseline_time
- final_energy += baseline_energy
- final_tensor_op = baseline_op
- else:
- print("**************** SMALLER ******************")
- print(curr_conf_name)
- print(baseline_time, baseline_energy, baseline_op, layer_ind)
- print(op_time, tensor_op, layer_ind)
- final_time += op_time
- final_energy += op_energy
-
- final_conf_layer.append((None, None, final_tensor_op)) # Don't care about the times and energies when writing
- final_conf.append((hardware, final_conf_layer))
- #print("\n")
- return final_time, final_energy, (curr_conf[0], final_conf)
-
- conf_index = 0
- print("RESULTS")
- for line in config_file:
- if line.startswith("conf"):
- orig_line_lst = line.split(' ')
- conf_name = orig_line_lst[0]
-
- if not fp32_baseline_conf:
- fp32_baseline_conf = self.__conf_results[conf_index] #conf_name]
- baseline_total_time, baseline_total_energy = get_baseline_times_energies(fp32_baseline_conf)
- results_file.write("%s\n" % repr(baseline_total_time))
- write_conf_to_file(conf_name, fp32_baseline_conf, 1, 1)
- else:
- curr_conf = self.__conf_results[conf_index] #conf_name]
- final_time, final_energy, curr_conf = get_final_times_energies_conf(curr_conf, conf_name)
- print("Baseline time: %f, final time: %f, baseline energy: %f, final energy: %f, rations: %f %f " % (baseline_total_time, final_time, baseline_total_energy, final_energy, baseline_total_time / final_time, baseline_total_energy / final_energy))
- write_conf_to_file(conf_name, curr_conf, baseline_total_time / final_time, baseline_total_energy / final_energy)
- conf_index += 1
- results_file.close()
- config_file.close()
-
-if __name__ == "__main__":
- if len(sys.argv) != 5:
- print("Usage: python driver.py <layer info> <tensor info> <configurations> <results file>")
- exit(1)
- Driver(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4]).driver()
diff --git a/llvm/projects/soc_simulator/src/driver_new_config_no_fp16_repl.py b/llvm/projects/soc_simulator/src/driver_new_config_no_fp16_repl.py
deleted file mode 100644
index d12477fd77533f94ff067e05771459ff4c830bb8..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/src/driver_new_config_no_fp16_repl.py
+++ /dev/null
@@ -1,464 +0,0 @@
-from collections import defaultdict
-import os
-import subprocess
-import sys
-
-class Driver:
- fp16_swing = 8
-
- class PrecisionTypes:
- FP16 = 0
- FP32 = 1
- PROMISE = 2
-
- class ApproxTypes:
- PERF = 3
- SAMP = 4
-
- results_time_key = "Time"
- results_energy_key = "Energy"
-
-
- def __init__(self, layer_filename, table_filename, config_filename, results_filename):
- self.__layer_filename = layer_filename
- self.__table_filename = table_filename
- self.__config_filename = config_filename
- self.__results_filename = results_filename
-
- # NOTE: Use an OrderedDict if we want to search by operation name
- # Using a list bc we care about the order the data is read in
- # since it corresponds to the data in the configuration file
- self.__tensor_layers = []
-
- # [layer_name][operation_name][cols]
- # Operation names need to be stored in order of insertion
- self.__tensor_table = defaultdict(lambda: list(defaultdict(str)))
-
- self.__conf_results = [] # indexed
- #self.__conf_results = {} # {conf name: (first line, [[layer value if promise], [tensor vals if gpu]])}
-
-
- @staticmethod
- def is_conv(operation_name):
- return operation_name.startswith("Conv")
-
-
- @staticmethod
- def is_nml(operation_name):
- return operation_name.startswith("NML")
-
-
- @staticmethod
- def is_fc(operation_name):
- return operation_name.startswith("FC")
-
-
- # FOR DEBUGGING ONLY
- def __get_str(self, appr):
- if appr == Driver.PrecisionTypes.FP16:
- return "FP16"
- elif appr == Driver.PrecisionTypes.FP32:
- return "FP32"
- elif appr == Driver.PrecisionTypes.PROMISE:
- return "PROMISE"
- elif appr == Driver.ApproxTypes.PERF:
- return "PERF"
- elif appr == Driver.ApproxTypes.SAMP:
- return "SAMP"
-
-
- def driver(self):
- self.__parse_tensor_layer_file()
- self.__parse_tensor_table()
- self.__run_simulations()
- self.__write_output()
-
-
- def __parse_tensor_layer_file(self):
- if not os.path.isfile(self.__layer_filename):
- print("ERROR: %s was not found." % self.__layer_filename)
- exit(1)
- layer_file = open(self.__layer_filename, "r")
- for line in layer_file:
- layer_data = line.strip().split(',')
- layer_name = layer_data[0]
-
- tensor_layer = defaultdict(str)
- tensor_layer["Name"] = layer_name
-
- if Driver.is_conv(layer_name):
- tensor_layer["N"] = float(layer_data[1])
- tensor_layer["Cin"] = float(layer_data[2])
- tensor_layer["H"] = float(layer_data[3])
- tensor_layer["W"] = float(layer_data[4])
- tensor_layer["Cout"] = float(layer_data[5])
- tensor_layer["Kh"] = float(layer_data[7])
- tensor_layer["Kw"] = float(layer_data[8])
- tensor_layer["Sh"] = float(layer_data[9])
- tensor_layer["Sw"] = float(layer_data[10])
-
- elif Driver.is_fc(layer_name):
- tensor_layer["RA"] = float(layer_data[1])
- tensor_layer["CA"] = float(layer_data[2])
- tensor_layer["RB"] = float(layer_data[3])
- tensor_layer["CB"] = float(layer_data[4])
-
- elif not Driver.is_nml(layer_name): # TODO should we store data for NMLs?
- print("ERROR: Invalid layer name %s" % layer_name)
- exit(1)
-
- self.__tensor_layers.append(tensor_layer)
- layer_file.close()
-
-
- def __parse_tensor_table(self):
- if not os.path.isfile(self.__table_filename):
- print("ERROR: %s was not found." % self.__table_filename)
- exit(1)
- table_file = open(self.__table_filename, "r")
- line = table_file.readline().strip()
-
- while line:
- # Line here MUST be a header or there's a bug
- # Get the description of the layer
- assert(line.startswith("**"))
- header_contents = line.split(' ')[1:]
- layer_name = header_contents[0]
- num_ops = int(header_contents[1])
- col_names = header_contents[2:]
-
- layer_operations = []
-
- # Go through all operations in the layer
- for op_count in range(num_ops):
- operation_data = defaultdict(str)
-
- line = table_file.readline().strip()
- op_data = line.split(' ')
- op_name = op_data[0]
- operation_data["Name"] = op_name
-
- # Number of data items (#s) needs to match up with the # of cols
- assert(len(op_data) - 1 == len(col_names))
-
- # Go through all data items (each col element) per operation
- for i in range(len(col_names)):
- operation_data[col_names[i]] = float(op_data[i + 1])
-
- layer_operations.append(operation_data)
-
- self.__tensor_table[layer_name] = layer_operations
- line = table_file.readline().strip()
- table_file.close()
-
-
- @staticmethod
- def is_promise(layer_hardware):
- return layer_hardware == "promise"
-
- @staticmethod
- def is_gpu(layer_hardware):
- return layer_hardware == "gpu"
-
- def __run_simulations(self):
- config_file = open(self.__config_filename, "r")
- line = config_file.readline().strip()
-
- while line:
- assert(line == "+++++")
- print("CONFIGURATION")
-
- curr_conf_results = []
-
- prev_layer = Driver.PrecisionTypes.FP32
- curr_layer = None
-
- line = config_file.readline().strip()
- first_line = line
- conf_name = line.split(' ')[0]
- print("CONF NAME: %s" % conf_name)
- assert(conf_name.startswith("conf"))
- line = config_file.readline().strip()
-
- while line != "-----":
- layer_as_lst = line.split(' ')
- layer_results = []
- # Skip softmax
- if line.find("softmax") != -1:
- layer_results.append((0, 0, ' '.join(layer_as_lst[2:])))
- curr_conf_results.append((layer_as_lst[1], layer_results))
- line = config_file.readline().strip()
- continue
-
- layer_ind = int(layer_as_lst[0]) - 1
- layer_table_data = self.__tensor_layers[layer_ind]
- layer_name = layer_table_data["Name"]
-
- if Driver.is_promise(layer_as_lst[1]):
- print("Running layer %s on PROMISE" % layer_name)
- curr_layer = Driver.PrecisionTypes.PROMISE
-
- total_time = 0
- total_energy = 0
-
- # To support multiple sets of <param> <number> in the future
- for i in range(2, len(layer_as_lst), 2):
- param_name = layer_as_lst[i] # Use when there's more than 1 type of param
- param_val = int(layer_as_lst[i + 1])
- time, energy = self.__run_promise_simulation(param_val, layer_table_data)
- total_time += time
- total_energy += energy
- layer_results.append((total_time, total_energy, ' '.join(layer_as_lst[2:])))
-
- elif Driver.is_gpu(layer_as_lst[1]):
- print("Running layer %s on the GPU" % layer_name)
-
- tensor_count = 0
-
- # 3 elements per tensor operation
- for i in range(2, len(layer_as_lst), 3):
- op_type = layer_as_lst[i]
- precision_type = layer_as_lst[i + 1]
- op_number = layer_as_lst[i + 2]
-
- approx_type = None
- if line.find("fp16") != -1:
- curr_layer = Driver.PrecisionTypes.FP16
- elif line.find("fp32") != -1:
- curr_layer = Driver.PrecisionTypes.FP32
-
- if precision_type == "perf" or precision_type == "samp": # Handle approx type
- if precision_type == "perf":
- approx_type = Driver.ApproxTypes.PERF
- elif precision_type == "samp":
- approx_type = Driver.ApproxTypes.SAMP
- if line.find("fp16") != -1:
- curr_layer = Driver.PrecisionTypes.FP16
- elif line.find("fp32") != -1:
- curr_layer = Driver.PrecisionTypes.FP32
- quant_time, quant_energy = self.__quantize(op_type, precision_type, op_number, curr_layer, prev_layer, \
- tensor_count, layer_table_data)
- if quant_time != 0:
- assert i == 2
- conv_time, conv_energy = self.__run_gpu_simulation(curr_layer, layer_name, \
- tensor_count, approx_type, op_number)
- print(quant_time, quant_energy, conv_time, conv_energy)
- layer_results.append((quant_time + conv_time, quant_energy + conv_energy, ' '.join(layer_as_lst[i : i + 3])))
- prev_layer = curr_layer
- tensor_count += 1
-
- line = config_file.readline().strip()
- prev_layer = curr_layer
- curr_conf_results.append((layer_as_lst[1], layer_results))
-
- self.__conf_results.append( (first_line, curr_conf_results) )
- line = config_file.readline().strip()
- config_file.close()
-
-
- def __quantize(self, op_type, precision_type, op_number, curr_layer, prev_layer, h2f_f2h_operation_ind, layer_data):
- if curr_layer == prev_layer or curr_layer == Driver.PrecisionTypes.PROMISE \
- or prev_layer == Driver.PrecisionTypes.PROMISE:
- return 0.0, 0.0
- print("IN QUANTIZE")
- layer_name = layer_data["Name"]
-
- # NOTE: Ignoring logic where curr == promise or prev == promise bc
- # smartDMA is always true so we'd return near the beginning of the method
-
- # Get h2f/f2h data using the first tensor operation in the layer
- # (which is why order matters in the tensor table)
- tensor_op_row = self.__tensor_table[layer_name][h2f_f2h_operation_ind]
- time_key = None
- energy_key = None
-
- print(precision_type, op_number)
- if op_number == "1":
- lookup_key = "_" #lookup_key = precision_type
- else:
- lookup_key = "_" + precision_type + str(op_number) + "_"
-
- print("QUANT LOOKUP KEY", lookup_key)
- if curr_layer == Driver.PrecisionTypes.FP32:
- time_key = "h2f%stime" % lookup_key
- energy_key = "h2f%senergy" % lookup_key
- elif curr_layer == Driver.PrecisionTypes.FP16:
- time_key = "f2h%stime" % lookup_key
- energy_key = "f2h%senergy" % lookup_key
- print(time_key, energy_key)
- time = tensor_op_row[time_key]
- energy = tensor_op_row[energy_key]
- print("Quantization: (%f, %f)" % (time, energy))
- return (time, energy)
-
-
- def __run_promise_simulation(self, swing, layer_data):
- layer_name = layer_data["Name"]
- patch_factor = 1
-
- if Driver.is_conv(layer_name):
- rows_a = layer_data["N"] * layer_data["H"] * layer_data["W"] \
- / (layer_data["Sh"] * layer_data["Sw"])
- cols_a = layer_data["Cin"] * layer_data["Kh"] * layer_data["Kw"]
- rows_b = cols_a
- cols_b = layer_data["Cout"]
- patch_factor = layer_data["Kh"] * layer_data["Kw"]
- elif Driver.is_fc(layer_name):
- rows_a = layer_data["RA"]
- cols_a = layer_data["CA"]
- rows_b = cols_
- cols_b = layer_data["CB"]
- else:
- print("PROMISE can't run whatever this layer is.")
- exit(1)
- # Run promise simulator
- # TODO need to print time and energy in the ptm runner so we can pipe it
- output = subprocess.Popen(["./ptm_new", str(rows_a), str(cols_a), str(rows_b), \
- str(cols_b), str(patch_factor), str(swing)], \
- stdout = subprocess.PIPE, stderr = subprocess.PIPE).communicate()[0]
- total_time_energy = output.strip().split(',')
-
- assert(len(total_time_energy) == 2)
- return float(total_time_energy[0]), float(total_time_energy[1])
-
-
- def __run_gpu_simulation(self, curr_layer, layer_name, tensor_ind, \
- approx_type = None, knob_number = None):
- tensor_info = self.__tensor_table[layer_name][tensor_ind]
- #print(tensor_info)
- #print(layer_name)
- #print(tensor_ind)
- time_key = None
- energy_key = None
-
- if approx_type == Driver.ApproxTypes.PERF or approx_type == Driver.ApproxTypes.SAMP: # fp16_perf2_energy
- approx_type_str = None
- if approx_type == Driver.ApproxTypes.PERF:
- approx_type_str = "perf"
- elif approx_type == Driver.ApproxTypes.SAMP:
- approx_type_str = "samp"
-
- if curr_layer == Driver.PrecisionTypes.FP32:
- time_key = "fp32_%s%s_time" % (approx_type_str, knob_number)
- energy_key = "fp32_%s%s_energy" % (approx_type_str, knob_number)
-
- elif curr_layer == Driver.PrecisionTypes.FP16:
- time_key = "fp16_%s%s_time" % (approx_type_str, knob_number)
- energy_key = "fp16_%s%s_energy" % (approx_type_str, knob_number)
-
- else: # None for now
- if curr_layer == Driver.PrecisionTypes.FP32:
- time_key = "fp32_time"
- energy_key = "fp32_energy"
-
- elif curr_layer == Driver.PrecisionTypes.FP16:
- time_key = "fp16_time"
- energy_key = "fp16_energy"
- print(time_key, energy_key)
- conversion_time = tensor_info[time_key]
- conversion_energy = tensor_info[energy_key]
- #print("GPU: (%f, %f)\n" % (conversion_time, conversion_energy))
- return conversion_time, conversion_energy
-
-
- def __write_output(self):
- config_file = open(self.__config_filename, "r")
- results_file = open(self.__results_filename, "w")
-
- def write_conf_to_file(conf_name, final_conf, time_speedup, energy_speedup):
- # conf = [layer value if promise], [tensor vals if gpu]]
- conf_str = ["+++++"]
-
- # process the first line
- first_line, layers = final_conf
- first_line_lst = first_line.split(' ')
- assert first_line_lst[0] == conf_name
-
- new_header = [conf_name]
- new_header.append(repr(time_speedup))
- new_header.append(repr(energy_speedup))
- new_header.append(repr(abs(float(first_line_lst[-2]))))
- new_header.append(repr(abs(float(first_line_lst[-1]))))
- conf_str.append(' '.join(new_header))
-
- for ind, (hardware, layer) in enumerate(layers):
- print(layer)
- layer_lst = [str(ind + 1)]
- layer_lst.append(hardware)
- print(layer_lst)
- for op_time, op_energy, tensor_op in layer:
- layer_lst.append(tensor_op)
- conf_str.append(' '.join(layer_lst))
- conf_str.append("-----\n")
- results_file.write('\n'.join(conf_str))
-
- baseline_conf = None
- baseline_total_time = baseline_total_energy = 0
-
- def get_baseline_times_energies(conf):
- curr_time = curr_energy = 0
- print("RESULTS: ", conf[1])
- for hardware, layer in conf[1]:
- for op_time, op_energy, tensor_op in layer:
- curr_time += op_time
- curr_energy += op_energy
- return curr_time, curr_energy
-
- def get_final_times_energies_conf(curr_conf):
- final_time = final_energy = 0
-
- final_conf = [] # List (conf) of lists (layers) of tuples (operation data)
-
- for layer_ind, (hardware, layer) in enumerate(curr_conf[1]):
- final_conf_layer = []
-
- for tensor_ind, (op_time, op_energy, tensor_op) in enumerate(layer):
- baseline_time, baseline_energy, baseline_op = baseline_conf[1][layer_ind][tensor_ind]
- final_tensor_op = tensor_op
- if op_time > baseline_time:
- print("**************** BIGGER ******************")
- final_time += baseline_time
- final_energy += baseline_energy
- final_tensor_op = baseline_op
- else:
- final_time += op_time
- final_energy += op_energy
- '''
- # Ignoring bigger energies for now
- if op_energy > baseline_energy:
- final_time += baseline_energy
- final_energy += baseline_energy
- final_tensor_op = baseline_op
- else:
- final_time += op_time
- final_energy += op_energy
- '''
- final_conf_layer.append((None, None, final_tensor_op)) # Don't care about the times and energies when writing
- final_conf.append(final_conf_layer)
- return final_time, final_energy, (curr_conf[0], final_conf)
-
- conf_index = 0
- print("RESULTS")
- for line in config_file:
- if line.startswith("conf"):
- orig_line_lst = line.split(' ')
- conf_name = orig_line_lst[0]
-
- if not baseline_conf:
- baseline_conf = self.__conf_results[conf_index] #conf_name]
- baseline_total_time, baseline_total_energy = get_baseline_times_energies(baseline_conf)
- results_file.write("%s\n" % repr(baseline_total_time))
- write_conf_to_file(conf_name, baseline_conf, 1, 1)
- else:
- curr_conf = self.__conf_results[conf_index] #conf_name]
- final_time, final_energy = get_baseline_times_energies(curr_conf)
- write_conf_to_file(conf_name, curr_conf, baseline_total_time / final_time, baseline_total_energy / final_energy)
- conf_index += 1
- results_file.close()
- config_file.close()
-
-if __name__ == "__main__":
- if len(sys.argv) != 5:
- print("Usage: python driver.py <layer info> <tensor info> <configurations> <results file>")
- exit(1)
- Driver(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4]).driver()
diff --git a/llvm/projects/soc_simulator/src/ext_test b/llvm/projects/soc_simulator/src/ext_test
deleted file mode 100755
index 5c120650ec8efd65d57d15ea93092c80463b6e28..0000000000000000000000000000000000000000
Binary files a/llvm/projects/soc_simulator/src/ext_test and /dev/null differ
diff --git a/llvm/projects/soc_simulator/src/fp16_emu.cpp b/llvm/projects/soc_simulator/src/fp16_emu.cpp
deleted file mode 100644
index 81a541f31f7ce97d992181448c0174fbf032f7cb..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/src/fp16_emu.cpp
+++ /dev/null
@@ -1,147 +0,0 @@
-/*
- * Copyright 1993-2014 NVIDIA Corporation. All rights reserved.
- *
- * NOTICE TO LICENSEE:
- *
- * This source code and/or documentation ("Licensed Deliverables") are
- * subject to NVIDIA intellectual property rights under U.S. and
- * international Copyright laws.
- *
- * These Licensed Deliverables contained herein is PROPRIETARY and
- * CONFIDENTIAL to NVIDIA and is being provided under the terms and
- * conditions of a form of NVIDIA software license agreement by and
- * between NVIDIA and Licensee ("License Agreement") or electronically
- * accepted by Licensee. Notwithstanding any terms or conditions to
- * the contrary in the License Agreement, reproduction or disclosure
- * of the Licensed Deliverables to any third party without the express
- * written consent of NVIDIA is prohibited.
- *
- * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
- * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
- * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
- * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
- * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
- * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
- * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
- * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
- * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
- * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
- * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
- * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
- * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
- * OF THESE LICENSED DELIVERABLES.
- *
- * U.S. Government End Users. These Licensed Deliverables are a
- * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
- * 1995), consisting of "commercial computer software" and "commercial
- * computer software documentation" as such terms are used in 48
- * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
- * only as a commercial end item. Consistent with 48 C.F.R.12.212 and
- * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
- * U.S. Government End Users acquire the Licensed Deliverables with
- * only those rights set forth herein.
- *
- * Any use of the Licensed Deliverables in individual and commercial
- * software must include, in the user documentation and internal
- * comments to the code, the above Disclaimer and U.S. Government End
- * Users Notice.
- */
-
-#include "fp16_emu.h"
-
-#define STATIC_ASSERT(cond) do { typedef char compile_time_assert[(cond) ? 1 : -1]; } while (0)
-
-// Host functions for converting between FP32 and FP16 formats
-// Paulius Micikevicius (pauliusm@nvidia.com)
-
-half1 cpu_float2half_rn(float f)
-{
- unsigned x = *((int*)(void*)(&f));
- unsigned u = (x & 0x7fffffff), remainder, shift, lsb, lsb_s1, lsb_m1;
- unsigned sign, exponent, mantissa;
-
- __half_raw hr;
-
- // Get rid of +NaN/-NaN case first.
- if (u > 0x7f800000) {
- hr.x = 0x7fffU;
- return reinterpret_cast<half1&>(hr);
- }
-
- sign = ((x >> 16) & 0x8000);
-
- // Get rid of +Inf/-Inf, +0/-0.
- if (u > 0x477fefff) {
- hr.x = sign | 0x7c00U;
- return reinterpret_cast<half1&>(hr);
- }
- if (u < 0x33000001) {
- hr.x = sign | 0x0000U;
- return reinterpret_cast<half1&>(hr);
- }
-
- exponent = ((u >> 23) & 0xff);
- mantissa = (u & 0x7fffff);
-
- if (exponent > 0x70) {
- shift = 13;
- exponent -= 0x70;
- } else {
- shift = 0x7e - exponent;
- exponent = 0;
- mantissa |= 0x800000;
- }
- lsb = (1 << shift);
- lsb_s1 = (lsb >> 1);
- lsb_m1 = (lsb - 1);
-
- // Round to nearest even.
- remainder = (mantissa & lsb_m1);
- mantissa >>= shift;
- if (remainder > lsb_s1 || (remainder == lsb_s1 && (mantissa & 0x1))) {
- ++mantissa;
- if (!(mantissa & 0x3ff)) {
- ++exponent;
- mantissa = 0;
- }
- }
-
- hr.x = (sign | (exponent << 10) | mantissa);
-
- return reinterpret_cast<half1&>(hr);
-}
-
-
-float cpu_half2float(half1 h)
-{
- STATIC_ASSERT(sizeof(int) == sizeof(float));
-
- __half_raw hr = reinterpret_cast<__half_raw&>(h);
-
- unsigned sign = ((hr.x >> 15) & 1);
- unsigned exponent = ((hr.x >> 10) & 0x1f);
- unsigned mantissa = ((hr.x & 0x3ff) << 13);
-
- if (exponent == 0x1f) { /* NaN or Inf */
- mantissa = (mantissa ? (sign = 0, 0x7fffff) : 0);
- exponent = 0xff;
- } else if (!exponent) { /* Denorm or Zero */
- if (mantissa) {
- unsigned int msb;
- exponent = 0x71;
- do {
- msb = (mantissa & 0x400000);
- mantissa <<= 1; /* normalize */
- --exponent;
- } while (!msb);
- mantissa &= 0x7fffff; /* 1.mantissa is implicit */
- }
- } else {
- exponent += 0x70;
- }
-
- int temp = ((sign << 31) | (exponent << 23) | mantissa);
-
- return reinterpret_cast<float&>(temp);
-}
-
diff --git a/llvm/projects/soc_simulator/src/fp16_emu.h b/llvm/projects/soc_simulator/src/fp16_emu.h
deleted file mode 100644
index 8aef176b92e9d598e117a61098532e2190cf2554..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/src/fp16_emu.h
+++ /dev/null
@@ -1,157 +0,0 @@
-/*
- * Copyright 1993-2014 NVIDIA Corporation. All rights reserved.
- *
- * NOTICE TO LICENSEE:
- *
- * This source code and/or documentation ("Licensed Deliverables") are
- * subject to NVIDIA intellectual property rights under U.S. and
- * international Copyright laws.
- *
- * These Licensed Deliverables contained herein is PROPRIETARY and
- * CONFIDENTIAL to NVIDIA and is being provided under the terms and
- * conditions of a form of NVIDIA software license agreement by and
- * between NVIDIA and Licensee ("License Agreement") or electronically
- * accepted by Licensee. Notwithstanding any terms or conditions to
- * the contrary in the License Agreement, reproduction or disclosure
- * of the Licensed Deliverables to any third party without the express
- * written consent of NVIDIA is prohibited.
- *
- * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
- * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
- * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
- * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
- * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
- * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
- * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
- * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
- * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
- * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
- * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
- * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
- * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
- * OF THESE LICENSED DELIVERABLES.
- *
- * U.S. Government End Users. These Licensed Deliverables are a
- * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
- * 1995), consisting of "commercial computer software" and "commercial
- * computer software documentation" as such terms are used in 48
- * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
- * only as a commercial end item. Consistent with 48 C.F.R.12.212 and
- * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
- * U.S. Government End Users acquire the Licensed Deliverables with
- * only those rights set forth herein.
- *
- * Any use of the Licensed Deliverables in individual and commercial
- * software must include, in the user documentation and internal
- * comments to the code, the above Disclaimer and U.S. Government End
- * Users Notice.
- */
-
-// Conversion from/to 16-bit floating point (half-precision).
-
-#if !defined(_FP16_EMU_H_)
-#define _FP16_EMU_H_
-
-#include <driver_types.h>
-#include <cuda_fp16.h>
-
-// Necessary to ensure visibility of CUDART_VERSION macro
-#include <cuda_runtime_api.h>
-
-// Definition of '__half_raw' was not provided before CUDA 9.0.
-// '__half_raw' is our type where the unsigned 16-bit integer
-// data member 'x' can be accessed in both CUDA 9.0 and 8.0.
-#if CUDART_VERSION < 9000
-typedef __half __half_raw;
-#endif
-
-// Internally, in CUDNN we use half1 struct as the FP16 type.
-typedef __half half1;
-
-#define HLF_EPSILON 4.887581E-04
-#define HLF_MIN 6.103516E-05
-#define HLF_MAX 6.550400E+04
-
-half1 cpu_float2half_rn(float f);
-
-float cpu_half2float(half1 h);
-
-static __inline__ __device__ __host__ half1 habs(half1 h)
-{
- __half_raw hr = reinterpret_cast<__half_raw&>(h);
- hr.x &= 0x7fffU;
- return reinterpret_cast<half1&>(hr);
-}
-
-static __inline__ __device__ __host__ half1 hneg(half1 h)
-{
- __half_raw hr = reinterpret_cast<__half_raw&>(h);
- hr.x ^= 0x8000U;
- return reinterpret_cast<half1&>(hr);
-}
-
-static __inline__ __device__ __host__ int ishnan(half1 h)
-{
- // When input is NaN, exponent is all ones and mantissa is non-zero.
- __half_raw hr = reinterpret_cast<__half_raw&>(h);
- return (hr.x & 0x7c00U) == 0x7c00U && (hr.x & 0x03ffU) != 0;
-}
-
-static __inline__ __device__ __host__ int ishinf(half1 h)
-{
- // When input is +/- inf, exponent is all ones and mantissa is zero.
- __half_raw hr = reinterpret_cast<__half_raw&>(h);
- return (hr.x & 0x7c00U) == 0x7c00U && (hr.x & 0x03ffU) == 0;
-}
-
-static __inline__ __device__ __host__ int ishequ(half1 x, half1 y)
-{
- __half_raw xr = reinterpret_cast<__half_raw&>(x);
- __half_raw yr = reinterpret_cast<__half_raw&>(y);
- return ishnan(x) == 0 && ishnan(y) == 0 && xr.x == yr.x;
-}
-
-// Returns 0.0000 in FP16 binary form
-static __inline__ __device__ __host__ half1 hzero()
-{
- __half_raw hr;
- hr.x = 0x0000U;
- return reinterpret_cast<half1&>(hr);
-}
-
-// Returns 1.0000 in FP16 binary form
-static __inline__ __device__ __host__ half1 hone()
-{
- __half_raw hr;
- hr.x = 0x3c00U;
- return reinterpret_cast<half1&>(hr);
-}
-
-// Returns quiet NaN, the most significant fraction bit #9 is set
-static __inline__ __device__ __host__ half1 hnan()
-{
- __half_raw hr;
- hr.x = 0x7e00U;
- return reinterpret_cast<half1&>(hr);
-}
-
-// Largest positive FP16 value, corresponds to 6.5504e+04
-static __inline__ __device__ __host__ half1 hmax()
-{
- // Exponent all ones except LSB (0x1e), mantissa is all ones (0x3ff)
- __half_raw hr;
- hr.x = 0x7bffU;
- return reinterpret_cast<half1&>(hr);
-}
-
-// Smallest positive (normalized) FP16 value, corresponds to 6.1035e-05
-static __inline__ __device__ __host__ half1 hmin()
-{
- // Exponent is 0x01 (5 bits), mantissa is all zeros (10 bits)
- __half_raw hr;
- hr.x = 0x0400U;
- return reinterpret_cast<half1&>(hr);
-}
-
-#endif // _FP16_EMU_H_
-
diff --git a/llvm/projects/soc_simulator/src/gemm b/llvm/projects/soc_simulator/src/gemm
deleted file mode 100755
index 8730e084e099f740ad03cb3457862363579f76fe..0000000000000000000000000000000000000000
Binary files a/llvm/projects/soc_simulator/src/gemm and /dev/null differ
diff --git a/llvm/projects/soc_simulator/src/gemm.cu b/llvm/projects/soc_simulator/src/gemm.cu
deleted file mode 100644
index 040a8dbba973d89f2a6ac30b9f38864f1ba9d42e..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/src/gemm.cu
+++ /dev/null
@@ -1,255 +0,0 @@
-#include <iostream>
-#include <fstream>
-#include <string>
-#include <chrono>
-#include <cublas_v2.h>
-#include <cuda_fp16.h>
-#include "fp16_emu.h"
-
-#define NUM_ARGS (5)
-
-inline cudaError_t checkCuda(cudaError_t result) {
- if (result != cudaSuccess)
- std::cerr << "CUDA Runtime Error: " << cudaGetErrorString(result) << "\n";
- return result;
-}
-
-inline cublasStatus_t checkCublas(cublasStatus_t result) {
- if (result != CUBLAS_STATUS_SUCCESS)
- std::cerr << "cuBLAS Error: " << result << "\n";
- return result;
-}
-
-template <typename T>
-inline void printArray(const T * const __restrict__ array,
- const unsigned elements) {
- for (unsigned i = 0; i < elements; i++)
- std::cout << std::to_string(array[i]) << "\n";
-}
-
-// initialization
-template <typename T>
-__global__ void initKernel(T * const __restrict__ array,
- const unsigned elements) {
- const unsigned idx = blockIdx.x * blockDim.x + threadIdx.x;
- if (idx < elements)
- array[idx] = 1.2;
-}
-
-template <typename T>
-void init(T * const __restrict__ array,
- const unsigned elements) {
- const unsigned block_size = 512;
- const unsigned num_blocks = (elements + block_size - 1) / block_size;
- initKernel<<<num_blocks, block_size>>>(array, elements);
- checkCuda(cudaDeviceSynchronize());
-}
-
-// float to half
-__global__ void f2hKernel(const float * const __restrict__ input,
- const unsigned elements,
- half * const __restrict__ output) {
- const unsigned idx = blockIdx.x * blockDim.x + threadIdx.x;
- if (idx < elements)
- output[idx] = __float2half_rn(input[idx]);
-}
-
-void f2h(const float * const __restrict__ input,
- const unsigned elements,
- half * const __restrict__ output) {
- const unsigned block_size = 512;
- const unsigned num_blocks = (elements + block_size - 1) / block_size;
- f2hKernel<<<num_blocks, block_size>>>(input, elements, output);
- checkCuda(cudaDeviceSynchronize());
-}
-
-// half to float
-__global__ void h2fKernel(const half * const __restrict__ input,
- const unsigned elements,
- float * const __restrict__ output) {
- const unsigned idx = blockIdx.x * blockDim.x + threadIdx.x;
- if (idx < elements)
- output[idx] = __half2float(input[idx]);
-}
-
-void h2f(const half * const __restrict__ input,
- const unsigned elements,
- float * const __restrict__ output) {
- const unsigned block_size = 512;
- const unsigned num_blocks = (elements + block_size - 1) / block_size;
- h2fKernel<<<num_blocks, block_size>>>(input, elements, output);
- checkCuda(cudaDeviceSynchronize());
-}
-
-void sgemm(const float * const __restrict__ a,
- const unsigned num_rows_a,
- const unsigned num_cols_a,
- const float * const __restrict__ b,
- const unsigned num_rows_b,
- const unsigned num_cols_b,
- float * const __restrict__ c) {
- std::chrono::time_point<std::chrono::high_resolution_clock> begin;
- std::chrono::time_point<std::chrono::high_resolution_clock> end;
- std::ofstream ofs("profile_data.txt", std::ios::out);
-
- cublasHandle_t handle;
- checkCublas(cublasCreate(&handle));
-
- // Enable Tensor Cores
- checkCublas(cublasSetMathMode(handle, CUBLAS_TENSOR_OP_MATH));
-
- const float alpha_ = 1.0;
- const float beta_ = 0.0;
- const float *alpha = &alpha_;
- const float *beta = &beta_;
-
- begin = std::chrono::high_resolution_clock::now();
- checkCublas(cublasGemmEx(handle,
- CUBLAS_OP_N,
- CUBLAS_OP_N,
- // Dimensions
- num_rows_a,
- num_cols_b,
- num_cols_a,
- alpha,
- // A
- a,
- CUDA_R_32F,
- num_rows_a,
- // B
- b,
- CUDA_R_32F,
- num_rows_b,
- beta,
- // C
- c,
- CUDA_R_32F,
- num_rows_a,
- // Compute precision and algorithm
- CUDA_R_32F,
- CUBLAS_GEMM_DEFAULT_TENSOR_OP));
- checkCuda(cudaDeviceSynchronize());
- end = std::chrono::high_resolution_clock::now();
-
- ofs << "FP32_GEMM" << "\t" << std::to_string(std::chrono::duration<double>(begin.time_since_epoch()).count()) << "\n";
- ofs << "FP32_GEMM" << "\t" << std::to_string(std::chrono::duration<double>(end.time_since_epoch()).count()) << "\n";
- ofs.flush();
- ofs.close();
-}
-
-void hgemm(const float * const __restrict__ af,
- const unsigned num_rows_a,
- const unsigned num_cols_a,
- const float * const __restrict__ bf,
- const unsigned num_rows_b,
- const unsigned num_cols_b,
- float * const __restrict__ cf) {
- std::chrono::time_point<std::chrono::high_resolution_clock> begin;
- std::chrono::time_point<std::chrono::high_resolution_clock> end;
- std::ofstream ofs("profile_data.txt", std::ios::out);
-
- const unsigned num_elements_a = num_rows_a * num_cols_a;
- const unsigned num_elements_b = num_rows_b * num_cols_b;
- const unsigned num_elements_c = num_rows_a * num_cols_b;
-
- half *a;
- half *b;
- half *c;
-
- checkCuda(cudaMallocManaged(&a, sizeof(half) * num_elements_a));
- checkCuda(cudaMallocManaged(&b, sizeof(half) * num_elements_b));
- checkCuda(cudaMallocManaged(&c, sizeof(half) * num_elements_c));
-
- init(a, num_elements_a);
- init(b, num_elements_b);
- init(c, num_elements_c);
-
- // Convert floats to halfs
- f2h(af, num_elements_a, a);
- f2h(bf, num_elements_b, b);
-
- cublasHandle_t handle;
- checkCublas(cublasCreate(&handle));
- checkCublas(cublasSetMathMode(handle, CUBLAS_TENSOR_OP_MATH));
-
- const half alpha_ = cpu_float2half_rn(1.0);
- const half beta_ = cpu_float2half_rn(0.0);
- const half *alpha = &alpha_;
- const half *beta = &beta_;
-
- begin = std::chrono::high_resolution_clock::now();
- checkCublas(cublasGemmEx(handle,
- CUBLAS_OP_N,
- CUBLAS_OP_N,
- // Dimensions
- num_rows_a,
- num_cols_b,
- num_cols_a,
- alpha,
- // A
- a,
- CUDA_R_16F,
- num_rows_a,
- // B
- b,
- CUDA_R_16F,
- num_rows_b,
- beta,
- // C
- c,
- CUDA_R_16F,
- num_rows_a,
- // Compute precision and algorithm
- CUDA_R_16F,
- CUBLAS_GEMM_DEFAULT_TENSOR_OP));
- checkCuda(cudaDeviceSynchronize());
- end = std::chrono::high_resolution_clock::now();
-
- h2f(c, num_elements_c, cf);
-
- ofs << "FP16_GEMM" << "\t" << std::to_string(std::chrono::duration<double>(begin.time_since_epoch()).count()) << "\n";
- ofs << "FP16_GEMM" << "\t" << std::to_string(std::chrono::duration<double>(end.time_since_epoch()).count()) << "\n";
- ofs.flush();
- ofs.close();
-}
-
-int main(int argc, char *argv[]) {
- if (argc != NUM_ARGS) {
- std::cerr << "Usage: " << argv[0] << " <RA> <CA> <CB> <precision>\n";
- exit(1);
- }
-
- // Inputs
- const unsigned num_rows_a = std::atoi(argv[1]);
- const unsigned num_cols_a = std::atoi(argv[2]);
- const unsigned num_rows_b = num_cols_a;
- const unsigned num_cols_b = std::atoi(argv[3]);
- const std::string precision(argv[4]);
-
- const unsigned num_elements_a = num_rows_a * num_cols_a;
- const unsigned num_elements_b = num_rows_b * num_cols_b;
- const unsigned num_elements_c = num_rows_a * num_cols_b;
-
- float *a;
- float *b;
- float *c;
-
- checkCuda(cudaMallocManaged(&a, sizeof(float) * num_elements_a));
- checkCuda(cudaMallocManaged(&b, sizeof(float) * num_elements_b));
- checkCuda(cudaMallocManaged(&c, sizeof(float) * num_elements_c));
-
- init(a, num_elements_a);
- init(b, num_elements_b);
- init(c, num_elements_c);
-
- if (precision == "fp32")
- sgemm(a, num_rows_a, num_cols_a, b, num_rows_b, num_cols_b, c);
- else
- hgemm(a, num_rows_a, num_cols_a, b, num_rows_b, num_cols_b, c);
-
- checkCuda(cudaFree(a));
- checkCuda(cudaFree(b));
- checkCuda(cudaFree(c));
-
- return 0;
-}
diff --git a/llvm/projects/soc_simulator/src/hardware_knobs.pl b/llvm/projects/soc_simulator/src/hardware_knobs.pl
deleted file mode 100755
index fac6b6c748a706cd397040546fa551b72ac14f3c..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/src/hardware_knobs.pl
+++ /dev/null
@@ -1,104 +0,0 @@
-#!/usr/bin/perl
-
-use strict;
-use warnings;
-
-# Do a GEMM: [5000 x K] x [K x 256], where K = {128, 256, ..., 16384}
-my $rows_a = 5000;
-my $cols_b = 1024;
-my @K;
-for (my $i = 8; $i <= 15; $i++) {
- push @K, 2**$i;
-}
-
-# Header
-foreach my $element (@K) {
- print $element . ",";
-}
-print "\n\n";
-
-print "########## PROMISE 1 #########\n";
-
-# Time
-foreach my $element (@K) {
- my ($time, $energy) = promise($rows_a, $element, $element, $cols_b, 1);
- print $time . ",";
-}
-print "\n\n";
-
-# Energy
-foreach my $element (@K) {
- my ($time, $energy) = promise($rows_a, $element, $element, $cols_b, 1);
- print $energy . ",";
-}
-print "\n\n";
-
-print "########## PROMISE 7 #########\n";
-
-# Time
-foreach my $element (@K) {
- my ($time, $energy) = promise($rows_a, $element, $element, $cols_b, 7);
- print $time . ",";
-}
-print "\n\n";
-
-# Energy
-foreach my $element (@K) {
- my ($time, $energy) = promise($rows_a, $element, $element, $cols_b, 7);
- print $energy . ",";
-}
-print "\n\n";
-
-print "########## FP32 #########\n";
-
-foreach my $element (@K) {
- my ($time, $energy) = gpu($rows_a, $element, $element, $cols_b, "fp32");
- print $time . ",";
-}
-print "\n\n";
-
-# Energy
-foreach my $element (@K) {
- my ($time, $energy) = gpu($rows_a, $element, $element, $cols_b, "fp32");
- print $energy . ",";
-}
-print "\n\n";
-
-print "########## FP16 #########\n";
-
-foreach my $element (@K) {
- my ($time, $energy) = gpu($rows_a, $element, $element, $cols_b, "fp16");
- print $time . ",";
-}
-print "\n\n";
-
-# Energy
-foreach my $element (@K) {
- my ($time, $energy) = gpu($rows_a, $element, $element, $cols_b, "fp16");
- print $energy . ",";
-}
-print "\n\n";
-
-# Cleanup
-`rm -f blah profile_data.txt`;
-
-# PROMISE
-sub promise {
- my ($rows_a, $cols_a, $rows_b, $cols_b, $swing) = @_;
- my $patch_factor = 1;
-
- my $te = `./ptm $rows_a $cols_a $rows_b $cols_b $patch_factor $swing`;
- chomp $te;
- my @temp = split /,/, $te;
- return ($temp[0], $temp[1]);
-}
-
-# GPU
-sub gpu {
- my ($rows_a, $cols_a, $rows_b, $cols_b, $precision) = @_;
- my $iterations = 10;
- my $te = `~/awesome_profiler/pp "./gemm $rows_a $cols_a $cols_b $precision" $iterations blah`;
- chomp $te;
- my @temp = split /,/, $te;
- return ($temp[0], $temp[1]);
-}
diff --git a/llvm/projects/soc_simulator/src/patch b/llvm/projects/soc_simulator/src/patch
deleted file mode 100755
index 94d04d5ba8a88bdd4fb1cbb907f18c419ba248bd..0000000000000000000000000000000000000000
Binary files a/llvm/projects/soc_simulator/src/patch and /dev/null differ
diff --git a/llvm/projects/soc_simulator/src/patch.cu b/llvm/projects/soc_simulator/src/patch.cu
deleted file mode 100644
index 6d489978a24f828cc370cdb6e2add64877c514a7..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/src/patch.cu
+++ /dev/null
@@ -1,292 +0,0 @@
-#include <iostream>
-#include <fstream>
-#include <chrono>
-
-// NOTE: This benchmark was originally meant to measure the performance of
-// patch matrix generation on the GPU. However, it is now used for measuring
-// both performance and energy of the actual patch matrix generation that needs
-// to happen before offloading the computation to PROMISE.
-// In order to do so, we assume that initially only that tile of the patch
-// matrix is generated that PROMISE will actually work on, and the generation
-// of the remaining tiles can be pipelined with PROMISE's computation. This
-// has two implications:
-// 1. We only add the time for the first tile's generation. This means that
-// this program has to be profiled with the appropriate smaller batch size to
-// obtain the *time* overhead.
-// 2. Since pipelining doesn't affect energy, the program has to be profiled
-// with the full batch size to obtain the *energy* overhead.
-
-#define NUM_ARGS (9)
-#define BLOCK_SIZE (512)
-
-struct image_dim {
- unsigned n;
- unsigned cin;
- unsigned h;
- unsigned w;
-};
-
-struct kernel_dim {
- unsigned cout;
- unsigned cin;
- unsigned h;
- unsigned w;
-};
-
-inline cudaError_t checkCuda(cudaError_t result) {
- if (result != cudaSuccess)
- std::cerr << "CUDA Runtime Error: " << cudaGetErrorString(result) << "\n";
- return result;
-}
-
-// init kernel to bring all the pages to the GPU
-template <typename T>
-__global__ void initKernel(T * const __restrict__ array,
- const unsigned elements) {
- const unsigned idx = blockIdx.x * blockDim.x + threadIdx.x;
- if (idx < elements)
- array[idx] = 1;
-}
-
-template <typename T>
-void init(T * const __restrict__ array,
- const unsigned elements) {
- const unsigned num_blocks = (elements + BLOCK_SIZE - 1) / BLOCK_SIZE;
- initKernel<<<num_blocks, BLOCK_SIZE>>>(array, elements);
- checkCuda(cudaDeviceSynchronize());
-}
-
-// Patches input matrix
-template <typename T>
-__global__ void patchInputKernel(const T * const __restrict__ input,
- const image_dim idim,
- const kernel_dim kdim,
- T * const __restrict__ patch_input,
- const unsigned patch_rows,
- const unsigned patch_cols,
- const unsigned patch_n) {
- // Coalesced writes ftw
- const unsigned idx = blockIdx.x * blockDim.x + threadIdx.x;
- if (idx < patch_n) {
- // Index of output
- const unsigned patch_col = idx % patch_cols;
- const unsigned patch_row = idx / patch_cols;
-
- // Index of the source point in the input image batch
- const unsigned col_idx = patch_row % idim.w;
- const unsigned row_idx = patch_row / idim.w;
-
- // Index of my point in the (kh * kw * cin) kernel block. kw and kh are
- // flipped because I've assumed the source point lies at the bottom
- // right of the kernel cube and not the top left.
- const int kw_idx = kdim.w - (patch_col % kdim.w) - 1;
- const int kh_idx = kdim.h - ((patch_col / kdim.w) % kdim.h) - 1;
- const int kc_idx = patch_col / (kdim.w * kdim.h);
-
- // Index of the "kernel point" in the input image batch
- const int w_idx = col_idx - kw_idx;
- const int h_idx = row_idx - kh_idx;
- const int c_idx = kc_idx;
-
- // Are we inside the input cube?
- const bool inside = (w_idx >= 0 && h_idx >= 0);
- //printf("thread %d: dst (%u, %u); src point (%u, %u); kernel (%d, %d, %d); input (%d, %d, %d); inside %d\n", idx, patch_row, patch_col, row_idx, col_idx, kc_idx, kh_idx, kw_idx, c_idx, h_idx, w_idx, inside);
-
- if (inside)
- patch_input[idx] = input[(c_idx * idim.w * idim.h) + (h_idx * idim.w) + w_idx];
- else
- patch_input[idx] = 0;
- }
-}
-
-template <typename T>
-void patchInput(const T * const __restrict__ input,
- const image_dim idim,
- const kernel_dim kdim,
- T * const __restrict__ patch_input) {
- const auto patch_rows = (idim.n * idim.h * idim.w) / sizeof(unsigned);
- const auto patch_cols = kdim.cin * kdim.h * kdim.w;
- const auto patch_n = patch_rows * patch_cols;
-
- const unsigned num_blocks = (patch_n + BLOCK_SIZE - 1) / BLOCK_SIZE;
- patchInputKernel<<<num_blocks, BLOCK_SIZE>>>(input, idim, kdim, patch_input, patch_rows, patch_cols, patch_n);
- checkCuda(cudaDeviceSynchronize());
-}
-
-// Unpatches output matrix
-template <typename T>
-__global__ void unpatchOutputKernel(const T * const __restrict__ patch_output,
- const unsigned patch_rows,
- const unsigned patch_cols,
- const unsigned patch_n,
- T * const __restrict__ output,
- const image_dim odim) {
- const unsigned idx = blockIdx.x * blockDim.x + threadIdx.x;
- if (idx < patch_n) {
- // Read index in the patch matrix
- const unsigned image_size = odim.w * odim.h * odim.cin;
- const unsigned image_id = idx / image_size;
- const unsigned patch_row = (image_id * odim.w * odim.h) + (idx % (odim.w * odim.h));
- const unsigned patch_col = (idx / (odim.w * odim.h)) % odim.cin;
- //printf("thread %d: src (%u, %u)\n", idx, patch_row, patch_col);
-
- // Coalesced writes ftw
- output[idx] = patch_output[(patch_row * patch_cols) + patch_col];
- }
-}
-
-template <typename T>
-void unpatchOutput(const T * const __restrict__ patch_output,
- T * const __restrict__ output,
- const image_dim odim) {
- const auto patch_rows = (odim.n * odim.h * odim.w) / sizeof(unsigned);
- const auto patch_cols = odim.cin;
- const auto patch_n = patch_rows * patch_cols;
-
- const unsigned num_blocks = (patch_n + BLOCK_SIZE - 1) / BLOCK_SIZE;
- unpatchOutputKernel<<<num_blocks, BLOCK_SIZE>>>(patch_output, patch_rows, patch_cols, patch_n, output, odim);
- checkCuda(cudaDeviceSynchronize());
-}
-
-// Patches kernel matrix
-template <typename T>
-__global__ void patchKernelKernel(const T * const __restrict__ kernel,
- T * const __restrict__ patch_kernel,
- const unsigned patch_rows,
- const unsigned patch_cols,
- const unsigned patch_n) {
- // There are two ways to implement this: coalesced reads or coalesced
- // writes. Empirically, coalesced writes is about 3x faster and that's
- // what's used here. This is most likely because the coalesced reads
- // version results in different thread blocks writing to the same line
- // while presumably being on different cores. This results in ping-ponging
- // of the line, which hurts performance. The strided reads in the coalesced
- // writes version are just reads - they can be shared as many times as
- // needed without penalty.
- const unsigned idx = blockIdx.x * blockDim.x + threadIdx.x;
- if (idx < patch_n) {
- const unsigned col_idx = idx % patch_cols;
- const unsigned row_idx = idx / patch_cols;
- patch_kernel[idx] = kernel[(col_idx * patch_rows) + row_idx];
- }
-}
-
-template <typename T>
-void patchKernel(const T * const __restrict__ kernel,
- const kernel_dim kdim,
- T * const __restrict__ patch_kernel) {
- const auto patch_rows = kdim.cin * kdim.h * kdim.w;
- const auto patch_cols = kdim.cout;
- const auto patch_n = patch_rows * patch_cols;
-
- const unsigned num_blocks = (patch_n + BLOCK_SIZE - 1) / BLOCK_SIZE;
- patchKernelKernel<<<num_blocks, BLOCK_SIZE>>>(kernel, patch_kernel, patch_rows, patch_cols, patch_n);
- checkCuda(cudaDeviceSynchronize());
-}
-
-int main(int argc, char *argv[]) {
- if (argc != NUM_ARGS) {
- std::cerr << "Usage: " << argv[0] << " <N> <C> <H> <W> <Cout> <Kh> <Kw> <patch/unpatch>\n";
- exit(1);
- }
-
- // Inputs
- const unsigned n = std::atoi(argv[1]);
- const unsigned c = std::atoi(argv[2]);
- const unsigned h = std::atoi(argv[3]);
- const unsigned w = std::atoi(argv[4]);
-
- const unsigned cout = std::atoi(argv[5]);
- const unsigned kh = std::atoi(argv[6]);
- const unsigned kw = std::atoi(argv[7]);
-
- const std::string type(argv[8]);
-
- const image_dim idim = {n, c, h, w};
- const kernel_dim kdim = {cout, c, kh, kw};
- const image_dim odim = {n, cout, h, w};
-
- std::chrono::time_point<std::chrono::high_resolution_clock> begin;
- std::chrono::time_point<std::chrono::high_resolution_clock> end;
- std::ofstream ofs("profile_data.txt", std::ios::out);
-
- if (type == "patch") {
- const auto patch_rows = (idim.n * idim.h * idim.w) / sizeof(unsigned);
- const auto patch_cols = kdim.cin * kdim.h * kdim.w;
- const auto patch_n = patch_rows * patch_cols;
-
- unsigned *input;
- unsigned *output;
-
- checkCuda(cudaMallocManaged(&input, sizeof(unsigned) * patch_n));
- checkCuda(cudaMallocManaged(&output, sizeof(unsigned) * patch_n));
-
- init(input, patch_n);
- init(output, patch_n);
-
- begin = std::chrono::high_resolution_clock::now();
- patchInput(input, idim, kdim, output);
- end = std::chrono::high_resolution_clock::now();
-
- checkCuda(cudaFree(input));
- checkCuda(cudaFree(output));
-
- ofs << "Patch_Input" << "\t" << std::to_string(std::chrono::duration<double>(begin.time_since_epoch()).count()) << "\n";
- ofs << "Patch_Input" << "\t" << std::to_string(std::chrono::duration<double>(end.time_since_epoch()).count()) << "\n";
- ofs.flush();
- } else if (type == "unpatch") {
- const auto patch_rows = (odim.n * odim.h * odim.w) / sizeof(unsigned);
- const auto patch_cols = odim.cin;
- const auto patch_n = patch_rows * patch_cols;
-
- unsigned *input;
- unsigned *output;
-
- checkCuda(cudaMallocManaged(&input, sizeof(unsigned) * patch_n));
- checkCuda(cudaMallocManaged(&output, sizeof(unsigned) * patch_n));
-
- init(input, patch_n);
- init(output, patch_n);
-
- begin = std::chrono::high_resolution_clock::now();
- unpatchOutput(input, output, odim);
- end = std::chrono::high_resolution_clock::now();
-
- checkCuda(cudaFree(input));
- checkCuda(cudaFree(output));
-
- ofs << "Unpatch_Output" << "\t" << std::to_string(std::chrono::duration<double>(begin.time_since_epoch()).count()) << "\n";
- ofs << "Unpatch_Output" << "\t" << std::to_string(std::chrono::duration<double>(end.time_since_epoch()).count()) << "\n";
- ofs.flush();
- } else if (type == "kernel") {
- const auto patch_rows = kdim.cin * kdim.h * kdim.w;
- const auto patch_cols = kdim.cout;
- const auto patch_n = patch_rows * patch_cols;
-
- char *input;
- char *output;
-
- checkCuda(cudaMallocManaged(&input, sizeof(char) * patch_n));
- checkCuda(cudaMallocManaged(&output, sizeof(char) * patch_n));
-
- init(input, patch_n);
- init(output, patch_n);
-
- begin = std::chrono::high_resolution_clock::now();
- patchKernel(input, kdim, output);
- end = std::chrono::high_resolution_clock::now();
-
- checkCuda(cudaFree(input));
- checkCuda(cudaFree(output));
-
- ofs << "Patch_Kernel" << "\t" << std::to_string(std::chrono::duration<double>(begin.time_since_epoch()).count()) << "\n";
- ofs << "Patch_Kernel" << "\t" << std::to_string(std::chrono::duration<double>(end.time_since_epoch()).count()) << "\n";
- ofs.flush();
- } else {
- std::cerr << "Patch type not supported!\n";
- exit(1);
- }
-
- ofs.close();
- return 0;
-}
diff --git a/llvm/projects/soc_simulator/src/promise_timing_model.cpp b/llvm/projects/soc_simulator/src/promise_timing_model.cpp
deleted file mode 100644
index 87150f45a3a967443a947cf1b95b0e8d6fbae842..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/src/promise_timing_model.cpp
+++ /dev/null
@@ -1,374 +0,0 @@
-#include "promise_timing_model.h"
-
-// NOTE 1: This code uses seconds for all computations. Using clock cycles
-// would have been cleaner but it's not possible because we don't know anything
-// about Jetson's DRAM other than its bandwidth.
-
-// NOTE 2: All elements are assumed to be 1 byte long.
-
-// NOTE 3: PROMISE's frequency is fixed at 1 GHz and thus 1 cycle = 1 ns.
-
-Dram::Dram(const double latency, const double bandwidth)
- : latency_(latency), bandwidth_(bandwidth) {}
-
-// Calculates (time, energy) of accessing 'num_bytes' in memory
-std::pair<double, double> Dram::access(const unsigned num_bytes, const bool pipeline) const{
- const auto time = (pipeline ? 0.0 : latency_) + (static_cast<double>(num_bytes)/bandwidth_);
- const auto energy = energy_per_bit * static_cast<double>(num_bytes * 8);
- return std::make_pair(time, energy);
-}
-
-// Calculates the index within the scratchpad array
-unsigned Scratchpad::getIndex(const unsigned address) const {
- return ((address >> log_line_size) & (num_lines_ - 1));
-}
-
-Scratchpad::Scratchpad(const bool enable,
- const unsigned size,
- const double dram_latency,
- const double dram_bandwidth)
- : enable_(enable), dram_(dram_latency, dram_bandwidth) {
-
- num_lines_ = size / line_size;
- lines_.resize(num_lines_);
- clear();
-
-#ifdef DEBUG
- if (enable_) {
- std::cout << "Initialized " << (size / 1024) << " KB scratchpad "
- << "with geometry [" << num_lines_ << " x "
- << line_size << "]\n";
- }
-#endif
-}
-
-// Clears the scratchpad
-void Scratchpad::clear() {
- for (auto &x : lines_)
- x = -1;
-}
-
-// Calculates (time, energy) of accessing 'num_bytes' starting from 'address'
-std::pair<double, double> Scratchpad::access(const unsigned address,
- const unsigned num_bytes) {
- if (!enable_) {
- const auto load = dram_.access(num_bytes);
-#ifdef DEBUG
- std::cout << "Accessing " << num_bytes << " bytes from DRAM\n";
- std::cout << "Took " << std::to_string(load.first * 1e6) << " us and "
- << std::to_string(load.second * 1e6) << " uJ\n";
-#endif
- return load;
- }
-
- auto addr = address;
- int num_bytes_remaining = static_cast<int>(num_bytes);
-
- double time = 0.0;
- double energy = 0.0;
-
- double hits = 0.0;
- double accesses = 0.0;
-
-#ifdef DEBUG
- std::cout << "Accessing " << num_bytes << " bytes from the scratchpad, "
- << "starting at address " << addr << " (index "
- << getIndex(addr) << ")\n";
-#endif
-
- // Keep reading line by line until everything is read
- while (num_bytes_remaining > 0) {
- if (lines_[getIndex(addr)] == address) {
- // Hit
- hits++;
- } else {
- // We missed. Load the line from memory. If this is not the
- // first miss, the accesses can be pipelined (overlapped).
- const bool first_miss = (hits == accesses);
- const auto miss = dram_.access(line_size, !first_miss);
- time += miss.first;
- energy += miss.second;
-
- lines_[getIndex(addr)] = address;
- }
-
- // This is required in case we began in the middle of a line
- const auto bytes_accessed = line_size - (addr & (line_size - 1));
- addr += bytes_accessed;
- num_bytes_remaining -= bytes_accessed;
-
- time += line_latency;
- energy += line_energy;
- accesses++;
- }
-
-#ifdef DEBUG
- std::cout << "Took " << std::to_string(time * 1e6) << " us and "
- << std::to_string(energy * 1e6) << " uJ\n";
- std::cout << "Hit rate is " << ((hits * 100.0) / accesses) << "%\n";
-#endif
- return std::make_pair(time, energy);
-}
-
-// uint version of min
-unsigned Promise::min(const unsigned x, const unsigned y) const {
- return static_cast<unsigned>(std::min(x, y));
-}
-
-// Calculates energy of loading data into the SRAM
-double Promise::loadSRAM(const unsigned num_bytes) const {
- return (sram_energy_per_byte * static_cast<double>(num_bytes));
-}
-
-// Calculates (time, energy) of computing 'num_elements' elements
-std::pair<double, double> Promise::compute(const unsigned num_elements,
- const unsigned voltage_swing) const {
- const auto time = (pipeline_latency_ * static_cast<double>(num_elements)) + reduction_latency_;
- const auto energy = compute_energy_per_dot[voltage_swing] * static_cast<double>(num_elements);
- return std::make_pair(time, energy);
-}
-
-// Calculates the number of banks required to fill up an entire column; i.e. all the rows
-unsigned Promise::banksPerColumnTile(const unsigned num_rows) const {
- return static_cast<unsigned>(std::ceil(static_cast<double>(num_rows) / static_cast<double>(bank_x_)));
-}
-
-// Calculates the number of fully filled column tiles
-unsigned Promise::activeColumnTiles(const unsigned num_rows, const unsigned remaining_columns) const {
- const auto banks_per_column_tile = banksPerColumnTile(num_rows);
- const auto remaining_column_tiles = static_cast<unsigned>(std::ceil(static_cast<double>(remaining_columns) / static_cast<double>(bank_y_)));
- auto active_column_tiles = num_banks_ / banks_per_column_tile;
- active_column_tiles = min(active_column_tiles, remaining_column_tiles);
- return active_column_tiles;
-}
-
-// Calculates the number of rows of A that can be operated on in parallel
-// based on the tiling of *B*
-unsigned Promise::numRowsA(const unsigned num_rows, const unsigned num_cols) const {
- const auto banks_per_column_tile = banksPerColumnTile(num_rows);
- const auto total_column_tiles = static_cast<unsigned>(std::ceil(static_cast<double>(num_cols) / static_cast<double>(bank_y_)));
- const auto total_required_banks = banks_per_column_tile * total_column_tiles;
- const auto num_rows_a = num_banks_ < total_required_banks ? 1 : num_banks_ / total_required_banks;
- return num_rows_a;
-}
-
-// Calculates (time, energy) of A x B (GEMM)
-std::pair<double, double> Promise::run(const unsigned num_rows_a,
- const unsigned num_cols_a,
- const unsigned num_rows_b,
- const unsigned num_cols_b,
- const unsigned voltage_swing,
- const unsigned patch_factor) {
-#ifdef DEBUG
- std::cout << "Performing [" << num_rows_a << " x " << num_cols_a
- << "] x [" << num_rows_b << " x " << num_cols_b << "] GEMM\n";
-#endif
- scratch_.clear();
-
- double compute_time = 0.0;
- double compute_energy = 0.0;
-
- double leakage_energy = 0.0;
-
- double a_time = 0.0;
- double a_energy = 0.0;
-
- double b_time = 0.0;
- double b_energy = 0.0;
-
- double c_time = 0.0;
- double c_energy = 0.0;
-
- double average_bank_utilization;
- double iterations;
-
- // Load a tile of B, compute the corresponding part of C, repeat
- auto remaining_columns_b = num_cols_b;
- for (unsigned i = 0; i < num_cols_b;) {
- // Figure out how B is tiled. In a nutshell, we use as many banks
- // as will fill up entire columns of B (because we need an entire
- // column for the reduction to work). The corner cases are where
- // either #rows or #columns is not divisible by the bank size,
- // and/or the banks only fill up part of the column. Once the
- // tiling and #active banks is figured out, we can calculate the
- // tile size.
- // Furthermore, if B is sufficiently small, we may be able to
- // operate on multiple rows of A at the same time.
- const auto banks_per_column_tile = banksPerColumnTile(num_rows_b);
- const auto active_column_tiles = activeColumnTiles(num_rows_b, remaining_columns_b);
- const auto tile_x = min(num_rows_b, banks_per_column_tile * bank_x_);
- const auto tile_y = min(remaining_columns_b, active_column_tiles * bank_y_);
- const auto max_parallel_rows_a = min(num_rows_a, numRowsA(num_rows_b, num_cols_b));
- const auto max_active_banks = banks_per_column_tile * active_column_tiles * max_parallel_rows_a;
-
- // Load the required tiles of B into the active banks
- const auto num_bytes = (tile_x * tile_y) / patch_factor;
- const auto load_b = dram_.access(num_bytes);
- b_time += load_b.first;
- b_energy += load_b.second;
- b_energy += loadSRAM(num_bytes);
- leakage_energy += (load_b.first * leakage_energy_per_s * max_active_banks);
-
-#ifdef DEBUG
- std::cout << "\nLoading " << tile_x << " x " << tile_y << " tile of B from DRAM\n";
- std::cout << "There are " << active_column_tiles << " active column tiles of B "
- << "with " << banks_per_column_tile << " PROMISE banks per tile\n";
-#endif
-
- // Load row(s) of A, compute C, write the result back
- auto remaining_rows_a = num_rows_a;
- for (unsigned j = 0; j < num_rows_a; j += max_parallel_rows_a) {
- const auto active_rows_a = min(remaining_rows_a, max_parallel_rows_a);
- const auto active_banks = banks_per_column_tile * active_column_tiles * active_rows_a;
- const auto bank_utilization = (static_cast<double>(active_banks) * 100.0) / static_cast<double>(num_banks_);
- average_bank_utilization += bank_utilization;
- iterations++;
-
-#ifdef DEBUG
- std::cout << "There are a total of " << active_banks << " active banks "
- << "operating on " << active_rows_a << " rows of A in parallel\n";
- std::cout << "Bank utilization is " << bank_utilization << "%\n";
-#endif
-
- // Load the rows from the scratchpad
- for (unsigned k = 0; k < active_rows_a; k++) {
- const auto load_a = scratch_.access((j + k) * num_cols_a, num_cols_a);
- a_time += load_a.first;
- a_energy += load_a.second;
- leakage_energy += (load_a.first * leakage_energy_per_s * active_banks);
- }
-
- // All the banks operate in parallel, so use the biggest
- // computation and count the time only once. Computation
- // energy is energy per bank x active banks.
- const auto comp_c = compute(tile_y > bank_y_ ? bank_y_ : tile_y, voltage_swing);
- compute_time += comp_c.first;
- compute_energy += (comp_c.second * active_banks);
-
- // This is sequential, so use tile width and the number of active rows
- const auto store_c = dram_.access(tile_y * active_rows_a);
- c_time += store_c.first;
- c_energy += store_c.second;
-
- // Leakage is for the entire duration and across all active banks
- leakage_energy += ((comp_c.first + store_c.first) * leakage_energy_per_s * active_banks);
-
- remaining_rows_a -= active_rows_a;
- }
-
- auto processed_columns_b = active_column_tiles * bank_y_;
- i += processed_columns_b;
- remaining_columns_b -= processed_columns_b;
- }
-
- const auto memory_time = a_time + b_time + c_time;
- const auto memory_energy = a_energy + b_energy + c_energy;
- const auto total_time = compute_time + memory_time;
- const auto total_energy = compute_energy + memory_energy + leakage_energy;
-
-#ifdef DEBUG
- std::cout << "------------------------------\n";
- std::cout << "Compute time: " << std::to_string(compute_time * 1e3) << " ms\n";
- std::cout << "Compute energy: " << std::to_string(compute_energy * 1e3) << " mJ\n";
- std::cout << "Compute power: " << std::to_string((compute_energy/compute_time) * 1e3) << " mW\n";
- std::cout << "------------------------------\n";
-
- std::cout << "Memory time: " << std::to_string(memory_time * 1e3) << " ms\n";
- std::cout << " A: " << std::to_string(a_time * 1e3) << " ms\n";
- std::cout << " B: " << std::to_string(b_time * 1e3) << " ms\n";
- std::cout << " C: " << std::to_string(c_time * 1e3) << " ms\n";
- std::cout << "Memory energy: " << std::to_string(memory_energy * 1e3) << " mJ\n";
- std::cout << " A: " << std::to_string(a_energy * 1e3) << " mJ\n";
- std::cout << " B: " << std::to_string(b_energy * 1e3) << " mJ\n";
- std::cout << " C: " << std::to_string(c_energy * 1e3) << " mJ\n";
- std::cout << "Memory power: " << std::to_string((memory_energy/memory_time) * 1e3) << " mW\n";
- std::cout << "------------------------------\n";
-
- std::cout << "Leakage energy: " << std::to_string(leakage_energy * 1e3) << " mJ\n";
- std::cout << "Leakage power: " << std::to_string((leakage_energy/total_time) * 1e3) << " mW\n";
- std::cout << "------------------------------\n";
-
- std::cout << "Total time: " << std::to_string(total_time * 1e3) << " ms\n";
- std::cout << "Total energy: " << std::to_string(total_energy * 1e3) << " mJ\n";
- std::cout << "Average power: " << std::to_string((total_energy/total_time) * 1e3) << " mW\n";
- std::cout << "------------------------------\n";
-
- std::cout << "Average bank utilization was " << (average_bank_utilization / iterations) << "%\n";
- std::cout << "------------------------------\n";
-#endif
-
- //std::vector<double> result = {total_time, total_energy, compute_time, compute_energy, memory_time, memory_energy, leakage_energy};
- //return result;
- return std::make_pair(total_time, total_energy);
-}
-
-Promise::Promise() :
- scratch_(use_scratchpad_, scratchpad_size_, dram_latency_, dram_bandwidth_),
- dram_(dram_latency_, dram_bandwidth_) {
-#ifdef DEBUG
- std::cout << "Initialized PROMISE with " << num_banks_ << " ["
- << bank_x << " x " << bank_y << "] banks\n";
-#endif
-}
-
-// TODO better naming?
-std::pair<double, double> Promise::fc_profile(const unsigned num_rows_a,
- const unsigned num_cols_a,
- const unsigned num_rows_b,
- const unsigned num_cols_b,
- const unsigned voltage_swing,
- const unsigned patch_factor) {
- return num_rows_a <= num_cols_b ?
- run(num_rows_a, num_cols_a, num_rows_b, num_cols_b, voltage_swing, patch_factor) :
- run(num_cols_b, num_rows_b, num_cols_a, num_rows_a, voltage_swing, patch_factor);
-}
-
-std::pair<double, double> Promise::conv_profile(const unsigned n,
- const unsigned c,
- const unsigned h,
- const unsigned w,
- const unsigned c_out,
- const unsigned c_in,
- const unsigned k_h,
- const unsigned k_w,
- const unsigned s_h,
- const unsigned s_w,
- const unsigned voltage_swing,
- const unsigned patch_factor) {
- unsigned num_rows_a = n * h * w / (s_h * s_w);
- unsigned num_cols_a = c_in * k_h * k_w;
- unsigned num_rows_b = num_rows_a;
- unsigned num_cols_b = c_out;
-
- return num_rows_a <= num_cols_b ?
- run(num_rows_a, num_cols_a, num_rows_b, num_cols_b, voltage_swing, patch_factor) :
- run(num_cols_b, num_rows_b, num_cols_a, num_rows_a, voltage_swing, patch_factor);
-}
-
-/*
-int main(int argc, char *argv[]) {
- if (argc != NUM_ARGS) {
- std::cout << "Usage: " << argv[0] << " <#rows A> <#cols A> <#rows B> <#cols B> <patch factor> <voltage swing>\n";
- exit(1);
- }
-
- // Inputs
- const auto num_rows_a = std::atoi(argv[1]);
- const auto num_cols_a = std::atoi(argv[2]);
- const auto num_rows_b = std::atoi(argv[3]);
- const auto num_cols_b = std::atoi(argv[4]);
- const auto patch_factor = std::atoi(argv[5]);
- const auto voltage_swing = std::atoi(argv[6]);
-
- // Make sure the array dimensions make sense and the swing level is valid
- assert(num_cols_a == num_rows_b);
- assert(voltage_swing > 0 and voltage_swing <= VOLTAGE_LEVELS);
-
- Promise promise;
-
- auto result = promise.fc_profile(num_rows_a, num_cols_a, num_rows_b, num_cols_b, voltage_swing, patch_factor);
- std::cout << std::to_string(result.first * 1e3) << ","
- << std::to_string(result.second * 1e3) << std::endl;
- return 0;
-}
-*/
diff --git a/llvm/projects/soc_simulator/src/promise_timing_model_ext b/llvm/projects/soc_simulator/src/promise_timing_model_ext
deleted file mode 100755
index 960ac445a104c93fd8a5f71c9323f921cf2bc9e4..0000000000000000000000000000000000000000
Binary files a/llvm/projects/soc_simulator/src/promise_timing_model_ext and /dev/null differ
diff --git a/llvm/projects/soc_simulator/src/ptm b/llvm/projects/soc_simulator/src/ptm
deleted file mode 100755
index 0cdb86b8481a64e096766092c375e8ee682e7dac..0000000000000000000000000000000000000000
Binary files a/llvm/projects/soc_simulator/src/ptm and /dev/null differ
diff --git a/llvm/projects/soc_simulator/src/quantization.cu b/llvm/projects/soc_simulator/src/quantization.cu
deleted file mode 100644
index bb1f34b2bdfe869ebd79b67bf05b0776bfbd3d4a..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/src/quantization.cu
+++ /dev/null
@@ -1,275 +0,0 @@
-#include <iostream>
-#include <fstream>
-#include <chrono>
-#include "cuda_fp16.h"
-
-// NOTE: This benchmark was originally meant to measure the performance of
-// various conversion routines. However, it is now used for measuring both
-// performance and energy of the actual conversion that needs to happen before
-// offloading the computation to PROMISE.
-// In order to do so, we assume that the initial conversion is only performed
-// for the tile that PROMISE will actually work on, and the conversions of the
-// remaining tiles can be pipelined with PROMISE's computation. This has two
-// implications:
-// 1. We only add the time for the conversion of the very first tile. This
-// means that this program has to be profiled with the appropriate smaller
-// batch size to obtain the *time* overhead.
-// 2. Since pipelining doesn't affect energy, the program has to be profiled
-// with the full batch size to obtain the *energy* overhead.
-
-#define NUM_ARGS (3)
-#define BLOCK_SIZE (512)
-
-inline cudaError_t checkCuda(cudaError_t result) {
- if (result != cudaSuccess)
- std::cerr << "CUDA Runtime Error: " << cudaGetErrorString(result) << "\n";
- return result;
-}
-
-// init kernel to bring all the pages to the GPU
-template <typename T>
-__global__ void initKernel(T * const __restrict__ array,
- const unsigned elements) {
- const unsigned idx = blockIdx.x * blockDim.x + threadIdx.x;
- if (idx < elements)
- array[idx] = 1;
-}
-
-template <typename T>
-void init(T * const __restrict__ array,
- const unsigned elements) {
- const unsigned num_blocks = (elements + BLOCK_SIZE - 1) / BLOCK_SIZE;
- initKernel<<<num_blocks, BLOCK_SIZE>>>(array, elements);
- checkCuda(cudaDeviceSynchronize());
-}
-
-// float to half
-__global__ void f2hKernel(const float * const __restrict__ input,
- const unsigned elements,
- half * const __restrict__ output) {
- const unsigned idx = blockIdx.x * blockDim.x + threadIdx.x;
- if (idx < elements)
- output[idx] = __float2half_rn(input[idx]);
-}
-
-void f2h(const float * const __restrict__ input,
- const unsigned elements,
- half * const __restrict__ output) {
- const unsigned num_blocks = (elements + BLOCK_SIZE - 1) / BLOCK_SIZE;
- f2hKernel<<<num_blocks, BLOCK_SIZE>>>(input, elements, output);
- checkCuda(cudaDeviceSynchronize());
-}
-
-// half to float
-__global__ void h2fKernel(const half * const __restrict__ input,
- const unsigned elements,
- float * const __restrict__ output) {
- const unsigned idx = blockIdx.x * blockDim.x + threadIdx.x;
- if (idx < elements)
- output[idx] = __half2float(input[idx]);
-}
-
-void h2f(const half * const __restrict__ input,
- const unsigned elements,
- float * const __restrict__ output) {
- const unsigned num_blocks = (elements + BLOCK_SIZE - 1) / BLOCK_SIZE;
- h2fKernel<<<num_blocks, BLOCK_SIZE>>>(input, elements, output);
- checkCuda(cudaDeviceSynchronize());
-}
-
-// float to char ([-1.0, 1.0] to [-128, 127])
-__global__ void f2cKernel(const float * const __restrict__ input,
- const unsigned elements,
- char * const __restrict__ output) {
- const unsigned idx = blockIdx.x * blockDim.x + threadIdx.x;
- if (idx < elements)
- output[idx] = (char) ((127.5 * input[idx]) - 0.5);
-}
-
-void f2c(const float * const __restrict__ input,
- const unsigned elements,
- char * const __restrict__ output) {
- const unsigned num_blocks = (elements + BLOCK_SIZE - 1) / BLOCK_SIZE;
- f2cKernel<<<num_blocks, BLOCK_SIZE>>>(input, elements, output);
- checkCuda(cudaDeviceSynchronize());
-}
-
-// char to float ([-128, 127] to [-1.0, 1.0])
-__global__ void c2fKernel(const char * const __restrict__ input,
- const unsigned elements,
- float * const __restrict__ output) {
- const unsigned idx = blockIdx.x * blockDim.x + threadIdx.x;
- if (idx < elements)
- output[idx] = (((float) input[idx]) + 0.5) / 127.5;
-}
-
-void c2f(const char * const __restrict__ input,
- const unsigned elements,
- float * const __restrict__ output) {
- const unsigned num_blocks = (elements + BLOCK_SIZE - 1) / BLOCK_SIZE;
- c2fKernel<<<num_blocks, BLOCK_SIZE>>>(input, elements, output);
- checkCuda(cudaDeviceSynchronize());
-}
-
-// half to char ([-1.0, 1.0] to [-128, 127])
-__global__ void h2cKernel(const half * const __restrict__ input,
- const unsigned elements,
- char * const __restrict__ output) {
- const unsigned idx = blockIdx.x * blockDim.x + threadIdx.x;
- if (idx < elements)
- output[idx] = (char) ((127.5 * __half2float(input[idx])) - 0.5);
-}
-
-void h2c(const half * const __restrict__ input,
- const unsigned elements,
- char * const __restrict__ output) {
- const unsigned num_blocks = (elements + BLOCK_SIZE - 1) / BLOCK_SIZE;
- h2cKernel<<<num_blocks, BLOCK_SIZE>>>(input, elements, output);
- checkCuda(cudaDeviceSynchronize());
-}
-
-// char to half ([-128, 127] to [-1.0, 1.0])
-__global__ void c2hKernel(const char * const __restrict__ input,
- const unsigned elements,
- half * const __restrict__ output) {
- const unsigned idx = blockIdx.x * blockDim.x + threadIdx.x;
- if (idx < elements)
- output[idx] = __float2half_rn((((float) input[idx]) + 0.5) / 127.5);
-}
-
-void c2h(const char * const __restrict__ input,
- const unsigned elements,
- half * const __restrict__ output) {
- const unsigned num_blocks = (elements + BLOCK_SIZE - 1) / BLOCK_SIZE;
- c2hKernel<<<num_blocks, BLOCK_SIZE>>>(input, elements, output);
- checkCuda(cudaDeviceSynchronize());
-}
-
-int main(int argc, char *argv[]) {
- if (argc != NUM_ARGS) {
- std::cerr << "Usage: " << argv[0] << " <#elements> <conversion type>\n";
- exit(1);
- }
-
- float *floats;
- half *halfs;
- char *chars;
-
- // Inputs
- const unsigned n = std::atoi(argv[1]);
- const std::string type(argv[2]);
-
- std::chrono::time_point<std::chrono::high_resolution_clock> begin;
- std::chrono::time_point<std::chrono::high_resolution_clock> end;
- std::ofstream ofs("profile_data.txt", std::ios::out);
-
- if (type == "f2h") {
- checkCuda(cudaMallocManaged(&floats, sizeof(float) * n));
- checkCuda(cudaMallocManaged(&halfs, sizeof(half) * n));
-
- init(floats, n);
- init(halfs, n);
-
- begin = std::chrono::high_resolution_clock::now();
- f2h(floats, n, halfs);
- end = std::chrono::high_resolution_clock::now();
-
- checkCuda(cudaFree(floats));
- checkCuda(cudaFree(halfs));
-
- ofs << "f2h" << "\t" << std::to_string(std::chrono::duration<double>(begin.time_since_epoch()).count()) << "\n";
- ofs << "f2h" << "\t" << std::to_string(std::chrono::duration<double>(end.time_since_epoch()).count()) << "\n";
- ofs.flush();
- } else if (type == "h2f") {
- checkCuda(cudaMallocManaged(&floats, sizeof(float) * n));
- checkCuda(cudaMallocManaged(&halfs, sizeof(half) * n));
-
- init(floats, n);
- init(halfs, n);
-
- begin = std::chrono::high_resolution_clock::now();
- h2f(halfs, n, floats);
- end = std::chrono::high_resolution_clock::now();
-
- checkCuda(cudaFree(floats));
- checkCuda(cudaFree(halfs));
-
- ofs << "h2f" << "\t" << std::to_string(std::chrono::duration<double>(begin.time_since_epoch()).count()) << "\n";
- ofs << "h2f" << "\t" << std::to_string(std::chrono::duration<double>(end.time_since_epoch()).count()) << "\n";
- ofs.flush();
- } else if (type == "f2c") {
- checkCuda(cudaMallocManaged(&floats, sizeof(float) * n));
- checkCuda(cudaMallocManaged(&chars, sizeof(char) * n));
-
- init(floats, n);
- init(chars, n);
-
- begin = std::chrono::high_resolution_clock::now();
- f2c(floats, n, chars);
- end = std::chrono::high_resolution_clock::now();
-
- checkCuda(cudaFree(floats));
- checkCuda(cudaFree(chars));
-
- ofs << "f2c" << "\t" << std::to_string(std::chrono::duration<double>(begin.time_since_epoch()).count()) << "\n";
- ofs << "f2c" << "\t" << std::to_string(std::chrono::duration<double>(end.time_since_epoch()).count()) << "\n";
- ofs.flush();
- } else if (type == "c2f") {
- checkCuda(cudaMallocManaged(&floats, sizeof(float) * n));
- checkCuda(cudaMallocManaged(&chars, sizeof(char) * n));
-
- init(floats, n);
- init(chars, n);
-
- begin = std::chrono::high_resolution_clock::now();
- c2f(chars, n, floats);
- end = std::chrono::high_resolution_clock::now();
-
- checkCuda(cudaFree(floats));
- checkCuda(cudaFree(chars));
-
- ofs << "c2f" << "\t" << std::to_string(std::chrono::duration<double>(begin.time_since_epoch()).count()) << "\n";
- ofs << "c2f" << "\t" << std::to_string(std::chrono::duration<double>(end.time_since_epoch()).count()) << "\n";
- ofs.flush();
- } else if (type == "h2c") {
- checkCuda(cudaMallocManaged(&halfs, sizeof(half) * n));
- checkCuda(cudaMallocManaged(&chars, sizeof(char) * n));
-
- init(halfs, n);
- init(chars, n);
-
- begin = std::chrono::high_resolution_clock::now();
- h2c(halfs, n, chars);
- end = std::chrono::high_resolution_clock::now();
-
- checkCuda(cudaFree(halfs));
- checkCuda(cudaFree(chars));
-
- ofs << "h2c" << "\t" << std::to_string(std::chrono::duration<double>(begin.time_since_epoch()).count()) << "\n";
- ofs << "h2c" << "\t" << std::to_string(std::chrono::duration<double>(end.time_since_epoch()).count()) << "\n";
- ofs.flush();
- } else if (type == "c2h") {
- checkCuda(cudaMallocManaged(&halfs, sizeof(half) * n));
- checkCuda(cudaMallocManaged(&chars, sizeof(char) * n));
-
- init(halfs, n);
- init(chars, n);
-
- begin = std::chrono::high_resolution_clock::now();
- c2h(chars, n, halfs);
- end = std::chrono::high_resolution_clock::now();
-
- checkCuda(cudaFree(halfs));
- checkCuda(cudaFree(chars));
-
- ofs << "c2h" << "\t" << std::to_string(std::chrono::duration<double>(begin.time_since_epoch()).count()) << "\n";
- ofs << "c2h" << "\t" << std::to_string(std::chrono::duration<double>(end.time_since_epoch()).count()) << "\n";
- ofs.flush();
- } else {
- std::cerr << "Conversion type not supported!\n";
- exit(1);
- }
-
- ofs.close();
- return 0;
-}
diff --git a/llvm/projects/soc_simulator/src/quantize b/llvm/projects/soc_simulator/src/quantize
deleted file mode 100755
index d977c364e97cae9352e968a2469303cfdba8a650..0000000000000000000000000000000000000000
Binary files a/llvm/projects/soc_simulator/src/quantize and /dev/null differ
diff --git a/llvm/projects/soc_simulator/src/run_configs.pl b/llvm/projects/soc_simulator/src/run_configs.pl
deleted file mode 100755
index ad67ea75ee652f6e504d27135a5898a4503cae26..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/src/run_configs.pl
+++ /dev/null
@@ -1,126 +0,0 @@
-#!/usr/bin/perl
-
-use strict;
-use warnings;
-
-my $smart_dma = 1;
-my $detailed_results = 0;
-
-# Full experiments
-my @mnist_networks = ("lenet", "fc4");
-my @cifar10_networks = ("alexnet", "alexnet2", "resnet18", "vgg16");
-my @cifar100_networks = ("vgg16");
-my @pipelines = ("pipeline_GEMO", "pipeline_GEO", "pipeline_GEOM", "pipeline_GSM", "pipeline_GSME");
-
-# Naive experiments
-my @mnist_networks_naive = ("lenet", "fc4");
-my @cifar10_networks_naive = ("vgg16");
-
-#print "############### NAIVE 1% ##############\n";
-
-# MNIST networks
-foreach my $network (@mnist_networks_naive) {
- print "Running $network\n";
- print "Naive Loss1\n";
- `./driver.pl ../${network}_mnist/${network}_layers.txt ../${network}_mnist/${network}_tensors.txt ../${network}_mnist/${network}_naive1.txt ../${network}_mnist/${network}_naive_results1.csv $smart_dma $detailed_results`;
-}
-
-# CIFAR10 networks
-foreach my $network (@cifar10_networks_naive) {
- print "Running $network\n";
- print "Naive Loss1\n";
- `./driver.pl ../${network}_cifar10/${network}_layers.txt ../${network}_cifar10/${network}_tensors.txt ../${network}_cifar10/${network}_naive1.txt ../${network}_cifar10/${network}_naive_results1.csv $smart_dma $detailed_results`;
-}
-
-#print "############### 1% ##############\n";
-#
-## MNIST networks
-#foreach my $network (@mnist_networks) {
-# print "Running $network\n";
-# print "HA Loss1\n";
-# `./driver.pl ../${network}_mnist/${network}_layers.txt ../${network}_mnist/${network}_tensors.txt ../${network}_mnist/${network}_confs1.txt ../${network}_mnist/${network}_results1.csv $smart_dma $detailed_results`;
-# print "HS Loss1\n";
-# `./driver.pl ../${network}_mnist/${network}_layers.txt ../${network}_mnist/${network}_tensors.txt ../${network}_mnist/${network}_promise_confs1.txt ../${network}_mnist/${network}_promise_results1.csv $smart_dma $detailed_results`;
-#}
-#
-## CIFAR10 networks
-#foreach my $network (@cifar10_networks) {
-# print "Running $network\n";
-# print "HA Loss1\n";
-# `./driver.pl ../${network}_cifar10/${network}_layers.txt ../${network}_cifar10/${network}_tensors.txt ../${network}_cifar10/${network}_confs1.txt ../${network}_cifar10/${network}_results1.csv $smart_dma $detailed_results`;
-# print "HS Loss1\n";
-# `./driver.pl ../${network}_cifar10/${network}_layers.txt ../${network}_cifar10/${network}_tensors.txt ../${network}_cifar10/${network}_promise_confs1.txt ../${network}_cifar10/${network}_promise_results1.csv $smart_dma $detailed_results`;
-#}
-#
-## CIFAR100 networks
-#foreach my $network (@cifar100_networks) {
-# print "Running $network\n";
-# print "HA Loss1\n";
-# `./driver.pl ../${network}_cifar100/${network}_layers.txt ../${network}_cifar100/${network}_tensors.txt ../${network}_cifar100/${network}_confs1.txt ../${network}_cifar100/${network}_results1.csv $smart_dma $detailed_results`;
-# print "HS Loss1\n";
-# `./driver.pl ../${network}_cifar100/${network}_layers.txt ../${network}_cifar100/${network}_tensors.txt ../${network}_cifar100/${network}_promise_confs1.txt ../${network}_cifar100/${network}_promise_results1.csv $smart_dma $detailed_results`;
-#}
-#
-## Image pipelines
-#foreach my $pipeline (@pipelines) {
-# print "Running $pipeline\n";
-# print "HA Loss1\n";
-# `./driver.pl ../${pipeline}/${pipeline}_layers.txt ../${pipeline}/${pipeline}_tensors.txt ../${pipeline}/${pipeline}_confs1.txt ../${pipeline}/${pipeline}_results1.csv $smart_dma $detailed_results`;
-# print "HS Loss1\n";
-# `./driver.pl ../${pipeline}/${pipeline}_layers.txt ../${pipeline}/${pipeline}_tensors.txt ../${pipeline}/${pipeline}_promise_confs1.txt ../${pipeline}/${pipeline}_promise_results1.csv $smart_dma $detailed_results`;
-#}
-#
-## vgg16_cifar100_top5
-##print "Running vgg16_cifar100_top5\n";
-##`./driver.pl ../vgg16_cifar100_top5/vgg16_layers.txt ../vgg16_cifar100_top5/vgg16_tensors.txt ../vgg16_cifar100_top5/vgg16_confs1.txt ../vgg16_cifar100_top5/vgg16_results1.csv $smart_dma $detailed_results`;
-##`./driver.pl ../vgg16_cifar100_top5/vgg16_layers.txt ../vgg16_cifar100_top5/vgg16_tensors.txt ../vgg16_cifar100_top5/vgg16_promise_confs1.txt ../vgg16_cifar100_top5/vgg16_promise_results1.csv $smart_dma $detailed_results`;
-#
-#print "############### 2% ##############\n";
-#
-## MNIST networks
-#foreach my $network (@mnist_networks) {
-# print "Running $network\n";
-# print "HA Loss2\n";
-# `./driver.pl ../${network}_mnist/${network}_layers.txt ../${network}_mnist/${network}_tensors.txt ../${network}_mnist/${network}_confs2.txt ../${network}_mnist/${network}_results2.csv $smart_dma $detailed_results`;
-# print "HS Loss2\n";
-# `./driver.pl ../${network}_mnist/${network}_layers.txt ../${network}_mnist/${network}_tensors.txt ../${network}_mnist/${network}_promise_confs2.txt ../${network}_mnist/${network}_promise_results2.csv $smart_dma $detailed_results`;
-#}
-#
-## CIFAR10 networks
-#foreach my $network (@cifar10_networks) {
-# print "Running $network\n";
-# print "HA Loss2\n";
-# `./driver.pl ../${network}_cifar10/${network}_layers.txt ../${network}_cifar10/${network}_tensors.txt ../${network}_cifar10/${network}_confs2.txt ../${network}_cifar10/${network}_results2.csv $smart_dma $detailed_results`;
-# print "HS Loss2\n";
-# `./driver.pl ../${network}_cifar10/${network}_layers.txt ../${network}_cifar10/${network}_tensors.txt ../${network}_cifar10/${network}_promise_confs2.txt ../${network}_cifar10/${network}_promise_results2.csv $smart_dma $detailed_results`;
-#}
-#
-## CIFAR100 networks
-#foreach my $network (@cifar100_networks) {
-# print "Running $network\n";
-# print "HA Loss2\n";
-# `./driver.pl ../${network}_cifar100/${network}_layers.txt ../${network}_cifar100/${network}_tensors.txt ../${network}_cifar100/${network}_confs2.txt ../${network}_cifar100/${network}_results2.csv $smart_dma $detailed_results`;
-# print "HS Loss2\n";
-# `./driver.pl ../${network}_cifar100/${network}_layers.txt ../${network}_cifar100/${network}_tensors.txt ../${network}_cifar100/${network}_promise_confs2.txt ../${network}_cifar100/${network}_promise_results2.csv $smart_dma $detailed_results`;
-#}
-#
-## Image pipelines
-#foreach my $pipeline (@pipelines) {
-# print "Running $pipeline\n";
-# print "HA Loss2\n";
-# `./driver.pl ../${pipeline}/${pipeline}_layers.txt ../${pipeline}/${pipeline}_tensors.txt ../${pipeline}/${pipeline}_confs2.txt ../${pipeline}/${pipeline}_results2.csv $smart_dma $detailed_results`;
-# print "HS Loss2\n";
-# `./driver.pl ../${pipeline}/${pipeline}_layers.txt ../${pipeline}/${pipeline}_tensors.txt ../${pipeline}/${pipeline}_promise_confs2.txt ../${pipeline}/${pipeline}_promise_results2.csv $smart_dma $detailed_results`;
-#}
-#
-## vgg16_cifar100_top5
-##print "Running vgg16_cifar100_top5\n";
-##`./driver.pl ../vgg16_cifar100_top5/vgg16_layers.txt ../vgg16_cifar100_top5/vgg16_tensors.txt ../vgg16_cifar100_top5/vgg16_confs2.txt ../vgg16_cifar100_top5/vgg16_results2.csv $smart_dma $detailed_results`;
-##`./driver.pl ../vgg16_cifar100_top5/vgg16_layers.txt ../vgg16_cifar100_top5/vgg16_tensors.txt ../vgg16_cifar100_top5/vgg16_promise_confs2.txt ../vgg16_cifar100_top5/vgg16_promise_results2.csv $smart_dma $detailed_results`;
-#
-# Alexnet HA Loss2 without pDMA
-#print "Running Alexnet without pDMA\n";
-#$smart_dma = 0;
-#$detailed_results = 1;
-#`./driver.pl ../alexnet_cifar10/alexnet_layers.txt ../alexnet_cifar10/alexnet_tensors.txt ../alexnet_cifar10/alexnet_confs2.txt ../alexnet_cifar10/alexnet_results2_nodma.csv $smart_dma $detailed_results`;
-
diff --git a/llvm/projects/soc_simulator/src/table_generator.py b/llvm/projects/soc_simulator/src/table_generator.py
deleted file mode 100644
index 528b8e0ef5677cec9ccdba37abfde696544029cc..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/src/table_generator.py
+++ /dev/null
@@ -1,308 +0,0 @@
-import glob
-import os
-import subprocess
-import shutil
-import sys
-
-from collections import defaultdict
-
-'''
-FORMAT
-
-** LayerName NumOpsInLayer <cols>
-OpName Col1Val Col2Val ...
-
-** Conv1 1 h2f_time h2f_energy fp32_time fp32_energy f2h_time f2h_energy fp16_perf_time fp16_perf_energy fp16_time fp16_energy
-Conv1 51.8808 97.2844 319.582 601.966 12.81 18.758 388.092 650.649 340.037 590.664
-
-'''
-
-class TableGenerator:
-
- __ops_header_delimiter = "#"
- __table_header_delimter = "**"
- __time_col_name = "time"
- __energy_col_name = "energy"
-
- '''
- Stores all precision conversions used.
- '''
- precision_conversions = frozenset(["h2f", "f2h"])
-
- def __init__(self, network_name, dir_path, soc_ops_file, iters, profiler_binary_name):
- '''
- Args:
- dir_path: Path of directory containing network binaries
- iters: Number of iterations to run each binary for
- profiler_binary_name: Name of offline profiler binary to run
- '''
- self.__network_name = network_name
- self.__dir_path = dir_path
-
- # Name of the actual directory
- self.__soc_ops_filename = soc_ops_file
- self.__iters = iters
- self.__profiler_binary_name = profiler_binary_name
-
- # Path to results directory
- self.__results_dir_path = "%s_results" % self.__network_name
-
- # Outputted table file
- self.__table_filename = "%s_tensors.txt" % self.__network_name
-
- # Nested default dictionary of default dicts
- self.__table = self.__build_nested_default_dict()
-
-
- def generate_table(self):
- '''
- Generates a table file called <network_name>_tensors.txt in the following
- steps:
- 1. Runs the offline profiler against the inputted binaries to generate
- results files
- 2. Builds an internal table storing all data from the parsed results files
- the offline profiler generated
- 3. Writes the internal table to <network_name>_tensors.txt file and uses the
- <network_name>_ops.txt file as a guideline in terms of row order
- '''
- #self.__run_inputted_binaries()
- self.__build_internal_table()
- self.__output_table_to_file()
-
-
- def __run_inputted_binaries(self):
- '''
- Invokes the profiler to run all appropriate binaries (must start with the network
- name) in the inputted directory. Result files generated by the profiler are
- stored in the results file directory and are named <binary_name>.txt. These results
- files are then parsed in a later step to generate the table
- '''
- if not os.path.isdir(self.__dir_path):
- print("ERROR: Directory %s not found" % self.__dir_path)
- exit(1)
-
- try:
- os.mkdir(self.__results_dir_path)
- except OSError:
- if os.path.isdir(self.__results_dir_path):
- print("Directory already exists. Clearing directory.")
- for old_file in glob.glob(os.path.join(self.__results_dir_path, "*")):
- os.remove(old_file)
- else:
- print("ERROR: Directory doesn't exist but failed to create dir")
-
- for binary_name in os.listdir(self.__dir_path):
- binary_path = os.path.join(self.__dir_path, binary_name)
-
- if not self.__should_execute_file(binary_path):
- continue
-
- output_file = os.path.join(self.__results_dir_path, binary_name + ".txt")
- # No stdout/stderr piping needed for now
- subprocess.Popen([profiler_binary_name, binary_path, str(self.__iters), \
- output_file]).communicate()
-
-
- def __build_internal_table(self):
- '''
- Iterates through each results file generated by the runs of the offline
- profiler and stores the data in a dictionary in the following format:
- [operation name][approximation type OR conversion type][time/energy]
- '''
- for results_file_name in os.listdir(self.__results_dir_path):
- # Ignore if it's not a results file
- if results_file_name == self.__table_filename or \
- not results_file_name.startswith(self.__network_name):
- continue
-
- approx_type = self.__get_approximation_type(results_file_name)
- results_file = open(os.path.join(self.__results_dir_path, results_file_name), "r")
- for line in results_file:
- line = line.strip()
- op_name, total_time, total_energy = self.__parse_tensor_operation_line(line)
- # If the current operation is f2h or h2f
- if any(op_name.endswith(prec_conv) for prec_conv in TableGenerator.precision_conversions):
- # Get the original operation name (without the f2h/h2f) and the conversion type
- orig_op_name, conversion_type = self.__get_original_operation_name(op_name)
-
- if orig_op_name not in self.__table:
- print("ERROR: Conversion found but original %s is not in the table" % orig_op_name)
- exit(1)
-
- # Store f2h and h2f as columns in the row belonging to the original operation
- approx_type_no_fp_prefix = approx_type[5 : ]
- self.__table[orig_op_name][conversion_type + "_" + approx_type_no_fp_prefix][TableGenerator.__time_col_name] = total_time
- self.__table[orig_op_name][conversion_type + "_" + approx_type_no_fp_prefix][TableGenerator.__energy_col_name] = total_energy
-
- # Create a new row in the dictionary
- else:
- self.__table[op_name][approx_type][TableGenerator.__time_col_name] = total_time
- self.__table[op_name][approx_type][TableGenerator.__energy_col_name] = total_energy
- results_file.close()
-
-
- def __output_table_to_file(self):
- '''
- Outputs the internally stored table to a file using the <network_name>_ops.txt file as
- a guideline in the following steps:
- 1. Opens the ops file and the file to output the table to
- 2. Reads a line from the ops file (guaranteed to be the layers/NML header)
- 3. For each operation in the layer (or 1 operation if the "layer" is a NML), we store the
- time and the energy
- '''
- table_file_path = os.path.join(self.__results_dir_path, self.__table_filename)
- soc_operations_file = open(self.__soc_ops_filename, "r")
- table_file = open(table_file_path, "w")
-
- curr_line = soc_operations_file.readline().strip()
-
- while curr_line:
- # First line is always the layers line (#layer_name,num_ops)
- layer_name, num_ops = self.__parse_layer_info_line(curr_line)
-
- # List of strings, where each string is a row corresponding to an operation
- # in the layer
- ops_in_layer = []
-
- # Stores a list of elements in the header, which will be joined into a string
- # The header is only generated for the first operation in the layer
- # CRITICAL ASSUMPTION: All operations within a layer have the same # columns
- # or everything breaks bc the header is per layer, not per operation
- header = [TableGenerator.__table_header_delimter, layer_name, str(num_ops)]
-
- # Iterate through all operations within the layer
- for op_in_layer_count in range(num_ops):
- # Contains the operation name
- curr_line = soc_operations_file.readline().strip()
-
- # Stores a list of elements that will be joined to make up a row
- curr_op = [curr_line]
- operation_data = self.__table[curr_line]
- # Iterate through time/energy data for each approximation type corresponding
- # to the current operation
- for approx_type in operation_data:
- op_time = operation_data[approx_type][TableGenerator.__time_col_name]
- op_energy = operation_data[approx_type][TableGenerator.__energy_col_name]
- curr_op.append(op_time)
- curr_op.append(op_energy)
- if op_in_layer_count == 0:
- if approx_type == "fp32_perf20":
- header.append("fp32_time")
- header.append("fp32_energy")
- elif approx_type == "fp16_perf20":
- header.append("fp16_time")
- header.append("fp16_energy")
- elif approx_type.find("f2h_perf20") != -1:
- header.append("f2h_time")
- header.append("f2h_energy")
- else:
- header.append("%s_time" % approx_type)
- header.append("%s_energy" % approx_type)
- ops_in_layer.append(' '.join(curr_op))
- # Getting all operation rows and then writing everything because
- # calls to write() are slow (memory vs time tradeoff)
- table_file.write("%s\n%s\n" % (' '.join(header), '\n'.join(ops_in_layer)))
-
- curr_line = soc_operations_file.readline().strip()
-
-
- def __should_execute_file(self, file_path):
- '''
- Checks if the file at the given file path is a binary that should be run
- by the profiler. Must exist, be a binary, and must start with the network
- name as per our naming standards.
-
- Args:
- file_path: Path of the file to check
- '''
- return os.path.isfile(file_path) and os.access(file_path, os.X_OK) and \
- file_path.find(self.__network_name) != -1
-
-
- def __get_approximation_type(self, results_filename):
- '''
- Parses a given results filename for the approximation type.
- Format assumption: <network_name>_<approx_type>.txt
-
- Args:
- results_filename: Name of results file
-
- Returns:
- the approximation technique (ex: fp16)
- '''
- approx_type_start_ind = results_filename.find("_", results_filename.find("_") + 1) + 1
- approx_type_end_ind = results_filename.find(".txt")
- return results_filename[approx_type_start_ind : approx_type_end_ind]
-
-
- def __parse_tensor_operation_line(self, tensor_op_line):
- '''
- Parses a tensor operation line (within a output file from the offline
- profiler for the operation name, the total time used, and the total
- energy used
-
- Args:
- tensor_op_line: Tensor operation line from output file
-
- Returns:
- operation name
- total time used
- total energy used
- '''
- line_as_list = tensor_op_line.split(",")
- return line_as_list[0], line_as_list[1], line_as_list[2]
-
-
- def __build_nested_default_dict(self):
- '''
- Builds a nested default dictionary with an arbitrary number of levels
- '''
- return defaultdict(self.__build_nested_default_dict)
-
- def __get_original_operation_name(self, op_name):
- '''
- Parses an operation name containing _<conversion type> for the original
- operation name.
- Format assumption: <original_op_name>_<conversion type>
-
- Args:
- op_name: Name of the operation
-
- Returns:
- the original operation name
- '''
- underscore_ind = op_name.find("_")
- return op_name[ : underscore_ind], op_name[underscore_ind + 1 : ]
-
-
- def __parse_layer_info_line(self, layer_info_line): #layer_name,num_ops
- '''
- Parses a layer header (from the original ops.txt file) into the layer name
- and the number of operations
- Assumed format: #layer_name,num_ops
-
- Args:
- layer_info_line: Line at the beginning of each layer in the ops file
-
- Returns:
- layer name
- number of ops in the layer
- '''
- comma_ind = layer_info_line.find(",")
- return layer_info_line[layer_info_line.find(TableGenerator.__ops_header_delimiter) + 1 : comma_ind], \
- int(layer_info_line[comma_ind + 1 : ])
-
-
-if __name__ == "__main__":
- if len(sys.argv) != 6:
- print("python table_generator.py <network name> <binary dir path> <soc_ops file> <num itrs> <profiler bin path>")
- print("soc ops file: ~/soc_simular/%s_cifar10/%s_ops.txt")
- exit(1)
-
- network_name = sys.argv[1]
- binary_dir_path = sys.argv[2]
- soc_ops_file = sys.argv[3]
- num_iters = int(sys.argv[4])
- profiler_binary_name = sys.argv[5]
- table_gen = TableGenerator(network_name, binary_dir_path, soc_ops_file, num_iters, profiler_binary_name)
- table_gen.generate_table()
diff --git a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_confs1.txt b/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_confs1.txt
deleted file mode 100644
index 69a3b7cc2fbbad37d571bf1009d4efbd982ce0f7..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_confs1.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-9 9 9,9 9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9
-9 9 9,7,7,9 9 9 9,9 9 9,7,7,8 8 8,7,7,8 8 8,8 8 8,7,9 9 9,9 9
-9 9 9,7,7,8 8 8 8,8 8 8,9 9 9,7,7,8 8 8,7,7,7,8 8 8 8,9 9 9,9 9
-9 9 9,7,7,7,9 9 9,8 8 8,7,8 8 8,7,7,8 8 8,8 8 8,7,9 9 9,9 9
-9 9 9,7,7,7,8 8 8,8 8 8,7,7,9 9 9,7,7,7,9 9 9 9,9 9 9,9 9
-9 9 9,7,7,9 9 9 9,8 8 8,7,7,8 8 8,7,7,8 8 8,8 8 8,7,9 9 9,9 9
-9 9 9,7,7,7,9 9 9,8 8 8,7,8 8 8,7,7,8 8 8,8 8 8,7,9 9 9,9 9
-9 9 9,7,7,8 8 8 8,8 8 8,9 9 9,7,7,8 8 8,7,7,7,8 8 8 8,9 9 9,9 9
-9 9 9,7,7,9 9 9 9,9 9 9,7,7,8 8 8,7,7,8 8 8,8 8 8,7,9 9 9,9 9
-9 9 9,7,7,7,8 8 8,8 8 8,7,7,9 9 9,7,7,7,9 9 9 9,9 9 9,9 9
-9 9 9,7,7,9 9 9 9,8 8 8,7,7,8 8 8,7,7,8 8 8,8 8 8,7,9 9 9,9 9
-9 9 9,7,7,9 9 9 9,9 9 9,7,7,8 8 8,7,7,8 8 8,8 8 8,7,9 9 9,9 9
-9 9 9,7,7,7,9 9 9,8 8 8,7,8 8 8,7,7,8 8 8,8 8 8,7,9 9 9,9 9
-9 9 9,7,7,9 9 9 9,8 8 8,7,7,8 8 8,7,7,8 8 8,8 8 8,7,9 9 9,9 9
-9 9 9,7,7,8 8 8 8,8 8 8,9 9 9,7,7,8 8 8,7,7,7,8 8 8 8,9 9 9,9 9
diff --git a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_confs2.txt b/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_confs2.txt
deleted file mode 100644
index 73bef415abe86b2cfb918aa56ab7a2ba13b9021d..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_confs2.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-9 9 9,9 9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,7,8 8 8,4,7,7,4,8 8 8,8 8 8,7,8 8 8,8 8
-9 9 9,5,8 8 8,8 8 8 8,7,6,7,7,8 8 8,4,4,4,8 8 8 8,8 8 8,8 8
-9 9 9,7,4,8 8 8 8,7,8 8 8,4,7,8 8 8,4,4,8 8 8,7,8 8 8,8 8
-9 9 9,8 8 8 8,4,8 8 8 8,7,5,4,7,7,7,4,8 8 8,7,8 8 8,8 8
-9 9 9,8 8 8 8,7,7,7,6,7,7,8 8 8,4,4,4,7,8 8 8,8 8
-9 9 9,8 8 8 8,5,7,4,6,8 8 8 8,7,4,7,4,4,7,8 8 8,8 8
-9 9 9,8 8 8 8,4,8 8 8 8,8 8 8,4,4,7,7,8 8 8 8,8 8 8,5,7,8 8 8,8 8
-9 9 9,7,8 8 8,8 8 8 8,7,8 8 8,4,7,7,4,4,8 8 8,6,8 8 8,8 8
-9 9 9,8 8 8 8,4,8 8 8 8,7,4,7,8 8 8,7,7,8 8 8,8 8 8,7,8 8 8,8 8
-9 9 9,7,4,8 8 8 8,7,6,4,7,8 8 8,4,7,4,8 8 8 8,8 8 8,8 8
-9 9 9,8 8 8 8,4,8 8 8 8,7,4,7,8 8 8,7,7,8 8 8,8 8 8,7,8 8 8,8 8
-9 9 9,8 8 8 8,4,8 8 8 8,7,5,4,7,7,7,4,8 8 8,7,8 8 8,8 8
-9 9 9,7,4,8 8 8 8,7,8 8 8,4,7,8 8 8,4,4,8 8 8,7,8 8 8,8 8
-9 9 9,8 8 8 8,4,8 8 8 8,8 8 8,4,4,7,7,8 8 8 8,8 8 8,5,7,8 8 8,8 8
-9 9 9,5,8 8 8,8 8 8 8,7,6,7,7,8 8 8,4,4,4,8 8 8 8,8 8 8,8 8
-9 9 9,8 8 8 8,5,7,4,6,8 8 8 8,7,4,7,4,4,7,8 8 8,8 8
-9 9 9,7,8 8 8,8 8 8 8,7,8 8 8,4,7,7,4,4,8 8 8,6,8 8 8,8 8
-9 9 9,7,4,8 8 8 8,7,6,4,7,8 8 8,4,7,4,8 8 8 8,8 8 8,8 8
-9 9 9,5,5,8 8 8 8,4,6,4,7,8 8 8,4,4,4,7,8 8 8,8 8
-9 9 9,8 8 8 8,7,7,7,6,7,7,8 8 8,4,4,4,7,8 8 8,8 8
-9 9 9,7,4,8 8 8 8,7,8 8 8,8 8 8 8,7,7,4,4,8 8 8,7,8 8 8,8 8
-9 9 9,8 8 8 8,4,8 8 8 8,8 8 8,4,4,7,7,8 8 8 8,8 8 8,5,7,8 8 8,8 8
-9 9 9,8 8 8 8,7,7,7,6,7,7,8 8 8,4,4,4,7,8 8 8,8 8
-9 9 9,8 8 8 8,5,8 8 8 8,8 8 8,8 8 8,7,8 8 8,7,7,8 8 8,8 8 8,7,8 8 8,8 8
-9 9 9,5,8 8 8,8 8 8 8,7,6,7,7,8 8 8,4,4,4,8 8 8 8,8 8 8,8 8
-9 9 9,7,8 8 8,8 8 8 8,7,8 8 8,4,7,7,4,4,8 8 8,6,8 8 8,8 8
-9 9 9,8 8 8 8,5,7,4,6,8 8 8 8,7,4,7,4,4,7,8 8 8,8 8
-9 9 9,8 8 8 8,4,8 8 8 8,7,4,7,8 8 8,7,7,8 8 8,8 8 8,7,8 8 8,8 8
-9 9 9,8 8 8 8,4,8 8 8 8,7,5,4,7,7,7,4,8 8 8,7,8 8 8,8 8
-9 9 9,7,4,8 8 8 8,7,6,4,7,8 8 8,4,7,4,8 8 8 8,8 8 8,8 8
diff --git a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_fp16.csv b/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_fp16.csv
deleted file mode 100644
index 04c4cfc4efb2b0fe6f94ddc332d356ba2966da72..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_fp16.csv
+++ /dev/null
@@ -1,148 +0,0 @@
-Add1,69.7114,211.862,107.8,104.062,6085.83,3099.48,2986.34,49.2963,152.515,80.4532,73.6474,4385.91,2317.11,2113.98
-Add10,41.8232,272.933,216.003,56.9294,13052.8,10330.2,2722.54,29.5763,193.08,152.806,40.2755,9233.85,7307.84,1926.04
-Add10_f2h,40.0563,257.396,205.254,52.142,12850.3,10247,2603.29,28.3409,182.148,145.252,36.8963,9087.18,7246.3,1840.92
-Add10_h2f,10.5152,61.7303,48.6618,13.0686,11780.8,9286.75,2494.08,7.45143,43.7235,34.4675,9.2562,8348.86,6581.42,1767.47
-Add11,32.499,205.175,160.15,45.0253,12627.7,9856.71,2770.94,22.9909,145.16,113.305,31.856,8930.63,6971.08,1959.62
-Add11_f2h,4.84751,25.3885,19.8298,5.55869,10456,8166.49,2289.46,3.43461,18.1215,14.1539,3.96771,7436.9,5808.46,1628.52
-Add11_h2f,2.71712,12.3719,9.65008,2.72179,9106.03,7102.66,2003.36,1.92229,8.92178,6.95893,1.96292,6563.12,5119.11,1444.06
-Add12,32.3458,201.907,158.806,43.1007,12485.6,9820.54,2665.04,22.8734,142.79,112.309,30.483,8830.29,6945.65,1884.77
-Add12_f2h,4.88662,25.364,19.9643,5.39974,10378.2,8169.14,2209.1,3.45883,18.0489,14.2063,3.84293,7376.52,5806.53,1570.11
-Add12_h2f,2.69777,11.7633,9.24658,2.51676,8720.78,6854.97,1865.81,1.90775,8.49592,6.67815,1.81791,6297.92,4950.42,1347.6
-Add13,32.2976,199.983,158.214,41.7697,12384.7,9798.1,2586.56,22.8397,141.436,111.896,29.5422,8758.82,6929.74,1829.23
-Add13_f2h,4.91853,25.4026,20.1163,5.28634,10314.5,8167.97,2146.57,3.50657,18.2597,14.4609,3.79914,7330.34,5804.95,1525.56
-Add13_h2f,2.71496,12.0093,9.49625,2.51304,8840.31,6990.41,1849.9,1.92046,8.68342,6.8666,1.81698,6383.37,5047.78,1335.71
-Add14,0.999932,9.47008,7.42388,2.0462,18954.6,14861.4,4093.23,0.708467,7.11012,5.57305,1.53723,14214.1,11144.8,3069.6
-Add14_f2h,1.88605,7.19323,5.64536,1.54786,7493.19,5878.86,1614.33,1.34509,5.54955,4.35727,1.1924,5686.64,4462.19,1224.59
-Add14_h2f,0.760782,0.960605,0.753025,0.20758,2523.02,1977.84,545.18,0.538123,1.10915,0.869399,0.23978,2911.43,2282.18,629.323
-Add15,0.354443,1.81062,1.41722,0.393395,10528.4,8243.7,2284.66,0.255481,2.29175,1.79496,0.496829,13969.3,10946.3,3023.19
-Add15_f2h,0.764484,1.36953,1.07351,0.296021,3102.43,2430.16,672.268,0.572894,1.59231,1.24879,0.343541,3452.8,2704.95,747.932
-Add15_h2f,0.120211,0.0438951,0.0343584,0.00953666,722.085,565.205,156.881,0.0858258,0.0897805,0.0702545,0.0195276,1484.29,1161.5,322.825
-Add1_f2h,314.261,894.668,472.564,422.104,5684.96,2999.37,2685.59,222.29,654.332,365.302,299.156,4151.15,2314.09,1902.21
-Add1_h2f,83.2799,265.625,135.314,130.311,6379.44,3249.94,3129.5,58.8888,190.41,99.6739,92.2071,4573.2,2394.11,2214.4
-Add2,69.1905,303.338,199.548,103.79,8768.52,5768.33,3000.19,48.9274,214.509,141.116,73.3959,6200.7,4079.22,2121.55
-Add2_f2h,336.556,1525.29,1077.85,447.443,9065.06,6405.97,2659.09,238.064,1078.88,762.399,316.488,6410.37,4530.13,1880.3
-Add2_h2f,83.1178,365.325,234.29,131.036,8790.52,5637.51,3153.01,58.7743,258.343,165.686,92.6601,6216.16,3986.69,2229.56
-Add3,40.9358,206.435,132.407,74.0277,10086.1,6469.28,3616.86,28.9503,146.001,93.6465,52.3555,7132.63,4575.01,2557.68
-Add3_f2h,52.9835,255.846,166.622,89.2241,9657.91,6289.83,3368.08,37.4671,180.924,117.829,63.0955,6829.52,4447.85,2381.69
-Add3_h2f,41.6412,204.32,129.501,74.8188,9814.92,6220.84,3594.07,29.4469,144.508,91.593,52.9164,6942.13,4400.1,2542.08
-Add4,37.6205,200.601,138.934,61.6676,10664.8,7386.36,3278.47,26.6043,141.874,98.2614,43.6138,7542.19,5223.74,2318.49
-Add4_f2h,146.069,779.141,555.206,223.935,10669.7,7603.38,3066.3,103.384,551.386,392.897,158.49,7544.72,5376.51,2168.23
-Add4_h2f,41.3499,213.133,145.707,67.426,10308.5,7047.34,3261.17,29.2407,150.732,103.047,47.6846,7289.72,4983.6,2306.13
-Add5,39.1711,216.321,149.235,67.0856,11045,7619.75,3425.3,27.7004,152.99,105.545,47.445,7810.9,5388.61,2422.31
-Add5_f2h,27.4739,144.775,100.707,44.0675,10539.5,7331.45,3208.09,19.4282,102.386,71.2208,31.165,7453.51,5184.78,2268.74
-Add5_h2f,20.8433,109.029,74.8169,34.2117,10461.4,7178.79,3282.61,14.7445,77.147,52.9395,24.2078,7398.96,5077.34,2321.65
-Add6,38.2773,222.684,163.353,59.3305,11635.7,8535.58,3100.14,27.0679,157.484,115.525,41.9589,8228.62,6036.27,2192.36
-Add6_f2h,78.3804,453.985,337.891,116.094,11585.2,8622.75,2962.45,55.4654,321.237,239.086,82.1516,8192.13,6097.33,2094.81
-Add6_h2f,20.7551,113.98,83.0303,30.9501,10983.3,8000.93,2982.38,14.6773,80.6183,58.7275,21.891,7767.8,5658.58,2109.24
-Add7,38.3104,225.263,168.705,56.5575,11759.8,8807.22,2952.55,27.0935,159.323,119.322,40.0017,8316.1,6228.2,2087.92
-Add7_f2h,81.46,477.128,362.784,114.344,11714.6,8907.2,2807.39,57.624,337.519,256.632,80.8876,8283.65,6298.49,1985.17
-Add7_h2f,20.7616,115.225,85.6469,29.5778,11099.7,8250.44,2849.26,14.6822,81.4998,60.5791,20.9209,7850.09,5834.99,2015.11
-Add8,42.0846,253.744,188.878,64.8666,12058.8,8976.12,3082.68,29.7613,179.461,133.584,45.8767,8527.74,6347.77,2180
-Add8_f2h,14.6636,82.9186,61.9626,20.956,11309.1,8451,2858.12,10.3711,58.6733,43.8445,14.8289,8000.2,5978.33,2021.89
-Add8_h2f,10.5141,57.5583,42.7041,14.8542,10948.7,8123.2,2825.55,7.43528,40.7375,30.2242,10.5133,7748.37,5748.74,1999.65
-Add9,41.8259,265.047,205.792,59.2543,12674.6,9841.07,2833.5,29.5785,187.445,145.539,41.9062,8963.23,6959.46,2003.8
-Add9_f2h,39.8499,247.783,193.75,54.033,12435.2,9723.35,2711.83,28.1894,175.299,137.075,38.2246,8793.58,6875.93,1917.67
-Add9_h2f,10.4589,60.1816,46.5522,13.6294,11508.3,8902.04,2606.31,7.39612,42.594,32.9479,9.64626,8144.6,6300.12,1844.51
-Conv1,310.228,694.841,342.376,352.465,5137.93,2796.38,2341.55,227.742,502.069,283.327,253.153,4000.56,2481.3,1665.08
-Conv10,481.652,3018.22,2355.36,662.857,12534.5,9781.99,2752.5,340.625,2134.28,1665.53,468.767,8863.57,6917.32,1946.32
-Conv10_f2h,16.267,94.4725,71.7156,22.7569,11613.2,8815.27,2797.92,11.5068,66.8653,50.7629,16.1033,8215,6235.99,1979.12
-Conv10_h2f,10.5438,65.7062,52.8554,12.8508,12463.8,10026.1,2437.66,7.45594,46.5025,37.4079,9.09478,8820.88,7095.76,1725.14
-Conv11,173.774,1077.96,835.557,242.405,12410.5,9620.47,2790,122.957,762.523,591.018,171.513,8775.98,6803.22,1972.86
-Conv11_f2h,6.79928,37.3658,28.7179,8.64791,10989.1,8446.21,2542.86,4.81142,26.5033,20.3687,6.13521,7787.5,5985.57,1802.09
-Conv11_h2f,2.77964,12.8278,10.0359,2.79191,9229.29,7220.6,2008.69,1.96619,9.21135,7.20639,2.00505,6624.71,5182.79,1441.97
-Conv12,172.103,1054.21,824.356,229.857,12254.7,9583.51,2671.18,121.754,745.623,583.022,162.614,8665.94,6777.23,1888.86
-Conv12_f2h,6.35668,34.3,26.5952,7.70476,10790.7,8366.52,2424.14,4.49979,24.3436,18.8767,5.46737,7649.53,5931.31,1718.37
-Conv12_h2f,2.76929,12.2454,9.6556,2.58978,8837.54,6968.57,1868.97,1.95863,8.84718,6.97582,1.87148,6378.23,5029.25,1349.07
-Conv13,171.684,1041.08,819.652,221.43,12131.7,9552.2,2579.48,121.445,736.263,579.64,156.639,8579.07,6755.25,1824.02
-Conv13_f2h,6.20782,33.1375,25.8857,7.25183,10669.3,8333.91,2335.41,4.39486,23.5409,18.3911,5.15048,7566.21,5910.3,1656.12
-Conv13_h2f,2.76826,12.0417,9.55355,2.48819,8700.26,6902.56,1797.7,1.95823,8.68074,6.88708,1.79382,6270.3,4974.74,1295.67
-Conv1_f2h,7.62006,16.0868,8.21342,7.87343,4543.5,2445.6,2097.91,5.45871,12.1698,7.31434,5.59711,3581.38,2233.61,1494.73
-Conv1_h2f,83.4767,247.452,143.632,103.82,5928.39,3441,2487.39,59.0279,183.614,113.023,73.667,4398.72,2707.49,1764.94
-Conv2,1399.51,5412.29,3378.78,2033.51,7735.19,4829.01,2906.18,989.654,3830.09,2393.05,1438.02,5474.06,3420.35,2055.12
-Conv2_f2h,109.635,397.898,194.591,203.307,7252.79,3544.58,3708.21,77.5331,282.821,139.764,143.829,5150.57,2542.09,2622.66
-Conv2_h2f,83.3307,436.945,336.643,100.302,10486.9,8079.59,2407.3,58.9247,308.997,238.072,70.9279,7415.87,5713.65,1702.27
-Conv3,354.47,1585.56,948.24,637.318,8947.03,5350.96,3596.07,250.683,1121.23,670.544,450.697,6326.72,3783.93,2542.83
-Conv3_f2h,28.5399,124.01,71.14,52.8695,8689.56,4984.75,3704.81,20.1903,87.7504,50.3439,37.4082,6145.43,3525.53,2620.02
-Conv3_h2f,41.6322,203.92,135.55,68.3698,9796.09,6511.67,3284.41,29.4395,144.212,95.8623,48.3504,6927.44,4604.9,2322.58
-Conv4,543.421,2780.23,1853.46,926.763,10232.8,6821.89,3410.92,384.277,1965.96,1310.62,655.348,7235.83,4823.95,2411.91
-Conv4_f2h,52.8973,253.818,152.989,100.829,9599.81,5786.36,3813.45,37.4134,179.493,108.19,71.3037,6789.28,4092.37,2696.95
-Conv4_h2f,41.5238,236.022,174.197,61.8248,11368,8390.24,2977.78,29.3628,166.909,123.187,43.7213,8038.87,5933.13,2105.74
-Conv5,278.988,1427.88,945.3,482.585,10236.5,6776.91,3459.59,197.282,1009.7,668.449,341.252,7238.45,4792.15,2446.32
-Conv5_f2h,14.8008,70.9043,45.1842,25.7201,9581.37,6105.87,3475.5,10.4668,50.1662,31.9688,18.1977,6778.52,4319.78,2458.78
-Conv5_h2f,20.8418,110.25,77.5339,32.7165,10579.5,7440.09,3139.45,14.7375,78.0695,54.9032,23.1665,7491.32,5268.35,2222.99
-Conv6,490.314,2738.69,1950.74,787.949,11171.6,7957.5,3214.1,346.719,1936.58,1379.4,557.184,7899.63,5626.93,2272.73
-Conv6_f2h,28.103,144.6,96.262,48.3381,10291.3,6851.06,3440.28,19.8745,102.268,68.0815,34.1867,7277.97,4845.06,2432.93
-Conv6_h2f,20.8654,122.714,92.9633,29.7508,11762.4,8910.71,2851.66,14.7546,86.7919,65.7503,21.0417,8318.79,6302.01,2016.79
-Conv7,514.632,2922.64,2150.64,772,11358.7,8358.43,3000.25,363.913,2066.64,1520.74,545.901,8031.96,5910.47,2121.51
-Conv7_f2h,28.2053,152.501,107.622,44.8785,10813.7,7631.44,3182.25,19.9546,107.904,76.1494,31.7549,7647.43,5396.98,2250.48
-Conv7_h2f,20.8436,124.457,96.3593,28.0977,11942.1,9246.03,2696.05,14.7391,88.0253,68.1525,19.8729,8446.1,6539.32,1906.8
-Conv8,240.716,1378.49,1006.49,371.999,11454.2,8363.32,3090.86,170.247,974.885,711.794,263.094,8099.49,5913.93,2185.59
-Conv8_f2h,9.68469,49.5859,35.2796,14.3062,10241.5,7287.22,2954.3,6.87516,35.2358,25.0675,10.1686,7251.09,5159.52,2091.63
-Conv8_h2f,10.5597,59.0058,44.3452,14.6605,11175.6,8398.89,2776.67,7.46752,41.7631,31.3867,10.3764,7908.98,5943.95,1965.05
-Conv9,477.878,2885.71,2189.28,696.429,12080.4,9165.65,2914.72,338.05,2040.9,1548.28,492.646,8542.53,6481.6,2061.04
-Conv9_f2h,16.2347,90.1067,65.6407,24.466,11099.8,8085.77,3014.08,11.4848,63.772,46.458,17.3143,7851.77,5719.72,2132.09
-Conv9_h2f,10.5851,63.9065,50.4134,13.4931,12075.4,9525.82,2549.57,7.48625,45.2319,35.6818,9.55026,8545.5,6741.23,1804.29
-Mul1,5.49296,37.0659,29.09,7.97593,13530.7,10624,2906.69,3.93147,26.5587,20.8353,5.72401,9606.57,7543.24,2063.55
-Mul1_f2h,2.18449,8.22985,6.46583,1.76402,7507.4,5897.26,1610.14,1.54872,6.15957,4.84093,1.31877,5588.96,4391.09,1197.99
-Mul1_h2f,0.810781,1.37103,1.07631,0.294716,3393.71,2664.12,729.59,0.574493,1.51826,1.19191,0.326392,3760.1,2951.69,808.488
-Mul2,1.31517,8.1931,6.40985,1.78324,12447.5,9739.56,2707.96,0.931548,6.00801,4.69986,1.30826,9120.64,7136.62,1984.19
-Mul2_f2h,1.76803,6.49192,5.08483,1.40709,7151.61,5599.64,1551.97,1.26447,5.09282,3.99098,1.10192,5498.59,4306.31,1192.38
-Mul2_h2f,0.133188,0.0183427,0.0143504,0.0039923,261.068,204.28,56.7875,0.0949979,0.06404,0.0500845,0.013956,879.603,688.197,191.411
-Pool1,79.7262,360.302,206.802,153.501,9038.84,5188.08,3850.75,56.3789,254.792,146.244,108.551,6391.73,3668.8,2722.98
-Pool1_f2h,106.496,467.712,270.25,197.462,8783.74,5075.34,3708.4,75.3091,330.755,191.123,139.636,6211.29,3589.1,2622.27
-Pool1_h2f,20.9488,92.2916,53.56,38.7316,8810.98,5113.32,3697.66,14.8149,65.2847,37.8887,27.3971,6231.7,3616.65,2615.16
-Pool2,40.8451,210.857,135.193,75.6642,10325.2,6620.16,3705.01,28.8847,149.127,95.6146,53.5134,7301.99,4681.87,2620.15
-Pool2_f2h,52.6109,263.231,170.165,93.0664,10007,6469.01,3537.99,37.2057,186.157,120.341,65.8167,7076.35,4574.53,2501.84
-Pool2_h2f,10.5892,50.2118,32.2214,17.9904,9482.45,6084.99,3397.46,7.49375,35.5699,22.8258,12.7444,6710.71,4306.37,2404.39
-Pool3,21.7732,125.807,90.0357,35.7709,11556.7,8270.76,3285.91,15.3981,89.0019,63.696,25.3063,8175.07,5850.69,2324.42
-Pool3_f2h,27.105,148.004,106.551,41.4535,10921,7862.21,3058.75,19.1678,104.677,75.3585,29.3184,7723.33,5560.2,2163.15
-Pool3_h2f,5.37692,25.667,18.3482,7.31879,9547.33,6824.97,2722.36,3.80213,18.2207,13.0249,5.19588,6777.51,4844.84,1932.7
-Pool4,12.2268,81.0192,62.5832,18.436,13254.2,10238.3,3015.89,8.64895,57.3698,44.3151,13.055,9382.73,7247.8,2134.97
-Pool4_f2h,14.3183,85.3146,66.1354,19.1792,11917,9238.05,2678.99,10.1261,60.363,46.7931,13.5702,8430.6,6535.39,1895.24
-Pool4_h2f,2.76689,12.3171,9.50437,2.81277,8901.99,6869.12,2032.88,1.95715,8.87091,6.84512,2.02583,6408.16,4944.77,1463.42
-Pool5,4.24587,28.8535,22.6878,6.16569,13613.1,10704.3,2908.83,3.01182,20.5632,16.1689,4.39464,9690.26,7619.75,2070.69
-Pool5_f2h,4.30952,21.6535,17.0404,4.6131,10038.8,7899.74,2139.08,3.05175,15.4491,12.159,3.29042,7146.27,5623.84,1522.54
-Pool5_h2f,0.789328,1.12606,0.885458,0.240603,2854.59,2244.62,609.971,0.558354,1.28688,1.01189,0.27502,3258.62,2562.17,696.513
-Relu1,63.043,222.775,109.755,113.019,7068.58,3483,3585.58,44.5829,158.651,79.4284,79.9495,5034.27,2521.06,2536.23
-Relu10,7.8553,54.9037,42.8846,12.0191,13979.1,10918.9,3060.23,5.55636,38.9249,30.404,8.52113,9908.04,7739.05,2169.03
-Relu10_f2h,14.2674,85.1708,66.8096,18.3612,11939.8,9365.82,2573.95,10.0908,60.2697,47.2767,12.9932,8447.54,6626.47,1821.1
-Relu10_h2f,10.4698,61.6665,48.0288,13.6376,11779.2,9174.16,2605.03,7.40475,43.6532,33.9994,9.654,8336.17,6492.58,1843.64
-Relu11,2.09916,16.791,13.0701,3.72095,15998.3,12453,3545.28,1.48462,12.0634,9.39021,2.67327,11491.9,8945.38,2546.63
-Relu11_f2h,4.48398,23.4935,18.3062,5.18733,10483.3,8168.53,2314.74,3.17253,16.7097,13.0203,3.68949,7455.03,5808.96,1646.13
-Relu11_h2f,2.65339,11.6345,9.04902,2.58551,8767.81,6819.4,1948.41,1.87634,8.37685,6.51523,1.86173,6311.27,4908.75,1402.6
-Relu12,2.09976,17.1837,13.475,3.70873,16368.8,12836,3532.8,1.48516,12.3639,9.69591,2.66821,11777.3,9235.93,2541.54
-Relu12_f2h,4.44727,22.6715,17.7981,4.87339,10189.2,7998.76,2190.47,3.14699,16.1439,12.6738,3.47034,7246.5,5688.6,1558.02
-Relu12_h2f,2.64916,11.3108,8.86276,2.44801,8539.6,6691.38,1848.22,1.87333,8.17249,6.4039,1.76875,6170.04,4834.82,1335.34
-Relu13,2.10382,16.7344,13.1973,3.53714,15914.8,12551.1,3363.76,1.48869,12.0067,9.46927,2.53767,11417,9004.38,2412.85
-Relu13_f2h,4.36041,22.0096,17.3783,4.63133,10092.1,7968.08,2123.99,3.0874,15.6907,12.3898,3.30126,7183.38,5671.67,1511.87
-Relu13_h2f,2.72796,11.4339,9.01109,2.4228,8560.24,6746.45,1813.78,2.03488,8.23175,6.48774,1.74417,6172.8,4865.11,1307.81
-Relu14,1.14743,10.6248,8.32102,2.30382,18530.7,14512.9,4017.87,0.811516,7.89588,6.18393,1.71212,13778.4,10791.3,2987.38
-Relu14_f2h,1.5964,5.26066,4.12403,1.13663,6435.18,5042.95,1392.23,1.14442,4.24048,3.32542,0.91515,5108.03,4003.08,1105.06
-Relu14_h2f,0.762322,0.9319,0.729587,0.202312,2419.48,1894.24,525.243,0.539977,1.14939,0.899736,0.249678,2965.98,2321.79,644.251
-Relu1_f2h,116.511,379.888,188.365,191.522,6536.78,3247.4,3289.38,82.4051,270.74,136.938,135.452,4668.45,2369.38,2327.06
-Relu1_h2f,83.4086,300.518,148.77,151.748,7206.56,3567.67,3638.89,58.9804,213.679,107.062,107.336,5124.37,2567.66,2573.96
-Relu2,62.4233,282.215,169.665,112.55,9042.07,5436.06,3606.01,44.1442,199.587,119.994,79.596,6394.13,3844.28,2549.94
-Relu2_f2h,106.67,463.637,286.355,177.281,8692.96,5369.01,3323.95,75.4328,327.879,202.516,125.367,6147.13,3796.78,2350.42
-Relu2_h2f,83.0734,372.529,221.009,151.52,8968.66,5320.81,3647.85,58.7425,263.431,156.291,107.144,6342.03,3762.66,2579.46
-Relu3,31.1491,157.975,97.2597,60.7158,10143.1,6244.72,3898.34,22.0274,111.732,68.79,42.9423,7173.3,4416.4,2756.93
-Relu3_f2h,53.9219,262.044,163.271,98.7728,9719.42,6055.82,3663.6,38.1317,185.316,115.466,69.8509,6872.96,4282.31,2590.67
-Relu3_h2f,41.4745,201.753,123.336,78.4172,9731.62,5949.14,3782.48,29.3302,142.684,87.2272,55.4573,6883.19,4207.91,2675.32
-Relu4,31.1126,165.225,109.288,55.9374,10620.9,7025.18,3595.76,22.0035,116.872,77.306,39.5668,7511.45,4968.48,2543
-Relu4_f2h,52.5946,268.222,180.055,88.1672,10199.7,6846.98,3352.7,37.1932,189.687,127.336,62.3518,7212.66,4841.86,2370.81
-Relu4_h2f,41.3761,210.186,137.878,72.3072,10159.6,6664.53,3495.09,29.259,148.644,97.5098,51.1349,7184.42,4712.91,2471.53
-Relu5,15.6197,87.4008,58.9328,28.468,11190.8,7545.75,3645.04,11.048,61.8499,41.7045,20.1457,7916.78,5338.18,2578.64
-Relu5_f2h,27.1876,142.132,96.6157,45.5163,10455.9,7107.57,3348.35,19.2268,100.527,68.3345,32.1932,7394.61,5026.63,2368
-Relu5_h2f,20.7564,107.244,71.9782,35.2657,10333.7,6935.58,3398.08,14.6781,75.8517,50.9089,24.9429,7308.38,4905.11,2403.28
-Relu6,15.5774,91.8805,65.7777,26.1027,11796,8444.84,3351.21,11.0158,65.0123,46.543,18.4696,8345.51,5974.6,2370.94
-Relu6_f2h,26.9604,148.182,106.898,41.2837,10992.7,7930.13,3062.55,19.0651,104.8,75.6027,29.1976,7774.08,5608.24,2165.85
-Relu6_h2f,20.7925,111.946,79.7133,32.2323,10779.9,7676.09,3103.81,14.709,79.2691,56.4457,22.8237,7636.46,5437.77,2198.71
-Relu7,15.5863,93.2475,68.0626,25.1848,11966.2,8734.34,3231.84,11.0234,65.9937,48.1698,17.8242,8467.81,6180.86,2286.99
-Relu7_f2h,27.0202,149.73,110.21,39.5201,11082.9,8157.67,2925.21,19.1073,105.894,77.944,27.9502,7837.75,5769.07,2068.69
-Relu7_h2f,20.7296,113.484,82.3887,31.0951,10948.8,7948.82,3000.02,14.6587,80.2669,58.2735,21.9936,7743.7,5621.92,2121.8
-Relu8,7.88397,50.8594,37.3843,13.4751,12900,9482.44,3417.54,5.60997,36.5052,26.8322,9.67313,9199.71,6762.66,2437.09
-Relu8_f2h,14.4739,80.2487,59.2479,21.0008,11105.4,8199.18,2906.22,10.2403,56.8303,41.9579,14.8727,7866.72,5808.05,2058.7
-Relu8_h2f,10.4677,57.0219,41.8152,15.2067,10895,7989.54,2905.49,7.40341,40.3631,29.5989,10.7642,7710.49,5654.25,2056.24
-Relu9,7.84042,52.587,40.301,12.286,13413.6,10279.8,3133.81,5.54547,37.2786,28.5693,8.70947,9505.87,7285.1,2220.81
-Relu9_f2h,14.2726,83.3662,64.1642,19.202,11681.8,8991.1,2690.7,10.0949,58.9936,45.4054,13.5884,8264.16,6360.65,1903.53
-Relu9_h2f,10.4563,59.3499,45.3517,13.9982,11352.1,8674.64,2677.47,7.39481,42.0028,32.0961,9.90692,8032.96,6138.36,1894.63
-Softmax1,2.01231,8.10635,6.34144,1.76491,8058.61,6304.39,1754.22,1.42328,5.95817,4.66119,1.29711,5923.64,4634.63,1289.13
diff --git a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_fp32.csv b/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_fp32.csv
deleted file mode 100644
index 2e203cf73d4f5220f9f3217398c952496028fb62..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_fp32.csv
+++ /dev/null
@@ -1,50 +0,0 @@
-Add1,113.913,376.561,219.636,156.925,6609.15,3854.17,2754.97,80.5497,282.75,177.622,111.325,4961.17,3115.93,1954.23
-Add10,46.7852,353.064,295.37,57.6943,15092.7,12626.5,2466.15,33.0858,249.698,208.891,40.8191,10672.6,8928.67,1744.47
-Add11,32.7593,228.974,188.206,40.768,13979,11490.1,2488.93,23.1655,161.932,133.101,28.8363,9885.52,8125.42,1760.39
-Add12,32.7648,220.92,180.753,40.1662,13485,11033.3,2451.71,23.1723,156.257,127.848,28.4139,9536.19,7802.45,1733.98
-Add13,32.723,215.055,175.489,39.5662,13144.2,10726,2418.24,23.1396,152.085,124.105,27.9836,9295.27,7585.25,1710.24
-Add14,0.902452,1.91287,1.55297,0.359903,4206.37,3415.11,791.258,0.639486,1.96075,1.59154,0.36925,4301.37,3491.69,809.769
-Add15,0.226743,0.146973,0.119291,0.0276818,1268.32,1029.69,238.626,0.161374,0.274735,0.222974,0.0517663,2347.78,1906.32,441.514
-Add2,114.041,707.602,550.188,157.413,12409.4,9648.8,2760.62,80.6401,500.915,389.6,111.35,8784.41,6832.28,1952.72
-Add3,51.2818,306.368,231.407,74.9613,11948.4,9024.9,2923.5,36.2641,216.686,163.674,53.0143,8450.22,6382.88,2067.43
-Add4,51.329,344.524,275.839,68.6848,13425,10748.6,2676.4,36.2967,243.665,195.087,48.5789,9494.83,7601.95,1892.93
-Add5,48.2779,312.538,246.161,66.377,12947.4,10197.6,2749.79,34.1397,221.022,174.082,46.9412,9155.71,7211.24,1944.51
-Add6,48.3038,331.31,269.638,61.6728,13717.7,11164.2,2553.51,34.1584,234.299,190.684,43.6161,9700.3,7894.61,1805.74
-Add7,48.1751,337.048,277.011,60.0367,13992.6,11500.2,2492.4,34.0674,238.357,195.898,42.4598,9894.64,8132.16,1762.55
-Add8,46.6813,323.76,263.597,60.1628,13871.3,11293.7,2577.64,33.0123,228.964,186.417,42.5484,9808.96,7986.22,1822.79
-Add9,46.6775,338.552,280.794,57.7577,14509.8,12034.3,2475.46,33.0125,239.488,198.63,40.8649,10264.6,8513.31,1751.56
-Conv1,163.415,464.69,270.83,193.861,5719.79,3344.04,2375.74,115.618,364.219,239.324,137.798,4496.91,2961.18,1689.91
-Conv10,802.661,5895.34,4887.39,1007.95,14689.8,12178.3,2511.49,567.577,4168.67,3455.92,712.856,10387.4,8611.5,1776.16
-Conv11,308.859,2238.17,1841.36,396.805,14494.3,11924.8,2569.46,218.426,1582.75,1302.12,280.685,10249.2,8432.33,1817.28
-Conv12,308.957,2143.29,1754.35,388.942,13875.6,11357.9,2517.72,218.489,1515.61,1240.55,275.097,9811.8,8031.51,1780.54
-Conv13,308.526,2076.4,1693.52,382.878,13461.8,10980,2481.86,218.187,1468.29,1197.52,270.808,9519.36,7764.54,1755.1
-Conv2,1186.37,5295.79,3610.95,1684.84,9029.61,6180.44,2849.17,840.943,3760.72,2581.03,1191.77,6467.54,4470.42,2016.67
-Conv3,613.82,3267.16,2333.45,933.711,10647.5,7604.92,3042.61,434.112,2311.16,1650.96,660.338,7532.26,5381.04,2151.65
-Conv4,982.846,5808.36,4413.44,1394.92,11819.9,8981.38,2838.55,695.005,4107.39,3121,986.415,8358.48,6351.33,2007.22
-Conv5,491.91,3047.42,2348.81,698.608,12391.2,9550.74,2840.5,347.871,2154.95,1660.93,494.035,8762.18,6753.64,2008.58
-Conv6,858.952,5554.16,4403.19,1150.97,12932.7,10252.8,2679.96,607.382,3927.42,3113.55,813.89,9144.98,7249.96,1895.06
-Conv7,859.425,5760.38,4659.21,1101.17,13405.5,10842.9,2562.58,607.715,4073.23,3294.58,778.676,9479.18,7667.16,1812.06
-Conv8,434.046,2940.43,2368.34,572.081,13549.6,10913.5,2636.05,306.938,2079.26,1674.71,404.559,9581.1,7717.15,1864.02
-Conv9,801.893,5651.89,4624.92,1026.97,14096.7,11535.3,2561.33,567.034,3996.52,3270.33,726.23,9967.96,8156.84,1811.22
-Mul1,10.6715,65.8927,53.5391,12.3536,12394.8,10071.9,2322.88,7.57836,46.691,37.9335,8.75846,8780.28,7134.75,1645.72
-Mul2,0.821147,1.66598,1.35236,0.313621,4076.84,3309.78,767.066,0.582562,1.7168,1.39365,0.323189,4224.31,3430.04,794.372
-Pool1,175.393,1012.62,726.774,285.844,11549.1,8289.34,3259.78,124.05,716.6,514.49,202.181,8172.74,5868.18,2305.37
-Pool2,88.7044,575.997,444.625,131.372,12987.9,10025.6,2962.21,62.7347,407.367,314.456,92.9131,9184.52,7089.77,2094.78
-Pool3,45.2833,307.898,247.595,60.3023,13598.6,10935.2,2663.36,32.0292,217.79,175.137,42.6548,9616.06,7732.71,1883.4
-Pool4,24.6055,179.513,148.419,31.0941,14591.4,12063.9,2527.45,17.4004,126.964,104.972,21.9976,10319,8531.59,1787.91
-Pool5,8.20681,50.2013,40.8424,9.35894,12226.9,9947.17,2279.71,5.82151,35.6921,29.0395,6.65352,8656.71,7042.81,1614.13
-Relu1,75.1381,267.733,149.248,118.484,7127.84,3973.96,3153.88,53.1331,197.389,117.092,83.9442,5255.76,3118.27,2234.45
-Relu10,9.31493,65.6928,54.5384,11.1544,14104.6,11709.8,2394.86,6.58686,46.4969,38.6024,7.89659,9982.66,8287.84,1695.28
-Relu11,2.49349,12.8041,10.5081,2.29598,10267.6,8426.39,1841.24,1.76354,9.25156,7.5928,1.65903,7415.31,6085.61,1329.91
-Relu12,2.50068,12.3579,10.0966,2.26132,9872.52,8066.18,1806.35,1.77005,8.95586,7.31638,1.63973,7136.08,5830.14,1306.13
-Relu13,2.48762,12.2284,9.96443,2.26399,9833.11,8012.58,1820.53,1.75929,8.83304,7.19779,1.63547,7102.38,5787.52,1315.04
-Relu14,0.846016,1.57242,1.27739,0.29503,3703.4,3008.56,694.844,0.599154,1.69071,1.37363,0.317122,3975.03,3229.61,745.515
-Relu2,74.295,455.493,338.819,116.674,12261.1,9120.32,3140.75,52.5363,322.394,239.897,82.5246,8677.47,6456.88,2221.33
-Relu3,36.6237,218.815,161.907,56.9082,11949.1,8841.42,3107.67,25.8986,154.77,114.524,40.248,8450.92,6253.36,2197.68
-Relu4,36.7139,244.573,191.979,52.5941,13329.1,10462.8,2866.31,25.9761,173.026,135.817,37.2093,9428.22,7400.79,2027.47
-Relu5,18.3751,115.933,90.1275,25.8055,12618,9809.37,2808.64,12.9942,82.009,63.7545,18.2548,8924.67,6938.12,1986.59
-Relu6,18.3515,122.286,98.2308,24.0552,13327.1,10705.5,2621.61,12.9773,86.4993,69.4836,17.016,9426.46,7572.14,1854.36
-Relu7,18.3889,125.084,101.453,23.631,13603.6,11033.6,2570.02,13.0046,88.4856,71.7689,16.7172,9621.58,7803.86,1817.78
-Relu8,9.34284,60.6176,48.9763,11.6413,12976.3,10484.3,2492.01,6.60743,42.914,34.6725,8.24179,9185.09,7421.14,1764
-Relu9,9.34283,63.4928,52.2617,11.2311,13592.4,11188.1,2404.33,6.60725,44.9455,36.9955,7.95121,9621.07,7919.28,1702.05
-Softmax1,2.09116,9.17789,7.4508,1.72709,8772.87,7122.11,1650.76,1.47902,6.75862,5.48652,1.27228,6456.75,5241.62,1215.29
diff --git a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_layers.txt b/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_layers.txt
deleted file mode 100644
index af6469192145b246beaec42cf42a6629e5ed1a93..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_layers.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-Conv1,2000,3,32,32,64,3,3,3,1,1
-Conv2,2000,64,32,32,64,64,3,3,1,1
-Conv3,2000,64,16,16,128,64,3,3,1,1
-Conv4,2000,128,16,16,128,128,3,3,1,1
-Conv5,2000,128,8,8,256,128,3,3,1,1
-Conv6,2000,256,8,8,256,256,3,3,1,1
-Conv7,2000,256,8,8,256,256,3,3,1,1
-Conv8,2000,256,4,4,512,256,3,3,1,1
-Conv9,2000,512,4,4,512,512,3,3,1,1
-Conv10,2000,512,4,4,512,512,3,3,1,1
-Conv11,2000,512,2,2,512,512,3,3,1,1
-Conv12,2000,512,2,2,512,512,3,3,1,1
-Conv13,2000,512,2,2,512,512,3,3,1,1
-FC1,2000,512,512,512
-FC2,2000,512,512,10
diff --git a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_naive1.txt b/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_naive1.txt
deleted file mode 100644
index 90a09b7a87bfde672e576b124225a9efbebe069c..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_naive1.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-9 9 9,9 9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9
-8 8 8,7,7,7,7,7,7,7,7,7,7,7,7,7,7
diff --git a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_naive_results1.csv b/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_naive_results1.csv
deleted file mode 100644
index 3036f34f281fb2142622187df7d369b1a9dbcd3e..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_naive_results1.csv
+++ /dev/null
@@ -1,14 +0,0 @@
-Time
-Configuration,Total,Improvement
-c0,9504.422158,0.999999989478582
-c1,1323.9068,7.17907139693886
-
-c1,1323.9068
-
-Energy
-Configuration,Total,Improvement
-c0,58514.286843,0.999999998291016
-c1,4256.555187,13.7468640479566
-
-c1,4256.555187
-
diff --git a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_ops.txt b/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_ops.txt
deleted file mode 100644
index 2075774fde3e66afd1a1946cac46b87038a6486f..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_ops.txt
+++ /dev/null
@@ -1,64 +0,0 @@
-#Conv1,3
-Conv1
-Add1
-Relu1
-#Conv2,4
-Conv2
-Add2
-Relu2
-Pool1
-#Conv3,3
-Conv3
-Add3
-Relu3
-#Conv4,4
-Conv4
-Add4
-Relu4
-Pool2
-#Conv5,3
-Conv5
-Add5
-Relu5
-#Conv6,3
-Conv6
-Add6
-Relu6
-#Conv7,4
-Conv7
-Add7
-Relu7
-Pool3
-#Conv8,3
-Conv8
-Add8
-Relu8
-#Conv9,3
-Conv9
-Add9
-Relu9
-#Conv10,4
-Conv10
-Add10
-Relu10
-Pool4
-#Conv11,3
-Conv11
-Add11
-Relu11
-#Conv12,3
-Conv12
-Add12
-Relu12
-#Conv13,4
-Conv13
-Add13
-Relu13
-Pool5
-#FC1,3
-Mul1
-Add14
-Relu14
-#FC2,2
-Mul2
-Add15
diff --git a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_promise_confs1.txt b/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_promise_confs1.txt
deleted file mode 100644
index e7b8720b064ac873815b3222371f587b6d3eace9..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_promise_confs1.txt
+++ /dev/null
@@ -1,48 +0,0 @@
-9 9 9,9 9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9
-8 8 8,8 8 8 8,8 8 8,7,7,5,7,6,8 8 8,7,7,6,7,7,7
-8 8 8,5,8 8 8,7,7,6,8 8 8 8,8 8 8,7,5,8 8 8,6,7,9 9 9,9 9
-9 9 9,5,9 9 9,9 9 9 9,6,6,9 9 9 9,6,6,8 8 8 8,9 9 9,6,9 9 9 9,6,8 8
-8 8 8,8 8 8 8,8 8 8,7,6,5,7,7,5,5,5,8 8 8,7,7,5
-8 8 8,8 8 8 8,8 8 8,7,7,5,9 9 9 9,6,5,5,7,6,7,7,5
-8 8 8,8 8 8 8,8 8 8,5,7,5,7,8 8 8,6,5,7,6,7,9 9 9,7
-8 8 8,6,8 8 8,8 8 8 8,7,8 8 8,7,6,9 9 9,7,8 8 8,8 8 8,9 9 9 9,9 9 9,6
-9 9 9,8 8 8 8,8 8 8,7,7,6,7,6,5,9 9 9 9,7,7,9 9 9 9,7,7
-8 8 8,8 8 8 8,8 8 8,7,7,5,7,7,5,5,7,6,8 8 8 8,8 8 8,8 8
-9 9 9,9 9 9 9,8 8 8,7,7,5,7,6,7,5,7,9 9 9,7,7,5
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,8 8 8,6,9 9 9 9,7,6,9 9 9 9,7,5,8 8 8 8,6,8 8
-8 8 8,8 8 8 8,8 8 8,7,5,5,7,6,9 9 9,6,7,6,9 9 9 9,7,7
-8 8 8,8 8 8 8,8 8 8,7,7,8 8 8,8 8 8 8,6,5,5,5,6,7,9 9 9,7
-9 9 9,8 8 8 8,8 8 8,5,5,5,9 9 9 9,6,9 9 9,6,7,6,7,7,6
-9 9 9,8 8 8 8,8 8 8,7,7,9 9 9,9 9 9 9,6,5,5,7,6,7,7,6
-8 8 8,8 8 8 8,8 8 8,7,5,5,7,6,9 9 9,9 9 9 9,7,6,9 9 9 9,7,7
-9 9 9,8 8 8 8,8 8 8,7,7,8 8 8,8 8 8 8,6,7,5,7,6,7,7,6
-8 8 8,8 8 8 8,8 8 8,7,7,6,7,6,7,5,8 8 8,6,7,7,7
-8 8 8,8 8 8 8,8 8 8,7,7,5,7,6,5,5,9 9 9,6,7,7,7
-8 8 8,8 8 8 8,8 8 8,7,5,5,7,9 9 9,5,5,7,6,8 8 8 8,7,5
-9 9 9,8 8 8 8,8 8 8,5,6,6,8 8 8 8,6,6,7,7,5,8 8 8 8,7,8 8
-9 9 9,9 9 9 9,8 8 8,5,6,6,8 8 8 8,7,6,7,7,5,8 8 8 8,7,8 8
-8 8 8,8 8 8 8,8 8 8,7,7,5,7,9 9 9,7,5,7,9 9 9,7,9 9 9,7
-8 8 8,8 8 8 8,8 8 8,8 8 8 8,7,5,8 8 8 8,9 9 9,5,5,7,8 8 8,7,7,7
-8 8 8,8 8 8 8,8 8 8,7,5,8 8 8,6,8 8 8,7,5,6,9 9 9,7,5,7
-8 8 8,8 8 8 8,8 8 8,7,7,8 8 8,9 9 9 9,9 9 9,5,5,7,6,7,7,7
-9 9 9,8 8 8 8,8 8 8,6,7,5,9 9 9 9,6,7,5,7,9 9 9,7,7,8 8
-8 8 8,8 8 8 8,9 9 9,8 8 8 8,8 8 8,8 8 8,9 9 9 9,5,8 8 8,5,7,6,8 8 8 8,5,6
-9 9 9,8 8 8 8,8 8 8,7,7,7,9 9 9 9,6,7,5,7,8 8 8,7,7,7
-8 8 8,9 9 9 9,8 8 8,7,5,8 8 8,9 9 9 9,7,8 8 8,8 8 8 8,7,7,9 9 9 9,6,7
-8 8 8,8 8 8 8,9 9 9,8 8 8 8,8 8 8,8 8 8,9 9 9 9,5,8 8 8,5,7,6,8 8 8 8,5,7
-9 9 9,8 8 8 8,8 8 8,5,9 9 9,9 9 9,6,6,9 9 9,6,9 9 9,6,5,7,7
-8 8 8,8 8 8 8,8 8 8,7,7,5,8 8 8 8,5,5,7,9 9 9,6,7,7,8 8
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,5,5,6,6,5,7,8 8 8,8 8 8,9 9 9 9,5,6
-8 8 8,9 9 9 9,9 9 9,7,6,5,6,9 9 9,6,8 8 8 8,8 8 8,6,7,7,8 8
-8 8 8,8 8 8 8,9 9 9,5,5,7,7,8 8 8,9 9 9,5,6,9 9 9,9 9 9 9,7,7
-8 8 8,8 8 8 8,8 8 8,7,7,5,7,9 9 9,5,5,7,8 8 8,8 8 8 8,9 9 9,7
-8 8 8,8 8 8 8,8 8 8,7,7,9 9 9,8 8 8 8,9 9 9,5,8 8 8 8,8 8 8,6,7,7,7
-9 9 9,9 9 9 9,9 9 9,9 9 9 9,8 8 8,9 9 9,6,6,7,7,8 8 8,8 8 8,9 9 9 9,5,6
-8 8 8,8 8 8 8,8 8 8,7,5,9 9 9,8 8 8 8,9 9 9,5,6,9 9 9,8 8 8,5,7,7
-8 8 8,9 9 9 9,8 8 8,6,7,6,9 9 9 9,8 8 8,6,9 9 9 9,9 9 9,5,5,6,5
-8 8 8,8 8 8 8,9 9 9,8 8 8 8,8 8 8,8 8 8,8 8 8 8,6,8 8 8,9 9 9 9,9 9 9,6,8 8 8 8,7,6
-8 8 8,8 8 8 8,8 8 8,6,7,7,6,8 8 8,6,8 8 8 8,8 8 8,9 9 9,6,6,8 8
-8 8 8,9 9 9 9,8 8 8,6,7,6,9 9 9 9,8 8 8,6,9 9 9 9,9 9 9,8 8 8,9 9 9 9,6,5
-8 8 8,9 9 9 9,8 8 8,9 9 9 9,7,5,9 9 9 9,8 8 8,6,9 9 9 9,9 9 9,8 8 8,9 9 9 9,9 9 9,5
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,7,6,9 9 9 9,8 8 8,7,9 9 9 9,9 9 9,8 8 8,9 9 9 9,9 9 9,5
-9 9 9,8 8 8 8,8 8 8,9 9 9 9,7,7,9 9 9 9,8 8 8,6,9 9 9 9,9 9 9,8 8 8,9 9 9 9,6,5
diff --git a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_promise_confs2.txt b/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_promise_confs2.txt
deleted file mode 100644
index f846e81c4b2ab1c7681debe4ec84de99be49fade..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_promise_confs2.txt
+++ /dev/null
@@ -1,52 +0,0 @@
-9 9 9,9 9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9
-8 8 8,7,8 8 8,6,5,5,6,6,7,7,6,5,5,9 9 9,5
-8 8 8,5,5,6,5,9 9 9,6,6,7,7,6,5,5,6,5
-8 8 8,7,8 8 8,8 8 8 8,5,5,8 8 8 8,6,7,7,6,5,7,6,5
-8 8 8,8 8 8 8,5,6,5,5,9 9 9 9,6,7,7,6,5,9 9 9 9,6,5
-9 9 9,7,5,6,5,5,9 9 9 9,6,7,6,6,5,5,6,5
-8 8 8,8 8 8 8,5,6,7,5,6,6,7,7,9 9 9,5,5,6,5
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-9 9 9,6,5,7,7,6,5,5,6,8 8 8 8,6,8 8 8,8 8 8 8,5,6
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diff --git a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_promise_results1.csv b/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_promise_results1.csv
deleted file mode 100644
index 3cc252f7f64c7b9fecac6fd7a7793d5560c18285..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_promise_results1.csv
+++ /dev/null
@@ -1,572 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,1108.984,7471.505,3792.343,6973.454,3475.891,6007.756,6530.41,3324.8076,6053.9348,6493.6098,2479.9481,2376.5679,2353.8847,69.37799,1.812953,58514.286843,0.999999998291016
-c1,1129.478,6358.145,1949.97,157.113483,78.556742,77.680494,157.113483,59.008124,3203.344,157.113483,39.466907,29.645681,39.466907,1.117251,0.021821,13437.241376,4.35463535782335
-c2,1129.478,86.311660,1949.97,157.113483,78.556742,118.016247,3366.9575,1683.0934,157.113483,77.680494,1299.926,29.645681,39.466907,69.37799,1.812953,10244.52054,5.71176425908397
-c3,1108.984,86.311660,3792.343,6973.454,59.008124,118.016247,6530.41,59.008124,118.016247,3427.0759,2479.9481,29.645681,2353.8847,0.839227,10.00372,27146.94873,2.1554645868097
-c4,1129.478,6358.145,1949.97,157.113483,59.008124,77.680494,157.113483,78.556742,77.680494,77.680494,19.513340,1273.3007,39.466907,1.117251,0.010789,11455.835301,5.10781490784096
-c5,1129.478,6358.145,1949.97,157.113483,78.556742,77.680494,6530.41,59.008124,77.680494,77.680494,39.466907,29.645681,39.466907,1.117251,0.010789,16605.430366,3.52380427371692
-c6,1129.478,6358.145,1949.97,77.680494,78.556742,77.680494,157.113483,1683.0934,118.016247,77.680494,39.466907,29.645681,39.466907,69.37799,0.021821,11885.39366,4.92320978375394
-c7,1129.478,131.129164,1949.97,3356.913,78.556742,3053.2545,157.113483,59.008124,6053.9348,157.113483,1299.926,1273.3007,2353.8847,69.37799,0.016391,21122.977077,2.77017232716201
-c8,1108.984,6358.145,1949.97,157.113483,78.556742,118.016247,157.113483,59.008124,77.680494,6493.6098,39.466907,39.466907,2353.8847,1.117251,0.021821,18992.154959,3.08097141484064
-c9,1129.478,6358.145,1949.97,157.113483,78.556742,77.680494,157.113483,78.556742,77.680494,77.680494,39.466907,29.645681,1286.6509,57.16078,10.00372,11564.90292,5.05964353888719
-c10,1108.984,7471.505,1949.97,157.113483,78.556742,77.680494,157.113483,59.008124,157.113483,77.680494,39.466907,2376.5679,39.466907,1.117251,0.010789,13751.355057,4.25516512190538
-c11,1129.478,7471.505,1949.97,3356.913,1731.6018,118.016247,6530.41,78.556742,118.016247,6493.6098,39.466907,19.513340,1286.6509,0.839227,10.00372,30334.55093,1.92896498732192
-c12,1129.478,6358.145,1949.97,157.113483,38.840247,77.680494,157.113483,59.008124,6053.9348,118.016247,39.466907,29.645681,2353.8847,1.117251,0.021821,18523.436238,3.15893259626782
-c13,1129.478,6358.145,1949.97,157.113483,78.556742,3053.2545,3366.9575,59.008124,77.680494,77.680494,19.513340,29.645681,39.466907,69.37799,0.021821,16465.870076,3.55367109163099
-c14,1108.984,6358.145,1949.97,77.680494,38.840247,77.680494,6530.41,59.008124,6053.9348,118.016247,39.466907,29.645681,39.466907,1.117251,0.016391,22482.382543,2.60267284709785
-c15,1108.984,6358.145,1949.97,157.113483,78.556742,6007.756,6530.41,59.008124,77.680494,77.680494,39.466907,29.645681,39.466907,1.117251,0.016391,22515.017474,2.59890034065847
-c16,1129.478,6358.145,1949.97,157.113483,38.840247,77.680494,157.113483,59.008124,6053.9348,6493.6098,39.466907,29.645681,2353.8847,1.117251,0.021821,24899.029791,2.35006291807982
-c17,1108.984,6358.145,1949.97,157.113483,78.556742,3053.2545,3366.9575,59.008124,157.113483,77.680494,39.466907,29.645681,39.466907,1.117251,0.016391,16476.496463,3.55137917938216
-c18,1129.478,6358.145,1949.97,157.113483,78.556742,118.016247,157.113483,59.008124,157.113483,77.680494,1299.926,29.645681,39.466907,1.117251,0.021821,11612.372716,5.03896040629841
-c19,1129.478,6358.145,1949.97,157.113483,78.556742,77.680494,157.113483,59.008124,77.680494,77.680494,2479.9481,29.645681,39.466907,1.117251,0.021821,12672.626074,4.6173765437074
-c20,1129.478,6358.145,1949.97,157.113483,38.840247,77.680494,157.113483,3324.8076,77.680494,77.680494,39.466907,29.645681,1286.6509,1.117251,0.010789,14705.400823,3.97910177011772
-c21,1108.984,6358.145,1949.97,77.680494,59.008124,118.016247,3366.9575,59.008124,118.016247,157.113483,39.466907,19.513340,1286.6509,1.117251,10.00372,14729.651337,3.97255068073204
-c22,1108.984,7471.505,1949.97,77.680494,59.008124,118.016247,3366.9575,78.556742,118.016247,157.113483,39.466907,19.513340,1286.6509,1.117251,10.00372,15862.559955,3.68882996440136
-c23,1129.478,6358.145,1949.97,157.113483,78.556742,77.680494,157.113483,3324.8076,157.113483,77.680494,39.466907,2376.5679,39.466907,69.37799,0.021821,15992.560304,3.65884419785369
-c24,1129.478,6358.145,1949.97,3356.913,78.556742,77.680494,3366.9575,3324.8076,77.680494,77.680494,39.466907,1273.3007,39.466907,1.117251,0.021821,21151.24291,2.76647035899192
-c25,1129.478,6358.145,1949.97,157.113483,38.840247,3053.2545,118.016247,1683.0934,157.113483,77.680494,29.645681,2376.5679,39.466907,0.552395,0.021821,17168.959558,3.40814399990362
-c26,1129.478,6358.145,1949.97,157.113483,78.556742,3053.2545,6530.41,3324.8076,77.680494,77.680494,39.466907,29.645681,39.466907,1.117251,0.021821,22846.81488,2.56115729453857
-c27,1108.984,6358.145,1949.97,118.016247,78.556742,77.680494,6530.41,59.008124,157.113483,77.680494,39.466907,2376.5679,39.466907,1.117251,10.00372,18982.187269,3.08258925620765
-c28,1129.478,6358.145,3792.343,3356.913,1731.6018,3053.2545,6530.41,38.840247,3203.344,77.680494,39.466907,29.645681,1286.6509,0.552395,0.016391,30628.342315,1.91046208280416
-c29,1108.984,6358.145,1949.97,157.113483,78.556742,157.113483,6530.41,59.008124,157.113483,77.680494,39.466907,1273.3007,39.466907,1.117251,0.021821,17987.468395,3.25305847564182
-c30,1129.478,7471.505,1949.97,157.113483,38.840247,3053.2545,6530.41,78.556742,3203.344,3427.0759,39.466907,39.466907,2353.8847,0.839227,0.021821,29473.227434,1.9853369223136
-c31,1129.478,6358.145,3792.343,3356.913,1731.6018,3053.2545,6530.41,38.840247,3203.344,77.680494,39.466907,29.645681,1286.6509,0.552395,0.021821,30628.347745,1.91046174410457
-c32,1108.984,6358.145,1949.97,77.680494,3475.891,6007.756,118.016247,59.008124,6053.9348,118.016247,2479.9481,29.645681,19.513340,1.117251,0.021821,27857.648105,2.1004747569645
-c33,1129.478,6358.145,1949.97,157.113483,78.556742,77.680494,3366.9575,38.840247,77.680494,157.113483,2479.9481,29.645681,39.466907,1.117251,10.00372,15951.717102,3.6682124000834
-c34,1129.478,7471.505,1949.97,3356.913,38.840247,77.680494,118.016247,59.008124,77.680494,157.113483,1299.926,1273.3007,2353.8847,0.552395,0.016391,19363.885275,3.02182571884802
-c35,1129.478,7471.505,3792.343,157.113483,59.008124,77.680494,118.016247,3324.8076,118.016247,3427.0759,1299.926,29.645681,39.466907,1.117251,10.00372,21055.203654,2.77908908062139
-c36,1129.478,6358.145,3792.343,77.680494,38.840247,157.113483,157.113483,1683.0934,6053.9348,77.680494,29.645681,2376.5679,2353.8847,1.117251,0.021821,24286.659754,2.40931800398904
-c37,1129.478,6358.145,1949.97,157.113483,78.556742,77.680494,157.113483,3324.8076,77.680494,77.680494,39.466907,1273.3007,1286.6509,69.37799,0.021821,16057.044108,3.64415057248499
-c38,1129.478,6358.145,1949.97,157.113483,78.556742,6007.756,3366.9575,3324.8076,77.680494,3427.0759,1299.926,29.645681,39.466907,1.117251,0.021821,27247.718379,2.14749307866262
-c39,1108.984,7471.505,3792.343,6973.454,1731.6018,6007.756,118.016247,59.008124,157.113483,157.113483,1299.926,1273.3007,2353.8847,0.552395,0.016391,32504.575323,1.800186160918
-c40,1129.478,6358.145,1949.97,157.113483,38.840247,6007.756,3366.9575,3324.8076,77.680494,118.016247,2479.9481,1273.3007,19.513340,1.117251,0.021821,26302.665783,2.22465232624268
-c41,1129.478,7471.505,1949.97,118.016247,78.556742,118.016247,6530.41,1683.0934,118.016247,6493.6098,2479.9481,19.513340,19.513340,0.839227,0.010789,28210.496479,2.07420265287206
-c42,1129.478,6358.145,3792.343,3356.913,1731.6018,3053.2545,3366.9575,59.008124,3203.344,6493.6098,2479.9481,29.645681,1286.6509,1.117251,0.016391,36342.033047,1.61009942966909
-c43,1129.478,6358.145,1949.97,118.016247,78.556742,157.113483,118.016247,1683.0934,118.016247,3427.0759,1299.926,2376.5679,29.645681,0.839227,10.00372,18854.463794,3.10347126133991
-c44,1129.478,7471.505,1949.97,118.016247,78.556742,118.016247,6530.41,1683.0934,118.016247,6493.6098,2479.9481,1273.3007,2353.8847,0.839227,0.010789,31798.655199,1.84014972623198
-c45,1129.478,7471.505,1949.97,6973.454,78.556742,77.680494,6530.41,1683.0934,118.016247,6493.6098,2479.9481,1273.3007,2353.8847,69.37799,0.010789,38682.295962,1.51268908001772
-c46,1129.478,7471.505,1949.97,3356.913,78.556742,118.016247,6530.41,1683.0934,157.113483,6493.6098,2479.9481,1273.3007,2353.8847,69.37799,0.010789,35145.187951,1.66493025327076
-c47,1108.984,6358.145,1949.97,6973.454,78.556742,157.113483,6530.41,1683.0934,118.016247,6493.6098,2479.9481,1273.3007,2353.8847,0.839227,0.010789,37559.336188,1.55791589058764
-c2,10244.52054
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,352.4661,1550.099,701.7255,1159.5933,558.563,925.6073,971.2723,490.07014,857.91333,883.36663,344.11179,344.22248,351.94343,12.419968,1.04789,9504.422158,0.999999989478582
-c1,442.9824,1610.85,426.5549,82.574848,41.518080,82.574848,82.574848,41.518080,527.54432,83.036160,21.220352,21.220352,21.220352,0.922624,0.018020,3486.330184,2.72619671223325
-c2,442.9824,90.532480,426.5549,82.574848,41.518080,82.574848,590.3019,290.68457,83.036160,83.036160,208.37216,21.220352,21.220352,12.419968,1.04789,2478.077068,3.83540201279156
-c3,352.4661,90.532480,701.7255,1159.5933,41.518080,82.574848,971.2723,41.518080,83.036160,543.5573,344.11179,21.220352,351.94343,0.922624,1.669613,4787.661957,1.98519069325364
-c4,442.9824,1610.85,426.5549,82.574848,41.518080,82.574848,82.574848,41.518080,83.036160,83.036160,21.220352,206.54856,21.220352,0.922624,0.018020,3227.150232,2.94514391342585
-c5,442.9824,1610.85,426.5549,82.574848,41.518080,82.574848,971.2723,41.518080,83.036160,83.036160,21.220352,21.220352,21.220352,0.922624,0.018020,3930.519476,2.41810833764437
-c6,442.9824,1610.85,426.5549,82.574848,41.518080,82.574848,82.574848,290.68457,83.036160,83.036160,21.220352,21.220352,21.220352,12.419968,0.018020,3302.485858,2.87795990017045
-c7,442.9824,90.532480,426.5549,652.9992,41.518080,544.1687,82.574848,41.518080,857.91333,83.036160,208.37216,206.54856,351.94343,12.419968,0.018020,4043.100316,2.35077568699199
-c8,352.4661,1610.85,426.5549,82.574848,41.518080,82.574848,82.574848,41.518080,83.036160,883.36663,21.220352,21.220352,351.94343,0.922624,0.018020,4082.359272,2.3281689072228
-c9,442.9824,1610.85,426.5549,82.574848,41.518080,82.574848,82.574848,41.518080,83.036160,83.036160,21.220352,21.220352,210.33129,7.640322,1.669613,3239.302253,2.93409540767249
-c10,352.4661,1550.099,426.5549,82.574848,41.518080,82.574848,82.574848,41.518080,83.036160,83.036160,21.220352,344.22248,21.220352,0.922624,0.018020,3213.556852,2.95760190342505
-c11,442.9824,1550.099,426.5549,652.9992,333.7788,82.574848,971.2723,41.518080,83.036160,883.36663,21.220352,21.220352,210.33129,0.922624,1.669613,5723.546549,1.66058263186526
-c12,442.9824,1610.85,426.5549,82.574848,41.518080,82.574848,82.574848,41.518080,857.91333,83.036160,21.220352,21.220352,351.94343,0.922624,0.018020,4147.422272,2.29164558260718
-c13,442.9824,1610.85,426.5549,82.574848,41.518080,544.1687,590.3019,41.518080,83.036160,83.036160,21.220352,21.220352,21.220352,12.419968,0.018020,4022.640272,2.36273225520146
-c14,352.4661,1610.85,426.5549,82.574848,41.518080,82.574848,971.2723,41.518080,857.91333,83.036160,21.220352,21.220352,21.220352,0.922624,0.018020,4614.880346,2.05951644234642
-c15,352.4661,1610.85,426.5549,82.574848,41.518080,925.6073,971.2723,41.518080,83.036160,83.036160,21.220352,21.220352,21.220352,0.922624,0.018020,4683.035628,2.02954295248546
-c16,442.9824,1610.85,426.5549,82.574848,41.518080,82.574848,82.574848,41.518080,857.91333,883.36663,21.220352,21.220352,351.94343,0.922624,0.018020,4947.752742,1.92095734397236
-c17,352.4661,1610.85,426.5549,82.574848,41.518080,544.1687,590.3019,41.518080,83.036160,83.036160,21.220352,21.220352,21.220352,0.922624,0.018020,3920.626628,2.42420990759516
-c18,442.9824,1610.85,426.5549,82.574848,41.518080,82.574848,82.574848,41.518080,83.036160,83.036160,208.37216,21.220352,21.220352,0.922624,0.018020,3228.973832,2.9434806096787
-c19,442.9824,1610.85,426.5549,82.574848,41.518080,82.574848,82.574848,41.518080,83.036160,83.036160,344.11179,21.220352,21.220352,0.922624,0.018020,3364.713462,2.82473440394449
-c20,442.9824,1610.85,426.5549,82.574848,41.518080,82.574848,82.574848,490.07014,83.036160,83.036160,21.220352,21.220352,210.33129,0.922624,0.018020,3679.485022,2.58308481835465
-c21,352.4661,1610.85,426.5549,82.574848,41.518080,82.574848,590.3019,41.518080,83.036160,83.036160,21.220352,21.220352,210.33129,0.922624,1.669613,3649.795307,2.60409724330611
-c22,352.4661,1550.099,426.5549,82.574848,41.518080,82.574848,590.3019,41.518080,83.036160,83.036160,21.220352,21.220352,210.33129,0.922624,1.669613,3589.044307,2.64817624977355
-c23,442.9824,1610.85,426.5549,82.574848,41.518080,82.574848,82.574848,490.07014,83.036160,83.036160,21.220352,344.22248,21.220352,12.419968,0.018020,3824.873556,2.48489832941033
-c24,442.9824,1610.85,426.5549,652.9992,41.518080,82.574848,590.3019,490.07014,83.036160,83.036160,21.220352,206.54856,21.220352,0.922624,0.018020,4753.853696,1.99930889039903
-c25,442.9824,1610.85,426.5549,82.574848,41.518080,544.1687,82.574848,290.68457,83.036160,83.036160,21.220352,344.22248,21.220352,0.922624,0.018020,4075.584494,2.33203898454034
-c26,442.9824,1610.85,426.5549,82.574848,41.518080,544.1687,971.2723,490.07014,83.036160,83.036160,21.220352,21.220352,21.220352,0.922624,0.018020,4840.665388,1.96345361635945
-c27,352.4661,1610.85,426.5549,82.574848,41.518080,82.574848,971.2723,41.518080,83.036160,83.036160,21.220352,344.22248,21.220352,0.922624,1.669613,4164.656897,2.28216205196406
-c28,442.9824,1610.85,701.7255,652.9992,333.7788,544.1687,971.2723,41.518080,527.54432,83.036160,21.220352,21.220352,210.33129,0.922624,0.018020,6163.588098,1.54202744451423
-c29,352.4661,1610.85,426.5549,82.574848,41.518080,82.574848,971.2723,41.518080,83.036160,83.036160,21.220352,206.54856,21.220352,0.922624,0.018020,4025.331384,2.3611526642659
-c30,442.9824,1550.099,426.5549,82.574848,41.518080,544.1687,971.2723,41.518080,527.54432,543.5573,21.220352,21.220352,351.94343,0.922624,0.018020,5567.114706,1.70724378591161
-c31,442.9824,1610.85,701.7255,652.9992,333.7788,544.1687,971.2723,41.518080,527.54432,83.036160,21.220352,21.220352,210.33129,0.922624,0.018020,6163.588098,1.54202744451423
-c32,352.4661,1610.85,426.5549,82.574848,558.563,925.6073,82.574848,41.518080,857.91333,83.036160,344.11179,21.220352,21.220352,0.922624,0.018020,5409.151704,1.75710028159528
-c33,442.9824,1610.85,426.5549,82.574848,41.518080,82.574848,590.3019,41.518080,83.036160,83.036160,344.11179,21.220352,21.220352,0.922624,1.669613,3874.092107,2.45332884458111
-c34,442.9824,1550.099,426.5549,652.9992,41.518080,82.574848,82.574848,41.518080,83.036160,83.036160,208.37216,206.54856,351.94343,0.922624,0.018020,4254.69847,2.23386498517567
-c35,442.9824,1550.099,701.7255,82.574848,41.518080,82.574848,82.574848,490.07014,83.036160,543.5573,208.37216,21.220352,21.220352,0.922624,1.669613,4354.118225,2.18285803199893
-c36,442.9824,1610.85,701.7255,82.574848,41.518080,82.574848,82.574848,290.68457,857.91333,83.036160,21.220352,344.22248,351.94343,0.922624,0.018020,4994.76149,1.9028780426735
-c37,442.9824,1610.85,426.5549,82.574848,41.518080,82.574848,82.574848,490.07014,83.036160,83.036160,21.220352,206.54856,210.33129,12.419968,0.018020,3876.310574,2.45192477005263
-c38,442.9824,1610.85,426.5549,82.574848,41.518080,925.6073,590.3019,490.07014,83.036160,543.5573,208.37216,21.220352,21.220352,0.922624,0.018020,5488.806536,1.73160083571944
-c39,352.4661,1550.099,701.7255,1159.5933,333.7788,925.6073,82.574848,41.518080,83.036160,83.036160,208.37216,206.54856,351.94343,0.922624,0.018020,6081.240042,1.56290854103225
-c40,442.9824,1610.85,426.5549,82.574848,41.518080,925.6073,590.3019,490.07014,83.036160,83.036160,344.11179,206.54856,21.220352,0.922624,0.018020,5349.353234,1.77674226482494
-c41,442.9824,1550.099,426.5549,82.574848,41.518080,82.574848,971.2723,290.68457,83.036160,883.36663,344.11179,21.220352,21.220352,0.922624,0.018020,5242.156874,1.81307469523327
-c42,442.9824,1610.85,701.7255,652.9992,333.7788,544.1687,590.3019,41.518080,527.54432,883.36663,344.11179,21.220352,210.33129,0.922624,0.018020,6905.839606,1.37628768732386
-c43,442.9824,1610.85,426.5549,82.574848,41.518080,82.574848,82.574848,290.68457,83.036160,543.5573,208.37216,344.22248,21.220352,0.922624,1.669613,4263.315183,2.22935005438114
-c44,442.9824,1550.099,426.5549,82.574848,41.518080,82.574848,971.2723,290.68457,83.036160,883.36663,344.11179,206.54856,351.94343,0.922624,0.018020,5758.20816,1.65058673268618
-c45,442.9824,1550.099,426.5549,1159.5933,41.518080,82.574848,971.2723,290.68457,83.036160,883.36663,344.11179,206.54856,351.94343,12.419968,0.018020,6846.723956,1.38817076316534
-c46,442.9824,1550.099,426.5549,652.9992,41.518080,82.574848,971.2723,290.68457,83.036160,883.36663,344.11179,206.54856,351.94343,12.419968,0.018020,6340.129856,1.49908948617141
-c47,352.4661,1610.85,426.5549,1159.5933,41.518080,82.574848,971.2723,290.68457,83.036160,883.36663,344.11179,206.54856,351.94343,0.922624,0.018020,6805.461312,1.39658747329622
-c2,2478.077068
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,1108.984,7471.505,3792.343,6973.454,3475.891,6007.756,6530.41,3324.8076,6053.9348,6493.6098,2479.9481,2376.5679,2353.8847,69.37799,1.812953,58514.286843,0.999999998291016
-c1,1145.5648,6358.145,1949.97,375.615141,179.180675,272.76268,352.195669,152.266559,3203.344,357.330128,89.882488,80.061262,89.882488,3.135222,0.543719,14609.879831,4.00511757244776
-c2,1145.5648,384.623163,1949.97,375.615141,179.180675,313.098433,3366.9575,1683.0934,357.330128,277.897139,1299.926,80.061262,89.882488,69.37799,1.812953,11574.391072,5.0554958764962
-c3,1108.984,384.623163,3792.343,6973.454,159.632057,313.098433,6530.41,152.266559,318.232892,3427.0759,2492.7759,80.061262,2353.8847,2.857198,10.00372,28099.702784,2.08238098048781
-c4,1145.5648,6358.145,1949.97,375.615141,159.632057,272.76268,352.195669,171.815177,277.897139,277.897139,69.928921,1273.3007,89.882488,3.135222,0.532687,12778.27482,4.57920080835137
-c5,1145.5648,6358.145,1949.97,375.615141,179.180675,272.76268,6530.41,152.266559,277.897139,277.897139,89.882488,80.061262,89.882488,3.135222,0.532687,17783.20328,3.29042442987569
-c6,1145.5648,6358.145,1949.97,296.182152,179.180675,272.76268,352.195669,1683.0934,318.232892,277.897139,89.882488,80.061262,89.882488,69.37799,0.543719,13162.972354,4.44537030275548
-c7,1145.5648,429.440667,1949.97,3356.913,179.180675,3053.2545,352.195669,152.266559,6053.9348,357.330128,1299.926,1273.3007,2365.9264,69.37799,0.538289,22039.120177,2.65501917079991
-c8,1108.984,6756.043,1949.97,375.615141,179.180675,313.098433,352.195669,152.266559,277.897139,6493.6098,89.882488,89.882488,2353.8847,3.135222,0.543719,20496.189033,2.85488616753584
-c9,1145.5648,6358.145,1949.97,375.615141,179.180675,272.76268,352.195669,171.815177,277.897139,277.897139,89.882488,80.061262,1286.6509,57.16078,10.00372,12884.80257,4.54134132602902
-c10,1108.984,7471.505,2073.98,375.615141,179.180675,272.76268,352.195669,152.266559,357.330128,277.897139,89.882488,2376.5679,89.882488,3.135222,0.532687,15181.717776,3.85425992769252
-c11,1145.5648,7908.45,2073.98,3356.913,1731.6018,313.098433,6530.41,171.815177,318.232892,6493.6098,89.882488,69.928921,1286.6509,2.857198,10.00372,31502.999129,1.85741955607626
-c12,1145.5648,6358.145,1949.97,375.615141,139.46418,272.76268,352.195669,152.266559,6053.9348,318.232892,89.882488,80.061262,2353.8847,3.135222,0.543719,19645.659112,2.97848426522935
-c13,1145.5648,6358.145,1949.97,375.615141,179.180675,3053.2545,3366.9575,152.266559,277.897139,277.897139,69.928921,80.061262,89.882488,69.37799,0.543719,17446.542833,3.35391871430991
-c14,1108.984,6756.043,1949.97,296.182152,139.46418,272.76268,6530.41,152.266559,6053.9348,318.232892,89.882488,80.061262,89.882488,3.135222,0.538289,23841.750012,2.4542781703575
-c15,1108.984,6756.043,1949.97,375.615141,179.180675,6007.756,6530.41,152.266559,277.897139,277.897139,89.882488,80.061262,89.882488,3.135222,0.538289,23879.519402,2.45039632552486
-c16,1145.5648,6358.145,1949.97,375.615141,139.46418,272.76268,352.195669,152.266559,6053.9348,6493.6098,89.882488,80.061262,2353.8847,3.135222,0.543719,25821.03602,2.26614790247232
-c17,1108.984,6756.043,1949.97,375.615141,179.180675,3053.2545,3366.9575,152.266559,357.330128,277.897139,89.882488,80.061262,89.882488,3.135222,0.538289,17840.998391,3.27976524814561
-c18,1145.5648,6358.145,1949.97,375.615141,179.180675,313.098433,352.195669,152.266559,357.330128,277.897139,1299.926,80.061262,89.882488,3.135222,0.543719,12934.812235,4.5237832082548
-c19,1145.5648,6358.145,1949.97,375.615141,179.180675,272.76268,352.195669,152.266559,277.897139,277.897139,2479.9481,80.061262,89.882488,3.135222,0.543719,13995.065593,4.18106553599584
-c20,1145.5648,6358.145,1949.97,375.615141,139.46418,272.76268,352.195669,3324.8076,277.897139,277.897139,89.882488,80.061262,1286.6509,3.135222,0.532687,15934.581907,3.67215699899093
-c21,1108.984,6756.043,1949.97,296.182152,159.632057,313.098433,3366.9575,152.266559,318.232892,357.330128,89.882488,69.928921,1286.6509,3.135222,10.00372,16238.297972,3.60347411924266
-c22,1108.984,7471.505,2073.98,296.182152,159.632057,313.098433,3366.9575,171.815177,318.232892,357.330128,89.882488,69.928921,1286.6509,3.135222,10.00372,17097.31859,3.42242476168057
-c23,1145.5648,6358.145,1949.97,375.615141,179.180675,272.76268,352.195669,3324.8076,357.330128,277.897139,89.882488,2376.5679,89.882488,69.37799,0.543719,17219.723417,3.39809676881469
-c24,1145.5648,6358.145,1949.97,3356.913,179.180675,272.76268,3366.9575,3383.8134,277.897139,277.897139,89.882488,1273.3007,89.882488,3.135222,0.543719,22025.84595,2.6566192603984
-c25,1145.5648,6358.145,1949.97,375.615141,139.46418,3053.2545,313.098433,1683.0934,357.330128,277.897139,80.061262,2376.5679,89.882488,2.570366,0.543719,18203.058456,3.2145304956849
-c26,1145.5648,6358.145,1949.97,375.615141,179.180675,3053.2545,6654.867,3324.8076,277.897139,277.897139,89.882488,80.061262,89.882488,3.135222,0.543719,23860.704173,2.45232857226318
-c27,1108.984,6756.043,1949.97,336.517905,179.180675,272.76268,6530.41,152.266559,357.330128,277.897139,89.882488,2376.5679,89.882488,3.135222,10.00372,20490.833904,2.85563227107191
-c28,1145.5648,6358.145,3996.263,3610.731,1731.6018,3053.2545,6654.867,132.098682,3203.344,277.897139,89.882488,80.061262,1286.6509,2.570366,0.538289,31623.470226,1.85034362894989
-c29,1108.984,6756.043,1949.97,375.615141,179.180675,352.195669,6530.41,152.266559,357.330128,277.897139,89.882488,1273.3007,89.882488,3.135222,0.543719,19496.636928,3.0012502545421
-c30,1145.5648,7908.45,2073.98,375.615141,139.46418,3053.2545,6654.867,171.815177,3203.344,3427.0759,89.882488,89.882488,2353.8847,2.857198,0.543719,30690.481291,1.90659397281919
-c31,1145.5648,6358.145,3996.263,3610.731,1731.6018,3053.2545,6654.867,132.098682,3203.344,277.897139,89.882488,80.061262,1286.6509,2.570366,0.543719,31623.475656,1.85034331123131
-c32,1108.984,6756.043,1949.97,296.182152,3475.891,6007.756,313.098433,152.266559,6053.9348,318.232892,2479.9481,80.061262,69.928921,3.135222,0.543719,29065.97606,2.01315402314016
-c33,1145.5648,6358.145,1949.97,375.615141,179.180675,272.76268,3366.9575,132.098682,277.897139,357.330128,2479.9481,80.061262,89.882488,3.135222,10.00372,17078.552537,3.42618534993599
-c34,1145.5648,7908.45,2073.98,3356.913,139.46418,272.76268,313.098433,152.266559,277.897139,357.330128,1299.926,1273.3007,2365.9264,2.570366,0.538289,20939.988674,2.79438004836248
-c35,1145.5648,7908.45,3792.343,375.615141,159.632057,272.76268,313.098433,3324.8076,318.232892,3427.0759,1299.926,80.061262,89.882488,3.135222,10.00372,22520.591195,2.59825712728916
-c36,1145.5648,6358.145,3996.263,296.182152,139.46418,352.195669,352.195669,1683.0934,6117.8413,277.897139,80.061262,2376.5679,2353.8847,3.135222,0.543719,25533.035112,2.29170900980466
-c37,1145.5648,6358.145,1949.97,375.615141,179.180675,272.76268,352.195669,3324.8076,277.897139,277.897139,89.882488,1273.3007,1286.6509,70.74902,0.543719,17235.16267,3.39505275487456
-c38,1145.5648,6358.145,1949.97,375.615141,179.180675,6007.756,3519.4585,3383.8134,277.897139,3427.0759,1299.926,80.061262,89.882488,3.135222,0.543719,28098.025246,2.08250530499753
-c39,1108.984,7471.505,3792.343,6973.454,1802.5061,6130.47,313.098433,152.266559,357.330128,357.330128,1299.926,1273.3007,2365.9264,2.570366,0.538289,33401.549103,1.75184349945494
-c40,1145.5648,6358.145,1949.97,375.615141,139.46418,6007.756,3519.4585,3383.8134,277.897139,318.232892,2479.9481,1307.6007,69.928921,3.135222,0.543719,27337.073714,2.14047367472935
-c41,1145.5648,7908.45,2073.98,336.517905,179.180675,313.098433,6530.41,1732.6793,318.232892,6493.6098,2479.9481,69.928921,69.928921,2.857198,0.532687,29654.919632,1.97317299698702
-c42,1145.5648,6358.145,3996.263,3610.731,1731.6018,3053.2545,3366.9575,152.266559,3203.344,6559.316,2479.9481,80.061262,1286.6509,3.135222,0.538289,37027.777932,1.58028080411498
-c43,1145.5648,6358.145,1949.97,336.517905,179.180675,352.195669,313.098433,1683.0934,318.232892,3427.0759,1299.926,2388.8133,80.061262,2.857198,10.00372,19844.736154,2.94860491437399
-c44,1145.5648,7908.45,2073.98,336.517905,179.180675,313.098433,6530.41,1732.6793,318.232892,6493.6098,2479.9481,1307.6007,2365.9264,2.857198,0.532687,33188.58889,1.76308450053815
-c45,1145.5648,7908.45,2073.98,7209.476,179.180675,272.76268,6530.41,1732.6793,318.232892,6493.6098,2479.9481,1307.6007,2365.9264,69.37799,0.532687,40087.732024,1.45965570369515
-c46,1145.5648,7908.45,2073.98,3356.913,179.180675,313.098433,6530.41,1732.6793,357.330128,6493.6098,2479.9481,1307.6007,2365.9264,69.37799,0.532687,36314.602013,1.61131565371201
-c47,1108.984,6756.043,1949.97,7209.476,179.180675,352.195669,6530.41,1732.6793,318.232892,6493.6098,2479.9481,1307.6007,2365.9264,2.857198,0.532687,38787.646421,1.5085804912479
-c2,11574.391072
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,157.595858,76.708720,151.959867,151.959867,74.930729,0,153.127903,38.454652,38.454652,38.454652,1.156348,0.102999,882.906247,0
-c2,0,188.762345,0,157.595858,76.708720,151.959867,0,0,153.127903,153.127903,0,38.454652,38.454652,0,0,958.1919,0
-c3,0,188.762345,0,0,76.708720,151.959867,0,74.930729,153.127903,0,0,38.454652,0,1.156348,0,685.100564,0
-c4,0,0,0,157.595858,76.708720,151.959867,151.959867,74.930729,153.127903,153.127903,38.454652,0,38.454652,1.156348,0.102999,997.579498,0
-c5,0,0,0,157.595858,76.708720,151.959867,0,74.930729,153.127903,153.127903,38.454652,38.454652,38.454652,1.156348,0.102999,884.074283,0
-c6,0,0,0,157.595858,76.708720,151.959867,151.959867,0,153.127903,153.127903,38.454652,38.454652,38.454652,0,0.102999,959.947073,0
-c7,0,188.762345,0,0,76.708720,0,151.959867,74.930729,0,153.127903,0,0,0,0,0.102999,645.592563,0
-c8,0,0,0,157.595858,76.708720,151.959867,151.959867,74.930729,153.127903,0,38.454652,38.454652,0,1.156348,0.102999,844.451595,0
-c9,0,0,0,157.595858,76.708720,151.959867,151.959867,74.930729,153.127903,153.127903,38.454652,38.454652,0,0,0,996.320151,0
-c10,0,0,0,157.595858,76.708720,151.959867,151.959867,74.930729,153.127903,153.127903,38.454652,0,38.454652,1.156348,0.102999,997.579498,0
-c11,0,0,0,0,0,151.959867,0,74.930729,153.127903,0,38.454652,38.454652,0,1.156348,0,458.084151,0
-c12,0,0,0,157.595858,76.708720,151.959867,151.959867,74.930729,0,153.127903,38.454652,38.454652,0,1.156348,0.102999,844.451595,0
-c13,0,0,0,157.595858,76.708720,0,0,74.930729,153.127903,153.127903,38.454652,38.454652,38.454652,0,0.102999,730.958068,0
-c14,0,0,0,157.595858,76.708720,151.959867,0,74.930729,0,153.127903,38.454652,38.454652,38.454652,1.156348,0.102999,730.94638,0
-c15,0,0,0,157.595858,76.708720,0,0,74.930729,153.127903,153.127903,38.454652,38.454652,38.454652,1.156348,0.102999,732.114416,0
-c16,0,0,0,157.595858,76.708720,151.959867,151.959867,74.930729,0,0,38.454652,38.454652,0,1.156348,0.102999,691.323692,0
-c17,0,0,0,157.595858,76.708720,0,0,74.930729,153.127903,153.127903,38.454652,38.454652,38.454652,1.156348,0.102999,732.114416,0
-c18,0,0,0,157.595858,76.708720,151.959867,151.959867,74.930729,153.127903,153.127903,0,38.454652,38.454652,1.156348,0.102999,997.579498,0
-c19,0,0,0,157.595858,76.708720,151.959867,151.959867,74.930729,153.127903,153.127903,0,38.454652,38.454652,1.156348,0.102999,997.579498,0
-c20,0,0,0,157.595858,76.708720,151.959867,151.959867,0,153.127903,153.127903,38.454652,38.454652,0,1.156348,0.102999,922.648769,0
-c21,0,0,0,157.595858,76.708720,151.959867,0,74.930729,153.127903,153.127903,38.454652,38.454652,0,1.156348,0,845.516632,0
-c22,0,0,0,157.595858,76.708720,151.959867,0,74.930729,153.127903,153.127903,38.454652,38.454652,0,1.156348,0,845.516632,0
-c23,0,0,0,157.595858,76.708720,151.959867,151.959867,0,153.127903,153.127903,38.454652,0,38.454652,0,0.102999,921.492421,0
-c24,0,0,0,0,76.708720,151.959867,0,0,153.127903,153.127903,38.454652,0,38.454652,1.156348,0.102999,613.093044,0
-c25,0,0,0,157.595858,76.708720,0,151.959867,0,153.127903,153.127903,38.454652,0,38.454652,1.156348,0.102999,770.688902,0
-c26,0,0,0,157.595858,76.708720,0,0,0,153.127903,153.127903,38.454652,38.454652,38.454652,1.156348,0.102999,657.183687,0
-c27,0,0,0,157.595858,76.708720,151.959867,0,74.930729,153.127903,153.127903,38.454652,0,38.454652,1.156348,0,845.516632,0
-c28,0,0,0,0,0,0,0,74.930729,0,153.127903,38.454652,38.454652,0,1.156348,0.102999,306.227283,0
-c29,0,0,0,157.595858,76.708720,151.959867,0,74.930729,153.127903,153.127903,38.454652,0,38.454652,1.156348,0.102999,845.619631,0
-c30,0,0,0,157.595858,76.708720,0,0,74.930729,0,0,38.454652,38.454652,0,1.156348,0.102999,387.403958,0
-c31,0,0,0,0,0,0,0,74.930729,0,153.127903,38.454652,38.454652,0,1.156348,0.102999,306.227283,0
-c32,0,0,0,157.595858,0,0,151.959867,74.930729,0,153.127903,0,38.454652,38.454652,1.156348,0.102999,615.783008,0
-c33,0,0,0,157.595858,76.708720,151.959867,0,74.930729,153.127903,153.127903,0,38.454652,38.454652,1.156348,0,845.516632,0
-c34,0,0,0,0,76.708720,151.959867,151.959867,74.930729,153.127903,153.127903,0,0,0,1.156348,0.102999,763.074336,0
-c35,0,0,0,157.595858,76.708720,151.959867,151.959867,0,153.127903,0,0,38.454652,38.454652,1.156348,0,769.417867,0
-c36,0,0,0,157.595858,76.708720,151.959867,151.959867,0,0,153.127903,38.454652,0,0,1.156348,0.102999,731.066214,0
-c37,0,0,0,157.595858,76.708720,151.959867,151.959867,0,153.127903,153.127903,38.454652,0,0,0,0.102999,883.037769,0
-c38,0,0,0,157.595858,76.708720,0,0,0,153.127903,0,0,38.454652,38.454652,1.156348,0.102999,465.601132,0
-c39,0,0,0,0,0,0,151.959867,74.930729,153.127903,153.127903,0,0,0,1.156348,0.102999,534.405749,0
-c40,0,0,0,157.595858,76.708720,0,0,0,153.127903,153.127903,0,0,38.454652,1.156348,0.102999,580.274383,0
-c41,0,0,0,157.595858,76.708720,151.959867,0,0,153.127903,0,0,38.454652,38.454652,1.156348,0.102999,617.560999,0
-c42,0,0,0,0,0,0,0,74.930729,0,0,0,38.454652,0,1.156348,0.102999,114.644728,0
-c43,0,0,0,157.595858,76.708720,151.959867,151.959867,0,153.127903,0,0,0,38.454652,1.156348,0,730.963215,0
-c44,0,0,0,157.595858,76.708720,151.959867,0,0,153.127903,0,0,0,0,1.156348,0.102999,540.651695,0
-c45,0,0,0,0,76.708720,151.959867,0,0,153.127903,0,0,0,0,0,0.102999,381.899489,0
-c46,0,0,0,0,76.708720,151.959867,0,0,153.127903,0,0,0,0,0,0.102999,381.899489,0
-c47,0,0,0,0,76.708720,151.959867,0,0,153.127903,0,0,0,0,1.156348,0.102999,383.055837,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,60.905800,23.915213,43.122319,43.122319,18.327706,0,47.088742,11.960929,11.960929,11.960929,0.861623,0.418899,273.645408,0
-c2,0,109.549158,0,60.905800,23.915213,43.122319,0,0,47.088742,47.088742,0,11.960929,11.960929,0,0,355.591832,0
-c3,0,109.549158,0,0,23.915213,43.122319,0,18.327706,47.088742,0,0,11.960929,0,0.861623,0,254.82569,0
-c4,0,0,0,60.905800,23.915213,43.122319,43.122319,18.327706,47.088742,47.088742,11.960929,0,11.960929,0.861623,0.418899,308.773221,0
-c5,0,0,0,60.905800,23.915213,43.122319,0,18.327706,47.088742,47.088742,11.960929,11.960929,11.960929,0.861623,0.418899,277.611831,0
-c6,0,0,0,60.905800,23.915213,43.122319,43.122319,0,47.088742,47.088742,11.960929,11.960929,11.960929,0,0.418899,301.544821,0
-c7,0,109.549158,0,0,23.915213,0,43.122319,18.327706,0,47.088742,0,0,0,0,0.418899,242.422037,0
-c8,0,0,0,60.905800,23.915213,43.122319,43.122319,18.327706,47.088742,0,11.960929,11.960929,0,0.861623,0.418899,261.684479,0
-c9,0,0,0,60.905800,23.915213,43.122319,43.122319,18.327706,47.088742,47.088742,11.960929,11.960929,0,0,0,307.492699,0
-c10,0,0,0,60.905800,23.915213,43.122319,43.122319,18.327706,47.088742,47.088742,11.960929,0,11.960929,0.861623,0.418899,308.773221,0
-c11,0,0,0,0,0,43.122319,0,18.327706,47.088742,0,11.960929,11.960929,0,0.861623,0,133.322248,0
-c12,0,0,0,60.905800,23.915213,43.122319,43.122319,18.327706,0,47.088742,11.960929,11.960929,0,0.861623,0.418899,261.684479,0
-c13,0,0,0,60.905800,23.915213,0,0,18.327706,47.088742,47.088742,11.960929,11.960929,11.960929,0,0.418899,233.627889,0
-c14,0,0,0,60.905800,23.915213,43.122319,0,18.327706,0,47.088742,11.960929,11.960929,11.960929,0.861623,0.418899,230.523089,0
-c15,0,0,0,60.905800,23.915213,0,0,18.327706,47.088742,47.088742,11.960929,11.960929,11.960929,0.861623,0.418899,234.489512,0
-c16,0,0,0,60.905800,23.915213,43.122319,43.122319,18.327706,0,0,11.960929,11.960929,0,0.861623,0.418899,214.595737,0
-c17,0,0,0,60.905800,23.915213,0,0,18.327706,47.088742,47.088742,11.960929,11.960929,11.960929,0.861623,0.418899,234.489512,0
-c18,0,0,0,60.905800,23.915213,43.122319,43.122319,18.327706,47.088742,47.088742,0,11.960929,11.960929,0.861623,0.418899,308.773221,0
-c19,0,0,0,60.905800,23.915213,43.122319,43.122319,18.327706,47.088742,47.088742,0,11.960929,11.960929,0.861623,0.418899,308.773221,0
-c20,0,0,0,60.905800,23.915213,43.122319,43.122319,0,47.088742,47.088742,11.960929,11.960929,0,0.861623,0.418899,290.445515,0
-c21,0,0,0,60.905800,23.915213,43.122319,0,18.327706,47.088742,47.088742,11.960929,11.960929,0,0.861623,0,265.232003,0
-c22,0,0,0,60.905800,23.915213,43.122319,0,18.327706,47.088742,47.088742,11.960929,11.960929,0,0.861623,0,265.232003,0
-c23,0,0,0,60.905800,23.915213,43.122319,43.122319,0,47.088742,47.088742,11.960929,0,11.960929,0,0.418899,289.583892,0
-c24,0,0,0,0,23.915213,43.122319,0,0,47.088742,47.088742,11.960929,0,11.960929,0.861623,0.418899,186.417396,0
-c25,0,0,0,60.905800,23.915213,0,43.122319,0,47.088742,47.088742,11.960929,0,11.960929,0.861623,0.418899,247.323196,0
-c26,0,0,0,60.905800,23.915213,0,0,0,47.088742,47.088742,11.960929,11.960929,11.960929,0.861623,0.418899,216.161806,0
-c27,0,0,0,60.905800,23.915213,43.122319,0,18.327706,47.088742,47.088742,11.960929,0,11.960929,0.861623,0,265.232003,0
-c28,0,0,0,0,0,0,0,18.327706,0,47.088742,11.960929,11.960929,0,0.861623,0.418899,90.618828,0
-c29,0,0,0,60.905800,23.915213,43.122319,0,18.327706,47.088742,47.088742,11.960929,0,11.960929,0.861623,0.418899,265.650902,0
-c30,0,0,0,60.905800,23.915213,0,0,18.327706,0,0,11.960929,11.960929,0,0.861623,0.418899,128.351099,0
-c31,0,0,0,0,0,0,0,18.327706,0,47.088742,11.960929,11.960929,0,0.861623,0.418899,90.618828,0
-c32,0,0,0,60.905800,0,0,43.122319,18.327706,0,47.088742,0,11.960929,11.960929,0.861623,0.418899,194.646947,0
-c33,0,0,0,60.905800,23.915213,43.122319,0,18.327706,47.088742,47.088742,0,11.960929,11.960929,0.861623,0,265.232003,0
-c34,0,0,0,0,23.915213,43.122319,43.122319,18.327706,47.088742,47.088742,0,0,0,0.861623,0.418899,223.945563,0
-c35,0,0,0,60.905800,23.915213,43.122319,43.122319,0,47.088742,0,0,11.960929,11.960929,0.861623,0,242.937874,0
-c36,0,0,0,60.905800,23.915213,43.122319,43.122319,0,0,47.088742,11.960929,0,0,0.861623,0.418899,231.395844,0
-c37,0,0,0,60.905800,23.915213,43.122319,43.122319,0,47.088742,47.088742,11.960929,0,0,0,0.418899,277.622963,0
-c38,0,0,0,60.905800,23.915213,0,0,0,47.088742,0,0,11.960929,11.960929,0.861623,0.418899,157.112135,0
-c39,0,0,0,0,0,0,43.122319,18.327706,47.088742,47.088742,0,0,0,0.861623,0.418899,156.908031,0
-c40,0,0,0,60.905800,23.915213,0,0,0,47.088742,47.088742,0,0,11.960929,0.861623,0.418899,192.239948,0
-c41,0,0,0,60.905800,23.915213,43.122319,0,0,47.088742,0,0,11.960929,11.960929,0.861623,0.418899,200.234454,0
-c42,0,0,0,0,0,0,0,18.327706,0,0,0,11.960929,0,0.861623,0.418899,31.569157,0
-c43,0,0,0,60.905800,23.915213,43.122319,43.122319,0,47.088742,0,0,0,11.960929,0.861623,0,230.976945,0
-c44,0,0,0,60.905800,23.915213,43.122319,0,0,47.088742,0,0,0,0,0.861623,0.418899,176.312596,0
-c45,0,0,0,0,23.915213,43.122319,0,0,47.088742,0,0,0,0,0,0.418899,114.545173,0
-c46,0,0,0,0,23.915213,43.122319,0,0,47.088742,0,0,0,0,0,0.418899,114.545173,0
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-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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-c7,0,32.936441,0,0,9.597736,0,18.163317,8.429972,0,19.033555,0,0,0,0,0.089271,88.250292,0
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-c38,0,0,0,21.882908,9.597736,0,0,0,19.033555,0,0,4.848960,4.848960,0.281905,0.089271,60.583295,0
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-c0,0
-
-Patch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
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-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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-c0,0
-
-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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-c0,0
-
-Quantization Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
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-Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
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-Unpatch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
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diff --git a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_promise_results2.csv b/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_promise_results2.csv
deleted file mode 100644
index 3371feb04844bd486683dbd9d19495a0aa255099..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_promise_results2.csv
+++ /dev/null
@@ -1,616 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,1108.984,7471.505,3792.343,6973.454,3475.891,6007.756,6530.41,3324.8076,6053.9348,6493.6098,2479.9481,2376.5679,2353.8847,69.37799,1.812953,58514.286843,0.999999998291016
-c1,1129.478,174.570537,1949.97,118.016247,38.840247,77.680494,118.016247,59.008124,157.113483,157.113483,29.645681,19.513340,19.513340,69.37799,0.010789,4117.868002,14.2098497070803
-c2,1129.478,86.311660,43.155830,118.016247,38.840247,6007.756,118.016247,59.008124,157.113483,157.113483,29.645681,19.513340,19.513340,0.839227,0.010789,7984.331698,7.32863918025767
-c3,1129.478,174.570537,1949.97,3356.913,38.840247,77.680494,3366.9575,59.008124,157.113483,157.113483,29.645681,19.513340,39.466907,0.839227,0.010789,10557.120812,5.54263679754661
-c4,1129.478,6358.145,43.155830,118.016247,38.840247,77.680494,6530.41,59.008124,157.113483,157.113483,29.645681,19.513340,2353.8847,0.839227,0.010789,17072.854645,3.42732880452443
-c5,1108.984,174.570537,43.155830,118.016247,38.840247,77.680494,6530.41,59.008124,157.113483,118.016247,29.645681,19.513340,19.513340,0.839227,0.010789,8495.317586,6.88782797839681
-c6,1129.478,6358.145,43.155830,118.016247,78.556742,77.680494,118.016247,59.008124,157.113483,157.113483,2479.9481,19.513340,19.513340,0.839227,0.010789,10816.108446,5.40992045282679
-c7,1108.984,174.570537,87.285268,77.680494,59.008124,157.113483,118.016247,78.556742,6053.9348,157.113483,19.513340,39.466907,19.513340,0.839227,10.00372,8161.599712,7.16946287380649
-c8,1129.478,174.570537,43.155830,118.016247,38.840247,77.680494,3366.9575,59.008124,157.113483,157.113483,29.645681,19.513340,19.513340,69.37799,0.010789,5459.995085,10.7169118031008
-c9,1129.478,86.311660,1949.97,157.113483,3475.891,3053.2545,157.113483,78.556742,77.680494,118.016247,29.645681,39.466907,29.645681,0.839227,0.016391,10382.999496,5.63558596935151
-c10,1129.478,6358.145,65.564582,118.016247,59.008124,157.113483,118.016247,59.008124,157.113483,77.680494,2479.9481,19.513340,2353.8847,69.37799,0.016391,13221.884305,4.42556333504717
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-c12,1129.478,174.570537,87.285268,118.016247,38.840247,77.680494,118.016247,78.556742,157.113483,157.113483,1299.926,19.513340,19.513340,0.839227,0.010789,3476.473444,16.8315064396187
-c13,1129.478,174.570537,43.155830,118.016247,38.840247,77.680494,157.113483,1683.0934,157.113483,157.113483,29.645681,2376.5679,19.513340,0.839227,1.812953,6164.554305,9.49205457502974
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-c20,1108.984,131.129164,43.155830,157.113483,3475.891,6007.756,77.680494,78.556742,3203.344,77.680494,39.466907,19.513340,19.513340,0.839227,0.021821,14440.645842,4.05205467110122
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-c23,1108.984,174.570537,65.564582,118.016247,38.840247,77.680494,118.016247,78.556742,118.016247,3427.0759,19.513340,2376.5679,1286.6509,0.552395,0.016391,9008.622169,6.49536467350355
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-c35,1129.478,174.570537,43.155830,157.113483,78.556742,118.016247,77.680494,59.008124,118.016247,3427.0759,2479.9481,19.513340,39.466907,0.552395,0.016391,7922.168737,7.38614488619741
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-c41,1129.478,174.570537,3792.343,118.016247,38.840247,3053.2545,6530.41,59.008124,157.113483,77.680494,29.645681,19.513340,19.513340,0.839227,0.010789,15200.237009,3.84956408399405
-c42,1129.478,86.311660,1949.97,118.016247,38.840247,77.680494,118.016247,59.008124,6053.9348,157.113483,2479.9481,19.513340,19.513340,0.839227,0.010789,12308.194098,4.75409194083956
-c43,1129.478,131.129164,3792.343,118.016247,38.840247,3053.2545,118.016247,59.008124,157.113483,157.113483,39.466907,1273.3007,19.513340,0.839227,10.00372,10097.436389,5.79496458400552
-c44,1129.478,131.129164,43.155830,6973.454,3475.891,118.016247,157.113483,78.556742,157.113483,6493.6098,1299.926,19.513340,1286.6509,0.839227,10.00372,21374.450936,2.73758080356998
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-c46,1129.478,131.129164,1949.97,118.016247,59.008124,118.016247,157.113483,78.556742,118.016247,77.680494,2479.9481,19.513340,2353.8847,1.117251,10.00372,8801.451859,6.64825384670375
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-c48,1129.478,174.570537,3792.343,157.113483,38.840247,77.680494,6530.41,78.556742,157.113483,157.113483,29.645681,1273.3007,19.513340,57.16078,10.00372,13682.84369,4.27647115914345
-c49,1129.478,131.129164,1949.97,157.113483,3475.891,3053.2545,157.113483,3324.8076,6053.9348,118.016247,29.645681,39.466907,1286.6509,0.839227,0.010789,20907.321781,2.79874616060588
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-c51,1129.478,131.129164,43.155830,3356.913,1731.6018,3053.2545,77.680494,1683.0934,118.016247,157.113483,1299.926,39.466907,29.645681,0.552395,1.812953,12852.839854,4.55263483031153
-c12,3476.473444
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,352.4661,1550.099,701.7255,1159.5933,558.563,925.6073,971.2723,490.07014,857.91333,883.36663,344.11179,344.22248,351.94343,12.419968,1.04789,9504.422158,0.999999989478582
-c1,442.9824,90.532480,426.5549,82.574848,41.518080,82.574848,82.574848,41.518080,83.036160,83.036160,21.220352,21.220352,21.220352,12.419968,0.018020,1533.001848,6.19987611261665
-c2,442.9824,90.532480,45.439232,82.574848,41.518080,925.6073,82.574848,41.518080,83.036160,83.036160,21.220352,21.220352,21.220352,0.922624,0.018020,1983.421288,4.79193287694848
-c3,442.9824,90.532480,426.5549,652.9992,41.518080,82.574848,590.3019,41.518080,83.036160,83.036160,21.220352,21.220352,21.220352,0.922624,0.018020,2599.655908,3.6560306935809
-c4,442.9824,1610.85,45.439232,82.574848,41.518080,82.574848,971.2723,41.518080,83.036160,83.036160,21.220352,21.220352,351.94343,0.922624,0.018020,3880.126886,2.44951317116506
-c5,352.4661,90.532480,45.439232,82.574848,41.518080,82.574848,971.2723,41.518080,83.036160,83.036160,21.220352,21.220352,21.220352,0.922624,0.018020,1938.569988,4.90280037685179
-c6,442.9824,1610.85,45.439232,82.574848,41.518080,82.574848,82.574848,41.518080,83.036160,83.036160,344.11179,21.220352,21.220352,0.922624,0.018020,2983.597794,3.18555733569639
-c7,352.4661,90.532480,45.439232,82.574848,41.518080,82.574848,82.574848,41.518080,857.91333,83.036160,21.220352,21.220352,21.220352,0.922624,1.669613,1826.401299,5.20390652525995
-c8,442.9824,90.532480,45.439232,82.574848,41.518080,82.574848,590.3019,41.518080,83.036160,83.036160,21.220352,21.220352,21.220352,12.419968,0.018020,1659.613232,5.72688949572743
-c9,442.9824,90.532480,426.5549,82.574848,558.563,544.1687,82.574848,41.518080,83.036160,83.036160,21.220352,21.220352,21.220352,0.922624,0.018020,2500.143276,3.80155084273855
-c10,442.9824,1610.85,45.439232,82.574848,41.518080,82.574848,82.574848,41.518080,83.036160,83.036160,344.11179,21.220352,351.94343,12.419968,0.018020,3325.818216,2.85776950360628
-c11,442.9824,1550.099,45.439232,82.574848,333.7788,82.574848,971.2723,490.07014,83.036160,83.036160,21.220352,21.220352,21.220352,0.922624,1.04789,4230.495458,2.24664510993915
-c12,442.9824,90.532480,45.439232,82.574848,41.518080,82.574848,82.574848,41.518080,83.036160,83.036160,208.37216,21.220352,21.220352,0.922624,0.018020,1327.540644,7.15942030476769
-c13,442.9824,90.532480,45.439232,82.574848,41.518080,82.574848,82.574848,290.68457,83.036160,83.036160,21.220352,344.22248,21.220352,0.922624,1.04789,1713.587324,5.54650555021814
-c14,442.9824,1610.85,701.7255,1159.5933,41.518080,82.574848,82.574848,41.518080,83.036160,83.036160,21.220352,21.220352,21.220352,0.922624,0.018020,4394.011076,2.16304005094774
-c15,352.4661,90.532480,45.439232,82.574848,41.518080,82.574848,82.574848,41.518080,83.036160,83.036160,21.220352,344.22248,210.33129,0.922624,0.018020,1561.985602,6.08483300828576
-c16,442.9824,90.532480,45.439232,82.574848,41.518080,544.1687,82.574848,290.68457,527.54432,83.036160,21.220352,344.22248,21.220352,12.419968,0.018020,2630.15681,3.61363313415396
-c17,442.9824,90.532480,45.439232,82.574848,41.518080,925.6073,590.3019,41.518080,83.036160,83.036160,21.220352,21.220352,351.94343,0.922624,0.018020,2821.871418,3.36812717991365
-c18,442.9824,90.532480,45.439232,82.574848,41.518080,82.574848,82.574848,490.07014,83.036160,83.036160,21.220352,344.22248,21.220352,0.922624,0.018020,1911.943024,4.97107996503352
-c19,352.4661,90.532480,45.439232,82.574848,41.518080,82.574848,82.574848,41.518080,83.036160,83.036160,344.11179,21.220352,351.94343,0.922624,0.018020,1703.487052,5.57939174759329
-c20,352.4661,90.532480,45.439232,82.574848,558.563,925.6073,82.574848,41.518080,527.54432,83.036160,21.220352,21.220352,21.220352,0.922624,0.018020,2854.458068,3.32967645648118
-c21,442.9824,90.532480,45.439232,82.574848,41.518080,82.574848,82.574848,41.518080,83.036160,83.036160,21.220352,344.22248,351.94343,0.922624,0.018020,1794.114042,5.29755712610619
-c22,352.4661,90.532480,426.5549,652.9992,41.518080,82.574848,82.574848,41.518080,83.036160,83.036160,344.11179,21.220352,351.94343,0.922624,0.018020,2655.027072,3.57978338535814
-c23,352.4661,90.532480,45.439232,82.574848,41.518080,82.574848,82.574848,41.518080,83.036160,543.5573,21.220352,344.22248,210.33129,0.922624,0.018020,2022.506742,4.69932756746639
-c24,352.4661,90.532480,45.439232,652.9992,41.518080,544.1687,82.574848,41.518080,857.91333,83.036160,344.11179,21.220352,210.33129,7.640322,0.018020,3375.487984,2.81571788182322
-c25,442.9824,90.532480,45.439232,82.574848,41.518080,82.574848,971.2723,490.07014,83.036160,83.036160,21.220352,344.22248,21.220352,0.922624,0.018020,2800.640476,3.39366009314006
-c26,442.9824,90.532480,45.439232,82.574848,41.518080,544.1687,971.2723,41.518080,83.036160,83.036160,344.11179,21.220352,21.220352,0.922624,0.018020,2813.571578,3.37806291992395
-c27,442.9824,90.532480,45.439232,82.574848,41.518080,544.1687,590.3019,490.07014,83.036160,83.036160,208.37216,21.220352,21.220352,0.922624,0.018020,2745.413608,3.46192711513918
-c28,442.9824,90.532480,45.439232,82.574848,41.518080,544.1687,590.3019,490.07014,83.036160,83.036160,208.37216,21.220352,21.220352,0.922624,0.018020,2745.413608,3.46192711513918
-c29,442.9824,90.532480,45.439232,82.574848,41.518080,82.574848,82.574848,41.518080,83.036160,83.036160,208.37216,21.220352,351.94343,0.922624,1.04789,1659.293592,5.72799270184895
-c30,442.9824,90.532480,45.439232,82.574848,41.518080,925.6073,971.2723,41.518080,857.91333,83.036160,21.220352,344.22248,21.220352,0.922624,0.018020,3969.998038,2.39406211983468
-c31,442.9824,90.532480,45.439232,82.574848,41.518080,82.574848,971.2723,41.518080,83.036160,83.036160,208.37216,21.220352,21.220352,7.640322,0.018020,2222.955794,4.27557837906432
-c32,352.4661,90.532480,45.439232,82.574848,41.518080,82.574848,82.574848,41.518080,83.036160,543.5573,21.220352,206.54856,210.33129,0.922624,0.018020,1884.832822,5.04258072270662
-c33,442.9824,90.532480,45.439232,82.574848,333.7788,82.574848,590.3019,41.518080,857.91333,83.036160,344.11179,344.22248,21.220352,0.922624,0.018020,3361.147344,2.82773139719484
-c34,442.9824,90.532480,45.439232,82.574848,41.518080,82.574848,82.574848,41.518080,83.036160,543.5573,21.220352,344.22248,210.33129,0.922624,0.018020,2113.023042,4.49802085414169
-c35,442.9824,90.532480,45.439232,82.574848,41.518080,82.574848,82.574848,41.518080,83.036160,543.5573,344.11179,21.220352,21.220352,0.922624,0.018020,1923.801414,4.94043802795344
-c36,442.9824,90.532480,45.439232,82.574848,41.518080,82.574848,82.574848,290.68457,83.036160,83.036160,208.37216,206.54856,21.220352,0.922624,0.018020,1762.035342,5.39400169341202
-c37,352.4661,1610.85,45.439232,652.9992,558.563,544.1687,82.574848,41.518080,857.91333,83.036160,208.37216,21.220352,21.220352,7.640322,0.018020,5087.999856,1.86800751576107
-c38,442.9824,90.532480,45.439232,82.574848,41.518080,82.574848,82.574848,290.68457,83.036160,83.036160,208.37216,206.54856,21.220352,7.640322,0.018020,1768.75304,5.3735153555685
-c39,352.4661,90.532480,701.7255,82.574848,41.518080,925.6073,82.574848,290.68457,83.036160,83.036160,21.220352,21.220352,21.220352,0.922624,0.018020,2798.357746,3.39642843447835
-c40,442.9824,90.532480,45.439232,82.574848,333.7788,925.6073,590.3019,41.518080,83.036160,543.5573,21.220352,21.220352,21.220352,0.922624,0.018020,3243.9302,2.92990948603304
-c41,442.9824,90.532480,701.7255,82.574848,41.518080,544.1687,971.2723,41.518080,83.036160,83.036160,21.220352,21.220352,21.220352,0.922624,0.018020,3146.966408,3.0201853543209
-c42,442.9824,90.532480,426.5549,82.574848,41.518080,82.574848,82.574848,41.518080,857.91333,83.036160,344.11179,21.220352,21.220352,0.922624,0.018020,2619.273112,3.62864863216873
-c43,442.9824,90.532480,701.7255,82.574848,41.518080,544.1687,82.574848,41.518080,83.036160,83.036160,21.220352,206.54856,21.220352,0.922624,1.669613,2445.248757,3.88689361035452
-c44,442.9824,90.532480,45.439232,1159.5933,558.563,82.574848,82.574848,41.518080,83.036160,883.36663,208.37216,21.220352,210.33129,0.922624,1.669613,3912.697017,2.4291228975289
-c45,442.9824,90.532480,426.5549,82.574848,41.518080,82.574848,590.3019,41.518080,83.036160,83.036160,21.220352,206.54856,21.220352,0.922624,0.018020,2214.559764,4.29178831988441
-c46,442.9824,90.532480,426.5549,82.574848,41.518080,82.574848,82.574848,41.518080,83.036160,83.036160,344.11179,21.220352,351.94343,0.922624,1.669613,2176.770613,4.36629457629054
-c47,352.4661,90.532480,426.5549,82.574848,41.518080,82.574848,971.2723,41.518080,83.036160,883.36663,21.220352,21.220352,210.33129,0.922624,1.04789,3310.156934,2.87129041322696
-c48,442.9824,90.532480,701.7255,82.574848,41.518080,82.574848,971.2723,41.518080,83.036160,83.036160,21.220352,206.54856,21.220352,7.640322,1.669613,2879.070055,3.30121242148057
-c49,442.9824,90.532480,426.5549,82.574848,558.563,544.1687,82.574848,490.07014,857.91333,83.036160,21.220352,21.220352,210.33129,0.922624,0.018020,3912.683444,2.42913132409463
-c50,352.4661,90.532480,701.7255,82.574848,558.563,544.1687,82.574848,41.518080,527.54432,883.36663,208.37216,21.220352,21.220352,0.922624,1.669613,4118.439607,2.30777256295524
-c51,442.9824,90.532480,45.439232,652.9992,333.7788,544.1687,82.574848,290.68457,83.036160,83.036160,208.37216,21.220352,21.220352,0.922624,1.04789,2902.015928,3.2751101531821
-c12,1327.540644
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,1108.984,7471.505,3792.343,6973.454,3475.891,6007.756,6530.41,3324.8076,6053.9348,6493.6098,2479.9481,2376.5679,2353.8847,69.37799,1.812953,58514.286843,0.999999998291016
-c1,1145.5648,472.88204,1949.97,336.517905,139.46418,272.76268,313.098433,152.266559,357.330128,357.330128,80.061262,69.928921,69.928921,69.37799,0.532687,5787.016634,10.1113042406143
-c2,1145.5648,384.623163,174.989917,336.517905,139.46418,6007.756,313.098433,152.266559,357.330128,357.330128,80.061262,69.928921,69.928921,2.857198,0.532687,9592.250202,6.10016263136917
-c3,1145.5648,472.88204,1949.97,3356.913,139.46418,272.76268,3366.9575,152.266559,357.330128,357.330128,80.061262,69.928921,89.882488,2.857198,0.532687,11814.703571,4.95266648004278
-c4,1145.5648,6358.145,174.989917,336.517905,139.46418,272.76268,6530.41,152.266559,357.330128,357.330128,80.061262,69.928921,2353.8847,2.857198,0.532687,18332.046065,3.19191247481784
-c5,1108.984,472.88204,174.989917,336.517905,139.46418,272.76268,6530.41,152.266559,357.330128,318.232892,80.061262,69.928921,69.928921,2.857198,0.532687,10087.14929,5.80087441760392
-c6,1145.5648,6358.145,174.989917,336.517905,179.180675,272.76268,313.098433,152.266559,357.330128,357.330128,2479.9481,69.928921,69.928921,2.857198,0.532687,12270.382052,4.76874200967429
-c7,1108.984,472.88204,219.119355,296.182152,159.632057,352.195669,313.098433,171.815177,6053.9348,357.330128,69.928921,89.882488,69.928921,2.857198,10.00372,9747.775059,6.00283509709131
-c8,1145.5648,472.88204,174.989917,336.517905,139.46418,272.76268,3366.9575,152.266559,357.330128,357.330128,80.061262,69.928921,69.928921,69.37799,0.532687,7065.895618,8.28122706282488
-c9,1145.5648,384.623163,1949.97,375.615141,3475.891,3197.8545,352.195669,171.815177,277.897139,318.232892,80.061262,89.882488,80.061262,2.857198,0.538289,11903.05998,4.91590283924703
-c10,1145.5648,6358.145,197.398669,336.517905,159.632057,352.195669,313.098433,152.266559,357.330128,277.897139,2479.9481,69.928921,2353.8847,69.37799,0.538289,14623.724359,4.00132585970519
-c11,1145.5648,7908.45,219.119355,375.615141,1731.6018,313.098433,6530.41,3324.8076,357.330128,277.897139,80.061262,69.928921,69.928921,3.135222,1.812953,22408.761675,2.61122356650159
-c12,1145.5648,472.88204,219.119355,336.517905,139.46418,272.76268,313.098433,171.815177,357.330128,357.330128,1299.926,69.928921,69.928921,2.857198,0.532687,5229.058553,11.1902142863571
-c13,1145.5648,472.88204,174.989917,336.517905,139.46418,272.76268,352.195669,1683.0934,357.330128,357.330128,80.061262,2376.5679,69.928921,2.857198,1.812953,7823.359081,7.47943249047151
-c14,1145.5648,6358.145,3996.263,6973.454,159.632057,313.098433,352.195669,152.266559,357.330128,318.232892,69.928921,69.928921,69.928921,2.857198,0.543719,20339.370218,2.87689765848925
-c15,1108.984,472.88204,197.398669,336.517905,139.46418,272.76268,313.098433,171.815177,318.232892,277.897139,69.928921,2376.5679,1319.7884,2.570366,0.538289,7378.446991,7.93043388687762
-c16,1145.5648,429.440667,197.398669,336.517905,139.46418,3053.2545,352.195669,1683.0934,3203.344,357.330128,69.928921,2376.5679,69.928921,69.37799,0.538289,13483.945939,4.33955213657467
-c17,1145.5648,429.440667,174.989917,336.517905,139.46418,6007.756,3519.4585,152.266559,357.330128,357.330128,80.061262,69.928921,2353.8847,2.570366,0.532687,15127.09672,3.86817692378596
-c18,1145.5648,472.88204,174.989917,336.517905,139.46418,272.76268,313.098433,3324.8076,357.330128,277.897139,80.061262,2376.5679,69.928921,2.857198,0.538289,9345.268392,6.26138102860191
-c19,1108.984,472.88204,174.989917,375.615141,139.46418,272.76268,313.098433,152.266559,357.330128,318.232892,2479.9481,69.928921,2353.8847,2.857198,0.532687,8592.777576,6.80970566786953
-c20,1108.984,429.440667,174.989917,375.615141,3475.891,6007.756,272.76268,171.815177,3203.344,277.897139,89.882488,69.928921,69.928921,2.857198,0.543719,15731.636968,3.71952941642831
-c21,1145.5648,472.88204,174.989917,336.517905,139.46418,272.76268,313.098433,152.266559,357.330128,277.897139,80.061262,2376.5679,2353.8847,2.857198,0.532687,8456.677528,6.91929968445996
-c22,1108.984,429.440667,1949.97,3356.913,139.46418,352.195669,272.76268,171.815177,357.330128,277.897139,2479.9481,80.061262,2353.8847,2.570366,0.543719,13333.780787,4.38842420907408
-c23,1108.984,472.88204,197.398669,336.517905,139.46418,272.76268,313.098433,171.815177,318.232892,3427.0759,69.928921,2376.5679,1319.7884,2.570366,0.538289,10527.625752,5.5581655033726
-c24,1108.984,384.623163,174.989917,3356.913,139.46418,3053.2545,352.195669,132.098682,6053.9348,277.897139,2479.9481,89.882488,1286.6509,57.16078,0.532687,18948.530005,3.08806469519025
-c25,1145.5648,472.88204,174.989917,336.517905,139.46418,313.098433,6530.41,3324.8076,357.330128,357.330128,80.061262,2376.5679,80.061262,2.857198,0.532687,15692.47544,3.72881172851584
-c26,1145.5648,429.440667,174.989917,336.517905,139.46418,3053.2545,6654.867,171.815177,357.330128,357.330128,2479.9481,69.928921,69.928921,2.857198,0.532687,15443.770229,3.78886020683195
-c27,1145.5648,429.440667,197.398669,336.517905,179.180675,3053.2545,3366.9575,3383.8134,357.330128,357.330128,1299.926,89.882488,80.061262,2.570366,0.532687,14279.761175,4.09770763783304
-c28,1145.5648,429.440667,197.398669,375.615141,179.180675,3053.2545,3366.9575,3383.8134,318.232892,357.330128,1299.926,89.882488,80.061262,2.570366,0.532687,14279.761175,4.09770763783304
-c29,1145.5648,472.88204,197.398669,336.517905,139.46418,352.195669,313.098433,171.815177,318.232892,357.330128,1299.926,69.928921,2353.8847,2.857198,1.812953,7532.909665,7.76781996179932
-c30,1145.5648,472.88204,219.119355,375.615141,159.632057,6007.756,6530.41,171.815177,6053.9348,357.330128,80.061262,2376.5679,80.061262,2.857198,0.538289,24034.145409,2.43463146301949
-c31,1145.5648,472.88204,197.398669,375.615141,139.46418,352.195669,6530.41,132.098682,318.232892,357.330128,1299.926,89.882488,89.882488,57.16078,0.532687,11558.576644,5.06241279864967
-c32,1108.984,429.440667,174.989917,375.615141,179.180675,313.098433,272.76268,132.098682,318.232892,3427.0759,80.061262,1273.3007,1286.6509,2.570366,0.538289,9374.600504,6.24178984414897
-c33,1145.5648,429.440667,174.989917,296.182152,1731.6018,272.76268,3366.9575,171.815177,6053.9348,318.232892,2479.9481,2376.5679,69.928921,3.135222,0.538289,18891.600817,3.09737047167584
-c34,1145.5648,472.88204,174.989917,336.517905,139.46418,272.76268,313.098433,152.266559,357.330128,3427.0759,80.061262,2376.5679,1319.7884,2.857198,0.538289,10571.765591,5.53495873379171
-c35,1145.5648,472.88204,174.989917,375.615141,179.180675,313.098433,272.76268,152.266559,318.232892,3427.0759,2492.7759,69.928921,89.882488,2.570366,0.538289,9487.365001,6.16760145942232
-c36,1145.5648,472.88204,197.398669,375.615141,139.46418,352.195669,272.76268,1683.0934,277.897139,318.232892,1299.926,1273.3007,69.928921,2.857198,0.543719,7881.663148,7.42410389810148
-c37,1108.984,6756.043,174.989917,3356.913,3586.141,3197.8545,352.195669,132.098682,6053.9348,357.330128,1299.926,89.882488,80.061262,57.16078,0.532687,26604.047913,2.19945050521662
-c38,1145.5648,472.88204,197.398669,375.615141,139.46418,352.195669,272.76268,1683.0934,277.897139,318.232892,1299.926,1273.3007,69.928921,57.16078,0.543719,7935.96673,7.37330284972978
-c39,1108.984,429.440667,3792.343,296.182152,159.632057,6007.756,352.195669,1683.0934,318.232892,277.897139,89.882488,89.882488,69.928921,3.135222,0.543719,14679.129814,3.98622310626142
-c40,1145.5648,429.440667,197.398669,336.517905,1731.6018,6130.47,3519.4585,152.266559,318.232892,3427.0759,89.882488,80.061262,80.061262,2.857198,0.532687,17641.422589,3.31686893254281
-c41,1145.5648,472.88204,3792.343,336.517905,139.46418,3053.2545,6654.867,152.266559,357.330128,277.897139,80.061262,69.928921,69.928921,2.857198,0.532687,16605.69624,3.52374785404513
-c42,1145.5648,384.623163,1949.97,336.517905,139.46418,272.76268,313.098433,152.266559,6053.9348,357.330128,2479.9481,69.928921,69.928921,2.857198,0.532687,13728.728475,4.26217814150354
-c43,1145.5648,429.440667,3792.343,336.517905,139.46418,3053.2545,313.098433,152.266559,357.330128,357.330128,89.882488,1273.3007,69.928921,2.857198,10.00372,11522.583327,5.0782263555487
-c44,1145.5648,429.440667,174.989917,6973.454,3475.891,313.098433,352.195669,171.815177,357.330128,6493.6098,1337.2918,69.928921,1286.6509,2.857198,10.00372,22594.12213,2.58980128758027
-c45,1145.5648,472.88204,1949.97,336.517905,139.46418,272.76268,3366.9575,152.266559,357.330128,357.330128,80.061262,1273.3007,69.928921,2.857198,0.532687,9977.726688,5.86449078896146
-c46,1145.5648,429.440667,1949.97,336.517905,159.632057,313.098433,352.195669,171.815177,318.232892,277.897139,2479.9481,69.928921,2353.8847,3.135222,10.00372,10371.265402,5.64196209534083
-c47,1108.984,472.88204,1949.97,336.517905,139.46418,272.76268,6530.41,152.266559,277.897139,6493.6098,89.882488,80.061262,1286.6509,2.857198,1.812953,19196.029104,3.04824952187544
-c48,1145.5648,472.88204,3792.343,375.615141,139.46418,272.76268,6530.41,171.815177,357.330128,357.330128,80.061262,1273.3007,69.928921,57.16078,10.00372,15105.972657,3.87358614928555
-c49,1145.5648,429.440667,1949.97,375.615141,3475.891,3197.8545,352.195669,3324.8076,6053.9348,318.232892,80.061262,89.882488,1286.6509,2.857198,0.532687,22083.491604,2.64968455293695
-c50,1108.984,429.440667,3792.343,375.615141,3475.891,3197.8545,272.76268,171.815177,3203.344,6559.316,1337.2918,69.928921,69.928921,2.857198,10.00372,24077.376725,2.43026004320552
-c51,1145.5648,429.440667,174.989917,3356.913,1731.6018,3053.2545,272.76268,1683.0934,318.232892,357.330128,1299.926,89.882488,80.061262,2.570366,1.812953,13997.436853,4.18035723536222
-c12,5229.058553
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,188.762345,0,157.595858,76.708720,151.959867,151.959867,74.930729,153.127903,153.127903,38.454652,38.454652,38.454652,0,0.102999,1223.640147,0
-c2,0,188.762345,90.173220,157.595858,76.708720,0,151.959867,74.930729,153.127903,153.127903,38.454652,38.454652,38.454652,1.156348,0.102999,1163.009848,0
-c3,0,188.762345,0,0,76.708720,151.959867,0,74.930729,153.127903,153.127903,38.454652,38.454652,38.454652,1.156348,0.102999,915.24077,0
-c4,0,0,90.173220,157.595858,76.708720,151.959867,0,74.930729,153.127903,153.127903,38.454652,38.454652,0,1.156348,0.102999,935.792851,0
-c5,0,188.762345,90.173220,157.595858,76.708720,151.959867,0,74.930729,153.127903,153.127903,38.454652,38.454652,38.454652,1.156348,0.102999,1163.009848,0
-c6,0,0,90.173220,157.595858,76.708720,151.959867,151.959867,74.930729,153.127903,153.127903,0,38.454652,38.454652,1.156348,0.102999,1087.752718,0
-c7,0,188.762345,90.173220,157.595858,76.708720,151.959867,151.959867,74.930729,0,153.127903,38.454652,38.454652,38.454652,1.156348,0,1161.738813,0
-c8,0,188.762345,90.173220,157.595858,76.708720,151.959867,0,74.930729,153.127903,153.127903,38.454652,38.454652,38.454652,0,0.102999,1161.8535,0
-c9,0,188.762345,0,157.595858,0,0,151.959867,74.930729,153.127903,153.127903,38.454652,38.454652,38.454652,1.156348,0.102999,996.127908,0
-c10,0,0,90.173220,157.595858,76.708720,151.959867,151.959867,74.930729,153.127903,153.127903,0,38.454652,0,0,0.102999,1048.141718,0
-c11,0,0,90.173220,157.595858,0,151.959867,0,0,153.127903,153.127903,38.454652,38.454652,38.454652,1.156348,0,822.505055,0
-c12,0,188.762345,90.173220,157.595858,76.708720,151.959867,151.959867,74.930729,153.127903,153.127903,0,38.454652,38.454652,1.156348,0.102999,1276.515063,0
-c13,0,188.762345,90.173220,157.595858,76.708720,151.959867,151.959867,0,153.127903,153.127903,38.454652,0,38.454652,1.156348,0,1201.481335,0
-c14,0,0,0,0,76.708720,151.959867,151.959867,74.930729,153.127903,153.127903,38.454652,38.454652,38.454652,1.156348,0.102999,878.438292,0
-c15,0,188.762345,90.173220,157.595858,76.708720,151.959867,151.959867,74.930729,153.127903,153.127903,38.454652,0,0,1.156348,0.102999,1238.060411,0
-c16,0,188.762345,90.173220,157.595858,76.708720,0,151.959867,0,0,153.127903,38.454652,0,38.454652,0,0.102999,895.340216,0
-c17,0,188.762345,90.173220,157.595858,76.708720,0,0,74.930729,153.127903,153.127903,38.454652,38.454652,0,1.156348,0.102999,972.595329,0
-c18,0,188.762345,90.173220,157.595858,76.708720,151.959867,151.959867,0,153.127903,153.127903,38.454652,0,38.454652,1.156348,0.102999,1201.584334,0
-c19,0,188.762345,90.173220,157.595858,76.708720,151.959867,151.959867,74.930729,153.127903,153.127903,0,38.454652,0,1.156348,0.102999,1238.060411,0
-c20,0,188.762345,90.173220,157.595858,0,0,151.959867,74.930729,0,153.127903,38.454652,38.454652,38.454652,1.156348,0.102999,933.173225,0
-c21,0,188.762345,90.173220,157.595858,76.708720,151.959867,151.959867,74.930729,153.127903,153.127903,38.454652,0,0,1.156348,0.102999,1238.060411,0
-c22,0,188.762345,0,0,76.708720,151.959867,151.959867,74.930729,153.127903,153.127903,0,38.454652,0,1.156348,0.102999,990.291333,0
-c23,0,188.762345,90.173220,157.595858,76.708720,151.959867,151.959867,74.930729,153.127903,0,38.454652,0,0,1.156348,0.102999,1084.932508,0
-c24,0,188.762345,90.173220,0,76.708720,0,151.959867,74.930729,0,153.127903,0,38.454652,0,0,0.102999,774.220435,0
-c25,0,188.762345,90.173220,157.595858,76.708720,151.959867,0,0,153.127903,153.127903,38.454652,0,38.454652,1.156348,0.102999,1049.624467,0
-c26,0,188.762345,90.173220,157.595858,76.708720,0,0,74.930729,153.127903,153.127903,0,38.454652,38.454652,1.156348,0.102999,972.595329,0
-c27,0,188.762345,90.173220,157.595858,76.708720,0,0,0,153.127903,153.127903,0,38.454652,38.454652,1.156348,0.102999,897.6646,0
-c28,0,188.762345,90.173220,157.595858,76.708720,0,0,0,153.127903,153.127903,0,38.454652,38.454652,1.156348,0.102999,897.6646,0
-c29,0,188.762345,90.173220,157.595858,76.708720,151.959867,151.959867,74.930729,153.127903,153.127903,0,38.454652,0,1.156348,0,1237.957412,0
-c30,0,188.762345,90.173220,157.595858,76.708720,0,0,74.930729,0,153.127903,38.454652,0,38.454652,1.156348,0.102999,819.467426,0
-c31,0,188.762345,90.173220,157.595858,76.708720,151.959867,0,74.930729,153.127903,153.127903,0,38.454652,38.454652,0,0.102999,1123.398848,0
-c32,0,188.762345,90.173220,157.595858,76.708720,151.959867,151.959867,74.930729,153.127903,0,38.454652,0,0,1.156348,0.102999,1084.932508,0
-c33,0,188.762345,90.173220,157.595858,0,151.959867,0,74.930729,0,153.127903,0,0,38.454652,1.156348,0.102999,856.263921,0
-c34,0,188.762345,90.173220,157.595858,76.708720,151.959867,151.959867,74.930729,153.127903,0,38.454652,0,0,1.156348,0.102999,1084.932508,0
-c35,0,188.762345,90.173220,157.595858,76.708720,151.959867,151.959867,74.930729,153.127903,0,0,38.454652,38.454652,1.156348,0.102999,1123.38716,0
-c36,0,188.762345,90.173220,157.595858,76.708720,151.959867,151.959867,0,153.127903,153.127903,0,0,38.454652,1.156348,0.102999,1163.129682,0
-c37,0,0,90.173220,0,0,0,151.959867,74.930729,0,153.127903,0,38.454652,38.454652,0,0.102999,547.204022,0
-c38,0,188.762345,90.173220,157.595858,76.708720,151.959867,151.959867,0,153.127903,153.127903,0,0,38.454652,0,0.102999,1161.973334,0
-c39,0,188.762345,0,157.595858,76.708720,0,151.959867,0,153.127903,153.127903,38.454652,38.454652,38.454652,1.156348,0.102999,997.905899,0
-c40,0,188.762345,90.173220,157.595858,0,0,0,74.930729,153.127903,0,38.454652,38.454652,38.454652,1.156348,0.102999,781.213358,0
-c41,0,188.762345,0,157.595858,76.708720,0,0,74.930729,153.127903,153.127903,38.454652,38.454652,38.454652,1.156348,0.102999,920.876761,0
-c42,0,188.762345,0,157.595858,76.708720,151.959867,151.959867,74.930729,0,153.127903,0,38.454652,38.454652,1.156348,0.102999,1033.21394,0
-c43,0,188.762345,0,157.595858,76.708720,0,151.959867,74.930729,153.127903,153.127903,38.454652,0,38.454652,1.156348,0,1034.278977,0
-c44,0,188.762345,90.173220,0,0,151.959867,151.959867,74.930729,153.127903,0,0,38.454652,0,1.156348,0,850.524931,0
-c45,0,188.762345,0,157.595858,76.708720,151.959867,0,74.930729,153.127903,153.127903,38.454652,0,38.454652,1.156348,0.102999,1034.381976,0
-c46,0,188.762345,0,157.595858,76.708720,151.959867,151.959867,74.930729,153.127903,153.127903,0,38.454652,0,1.156348,0,1147.784192,0
-c47,0,188.762345,0,157.595858,76.708720,151.959867,0,74.930729,153.127903,0,38.454652,38.454652,0,1.156348,0,881.151074,0
-c48,0,188.762345,0,157.595858,76.708720,151.959867,0,74.930729,153.127903,153.127903,38.454652,0,38.454652,0,0,1033.122629,0
-c49,0,188.762345,0,157.595858,0,0,151.959867,0,0,153.127903,38.454652,38.454652,0,1.156348,0.102999,729.614624,0
-c50,0,188.762345,0,157.595858,0,0,151.959867,74.930729,0,0,0,38.454652,38.454652,1.156348,0,651.314451,0
-c51,0,188.762345,90.173220,0,0,0,151.959867,0,153.127903,153.127903,0,38.454652,38.454652,1.156348,0,815.21689,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,109.549158,0,60.905800,23.915213,43.122319,43.122319,18.327706,47.088742,47.088742,11.960929,11.960929,11.960929,0,0.418899,429.421685,0
-c2,0,109.549158,41.660867,60.905800,23.915213,0,43.122319,18.327706,47.088742,47.088742,11.960929,11.960929,11.960929,0.861623,0.418899,428.821856,0
-c3,0,109.549158,0,0,23.915213,43.122319,0,18.327706,47.088742,47.088742,11.960929,11.960929,11.960929,0.861623,0.418899,326.255189,0
-c4,0,0,41.660867,60.905800,23.915213,43.122319,0,18.327706,47.088742,47.088742,11.960929,11.960929,0,0.861623,0.418899,307.311769,0
-c5,0,109.549158,41.660867,60.905800,23.915213,43.122319,0,18.327706,47.088742,47.088742,11.960929,11.960929,11.960929,0.861623,0.418899,428.821856,0
-c6,0,0,41.660867,60.905800,23.915213,43.122319,43.122319,18.327706,47.088742,47.088742,0,11.960929,11.960929,0.861623,0.418899,350.434088,0
-c7,0,109.549158,41.660867,60.905800,23.915213,43.122319,43.122319,18.327706,0,47.088742,11.960929,11.960929,11.960929,0.861623,0,424.436534,0
-c8,0,109.549158,41.660867,60.905800,23.915213,43.122319,0,18.327706,47.088742,47.088742,11.960929,11.960929,11.960929,0,0.418899,427.960233,0
-c9,0,109.549158,0,60.905800,0,0,43.122319,18.327706,47.088742,47.088742,11.960929,11.960929,11.960929,0.861623,0.418899,363.245776,0
-c10,0,0,41.660867,60.905800,23.915213,43.122319,43.122319,18.327706,47.088742,47.088742,0,11.960929,0,0,0.418899,337.611536,0
-c11,0,0,41.660867,60.905800,0,43.122319,0,0,47.088742,47.088742,11.960929,11.960929,11.960929,0.861623,0,276.61088,0
-c12,0,109.549158,41.660867,60.905800,23.915213,43.122319,43.122319,18.327706,47.088742,47.088742,0,11.960929,11.960929,0.861623,0.418899,459.983246,0
-c13,0,109.549158,41.660867,60.905800,23.915213,43.122319,43.122319,0,47.088742,47.088742,11.960929,0,11.960929,0.861623,0,441.236641,0
-c14,0,0,0,0,23.915213,43.122319,43.122319,18.327706,47.088742,47.088742,11.960929,11.960929,11.960929,0.861623,0.418899,259.82835,0
-c15,0,109.549158,41.660867,60.905800,23.915213,43.122319,43.122319,18.327706,47.088742,47.088742,11.960929,0,0,0.861623,0.418899,448.022317,0
-c16,0,109.549158,41.660867,60.905800,23.915213,0,43.122319,0,0,47.088742,11.960929,0,11.960929,0,0.418899,350.582856,0
-c17,0,109.549158,41.660867,60.905800,23.915213,0,0,18.327706,47.088742,47.088742,11.960929,11.960929,0,0.861623,0.418899,373.738608,0
-c18,0,109.549158,41.660867,60.905800,23.915213,43.122319,43.122319,0,47.088742,47.088742,11.960929,0,11.960929,0.861623,0.418899,441.65554,0
-c19,0,109.549158,41.660867,60.905800,23.915213,43.122319,43.122319,18.327706,47.088742,47.088742,0,11.960929,0,0.861623,0.418899,448.022317,0
-c20,0,109.549158,41.660867,60.905800,0,0,43.122319,18.327706,0,47.088742,11.960929,11.960929,11.960929,0.861623,0.418899,357.817901,0
-c21,0,109.549158,41.660867,60.905800,23.915213,43.122319,43.122319,18.327706,47.088742,47.088742,11.960929,0,0,0.861623,0.418899,448.022317,0
-c22,0,109.549158,0,0,23.915213,43.122319,43.122319,18.327706,47.088742,47.088742,0,11.960929,0,0.861623,0.418899,345.45565,0
-c23,0,109.549158,41.660867,60.905800,23.915213,43.122319,43.122319,18.327706,47.088742,0,11.960929,0,0,0.861623,0.418899,400.933575,0
-c24,0,109.549158,41.660867,0,23.915213,0,43.122319,18.327706,0,47.088742,0,11.960929,0,0,0.418899,296.043833,0
-c25,0,109.549158,41.660867,60.905800,23.915213,43.122319,0,0,47.088742,47.088742,11.960929,0,11.960929,0.861623,0.418899,398.533221,0
-c26,0,109.549158,41.660867,60.905800,23.915213,0,0,18.327706,47.088742,47.088742,0,11.960929,11.960929,0.861623,0.418899,373.738608,0
-c27,0,109.549158,41.660867,60.905800,23.915213,0,0,0,47.088742,47.088742,0,11.960929,11.960929,0.861623,0.418899,355.410902,0
-c28,0,109.549158,41.660867,60.905800,23.915213,0,0,0,47.088742,47.088742,0,11.960929,11.960929,0.861623,0.418899,355.410902,0
-c29,0,109.549158,41.660867,60.905800,23.915213,43.122319,43.122319,18.327706,47.088742,47.088742,0,11.960929,0,0.861623,0,447.603418,0
-c30,0,109.549158,41.660867,60.905800,23.915213,0,0,18.327706,0,47.088742,11.960929,0,11.960929,0.861623,0.418899,326.649866,0
-c31,0,109.549158,41.660867,60.905800,23.915213,43.122319,0,18.327706,47.088742,47.088742,0,11.960929,11.960929,0,0.418899,415.999304,0
-c32,0,109.549158,41.660867,60.905800,23.915213,43.122319,43.122319,18.327706,47.088742,0,11.960929,0,0,0.861623,0.418899,400.933575,0
-c33,0,109.549158,41.660867,60.905800,0,43.122319,0,18.327706,0,47.088742,0,0,11.960929,0.861623,0.418899,333.896043,0
-c34,0,109.549158,41.660867,60.905800,23.915213,43.122319,43.122319,18.327706,47.088742,0,11.960929,0,0,0.861623,0.418899,400.933575,0
-c35,0,109.549158,41.660867,60.905800,23.915213,43.122319,43.122319,18.327706,47.088742,0,0,11.960929,11.960929,0.861623,0.418899,412.894504,0
-c36,0,109.549158,41.660867,60.905800,23.915213,43.122319,43.122319,0,47.088742,47.088742,0,0,11.960929,0.861623,0.418899,429.694611,0
-c37,0,0,41.660867,0,0,0,43.122319,18.327706,0,47.088742,0,11.960929,11.960929,0,0.418899,174.540391,0
-c38,0,109.549158,41.660867,60.905800,23.915213,43.122319,43.122319,0,47.088742,47.088742,0,0,11.960929,0,0.418899,428.832988,0
-c39,0,109.549158,0,60.905800,23.915213,0,43.122319,0,47.088742,47.088742,11.960929,11.960929,11.960929,0.861623,0.418899,368.833283,0
-c40,0,109.549158,41.660867,60.905800,0,0,0,18.327706,47.088742,0,11.960929,11.960929,11.960929,0.861623,0.418899,314.695582,0
-c41,0,109.549158,0,60.905800,23.915213,0,0,18.327706,47.088742,47.088742,11.960929,11.960929,11.960929,0.861623,0.418899,344.03867,0
-c42,0,109.549158,0,60.905800,23.915213,43.122319,43.122319,18.327706,0,47.088742,0,11.960929,11.960929,0.861623,0.418899,371.233637,0
-c43,0,109.549158,0,60.905800,23.915213,0,43.122319,18.327706,47.088742,47.088742,11.960929,0,11.960929,0.861623,0,374.781161,0
-c44,0,109.549158,41.660867,0,0,43.122319,43.122319,18.327706,47.088742,0,0,11.960929,0,0.861623,0,315.693663,0
-c45,0,109.549158,0,60.905800,23.915213,43.122319,0,18.327706,47.088742,47.088742,11.960929,0,11.960929,0.861623,0.418899,375.20006,0
-c46,0,109.549158,0,60.905800,23.915213,43.122319,43.122319,18.327706,47.088742,47.088742,0,11.960929,0,0.861623,0,405.942551,0
-c47,0,109.549158,0,60.905800,23.915213,43.122319,0,18.327706,47.088742,0,11.960929,11.960929,0,0.861623,0,327.692419,0
-c48,0,109.549158,0,60.905800,23.915213,43.122319,0,18.327706,47.088742,47.088742,11.960929,0,11.960929,0,0,373.919538,0
-c49,0,109.549158,0,60.905800,0,0,43.122319,0,0,47.088742,11.960929,11.960929,0,0.861623,0.418899,285.868399,0
-c50,0,109.549158,0,60.905800,0,0,43.122319,18.327706,0,0,0,11.960929,11.960929,0.861623,0,256.688464,0
-c51,0,109.549158,41.660867,0,0,0,43.122319,0,47.088742,47.088742,0,11.960929,11.960929,0.861623,0,313.293309,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,32.936441,0,21.882908,9.597736,18.163317,18.163317,8.429972,19.033555,19.033555,4.848960,4.848960,4.848960,0,0.089271,161.876952,0
-c2,0,32.936441,13.739047,21.882908,9.597736,0,18.163317,8.429972,19.033555,19.033555,4.848960,4.848960,4.848960,0.281905,0.089271,157.734587,0
-c3,0,32.936441,0,0,9.597736,18.163317,0,8.429972,19.033555,19.033555,4.848960,4.848960,4.848960,0.281905,0.089271,122.112632,0
-c4,0,0,13.739047,21.882908,9.597736,18.163317,0,8.429972,19.033555,19.033555,4.848960,4.848960,0,0.281905,0.089271,119.949186,0
-c5,0,32.936441,13.739047,21.882908,9.597736,18.163317,0,8.429972,19.033555,19.033555,4.848960,4.848960,4.848960,0.281905,0.089271,157.734587,0
-c6,0,0,13.739047,21.882908,9.597736,18.163317,18.163317,8.429972,19.033555,19.033555,0,4.848960,4.848960,0.281905,0.089271,138.112503,0
-c7,0,32.936441,13.739047,21.882908,9.597736,18.163317,18.163317,8.429972,0,19.033555,4.848960,4.848960,4.848960,0.281905,0,156.775078,0
-c8,0,32.936441,13.739047,21.882908,9.597736,18.163317,0,8.429972,19.033555,19.033555,4.848960,4.848960,4.848960,0,0.089271,157.452682,0
-c9,0,32.936441,0,21.882908,0,0,18.163317,8.429972,19.033555,19.033555,4.848960,4.848960,4.848960,0.281905,0.089271,134.397804,0
-c10,0,0,13.739047,21.882908,9.597736,18.163317,18.163317,8.429972,19.033555,19.033555,0,4.848960,0,0,0.089271,132.981638,0
-c11,0,0,13.739047,21.882908,0,18.163317,0,0,19.033555,19.033555,4.848960,4.848960,4.848960,0.281905,0,106.681167,0
-c12,0,32.936441,13.739047,21.882908,9.597736,18.163317,18.163317,8.429972,19.033555,19.033555,0,4.848960,4.848960,0.281905,0.089271,171.048944,0
-c13,0,32.936441,13.739047,21.882908,9.597736,18.163317,18.163317,0,19.033555,19.033555,4.848960,0,4.848960,0.281905,0,162.529701,0
-c14,0,0,0,0,9.597736,18.163317,18.163317,8.429972,19.033555,19.033555,4.848960,4.848960,4.848960,0.281905,0.089271,107.339508,0
-c15,0,32.936441,13.739047,21.882908,9.597736,18.163317,18.163317,8.429972,19.033555,19.033555,4.848960,0,0,0.281905,0.089271,166.199984,0
-c16,0,32.936441,13.739047,21.882908,9.597736,0,18.163317,0,0,19.033555,4.848960,0,4.848960,0,0.089271,125.140195,0
-c17,0,32.936441,13.739047,21.882908,9.597736,0,0,8.429972,19.033555,19.033555,4.848960,4.848960,0,0.281905,0.089271,134.72231,0
-c18,0,32.936441,13.739047,21.882908,9.597736,18.163317,18.163317,0,19.033555,19.033555,4.848960,0,4.848960,0.281905,0.089271,162.618972,0
-c19,0,32.936441,13.739047,21.882908,9.597736,18.163317,18.163317,8.429972,19.033555,19.033555,0,4.848960,0,0.281905,0.089271,166.199984,0
-c20,0,32.936441,13.739047,21.882908,0,0,18.163317,8.429972,0,19.033555,4.848960,4.848960,4.848960,0.281905,0.089271,129.103296,0
-c21,0,32.936441,13.739047,21.882908,9.597736,18.163317,18.163317,8.429972,19.033555,19.033555,4.848960,0,0,0.281905,0.089271,166.199984,0
-c22,0,32.936441,0,0,9.597736,18.163317,18.163317,8.429972,19.033555,19.033555,0,4.848960,0,0.281905,0.089271,130.578029,0
-c23,0,32.936441,13.739047,21.882908,9.597736,18.163317,18.163317,8.429972,19.033555,0,4.848960,0,0,0.281905,0.089271,147.166429,0
-c24,0,32.936441,13.739047,0,9.597736,0,18.163317,8.429972,0,19.033555,0,4.848960,0,0,0.089271,106.838299,0
-c25,0,32.936441,13.739047,21.882908,9.597736,18.163317,0,0,19.033555,19.033555,4.848960,0,4.848960,0.281905,0.089271,144.455655,0
-c26,0,32.936441,13.739047,21.882908,9.597736,0,0,8.429972,19.033555,19.033555,0,4.848960,4.848960,0.281905,0.089271,134.72231,0
-c27,0,32.936441,13.739047,21.882908,9.597736,0,0,0,19.033555,19.033555,0,4.848960,4.848960,0.281905,0.089271,126.292338,0
-c28,0,32.936441,13.739047,21.882908,9.597736,0,0,0,19.033555,19.033555,0,4.848960,4.848960,0.281905,0.089271,126.292338,0
-c29,0,32.936441,13.739047,21.882908,9.597736,18.163317,18.163317,8.429972,19.033555,19.033555,0,4.848960,0,0.281905,0,166.110713,0
-c30,0,32.936441,13.739047,21.882908,9.597736,0,0,8.429972,0,19.033555,4.848960,0,4.848960,0.281905,0.089271,115.688755,0
-c31,0,32.936441,13.739047,21.882908,9.597736,18.163317,0,8.429972,19.033555,19.033555,0,4.848960,4.848960,0,0.089271,152.603722,0
-c32,0,32.936441,13.739047,21.882908,9.597736,18.163317,18.163317,8.429972,19.033555,0,4.848960,0,0,0.281905,0.089271,147.166429,0
-c33,0,32.936441,13.739047,21.882908,0,18.163317,0,8.429972,0,19.033555,0,0,4.848960,0.281905,0.089271,119.405376,0
-c34,0,32.936441,13.739047,21.882908,9.597736,18.163317,18.163317,8.429972,19.033555,0,4.848960,0,0,0.281905,0.089271,147.166429,0
-c35,0,32.936441,13.739047,21.882908,9.597736,18.163317,18.163317,8.429972,19.033555,0,0,4.848960,4.848960,0.281905,0.089271,152.015389,0
-c36,0,32.936441,13.739047,21.882908,9.597736,18.163317,18.163317,0,19.033555,19.033555,0,0,4.848960,0.281905,0.089271,157.770012,0
-c37,0,0,13.739047,0,0,0,18.163317,8.429972,0,19.033555,0,4.848960,4.848960,0,0.089271,69.153082,0
-c38,0,32.936441,13.739047,21.882908,9.597736,18.163317,18.163317,0,19.033555,19.033555,0,0,4.848960,0,0.089271,157.488107,0
-c39,0,32.936441,0,21.882908,9.597736,0,18.163317,0,19.033555,19.033555,4.848960,4.848960,4.848960,0.281905,0.089271,135.565568,0
-c40,0,32.936441,13.739047,21.882908,0,0,0,8.429972,19.033555,0,4.848960,4.848960,4.848960,0.281905,0.089271,110.939979,0
-c41,0,32.936441,0,21.882908,9.597736,0,0,8.429972,19.033555,19.033555,4.848960,4.848960,4.848960,0.281905,0.089271,125.832223,0
-c42,0,32.936441,0,21.882908,9.597736,18.163317,18.163317,8.429972,0,19.033555,0,4.848960,4.848960,0.281905,0.089271,138.276342,0
-c43,0,32.936441,0,21.882908,9.597736,0,18.163317,8.429972,19.033555,19.033555,4.848960,0,4.848960,0.281905,0,139.057309,0
-c44,0,32.936441,13.739047,0,0,18.163317,18.163317,8.429972,19.033555,0,0,4.848960,0,0.281905,0,115.596514,0
-c45,0,32.936441,0,21.882908,9.597736,18.163317,0,8.429972,19.033555,19.033555,4.848960,0,4.848960,0.281905,0.089271,139.14658,0
-c46,0,32.936441,0,21.882908,9.597736,18.163317,18.163317,8.429972,19.033555,19.033555,0,4.848960,0,0.281905,0,152.371666,0
-c47,0,32.936441,0,21.882908,9.597736,18.163317,0,8.429972,19.033555,0,4.848960,4.848960,0,0.281905,0,120.023754,0
-c48,0,32.936441,0,21.882908,9.597736,18.163317,0,8.429972,19.033555,19.033555,4.848960,0,4.848960,0,0,138.775404,0
-c49,0,32.936441,0,21.882908,0,0,18.163317,0,0,19.033555,4.848960,4.848960,0,0.281905,0.089271,102.085317,0
-c50,0,32.936441,0,21.882908,0,0,18.163317,8.429972,0,0,0,4.848960,4.848960,0.281905,0,91.392463,0
-c51,0,32.936441,13.739047,0,0,0,18.163317,0,19.033555,19.033555,0,4.848960,4.848960,0.281905,0,112.88574,0
-c0,0
-
-Patch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c30,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c39,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c40,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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-c51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c0,0
-
-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,16.0868,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.0868,0
-c2,16.0868,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.0868,0
-c3,16.0868,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.0868,0
-c4,16.0868,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.0868,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c6,16.0868,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.0868,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c8,16.0868,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.0868,0
-c9,16.0868,0,0,0,0,144.6,0,0,0,0,0,0,0,0,0,160.6868,0
-c10,16.0868,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.0868,0
-c11,16.0868,436.945,0,0,0,0,0,0,0,0,0,0,0,0,0,453.0318,0
-c12,16.0868,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.0868,0
-c13,16.0868,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.0868,0
-c14,16.0868,0,203.92,0,0,0,0,0,0,0,0,0,0,0,0,220.0068,0
-c15,0,0,0,0,0,0,0,0,0,0,0,0,33.1375,0,0,33.1375,0
-c16,16.0868,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.0868,0
-c17,16.0868,0,0,0,0,0,152.501,0,0,0,0,0,0,0,0,168.5878,0
-c18,16.0868,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.0868,0
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-c21,16.0868,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.0868,0
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-c26,16.0868,0,0,0,0,0,124.457,0,0,0,0,0,0,0,0,140.5438,0
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-c30,16.0868,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.0868,0
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-c37,0,397.898,0,0,110.25,144.6,0,0,0,0,0,0,0,0,0,652.748,0
-c38,16.0868,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.0868,0
-c39,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c40,16.0868,0,0,0,0,122.714,152.501,0,0,0,0,0,0,0,0,291.3018,0
-c41,16.0868,0,0,0,0,0,124.457,0,0,0,0,0,0,0,0,140.5438,0
-c42,16.0868,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.0868,0
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-c44,16.0868,0,0,0,0,0,0,0,0,0,37.3658,0,0,0,0,53.4526,0
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-c47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c48,16.0868,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.0868,0
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-c51,16.0868,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.0868,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,7.62006,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.62006,0
-c2,7.62006,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.62006,0
-c3,7.62006,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.62006,0
-c4,7.62006,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.62006,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c6,7.62006,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.62006,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c8,7.62006,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.62006,0
-c9,7.62006,0,0,0,0,28.103,0,0,0,0,0,0,0,0,0,35.72306,0
-c10,7.62006,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.62006,0
-c11,7.62006,83.3307,0,0,0,0,0,0,0,0,0,0,0,0,0,90.95076,0
-c12,7.62006,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.62006,0
-c13,7.62006,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.62006,0
-c14,7.62006,0,41.6322,0,0,0,0,0,0,0,0,0,0,0,0,49.25226,0
-c15,0,0,0,0,0,0,0,0,0,0,0,0,6.20782,0,0,6.20782,0
-c16,7.62006,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.62006,0
-c17,7.62006,0,0,0,0,0,28.2053,0,0,0,0,0,0,0,0,35.82536,0
-c18,7.62006,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.62006,0
-c19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c21,7.62006,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.62006,0
-c22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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-c26,7.62006,0,0,0,0,0,20.8436,0,0,0,0,0,0,0,0,28.46366,0
-c27,7.62006,0,0,0,0,0,0,10.5597,0,0,0,0,0,0,0,18.17976,0
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-c30,7.62006,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.62006,0
-c31,7.62006,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.62006,0
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-c34,7.62006,0,0,0,0,0,0,0,0,0,0,0,6.20782,0,0,13.82788,0
-c35,7.62006,0,0,0,0,0,0,0,0,0,2.77964,0,0,0,0,10.3997,0
-c36,7.62006,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.62006,0
-c37,0,109.635,0,0,20.8418,28.103,0,0,0,0,0,0,0,0,0,158.5798,0
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-c40,7.62006,0,0,0,0,20.8654,28.2053,0,0,0,0,0,0,0,0,56.69076,0
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-c42,7.62006,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.62006,0
-c43,7.62006,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.62006,0
-c44,7.62006,0,0,0,0,0,0,0,0,0,6.79928,0,0,0,0,14.41934,0
-c45,7.62006,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.62006,0
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-c0,0
-
-Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,352.4661,1550.099,701.7255,1159.5933,558.563,925.6073,971.2723,490.07014,857.91333,883.36663,344.11179,344.22248,351.94343,12.419968,1.04789,9504.422158,0.999999989478582
-c1,450.60246,123.468921,426.5549,104.457756,51.115816,100.738165,100.738165,49.948052,102.069715,102.069715,26.069312,26.069312,26.069312,12.419968,0.107291,1702.49886,5.5826302284496
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-c4,450.60246,1610.85,59.178279,104.457756,51.115816,100.738165,971.2723,49.948052,102.069715,102.069715,26.069312,26.069312,351.94343,1.204529,0.107291,4007.696132,2.37154255407649
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-c6,450.60246,1610.85,59.178279,104.457756,51.115816,100.738165,100.738165,49.948052,102.069715,102.069715,344.11179,26.069312,26.069312,1.204529,0.107291,3129.330357,3.03720629335887
-c7,352.4661,123.468921,59.178279,104.457756,51.115816,100.738165,100.738165,49.948052,857.91333,102.069715,26.069312,26.069312,26.069312,1.204529,1.669613,1983.176377,4.79252465336699
-c8,450.60246,123.468921,59.178279,104.457756,51.115816,100.738165,590.3019,49.948052,102.069715,102.069715,26.069312,26.069312,26.069312,12.419968,0.107291,1824.685974,5.20879853988517
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-c13,450.60246,123.468921,59.178279,104.457756,51.115816,100.738165,100.738165,290.68457,102.069715,102.069715,26.069312,344.22248,26.069312,1.204529,1.04789,1883.737085,5.04551390378802
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-c20,352.4661,123.468921,59.178279,104.457756,558.563,925.6073,100.738165,49.948052,527.54432,102.069715,26.069312,26.069312,26.069312,1.204529,0.107291,2983.561364,3.18559623211436
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-
-Unpatch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
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-
diff --git a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_results1.csv b/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_results1.csv
deleted file mode 100644
index fa38a02ff27be3de0da8d5ce5598fcea3f304bb2..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_results1.csv
+++ /dev/null
@@ -1,209 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,1108.984,7471.505,3792.343,6973.454,3475.891,6007.756,6530.41,3324.8076,6053.9348,6493.6098,2479.9481,2376.5679,2353.8847,69.37799,1.812953,58514.286843,0.999999998291016
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-c5,1108.984,174.570537,87.285268,6973.454,1731.6018,157.113483,157.113483,1683.0934,157.113483,157.113483,1299.926,1273.3007,39.466907,69.37799,1.812953,15071.327487,3.88249054406278
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-c7,1108.984,174.570537,87.285268,3356.913,1731.6018,6007.756,157.113483,78.556742,3203.344,157.113483,39.466907,39.466907,1286.6509,69.37799,1.812953,17500.01397,3.34367084557435
-c8,1108.984,174.570537,87.285268,6973.454,3475.891,157.113483,157.113483,1683.0934,157.113483,157.113483,1299.926,1273.3007,39.466907,69.37799,1.812953,16815.616687,3.47975858299987
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-c12,1108.984,174.570537,87.285268,157.113483,3475.891,3053.2545,157.113483,1683.0934,157.113483,157.113483,1299.926,1273.3007,39.466907,69.37799,1.812953,12895.417187,4.53760320745796
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-c3,12895.417187
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,352.4661,1550.099,701.7255,1159.5933,558.563,925.6073,971.2723,490.07014,857.91333,883.36663,344.11179,344.22248,351.94343,12.419968,1.04789,9504.422158,0.999999989478582
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-c4,352.4661,90.532480,45.439232,82.574848,333.7788,544.1687,82.574848,41.518080,857.91333,83.036160,21.220352,21.220352,351.94343,12.419968,1.04789,2921.85457,3.25287299727334
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-c8,352.4661,90.532480,45.439232,1159.5933,558.563,82.574848,82.574848,290.68457,83.036160,83.036160,208.37216,206.54856,21.220352,12.419968,1.04789,3278.109628,2.89936059089722
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-c12,352.4661,90.532480,45.439232,82.574848,558.563,544.1687,82.574848,290.68457,83.036160,83.036160,208.37216,206.54856,21.220352,12.419968,1.04789,2662.685028,3.5694878294299
-c13,352.4661,90.532480,45.439232,1159.5933,333.7788,82.574848,82.574848,290.68457,83.036160,83.036160,208.37216,206.54856,21.220352,12.419968,1.04789,3053.325428,3.11280997418759
-c14,352.4661,90.532480,45.439232,652.9992,333.7788,925.6073,82.574848,41.518080,527.54432,83.036160,21.220352,21.220352,210.33129,12.419968,1.04789,3401.736372,2.79399131479812
-c3,2662.685028
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,1108.984,7471.505,3792.343,6973.454,3475.891,6007.756,6530.41,3324.8076,6053.9348,6493.6098,2479.9481,2376.5679,2353.8847,69.37799,1.812953,58514.286843,0.999999998291016
-c1,1108.984,472.88204,219.119355,6973.454,3475.891,352.195669,352.195669,1683.0934,357.330128,357.330128,1299.926,1273.3007,89.882488,69.37799,1.812953,18086.77552,3.23519725529718
-c2,1108.984,472.88204,219.119355,3356.913,1731.6018,6130.47,352.195669,171.815177,3203.344,357.330128,89.882488,89.882488,1286.6509,70.74902,1.812953,18643.633018,3.13856674139901
-c3,1108.984,472.88204,219.119355,375.615141,3475.891,3197.8545,352.195669,1683.0934,357.330128,357.330128,1299.926,1273.3007,89.882488,69.37799,1.812953,14334.595492,4.08203262292633
-c4,1108.984,472.88204,219.119355,375.615141,1731.6018,3053.2545,352.195669,171.815177,6053.9348,357.330128,89.882488,89.882488,2353.8847,69.37799,1.812953,16501.573229,3.54598229371053
-c5,1108.984,472.88204,219.119355,6973.454,1802.5061,352.195669,352.195669,1683.0934,357.330128,357.330128,1299.926,1273.3007,89.882488,69.37799,1.812953,16413.39062,3.56503344380265
-c6,1108.984,472.88204,219.119355,375.615141,3475.891,3197.8545,352.195669,1683.0934,357.330128,357.330128,1299.926,1273.3007,89.882488,69.37799,1.812953,14334.595492,4.08203262292633
-c7,1108.984,472.88204,219.119355,3356.913,1731.6018,6130.47,352.195669,171.815177,3203.344,357.330128,89.882488,89.882488,1286.6509,70.74902,1.812953,18643.633018,3.13856674139901
-c8,1108.984,472.88204,219.119355,6973.454,3475.891,352.195669,352.195669,1683.0934,357.330128,357.330128,1299.926,1273.3007,89.882488,69.37799,1.812953,18086.77552,3.23519725529718
-c9,1108.984,472.88204,219.119355,375.615141,1731.6018,3053.2545,352.195669,171.815177,6053.9348,357.330128,89.882488,89.882488,2353.8847,69.37799,1.812953,16501.573229,3.54598229371053
-c10,1108.984,472.88204,219.119355,6973.454,1802.5061,352.195669,352.195669,1683.0934,357.330128,357.330128,1299.926,1273.3007,89.882488,69.37799,1.812953,16413.39062,3.56503344380265
-c11,1108.984,472.88204,219.119355,6973.454,3475.891,352.195669,352.195669,1683.0934,357.330128,357.330128,1299.926,1273.3007,89.882488,69.37799,1.812953,18086.77552,3.23519725529718
-c12,1108.984,472.88204,219.119355,375.615141,3475.891,3197.8545,352.195669,1683.0934,357.330128,357.330128,1299.926,1273.3007,89.882488,69.37799,1.812953,14334.595492,4.08203262292633
-c13,1108.984,472.88204,219.119355,6973.454,1802.5061,352.195669,352.195669,1683.0934,357.330128,357.330128,1299.926,1273.3007,89.882488,69.37799,1.812953,16413.39062,3.56503344380265
-c14,1108.984,472.88204,219.119355,3356.913,1731.6018,6130.47,352.195669,171.815177,3203.344,357.330128,89.882488,89.882488,1286.6509,70.74902,1.812953,18643.633018,3.13856674139901
-c3,14334.595492
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,188.762345,90.173220,0,0,151.959867,151.959867,0,153.127903,153.127903,0,0,38.454652,0,0,927.565757,0
-c2,0,188.762345,90.173220,0,0,0,151.959867,74.930729,0,153.127903,38.454652,38.454652,0,0,0,735.863368,0
-c3,0,188.762345,90.173220,157.595858,0,0,151.959867,0,153.127903,153.127903,0,0,38.454652,0,0,933.201748,0
-c4,0,188.762345,90.173220,157.595858,0,0,151.959867,74.930729,0,153.127903,38.454652,38.454652,0,0,0,893.459226,0
-c5,0,188.762345,90.173220,0,0,151.959867,151.959867,0,153.127903,153.127903,0,0,38.454652,0,0,927.565757,0
-c6,0,188.762345,90.173220,157.595858,0,0,151.959867,0,153.127903,153.127903,0,0,38.454652,0,0,933.201748,0
-c7,0,188.762345,90.173220,0,0,0,151.959867,74.930729,0,153.127903,38.454652,38.454652,0,0,0,735.863368,0
-c8,0,188.762345,90.173220,0,0,151.959867,151.959867,0,153.127903,153.127903,0,0,38.454652,0,0,927.565757,0
-c9,0,188.762345,90.173220,157.595858,0,0,151.959867,74.930729,0,153.127903,38.454652,38.454652,0,0,0,893.459226,0
-c10,0,188.762345,90.173220,0,0,151.959867,151.959867,0,153.127903,153.127903,0,0,38.454652,0,0,927.565757,0
-c11,0,188.762345,90.173220,0,0,151.959867,151.959867,0,153.127903,153.127903,0,0,38.454652,0,0,927.565757,0
-c12,0,188.762345,90.173220,157.595858,0,0,151.959867,0,153.127903,153.127903,0,0,38.454652,0,0,933.201748,0
-c13,0,188.762345,90.173220,0,0,151.959867,151.959867,0,153.127903,153.127903,0,0,38.454652,0,0,927.565757,0
-c14,0,188.762345,90.173220,0,0,0,151.959867,74.930729,0,153.127903,38.454652,38.454652,0,0,0,735.863368,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,109.549158,41.660867,0,0,43.122319,43.122319,0,47.088742,47.088742,0,0,11.960929,0,0,343.593076,0
-c2,0,109.549158,41.660867,0,0,0,43.122319,18.327706,0,47.088742,11.960929,11.960929,0,0,0,283.67065,0
-c3,0,109.549158,41.660867,60.905800,0,0,43.122319,0,47.088742,47.088742,0,0,11.960929,0,0,361.376557,0
-c4,0,109.549158,41.660867,60.905800,0,0,43.122319,18.327706,0,47.088742,11.960929,11.960929,0,0,0,344.57645,0
-c5,0,109.549158,41.660867,0,0,43.122319,43.122319,0,47.088742,47.088742,0,0,11.960929,0,0,343.593076,0
-c6,0,109.549158,41.660867,60.905800,0,0,43.122319,0,47.088742,47.088742,0,0,11.960929,0,0,361.376557,0
-c7,0,109.549158,41.660867,0,0,0,43.122319,18.327706,0,47.088742,11.960929,11.960929,0,0,0,283.67065,0
-c8,0,109.549158,41.660867,0,0,43.122319,43.122319,0,47.088742,47.088742,0,0,11.960929,0,0,343.593076,0
-c9,0,109.549158,41.660867,60.905800,0,0,43.122319,18.327706,0,47.088742,11.960929,11.960929,0,0,0,344.57645,0
-c10,0,109.549158,41.660867,0,0,43.122319,43.122319,0,47.088742,47.088742,0,0,11.960929,0,0,343.593076,0
-c11,0,109.549158,41.660867,0,0,43.122319,43.122319,0,47.088742,47.088742,0,0,11.960929,0,0,343.593076,0
-c12,0,109.549158,41.660867,60.905800,0,0,43.122319,0,47.088742,47.088742,0,0,11.960929,0,0,361.376557,0
-c13,0,109.549158,41.660867,0,0,43.122319,43.122319,0,47.088742,47.088742,0,0,11.960929,0,0,343.593076,0
-c14,0,109.549158,41.660867,0,0,0,43.122319,18.327706,0,47.088742,11.960929,11.960929,0,0,0,283.67065,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,32.936441,13.739047,0,0,18.163317,18.163317,0,19.033555,19.033555,0,0,4.848960,0,0,125.918192,0
-c2,0,32.936441,13.739047,0,0,0,18.163317,8.429972,0,19.033555,4.848960,4.848960,0,0,0,102.000252,0
-c3,0,32.936441,13.739047,21.882908,0,0,18.163317,0,19.033555,19.033555,0,0,4.848960,0,0,129.637783,0
-c4,0,32.936441,13.739047,21.882908,0,0,18.163317,8.429972,0,19.033555,4.848960,4.848960,0,0,0,123.88316,0
-c5,0,32.936441,13.739047,0,0,18.163317,18.163317,0,19.033555,19.033555,0,0,4.848960,0,0,125.918192,0
-c6,0,32.936441,13.739047,21.882908,0,0,18.163317,0,19.033555,19.033555,0,0,4.848960,0,0,129.637783,0
-c7,0,32.936441,13.739047,0,0,0,18.163317,8.429972,0,19.033555,4.848960,4.848960,0,0,0,102.000252,0
-c8,0,32.936441,13.739047,0,0,18.163317,18.163317,0,19.033555,19.033555,0,0,4.848960,0,0,125.918192,0
-c9,0,32.936441,13.739047,21.882908,0,0,18.163317,8.429972,0,19.033555,4.848960,4.848960,0,0,0,123.88316,0
-c10,0,32.936441,13.739047,0,0,18.163317,18.163317,0,19.033555,19.033555,0,0,4.848960,0,0,125.918192,0
-c11,0,32.936441,13.739047,0,0,18.163317,18.163317,0,19.033555,19.033555,0,0,4.848960,0,0,125.918192,0
-c12,0,32.936441,13.739047,21.882908,0,0,18.163317,0,19.033555,19.033555,0,0,4.848960,0,0,129.637783,0
-c13,0,32.936441,13.739047,0,0,18.163317,18.163317,0,19.033555,19.033555,0,0,4.848960,0,0,125.918192,0
-c14,0,32.936441,13.739047,0,0,0,18.163317,8.429972,0,19.033555,4.848960,4.848960,0,0,0,102.000252,0
-c0,0
-
-Patch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c0,0
-
-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,122.714,0,0,0,0,0,0,0,1.37103,0,124.08503,0
-c3,0,0,0,0,0,144.6,0,0,0,0,0,0,0,0,0,144.6,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c5,0,0,0,0,70.9043,0,0,0,0,0,0,0,0,0,0,70.9043,0
-c6,0,0,0,0,0,144.6,0,0,0,0,0,0,0,0,0,144.6,0
-c7,0,0,0,0,0,122.714,0,0,0,0,0,0,0,1.37103,0,124.08503,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c10,0,0,0,0,70.9043,0,0,0,0,0,0,0,0,0,0,70.9043,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c12,0,0,0,0,0,144.6,0,0,0,0,0,0,0,0,0,144.6,0
-c13,0,0,0,0,70.9043,0,0,0,0,0,0,0,0,0,0,70.9043,0
-c14,0,0,0,0,0,122.714,0,0,0,0,0,0,0,1.37103,0,124.08503,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,20.8654,0,0,0,0,0,0,0,0.810781,0,21.676181,0
-c3,0,0,0,0,0,28.103,0,0,0,0,0,0,0,0,0,28.103,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c5,0,0,0,0,14.8008,0,0,0,0,0,0,0,0,0,0,14.8008,0
-c6,0,0,0,0,0,28.103,0,0,0,0,0,0,0,0,0,28.103,0
-c7,0,0,0,0,0,20.8654,0,0,0,0,0,0,0,0.810781,0,21.676181,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c10,0,0,0,0,14.8008,0,0,0,0,0,0,0,0,0,0,14.8008,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c12,0,0,0,0,0,28.103,0,0,0,0,0,0,0,0,0,28.103,0
-c13,0,0,0,0,14.8008,0,0,0,0,0,0,0,0,0,0,14.8008,0
-c14,0,0,0,0,0,20.8654,0,0,0,0,0,0,0,0.810781,0,21.676181,0
-c0,0
-
-Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,352.4661,1550.099,701.7255,1159.5933,558.563,925.6073,971.2723,490.07014,857.91333,883.36663,344.11179,344.22248,351.94343,12.419968,1.04789,9504.422158,0.999999989478582
-c1,352.4661,123.468921,59.178279,1159.5933,558.563,100.738165,100.738165,290.68457,102.069715,102.069715,208.37216,206.54856,26.069312,12.419968,1.04789,3404.02782,2.79211051770692
-c2,352.4661,123.468921,59.178279,652.9992,333.7788,946.4727,100.738165,49.948052,527.54432,102.069715,26.069312,26.069312,210.33129,13.230749,1.04789,3525.412805,2.69597417781053
-c3,352.4661,123.468921,59.178279,104.457756,558.563,572.2717,100.738165,290.68457,102.069715,102.069715,208.37216,206.54856,26.069312,12.419968,1.04789,2820.425811,3.36985351075226
-c4,352.4661,123.468921,59.178279,104.457756,333.7788,544.1687,100.738165,49.948052,857.91333,102.069715,26.069312,26.069312,351.94343,12.419968,1.04789,3045.73773,3.12056476574676
-c5,352.4661,123.468921,59.178279,1159.5933,348.5796,100.738165,100.738165,290.68457,102.069715,102.069715,208.37216,206.54856,26.069312,12.419968,1.04789,3194.04442,2.975669906442
-c6,352.4661,123.468921,59.178279,104.457756,558.563,572.2717,100.738165,290.68457,102.069715,102.069715,208.37216,206.54856,26.069312,12.419968,1.04789,2820.425811,3.36985351075226
-c7,352.4661,123.468921,59.178279,652.9992,333.7788,946.4727,100.738165,49.948052,527.54432,102.069715,26.069312,26.069312,210.33129,13.230749,1.04789,3525.412805,2.69597417781053
-c8,352.4661,123.468921,59.178279,1159.5933,558.563,100.738165,100.738165,290.68457,102.069715,102.069715,208.37216,206.54856,26.069312,12.419968,1.04789,3404.02782,2.79211051770692
-c9,352.4661,123.468921,59.178279,104.457756,333.7788,544.1687,100.738165,49.948052,857.91333,102.069715,26.069312,26.069312,351.94343,12.419968,1.04789,3045.73773,3.12056476574676
-c10,352.4661,123.468921,59.178279,1159.5933,348.5796,100.738165,100.738165,290.68457,102.069715,102.069715,208.37216,206.54856,26.069312,12.419968,1.04789,3194.04442,2.975669906442
-c11,352.4661,123.468921,59.178279,1159.5933,558.563,100.738165,100.738165,290.68457,102.069715,102.069715,208.37216,206.54856,26.069312,12.419968,1.04789,3404.02782,2.79211051770692
-c12,352.4661,123.468921,59.178279,104.457756,558.563,572.2717,100.738165,290.68457,102.069715,102.069715,208.37216,206.54856,26.069312,12.419968,1.04789,2820.425811,3.36985351075226
-c13,352.4661,123.468921,59.178279,1159.5933,348.5796,100.738165,100.738165,290.68457,102.069715,102.069715,208.37216,206.54856,26.069312,12.419968,1.04789,3194.04442,2.975669906442
-c14,352.4661,123.468921,59.178279,652.9992,333.7788,946.4727,100.738165,49.948052,527.54432,102.069715,26.069312,26.069312,210.33129,13.230749,1.04789,3525.412805,2.69597417781053
-c3,2820.425811
-
-Unpatch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c0,0
-
diff --git a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_results2.csv b/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_results2.csv
deleted file mode 100644
index 03e64efef1e92da5898265ce6982aebb2ff9a7c7..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_results2.csv
+++ /dev/null
@@ -1,385 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,1108.984,7471.505,3792.343,6973.454,3475.891,6007.756,6530.41,3324.8076,6053.9348,6493.6098,2479.9481,2376.5679,2353.8847,69.37799,1.812953,58514.286843,0.999999998291016
-c1,1108.984,6358.145,1949.97,3356.913,78.556742,3053.2545,69.626370,78.556742,157.113483,69.626370,1299.926,1273.3007,39.466907,57.16078,10.00372,18960.604314,3.08609818365255
-c2,1108.984,86.311660,1949.97,3356.913,78.556742,118.016247,157.113483,78.556742,3203.344,69.626370,17.490144,17.490144,1286.6509,57.16078,10.00372,11596.187932,5.04599327654297
-c3,1108.984,174.570537,38.681317,3356.913,78.556742,3053.2545,69.626370,78.556742,3203.344,69.626370,17.490144,1273.3007,39.466907,57.16078,10.00372,12629.535829,4.63313039940282
-c4,1108.984,6358.145,38.681317,3356.913,78.556742,77.680494,69.626370,78.556742,157.113483,157.113483,17.490144,1273.3007,39.466907,57.16078,10.00372,12878.792882,4.54346047217176
-c5,1108.984,6358.145,87.285268,157.113483,78.556742,118.016247,157.113483,78.556742,3203.344,69.626370,17.490144,17.490144,39.466907,57.16078,10.00372,11558.35303,5.06251073875955
-c6,1108.984,6358.145,43.155830,157.113483,34.813185,118.016247,3366.9575,78.556742,69.626370,157.113483,17.490144,17.490144,39.466907,57.16078,10.00372,11634.093535,5.02955268186648
-c7,1108.984,6358.145,38.681317,3356.913,1731.6018,69.626370,69.626370,78.556742,157.113483,3427.0759,1299.926,19.513340,39.466907,57.16078,10.00372,17822.394729,3.2831887860428
-c8,1108.984,174.570537,1949.97,3356.913,78.556742,3053.2545,69.626370,78.556742,157.113483,69.626370,17.490144,1273.3007,29.645681,57.16078,10.00372,11484.772769,5.09494506425489
-c9,1108.984,6358.145,38.681317,3356.913,78.556742,69.626370,157.113483,1683.0934,157.113483,157.113483,1299.926,1273.3007,39.466907,57.16078,10.00372,15845.198385,3.69287181213874
-c10,1108.984,174.570537,38.681317,3356.913,78.556742,118.016247,69.626370,78.556742,3203.344,69.626370,39.466907,17.490144,1286.6509,57.16078,10.00372,9707.647776,6.02764826149737
-c11,1108.984,6358.145,38.681317,3356.913,78.556742,69.626370,157.113483,1683.0934,157.113483,157.113483,1299.926,1273.3007,39.466907,57.16078,10.00372,15845.198385,3.69287181213874
-c12,1108.984,6358.145,38.681317,3356.913,78.556742,77.680494,69.626370,78.556742,157.113483,157.113483,17.490144,1273.3007,39.466907,57.16078,10.00372,12878.792882,4.54346047217176
-c13,1108.984,174.570537,38.681317,3356.913,78.556742,3053.2545,69.626370,78.556742,3203.344,69.626370,17.490144,1273.3007,39.466907,57.16078,10.00372,12629.535829,4.63313039940282
-c14,1108.984,6358.145,38.681317,3356.913,1731.6018,69.626370,69.626370,78.556742,157.113483,3427.0759,1299.926,19.513340,39.466907,57.16078,10.00372,17822.394729,3.2831887860428
-c15,1108.984,86.311660,1949.97,3356.913,78.556742,118.016247,157.113483,78.556742,3203.344,69.626370,17.490144,17.490144,1286.6509,57.16078,10.00372,11596.187932,5.04599327654297
-c16,1108.984,6358.145,43.155830,157.113483,34.813185,118.016247,3366.9575,78.556742,69.626370,157.113483,17.490144,17.490144,39.466907,57.16078,10.00372,11634.093535,5.02955268186648
-c17,1108.984,174.570537,1949.97,3356.913,78.556742,3053.2545,69.626370,78.556742,157.113483,69.626370,17.490144,1273.3007,29.645681,57.16078,10.00372,11484.772769,5.09494506425489
-c18,1108.984,174.570537,38.681317,3356.913,78.556742,118.016247,69.626370,78.556742,3203.344,69.626370,39.466907,17.490144,1286.6509,57.16078,10.00372,9707.647776,6.02764826149737
-c19,1108.984,86.311660,43.155830,3356.913,34.813185,118.016247,69.626370,78.556742,3203.344,69.626370,17.490144,17.490144,39.466907,57.16078,10.00372,8310.959099,7.04061774843518
-c20,1108.984,6358.145,87.285268,157.113483,78.556742,118.016247,157.113483,78.556742,3203.344,69.626370,17.490144,17.490144,39.466907,57.16078,10.00372,11558.35303,5.06251073875955
-c21,1108.984,174.570537,38.681317,3356.913,78.556742,3053.2545,3366.9575,78.556742,157.113483,69.626370,17.490144,1273.3007,39.466907,57.16078,10.00372,12880.636442,4.54281018272676
-c22,1108.984,6358.145,38.681317,3356.913,1731.6018,69.626370,69.626370,78.556742,157.113483,3427.0759,1299.926,19.513340,39.466907,57.16078,10.00372,17822.394729,3.2831887860428
-c23,1108.984,6358.145,87.285268,157.113483,78.556742,118.016247,157.113483,78.556742,3203.344,69.626370,17.490144,17.490144,39.466907,57.16078,10.00372,11558.35303,5.06251073875955
-c24,1108.984,6358.145,43.155830,3356.913,1731.6018,3053.2545,157.113483,1683.0934,157.113483,157.113483,1299.926,1273.3007,39.466907,57.16078,10.00372,20486.346086,2.8562578368898
-c25,1108.984,86.311660,1949.97,3356.913,78.556742,118.016247,157.113483,78.556742,3203.344,69.626370,17.490144,17.490144,1286.6509,57.16078,10.00372,11596.187932,5.04599327654297
-c26,1108.984,174.570537,1949.97,3356.913,78.556742,3053.2545,69.626370,78.556742,157.113483,69.626370,17.490144,1273.3007,29.645681,57.16078,10.00372,11484.772769,5.09494506425489
-c27,1108.984,6358.145,43.155830,157.113483,34.813185,118.016247,3366.9575,78.556742,69.626370,157.113483,17.490144,17.490144,39.466907,57.16078,10.00372,11634.093535,5.02955268186648
-c28,1108.984,6358.145,38.681317,3356.913,78.556742,69.626370,157.113483,1683.0934,157.113483,157.113483,1299.926,1273.3007,39.466907,57.16078,10.00372,15845.198385,3.69287181213874
-c29,1108.984,6358.145,38.681317,3356.913,78.556742,77.680494,69.626370,78.556742,157.113483,157.113483,17.490144,1273.3007,39.466907,57.16078,10.00372,12878.792882,4.54346047217176
-c30,1108.984,174.570537,38.681317,3356.913,78.556742,118.016247,69.626370,78.556742,3203.344,69.626370,39.466907,17.490144,1286.6509,57.16078,10.00372,9707.647776,6.02764826149737
-c19,8310.959099
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,352.4661,1550.099,701.7255,1159.5933,558.563,925.6073,971.2723,490.07014,857.91333,883.36663,344.11179,344.22248,351.94343,12.419968,1.04789,9504.422158,0.999999989478582
-c1,352.4661,1610.85,426.5549,652.9992,41.518080,544.1687,82.574848,41.518080,83.036160,83.036160,208.37216,206.54856,21.220352,7.640322,1.669613,4364.173235,2.17782874978315
-c2,352.4661,90.532480,426.5549,652.9992,41.518080,82.574848,82.574848,41.518080,527.54432,83.036160,21.220352,21.220352,210.33129,7.640322,1.669613,2643.400945,3.59552788101436
-c3,352.4661,90.532480,45.439232,652.9992,41.518080,544.1687,82.574848,41.518080,527.54432,83.036160,21.220352,206.54856,21.220352,7.640322,1.669613,2720.096399,3.49414888828177
-c4,352.4661,1610.85,45.439232,652.9992,41.518080,82.574848,82.574848,41.518080,83.036160,83.036160,21.220352,206.54856,21.220352,7.640322,1.669613,3334.311907,2.850489737627
-c5,352.4661,1610.85,45.439232,82.574848,41.518080,82.574848,82.574848,41.518080,527.54432,83.036160,21.220352,21.220352,21.220352,7.640322,1.669613,3023.067507,3.1439661276487
-c6,352.4661,1610.85,45.439232,82.574848,41.518080,82.574848,590.3019,41.518080,83.036160,83.036160,21.220352,21.220352,21.220352,7.640322,1.669613,3086.286399,3.07956573736092
-c7,352.4661,1610.85,45.439232,652.9992,333.7788,82.574848,82.574848,41.518080,83.036160,543.5573,208.37216,21.220352,21.220352,7.640322,1.669613,4088.917367,2.32443482528233
-c8,352.4661,90.532480,426.5549,652.9992,41.518080,544.1687,82.574848,41.518080,83.036160,83.036160,21.220352,206.54856,21.220352,7.640322,1.669613,2656.703907,3.57752392925871
-c9,352.4661,1610.85,45.439232,652.9992,41.518080,82.574848,82.574848,290.68457,83.036160,83.036160,208.37216,206.54856,21.220352,7.640322,1.669613,3770.630205,2.52064545956594
-c10,352.4661,90.532480,45.439232,652.9992,41.518080,82.574848,82.574848,41.518080,527.54432,83.036160,21.220352,21.220352,210.33129,7.640322,1.669613,2262.285277,4.20124810717017
-c11,352.4661,1610.85,45.439232,652.9992,41.518080,82.574848,82.574848,290.68457,83.036160,83.036160,208.37216,206.54856,21.220352,7.640322,1.669613,3770.630205,2.52064545956594
-c12,352.4661,1610.85,45.439232,652.9992,41.518080,82.574848,82.574848,41.518080,83.036160,83.036160,21.220352,206.54856,21.220352,7.640322,1.669613,3334.311907,2.850489737627
-c13,352.4661,90.532480,45.439232,652.9992,41.518080,544.1687,82.574848,41.518080,527.54432,83.036160,21.220352,206.54856,21.220352,7.640322,1.669613,2720.096399,3.49414888828177
-c14,352.4661,1610.85,45.439232,652.9992,333.7788,82.574848,82.574848,41.518080,83.036160,543.5573,208.37216,21.220352,21.220352,7.640322,1.669613,4088.917367,2.32443482528233
-c15,352.4661,90.532480,426.5549,652.9992,41.518080,82.574848,82.574848,41.518080,527.54432,83.036160,21.220352,21.220352,210.33129,7.640322,1.669613,2643.400945,3.59552788101436
-c16,352.4661,1610.85,45.439232,82.574848,41.518080,82.574848,590.3019,41.518080,83.036160,83.036160,21.220352,21.220352,21.220352,7.640322,1.669613,3086.286399,3.07956573736092
-c17,352.4661,90.532480,426.5549,652.9992,41.518080,544.1687,82.574848,41.518080,83.036160,83.036160,21.220352,206.54856,21.220352,7.640322,1.669613,2656.703907,3.57752392925871
-c18,352.4661,90.532480,45.439232,652.9992,41.518080,82.574848,82.574848,41.518080,527.54432,83.036160,21.220352,21.220352,210.33129,7.640322,1.669613,2262.285277,4.20124810717017
-c19,352.4661,90.532480,45.439232,652.9992,41.518080,82.574848,82.574848,41.518080,527.54432,83.036160,21.220352,21.220352,21.220352,7.640322,1.669613,2073.174339,4.58447778402307
-c20,352.4661,1610.85,45.439232,82.574848,41.518080,82.574848,82.574848,41.518080,527.54432,83.036160,21.220352,21.220352,21.220352,7.640322,1.669613,3023.067507,3.1439661276487
-c21,352.4661,90.532480,45.439232,652.9992,41.518080,544.1687,590.3019,41.518080,83.036160,83.036160,21.220352,206.54856,21.220352,7.640322,1.669613,2783.315291,3.41478446486268
-c22,352.4661,1610.85,45.439232,652.9992,333.7788,82.574848,82.574848,41.518080,83.036160,543.5573,208.37216,21.220352,21.220352,7.640322,1.669613,4088.917367,2.32443482528233
-c23,352.4661,1610.85,45.439232,82.574848,41.518080,82.574848,82.574848,41.518080,527.54432,83.036160,21.220352,21.220352,21.220352,7.640322,1.669613,3023.067507,3.1439661276487
-c24,352.4661,1610.85,45.439232,652.9992,333.7788,544.1687,82.574848,290.68457,83.036160,83.036160,208.37216,206.54856,21.220352,7.640322,1.669613,4524.484777,2.1006639244868
-c25,352.4661,90.532480,426.5549,652.9992,41.518080,82.574848,82.574848,41.518080,527.54432,83.036160,21.220352,21.220352,210.33129,7.640322,1.669613,2643.400945,3.59552788101436
-c26,352.4661,90.532480,426.5549,652.9992,41.518080,544.1687,82.574848,41.518080,83.036160,83.036160,21.220352,206.54856,21.220352,7.640322,1.669613,2656.703907,3.57752392925871
-c27,352.4661,1610.85,45.439232,82.574848,41.518080,82.574848,590.3019,41.518080,83.036160,83.036160,21.220352,21.220352,21.220352,7.640322,1.669613,3086.286399,3.07956573736092
-c28,352.4661,1610.85,45.439232,652.9992,41.518080,82.574848,82.574848,290.68457,83.036160,83.036160,208.37216,206.54856,21.220352,7.640322,1.669613,3770.630205,2.52064545956594
-c29,352.4661,1610.85,45.439232,652.9992,41.518080,82.574848,82.574848,41.518080,83.036160,83.036160,21.220352,206.54856,21.220352,7.640322,1.669613,3334.311907,2.850489737627
-c30,352.4661,90.532480,45.439232,652.9992,41.518080,82.574848,82.574848,41.518080,527.54432,83.036160,21.220352,21.220352,210.33129,7.640322,1.669613,2262.285277,4.20124810717017
-c19,2073.174339
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,1108.984,7471.505,3792.343,6973.454,3475.891,6007.756,6530.41,3324.8076,6053.9348,6493.6098,2479.9481,2376.5679,2353.8847,69.37799,1.812953,58514.286843,0.999999998291016
-c1,1108.984,6756.043,1949.97,3356.913,179.180675,3053.2545,264.708556,171.815177,357.330128,269.843015,1299.926,1273.3007,89.882488,57.16078,10.00372,20198.315739,2.89698840781653
-c2,1108.984,384.623163,1949.97,3356.913,179.180675,313.098433,352.195669,171.815177,3203.344,269.843015,67.905725,67.905725,1286.6509,57.16078,10.00372,12779.593982,4.57872812450413
-c3,1108.984,472.88204,170.515404,3356.913,179.180675,3053.2545,264.708556,171.815177,3203.344,269.843015,67.905725,1273.3007,89.882488,57.16078,10.00372,13749.69378,4.25567924302035
-c4,1108.984,6756.043,170.515404,3356.913,179.180675,272.76268,264.708556,171.815177,357.330128,357.330128,67.905725,1273.3007,89.882488,57.16078,10.00372,14493.836161,4.03718420639608
-c5,1108.984,6756.043,219.119355,375.615141,179.180675,313.098433,352.195669,171.815177,3203.344,269.843015,67.905725,67.905725,89.882488,57.16078,10.00372,13242.096903,4.41880820158194
-c6,1108.984,6756.043,174.989917,375.615141,135.437118,313.098433,3366.9575,171.815177,269.843015,357.330128,67.905725,67.905725,89.882488,57.16078,10.00372,13322.971867,4.39198453527753
-c7,1108.984,6756.043,170.515404,3356.913,1731.6018,264.708556,264.708556,171.815177,357.330128,3427.0759,1299.926,69.928921,89.882488,57.16078,10.00372,19136.59743,3.05771633391299
-c8,1108.984,472.88204,1949.97,3356.913,179.180675,3053.2545,264.708556,171.815177,357.330128,269.843015,67.905725,1273.3007,80.061262,57.16078,10.00372,12673.313278,4.61712616880261
-c9,1108.984,6756.043,170.515404,3356.913,179.180675,264.708556,352.195669,1683.0934,357.330128,357.330128,1299.926,1273.3007,89.882488,57.16078,10.00372,17316.567648,3.37909265245465
-c10,1108.984,472.88204,170.515404,3356.913,179.180675,313.098433,264.708556,171.815177,3203.344,269.843015,89.882488,67.905725,1286.6509,57.16078,10.00372,11022.887913,5.30843520990056
-c11,1108.984,6756.043,170.515404,3356.913,179.180675,264.708556,352.195669,1683.0934,357.330128,357.330128,1299.926,1273.3007,89.882488,57.16078,10.00372,17316.567648,3.37909265245465
-c12,1108.984,6756.043,170.515404,3356.913,179.180675,272.76268,264.708556,171.815177,357.330128,357.330128,67.905725,1273.3007,89.882488,57.16078,10.00372,14493.836161,4.03718420639608
-c13,1108.984,472.88204,170.515404,3356.913,179.180675,3053.2545,264.708556,171.815177,3203.344,269.843015,67.905725,1273.3007,89.882488,57.16078,10.00372,13749.69378,4.25567924302035
-c14,1108.984,6756.043,170.515404,3356.913,1731.6018,264.708556,264.708556,171.815177,357.330128,3427.0759,1299.926,69.928921,89.882488,57.16078,10.00372,19136.59743,3.05771633391299
-c15,1108.984,384.623163,1949.97,3356.913,179.180675,313.098433,352.195669,171.815177,3203.344,269.843015,67.905725,67.905725,1286.6509,57.16078,10.00372,12779.593982,4.57872812450413
-c16,1108.984,6756.043,174.989917,375.615141,135.437118,313.098433,3366.9575,171.815177,269.843015,357.330128,67.905725,67.905725,89.882488,57.16078,10.00372,13322.971867,4.39198453527753
-c17,1108.984,472.88204,1949.97,3356.913,179.180675,3053.2545,264.708556,171.815177,357.330128,269.843015,67.905725,1273.3007,80.061262,57.16078,10.00372,12673.313278,4.61712616880261
-c18,1108.984,472.88204,170.515404,3356.913,179.180675,313.098433,264.708556,171.815177,3203.344,269.843015,89.882488,67.905725,1286.6509,57.16078,10.00372,11022.887913,5.30843520990056
-c19,1108.984,384.623163,174.989917,3356.913,135.437118,313.098433,264.708556,171.815177,3203.344,269.843015,67.905725,67.905725,89.882488,57.16078,10.00372,9676.614817,6.04697896370779
-c20,1108.984,6756.043,219.119355,375.615141,179.180675,313.098433,352.195669,171.815177,3203.344,269.843015,67.905725,67.905725,89.882488,57.16078,10.00372,13242.096903,4.41880820158194
-c21,1108.984,472.88204,170.515404,3356.913,179.180675,3053.2545,3366.9575,171.815177,357.330128,269.843015,67.905725,1273.3007,89.882488,57.16078,10.00372,14005.928852,4.17782262379993
-c22,1108.984,6756.043,170.515404,3356.913,1731.6018,264.708556,264.708556,171.815177,357.330128,3427.0759,1299.926,69.928921,89.882488,57.16078,10.00372,19136.59743,3.05771633391299
-c23,1108.984,6756.043,219.119355,375.615141,179.180675,313.098433,352.195669,171.815177,3203.344,269.843015,67.905725,67.905725,89.882488,57.16078,10.00372,13242.096903,4.41880820158194
-c24,1108.984,6756.043,174.989917,3356.913,1731.6018,3053.2545,352.195669,1683.0934,357.330128,357.330128,1299.926,1273.3007,89.882488,57.16078,10.00372,21662.00923,2.70124003510435
-c25,1108.984,384.623163,1949.97,3356.913,179.180675,313.098433,352.195669,171.815177,3203.344,269.843015,67.905725,67.905725,1286.6509,57.16078,10.00372,12779.593982,4.57872812450413
-c26,1108.984,472.88204,1949.97,3356.913,179.180675,3053.2545,264.708556,171.815177,357.330128,269.843015,67.905725,1273.3007,80.061262,57.16078,10.00372,12673.313278,4.61712616880261
-c27,1108.984,6756.043,174.989917,375.615141,135.437118,313.098433,3366.9575,171.815177,269.843015,357.330128,67.905725,67.905725,89.882488,57.16078,10.00372,13322.971867,4.39198453527753
-c28,1108.984,6756.043,170.515404,3356.913,179.180675,264.708556,352.195669,1683.0934,357.330128,357.330128,1299.926,1273.3007,89.882488,57.16078,10.00372,17316.567648,3.37909265245465
-c29,1108.984,6756.043,170.515404,3356.913,179.180675,272.76268,264.708556,171.815177,357.330128,357.330128,67.905725,1273.3007,89.882488,57.16078,10.00372,14493.836161,4.03718420639608
-c30,1108.984,472.88204,170.515404,3356.913,179.180675,313.098433,264.708556,171.815177,3203.344,269.843015,89.882488,67.905725,1286.6509,57.16078,10.00372,11022.887913,5.30843520990056
-c19,9676.614817
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,76.708720,0,151.959867,74.930729,153.127903,153.127903,0,0,38.454652,0,0,648.309774,0
-c2,0,188.762345,0,0,76.708720,151.959867,151.959867,74.930729,0,153.127903,38.454652,38.454652,0,0,0,874.358735,0
-c3,0,188.762345,90.173220,0,76.708720,0,151.959867,74.930729,0,153.127903,38.454652,0,38.454652,0,0,812.572088,0
-c4,0,0,90.173220,0,76.708720,151.959867,151.959867,74.930729,153.127903,153.127903,38.454652,0,38.454652,0,0,928.897513,0
-c5,0,0,90.173220,157.595858,76.708720,151.959867,151.959867,74.930729,0,153.127903,38.454652,38.454652,38.454652,0,0,971.82012,0
-c6,0,0,90.173220,157.595858,76.708720,151.959867,0,74.930729,153.127903,153.127903,38.454652,38.454652,38.454652,0,0,972.988156,0
-c7,0,0,90.173220,0,0,151.959867,151.959867,74.930729,153.127903,0,0,38.454652,38.454652,0,0,699.06089,0
-c8,0,188.762345,0,0,76.708720,0,151.959867,74.930729,153.127903,153.127903,38.454652,0,38.454652,0,0,875.526771,0
-c9,0,0,90.173220,0,76.708720,151.959867,151.959867,0,153.127903,153.127903,0,0,38.454652,0,0,815.512132,0
-c10,0,188.762345,90.173220,0,76.708720,151.959867,151.959867,74.930729,0,153.127903,38.454652,38.454652,0,0,0,964.531955,0
-c11,0,0,90.173220,0,76.708720,151.959867,151.959867,0,153.127903,153.127903,0,0,38.454652,0,0,815.512132,0
-c12,0,0,90.173220,0,76.708720,151.959867,151.959867,74.930729,153.127903,153.127903,38.454652,0,38.454652,0,0,928.897513,0
-c13,0,188.762345,90.173220,0,76.708720,0,151.959867,74.930729,0,153.127903,38.454652,0,38.454652,0,0,812.572088,0
-c14,0,0,90.173220,0,0,151.959867,151.959867,74.930729,153.127903,0,0,38.454652,38.454652,0,0,699.06089,0
-c15,0,188.762345,0,0,76.708720,151.959867,151.959867,74.930729,0,153.127903,38.454652,38.454652,0,0,0,874.358735,0
-c16,0,0,90.173220,157.595858,76.708720,151.959867,0,74.930729,153.127903,153.127903,38.454652,38.454652,38.454652,0,0,972.988156,0
-c17,0,188.762345,0,0,76.708720,0,151.959867,74.930729,153.127903,153.127903,38.454652,0,38.454652,0,0,875.526771,0
-c18,0,188.762345,90.173220,0,76.708720,151.959867,151.959867,74.930729,0,153.127903,38.454652,38.454652,0,0,0,964.531955,0
-c19,0,188.762345,90.173220,0,76.708720,151.959867,151.959867,74.930729,0,153.127903,38.454652,38.454652,38.454652,0,0,1002.986607,0
-c20,0,0,90.173220,157.595858,76.708720,151.959867,151.959867,74.930729,0,153.127903,38.454652,38.454652,38.454652,0,0,971.82012,0
-c21,0,188.762345,90.173220,0,76.708720,0,0,74.930729,153.127903,153.127903,38.454652,0,38.454652,0,0,813.740124,0
-c22,0,0,90.173220,0,0,151.959867,151.959867,74.930729,153.127903,0,0,38.454652,38.454652,0,0,699.06089,0
-c23,0,0,90.173220,157.595858,76.708720,151.959867,151.959867,74.930729,0,153.127903,38.454652,38.454652,38.454652,0,0,971.82012,0
-c24,0,0,90.173220,0,0,0,151.959867,0,153.127903,153.127903,0,0,38.454652,0,0,586.843545,0
-c25,0,188.762345,0,0,76.708720,151.959867,151.959867,74.930729,0,153.127903,38.454652,38.454652,0,0,0,874.358735,0
-c26,0,188.762345,0,0,76.708720,0,151.959867,74.930729,153.127903,153.127903,38.454652,0,38.454652,0,0,875.526771,0
-c27,0,0,90.173220,157.595858,76.708720,151.959867,0,74.930729,153.127903,153.127903,38.454652,38.454652,38.454652,0,0,972.988156,0
-c28,0,0,90.173220,0,76.708720,151.959867,151.959867,0,153.127903,153.127903,0,0,38.454652,0,0,815.512132,0
-c29,0,0,90.173220,0,76.708720,151.959867,151.959867,74.930729,153.127903,153.127903,38.454652,0,38.454652,0,0,928.897513,0
-c30,0,188.762345,90.173220,0,76.708720,151.959867,151.959867,74.930729,0,153.127903,38.454652,38.454652,0,0,0,964.531955,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,23.915213,0,43.122319,18.327706,47.088742,47.088742,0,0,11.960929,0,0,191.503651,0
-c2,0,109.549158,0,0,23.915213,43.122319,43.122319,18.327706,0,47.088742,11.960929,11.960929,0,0,0,309.047315,0
-c3,0,109.549158,41.660867,0,23.915213,0,43.122319,18.327706,0,47.088742,11.960929,0,11.960929,0,0,307.585863,0
-c4,0,0,41.660867,0,23.915213,43.122319,43.122319,18.327706,47.088742,47.088742,11.960929,0,11.960929,0,0,288.247766,0
-c5,0,0,41.660867,60.905800,23.915213,43.122319,43.122319,18.327706,0,47.088742,11.960929,11.960929,11.960929,0,0,314.025753,0
-c6,0,0,41.660867,60.905800,23.915213,43.122319,0,18.327706,47.088742,47.088742,11.960929,11.960929,11.960929,0,0,317.992176,0
-c7,0,0,41.660867,0,0,43.122319,43.122319,18.327706,47.088742,0,0,11.960929,11.960929,0,0,217.243811,0
-c8,0,109.549158,0,0,23.915213,0,43.122319,18.327706,47.088742,47.088742,11.960929,0,11.960929,0,0,313.013738,0
-c9,0,0,41.660867,0,23.915213,43.122319,43.122319,0,47.088742,47.088742,0,0,11.960929,0,0,257.959131,0
-c10,0,109.549158,41.660867,0,23.915213,43.122319,43.122319,18.327706,0,47.088742,11.960929,11.960929,0,0,0,350.708182,0
-c11,0,0,41.660867,0,23.915213,43.122319,43.122319,0,47.088742,47.088742,0,0,11.960929,0,0,257.959131,0
-c12,0,0,41.660867,0,23.915213,43.122319,43.122319,18.327706,47.088742,47.088742,11.960929,0,11.960929,0,0,288.247766,0
-c13,0,109.549158,41.660867,0,23.915213,0,43.122319,18.327706,0,47.088742,11.960929,0,11.960929,0,0,307.585863,0
-c14,0,0,41.660867,0,0,43.122319,43.122319,18.327706,47.088742,0,0,11.960929,11.960929,0,0,217.243811,0
-c15,0,109.549158,0,0,23.915213,43.122319,43.122319,18.327706,0,47.088742,11.960929,11.960929,0,0,0,309.047315,0
-c16,0,0,41.660867,60.905800,23.915213,43.122319,0,18.327706,47.088742,47.088742,11.960929,11.960929,11.960929,0,0,317.992176,0
-c17,0,109.549158,0,0,23.915213,0,43.122319,18.327706,47.088742,47.088742,11.960929,0,11.960929,0,0,313.013738,0
-c18,0,109.549158,41.660867,0,23.915213,43.122319,43.122319,18.327706,0,47.088742,11.960929,11.960929,0,0,0,350.708182,0
-c19,0,109.549158,41.660867,0,23.915213,43.122319,43.122319,18.327706,0,47.088742,11.960929,11.960929,11.960929,0,0,362.669111,0
-c20,0,0,41.660867,60.905800,23.915213,43.122319,43.122319,18.327706,0,47.088742,11.960929,11.960929,11.960929,0,0,314.025753,0
-c21,0,109.549158,41.660867,0,23.915213,0,0,18.327706,47.088742,47.088742,11.960929,0,11.960929,0,0,311.552286,0
-c22,0,0,41.660867,0,0,43.122319,43.122319,18.327706,47.088742,0,0,11.960929,11.960929,0,0,217.243811,0
-c23,0,0,41.660867,60.905800,23.915213,43.122319,43.122319,18.327706,0,47.088742,11.960929,11.960929,11.960929,0,0,314.025753,0
-c24,0,0,41.660867,0,0,0,43.122319,0,47.088742,47.088742,0,0,11.960929,0,0,190.921599,0
-c25,0,109.549158,0,0,23.915213,43.122319,43.122319,18.327706,0,47.088742,11.960929,11.960929,0,0,0,309.047315,0
-c26,0,109.549158,0,0,23.915213,0,43.122319,18.327706,47.088742,47.088742,11.960929,0,11.960929,0,0,313.013738,0
-c27,0,0,41.660867,60.905800,23.915213,43.122319,0,18.327706,47.088742,47.088742,11.960929,11.960929,11.960929,0,0,317.992176,0
-c28,0,0,41.660867,0,23.915213,43.122319,43.122319,0,47.088742,47.088742,0,0,11.960929,0,0,257.959131,0
-c29,0,0,41.660867,0,23.915213,43.122319,43.122319,18.327706,47.088742,47.088742,11.960929,0,11.960929,0,0,288.247766,0
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-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
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-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
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-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
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-Quantization Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
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-Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
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-c13,352.4661,123.468921,59.178279,652.9992,51.115816,544.1687,100.738165,49.948052,527.54432,102.069715,26.069312,206.54856,26.069312,7.640322,1.669613,2831.694387,3.3564433598447
-c14,352.4661,1720.485,59.178279,652.9992,333.7788,100.738165,100.738165,49.948052,102.069715,543.5573,208.37216,26.069312,26.069312,7.640322,1.669613,4285.779495,2.21766470891044
-c15,352.4661,123.468921,426.5549,652.9992,51.115816,100.738165,100.738165,49.948052,527.54432,102.069715,26.069312,26.069312,210.33129,7.640322,1.669613,2759.423203,3.44435090754903
-c16,352.4661,1720.485,59.178279,104.457756,51.115816,100.738165,590.3019,49.948052,102.069715,102.069715,26.069312,26.069312,26.069312,7.640322,1.669613,3320.348369,2.8624773112632
-c17,352.4661,123.468921,426.5549,652.9992,51.115816,544.1687,100.738165,49.948052,102.069715,102.069715,26.069312,206.54856,26.069312,7.640322,1.669613,2773.596403,3.42675012306936
-c18,352.4661,123.468921,59.178279,652.9992,51.115816,100.738165,100.738165,49.948052,527.54432,102.069715,26.069312,26.069312,210.33129,7.640322,1.669613,2392.046582,3.97334309130343
-c19,352.4661,123.468921,59.178279,652.9992,51.115816,100.738165,100.738165,49.948052,527.54432,102.069715,26.069312,26.069312,26.069312,7.640322,1.669613,2207.784604,4.30495878551027
-c20,352.4661,1720.485,59.178279,104.457756,51.115816,100.738165,100.738165,49.948052,527.54432,102.069715,26.069312,26.069312,26.069312,7.640322,1.669613,3256.259239,2.91881609187762
-c21,352.4661,123.468921,59.178279,652.9992,51.115816,544.1687,590.3019,49.948052,102.069715,102.069715,26.069312,206.54856,26.069312,7.640322,1.669613,2895.783517,3.28215896457294
-c22,352.4661,1720.485,59.178279,652.9992,333.7788,100.738165,100.738165,49.948052,102.069715,543.5573,208.37216,26.069312,26.069312,7.640322,1.669613,4285.779495,2.21766470891044
-c23,352.4661,1720.485,59.178279,104.457756,51.115816,100.738165,100.738165,49.948052,527.54432,102.069715,26.069312,26.069312,26.069312,7.640322,1.669613,3256.259239,2.91881609187762
-c24,352.4661,1720.485,59.178279,652.9992,333.7788,544.1687,100.738165,290.68457,102.069715,102.069715,208.37216,206.54856,26.069312,7.640322,1.669613,4708.938211,2.01837899124689
-c25,352.4661,123.468921,426.5549,652.9992,51.115816,100.738165,100.738165,49.948052,527.54432,102.069715,26.069312,26.069312,210.33129,7.640322,1.669613,2759.423203,3.44435090754903
-c26,352.4661,123.468921,426.5549,652.9992,51.115816,544.1687,100.738165,49.948052,102.069715,102.069715,26.069312,206.54856,26.069312,7.640322,1.669613,2773.596403,3.42675012306936
-c27,352.4661,1720.485,59.178279,104.457756,51.115816,100.738165,590.3019,49.948052,102.069715,102.069715,26.069312,26.069312,26.069312,7.640322,1.669613,3320.348369,2.8624773112632
-c28,352.4661,1720.485,59.178279,652.9992,51.115816,100.738165,100.738165,290.68457,102.069715,102.069715,208.37216,206.54856,26.069312,7.640322,1.669613,3982.844692,2.38634007960609
-c29,352.4661,1720.485,59.178279,652.9992,51.115816,100.738165,100.738165,49.948052,102.069715,102.069715,26.069312,206.54856,26.069312,7.640322,1.669613,3559.805326,2.66992743158991
-c30,352.4661,123.468921,59.178279,652.9992,51.115816,100.738165,100.738165,49.948052,527.54432,102.069715,26.069312,26.069312,210.33129,7.640322,1.669613,2392.046582,3.97334309130343
-c19,2207.784604
-
-Unpatch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c30,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c0,0
-
diff --git a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_tensors.txt b/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_tensors.txt
deleted file mode 100644
index 6c6b42b93c0446c298489429261592fe99e2f81b..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar10/vgg16_tensors.txt
+++ /dev/null
@@ -1,64 +0,0 @@
-#Conv1,3
-Conv1,163.415,464.69,310.228,694.841,7.62006,16.0868,83.4767,247.452
-Add1,113.913,376.561,69.7114,211.862,314.261,894.668,83.2799,265.625
-Relu1,75.1381,267.733,63.043,222.775,116.511,379.888,83.4086,300.518
-#Conv2,4
-Conv2,1186.37,5295.79,1399.51,5412.29,109.635,397.898,83.3307,436.945
-Add2,114.041,707.602,69.1905,303.338,336.556,1525.29,83.1178,365.325
-Relu2,74.295,455.493,62.4233,282.215,106.67,463.637,83.0734,372.529
-Pool1,175.393,1012.62,79.7262,360.302,106.496,467.712,20.9488,92.2916
-#Conv3,3
-Conv3,613.82,3267.16,354.47,1585.56,28.5399,124.01,41.6322,203.92
-Add3,51.2818,306.368,40.9358,206.435,52.9835,255.846,41.6412,204.32
-Relu3,36.6237,218.815,31.1491,157.975,53.9219,262.044,41.4745,201.753
-#Conv4,4
-Conv4,982.846,5808.36,543.421,2780.23,52.8973,253.818,41.5238,236.022
-Add4,51.329,344.524,37.6205,200.601,146.069,779.141,41.3499,213.133
-Relu4,36.7139,244.573,31.1126,165.225,52.5946,268.222,41.3761,210.186
-Pool2,88.7044,575.997,40.8451,210.857,52.6109,263.231,10.5892,50.2118
-#Conv5,3
-Conv5,491.91,3047.42,278.988,1427.88,14.8008,70.9043,20.8418,110.25
-Add5,48.2779,312.538,39.1711,216.321,27.4739,144.775,20.8433,109.029
-Relu5,18.3751,115.933,15.6197,87.4008,27.1876,142.132,20.7564,107.244
-#Conv6,3
-Conv6,858.952,5554.16,490.314,2738.69,28.103,144.6,20.8654,122.714
-Add6,48.3038,331.31,38.2773,222.684,78.3804,453.985,20.7551,113.98
-Relu6,18.3515,122.286,15.5774,91.8805,26.9604,148.182,20.7925,111.946
-#Conv7,4
-Conv7,859.425,5760.38,514.632,2922.64,28.2053,152.501,20.8436,124.457
-Add7,48.1751,337.048,38.3104,225.263,81.46,477.128,20.7616,115.225
-Relu7,18.3889,125.084,15.5863,93.2475,27.0202,149.73,20.7296,113.484
-Pool3,45.2833,307.898,21.7732,125.807,27.105,148.004,5.37692,25.667
-#Conv8,3
-Conv8,434.046,2940.43,240.716,1378.49,9.68469,49.5859,10.5597,59.0058
-Add8,46.6813,323.76,42.0846,253.744,14.6636,82.9186,10.5141,57.5583
-Relu8,9.34284,60.6176,7.88397,50.8594,14.4739,80.2487,10.4677,57.0219
-#Conv9,3
-Conv9,801.893,5651.89,477.878,2885.71,16.2347,90.1067,10.5851,63.9065
-Add9,46.6775,338.552,41.8259,265.047,39.8499,247.783,10.4589,60.1816
-Relu9,9.34283,63.4928,7.84042,52.587,14.2726,83.3662,10.4563,59.3499
-#Conv10,4
-Conv10,802.661,5895.34,481.652,3018.22,16.267,94.4725,10.5438,65.7062
-Add10,46.7852,353.064,41.8232,272.933,40.0563,257.396,10.5152,61.7303
-Relu10,9.31493,65.6928,7.8553,54.9037,14.2674,85.1708,10.4698,61.6665
-Pool4,24.6055,179.513,12.2268,81.0192,14.3183,85.3146,2.76689,12.3171
-#Conv11,3
-Conv11,308.859,2238.17,173.774,1077.96,6.79928,37.3658,2.77964,12.8278
-Add11,32.7593,228.974,32.499,205.175,4.84751,25.3885,2.71712,12.3719
-Relu11,2.49349,12.8041,2.09916,16.791,4.48398,23.4935,2.65339,11.6345
-#Conv12,3
-Conv12,308.957,2143.29,172.103,1054.21,6.35668,34.3,2.76929,12.2454
-Add12,32.7648,220.92,32.3458,201.907,4.88662,25.364,2.69777,11.7633
-Relu12,2.50068,12.3579,2.09976,17.1837,4.44727,22.6715,2.64916,11.3108
-#Conv13,4
-Conv13,308.526,2076.4,171.684,1041.08,6.20782,33.1375,2.76826,12.0417
-Add13,32.723,215.055,32.2976,199.983,4.91853,25.4026,2.71496,12.0093
-Relu13,2.48762,12.2284,2.10382,16.7344,4.36041,22.0096,2.72796,11.4339
-Pool5,8.20681,50.2013,4.24587,28.8535,4.30952,21.6535,0.789328,1.12606
-#FC1,3
-Mul1,10.6715,65.8927,5.49296,37.0659,2.18449,8.22985,0.810781,1.37103
-Add14,0.902452,1.91287,0.999932,9.47008,1.88605,7.19323,0.760782,0.960605
-Relu14,0.846016,1.57242,1.14743,10.6248,1.5964,5.26066,0.762322,0.9319
-#FC2,2
-Mul2,0.821147,1.66598,1.31517,8.1931,1.76803,6.49192,0.133188,0.0183427
-Add15,0.226743,0.146973,0.354443,1.81062,0.764484,1.36953,0.120211,0.0438951
diff --git a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_confs1.txt b/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_confs1.txt
deleted file mode 100644
index 2c88b81aaa9620b01f75897e5e082dd78c1d3d57..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_confs1.txt
+++ /dev/null
@@ -1,13 +0,0 @@
-9 9 9,9 9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,8 8 8 8,8 8 8,8 8
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,7,7,7,8 8 8,8 8 8 8,7,7,8 8 8 8,8 8 8,8 8
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,7,8 8 8,7,8 8 8 8,8 8 8,8 8
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,7,8 8 8,7,8 8 8 8,8 8 8,8 8
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,8 8 8 8,8 8 8,8 8
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,7,7,7,8 8 8,8 8 8 8,7,7,8 8 8 8,8 8 8,8 8
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,7,8 8 8,7,8 8 8 8,8 8 8,8 8
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,7,8 8 8,7,8 8 8 8,8 8 8,8 8
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,8 8 8 8,8 8 8,8 8
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,7,8 8 8,7,8 8 8 8,8 8 8,8 8
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,7,7,7,8 8 8,8 8 8 8,7,7,8 8 8 8,8 8 8,8 8
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,7,8 8 8,7,8 8 8 8,8 8 8,8 8
diff --git a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_confs2.txt b/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_confs2.txt
deleted file mode 100644
index 8dacc6e3fe910098503d504feece0b5ecd1753dc..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_confs2.txt
+++ /dev/null
@@ -1,13 +0,0 @@
-9 9 9,9 9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,8 8 8 8,7,8 8 8,7,8 8 8,8 8 8,7,8 8 8,8 8
-9 9 9,8 8 8 8,8 8 8,7,8 8 8,8 8 8,8 8 8 8,6,6,7,8 8 8,7,6,8 8 8,8 8
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,7,8 8 8 8,8 8 8,7,7,8 8 8,7,7,8 8 8,8 8
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,7,8 8 8 8,8 8 8,7,7,8 8 8,8 8 8,7,8 8 8,8 8
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,8 8 8 8,7,8 8 8,7,8 8 8,8 8 8,7,8 8 8,8 8
-9 9 9,8 8 8 8,8 8 8,7,8 8 8,8 8 8,8 8 8 8,6,6,7,8 8 8,7,6,8 8 8,8 8
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,7,8 8 8 8,8 8 8,7,7,8 8 8,7,7,8 8 8,8 8
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,7,8 8 8 8,8 8 8,7,7,8 8 8,8 8 8,7,8 8 8,8 8
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,8 8 8 8,7,8 8 8,7,8 8 8,8 8 8,7,8 8 8,8 8
-9 9 9,8 8 8 8,8 8 8,7,8 8 8,8 8 8,8 8 8 8,6,6,7,8 8 8,7,6,8 8 8,8 8
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,7,8 8 8 8,8 8 8,7,7,8 8 8,7,7,8 8 8,8 8
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,7,8 8 8 8,8 8 8,7,7,8 8 8,8 8 8,7,8 8 8,8 8
diff --git a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_fp16.csv b/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_fp16.csv
deleted file mode 100644
index 04c4cfc4efb2b0fe6f94ddc332d356ba2966da72..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_fp16.csv
+++ /dev/null
@@ -1,148 +0,0 @@
-Add1,69.7114,211.862,107.8,104.062,6085.83,3099.48,2986.34,49.2963,152.515,80.4532,73.6474,4385.91,2317.11,2113.98
-Add10,41.8232,272.933,216.003,56.9294,13052.8,10330.2,2722.54,29.5763,193.08,152.806,40.2755,9233.85,7307.84,1926.04
-Add10_f2h,40.0563,257.396,205.254,52.142,12850.3,10247,2603.29,28.3409,182.148,145.252,36.8963,9087.18,7246.3,1840.92
-Add10_h2f,10.5152,61.7303,48.6618,13.0686,11780.8,9286.75,2494.08,7.45143,43.7235,34.4675,9.2562,8348.86,6581.42,1767.47
-Add11,32.499,205.175,160.15,45.0253,12627.7,9856.71,2770.94,22.9909,145.16,113.305,31.856,8930.63,6971.08,1959.62
-Add11_f2h,4.84751,25.3885,19.8298,5.55869,10456,8166.49,2289.46,3.43461,18.1215,14.1539,3.96771,7436.9,5808.46,1628.52
-Add11_h2f,2.71712,12.3719,9.65008,2.72179,9106.03,7102.66,2003.36,1.92229,8.92178,6.95893,1.96292,6563.12,5119.11,1444.06
-Add12,32.3458,201.907,158.806,43.1007,12485.6,9820.54,2665.04,22.8734,142.79,112.309,30.483,8830.29,6945.65,1884.77
-Add12_f2h,4.88662,25.364,19.9643,5.39974,10378.2,8169.14,2209.1,3.45883,18.0489,14.2063,3.84293,7376.52,5806.53,1570.11
-Add12_h2f,2.69777,11.7633,9.24658,2.51676,8720.78,6854.97,1865.81,1.90775,8.49592,6.67815,1.81791,6297.92,4950.42,1347.6
-Add13,32.2976,199.983,158.214,41.7697,12384.7,9798.1,2586.56,22.8397,141.436,111.896,29.5422,8758.82,6929.74,1829.23
-Add13_f2h,4.91853,25.4026,20.1163,5.28634,10314.5,8167.97,2146.57,3.50657,18.2597,14.4609,3.79914,7330.34,5804.95,1525.56
-Add13_h2f,2.71496,12.0093,9.49625,2.51304,8840.31,6990.41,1849.9,1.92046,8.68342,6.8666,1.81698,6383.37,5047.78,1335.71
-Add14,0.999932,9.47008,7.42388,2.0462,18954.6,14861.4,4093.23,0.708467,7.11012,5.57305,1.53723,14214.1,11144.8,3069.6
-Add14_f2h,1.88605,7.19323,5.64536,1.54786,7493.19,5878.86,1614.33,1.34509,5.54955,4.35727,1.1924,5686.64,4462.19,1224.59
-Add14_h2f,0.760782,0.960605,0.753025,0.20758,2523.02,1977.84,545.18,0.538123,1.10915,0.869399,0.23978,2911.43,2282.18,629.323
-Add15,0.354443,1.81062,1.41722,0.393395,10528.4,8243.7,2284.66,0.255481,2.29175,1.79496,0.496829,13969.3,10946.3,3023.19
-Add15_f2h,0.764484,1.36953,1.07351,0.296021,3102.43,2430.16,672.268,0.572894,1.59231,1.24879,0.343541,3452.8,2704.95,747.932
-Add15_h2f,0.120211,0.0438951,0.0343584,0.00953666,722.085,565.205,156.881,0.0858258,0.0897805,0.0702545,0.0195276,1484.29,1161.5,322.825
-Add1_f2h,314.261,894.668,472.564,422.104,5684.96,2999.37,2685.59,222.29,654.332,365.302,299.156,4151.15,2314.09,1902.21
-Add1_h2f,83.2799,265.625,135.314,130.311,6379.44,3249.94,3129.5,58.8888,190.41,99.6739,92.2071,4573.2,2394.11,2214.4
-Add2,69.1905,303.338,199.548,103.79,8768.52,5768.33,3000.19,48.9274,214.509,141.116,73.3959,6200.7,4079.22,2121.55
-Add2_f2h,336.556,1525.29,1077.85,447.443,9065.06,6405.97,2659.09,238.064,1078.88,762.399,316.488,6410.37,4530.13,1880.3
-Add2_h2f,83.1178,365.325,234.29,131.036,8790.52,5637.51,3153.01,58.7743,258.343,165.686,92.6601,6216.16,3986.69,2229.56
-Add3,40.9358,206.435,132.407,74.0277,10086.1,6469.28,3616.86,28.9503,146.001,93.6465,52.3555,7132.63,4575.01,2557.68
-Add3_f2h,52.9835,255.846,166.622,89.2241,9657.91,6289.83,3368.08,37.4671,180.924,117.829,63.0955,6829.52,4447.85,2381.69
-Add3_h2f,41.6412,204.32,129.501,74.8188,9814.92,6220.84,3594.07,29.4469,144.508,91.593,52.9164,6942.13,4400.1,2542.08
-Add4,37.6205,200.601,138.934,61.6676,10664.8,7386.36,3278.47,26.6043,141.874,98.2614,43.6138,7542.19,5223.74,2318.49
-Add4_f2h,146.069,779.141,555.206,223.935,10669.7,7603.38,3066.3,103.384,551.386,392.897,158.49,7544.72,5376.51,2168.23
-Add4_h2f,41.3499,213.133,145.707,67.426,10308.5,7047.34,3261.17,29.2407,150.732,103.047,47.6846,7289.72,4983.6,2306.13
-Add5,39.1711,216.321,149.235,67.0856,11045,7619.75,3425.3,27.7004,152.99,105.545,47.445,7810.9,5388.61,2422.31
-Add5_f2h,27.4739,144.775,100.707,44.0675,10539.5,7331.45,3208.09,19.4282,102.386,71.2208,31.165,7453.51,5184.78,2268.74
-Add5_h2f,20.8433,109.029,74.8169,34.2117,10461.4,7178.79,3282.61,14.7445,77.147,52.9395,24.2078,7398.96,5077.34,2321.65
-Add6,38.2773,222.684,163.353,59.3305,11635.7,8535.58,3100.14,27.0679,157.484,115.525,41.9589,8228.62,6036.27,2192.36
-Add6_f2h,78.3804,453.985,337.891,116.094,11585.2,8622.75,2962.45,55.4654,321.237,239.086,82.1516,8192.13,6097.33,2094.81
-Add6_h2f,20.7551,113.98,83.0303,30.9501,10983.3,8000.93,2982.38,14.6773,80.6183,58.7275,21.891,7767.8,5658.58,2109.24
-Add7,38.3104,225.263,168.705,56.5575,11759.8,8807.22,2952.55,27.0935,159.323,119.322,40.0017,8316.1,6228.2,2087.92
-Add7_f2h,81.46,477.128,362.784,114.344,11714.6,8907.2,2807.39,57.624,337.519,256.632,80.8876,8283.65,6298.49,1985.17
-Add7_h2f,20.7616,115.225,85.6469,29.5778,11099.7,8250.44,2849.26,14.6822,81.4998,60.5791,20.9209,7850.09,5834.99,2015.11
-Add8,42.0846,253.744,188.878,64.8666,12058.8,8976.12,3082.68,29.7613,179.461,133.584,45.8767,8527.74,6347.77,2180
-Add8_f2h,14.6636,82.9186,61.9626,20.956,11309.1,8451,2858.12,10.3711,58.6733,43.8445,14.8289,8000.2,5978.33,2021.89
-Add8_h2f,10.5141,57.5583,42.7041,14.8542,10948.7,8123.2,2825.55,7.43528,40.7375,30.2242,10.5133,7748.37,5748.74,1999.65
-Add9,41.8259,265.047,205.792,59.2543,12674.6,9841.07,2833.5,29.5785,187.445,145.539,41.9062,8963.23,6959.46,2003.8
-Add9_f2h,39.8499,247.783,193.75,54.033,12435.2,9723.35,2711.83,28.1894,175.299,137.075,38.2246,8793.58,6875.93,1917.67
-Add9_h2f,10.4589,60.1816,46.5522,13.6294,11508.3,8902.04,2606.31,7.39612,42.594,32.9479,9.64626,8144.6,6300.12,1844.51
-Conv1,310.228,694.841,342.376,352.465,5137.93,2796.38,2341.55,227.742,502.069,283.327,253.153,4000.56,2481.3,1665.08
-Conv10,481.652,3018.22,2355.36,662.857,12534.5,9781.99,2752.5,340.625,2134.28,1665.53,468.767,8863.57,6917.32,1946.32
-Conv10_f2h,16.267,94.4725,71.7156,22.7569,11613.2,8815.27,2797.92,11.5068,66.8653,50.7629,16.1033,8215,6235.99,1979.12
-Conv10_h2f,10.5438,65.7062,52.8554,12.8508,12463.8,10026.1,2437.66,7.45594,46.5025,37.4079,9.09478,8820.88,7095.76,1725.14
-Conv11,173.774,1077.96,835.557,242.405,12410.5,9620.47,2790,122.957,762.523,591.018,171.513,8775.98,6803.22,1972.86
-Conv11_f2h,6.79928,37.3658,28.7179,8.64791,10989.1,8446.21,2542.86,4.81142,26.5033,20.3687,6.13521,7787.5,5985.57,1802.09
-Conv11_h2f,2.77964,12.8278,10.0359,2.79191,9229.29,7220.6,2008.69,1.96619,9.21135,7.20639,2.00505,6624.71,5182.79,1441.97
-Conv12,172.103,1054.21,824.356,229.857,12254.7,9583.51,2671.18,121.754,745.623,583.022,162.614,8665.94,6777.23,1888.86
-Conv12_f2h,6.35668,34.3,26.5952,7.70476,10790.7,8366.52,2424.14,4.49979,24.3436,18.8767,5.46737,7649.53,5931.31,1718.37
-Conv12_h2f,2.76929,12.2454,9.6556,2.58978,8837.54,6968.57,1868.97,1.95863,8.84718,6.97582,1.87148,6378.23,5029.25,1349.07
-Conv13,171.684,1041.08,819.652,221.43,12131.7,9552.2,2579.48,121.445,736.263,579.64,156.639,8579.07,6755.25,1824.02
-Conv13_f2h,6.20782,33.1375,25.8857,7.25183,10669.3,8333.91,2335.41,4.39486,23.5409,18.3911,5.15048,7566.21,5910.3,1656.12
-Conv13_h2f,2.76826,12.0417,9.55355,2.48819,8700.26,6902.56,1797.7,1.95823,8.68074,6.88708,1.79382,6270.3,4974.74,1295.67
-Conv1_f2h,7.62006,16.0868,8.21342,7.87343,4543.5,2445.6,2097.91,5.45871,12.1698,7.31434,5.59711,3581.38,2233.61,1494.73
-Conv1_h2f,83.4767,247.452,143.632,103.82,5928.39,3441,2487.39,59.0279,183.614,113.023,73.667,4398.72,2707.49,1764.94
-Conv2,1399.51,5412.29,3378.78,2033.51,7735.19,4829.01,2906.18,989.654,3830.09,2393.05,1438.02,5474.06,3420.35,2055.12
-Conv2_f2h,109.635,397.898,194.591,203.307,7252.79,3544.58,3708.21,77.5331,282.821,139.764,143.829,5150.57,2542.09,2622.66
-Conv2_h2f,83.3307,436.945,336.643,100.302,10486.9,8079.59,2407.3,58.9247,308.997,238.072,70.9279,7415.87,5713.65,1702.27
-Conv3,354.47,1585.56,948.24,637.318,8947.03,5350.96,3596.07,250.683,1121.23,670.544,450.697,6326.72,3783.93,2542.83
-Conv3_f2h,28.5399,124.01,71.14,52.8695,8689.56,4984.75,3704.81,20.1903,87.7504,50.3439,37.4082,6145.43,3525.53,2620.02
-Conv3_h2f,41.6322,203.92,135.55,68.3698,9796.09,6511.67,3284.41,29.4395,144.212,95.8623,48.3504,6927.44,4604.9,2322.58
-Conv4,543.421,2780.23,1853.46,926.763,10232.8,6821.89,3410.92,384.277,1965.96,1310.62,655.348,7235.83,4823.95,2411.91
-Conv4_f2h,52.8973,253.818,152.989,100.829,9599.81,5786.36,3813.45,37.4134,179.493,108.19,71.3037,6789.28,4092.37,2696.95
-Conv4_h2f,41.5238,236.022,174.197,61.8248,11368,8390.24,2977.78,29.3628,166.909,123.187,43.7213,8038.87,5933.13,2105.74
-Conv5,278.988,1427.88,945.3,482.585,10236.5,6776.91,3459.59,197.282,1009.7,668.449,341.252,7238.45,4792.15,2446.32
-Conv5_f2h,14.8008,70.9043,45.1842,25.7201,9581.37,6105.87,3475.5,10.4668,50.1662,31.9688,18.1977,6778.52,4319.78,2458.78
-Conv5_h2f,20.8418,110.25,77.5339,32.7165,10579.5,7440.09,3139.45,14.7375,78.0695,54.9032,23.1665,7491.32,5268.35,2222.99
-Conv6,490.314,2738.69,1950.74,787.949,11171.6,7957.5,3214.1,346.719,1936.58,1379.4,557.184,7899.63,5626.93,2272.73
-Conv6_f2h,28.103,144.6,96.262,48.3381,10291.3,6851.06,3440.28,19.8745,102.268,68.0815,34.1867,7277.97,4845.06,2432.93
-Conv6_h2f,20.8654,122.714,92.9633,29.7508,11762.4,8910.71,2851.66,14.7546,86.7919,65.7503,21.0417,8318.79,6302.01,2016.79
-Conv7,514.632,2922.64,2150.64,772,11358.7,8358.43,3000.25,363.913,2066.64,1520.74,545.901,8031.96,5910.47,2121.51
-Conv7_f2h,28.2053,152.501,107.622,44.8785,10813.7,7631.44,3182.25,19.9546,107.904,76.1494,31.7549,7647.43,5396.98,2250.48
-Conv7_h2f,20.8436,124.457,96.3593,28.0977,11942.1,9246.03,2696.05,14.7391,88.0253,68.1525,19.8729,8446.1,6539.32,1906.8
-Conv8,240.716,1378.49,1006.49,371.999,11454.2,8363.32,3090.86,170.247,974.885,711.794,263.094,8099.49,5913.93,2185.59
-Conv8_f2h,9.68469,49.5859,35.2796,14.3062,10241.5,7287.22,2954.3,6.87516,35.2358,25.0675,10.1686,7251.09,5159.52,2091.63
-Conv8_h2f,10.5597,59.0058,44.3452,14.6605,11175.6,8398.89,2776.67,7.46752,41.7631,31.3867,10.3764,7908.98,5943.95,1965.05
-Conv9,477.878,2885.71,2189.28,696.429,12080.4,9165.65,2914.72,338.05,2040.9,1548.28,492.646,8542.53,6481.6,2061.04
-Conv9_f2h,16.2347,90.1067,65.6407,24.466,11099.8,8085.77,3014.08,11.4848,63.772,46.458,17.3143,7851.77,5719.72,2132.09
-Conv9_h2f,10.5851,63.9065,50.4134,13.4931,12075.4,9525.82,2549.57,7.48625,45.2319,35.6818,9.55026,8545.5,6741.23,1804.29
-Mul1,5.49296,37.0659,29.09,7.97593,13530.7,10624,2906.69,3.93147,26.5587,20.8353,5.72401,9606.57,7543.24,2063.55
-Mul1_f2h,2.18449,8.22985,6.46583,1.76402,7507.4,5897.26,1610.14,1.54872,6.15957,4.84093,1.31877,5588.96,4391.09,1197.99
-Mul1_h2f,0.810781,1.37103,1.07631,0.294716,3393.71,2664.12,729.59,0.574493,1.51826,1.19191,0.326392,3760.1,2951.69,808.488
-Mul2,1.31517,8.1931,6.40985,1.78324,12447.5,9739.56,2707.96,0.931548,6.00801,4.69986,1.30826,9120.64,7136.62,1984.19
-Mul2_f2h,1.76803,6.49192,5.08483,1.40709,7151.61,5599.64,1551.97,1.26447,5.09282,3.99098,1.10192,5498.59,4306.31,1192.38
-Mul2_h2f,0.133188,0.0183427,0.0143504,0.0039923,261.068,204.28,56.7875,0.0949979,0.06404,0.0500845,0.013956,879.603,688.197,191.411
-Pool1,79.7262,360.302,206.802,153.501,9038.84,5188.08,3850.75,56.3789,254.792,146.244,108.551,6391.73,3668.8,2722.98
-Pool1_f2h,106.496,467.712,270.25,197.462,8783.74,5075.34,3708.4,75.3091,330.755,191.123,139.636,6211.29,3589.1,2622.27
-Pool1_h2f,20.9488,92.2916,53.56,38.7316,8810.98,5113.32,3697.66,14.8149,65.2847,37.8887,27.3971,6231.7,3616.65,2615.16
-Pool2,40.8451,210.857,135.193,75.6642,10325.2,6620.16,3705.01,28.8847,149.127,95.6146,53.5134,7301.99,4681.87,2620.15
-Pool2_f2h,52.6109,263.231,170.165,93.0664,10007,6469.01,3537.99,37.2057,186.157,120.341,65.8167,7076.35,4574.53,2501.84
-Pool2_h2f,10.5892,50.2118,32.2214,17.9904,9482.45,6084.99,3397.46,7.49375,35.5699,22.8258,12.7444,6710.71,4306.37,2404.39
-Pool3,21.7732,125.807,90.0357,35.7709,11556.7,8270.76,3285.91,15.3981,89.0019,63.696,25.3063,8175.07,5850.69,2324.42
-Pool3_f2h,27.105,148.004,106.551,41.4535,10921,7862.21,3058.75,19.1678,104.677,75.3585,29.3184,7723.33,5560.2,2163.15
-Pool3_h2f,5.37692,25.667,18.3482,7.31879,9547.33,6824.97,2722.36,3.80213,18.2207,13.0249,5.19588,6777.51,4844.84,1932.7
-Pool4,12.2268,81.0192,62.5832,18.436,13254.2,10238.3,3015.89,8.64895,57.3698,44.3151,13.055,9382.73,7247.8,2134.97
-Pool4_f2h,14.3183,85.3146,66.1354,19.1792,11917,9238.05,2678.99,10.1261,60.363,46.7931,13.5702,8430.6,6535.39,1895.24
-Pool4_h2f,2.76689,12.3171,9.50437,2.81277,8901.99,6869.12,2032.88,1.95715,8.87091,6.84512,2.02583,6408.16,4944.77,1463.42
-Pool5,4.24587,28.8535,22.6878,6.16569,13613.1,10704.3,2908.83,3.01182,20.5632,16.1689,4.39464,9690.26,7619.75,2070.69
-Pool5_f2h,4.30952,21.6535,17.0404,4.6131,10038.8,7899.74,2139.08,3.05175,15.4491,12.159,3.29042,7146.27,5623.84,1522.54
-Pool5_h2f,0.789328,1.12606,0.885458,0.240603,2854.59,2244.62,609.971,0.558354,1.28688,1.01189,0.27502,3258.62,2562.17,696.513
-Relu1,63.043,222.775,109.755,113.019,7068.58,3483,3585.58,44.5829,158.651,79.4284,79.9495,5034.27,2521.06,2536.23
-Relu10,7.8553,54.9037,42.8846,12.0191,13979.1,10918.9,3060.23,5.55636,38.9249,30.404,8.52113,9908.04,7739.05,2169.03
-Relu10_f2h,14.2674,85.1708,66.8096,18.3612,11939.8,9365.82,2573.95,10.0908,60.2697,47.2767,12.9932,8447.54,6626.47,1821.1
-Relu10_h2f,10.4698,61.6665,48.0288,13.6376,11779.2,9174.16,2605.03,7.40475,43.6532,33.9994,9.654,8336.17,6492.58,1843.64
-Relu11,2.09916,16.791,13.0701,3.72095,15998.3,12453,3545.28,1.48462,12.0634,9.39021,2.67327,11491.9,8945.38,2546.63
-Relu11_f2h,4.48398,23.4935,18.3062,5.18733,10483.3,8168.53,2314.74,3.17253,16.7097,13.0203,3.68949,7455.03,5808.96,1646.13
-Relu11_h2f,2.65339,11.6345,9.04902,2.58551,8767.81,6819.4,1948.41,1.87634,8.37685,6.51523,1.86173,6311.27,4908.75,1402.6
-Relu12,2.09976,17.1837,13.475,3.70873,16368.8,12836,3532.8,1.48516,12.3639,9.69591,2.66821,11777.3,9235.93,2541.54
-Relu12_f2h,4.44727,22.6715,17.7981,4.87339,10189.2,7998.76,2190.47,3.14699,16.1439,12.6738,3.47034,7246.5,5688.6,1558.02
-Relu12_h2f,2.64916,11.3108,8.86276,2.44801,8539.6,6691.38,1848.22,1.87333,8.17249,6.4039,1.76875,6170.04,4834.82,1335.34
-Relu13,2.10382,16.7344,13.1973,3.53714,15914.8,12551.1,3363.76,1.48869,12.0067,9.46927,2.53767,11417,9004.38,2412.85
-Relu13_f2h,4.36041,22.0096,17.3783,4.63133,10092.1,7968.08,2123.99,3.0874,15.6907,12.3898,3.30126,7183.38,5671.67,1511.87
-Relu13_h2f,2.72796,11.4339,9.01109,2.4228,8560.24,6746.45,1813.78,2.03488,8.23175,6.48774,1.74417,6172.8,4865.11,1307.81
-Relu14,1.14743,10.6248,8.32102,2.30382,18530.7,14512.9,4017.87,0.811516,7.89588,6.18393,1.71212,13778.4,10791.3,2987.38
-Relu14_f2h,1.5964,5.26066,4.12403,1.13663,6435.18,5042.95,1392.23,1.14442,4.24048,3.32542,0.91515,5108.03,4003.08,1105.06
-Relu14_h2f,0.762322,0.9319,0.729587,0.202312,2419.48,1894.24,525.243,0.539977,1.14939,0.899736,0.249678,2965.98,2321.79,644.251
-Relu1_f2h,116.511,379.888,188.365,191.522,6536.78,3247.4,3289.38,82.4051,270.74,136.938,135.452,4668.45,2369.38,2327.06
-Relu1_h2f,83.4086,300.518,148.77,151.748,7206.56,3567.67,3638.89,58.9804,213.679,107.062,107.336,5124.37,2567.66,2573.96
-Relu2,62.4233,282.215,169.665,112.55,9042.07,5436.06,3606.01,44.1442,199.587,119.994,79.596,6394.13,3844.28,2549.94
-Relu2_f2h,106.67,463.637,286.355,177.281,8692.96,5369.01,3323.95,75.4328,327.879,202.516,125.367,6147.13,3796.78,2350.42
-Relu2_h2f,83.0734,372.529,221.009,151.52,8968.66,5320.81,3647.85,58.7425,263.431,156.291,107.144,6342.03,3762.66,2579.46
-Relu3,31.1491,157.975,97.2597,60.7158,10143.1,6244.72,3898.34,22.0274,111.732,68.79,42.9423,7173.3,4416.4,2756.93
-Relu3_f2h,53.9219,262.044,163.271,98.7728,9719.42,6055.82,3663.6,38.1317,185.316,115.466,69.8509,6872.96,4282.31,2590.67
-Relu3_h2f,41.4745,201.753,123.336,78.4172,9731.62,5949.14,3782.48,29.3302,142.684,87.2272,55.4573,6883.19,4207.91,2675.32
-Relu4,31.1126,165.225,109.288,55.9374,10620.9,7025.18,3595.76,22.0035,116.872,77.306,39.5668,7511.45,4968.48,2543
-Relu4_f2h,52.5946,268.222,180.055,88.1672,10199.7,6846.98,3352.7,37.1932,189.687,127.336,62.3518,7212.66,4841.86,2370.81
-Relu4_h2f,41.3761,210.186,137.878,72.3072,10159.6,6664.53,3495.09,29.259,148.644,97.5098,51.1349,7184.42,4712.91,2471.53
-Relu5,15.6197,87.4008,58.9328,28.468,11190.8,7545.75,3645.04,11.048,61.8499,41.7045,20.1457,7916.78,5338.18,2578.64
-Relu5_f2h,27.1876,142.132,96.6157,45.5163,10455.9,7107.57,3348.35,19.2268,100.527,68.3345,32.1932,7394.61,5026.63,2368
-Relu5_h2f,20.7564,107.244,71.9782,35.2657,10333.7,6935.58,3398.08,14.6781,75.8517,50.9089,24.9429,7308.38,4905.11,2403.28
-Relu6,15.5774,91.8805,65.7777,26.1027,11796,8444.84,3351.21,11.0158,65.0123,46.543,18.4696,8345.51,5974.6,2370.94
-Relu6_f2h,26.9604,148.182,106.898,41.2837,10992.7,7930.13,3062.55,19.0651,104.8,75.6027,29.1976,7774.08,5608.24,2165.85
-Relu6_h2f,20.7925,111.946,79.7133,32.2323,10779.9,7676.09,3103.81,14.709,79.2691,56.4457,22.8237,7636.46,5437.77,2198.71
-Relu7,15.5863,93.2475,68.0626,25.1848,11966.2,8734.34,3231.84,11.0234,65.9937,48.1698,17.8242,8467.81,6180.86,2286.99
-Relu7_f2h,27.0202,149.73,110.21,39.5201,11082.9,8157.67,2925.21,19.1073,105.894,77.944,27.9502,7837.75,5769.07,2068.69
-Relu7_h2f,20.7296,113.484,82.3887,31.0951,10948.8,7948.82,3000.02,14.6587,80.2669,58.2735,21.9936,7743.7,5621.92,2121.8
-Relu8,7.88397,50.8594,37.3843,13.4751,12900,9482.44,3417.54,5.60997,36.5052,26.8322,9.67313,9199.71,6762.66,2437.09
-Relu8_f2h,14.4739,80.2487,59.2479,21.0008,11105.4,8199.18,2906.22,10.2403,56.8303,41.9579,14.8727,7866.72,5808.05,2058.7
-Relu8_h2f,10.4677,57.0219,41.8152,15.2067,10895,7989.54,2905.49,7.40341,40.3631,29.5989,10.7642,7710.49,5654.25,2056.24
-Relu9,7.84042,52.587,40.301,12.286,13413.6,10279.8,3133.81,5.54547,37.2786,28.5693,8.70947,9505.87,7285.1,2220.81
-Relu9_f2h,14.2726,83.3662,64.1642,19.202,11681.8,8991.1,2690.7,10.0949,58.9936,45.4054,13.5884,8264.16,6360.65,1903.53
-Relu9_h2f,10.4563,59.3499,45.3517,13.9982,11352.1,8674.64,2677.47,7.39481,42.0028,32.0961,9.90692,8032.96,6138.36,1894.63
-Softmax1,2.01231,8.10635,6.34144,1.76491,8058.61,6304.39,1754.22,1.42328,5.95817,4.66119,1.29711,5923.64,4634.63,1289.13
diff --git a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_fp32.csv b/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_fp32.csv
deleted file mode 100644
index 2e203cf73d4f5220f9f3217398c952496028fb62..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_fp32.csv
+++ /dev/null
@@ -1,50 +0,0 @@
-Add1,113.913,376.561,219.636,156.925,6609.15,3854.17,2754.97,80.5497,282.75,177.622,111.325,4961.17,3115.93,1954.23
-Add10,46.7852,353.064,295.37,57.6943,15092.7,12626.5,2466.15,33.0858,249.698,208.891,40.8191,10672.6,8928.67,1744.47
-Add11,32.7593,228.974,188.206,40.768,13979,11490.1,2488.93,23.1655,161.932,133.101,28.8363,9885.52,8125.42,1760.39
-Add12,32.7648,220.92,180.753,40.1662,13485,11033.3,2451.71,23.1723,156.257,127.848,28.4139,9536.19,7802.45,1733.98
-Add13,32.723,215.055,175.489,39.5662,13144.2,10726,2418.24,23.1396,152.085,124.105,27.9836,9295.27,7585.25,1710.24
-Add14,0.902452,1.91287,1.55297,0.359903,4206.37,3415.11,791.258,0.639486,1.96075,1.59154,0.36925,4301.37,3491.69,809.769
-Add15,0.226743,0.146973,0.119291,0.0276818,1268.32,1029.69,238.626,0.161374,0.274735,0.222974,0.0517663,2347.78,1906.32,441.514
-Add2,114.041,707.602,550.188,157.413,12409.4,9648.8,2760.62,80.6401,500.915,389.6,111.35,8784.41,6832.28,1952.72
-Add3,51.2818,306.368,231.407,74.9613,11948.4,9024.9,2923.5,36.2641,216.686,163.674,53.0143,8450.22,6382.88,2067.43
-Add4,51.329,344.524,275.839,68.6848,13425,10748.6,2676.4,36.2967,243.665,195.087,48.5789,9494.83,7601.95,1892.93
-Add5,48.2779,312.538,246.161,66.377,12947.4,10197.6,2749.79,34.1397,221.022,174.082,46.9412,9155.71,7211.24,1944.51
-Add6,48.3038,331.31,269.638,61.6728,13717.7,11164.2,2553.51,34.1584,234.299,190.684,43.6161,9700.3,7894.61,1805.74
-Add7,48.1751,337.048,277.011,60.0367,13992.6,11500.2,2492.4,34.0674,238.357,195.898,42.4598,9894.64,8132.16,1762.55
-Add8,46.6813,323.76,263.597,60.1628,13871.3,11293.7,2577.64,33.0123,228.964,186.417,42.5484,9808.96,7986.22,1822.79
-Add9,46.6775,338.552,280.794,57.7577,14509.8,12034.3,2475.46,33.0125,239.488,198.63,40.8649,10264.6,8513.31,1751.56
-Conv1,163.415,464.69,270.83,193.861,5719.79,3344.04,2375.74,115.618,364.219,239.324,137.798,4496.91,2961.18,1689.91
-Conv10,802.661,5895.34,4887.39,1007.95,14689.8,12178.3,2511.49,567.577,4168.67,3455.92,712.856,10387.4,8611.5,1776.16
-Conv11,308.859,2238.17,1841.36,396.805,14494.3,11924.8,2569.46,218.426,1582.75,1302.12,280.685,10249.2,8432.33,1817.28
-Conv12,308.957,2143.29,1754.35,388.942,13875.6,11357.9,2517.72,218.489,1515.61,1240.55,275.097,9811.8,8031.51,1780.54
-Conv13,308.526,2076.4,1693.52,382.878,13461.8,10980,2481.86,218.187,1468.29,1197.52,270.808,9519.36,7764.54,1755.1
-Conv2,1186.37,5295.79,3610.95,1684.84,9029.61,6180.44,2849.17,840.943,3760.72,2581.03,1191.77,6467.54,4470.42,2016.67
-Conv3,613.82,3267.16,2333.45,933.711,10647.5,7604.92,3042.61,434.112,2311.16,1650.96,660.338,7532.26,5381.04,2151.65
-Conv4,982.846,5808.36,4413.44,1394.92,11819.9,8981.38,2838.55,695.005,4107.39,3121,986.415,8358.48,6351.33,2007.22
-Conv5,491.91,3047.42,2348.81,698.608,12391.2,9550.74,2840.5,347.871,2154.95,1660.93,494.035,8762.18,6753.64,2008.58
-Conv6,858.952,5554.16,4403.19,1150.97,12932.7,10252.8,2679.96,607.382,3927.42,3113.55,813.89,9144.98,7249.96,1895.06
-Conv7,859.425,5760.38,4659.21,1101.17,13405.5,10842.9,2562.58,607.715,4073.23,3294.58,778.676,9479.18,7667.16,1812.06
-Conv8,434.046,2940.43,2368.34,572.081,13549.6,10913.5,2636.05,306.938,2079.26,1674.71,404.559,9581.1,7717.15,1864.02
-Conv9,801.893,5651.89,4624.92,1026.97,14096.7,11535.3,2561.33,567.034,3996.52,3270.33,726.23,9967.96,8156.84,1811.22
-Mul1,10.6715,65.8927,53.5391,12.3536,12394.8,10071.9,2322.88,7.57836,46.691,37.9335,8.75846,8780.28,7134.75,1645.72
-Mul2,0.821147,1.66598,1.35236,0.313621,4076.84,3309.78,767.066,0.582562,1.7168,1.39365,0.323189,4224.31,3430.04,794.372
-Pool1,175.393,1012.62,726.774,285.844,11549.1,8289.34,3259.78,124.05,716.6,514.49,202.181,8172.74,5868.18,2305.37
-Pool2,88.7044,575.997,444.625,131.372,12987.9,10025.6,2962.21,62.7347,407.367,314.456,92.9131,9184.52,7089.77,2094.78
-Pool3,45.2833,307.898,247.595,60.3023,13598.6,10935.2,2663.36,32.0292,217.79,175.137,42.6548,9616.06,7732.71,1883.4
-Pool4,24.6055,179.513,148.419,31.0941,14591.4,12063.9,2527.45,17.4004,126.964,104.972,21.9976,10319,8531.59,1787.91
-Pool5,8.20681,50.2013,40.8424,9.35894,12226.9,9947.17,2279.71,5.82151,35.6921,29.0395,6.65352,8656.71,7042.81,1614.13
-Relu1,75.1381,267.733,149.248,118.484,7127.84,3973.96,3153.88,53.1331,197.389,117.092,83.9442,5255.76,3118.27,2234.45
-Relu10,9.31493,65.6928,54.5384,11.1544,14104.6,11709.8,2394.86,6.58686,46.4969,38.6024,7.89659,9982.66,8287.84,1695.28
-Relu11,2.49349,12.8041,10.5081,2.29598,10267.6,8426.39,1841.24,1.76354,9.25156,7.5928,1.65903,7415.31,6085.61,1329.91
-Relu12,2.50068,12.3579,10.0966,2.26132,9872.52,8066.18,1806.35,1.77005,8.95586,7.31638,1.63973,7136.08,5830.14,1306.13
-Relu13,2.48762,12.2284,9.96443,2.26399,9833.11,8012.58,1820.53,1.75929,8.83304,7.19779,1.63547,7102.38,5787.52,1315.04
-Relu14,0.846016,1.57242,1.27739,0.29503,3703.4,3008.56,694.844,0.599154,1.69071,1.37363,0.317122,3975.03,3229.61,745.515
-Relu2,74.295,455.493,338.819,116.674,12261.1,9120.32,3140.75,52.5363,322.394,239.897,82.5246,8677.47,6456.88,2221.33
-Relu3,36.6237,218.815,161.907,56.9082,11949.1,8841.42,3107.67,25.8986,154.77,114.524,40.248,8450.92,6253.36,2197.68
-Relu4,36.7139,244.573,191.979,52.5941,13329.1,10462.8,2866.31,25.9761,173.026,135.817,37.2093,9428.22,7400.79,2027.47
-Relu5,18.3751,115.933,90.1275,25.8055,12618,9809.37,2808.64,12.9942,82.009,63.7545,18.2548,8924.67,6938.12,1986.59
-Relu6,18.3515,122.286,98.2308,24.0552,13327.1,10705.5,2621.61,12.9773,86.4993,69.4836,17.016,9426.46,7572.14,1854.36
-Relu7,18.3889,125.084,101.453,23.631,13603.6,11033.6,2570.02,13.0046,88.4856,71.7689,16.7172,9621.58,7803.86,1817.78
-Relu8,9.34284,60.6176,48.9763,11.6413,12976.3,10484.3,2492.01,6.60743,42.914,34.6725,8.24179,9185.09,7421.14,1764
-Relu9,9.34283,63.4928,52.2617,11.2311,13592.4,11188.1,2404.33,6.60725,44.9455,36.9955,7.95121,9621.07,7919.28,1702.05
-Softmax1,2.09116,9.17789,7.4508,1.72709,8772.87,7122.11,1650.76,1.47902,6.75862,5.48652,1.27228,6456.75,5241.62,1215.29
diff --git a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_layers.txt b/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_layers.txt
deleted file mode 100644
index af6469192145b246beaec42cf42a6629e5ed1a93..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_layers.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-Conv1,2000,3,32,32,64,3,3,3,1,1
-Conv2,2000,64,32,32,64,64,3,3,1,1
-Conv3,2000,64,16,16,128,64,3,3,1,1
-Conv4,2000,128,16,16,128,128,3,3,1,1
-Conv5,2000,128,8,8,256,128,3,3,1,1
-Conv6,2000,256,8,8,256,256,3,3,1,1
-Conv7,2000,256,8,8,256,256,3,3,1,1
-Conv8,2000,256,4,4,512,256,3,3,1,1
-Conv9,2000,512,4,4,512,512,3,3,1,1
-Conv10,2000,512,4,4,512,512,3,3,1,1
-Conv11,2000,512,2,2,512,512,3,3,1,1
-Conv12,2000,512,2,2,512,512,3,3,1,1
-Conv13,2000,512,2,2,512,512,3,3,1,1
-FC1,2000,512,512,512
-FC2,2000,512,512,10
diff --git a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_ops.txt b/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_ops.txt
deleted file mode 100644
index 2075774fde3e66afd1a1946cac46b87038a6486f..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_ops.txt
+++ /dev/null
@@ -1,64 +0,0 @@
-#Conv1,3
-Conv1
-Add1
-Relu1
-#Conv2,4
-Conv2
-Add2
-Relu2
-Pool1
-#Conv3,3
-Conv3
-Add3
-Relu3
-#Conv4,4
-Conv4
-Add4
-Relu4
-Pool2
-#Conv5,3
-Conv5
-Add5
-Relu5
-#Conv6,3
-Conv6
-Add6
-Relu6
-#Conv7,4
-Conv7
-Add7
-Relu7
-Pool3
-#Conv8,3
-Conv8
-Add8
-Relu8
-#Conv9,3
-Conv9
-Add9
-Relu9
-#Conv10,4
-Conv10
-Add10
-Relu10
-Pool4
-#Conv11,3
-Conv11
-Add11
-Relu11
-#Conv12,3
-Conv12
-Add12
-Relu12
-#Conv13,4
-Conv13
-Add13
-Relu13
-Pool5
-#FC1,3
-Mul1
-Add14
-Relu14
-#FC2,2
-Mul2
-Add15
diff --git a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_promise_confs1.txt b/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_promise_confs1.txt
deleted file mode 100644
index e6989e16ee3869ecb13ecc9f73af7f9a66c24dee..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_promise_confs1.txt
+++ /dev/null
@@ -1,33 +0,0 @@
-9 9 9,9 9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9
-9 9 9,9 9 9 9,8 8 8,8 8 8 8,8 8 8,6,7,8 8 8,5,7,8 8 8,5,8 8 8 8,6,7
-9 9 9,9 9 9 9,8 8 8,8 8 8 8,8 8 8,6,7,8 8 8,5,7,8 8 8,8 8 8,6,6,7
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,8 8 8,6,7,8 8 8,5,7,8 8 8,6,8 8 8 8,6,7
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,8 8 8,6,7,8 8 8,5,7,8 8 8,5,8 8 8 8,6,7
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,8 8 8,7,8 8 8 8,8 8 8,5,7,8 8 8,5,6,6,5
-9 9 9,9 9 9 9,8 8 8,8 8 8 8,8 8 8,6,7,8 8 8,5,8 8 8 8,8 8 8,8 8 8,6,6,7
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,8 8 8,6,8 8 8 8,8 8 8,5,5,8 8 8,5,6,8 8 8,7
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,8 8 8,7,7,8 8 8,8 8 8,7,8 8 8,5,6,8 8 8,8 8
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,8 8 8,6,7,8 8 8,8 8 8,7,8 8 8,5,6,6,7
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,5,5,8 8 8 8,8 8 8,8 8 8,8 8 8 8,5,5,7,7,6
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,8 8 8,8 8 8,8 8 8 8,8 8 8,5,5,8 8 8,5,6,5,7
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,8 8 8,6,7,8 8 8,6,8 8 8 8,8 8 8,8 8 8,8 8 8 8,6,7
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,8 8 8,6,8 8 8 8,8 8 8,5,7,8 8 8,5,7,6,6
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,8 8 8,7,8 8 8 8,8 8 8,5,7,8 8 8,5,6,6,7
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,8 8 8,6,7,8 8 8,5,8 8 8 8,8 8 8,5,5,6,7
-8 8 8,8 8 8 8,8 8 8,8 8 8 8,6,6,8 8 8 8,8 8 8,6,8 8 8 8,8 8 8,8 8 8,8 8 8 8,8 8 8,6
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,8 8 8,6,7,8 8 8,5,8 8 8 8,8 8 8,5,6,5,7
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,8 8 8,6,7,8 8 8,8 8 8,7,8 8 8,8 8 8,6,6,7
-9 9 9,9 9 9 9,8 8 8,8 8 8 8,7,7,6,8 8 8,8 8 8,8 8 8 8,8 8 8,7,5,8 8 8,8 8
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,8 8 8,6,7,8 8 8,6,8 8 8 8,8 8 8,5,5,6,7
-8 8 8,8 8 8 8,8 8 8,8 8 8 8,6,8 8 8,8 8 8 8,8 8 8,6,8 8 8 8,8 8 8,8 8 8,5,6,6
-8 8 8,8 8 8 8,8 8 8,8 8 8 8,7,8 8 8,8 8 8 8,8 8 8,6,8 8 8 8,5,8 8 8,8 8 8 8,6,6
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,6,8 8 8,8 8 8 8,8 8 8,8 8 8,7,7,5,7,7,8 8
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,5,8 8 8,8 8 8 8,8 8 8,8 8 8,6,8 8 8,7,6,8 8 8,5
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,8 8 8,7,5,8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,8 8 8 8,8 8 8,8 8
-8 8 8,8 8 8 8,8 8 8,8 8 8 8,8 8 8,5,5,8 8 8,8 8 8,8 8 8 8,7,8 8 8,8 8 8 8,5,8 8
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,8 8 8,7,5,8 8 8,8 8 8,8 8 8 8,8 8 8,7,7,7,8 8
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,7,7,5,6,6,7
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,8 8 8,6,7,8 8 8,8 8 8,8 8 8 8,8 8 8,5,5,6,5
-9 9 9,9 9 9 9,8 8 8,8 8 8 8,8 8 8,6,7,8 8 8,8 8 8,8 8 8 8,8 8 8,6,6,6,7
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,8 8 8,6,8 8 8 8,8 8 8,8 8 8,7,8 8 8,5,8 8 8 8,6,7
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,7,8 8 8 8,8 8 8,8 8 8,8 8 8 8,8 8 8,7,6,8 8 8,5
diff --git a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_promise_confs2.txt b/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_promise_confs2.txt
deleted file mode 100644
index 4732b0c143c403151aa411ec9b97007a2fc4898b..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_promise_confs2.txt
+++ /dev/null
@@ -1,52 +0,0 @@
-9 9 9,9 9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9 9,9 9 9 9,9 9 9,9 9
-9 9 9,7,8 8 8,9 9 9 9,9 9 9,5,9 9 9 9,8 8 8,8 8 8,8 8 8 8,8 8 8,5,6,9 9 9,8 8
-9 9 9,7,8 8 8,9 9 9 9,9 9 9,6,8 8 8 8,8 8 8,7,5,8 8 8,9 9 9,8 8 8 8,7,8 8
-9 9 9,7,8 8 8,9 9 9 9,9 9 9,5,8 8 8 8,8 8 8,9 9 9,8 8 8 8,8 8 8,9 9 9,7,7,8 8
-8 8 8,7,8 8 8,9 9 9 9,7,7,8 8 8 8,8 8 8,7,8 8 8 8,8 8 8,7,6,7,8 8
-8 8 8,8 8 8 8,9 9 9,7,7,7,9 9 9 9,8 8 8,5,5,6,8 8 8,9 9 9 9,5,7
-9 9 9,9 9 9 9,8 8 8,9 9 9 9,8 8 8,8 8 8,8 8 8 8,7,5,6,7,7,7,8 8 8,8 8
-9 9 9,7,8 8 8,9 9 9 9,6,5,9 9 9 9,8 8 8,7,8 8 8 8,8 8 8,9 9 9,6,7,7
-9 9 9,9 9 9 9,8 8 8,7,7,9 9 9,9 9 9 9,9 9 9,7,7,8 8 8,6,5,9 9 9,7
-9 9 9,7,8 8 8,9 9 9 9,7,6,8 8 8 8,8 8 8,7,8 8 8 8,8 8 8,9 9 9,6,5,8 8
-9 9 9,7,8 8 8,9 9 9 9,7,6,8 8 8 8,8 8 8,7,8 8 8 8,8 8 8,9 9 9,6,7,8 8
-9 9 9,7,8 8 8,9 9 9 9,6,9 9 9,8 8 8 8,8 8 8,8 8 8,5,8 8 8,9 9 9,6,7,8 8
-9 9 9,9 9 9 9,8 8 8,9 9 9 9,5,5,8 8 8 8,7,7,8 8 8 8,9 9 9,9 9 9,9 9 9 9,7,8 8
-9 9 9,9 9 9 9,8 8 8,7,6,9 9 9,8 8 8 8,9 9 9,7,8 8 8 8,5,9 9 9,6,7,8 8
-9 9 9,8 8 8 8,9 9 9,8 8 8 8,8 8 8,9 9 9,9 9 9 9,5,6,6,8 8 8,6,5,6,6
-8 8 8,8 8 8 8,9 9 9,8 8 8 8,5,9 9 9,7,9 9 9,7,8 8 8 8,7,6,5,7,6
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,5,6,7,8 8 8,7,5,8 8 8,6,5,5,9 9
-8 8 8,8 8 8 8,9 9 9,8 8 8 8,9 9 9,8 8 8,5,5,8 8 8,9 9 9 9,6,8 8 8,6,5,8 8
-9 9 9,7,8 8 8,9 9 9 9,7,5,8 8 8 8,8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,6,7,6
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,9 9 9,8 8 8,7,9 9 9,5,8 8 8 8,8 8 8,6,6,8 8 8,5
-9 9 9,8 8 8 8,8 8 8,9 9 9 9,7,9 9 9,7,9 9 9,7,9 9 9 9,6,7,6,5,8 8
-9 9 9,8 8 8 8,9 9 9,8 8 8 8,9 9 9,8 8 8,5,7,7,9 9 9 9,6,8 8 8,9 9 9 9,6,6
-9 9 9,9 9 9 9,8 8 8,5,6,5,8 8 8 8,8 8 8,7,8 8 8 8,8 8 8,9 9 9,6,5,8 8
-8 8 8,8 8 8 8,8 8 8,9 9 9 9,8 8 8,8 8 8,7,7,8 8 8,8 8 8 8,8 8 8,5,6,9 9 9,8 8
-9 9 9,8 8 8 8,8 8 8,9 9 9 9,8 8 8,8 8 8,7,7,5,8 8 8 8,8 8 8,6,6,9 9 9,5
-8 8 8,8 8 8 8,8 8 8,8 8 8 8,6,5,7,8 8 8,7,5,8 8 8,8 8 8,8 8 8 8,8 8 8,9 9
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,9 9 9,5,6,8 8 8,8 8 8 8,8 8 8,9 9 9,6,6,8 8
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,9 9 9,5,6,8 8 8,8 8 8 8,8 8 8,9 9 9,7,9 9 9,8 8
-9 9 9,8 8 8 8,8 8 8,9 9 9 9,9 9 9,8 8 8,7,7,8 8 8,8 8 8 8,8 8 8,6,6,9 9 9,7
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,8 8 8,9 9 9,5,7,8 8 8,8 8 8 8,8 8 8,9 9 9,6,6,8 8
-9 9 9,9 9 9 9,9 9 9,8 8 8 8,7,7,8 8 8 8,8 8 8,7,7,7,9 9 9,6,7,5
-8 8 8,9 9 9 9,9 9 9,7,9 9 9,8 8 8,9 9 9 9,9 9 9,9 9 9,6,9 9 9,7,8 8 8 8,7,6
-8 8 8,9 9 9 9,9 9 9,7,8 8 8,5,8 8 8 8,9 9 9,8 8 8,5,8 8 8,6,6,7,7
-8 8 8,9 9 9 9,8 8 8,7,9 9 9,8 8 8,9 9 9 9,9 9 9,9 9 9,6,9 9 9,7,8 8 8 8,7,6
-9 9 9,8 8 8 8,8 8 8,8 8 8 8,6,5,9 9 9 9,9 9 9,7,7,8 8 8,7,7,6,9 9
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,5,9 9 9,5,6,9 9 9,9 9 9 9,8 8 8,6,8 8 8 8,5,9 9
-8 8 8,8 8 8 8,8 8 8,8 8 8 8,8 8 8,8 8 8,9 9 9 9,5,8 8 8,9 9 9 9,6,8 8 8,7,6,8 8
-8 8 8,9 9 9 9,8 8 8,9 9 9 9,9 9 9,6,6,8 8 8,7,5,8 8 8,9 9 9,6,7,5
-9 9 9,9 9 9 9,8 8 8,9 9 9 9,8 8 8,7,7,8 8 8,6,7,8 8 8,9 9 9,7,5,9 9
-9 9 9,9 9 9 9,9 9 9,8 8 8 8,6,5,9 9 9 9,9 9 9,6,8 8 8 8,6,9 9 9,6,9 9 9,9 9
-8 8 8,9 9 9 9,9 9 9,8 8 8 8,8 8 8,6,6,9 9 9,7,7,8 8 8,5,8 8 8 8,6,7
-9 9 9,9 9 9 9,8 8 8,9 9 9 9,9 9 9,8 8 8,8 8 8 8,7,7,8 8 8 8,8 8 8,8 8 8,5,7,8 8
-8 8 8,9 9 9 9,8 8 8,9 9 9 9,8 8 8,6,6,8 8 8,7,7,8 8 8,9 9 9,8 8 8 8,7,9 9
-9 9 9,9 9 9 9,8 8 8,9 9 9 9,7,5,8 8 8 8,8 8 8,7,9 9 9 9,8 8 8,9 9 9,6,9 9 9,8 8
-9 9 9,9 9 9 9,8 8 8,9 9 9 9,8 8 8,7,8 8 8 8,8 8 8,6,7,7,7,7,7,8 8
-9 9 9,8 8 8 8,8 8 8,9 9 9 9,8 8 8,7,6,8 8 8,7,8 8 8 8,7,6,6,7,8 8
-9 9 9,9 9 9 9,8 8 8,6,9 9 9,5,8 8 8 8,9 9 9,8 8 8,8 8 8 8,8 8 8,9 9 9,6,7,8 8
-8 8 8,9 9 9 9,8 8 8,8 8 8 8,9 9 9,7,5,9 9 9,7,8 8 8 8,9 9 9,8 8 8,8 8 8 8,6,6
-9 9 9,9 9 9 9,8 8 8,9 9 9 9,9 9 9,5,8 8 8 8,8 8 8,7,6,8 8 8,7,6,7,6
-8 8 8,9 9 9 9,8 8 8,9 9 9 9,8 8 8,6,6,9 9 9,9 9 9,7,8 8 8,5,8 8 8 8,8 8 8,8 8
-9 9 9,8 8 8 8,8 8 8,9 9 9 9,6,9 9 9,8 8 8 8,8 8 8,7,7,8 8 8,9 9 9,6,7,8 8
-9 9 9,8 8 8 8,8 8 8,9 9 9 9,9 9 9,5,8 8 8 8,8 8 8,7,7,8 8 8,9 9 9,6,8 8 8,8 8
diff --git a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_promise_results1.csv b/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_promise_results1.csv
deleted file mode 100644
index f5825603b85a2a5a4313c427ce35c6c0f97996c4..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_promise_results1.csv
+++ /dev/null
@@ -1,407 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,1108.984,7471.505,3792.343,6973.454,3475.891,6007.756,6530.41,3324.8076,6053.9348,6493.6098,2479.9481,2376.5679,2353.8847,69.37799,1.812953,58514.286843,0.999999998291016
-c1,1108.984,7471.505,1949.97,3356.913,1731.6018,118.016247,157.113483,1683.0934,77.680494,157.113483,1299.926,19.513340,1286.6509,0.839227,0.021821,20418.942195,2.86568647864431
-c2,1108.984,7471.505,1949.97,3356.913,1731.6018,118.016247,157.113483,1683.0934,77.680494,157.113483,1299.926,1273.3007,29.645681,0.839227,0.021821,20415.724336,2.86613815867435
-c3,1129.478,7471.505,1949.97,3356.913,1731.6018,118.016247,157.113483,1683.0934,77.680494,157.113483,1299.926,29.645681,1286.6509,0.839227,0.021821,20449.568536,2.86139467704907
-c4,1129.478,7471.505,1949.97,3356.913,1731.6018,118.016247,157.113483,1683.0934,77.680494,157.113483,1299.926,19.513340,1286.6509,0.839227,0.021821,20439.436195,2.86281314212731
-c5,1129.478,7471.505,1949.97,3356.913,1731.6018,157.113483,3366.9575,1683.0934,77.680494,157.113483,1299.926,19.513340,29.645681,0.839227,0.010789,22431.361197,2.60859276743163
-c6,1108.984,7471.505,1949.97,3356.913,1731.6018,118.016247,157.113483,1683.0934,77.680494,3427.0759,1299.926,1273.3007,29.645681,0.839227,0.021821,23685.686753,2.47044922978827
-c7,1129.478,7471.505,1949.97,3356.913,1731.6018,118.016247,3366.9575,1683.0934,77.680494,77.680494,1299.926,19.513340,29.645681,57.16078,0.021821,22369.163557,2.61584598066495
-c8,1129.478,7471.505,1949.97,3356.913,1731.6018,157.113483,157.113483,1683.0934,3203.344,157.113483,1299.926,19.513340,29.645681,57.16078,10.00372,22413.49517,2.6106721034858
-c9,1129.478,7471.505,1949.97,3356.913,1731.6018,118.016247,157.113483,1683.0934,3203.344,157.113483,1299.926,19.513340,29.645681,0.839227,0.021821,22308.094482,2.62300693714174
-c10,1129.478,7471.505,1949.97,3356.913,38.840247,77.680494,3366.9575,1683.0934,3203.344,3427.0759,19.513340,19.513340,39.466907,1.117251,0.016391,25784.48477,2.26936032028628
-c11,1129.478,7471.505,1949.97,3356.913,1731.6018,3053.2545,3366.9575,1683.0934,77.680494,77.680494,1299.926,19.513340,29.645681,0.552395,0.021821,25247.793425,2.31760002255484
-c12,1129.478,7471.505,1949.97,3356.913,1731.6018,118.016247,157.113483,1683.0934,118.016247,3427.0759,1299.926,1273.3007,1286.6509,0.839227,0.021821,25003.521725,2.34024179683735
-c13,1129.478,7471.505,1949.97,3356.913,1731.6018,118.016247,3366.9575,1683.0934,77.680494,157.113483,1299.926,19.513340,39.466907,0.839227,0.016391,22402.090789,2.61200113564989
-c14,1129.478,7471.505,1949.97,3356.913,1731.6018,157.113483,3366.9575,1683.0934,77.680494,157.113483,1299.926,19.513340,29.645681,0.839227,0.021821,22431.372229,2.60859148449651
-c15,1129.478,7471.505,1949.97,3356.913,1731.6018,118.016247,157.113483,1683.0934,77.680494,3427.0759,1299.926,19.513340,19.513340,0.839227,0.021821,22442.261052,2.60732581475131
-c16,1129.478,6358.145,1949.97,3356.913,59.008124,118.016247,3366.9575,1683.0934,118.016247,3427.0759,1299.926,1273.3007,1286.6509,57.16078,0.016391,25483.728189,2.29614309882034
-c17,1129.478,7471.505,1949.97,3356.913,1731.6018,118.016247,157.113483,1683.0934,77.680494,3427.0759,1299.926,19.513340,29.645681,0.552395,0.021821,22452.106561,2.60618247216156
-c18,1129.478,7471.505,1949.97,3356.913,1731.6018,118.016247,157.113483,1683.0934,3203.344,157.113483,1299.926,1273.3007,29.645681,0.839227,0.021821,23561.881842,2.48343010066169
-c19,1108.984,7471.505,1949.97,3356.913,78.556742,157.113483,118.016247,1683.0934,3203.344,3427.0759,1299.926,39.466907,19.513340,57.16078,10.00372,23980.642519,2.44006333661129
-c20,1129.478,7471.505,1949.97,3356.913,1731.6018,118.016247,157.113483,1683.0934,118.016247,3427.0759,1299.926,19.513340,19.513340,0.839227,0.021821,22482.596805,2.60264804329551
-c21,1129.478,6358.145,1949.97,3356.913,59.008124,3053.2545,3366.9575,1683.0934,118.016247,3427.0759,1299.926,1273.3007,19.513340,0.839227,0.016391,27095.507329,2.15955678247874
-c22,1129.478,6358.145,1949.97,3356.913,78.556742,3053.2545,3366.9575,1683.0934,118.016247,3427.0759,19.513340,1273.3007,1286.6509,0.839227,0.016391,27101.780847,2.15905688845431
-c23,1129.478,7471.505,1949.97,3356.913,59.008124,3053.2545,3366.9575,1683.0934,3203.344,157.113483,39.466907,19.513340,39.466907,1.117251,10.00372,25540.205132,2.29106564772964
-c24,1108.984,6358.145,1949.97,3356.913,38.840247,3053.2545,3366.9575,1683.0934,3203.344,118.016247,1299.926,39.466907,29.645681,57.16078,0.010789,25663.728051,2.28003844565038
-c25,1129.478,7471.505,1949.97,3356.913,1731.6018,157.113483,77.680494,1683.0934,3203.344,3427.0759,1299.926,1273.3007,1286.6509,57.16078,10.00372,28114.817177,2.08126150230644
-c26,1129.478,6358.145,1949.97,3356.913,1731.6018,77.680494,77.680494,1683.0934,3203.344,3427.0759,39.466907,1273.3007,1286.6509,0.552395,10.00372,25604.95671,2.28527184315137
-c27,1129.478,7471.505,1949.97,3356.913,1731.6018,157.113483,77.680494,1683.0934,3203.344,3427.0759,1299.926,39.466907,39.466907,1.117251,10.00372,25577.755862,2.28770213188102
-c28,1129.478,7471.505,1949.97,3356.913,1731.6018,3053.2545,3366.9575,1683.0934,3203.344,157.113483,39.466907,19.513340,29.645681,0.839227,0.021821,27192.717659,2.1518366557397
-c29,1129.478,7471.505,1949.97,3356.913,1731.6018,118.016247,157.113483,1683.0934,3203.344,3427.0759,1299.926,19.513340,19.513340,0.839227,0.010789,25567.913526,2.28858277992213
-c30,1108.984,7471.505,1949.97,3356.913,1731.6018,118.016247,157.113483,1683.0934,3203.344,3427.0759,1299.926,29.645681,29.645681,0.839227,0.021821,25567.69524,2.28860231885883
-c31,1129.478,7471.505,1949.97,3356.913,1731.6018,118.016247,3366.9575,1683.0934,3203.344,157.113483,1299.926,19.513340,1286.6509,0.839227,0.021821,26774.943718,2.18541212413908
-c32,1108.984,6358.145,1949.97,3356.913,1731.6018,157.113483,3366.9575,1683.0934,3203.344,3427.0759,1299.926,39.466907,29.645681,57.16078,0.010789,27769.40824,2.10714920989923
-c2,20415.724336
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,352.4661,1550.099,701.7255,1159.5933,558.563,925.6073,971.2723,490.07014,857.91333,883.36663,344.11179,344.22248,351.94343,12.419968,1.04789,9504.422158,0.999999989478582
-c1,352.4661,1550.099,426.5549,652.9992,333.7788,82.574848,82.574848,290.68457,83.036160,83.036160,208.37216,21.220352,210.33129,0.922624,0.018020,4378.669032,2.17061894184701
-c2,352.4661,1550.099,426.5549,652.9992,333.7788,82.574848,82.574848,290.68457,83.036160,83.036160,208.37216,206.54856,21.220352,0.922624,0.018020,4374.886302,2.17249575981104
-c3,442.9824,1550.099,426.5549,652.9992,333.7788,82.574848,82.574848,290.68457,83.036160,83.036160,208.37216,21.220352,210.33129,0.922624,0.018020,4469.185332,2.12665647971261
-c4,442.9824,1550.099,426.5549,652.9992,333.7788,82.574848,82.574848,290.68457,83.036160,83.036160,208.37216,21.220352,210.33129,0.922624,0.018020,4469.185332,2.12665647971261
-c5,442.9824,1550.099,426.5549,652.9992,333.7788,82.574848,590.3019,290.68457,83.036160,83.036160,208.37216,21.220352,21.220352,0.922624,0.018020,4787.801446,1.98513285621467
-c6,352.4661,1550.099,426.5549,652.9992,333.7788,82.574848,82.574848,290.68457,83.036160,543.5573,208.37216,206.54856,21.220352,0.922624,0.018020,4835.407442,1.96558864489607
-c7,442.9824,1550.099,426.5549,652.9992,333.7788,82.574848,590.3019,290.68457,83.036160,83.036160,208.37216,21.220352,21.220352,7.640322,0.018020,4794.519144,1.98235144637163
-c8,442.9824,1550.099,426.5549,652.9992,333.7788,82.574848,82.574848,290.68457,527.54432,83.036160,208.37216,21.220352,21.220352,7.640322,1.669613,4732.951845,2.00813831799849
-c9,442.9824,1550.099,426.5549,652.9992,333.7788,82.574848,82.574848,290.68457,527.54432,83.036160,208.37216,21.220352,21.220352,0.922624,0.018020,4724.582554,2.01169560446852
-c10,442.9824,1550.099,426.5549,652.9992,41.518080,82.574848,590.3019,290.68457,527.54432,543.5573,21.220352,21.220352,21.220352,0.922624,0.018020,5213.418218,1.82306916082002
-c11,442.9824,1550.099,426.5549,652.9992,333.7788,544.1687,590.3019,290.68457,83.036160,83.036160,208.37216,21.220352,21.220352,0.922624,0.018020,5249.395298,1.81057463524678
-c12,442.9824,1550.099,426.5549,652.9992,333.7788,82.574848,82.574848,290.68457,83.036160,543.5573,208.37216,206.54856,210.33129,0.922624,0.018020,5115.03468,1.85813441487489
-c13,442.9824,1550.099,426.5549,652.9992,333.7788,82.574848,590.3019,290.68457,83.036160,83.036160,208.37216,21.220352,21.220352,0.922624,0.018020,4787.801446,1.98513285621467
-c14,442.9824,1550.099,426.5549,652.9992,333.7788,82.574848,590.3019,290.68457,83.036160,83.036160,208.37216,21.220352,21.220352,0.922624,0.018020,4787.801446,1.98513285621467
-c15,442.9824,1550.099,426.5549,652.9992,333.7788,82.574848,82.574848,290.68457,83.036160,543.5573,208.37216,21.220352,21.220352,0.922624,0.018020,4740.595534,2.00490041585352
-c16,442.9824,1610.85,426.5549,652.9992,41.518080,82.574848,590.3019,290.68457,83.036160,543.5573,208.37216,206.54856,210.33129,7.640322,0.018020,5397.96971,1.76074014722954
-c17,442.9824,1550.099,426.5549,652.9992,333.7788,82.574848,82.574848,290.68457,83.036160,543.5573,208.37216,21.220352,21.220352,0.922624,0.018020,4740.595534,2.00490041585352
-c18,442.9824,1550.099,426.5549,652.9992,333.7788,82.574848,82.574848,290.68457,527.54432,83.036160,208.37216,206.54856,21.220352,0.922624,0.018020,4909.910762,1.93576266965638
-c19,352.4661,1550.099,426.5549,652.9992,41.518080,82.574848,82.574848,290.68457,527.54432,543.5573,208.37216,21.220352,21.220352,7.640322,1.669613,4810.695965,1.97568543711356
-c20,442.9824,1550.099,426.5549,652.9992,333.7788,82.574848,82.574848,290.68457,83.036160,543.5573,208.37216,21.220352,21.220352,0.922624,0.018020,4740.595534,2.00490041585352
-c21,442.9824,1610.85,426.5549,652.9992,41.518080,544.1687,590.3019,290.68457,83.036160,543.5573,208.37216,206.54856,21.220352,0.922624,0.018020,5663.734926,1.67811914123258
-c22,442.9824,1610.85,426.5549,652.9992,41.518080,544.1687,590.3019,290.68457,83.036160,543.5573,21.220352,206.54856,210.33129,0.922624,0.018020,5665.694056,1.67753886748983
-c23,442.9824,1550.099,426.5549,652.9992,41.518080,544.1687,590.3019,290.68457,527.54432,83.036160,21.220352,21.220352,21.220352,0.922624,1.669613,5216.142523,1.82211700195683
-c24,352.4661,1610.85,426.5549,652.9992,41.518080,544.1687,590.3019,290.68457,527.54432,83.036160,208.37216,21.220352,21.220352,7.640322,0.018020,5378.595136,1.7670826193399
-c25,442.9824,1550.099,426.5549,652.9992,333.7788,82.574848,82.574848,290.68457,527.54432,543.5573,208.37216,206.54856,210.33129,7.640322,1.669613,5567.912131,1.70699927794893
-c26,442.9824,1610.85,426.5549,652.9992,333.7788,82.574848,82.574848,290.68457,527.54432,543.5573,21.220352,206.54856,210.33129,0.922624,1.669613,5434.793625,1.74881010005582
-c27,442.9824,1550.099,426.5549,652.9992,333.7788,82.574848,82.574848,290.68457,527.54432,543.5573,208.37216,21.220352,21.220352,0.922624,1.669613,5186.755287,1.83244079368418
-c28,442.9824,1550.099,426.5549,652.9992,333.7788,544.1687,590.3019,290.68457,527.54432,83.036160,21.220352,21.220352,21.220352,0.922624,0.018020,5506.75165,1.72595798566732
-c29,442.9824,1550.099,426.5549,652.9992,333.7788,82.574848,82.574848,290.68457,527.54432,543.5573,208.37216,21.220352,21.220352,0.922624,0.018020,5185.103694,1.8330244746495
-c30,352.4661,1550.099,426.5549,652.9992,333.7788,82.574848,82.574848,290.68457,527.54432,543.5573,208.37216,21.220352,21.220352,0.922624,0.018020,5094.587394,1.86559209537446
-c31,442.9824,1550.099,426.5549,652.9992,333.7788,82.574848,590.3019,290.68457,527.54432,83.036160,208.37216,21.220352,210.33129,0.922624,0.018020,5421.420544,1.7531239101542
-c32,352.4661,1610.85,426.5549,652.9992,333.7788,82.574848,590.3019,290.68457,527.54432,543.5573,208.37216,21.220352,21.220352,7.640322,0.018020,5669.783144,1.67632901452767
-c2,4374.886302
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,1108.984,7471.505,3792.343,6973.454,3475.891,6007.756,6530.41,3324.8076,6053.9348,6493.6098,2479.9481,2376.5679,2353.8847,69.37799,1.812953,58514.286843,0.999999998291016
-c1,1108.984,7471.505,2073.98,3356.913,1731.6018,313.098433,352.195669,1683.0934,277.897139,357.330128,1299.926,69.928921,1286.6509,2.857198,0.543719,21386.505307,2.73603778314562
-c2,1108.984,7471.505,2073.98,3356.913,1731.6018,313.098433,352.195669,1683.0934,277.897139,357.330128,1299.926,1273.3007,80.061262,2.857198,0.543719,21383.287448,2.73644951514824
-c3,1145.5648,7908.45,2073.98,3356.913,1731.6018,313.098433,352.195669,1683.0934,277.897139,357.330128,1299.926,80.061262,1286.6509,2.857198,0.543719,21870.163448,2.67553037335887
-c4,1145.5648,7908.45,2073.98,3356.913,1731.6018,313.098433,352.195669,1683.0934,277.897139,357.330128,1299.926,69.928921,1286.6509,2.857198,0.543719,21860.031107,2.67677050818951
-c5,1145.5648,7908.45,2073.98,3356.913,1731.6018,352.195669,3366.9575,1683.0934,277.897139,357.330128,1299.926,69.928921,80.061262,2.857198,0.532687,23707.289504,2.46819808676599
-c6,1108.984,7471.505,2073.98,3356.913,1731.6018,313.098433,352.195669,1683.0934,277.897139,3427.0759,1299.926,1273.3007,80.061262,2.857198,0.543719,24453.03322,2.39292549424294
-c7,1145.5648,7908.45,2073.98,3356.913,1731.6018,313.098433,3366.9575,1683.0934,277.897139,277.897139,1299.926,69.928921,80.061262,57.16078,0.543719,23643.073893,2.4749018194641
-c8,1145.5648,7908.45,2073.98,3356.913,1731.6018,352.195669,352.195669,1683.0934,3203.344,357.330128,1299.926,69.928921,80.061262,57.16078,10.00372,23681.749149,2.47085999550776
-c9,1145.5648,7908.45,2073.98,3356.913,1731.6018,313.098433,352.195669,1683.0934,3203.344,357.330128,1299.926,69.928921,80.061262,2.857198,0.543719,23578.88833,2.48163890408637
-c10,1145.5648,7908.45,2073.98,3356.913,139.46418,272.76268,3366.9575,1683.0934,3203.344,3427.0759,69.928921,69.928921,89.882488,3.135222,0.538289,26811.019301,2.1824715415639
-c11,1145.5648,7908.45,2073.98,3356.913,1731.6018,3053.2545,3366.9575,1683.0934,277.897139,277.897139,1299.926,69.928921,80.061262,2.570366,0.543719,26328.639546,2.22245765940626
-c12,1145.5648,7908.45,2073.98,3356.913,1731.6018,313.098433,352.195669,1683.0934,318.232892,3427.0759,1299.926,1273.3007,1286.6509,2.857198,0.543719,26173.484411,2.23563227962284
-c13,1145.5648,7908.45,2073.98,3356.913,1731.6018,313.098433,3366.9575,1683.0934,277.897139,357.330128,1299.926,69.928921,89.882488,2.857198,0.538289,23678.019096,2.47124923578426
-c14,1145.5648,7908.45,2073.98,3356.913,1731.6018,352.195669,3366.9575,1683.0934,277.897139,357.330128,1299.926,69.928921,80.061262,2.857198,0.543719,23707.300536,2.46819693821003
-c15,1145.5648,7908.45,2073.98,3356.913,1731.6018,313.098433,352.195669,1683.0934,277.897139,3427.0759,1299.926,69.928921,69.928921,2.857198,0.543719,23713.0549,2.46759798950409
-c16,1145.5648,6358.145,1949.97,3356.913,159.632057,313.098433,3366.9575,1683.0934,318.232892,3427.0759,1299.926,1273.3007,1286.6509,57.16078,0.538289,25996.259651,2.25087329498426
-c17,1145.5648,7908.45,2073.98,3356.913,1731.6018,313.098433,352.195669,1683.0934,277.897139,3427.0759,1299.926,69.928921,80.061262,2.570366,0.543719,23722.900409,2.46657388377955
-c18,1145.5648,7908.45,2073.98,3356.913,1731.6018,313.098433,352.195669,1683.0934,3203.344,357.330128,1299.926,1273.3007,80.061262,2.857198,0.543719,24782.260109,2.36113600412241
-c19,1108.984,7471.505,2073.98,3356.913,179.180675,352.195669,313.098433,1683.0934,3203.344,3427.0759,1299.926,89.882488,69.928921,57.16078,10.00372,24696.271986,2.3693570689226
-c20,1145.5648,7908.45,2073.98,3356.913,1731.6018,313.098433,352.195669,1683.0934,318.232892,3427.0759,1299.926,69.928921,69.928921,2.857198,0.543719,23753.390653,2.46340774887517
-c21,1145.5648,6358.145,1949.97,3356.913,159.632057,3053.2545,3366.9575,1683.0934,318.232892,3427.0759,1299.926,1273.3007,69.928921,2.857198,0.538289,27465.390157,2.13047352669918
-c22,1145.5648,6358.145,1949.97,3356.913,179.180675,3053.2545,3366.9575,1683.0934,318.232892,3427.0759,69.928921,1273.3007,1286.6509,2.857198,0.538289,27471.663675,2.12998700487335
-c23,1145.5648,7908.45,2073.98,3356.913,159.632057,3053.2545,3366.9575,1683.0934,3203.344,357.330128,89.882488,69.928921,89.882488,3.135222,10.00372,26571.352224,2.20215689926132
-c24,1108.984,6756.043,1949.97,3356.913,139.46418,3053.2545,3366.9575,1683.0934,3203.344,318.232892,1299.926,89.882488,80.061262,57.16078,0.532687,26463.819689,2.21110509781064
-c25,1145.5648,7908.45,2073.98,3356.913,1731.6018,352.195669,272.76268,1683.0934,3203.344,3427.0759,1299.926,1273.3007,1286.6509,57.16078,10.00372,29082.023349,2.0120431766247
-c26,1145.5648,6358.145,1949.97,3356.913,1731.6018,272.76268,272.76268,1683.0934,3203.344,3427.0759,89.882488,1273.3007,1286.6509,2.570366,10.00372,26063.641434,2.24505415970628
-c27,1145.5648,7908.45,2073.98,3356.913,1731.6018,352.195669,272.76268,1683.0934,3203.344,3427.0759,1299.926,89.882488,89.882488,3.135222,10.00372,26647.811167,2.19583838450037
-c28,1145.5648,7908.45,2073.98,3356.913,1731.6018,3053.2545,3366.9575,1683.0934,3203.344,357.330128,89.882488,69.928921,80.061262,2.857198,0.543719,28123.762716,2.08059949964129
-c29,1145.5648,7908.45,2073.98,3356.913,1731.6018,313.098433,352.195669,1683.0934,3203.344,3427.0759,1299.926,69.928921,69.928921,2.857198,0.532687,26638.490729,2.19660667785648
-c30,1108.984,7471.505,2073.98,3356.913,1731.6018,313.098433,352.195669,1683.0934,3203.344,3427.0759,1299.926,80.061262,80.061262,2.857198,0.543719,26185.240643,2.23462856107758
-c31,1145.5648,7908.45,2073.98,3356.913,1731.6018,313.098433,3366.9575,1683.0934,3203.344,357.330128,1299.926,69.928921,1286.6509,2.857198,0.543719,27800.239799,2.10481229858397
-c32,1108.984,6756.043,1949.97,3356.913,1731.6018,352.195669,3366.9575,1683.0934,3203.344,3427.0759,1299.926,89.882488,80.061262,57.16078,0.532687,28463.741486,2.05574824610481
-c2,21383.287448
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,151.959867,151.959867,0,153.127903,153.127903,0,38.454652,0,1.156348,0.102999,649.889539,0
-c2,0,0,0,0,0,151.959867,151.959867,0,153.127903,153.127903,0,0,38.454652,1.156348,0.102999,649.889539,0
-c3,0,0,0,0,0,151.959867,151.959867,0,153.127903,153.127903,0,38.454652,0,1.156348,0.102999,649.889539,0
-c4,0,0,0,0,0,151.959867,151.959867,0,153.127903,153.127903,0,38.454652,0,1.156348,0.102999,649.889539,0
-c5,0,0,0,0,0,151.959867,0,0,153.127903,153.127903,0,38.454652,38.454652,1.156348,0.102999,536.384324,0
-c6,0,0,0,0,0,151.959867,151.959867,0,153.127903,0,0,0,38.454652,1.156348,0.102999,496.761636,0
-c7,0,0,0,0,0,151.959867,0,0,153.127903,153.127903,0,38.454652,38.454652,0,0.102999,535.227976,0
-c8,0,0,0,0,0,151.959867,151.959867,0,0,153.127903,0,38.454652,38.454652,0,0,533.956941,0
-c9,0,0,0,0,0,151.959867,151.959867,0,0,153.127903,0,38.454652,38.454652,1.156348,0.102999,535.216288,0
-c10,0,0,0,0,76.708720,151.959867,0,0,0,0,38.454652,38.454652,38.454652,1.156348,0.102999,345.29189,0
-c11,0,0,0,0,0,0,0,0,153.127903,153.127903,0,38.454652,38.454652,1.156348,0.102999,384.424457,0
-c12,0,0,0,0,0,151.959867,151.959867,0,153.127903,0,0,0,0,1.156348,0.102999,458.306984,0
-c13,0,0,0,0,0,151.959867,0,0,153.127903,153.127903,0,38.454652,38.454652,1.156348,0.102999,536.384324,0
-c14,0,0,0,0,0,151.959867,0,0,153.127903,153.127903,0,38.454652,38.454652,1.156348,0.102999,536.384324,0
-c15,0,0,0,0,0,151.959867,151.959867,0,153.127903,0,0,38.454652,38.454652,1.156348,0.102999,535.216288,0
-c16,0,0,0,0,76.708720,151.959867,0,0,153.127903,0,0,0,0,0,0.102999,381.899489,0
-c17,0,0,0,0,0,151.959867,151.959867,0,153.127903,0,0,38.454652,38.454652,1.156348,0.102999,535.216288,0
-c18,0,0,0,0,0,151.959867,151.959867,0,0,153.127903,0,0,38.454652,1.156348,0.102999,496.761636,0
-c19,0,0,0,0,76.708720,151.959867,151.959867,0,0,0,0,38.454652,38.454652,0,0,457.537758,0
-c20,0,0,0,0,0,151.959867,151.959867,0,153.127903,0,0,38.454652,38.454652,1.156348,0.102999,535.216288,0
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-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
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-c3,7.62006,83.3307,28.5399,0,0,0,0,0,0,0,0,0,0,0,0,119.49066,0
-c4,7.62006,83.3307,28.5399,0,0,0,0,0,0,0,0,0,0,0,0,119.49066,0
-c5,7.62006,83.3307,28.5399,0,0,0,0,0,0,0,0,0,0,0,0,119.49066,0
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-c8,7.62006,83.3307,28.5399,0,0,0,0,0,0,0,0,0,0,0,0,119.49066,0
-c9,7.62006,83.3307,28.5399,0,0,0,0,0,0,0,0,0,0,0,0,119.49066,0
-c10,7.62006,83.3307,28.5399,0,0,0,0,0,0,0,0,0,0,0,0,119.49066,0
-c11,7.62006,83.3307,28.5399,0,0,0,0,0,0,0,0,0,0,0,0,119.49066,0
-c12,7.62006,83.3307,28.5399,0,0,0,0,0,0,0,0,0,0,0,0,119.49066,0
-c13,7.62006,83.3307,28.5399,0,0,0,0,0,0,0,0,0,0,0,0,119.49066,0
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-c17,7.62006,83.3307,28.5399,0,0,0,0,0,0,0,0,0,0,0,0,119.49066,0
-c18,7.62006,83.3307,28.5399,0,0,0,0,0,0,0,0,0,0,0,0,119.49066,0
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-c25,7.62006,83.3307,28.5399,0,0,0,0,0,0,0,0,0,0,0,0,119.49066,0
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-c0,0
-
-Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,352.4661,1550.099,701.7255,1159.5933,558.563,925.6073,971.2723,490.07014,857.91333,883.36663,344.11179,344.22248,351.94343,12.419968,1.04789,9504.422158,0.999999989478582
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-c3,450.60246,1633.4297,455.0948,652.9992,333.7788,100.738165,100.738165,290.68457,102.069715,102.069715,208.37216,26.069312,210.33129,1.204529,0.107291,4668.289872,2.03595368218485
-c4,450.60246,1633.4297,455.0948,652.9992,333.7788,100.738165,100.738165,290.68457,102.069715,102.069715,208.37216,26.069312,210.33129,1.204529,0.107291,4668.289872,2.03595368218485
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-c6,352.4661,1550.099,455.0948,652.9992,333.7788,100.738165,100.738165,290.68457,102.069715,543.5573,208.37216,206.54856,26.069312,1.204529,0.107291,4924.527667,1.93001696968602
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-c8,450.60246,1633.4297,455.0948,652.9992,333.7788,100.738165,100.738165,290.68457,527.54432,102.069715,208.37216,26.069312,26.069312,7.640322,1.669613,4917.500614,1.93277494214513
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-c11,450.60246,1633.4297,455.0948,652.9992,333.7788,544.1687,590.3019,290.68457,102.069715,102.069715,208.37216,26.069312,26.069312,1.204529,0.107291,5417.022164,1.75454736842485
-c12,450.60246,1633.4297,455.0948,652.9992,333.7788,100.738165,100.738165,290.68457,102.069715,543.5573,208.37216,206.54856,210.33129,1.204529,0.107291,5290.256705,1.79658994040082
-c13,450.60246,1633.4297,455.0948,652.9992,333.7788,100.738165,590.3019,290.68457,102.069715,102.069715,208.37216,26.069312,26.069312,1.204529,0.107291,4973.591629,1.91097755422538
-c14,450.60246,1633.4297,455.0948,652.9992,333.7788,100.738165,590.3019,290.68457,102.069715,102.069715,208.37216,26.069312,26.069312,1.204529,0.107291,4973.591629,1.91097755422538
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-c16,450.60246,1610.85,426.5549,652.9992,51.115816,100.738165,590.3019,290.68457,102.069715,543.5573,208.37216,206.54856,210.33129,7.640322,0.107291,5452.473649,1.74313946210986
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-c19,352.4661,1550.099,455.0948,652.9992,51.115816,100.738165,100.738165,290.68457,527.54432,543.5573,208.37216,26.069312,26.069312,7.640322,1.669613,4894.858155,1.9417155028527
-c20,450.60246,1633.4297,455.0948,652.9992,333.7788,100.738165,100.738165,290.68457,102.069715,543.5573,208.37216,26.069312,26.069312,1.204529,0.107291,4925.515479,1.92962990484128
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-c22,450.60246,1610.85,426.5549,652.9992,51.115816,544.1687,590.3019,290.68457,102.069715,543.5573,26.069312,206.54856,210.33129,1.204529,0.107291,5707.165543,1.66534892318351
-c23,450.60246,1633.4297,455.0948,652.9992,51.115816,544.1687,590.3019,290.68457,527.54432,102.069715,26.069312,26.069312,26.069312,1.204529,1.669613,5379.093259,1.76691898126247
-c24,352.4661,1720.485,426.5549,652.9992,51.115816,544.1687,590.3019,290.68457,527.54432,102.069715,208.37216,26.069312,26.069312,7.640322,0.107291,5526.648618,1.71974421443588
-c25,450.60246,1633.4297,455.0948,652.9992,333.7788,100.738165,100.738165,290.68457,527.54432,543.5573,208.37216,206.54856,210.33129,7.640322,1.669613,5723.729425,1.66052957542574
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-c27,450.60246,1633.4297,455.0948,652.9992,333.7788,100.738165,100.738165,290.68457,527.54432,543.5573,208.37216,26.069312,26.069312,1.204529,1.669613,5352.552406,1.77568032211658
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-c2,4483.040082
-
-Unpatch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
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diff --git a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_promise_results2.csv b/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_promise_results2.csv
deleted file mode 100644
index d02d164d68835e458f2902e02bbd6c3acff2e1c7..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_promise_results2.csv
+++ /dev/null
@@ -1,616 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
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-c12,1108.984,7471.505,1949.97,6973.454,38.840247,77.680494,3366.9575,78.556742,157.113483,3427.0759,2479.9481,2376.5679,2353.8847,1.117251,10.00372,31871.659037,1.83593475920023
-c13,1108.984,7471.505,1949.97,157.113483,59.008124,6007.756,3366.9575,3324.8076,157.113483,3427.0759,19.513340,2376.5679,29.645681,1.117251,10.00372,29467.138982,1.98574712937584
-c14,1108.984,6358.145,3792.343,3356.913,1731.6018,6007.756,6530.41,38.840247,118.016247,118.016247,1299.926,29.645681,19.513340,0.839227,0.016391,30510.96618,1.91781165847101
-c15,1129.478,6358.145,3792.343,3356.913,38.840247,6007.756,157.113483,3324.8076,157.113483,3427.0759,39.466907,29.645681,19.513340,1.117251,0.016391,27839.345283,2.10185570235181
-c16,1108.984,6358.145,1949.97,3356.913,38.840247,118.016247,157.113483,1683.0934,157.113483,77.680494,1299.926,29.645681,19.513340,0.552395,1.812953,16357.319723,3.57725394356618
-c17,1129.478,6358.145,3792.343,3356.913,3475.891,3053.2545,77.680494,38.840247,3203.344,6493.6098,29.645681,1273.3007,29.645681,0.552395,10.00372,32322.647218,1.81031851343483
-c18,1108.984,174.570537,1949.97,6973.454,78.556742,77.680494,3366.9575,1683.0934,3203.344,3427.0759,1299.926,1273.3007,29.645681,1.117251,0.016391,24647.692596,2.37402695516794
-c19,1108.984,6358.145,1949.97,3356.913,3475.891,3053.2545,157.113483,3324.8076,77.680494,3427.0759,1299.926,29.645681,29.645681,57.16078,0.010789,27706.223908,2.11195458558714
-c20,1108.984,6358.145,1949.97,6973.454,78.556742,6007.756,157.113483,3324.8076,157.113483,6493.6098,29.645681,39.466907,29.645681,0.552395,10.00372,32718.824492,1.78839819500445
-c21,1108.984,6358.145,3792.343,3356.913,3475.891,3053.2545,77.680494,78.556742,157.113483,6493.6098,29.645681,1273.3007,2353.8847,0.839227,0.016391,31610.177718,1.85112172351273
-c22,1108.984,7471.505,1949.97,77.680494,59.008124,77.680494,3366.9575,1683.0934,157.113483,3427.0759,1299.926,2376.5679,29.645681,0.552395,10.00372,23095.764091,2.53355058352224
-c23,1129.478,6358.145,1949.97,6973.454,1731.6018,3053.2545,157.113483,78.556742,3203.344,3427.0759,1299.926,19.513340,29.645681,69.37799,10.00372,29490.460156,1.98417679259838
-c24,1108.984,6358.145,1949.97,6973.454,1731.6018,3053.2545,157.113483,78.556742,77.680494,3427.0759,1299.926,29.645681,29.645681,69.37799,0.010789,26344.44206,2.22112453502033
-c25,1129.478,6358.145,1949.97,3356.913,59.008124,77.680494,157.113483,1683.0934,157.113483,77.680494,1299.926,1273.3007,1286.6509,57.16078,1.812953,18925.046811,3.09189652835096
-c26,1108.984,6358.145,1949.97,3356.913,1731.6018,6007.756,77.680494,59.008124,3203.344,3427.0759,1299.926,2376.5679,29.645681,0.839227,10.00372,30997.460846,1.88771225310797
-c27,1108.984,6358.145,1949.97,3356.913,1731.6018,6007.756,77.680494,59.008124,3203.344,3427.0759,1299.926,2376.5679,39.466907,69.37799,10.00372,31075.820835,1.88295224655181
-c28,1108.984,6358.145,1949.97,6973.454,3475.891,3053.2545,157.113483,78.556742,3203.344,3427.0759,1299.926,29.645681,29.645681,69.37799,0.021821,31214.405798,1.87459236079035
-c29,1108.984,6358.145,1949.97,3356.913,1731.6018,6007.756,77.680494,78.556742,3203.344,3427.0759,1299.926,2376.5679,29.645681,0.839227,10.00372,31017.009464,1.88652251346999
-c30,1108.984,7471.505,3792.343,3356.913,78.556742,157.113483,3366.9575,1683.0934,157.113483,157.113483,39.466907,2376.5679,29.645681,1.117251,0.010789,23776.501619,2.46101329516616
-c31,1129.478,7471.505,3792.343,157.113483,3475.891,3053.2545,6530.41,3324.8076,6053.9348,118.016247,2479.9481,39.466907,1286.6509,1.117251,0.016391,38913.953179,1.50368394656467
-c32,1129.478,7471.505,3792.343,157.113483,1731.6018,77.680494,3366.9575,3324.8076,3203.344,77.680494,1299.926,29.645681,29.645681,1.117251,0.021821,25692.867805,2.27745252337567
-c33,1129.478,7471.505,1949.97,157.113483,3475.891,3053.2545,6530.41,3324.8076,6053.9348,118.016247,2479.9481,39.466907,1286.6509,1.117251,0.016391,37071.580179,1.57841360963365
-c34,1108.984,6358.145,1949.97,3356.913,59.008124,77.680494,6530.41,3324.8076,157.113483,157.113483,1299.926,39.466907,39.466907,0.839227,1.812953,24461.657178,2.39208186828886
-c35,1129.478,7471.505,1949.97,3356.913,38.840247,6007.756,77.680494,59.008124,6053.9348,6493.6098,1299.926,29.645681,1286.6509,0.552395,1.812953,35257.283394,1.65963684788585
-c36,1129.478,6358.145,1949.97,3356.913,1731.6018,3053.2545,6530.41,38.840247,3203.344,6493.6098,29.645681,1273.3007,39.466907,0.839227,10.00372,35198.822582,1.66239329569745
-c37,1129.478,7471.505,1949.97,6973.454,3475.891,118.016247,118.016247,1683.0934,157.113483,77.680494,1299.926,2376.5679,29.645681,1.117251,0.010789,26861.485492,2.17837120894114
-c38,1108.984,7471.505,1949.97,6973.454,1731.6018,157.113483,157.113483,1683.0934,118.016247,157.113483,1299.926,2376.5679,39.466907,0.552395,1.812953,25226.291051,2.31957549735489
-c39,1108.984,7471.505,3792.343,3356.913,59.008124,77.680494,6530.41,3324.8076,118.016247,3427.0759,29.645681,2376.5679,29.645681,69.37799,1.812953,31773.79357,1.8415895643663
-c40,1129.478,7471.505,3792.343,3356.913,1731.6018,118.016247,118.016247,3324.8076,157.113483,157.113483,1299.926,19.513340,1286.6509,0.839227,0.021821,23963.859148,2.44177226370095
-c41,1108.984,7471.505,1949.97,6973.454,3475.891,3053.2545,3366.9575,78.556742,157.113483,3427.0759,1299.926,1273.3007,19.513340,1.117251,10.00372,33666.623136,1.73805036616889
-c42,1129.478,7471.505,1949.97,6973.454,1731.6018,118.016247,118.016247,1683.0934,157.113483,157.113483,1299.926,2376.5679,1286.6509,1.117251,1.812953,26455.436664,2.21180573826795
-c43,1108.984,7471.505,1949.97,6973.454,78.556742,77.680494,3366.9575,1683.0934,157.113483,6493.6098,1299.926,2376.5679,29.645681,69.37799,10.00372,33146.44571,1.76532612812885
-c44,1108.984,7471.505,1949.97,6973.454,1731.6018,157.113483,3366.9575,1683.0934,118.016247,157.113483,39.466907,39.466907,39.466907,1.117251,10.00372,24847.330605,2.3549526320437
-c45,1108.984,6358.145,1949.97,6973.454,1731.6018,157.113483,118.016247,1683.0934,157.113483,3427.0759,39.466907,29.645681,29.645681,1.117251,10.00372,23774.446553,2.46122602544847
-c46,1108.984,7471.505,1949.97,118.016247,3475.891,77.680494,3366.9575,3324.8076,3203.344,3427.0759,1299.926,2376.5679,29.645681,1.117251,10.00372,31241.492293,1.87296708194743
-c47,1129.478,7471.505,1949.97,3356.913,3475.891,157.113483,77.680494,3324.8076,157.113483,3427.0759,2479.9481,1273.3007,1286.6509,0.839227,0.016391,29568.303278,1.97895314096841
-c48,1108.984,7471.505,1949.97,6973.454,3475.891,77.680494,3366.9575,1683.0934,157.113483,118.016247,1299.926,39.466907,29.645681,1.117251,0.016391,27752.837354,2.10840736339074
-c49,1129.478,7471.505,1949.97,6973.454,1731.6018,118.016247,118.016247,3324.8076,6053.9348,157.113483,1299.926,19.513340,1286.6509,57.16078,10.00372,31701.151917,1.84580947757423
-c50,1108.984,6358.145,1949.97,6973.454,59.008124,6007.756,3366.9575,1683.0934,157.113483,157.113483,1299.926,2376.5679,29.645681,1.117251,10.00372,31538.855542,1.85530786237777
-c51,1108.984,6358.145,1949.97,6973.454,3475.891,77.680494,3366.9575,1683.0934,157.113483,157.113483,1299.926,2376.5679,29.645681,57.16078,10.00372,29081.706441,2.01206510218048
-c16,16357.319723
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,352.4661,1550.099,701.7255,1159.5933,558.563,925.6073,971.2723,490.07014,857.91333,883.36663,344.11179,344.22248,351.94343,12.419968,1.04789,9504.422158,0.999999989478582
-c1,352.4661,90.532480,426.5549,1159.5933,558.563,82.574848,971.2723,290.68457,527.54432,543.5573,208.37216,21.220352,21.220352,12.419968,1.669613,5268.245563,1.80409623354347
-c2,352.4661,90.532480,426.5549,1159.5933,558.563,82.574848,590.3019,290.68457,83.036160,83.036160,208.37216,344.22248,210.33129,0.922624,1.669613,4482.861585,2.12016850526585
-c3,352.4661,90.532480,426.5549,1159.5933,558.563,82.574848,590.3019,290.68457,857.91333,543.5573,208.37216,344.22248,21.220352,0.922624,1.669613,5529.148957,1.71896652812556
-c4,442.9824,90.532480,426.5549,1159.5933,41.518080,82.574848,590.3019,290.68457,83.036160,543.5573,208.37216,21.220352,21.220352,0.922624,1.669613,4004.741039,2.37329251207814
-c5,442.9824,1610.85,701.7255,82.574848,41.518080,82.574848,971.2723,290.68457,83.036160,83.036160,21.220352,206.54856,351.94343,0.922624,0.018020,4970.907852,1.91200928477784
-c6,352.4661,1550.099,426.5549,1159.5933,333.7788,544.1687,590.3019,41.518080,83.036160,83.036160,21.220352,21.220352,21.220352,7.640322,1.669613,5237.524091,1.81467842656118
-c7,352.4661,90.532480,426.5549,1159.5933,41.518080,82.574848,971.2723,290.68457,83.036160,543.5573,208.37216,344.22248,21.220352,0.922624,0.018020,4616.545674,2.05877351233646
-c8,352.4661,1550.099,426.5549,82.574848,41.518080,925.6073,971.2723,490.07014,83.036160,83.036160,208.37216,21.220352,21.220352,12.419968,0.018020,5269.48584,1.80367160406542
-c9,352.4661,90.532480,426.5549,1159.5933,41.518080,82.574848,590.3019,290.68457,83.036160,543.5573,208.37216,344.22248,21.220352,0.922624,1.669613,4237.226867,2.24307600985775
-c10,352.4661,90.532480,426.5549,1159.5933,41.518080,82.574848,590.3019,290.68457,83.036160,543.5573,208.37216,344.22248,21.220352,0.922624,1.669613,4237.226867,2.24307600985775
-c11,352.4661,90.532480,426.5549,1159.5933,41.518080,925.6073,590.3019,290.68457,527.54432,83.036160,208.37216,344.22248,21.220352,0.922624,1.669613,5064.246339,1.87676928294919
-c12,352.4661,1550.099,426.5549,1159.5933,41.518080,82.574848,590.3019,41.518080,83.036160,543.5573,344.11179,344.22248,351.94343,0.922624,1.669613,5914.089605,1.6070811624593
-c13,352.4661,1550.099,426.5549,82.574848,41.518080,925.6073,590.3019,490.07014,83.036160,543.5573,21.220352,344.22248,21.220352,0.922624,1.669613,5475.041149,1.73595443865121
-c14,352.4661,1610.85,701.7255,652.9992,333.7788,925.6073,971.2723,41.518080,83.036160,83.036160,208.37216,21.220352,21.220352,0.922624,0.018020,6008.043108,1.58194970125122
-c15,442.9824,1610.85,701.7255,652.9992,41.518080,925.6073,82.574848,490.07014,83.036160,543.5573,21.220352,21.220352,21.220352,0.922624,0.018020,5639.522628,1.68532385033417
-c16,352.4661,1610.85,426.5549,652.9992,41.518080,82.574848,82.574848,290.68457,83.036160,83.036160,208.37216,21.220352,21.220352,0.922624,1.04789,3959.078244,2.40066533980162
-c17,442.9824,1610.85,701.7255,652.9992,558.563,544.1687,82.574848,41.518080,527.54432,883.36663,21.220352,206.54856,21.220352,0.922624,1.669613,6297.874179,1.50914764838862
-c18,352.4661,90.532480,426.5549,1159.5933,41.518080,82.574848,590.3019,290.68457,527.54432,543.5573,208.37216,206.54856,21.220352,0.922624,0.018020,4542.409514,2.09237452490122
-c19,352.4661,1610.85,426.5549,652.9992,558.563,544.1687,82.574848,490.07014,83.036160,543.5573,208.37216,21.220352,21.220352,7.640322,0.018020,5603.311554,1.69621515719461
-c20,352.4661,1610.85,426.5549,1159.5933,41.518080,925.6073,82.574848,490.07014,83.036160,883.36663,21.220352,21.220352,21.220352,0.922624,1.669613,6121.890751,1.55253048270984
-c21,352.4661,1610.85,701.7255,652.9992,558.563,544.1687,82.574848,41.518080,83.036160,883.36663,21.220352,206.54856,351.94343,0.922624,0.018020,6091.921204,1.56016824310572
-c22,352.4661,1550.099,426.5549,82.574848,41.518080,82.574848,590.3019,290.68457,83.036160,543.5573,208.37216,344.22248,21.220352,0.922624,1.669613,4619.774935,2.05733441260522
-c23,442.9824,1610.85,426.5549,1159.5933,333.7788,544.1687,82.574848,41.518080,527.54432,543.5573,208.37216,21.220352,21.220352,12.419968,1.669613,5978.025093,1.58989329270965
-c24,352.4661,1610.85,426.5549,1159.5933,333.7788,544.1687,82.574848,41.518080,83.036160,543.5573,208.37216,21.220352,21.220352,12.419968,0.018020,5441.34904,1.74670323727839
-c25,442.9824,1610.85,426.5549,652.9992,41.518080,82.574848,82.574848,290.68457,83.036160,83.036160,208.37216,206.54856,210.33129,7.640322,1.04789,4430.751388,2.14510386866455
-c26,352.4661,1610.85,426.5549,652.9992,333.7788,925.6073,82.574848,41.518080,527.54432,543.5573,208.37216,344.22248,21.220352,0.922624,1.669613,6073.858077,1.56480804803619
-c27,352.4661,1610.85,426.5549,652.9992,333.7788,925.6073,82.574848,41.518080,527.54432,543.5573,208.37216,344.22248,21.220352,12.419968,1.669613,6085.355421,1.56185158372442
-c28,352.4661,1610.85,426.5549,1159.5933,558.563,544.1687,82.574848,41.518080,527.54432,543.5573,208.37216,21.220352,21.220352,12.419968,0.018020,6110.6414,1.55538860494434
-c29,352.4661,1610.85,426.5549,652.9992,333.7788,925.6073,82.574848,41.518080,527.54432,543.5573,208.37216,344.22248,21.220352,0.922624,1.669613,6073.858077,1.56480804803619
-c30,352.4661,1550.099,701.7255,652.9992,41.518080,82.574848,590.3019,290.68457,83.036160,83.036160,21.220352,344.22248,21.220352,0.922624,0.018020,4816.045346,1.97349096153027
-c31,442.9824,1550.099,701.7255,82.574848,558.563,544.1687,971.2723,490.07014,857.91333,83.036160,344.11179,21.220352,210.33129,0.922624,0.018020,6859.009454,1.3856843445359
-c32,442.9824,1550.099,701.7255,82.574848,333.7788,82.574848,590.3019,490.07014,527.54432,83.036160,208.37216,21.220352,21.220352,0.922624,0.018020,5136.441424,1.8503904139842
-c33,442.9824,1550.099,426.5549,82.574848,558.563,544.1687,971.2723,490.07014,857.91333,83.036160,344.11179,21.220352,210.33129,0.922624,0.018020,6583.838854,1.44359882196474
-c34,352.4661,1610.85,426.5549,652.9992,41.518080,82.574848,971.2723,490.07014,83.036160,83.036160,208.37216,21.220352,21.220352,0.922624,1.04789,5047.161266,1.8831223075264
-c35,442.9824,1550.099,426.5549,652.9992,41.518080,925.6073,82.574848,41.518080,857.91333,883.36663,208.37216,21.220352,210.33129,0.922624,1.04789,6347.028084,1.49746021011209
-c36,442.9824,1610.85,426.5549,652.9992,333.7788,544.1687,971.2723,41.518080,527.54432,883.36663,21.220352,206.54856,21.220352,0.922624,1.669613,6686.616831,1.42140969881739
-c37,442.9824,1550.099,426.5549,1159.5933,558.563,82.574848,82.574848,290.68457,83.036160,83.036160,208.37216,344.22248,21.220352,0.922624,0.018020,5334.454822,1.78170446596193
-c38,352.4661,1550.099,426.5549,1159.5933,333.7788,82.574848,82.574848,290.68457,83.036160,83.036160,208.37216,344.22248,21.220352,0.922624,1.04789,5020.184192,1.89324168300872
-c39,352.4661,1550.099,701.7255,652.9992,41.518080,82.574848,971.2723,490.07014,83.036160,543.5573,21.220352,344.22248,21.220352,12.419968,1.04789,5869.44967,1.61930377300085
-c40,442.9824,1550.099,701.7255,652.9992,333.7788,82.574848,82.574848,490.07014,83.036160,83.036160,208.37216,21.220352,210.33129,0.922624,0.018020,4943.741502,1.92251596526707
-c41,352.4661,1550.099,426.5549,1159.5933,558.563,544.1687,590.3019,41.518080,83.036160,543.5573,208.37216,206.54856,21.220352,0.922624,1.669613,6288.591749,1.51137526273253
-c42,442.9824,1550.099,426.5549,1159.5933,333.7788,82.574848,82.574848,290.68457,83.036160,83.036160,208.37216,344.22248,210.33129,0.922624,1.04789,5299.81143,1.79335097185994
-c43,352.4661,1550.099,426.5549,1159.5933,41.518080,82.574848,590.3019,290.68457,83.036160,883.36663,208.37216,344.22248,21.220352,12.419968,1.669613,6048.100061,1.57147234751293
-c44,352.4661,1550.099,426.5549,1159.5933,333.7788,82.574848,590.3019,290.68457,83.036160,83.036160,21.220352,21.220352,21.220352,0.922624,1.669613,5018.379031,1.8939227009152
-c45,352.4661,1610.85,426.5549,1159.5933,333.7788,82.574848,82.574848,290.68457,83.036160,543.5573,21.220352,21.220352,21.220352,0.922624,1.669613,5031.924119,1.88882458168037
-c46,352.4661,1550.099,426.5549,82.574848,558.563,82.574848,590.3019,490.07014,527.54432,543.5573,208.37216,344.22248,21.220352,0.922624,1.669613,5780.713585,1.64416068255766
-c47,442.9824,1550.099,426.5549,652.9992,558.563,82.574848,82.574848,490.07014,83.036160,543.5573,344.11179,206.54856,210.33129,0.922624,0.018020,5674.94408,1.67480451904639
-c48,352.4661,1550.099,426.5549,1159.5933,558.563,82.574848,590.3019,290.68457,83.036160,83.036160,208.37216,21.220352,21.220352,0.922624,0.018020,5428.663446,1.75078489898368
-c49,442.9824,1550.099,426.5549,1159.5933,333.7788,82.574848,82.574848,490.07014,857.91333,83.036160,208.37216,21.220352,210.33129,7.640322,1.669613,5958.411463,1.595126831624
-c50,352.4661,1610.85,426.5549,1159.5933,41.518080,925.6073,590.3019,290.68457,83.036160,83.036160,208.37216,344.22248,21.220352,0.922624,1.669613,6140.055699,1.54793742420841
-c51,352.4661,1610.85,426.5549,1159.5933,558.563,82.574848,590.3019,290.68457,83.036160,83.036160,208.37216,344.22248,21.220352,7.640322,1.669613,5820.785865,1.63284171848088
-c16,3959.078244
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,1108.984,7471.505,3792.343,6973.454,3475.891,6007.756,6530.41,3324.8076,6053.9348,6493.6098,2479.9481,2376.5679,2353.8847,69.37799,1.812953,58514.286843,0.999999998291016
-c1,1108.984,472.88204,1949.97,7209.476,3475.891,272.76268,6530.41,1732.6793,3203.344,3427.0759,1299.926,69.928921,80.061262,69.37799,16.49564,30919.264733,1.89248635629098
-c2,1108.984,472.88204,1949.97,7209.476,3475.891,313.098433,3366.9575,1683.0934,357.330128,277.897139,1299.926,2388.8133,1319.7884,3.135222,10.00372,25237.246282,2.31856859331271
-c3,1108.984,472.88204,1949.97,7209.476,3475.891,272.76268,3366.9575,1683.0934,6117.8413,3521.5484,1299.926,2388.8133,89.882488,3.135222,10.00372,32971.16705,1.77471081253488
-c4,1145.5648,472.88204,1949.97,7209.476,179.180675,352.195669,3366.9575,1683.0934,357.330128,3427.0759,1299.926,89.882488,80.061262,3.135222,10.00372,21626.734804,2.70564591015435
-c5,1145.5648,6358.145,3996.263,375.615141,179.180675,352.195669,6530.41,1732.6793,277.897139,277.897139,80.061262,1273.3007,2365.9264,2.570366,0.543719,24948.25031,2.34542646804386
-c6,1108.984,7471.505,2073.98,7209.476,1802.5061,3053.2545,3366.9575,171.815177,277.897139,318.232892,89.882488,89.882488,89.882488,57.16078,10.00372,27191.420272,2.15193932654045
-c7,1108.984,472.88204,1949.97,7209.476,159.632057,272.76268,6530.41,1732.6793,357.330128,3427.0759,1299.926,2388.8133,80.061262,3.135222,0.543719,26993.681608,2.16770307496285
-c8,1108.984,7471.505,2073.98,375.615141,179.180675,6007.756,6530.41,3324.8076,357.330128,357.330128,1299.926,80.061262,69.928921,69.37799,0.543719,29306.736564,1.99661557388197
-c9,1108.984,472.88204,1949.97,7209.476,179.180675,313.098433,3366.9575,1683.0934,357.330128,3427.0759,1299.926,2388.8133,80.061262,2.570366,10.00372,23849.422724,2.45348859277702
-c10,1108.984,472.88204,1949.97,7209.476,179.180675,313.098433,3366.9575,1683.0934,357.330128,3427.0759,1299.926,2388.8133,80.061262,3.135222,10.00372,23849.98758,2.45343048508443
-c11,1108.984,472.88204,1949.97,7209.476,159.632057,6007.756,3519.4585,1683.0934,3203.344,277.897139,1299.926,2388.8133,80.061262,3.135222,10.00372,29374.43264,1.99201419005853
-c12,1108.984,7471.505,2073.98,7209.476,139.46418,272.76268,3366.9575,171.815177,357.330128,3427.0759,2492.7759,2376.5679,2353.8847,3.135222,10.00372,32835.718007,1.78203158683244
-c13,1108.984,7471.505,2073.98,375.615141,159.632057,6007.756,3519.4585,3383.8134,357.330128,3427.0759,69.928921,2376.5679,80.061262,3.135222,10.00372,30424.847151,1.92324011884979
-c14,1108.984,6756.043,3996.263,3610.731,1731.6018,6130.47,6530.41,132.098682,318.232892,318.232892,1299.926,80.061262,69.928921,2.857198,0.538289,32086.378936,1.82364880678336
-c15,1145.5648,6358.145,3996.263,3610.731,139.46418,6007.756,352.195669,3324.8076,357.330128,3427.0759,89.882488,80.061262,69.928921,3.135222,0.538289,28962.879459,2.0203200694807
-c16,1108.984,6756.043,1949.97,3356.913,139.46418,313.098433,352.195669,1683.0934,357.330128,277.897139,1299.926,80.061262,69.928921,2.570366,1.812953,17749.288451,3.29671167804094
-c17,1145.5648,6358.145,3996.263,3610.731,3586.141,3197.8545,272.76268,132.098682,3203.344,6559.316,80.061262,1273.3007,80.061262,2.570366,10.00372,33508.217972,1.7462667431992
-c18,1108.984,472.88204,1949.97,7209.476,179.180675,272.76268,3366.9575,1683.0934,3203.344,3427.0759,1299.926,1273.3007,80.061262,3.135222,0.538289,25530.687668,2.29191972322584
-c19,1108.984,6756.043,1949.97,3356.913,3586.141,3197.8545,352.195669,3324.8076,277.897139,3427.0759,1299.926,80.061262,80.061262,57.16078,0.532687,28855.623799,2.02782955058642
-c20,1108.984,6756.043,1949.97,7209.476,179.180675,6007.756,352.195669,3324.8076,357.330128,6493.6098,80.061262,89.882488,80.061262,2.570366,10.00372,34001.93197,1.72091064479913
-c21,1108.984,6756.043,3996.263,3610.731,3586.141,3197.8545,272.76268,171.815177,357.330128,6493.6098,80.061262,1273.3007,2365.9264,2.857198,0.538289,33274.218134,1.75854730625074
-c22,1108.984,7471.505,2073.98,296.182152,159.632057,272.76268,3366.9575,1683.0934,357.330128,3427.0759,1299.926,2388.8133,80.061262,2.570366,10.00372,23998.877465,2.43820931560305
-c23,1145.5648,6358.145,1949.97,7209.476,1802.5061,3053.2545,352.195669,171.815177,3203.344,3427.0759,1299.926,69.928921,80.061262,69.37799,16.49564,30209.136959,1.93697313262271
-c24,1108.984,6756.043,1949.97,7209.476,1802.5061,3053.2545,352.195669,171.815177,277.897139,3427.0759,1299.926,80.061262,80.061262,69.37799,0.532687,27639.176686,2.11707777319327
-c25,1145.5648,6358.145,1949.97,3356.913,159.632057,272.76268,352.195669,1683.0934,357.330128,277.897139,1299.926,1273.3007,1286.6509,57.16078,1.8312957,19832.3735487,2.95044294139928
-c26,1108.984,6756.043,1949.97,3356.913,1731.6018,6130.47,272.76268,152.266559,3203.344,3427.0759,1299.926,2388.8133,80.061262,2.857198,10.00372,31871.092419,1.83596739923856
-c27,1108.984,6756.043,1949.97,3356.913,1731.6018,6130.47,272.76268,152.266559,3203.344,3427.0759,1299.926,2388.8133,89.882488,69.37799,16.49564,31953.926357,1.83120803390913
-c28,1108.984,6756.043,1949.97,7209.476,3475.891,3197.8545,352.195669,171.815177,3203.344,3427.0759,1299.926,80.061262,80.061262,69.37799,0.543719,32382.619479,1.80696582314009
-c29,1108.984,6756.043,1949.97,3356.913,1731.6018,6130.47,272.76268,171.815177,3203.344,3427.0759,1299.926,2388.8133,80.061262,2.857198,10.00372,31890.641037,1.83484197108571
-c30,1108.984,7471.505,3792.343,3610.731,179.180675,352.195669,3366.9575,1683.0934,357.330128,357.330128,89.882488,2376.5679,80.061262,3.135222,0.532687,24829.830059,2.35661244834534
-c31,1145.5648,7908.45,3792.343,375.615141,3475.891,3197.8545,6654.867,3324.8076,6053.9348,318.232892,2479.9481,89.882488,1286.6509,3.135222,0.538289,40107.715732,1.45892842883649
-c32,1145.5648,7908.45,3792.343,375.615141,1731.6018,272.76268,3366.9575,3383.8134,3293.4507,277.897139,1299.926,80.061262,80.061262,3.135222,0.543719,27012.183625,2.16621830499563
-c33,1145.5648,7908.45,2073.98,375.615141,3475.891,3197.8545,6654.867,3324.8076,6053.9348,318.232892,2479.9481,89.882488,1286.6509,3.135222,0.538289,38389.352732,1.52423217705886
-c34,1108.984,6756.043,1949.97,3356.913,159.632057,272.76268,6530.41,3324.8076,357.330128,357.330128,1299.926,89.882488,89.882488,2.857198,1.812953,25658.54372,2.28049912939292
-c35,1145.5648,7908.45,2073.98,3356.913,139.46418,6007.756,272.76268,152.266559,6053.9348,6493.6098,1337.2918,80.061262,1286.6509,2.570366,1.812953,36313.0891,1.61138278599002
-c36,1145.5648,6358.145,1949.97,3356.913,1731.6018,3053.2545,6654.867,132.098682,3203.344,6559.316,80.061262,1273.3007,89.882488,2.857198,10.00372,35601.18015,1.64360525218823
-c37,1145.5648,7908.45,2073.98,7209.476,3475.891,313.098433,313.098433,1683.0934,357.330128,277.897139,1299.926,2388.8133,80.061262,3.135222,0.532687,28530.347804,2.05094894180369
-c38,1108.984,7471.505,2073.98,7209.476,1802.5061,352.195669,352.195669,1683.0934,318.232892,357.330128,1299.926,2388.8133,89.882488,2.570366,1.812953,26512.503965,2.20704489849552
-c39,1108.984,7471.505,3792.343,3610.731,159.632057,272.76268,6530.41,3324.8076,318.232892,3427.0759,80.061262,2376.5679,80.061262,69.37799,1.812953,32624.365496,1.79357623586018
-c40,1145.5648,7908.45,3792.343,3610.731,1731.6018,313.098433,313.098433,3324.8076,357.330128,357.330128,1299.926,69.928921,1286.6509,2.857198,0.543719,25514.26206,2.29339521856665
-c41,1108.984,7471.505,2073.98,7209.476,3475.891,3197.8545,3366.9575,171.815177,357.330128,3427.0759,1299.926,1273.3007,69.928921,3.135222,10.00372,34517.163768,1.6952229061104
-c42,1145.5648,7908.45,2073.98,7209.476,1802.5061,313.098433,313.098433,1683.0934,357.330128,357.330128,1299.926,2388.8133,1319.7884,3.135222,1.812953,28177.403297,2.07663871715127
-c43,1108.984,7471.505,2073.98,7209.476,179.180675,272.76268,3366.9575,1683.0934,357.330128,6493.6098,1337.2918,2388.8133,80.061262,69.37799,16.49564,34108.919175,1.71551277750063
-c44,1108.984,7471.505,2073.98,7209.476,1802.5061,352.195669,3366.9575,1683.0934,318.232892,357.330128,89.882488,89.882488,89.882488,3.135222,10.00372,26027.047095,2.2482107326505
-c45,1108.984,6756.043,1949.97,7209.476,1802.5061,352.195669,313.098433,1683.0934,357.330128,3427.0759,89.882488,80.061262,80.061262,3.135222,10.00372,25222.916584,2.31988582351851
-c46,1108.984,7471.505,2073.98,336.517905,3475.891,272.76268,3366.9575,3383.8134,3293.4507,3427.0759,1299.926,2388.8133,80.061262,3.135222,10.00372,31992.877589,1.82897854365626
-c47,1145.5648,7908.45,2073.98,3356.913,3586.141,352.195669,272.76268,3324.8076,357.330128,3427.0759,2492.7759,1307.6007,1286.6509,2.857198,0.538289,30895.643764,1.89393323863309
-c48,1108.984,7471.505,2073.98,7209.476,3475.891,272.76268,3366.9575,1683.0934,357.330128,318.232892,1299.926,89.882488,80.061262,3.135222,0.538289,28811.755861,2.03091706462479
-c49,1145.5648,7908.45,2073.98,7209.476,1802.5061,313.098433,313.098433,3324.8076,6053.9348,357.330128,1299.926,69.928921,1286.6509,57.16078,10.00372,33225.916615,1.76110375960172
-c50,1108.984,6756.043,1949.97,7209.476,159.632057,6007.756,3519.4585,1683.0934,357.330128,357.330128,1299.926,2388.8133,80.061262,3.135222,10.00372,32891.012717,1.77903572530781
-c51,1108.984,6756.043,1949.97,7209.476,3475.891,272.76268,3366.9575,1683.0934,357.330128,357.330128,1299.926,2388.8133,80.061262,57.16078,10.00372,30373.802898,1.92647219206804
-c16,17749.288451
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,188.762345,0,0,0,151.959867,0,0,0,0,0,38.454652,38.454652,0,0,417.631516,0
-c2,0,188.762345,0,0,0,151.959867,0,0,153.127903,153.127903,0,0,0,1.156348,0,648.134366,0
-c3,0,188.762345,0,0,0,151.959867,0,0,0,0,0,0,38.454652,1.156348,0,380.333212,0
-c4,0,188.762345,0,0,76.708720,151.959867,0,0,153.127903,0,0,38.454652,38.454652,1.156348,0,648.624487,0
-c5,0,0,0,157.595858,76.708720,151.959867,0,0,153.127903,153.127903,38.454652,0,0,1.156348,0.102999,732.23425,0
-c6,0,0,0,0,0,0,0,74.930729,153.127903,153.127903,38.454652,38.454652,38.454652,0,0,496.550491,0
-c7,0,188.762345,0,0,76.708720,151.959867,0,0,153.127903,0,0,0,38.454652,1.156348,0.102999,610.272834,0
-c8,0,0,0,157.595858,76.708720,0,0,0,153.127903,153.127903,0,38.454652,38.454652,0,0.102999,617.572687,0
-c9,0,188.762345,0,0,76.708720,151.959867,0,0,153.127903,0,0,0,38.454652,1.156348,0,610.169835,0
-c10,0,188.762345,0,0,76.708720,151.959867,0,0,153.127903,0,0,0,38.454652,1.156348,0,610.169835,0
-c11,0,188.762345,0,0,76.708720,0,0,0,0,153.127903,0,0,38.454652,1.156348,0,458.209968,0
-c12,0,0,0,0,76.708720,151.959867,0,74.930729,153.127903,0,0,0,0,1.156348,0,457.883567,0
-c13,0,0,0,157.595858,76.708720,0,0,0,153.127903,0,38.454652,0,38.454652,1.156348,0,465.498133,0
-c14,0,0,0,0,0,0,0,74.930729,153.127903,153.127903,0,38.454652,38.454652,1.156348,0.102999,459.355186,0
-c15,0,0,0,0,76.708720,0,151.959867,0,153.127903,0,38.454652,38.454652,38.454652,1.156348,0.102999,498.419793,0
-c16,0,0,0,0,76.708720,151.959867,151.959867,0,153.127903,153.127903,0,38.454652,38.454652,1.156348,0,764.949912,0
-c17,0,0,0,0,0,0,151.959867,74.930729,0,0,38.454652,0,38.454652,1.156348,0,304.956248,0
-c18,0,188.762345,0,0,76.708720,151.959867,0,0,0,0,0,0,38.454652,1.156348,0.102999,457.144931,0
-c19,0,0,0,0,0,0,151.959867,0,153.127903,0,0,38.454652,38.454652,0,0.102999,382.100073,0
-c20,0,0,0,0,76.708720,0,151.959867,0,153.127903,0,38.454652,38.454652,38.454652,1.156348,0,498.316794,0
-c21,0,0,0,0,0,0,151.959867,74.930729,153.127903,0,38.454652,0,0,1.156348,0.102999,419.732498,0
-c22,0,0,0,157.595858,76.708720,151.959867,0,0,153.127903,0,0,0,38.454652,1.156348,0,579.003348,0
-c23,0,0,0,0,0,0,151.959867,74.930729,0,0,0,38.454652,38.454652,0,0,303.7999,0
-c24,0,0,0,0,0,0,151.959867,74.930729,153.127903,0,0,38.454652,38.454652,0,0.102999,457.030802,0
-c25,0,0,0,0,76.708720,151.959867,151.959867,0,153.127903,153.127903,0,0,0,0,0,686.88426,0
-c26,0,0,0,0,0,0,151.959867,74.930729,0,0,0,0,38.454652,1.156348,0,266.501596,0
-c27,0,0,0,0,0,0,151.959867,74.930729,0,0,0,0,38.454652,0,0,265.345248,0
-c28,0,0,0,0,0,0,151.959867,74.930729,0,0,0,38.454652,38.454652,0,0.102999,303.902899,0
-c29,0,0,0,0,0,0,151.959867,74.930729,0,0,0,0,38.454652,1.156348,0,266.501596,0
-c30,0,0,0,0,76.708720,151.959867,0,0,153.127903,153.127903,38.454652,0,38.454652,1.156348,0.102999,613.093044,0
-c31,0,0,0,157.595858,0,0,0,0,0,153.127903,0,38.454652,0,1.156348,0.102999,350.43776,0
-c32,0,0,0,157.595858,0,151.959867,0,0,0,153.127903,0,38.454652,38.454652,1.156348,0.102999,540.852279,0
-c33,0,0,0,157.595858,0,0,0,0,0,153.127903,0,38.454652,0,1.156348,0.102999,350.43776,0
-c34,0,0,0,0,76.708720,151.959867,0,0,153.127903,153.127903,0,38.454652,38.454652,1.156348,0,612.990045,0
-c35,0,0,0,0,76.708720,0,151.959867,74.930729,0,0,0,38.454652,0,1.156348,0,343.210316,0
-c36,0,0,0,0,0,0,0,74.930729,0,0,38.454652,0,38.454652,1.156348,0,152.996381,0
-c37,0,0,0,0,0,151.959867,151.959867,0,153.127903,153.127903,0,0,38.454652,1.156348,0.102999,649.889539,0
-c38,0,0,0,0,0,151.959867,151.959867,0,153.127903,153.127903,0,0,38.454652,1.156348,0,649.78654,0
-c39,0,0,0,0,76.708720,151.959867,0,0,153.127903,0,38.454652,0,38.454652,0,0,458.705794,0
-c40,0,0,0,0,0,151.959867,151.959867,0,153.127903,153.127903,0,38.454652,0,1.156348,0.102999,649.889539,0
-c41,0,0,0,0,0,0,0,74.930729,153.127903,0,0,0,38.454652,1.156348,0,267.669632,0
-c42,0,0,0,0,0,151.959867,151.959867,0,153.127903,153.127903,0,0,0,1.156348,0,611.331888,0
-c43,0,0,0,0,76.708720,151.959867,0,0,153.127903,0,0,0,38.454652,0,0,420.251142,0
-c44,0,0,0,0,0,151.959867,0,0,153.127903,153.127903,38.454652,38.454652,38.454652,1.156348,0,574.735977,0
-c45,0,0,0,0,0,151.959867,151.959867,0,153.127903,0,38.454652,38.454652,38.454652,1.156348,0,573.567941,0
-c46,0,0,0,157.595858,0,151.959867,0,0,0,0,0,0,38.454652,1.156348,0,349.166725,0
-c47,0,0,0,0,0,151.959867,151.959867,0,153.127903,0,0,0,0,1.156348,0.102999,458.306984,0
-c48,0,0,0,0,0,151.959867,0,0,153.127903,153.127903,0,38.454652,38.454652,1.156348,0.102999,536.384324,0
-c49,0,0,0,0,0,151.959867,151.959867,0,0,153.127903,0,38.454652,0,0,0,495.502289,0
-c50,0,0,0,0,76.708720,0,0,0,153.127903,153.127903,0,0,38.454652,1.156348,0,422.575526,0
-c51,0,0,0,0,0,151.959867,0,0,153.127903,153.127903,0,0,38.454652,0,0,496.670325,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,109.549158,0,0,0,43.122319,0,0,0,0,0,11.960929,11.960929,0,0,176.593335,0
-c2,0,109.549158,0,0,0,43.122319,0,0,47.088742,47.088742,0,0,0,0.861623,0,247.710584,0
-c3,0,109.549158,0,0,0,43.122319,0,0,0,0,0,0,11.960929,0.861623,0,165.494029,0
-c4,0,109.549158,0,0,23.915213,43.122319,0,0,47.088742,0,0,11.960929,11.960929,0.861623,0,248.458913,0
-c5,0,0,0,60.905800,23.915213,43.122319,0,0,47.088742,47.088742,11.960929,0,0,0.861623,0.418899,235.362267,0
-c6,0,0,0,0,0,0,0,18.327706,47.088742,47.088742,11.960929,11.960929,11.960929,0,0,148.387977,0
-c7,0,109.549158,0,0,23.915213,43.122319,0,0,47.088742,0,0,0,11.960929,0.861623,0.418899,236.916883,0
-c8,0,0,0,60.905800,23.915213,0,0,0,47.088742,47.088742,0,11.960929,11.960929,0,0.418899,203.339254,0
-c9,0,109.549158,0,0,23.915213,43.122319,0,0,47.088742,0,0,0,11.960929,0.861623,0,236.497984,0
-c10,0,109.549158,0,0,23.915213,43.122319,0,0,47.088742,0,0,0,11.960929,0.861623,0,236.497984,0
-c11,0,109.549158,0,0,23.915213,0,0,0,0,47.088742,0,0,11.960929,0.861623,0,193.375665,0
-c12,0,0,0,0,23.915213,43.122319,0,18.327706,47.088742,0,0,0,0,0.861623,0,133.315603,0
-c13,0,0,0,60.905800,23.915213,0,0,0,47.088742,0,11.960929,0,11.960929,0.861623,0,156.693236,0
-c14,0,0,0,0,0,0,0,18.327706,47.088742,47.088742,0,11.960929,11.960929,0.861623,0.418899,137.70757,0
-c15,0,0,0,0,23.915213,0,43.122319,0,47.088742,0,11.960929,11.960929,11.960929,0.861623,0.418899,151.289583,0
-c16,0,0,0,0,23.915213,43.122319,43.122319,0,47.088742,47.088742,0,11.960929,11.960929,0.861623,0,229.120816,0
-c17,0,0,0,0,0,0,43.122319,18.327706,0,0,11.960929,0,11.960929,0.861623,0,86.233506,0
-c18,0,109.549158,0,0,23.915213,43.122319,0,0,0,0,0,0,11.960929,0.861623,0.418899,189.828141,0
-c19,0,0,0,0,0,0,43.122319,0,47.088742,0,0,11.960929,11.960929,0,0.418899,114.551818,0
-c20,0,0,0,0,23.915213,0,43.122319,0,47.088742,0,11.960929,11.960929,11.960929,0.861623,0,150.870684,0
-c21,0,0,0,0,0,0,43.122319,18.327706,47.088742,0,11.960929,0,0,0.861623,0.418899,121.780218,0
-c22,0,0,0,60.905800,23.915213,43.122319,0,0,47.088742,0,0,0,11.960929,0.861623,0,187.854626,0
-c23,0,0,0,0,0,0,43.122319,18.327706,0,0,0,11.960929,11.960929,0,0,85.371883,0
-c24,0,0,0,0,0,0,43.122319,18.327706,47.088742,0,0,11.960929,11.960929,0,0.418899,132.879524,0
-c25,0,0,0,0,23.915213,43.122319,43.122319,0,47.088742,47.088742,0,0,0,0,0,204.337335,0
-c26,0,0,0,0,0,0,43.122319,18.327706,0,0,0,0,11.960929,0.861623,0,74.272577,0
-c27,0,0,0,0,0,0,43.122319,18.327706,0,0,0,0,11.960929,0,0,73.410954,0
-c28,0,0,0,0,0,0,43.122319,18.327706,0,0,0,11.960929,11.960929,0,0.418899,85.790782,0
-c29,0,0,0,0,0,0,43.122319,18.327706,0,0,0,0,11.960929,0.861623,0,74.272577,0
-c30,0,0,0,0,23.915213,43.122319,0,0,47.088742,47.088742,11.960929,0,11.960929,0.861623,0.418899,186.417396,0
-c31,0,0,0,60.905800,0,0,0,0,0,47.088742,0,11.960929,0,0.861623,0.418899,121.235993,0
-c32,0,0,0,60.905800,0,43.122319,0,0,0,47.088742,0,11.960929,11.960929,0.861623,0.418899,176.319241,0
-c33,0,0,0,60.905800,0,0,0,0,0,47.088742,0,11.960929,0,0.861623,0.418899,121.235993,0
-c34,0,0,0,0,23.915213,43.122319,0,0,47.088742,47.088742,0,11.960929,11.960929,0.861623,0,185.998497,0
-c35,0,0,0,0,23.915213,0,43.122319,18.327706,0,0,0,11.960929,0,0.861623,0,98.18779,0
-c36,0,0,0,0,0,0,0,18.327706,0,0,11.960929,0,11.960929,0.861623,0,43.111187,0
-c37,0,0,0,0,0,43.122319,43.122319,0,47.088742,47.088742,0,0,11.960929,0.861623,0.418899,193.663573,0
-c38,0,0,0,0,0,43.122319,43.122319,0,47.088742,47.088742,0,0,11.960929,0.861623,0,193.244674,0
-c39,0,0,0,0,23.915213,43.122319,0,0,47.088742,0,11.960929,0,11.960929,0,0,138.048132,0
-c40,0,0,0,0,0,43.122319,43.122319,0,47.088742,47.088742,0,11.960929,0,0.861623,0.418899,193.663573,0
-c41,0,0,0,0,0,0,0,18.327706,47.088742,0,0,0,11.960929,0.861623,0,78.239,0
-c42,0,0,0,0,0,43.122319,43.122319,0,47.088742,47.088742,0,0,0,0.861623,0,181.283745,0
-c43,0,0,0,0,23.915213,43.122319,0,0,47.088742,0,0,0,11.960929,0,0,126.087203,0
-c44,0,0,0,0,0,43.122319,0,0,47.088742,47.088742,11.960929,11.960929,11.960929,0.861623,0,174.044213,0
-c45,0,0,0,0,0,43.122319,43.122319,0,47.088742,0,11.960929,11.960929,11.960929,0.861623,0,170.07779,0
-c46,0,0,0,60.905800,0,43.122319,0,0,0,0,0,0,11.960929,0.861623,0,116.850671,0
-c47,0,0,0,0,0,43.122319,43.122319,0,47.088742,0,0,0,0,0.861623,0.418899,134.613902,0
-c48,0,0,0,0,0,43.122319,0,0,47.088742,47.088742,0,11.960929,11.960929,0.861623,0.418899,162.502183,0
-c49,0,0,0,0,0,43.122319,43.122319,0,0,47.088742,0,11.960929,0,0,0,145.294309,0
-c50,0,0,0,0,23.915213,0,0,0,47.088742,47.088742,0,0,11.960929,0.861623,0,130.915249,0
-c51,0,0,0,0,0,43.122319,0,0,47.088742,47.088742,0,0,11.960929,0,0,149.260732,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,32.936441,0,0,0,18.163317,0,0,0,0,0,4.848960,4.848960,0,0,60.797678,0
-c2,0,32.936441,0,0,0,18.163317,0,0,19.033555,19.033555,0,0,0,0.281905,0,89.448773,0
-c3,0,32.936441,0,0,0,18.163317,0,0,0,0,0,0,4.848960,0.281905,0,56.230623,0
-c4,0,32.936441,0,0,9.597736,18.163317,0,0,19.033555,0,0,4.848960,4.848960,0.281905,0,89.710874,0
-c5,0,0,0,21.882908,9.597736,18.163317,0,0,19.033555,19.033555,4.848960,0,0,0.281905,0.089271,92.931207,0
-c6,0,0,0,0,0,0,0,8.429972,19.033555,19.033555,4.848960,4.848960,4.848960,0,0,61.043962,0
-c7,0,32.936441,0,0,9.597736,18.163317,0,0,19.033555,0,0,0,4.848960,0.281905,0.089271,84.951185,0
-c8,0,0,0,21.882908,9.597736,0,0,0,19.033555,19.033555,0,4.848960,4.848960,0,0.089271,79.334945,0
-c9,0,32.936441,0,0,9.597736,18.163317,0,0,19.033555,0,0,0,4.848960,0.281905,0,84.861914,0
-c10,0,32.936441,0,0,9.597736,18.163317,0,0,19.033555,0,0,0,4.848960,0.281905,0,84.861914,0
-c11,0,32.936441,0,0,9.597736,0,0,0,0,19.033555,0,0,4.848960,0.281905,0,66.698597,0
-c12,0,0,0,0,9.597736,18.163317,0,8.429972,19.033555,0,0,0,0,0.281905,0,55.506485,0
-c13,0,0,0,21.882908,9.597736,0,0,0,19.033555,0,4.848960,0,4.848960,0.281905,0,60.494024,0
-c14,0,0,0,0,0,0,0,8.429972,19.033555,19.033555,0,4.848960,4.848960,0.281905,0.089271,56.566178,0
-c15,0,0,0,0,9.597736,0,18.163317,0,19.033555,0,4.848960,4.848960,4.848960,0.281905,0.089271,61.712664,0
-c16,0,0,0,0,9.597736,18.163317,18.163317,0,19.033555,19.033555,0,4.848960,4.848960,0.281905,0,93.971305,0
-c17,0,0,0,0,0,0,18.163317,8.429972,0,0,4.848960,0,4.848960,0.281905,0,36.573114,0
-c18,0,32.936441,0,0,9.597736,18.163317,0,0,0,0,0,0,4.848960,0.281905,0.089271,65.91763,0
-c19,0,0,0,0,0,0,18.163317,0,19.033555,0,0,4.848960,4.848960,0,0.089271,46.984063,0
-c20,0,0,0,0,9.597736,0,18.163317,0,19.033555,0,4.848960,4.848960,4.848960,0.281905,0,61.623393,0
-c21,0,0,0,0,0,0,18.163317,8.429972,19.033555,0,4.848960,0,0,0.281905,0.089271,50.84698,0
-c22,0,0,0,21.882908,9.597736,18.163317,0,0,19.033555,0,0,0,4.848960,0.281905,0,73.808381,0
-c23,0,0,0,0,0,0,18.163317,8.429972,0,0,0,4.848960,4.848960,0,0,36.291209,0
-c24,0,0,0,0,0,0,18.163317,8.429972,19.033555,0,0,4.848960,4.848960,0,0.089271,55.414035,0
-c25,0,0,0,0,9.597736,18.163317,18.163317,0,19.033555,19.033555,0,0,0,0,0,83.99148,0
-c26,0,0,0,0,0,0,18.163317,8.429972,0,0,0,0,4.848960,0.281905,0,31.724154,0
-c27,0,0,0,0,0,0,18.163317,8.429972,0,0,0,0,4.848960,0,0,31.442249,0
-c28,0,0,0,0,0,0,18.163317,8.429972,0,0,0,4.848960,4.848960,0,0.089271,36.38048,0
-c29,0,0,0,0,0,0,18.163317,8.429972,0,0,0,0,4.848960,0.281905,0,31.724154,0
-c30,0,0,0,0,9.597736,18.163317,0,0,19.033555,19.033555,4.848960,0,4.848960,0.281905,0.089271,75.897259,0
-c31,0,0,0,21.882908,0,0,0,0,0,19.033555,0,4.848960,0,0.281905,0.089271,46.136599,0
-c32,0,0,0,21.882908,0,18.163317,0,0,0,19.033555,0,4.848960,4.848960,0.281905,0.089271,69.148876,0
-c33,0,0,0,21.882908,0,0,0,0,0,19.033555,0,4.848960,0,0.281905,0.089271,46.136599,0
-c34,0,0,0,0,9.597736,18.163317,0,0,19.033555,19.033555,0,4.848960,4.848960,0.281905,0,75.807988,0
-c35,0,0,0,0,9.597736,0,18.163317,8.429972,0,0,0,4.848960,0,0.281905,0,41.32189,0
-c36,0,0,0,0,0,0,0,8.429972,0,0,4.848960,0,4.848960,0.281905,0,18.409797,0
-c37,0,0,0,0,0,18.163317,18.163317,0,19.033555,19.033555,0,0,4.848960,0.281905,0.089271,79.61388,0
-c38,0,0,0,0,0,18.163317,18.163317,0,19.033555,19.033555,0,0,4.848960,0.281905,0,79.524609,0
-c39,0,0,0,0,9.597736,18.163317,0,0,19.033555,0,4.848960,0,4.848960,0,0,56.492528,0
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diff --git a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_results1.csv b/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_results1.csv
deleted file mode 100644
index a03d6b7ac070f7afab0e1cd4a25ba2bd3913415d..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_results1.csv
+++ /dev/null
@@ -1,187 +0,0 @@
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-c3,1108.984,6358.145,1949.97,3356.913,1731.6018,3053.2545,3366.9575,1683.0934,3203.344,157.113483,1299.926,39.466907,1286.6509,57.16078,10.00372,28662.58499,2.041486720729
-c4,1108.984,6358.145,1949.97,3356.913,1731.6018,3053.2545,3366.9575,1683.0934,3203.344,157.113483,1299.926,39.466907,1286.6509,57.16078,10.00372,28662.58499,2.041486720729
-c5,1108.984,6358.145,1949.97,3356.913,1731.6018,3053.2545,3366.9575,1683.0934,3203.344,3427.0759,1299.926,1273.3007,1286.6509,57.16078,10.00372,33166.3812,1.76426503433463
-c6,1108.984,6358.145,1949.97,3356.913,1731.6018,157.113483,157.113483,78.556742,3203.344,3427.0759,39.466907,39.466907,1286.6509,57.16078,10.00372,22961.566622,2.54835776458303
-c7,1108.984,6358.145,1949.97,3356.913,1731.6018,3053.2545,3366.9575,1683.0934,3203.344,157.113483,1299.926,39.466907,1286.6509,57.16078,10.00372,28662.58499,2.041486720729
-c8,1108.984,6358.145,1949.97,3356.913,1731.6018,3053.2545,3366.9575,1683.0934,3203.344,157.113483,1299.926,39.466907,1286.6509,57.16078,10.00372,28662.58499,2.041486720729
-c9,1108.984,6358.145,1949.97,3356.913,1731.6018,3053.2545,3366.9575,1683.0934,3203.344,3427.0759,1299.926,1273.3007,1286.6509,57.16078,10.00372,33166.3812,1.76426503433463
-c10,1108.984,6358.145,1949.97,3356.913,1731.6018,3053.2545,3366.9575,1683.0934,3203.344,157.113483,1299.926,39.466907,1286.6509,57.16078,10.00372,28662.58499,2.041486720729
-c11,1108.984,6358.145,1949.97,3356.913,1731.6018,157.113483,157.113483,78.556742,3203.344,3427.0759,39.466907,39.466907,1286.6509,57.16078,10.00372,22961.566622,2.54835776458303
-c12,1108.984,6358.145,1949.97,3356.913,1731.6018,3053.2545,3366.9575,1683.0934,3203.344,157.113483,1299.926,39.466907,1286.6509,57.16078,10.00372,28662.58499,2.041486720729
-c2,22961.566622
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,352.4661,1550.099,701.7255,1159.5933,558.563,925.6073,971.2723,490.07014,857.91333,883.36663,344.11179,344.22248,351.94343,12.419968,1.04789,9504.422158,0.999999989478582
-c1,352.4661,1610.85,426.5549,652.9992,333.7788,544.1687,590.3019,290.68457,527.54432,543.5573,208.37216,206.54856,210.33129,7.640322,1.669613,6507.467735,1.46054078160495
-c2,352.4661,1610.85,426.5549,652.9992,333.7788,82.574848,82.574848,41.518080,527.54432,543.5573,21.220352,21.220352,210.33129,7.640322,1.669613,4916.500325,1.93316817581685
-c3,352.4661,1610.85,426.5549,652.9992,333.7788,544.1687,590.3019,290.68457,527.54432,83.036160,208.37216,21.220352,210.33129,7.640322,1.669613,5861.618387,1.6214672072362
-c4,352.4661,1610.85,426.5549,652.9992,333.7788,544.1687,590.3019,290.68457,527.54432,83.036160,208.37216,21.220352,210.33129,7.640322,1.669613,5861.618387,1.6214672072362
-c5,352.4661,1610.85,426.5549,652.9992,333.7788,544.1687,590.3019,290.68457,527.54432,543.5573,208.37216,206.54856,210.33129,7.640322,1.669613,6507.467735,1.46054078160495
-c6,352.4661,1610.85,426.5549,652.9992,333.7788,82.574848,82.574848,41.518080,527.54432,543.5573,21.220352,21.220352,210.33129,7.640322,1.669613,4916.500325,1.93316817581685
-c7,352.4661,1610.85,426.5549,652.9992,333.7788,544.1687,590.3019,290.68457,527.54432,83.036160,208.37216,21.220352,210.33129,7.640322,1.669613,5861.618387,1.6214672072362
-c8,352.4661,1610.85,426.5549,652.9992,333.7788,544.1687,590.3019,290.68457,527.54432,83.036160,208.37216,21.220352,210.33129,7.640322,1.669613,5861.618387,1.6214672072362
-c9,352.4661,1610.85,426.5549,652.9992,333.7788,544.1687,590.3019,290.68457,527.54432,543.5573,208.37216,206.54856,210.33129,7.640322,1.669613,6507.467735,1.46054078160495
-c10,352.4661,1610.85,426.5549,652.9992,333.7788,544.1687,590.3019,290.68457,527.54432,83.036160,208.37216,21.220352,210.33129,7.640322,1.669613,5861.618387,1.6214672072362
-c11,352.4661,1610.85,426.5549,652.9992,333.7788,82.574848,82.574848,41.518080,527.54432,543.5573,21.220352,21.220352,210.33129,7.640322,1.669613,4916.500325,1.93316817581685
-c12,352.4661,1610.85,426.5549,652.9992,333.7788,544.1687,590.3019,290.68457,527.54432,83.036160,208.37216,21.220352,210.33129,7.640322,1.669613,5861.618387,1.6214672072362
-c2,4916.500325
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,1108.984,7471.505,3792.343,6973.454,3475.891,6007.756,6530.41,3324.8076,6053.9348,6493.6098,2479.9481,2376.5679,2353.8847,69.37799,1.812953,58514.286843,0.999999998291016
-c1,1108.984,6756.043,1949.97,3356.913,1731.6018,3053.2545,3366.9575,1683.0934,3203.344,3427.0759,1299.926,1273.3007,1286.6509,57.16078,10.00372,33564.2792,1.74335001565191
-c2,1108.984,6756.043,1949.97,3356.913,1731.6018,352.195669,352.195669,171.815177,3203.344,3427.0759,89.882488,89.882488,1286.6509,57.16078,10.00372,23943.718591,2.44382619083286
-c3,1108.984,6756.043,1949.97,3356.913,1731.6018,3053.2545,3366.9575,1683.0934,3203.344,357.330128,1299.926,89.882488,1286.6509,57.16078,10.00372,29311.115216,1.99631730871254
-c4,1108.984,6756.043,1949.97,3356.913,1731.6018,3053.2545,3366.9575,1683.0934,3203.344,357.330128,1299.926,89.882488,1286.6509,57.16078,10.00372,29311.115216,1.99631730871254
-c5,1108.984,6756.043,1949.97,3356.913,1731.6018,3053.2545,3366.9575,1683.0934,3203.344,3427.0759,1299.926,1273.3007,1286.6509,57.16078,10.00372,33564.2792,1.74335001565191
-c6,1108.984,6756.043,1949.97,3356.913,1731.6018,352.195669,352.195669,171.815177,3203.344,3427.0759,89.882488,89.882488,1286.6509,57.16078,10.00372,23943.718591,2.44382619083286
-c7,1108.984,6756.043,1949.97,3356.913,1731.6018,3053.2545,3366.9575,1683.0934,3203.344,357.330128,1299.926,89.882488,1286.6509,57.16078,10.00372,29311.115216,1.99631730871254
-c8,1108.984,6756.043,1949.97,3356.913,1731.6018,3053.2545,3366.9575,1683.0934,3203.344,357.330128,1299.926,89.882488,1286.6509,57.16078,10.00372,29311.115216,1.99631730871254
-c9,1108.984,6756.043,1949.97,3356.913,1731.6018,3053.2545,3366.9575,1683.0934,3203.344,3427.0759,1299.926,1273.3007,1286.6509,57.16078,10.00372,33564.2792,1.74335001565191
-c10,1108.984,6756.043,1949.97,3356.913,1731.6018,3053.2545,3366.9575,1683.0934,3203.344,357.330128,1299.926,89.882488,1286.6509,57.16078,10.00372,29311.115216,1.99631730871254
-c11,1108.984,6756.043,1949.97,3356.913,1731.6018,352.195669,352.195669,171.815177,3203.344,3427.0759,89.882488,89.882488,1286.6509,57.16078,10.00372,23943.718591,2.44382619083286
-c12,1108.984,6756.043,1949.97,3356.913,1731.6018,3053.2545,3366.9575,1683.0934,3203.344,357.330128,1299.926,89.882488,1286.6509,57.16078,10.00372,29311.115216,1.99631730871254
-c2,23943.718591
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,151.959867,151.959867,74.930729,0,0,38.454652,38.454652,0,0,0,455.759767,0
-c3,0,0,0,0,0,0,0,0,0,153.127903,0,38.454652,0,0,0,191.582555,0
-c4,0,0,0,0,0,0,0,0,0,153.127903,0,38.454652,0,0,0,191.582555,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,151.959867,151.959867,74.930729,0,0,38.454652,38.454652,0,0,0,455.759767,0
-c7,0,0,0,0,0,0,0,0,0,153.127903,0,38.454652,0,0,0,191.582555,0
-c8,0,0,0,0,0,0,0,0,0,153.127903,0,38.454652,0,0,0,191.582555,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0,0,153.127903,0,38.454652,0,0,0,191.582555,0
-c11,0,0,0,0,0,151.959867,151.959867,74.930729,0,0,38.454652,38.454652,0,0,0,455.759767,0
-c12,0,0,0,0,0,0,0,0,0,153.127903,0,38.454652,0,0,0,191.582555,0
-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,43.122319,43.122319,18.327706,0,0,11.960929,11.960929,0,0,0,128.494202,0
-c3,0,0,0,0,0,0,0,0,0,47.088742,0,11.960929,0,0,0,59.049671,0
-c4,0,0,0,0,0,0,0,0,0,47.088742,0,11.960929,0,0,0,59.049671,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,43.122319,43.122319,18.327706,0,0,11.960929,11.960929,0,0,0,128.494202,0
-c7,0,0,0,0,0,0,0,0,0,47.088742,0,11.960929,0,0,0,59.049671,0
-c8,0,0,0,0,0,0,0,0,0,47.088742,0,11.960929,0,0,0,59.049671,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0,0,47.088742,0,11.960929,0,0,0,59.049671,0
-c11,0,0,0,0,0,43.122319,43.122319,18.327706,0,0,11.960929,11.960929,0,0,0,128.494202,0
-c12,0,0,0,0,0,0,0,0,0,47.088742,0,11.960929,0,0,0,59.049671,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,18.163317,18.163317,8.429972,0,0,4.848960,4.848960,0,0,0,54.454526,0
-c3,0,0,0,0,0,0,0,0,0,19.033555,0,4.848960,0,0,0,23.882515,0
-c4,0,0,0,0,0,0,0,0,0,19.033555,0,4.848960,0,0,0,23.882515,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,18.163317,18.163317,8.429972,0,0,4.848960,4.848960,0,0,0,54.454526,0
-c7,0,0,0,0,0,0,0,0,0,19.033555,0,4.848960,0,0,0,23.882515,0
-c8,0,0,0,0,0,0,0,0,0,19.033555,0,4.848960,0,0,0,23.882515,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0,0,19.033555,0,4.848960,0,0,0,23.882515,0
-c11,0,0,0,0,0,18.163317,18.163317,8.429972,0,0,4.848960,4.848960,0,0,0,54.454526,0
-c12,0,0,0,0,0,0,0,0,0,19.033555,0,4.848960,0,0,0,23.882515,0
-c0,0
-
-Patch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c0,0
-
-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,397.898,0,0,0,0,0,0,0,0,0,0,0,0,0,397.898,0
-c2,0,397.898,0,0,0,0,0,0,0,0,0,0,0,0,0,397.898,0
-c3,0,397.898,0,0,0,0,0,0,0,0,0,0,0,0,0,397.898,0
-c4,0,397.898,0,0,0,0,0,0,0,0,0,0,0,0,0,397.898,0
-c5,0,397.898,0,0,0,0,0,0,0,0,0,0,0,0,0,397.898,0
-c6,0,397.898,0,0,0,0,0,0,0,0,0,0,0,0,0,397.898,0
-c7,0,397.898,0,0,0,0,0,0,0,0,0,0,0,0,0,397.898,0
-c8,0,397.898,0,0,0,0,0,0,0,0,0,0,0,0,0,397.898,0
-c9,0,397.898,0,0,0,0,0,0,0,0,0,0,0,0,0,397.898,0
-c10,0,397.898,0,0,0,0,0,0,0,0,0,0,0,0,0,397.898,0
-c11,0,397.898,0,0,0,0,0,0,0,0,0,0,0,0,0,397.898,0
-c12,0,397.898,0,0,0,0,0,0,0,0,0,0,0,0,0,397.898,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,109.635,0,0,0,0,0,0,0,0,0,0,0,0,0,109.635,0
-c2,0,109.635,0,0,0,0,0,0,0,0,0,0,0,0,0,109.635,0
-c3,0,109.635,0,0,0,0,0,0,0,0,0,0,0,0,0,109.635,0
-c4,0,109.635,0,0,0,0,0,0,0,0,0,0,0,0,0,109.635,0
-c5,0,109.635,0,0,0,0,0,0,0,0,0,0,0,0,0,109.635,0
-c6,0,109.635,0,0,0,0,0,0,0,0,0,0,0,0,0,109.635,0
-c7,0,109.635,0,0,0,0,0,0,0,0,0,0,0,0,0,109.635,0
-c8,0,109.635,0,0,0,0,0,0,0,0,0,0,0,0,0,109.635,0
-c9,0,109.635,0,0,0,0,0,0,0,0,0,0,0,0,0,109.635,0
-c10,0,109.635,0,0,0,0,0,0,0,0,0,0,0,0,0,109.635,0
-c11,0,109.635,0,0,0,0,0,0,0,0,0,0,0,0,0,109.635,0
-c12,0,109.635,0,0,0,0,0,0,0,0,0,0,0,0,0,109.635,0
-c0,0
-
-Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,352.4661,1550.099,701.7255,1159.5933,558.563,925.6073,971.2723,490.07014,857.91333,883.36663,344.11179,344.22248,351.94343,12.419968,1.04789,9504.422158,0.999999989478582
-c1,352.4661,1720.485,426.5549,652.9992,333.7788,544.1687,590.3019,290.68457,527.54432,543.5573,208.37216,206.54856,210.33129,7.640322,1.669613,6617.102735,1.43634191503388
-c2,352.4661,1720.485,426.5549,652.9992,333.7788,100.738165,100.738165,49.948052,527.54432,543.5573,26.069312,26.069312,210.33129,7.640322,1.669613,5080.589851,1.8707319916911
-c3,352.4661,1720.485,426.5549,652.9992,333.7788,544.1687,590.3019,290.68457,527.54432,102.069715,208.37216,26.069312,210.33129,7.640322,1.669613,5995.135902,1.58535555404069
-c4,352.4661,1720.485,426.5549,652.9992,333.7788,544.1687,590.3019,290.68457,527.54432,102.069715,208.37216,26.069312,210.33129,7.640322,1.669613,5995.135902,1.58535555404069
-c5,352.4661,1720.485,426.5549,652.9992,333.7788,544.1687,590.3019,290.68457,527.54432,543.5573,208.37216,206.54856,210.33129,7.640322,1.669613,6617.102735,1.43634191503388
-c6,352.4661,1720.485,426.5549,652.9992,333.7788,100.738165,100.738165,49.948052,527.54432,543.5573,26.069312,26.069312,210.33129,7.640322,1.669613,5080.589851,1.8707319916911
-c7,352.4661,1720.485,426.5549,652.9992,333.7788,544.1687,590.3019,290.68457,527.54432,102.069715,208.37216,26.069312,210.33129,7.640322,1.669613,5995.135902,1.58535555404069
-c8,352.4661,1720.485,426.5549,652.9992,333.7788,544.1687,590.3019,290.68457,527.54432,102.069715,208.37216,26.069312,210.33129,7.640322,1.669613,5995.135902,1.58535555404069
-c9,352.4661,1720.485,426.5549,652.9992,333.7788,544.1687,590.3019,290.68457,527.54432,543.5573,208.37216,206.54856,210.33129,7.640322,1.669613,6617.102735,1.43634191503388
-c10,352.4661,1720.485,426.5549,652.9992,333.7788,544.1687,590.3019,290.68457,527.54432,102.069715,208.37216,26.069312,210.33129,7.640322,1.669613,5995.135902,1.58535555404069
-c11,352.4661,1720.485,426.5549,652.9992,333.7788,100.738165,100.738165,49.948052,527.54432,543.5573,26.069312,26.069312,210.33129,7.640322,1.669613,5080.589851,1.8707319916911
-c12,352.4661,1720.485,426.5549,652.9992,333.7788,544.1687,590.3019,290.68457,527.54432,102.069715,208.37216,26.069312,210.33129,7.640322,1.669613,5995.135902,1.58535555404069
-c2,5080.589851
-
-Unpatch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c0,0
-
diff --git a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_results2.csv b/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_results2.csv
deleted file mode 100644
index ef1faa1dec83dc5120ac7f2c1810f0cad3edbb1b..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_results2.csv
+++ /dev/null
@@ -1,187 +0,0 @@
-Compute Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,1108.984,7471.505,3792.343,6973.454,3475.891,6007.756,6530.41,3324.8076,6053.9348,6493.6098,2479.9481,2376.5679,2353.8847,69.37799,1.812953,58514.286843,0.999999998291016
-c1,1108.984,6358.145,1949.97,3356.913,1731.6018,3053.2545,3366.9575,78.556742,3203.344,157.113483,1299.926,1273.3007,39.466907,57.16078,10.00372,27044.698132,2.16361396755259
-c2,1108.984,6358.145,1949.97,157.113483,1731.6018,3053.2545,3366.9575,59.008124,118.016247,157.113483,1299.926,39.466907,29.645681,57.16078,10.00372,19496.367225,3.0012917723379
-c3,1108.984,6358.145,1949.97,3356.913,1731.6018,157.113483,3366.9575,1683.0934,157.113483,157.113483,1299.926,39.466907,39.466907,57.16078,10.00372,21473.029463,2.725013099401
-c4,1108.984,6358.145,1949.97,3356.913,1731.6018,157.113483,3366.9575,1683.0934,157.113483,157.113483,1299.926,1273.3007,39.466907,57.16078,10.00372,22706.863256,2.57694274746839
-c5,1108.984,6358.145,1949.97,3356.913,1731.6018,3053.2545,3366.9575,78.556742,3203.344,157.113483,1299.926,1273.3007,39.466907,57.16078,10.00372,27044.698132,2.16361396755259
-c6,1108.984,6358.145,1949.97,157.113483,1731.6018,3053.2545,3366.9575,59.008124,118.016247,157.113483,1299.926,39.466907,29.645681,57.16078,10.00372,19496.367225,3.0012917723379
-c7,1108.984,6358.145,1949.97,3356.913,1731.6018,157.113483,3366.9575,1683.0934,157.113483,157.113483,1299.926,39.466907,39.466907,57.16078,10.00372,21473.029463,2.725013099401
-c8,1108.984,6358.145,1949.97,3356.913,1731.6018,157.113483,3366.9575,1683.0934,157.113483,157.113483,1299.926,1273.3007,39.466907,57.16078,10.00372,22706.863256,2.57694274746839
-c9,1108.984,6358.145,1949.97,3356.913,1731.6018,3053.2545,3366.9575,78.556742,3203.344,157.113483,1299.926,1273.3007,39.466907,57.16078,10.00372,27044.698132,2.16361396755259
-c10,1108.984,6358.145,1949.97,157.113483,1731.6018,3053.2545,3366.9575,59.008124,118.016247,157.113483,1299.926,39.466907,29.645681,57.16078,10.00372,19496.367225,3.0012917723379
-c11,1108.984,6358.145,1949.97,3356.913,1731.6018,157.113483,3366.9575,1683.0934,157.113483,157.113483,1299.926,39.466907,39.466907,57.16078,10.00372,21473.029463,2.725013099401
-c12,1108.984,6358.145,1949.97,3356.913,1731.6018,157.113483,3366.9575,1683.0934,157.113483,157.113483,1299.926,1273.3007,39.466907,57.16078,10.00372,22706.863256,2.57694274746839
-c2,19496.367225
-
-Compute Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,352.4661,1550.099,701.7255,1159.5933,558.563,925.6073,971.2723,490.07014,857.91333,883.36663,344.11179,344.22248,351.94343,12.419968,1.04789,9504.422158,0.999999989478582
-c1,352.4661,1610.85,426.5549,652.9992,333.7788,544.1687,590.3019,41.518080,527.54432,83.036160,208.37216,206.54856,21.220352,7.640322,1.669613,5608.669167,1.69459486832672
-c2,352.4661,1610.85,426.5549,82.574848,333.7788,544.1687,590.3019,41.518080,83.036160,83.036160,208.37216,21.220352,21.220352,7.640322,1.669613,4408.408447,2.15597580321087
-c3,352.4661,1610.85,426.5549,652.9992,333.7788,82.574848,590.3019,290.68457,83.036160,83.036160,208.37216,21.220352,21.220352,7.640322,1.669613,4766.405437,1.99404395707003
-c4,352.4661,1610.85,426.5549,652.9992,333.7788,82.574848,590.3019,290.68457,83.036160,83.036160,208.37216,206.54856,21.220352,7.640322,1.669613,4951.733645,1.9194130071305
-c5,352.4661,1610.85,426.5549,652.9992,333.7788,544.1687,590.3019,41.518080,527.54432,83.036160,208.37216,206.54856,21.220352,7.640322,1.669613,5608.669167,1.69459486832672
-c6,352.4661,1610.85,426.5549,82.574848,333.7788,544.1687,590.3019,41.518080,83.036160,83.036160,208.37216,21.220352,21.220352,7.640322,1.669613,4408.408447,2.15597580321087
-c7,352.4661,1610.85,426.5549,652.9992,333.7788,82.574848,590.3019,290.68457,83.036160,83.036160,208.37216,21.220352,21.220352,7.640322,1.669613,4766.405437,1.99404395707003
-c8,352.4661,1610.85,426.5549,652.9992,333.7788,82.574848,590.3019,290.68457,83.036160,83.036160,208.37216,206.54856,21.220352,7.640322,1.669613,4951.733645,1.9194130071305
-c9,352.4661,1610.85,426.5549,652.9992,333.7788,544.1687,590.3019,41.518080,527.54432,83.036160,208.37216,206.54856,21.220352,7.640322,1.669613,5608.669167,1.69459486832672
-c10,352.4661,1610.85,426.5549,82.574848,333.7788,544.1687,590.3019,41.518080,83.036160,83.036160,208.37216,21.220352,21.220352,7.640322,1.669613,4408.408447,2.15597580321087
-c11,352.4661,1610.85,426.5549,652.9992,333.7788,82.574848,590.3019,290.68457,83.036160,83.036160,208.37216,21.220352,21.220352,7.640322,1.669613,4766.405437,1.99404395707003
-c12,352.4661,1610.85,426.5549,652.9992,333.7788,82.574848,590.3019,290.68457,83.036160,83.036160,208.37216,206.54856,21.220352,7.640322,1.669613,4951.733645,1.9194130071305
-c2,4408.408447
-
-Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,1108.984,7471.505,3792.343,6973.454,3475.891,6007.756,6530.41,3324.8076,6053.9348,6493.6098,2479.9481,2376.5679,2353.8847,69.37799,1.812953,58514.286843,0.999999998291016
-c1,1108.984,6756.043,1949.97,3356.913,1731.6018,3053.2545,3366.9575,171.815177,3203.344,357.330128,1299.926,1273.3007,89.882488,57.16078,10.00372,27786.486793,2.10585408181777
-c2,1108.984,6756.043,1949.97,375.615141,1731.6018,3053.2545,3366.9575,152.266559,318.232892,357.330128,1299.926,89.882488,80.061262,57.16078,10.00372,20707.28977,2.82578199321841
-c3,1108.984,6756.043,1949.97,3356.913,1731.6018,352.195669,3366.9575,1683.0934,357.330128,357.330128,1299.926,89.882488,89.882488,57.16078,10.00372,22567.274101,2.59288234466558
-c4,1108.984,6756.043,1949.97,3356.913,1731.6018,352.195669,3366.9575,1683.0934,357.330128,357.330128,1299.926,1273.3007,89.882488,57.16078,10.00372,23750.692313,2.46368761910167
-c5,1108.984,6756.043,1949.97,3356.913,1731.6018,3053.2545,3366.9575,171.815177,3203.344,357.330128,1299.926,1273.3007,89.882488,57.16078,10.00372,27786.486793,2.10585408181777
-c6,1108.984,6756.043,1949.97,375.615141,1731.6018,3053.2545,3366.9575,152.266559,318.232892,357.330128,1299.926,89.882488,80.061262,57.16078,10.00372,20707.28977,2.82578199321841
-c7,1108.984,6756.043,1949.97,3356.913,1731.6018,352.195669,3366.9575,1683.0934,357.330128,357.330128,1299.926,89.882488,89.882488,57.16078,10.00372,22567.274101,2.59288234466558
-c8,1108.984,6756.043,1949.97,3356.913,1731.6018,352.195669,3366.9575,1683.0934,357.330128,357.330128,1299.926,1273.3007,89.882488,57.16078,10.00372,23750.692313,2.46368761910167
-c9,1108.984,6756.043,1949.97,3356.913,1731.6018,3053.2545,3366.9575,171.815177,3203.344,357.330128,1299.926,1273.3007,89.882488,57.16078,10.00372,27786.486793,2.10585408181777
-c10,1108.984,6756.043,1949.97,375.615141,1731.6018,3053.2545,3366.9575,152.266559,318.232892,357.330128,1299.926,89.882488,80.061262,57.16078,10.00372,20707.28977,2.82578199321841
-c11,1108.984,6756.043,1949.97,3356.913,1731.6018,352.195669,3366.9575,1683.0934,357.330128,357.330128,1299.926,89.882488,89.882488,57.16078,10.00372,22567.274101,2.59288234466558
-c12,1108.984,6756.043,1949.97,3356.913,1731.6018,352.195669,3366.9575,1683.0934,357.330128,357.330128,1299.926,1273.3007,89.882488,57.16078,10.00372,23750.692313,2.46368761910167
-c2,20707.28977
-
-Leakage Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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-c3,0,0,0,0,0,151.959867,0,0,153.127903,153.127903,0,38.454652,38.454652,0,0,535.124977,0
-c4,0,0,0,0,0,151.959867,0,0,153.127903,153.127903,0,0,38.454652,0,0,496.670325,0
-c5,0,0,0,0,0,0,0,74.930729,0,153.127903,0,0,38.454652,0,0,266.513284,0
-c6,0,0,0,157.595858,0,0,0,74.930729,153.127903,153.127903,0,38.454652,38.454652,0,0,615.691697,0
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-c9,0,0,0,0,0,0,0,74.930729,0,153.127903,0,0,38.454652,0,0,266.513284,0
-c10,0,0,0,157.595858,0,0,0,74.930729,153.127903,153.127903,0,38.454652,38.454652,0,0,615.691697,0
-c11,0,0,0,0,0,151.959867,0,0,153.127903,153.127903,0,38.454652,38.454652,0,0,535.124977,0
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-c0,0
-
-Memory Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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-c4,0,0,0,0,0,43.122319,0,0,47.088742,47.088742,0,0,11.960929,0,0,149.260732,0
-c5,0,0,0,0,0,0,0,18.327706,0,47.088742,0,0,11.960929,0,0,77.377377,0
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-c7,0,0,0,0,0,43.122319,0,0,47.088742,47.088742,0,11.960929,11.960929,0,0,161.221661,0
-c8,0,0,0,0,0,43.122319,0,0,47.088742,47.088742,0,0,11.960929,0,0,149.260732,0
-c9,0,0,0,0,0,0,0,18.327706,0,47.088742,0,0,11.960929,0,0,77.377377,0
-c10,0,0,0,60.905800,0,0,0,18.327706,47.088742,47.088742,0,11.960929,11.960929,0,0,197.332848,0
-c11,0,0,0,0,0,43.122319,0,0,47.088742,47.088742,0,11.960929,11.960929,0,0,161.221661,0
-c12,0,0,0,0,0,43.122319,0,0,47.088742,47.088742,0,0,11.960929,0,0,149.260732,0
-c0,0
-
-Memory Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,8.429972,0,19.033555,0,0,4.848960,0,0,32.312487,0
-c2,0,0,0,21.882908,0,0,0,8.429972,19.033555,19.033555,0,4.848960,4.848960,0,0,78.07791,0
-c3,0,0,0,0,0,18.163317,0,0,19.033555,19.033555,0,4.848960,4.848960,0,0,65.928347,0
-c4,0,0,0,0,0,18.163317,0,0,19.033555,19.033555,0,0,4.848960,0,0,61.079387,0
-c5,0,0,0,0,0,0,0,8.429972,0,19.033555,0,0,4.848960,0,0,32.312487,0
-c6,0,0,0,21.882908,0,0,0,8.429972,19.033555,19.033555,0,4.848960,4.848960,0,0,78.07791,0
-c7,0,0,0,0,0,18.163317,0,0,19.033555,19.033555,0,4.848960,4.848960,0,0,65.928347,0
-c8,0,0,0,0,0,18.163317,0,0,19.033555,19.033555,0,0,4.848960,0,0,61.079387,0
-c9,0,0,0,0,0,0,0,8.429972,0,19.033555,0,0,4.848960,0,0,32.312487,0
-c10,0,0,0,21.882908,0,0,0,8.429972,19.033555,19.033555,0,4.848960,4.848960,0,0,78.07791,0
-c11,0,0,0,0,0,18.163317,0,0,19.033555,19.033555,0,4.848960,4.848960,0,0,65.928347,0
-c12,0,0,0,0,0,18.163317,0,0,19.033555,19.033555,0,0,4.848960,0,0,61.079387,0
-c0,0
-
-Patch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c0,0
-
-Quantization Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,397.898,0,0,0,0,0,0,0,0,0,0,0,0,0,397.898,0
-c2,0,397.898,0,0,0,0,0,0,0,0,0,0,0,0,0,397.898,0
-c3,0,397.898,0,0,0,0,0,0,0,0,0,0,0,0,0,397.898,0
-c4,0,397.898,0,0,0,0,0,0,0,0,0,0,0,0,0,397.898,0
-c5,0,397.898,0,0,0,0,0,0,0,0,0,0,0,0,0,397.898,0
-c6,0,397.898,0,0,0,0,0,0,0,0,0,0,0,0,0,397.898,0
-c7,0,397.898,0,0,0,0,0,0,0,0,0,0,0,0,0,397.898,0
-c8,0,397.898,0,0,0,0,0,0,0,0,0,0,0,0,0,397.898,0
-c9,0,397.898,0,0,0,0,0,0,0,0,0,0,0,0,0,397.898,0
-c10,0,397.898,0,0,0,0,0,0,0,0,0,0,0,0,0,397.898,0
-c11,0,397.898,0,0,0,0,0,0,0,0,0,0,0,0,0,397.898,0
-c12,0,397.898,0,0,0,0,0,0,0,0,0,0,0,0,0,397.898,0
-c0,0
-
-Quantization Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,109.635,0,0,0,0,0,0,0,0,0,0,0,0,0,109.635,0
-c2,0,109.635,0,0,0,0,0,0,0,0,0,0,0,0,0,109.635,0
-c3,0,109.635,0,0,0,0,0,0,0,0,0,0,0,0,0,109.635,0
-c4,0,109.635,0,0,0,0,0,0,0,0,0,0,0,0,0,109.635,0
-c5,0,109.635,0,0,0,0,0,0,0,0,0,0,0,0,0,109.635,0
-c6,0,109.635,0,0,0,0,0,0,0,0,0,0,0,0,0,109.635,0
-c7,0,109.635,0,0,0,0,0,0,0,0,0,0,0,0,0,109.635,0
-c8,0,109.635,0,0,0,0,0,0,0,0,0,0,0,0,0,109.635,0
-c9,0,109.635,0,0,0,0,0,0,0,0,0,0,0,0,0,109.635,0
-c10,0,109.635,0,0,0,0,0,0,0,0,0,0,0,0,0,109.635,0
-c11,0,109.635,0,0,0,0,0,0,0,0,0,0,0,0,0,109.635,0
-c12,0,109.635,0,0,0,0,0,0,0,0,0,0,0,0,0,109.635,0
-c0,0
-
-Time
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,352.4661,1550.099,701.7255,1159.5933,558.563,925.6073,971.2723,490.07014,857.91333,883.36663,344.11179,344.22248,351.94343,12.419968,1.04789,9504.422158,0.999999989478582
-c1,352.4661,1720.485,426.5549,652.9992,333.7788,544.1687,590.3019,49.948052,527.54432,102.069715,208.37216,206.54856,26.069312,7.640322,1.669613,5750.616654,1.65276570576346
-c2,352.4661,1720.485,426.5549,104.457756,333.7788,544.1687,590.3019,49.948052,102.069715,102.069715,208.37216,26.069312,26.069312,7.640322,1.669613,4596.121357,2.06792232253231
-c3,352.4661,1720.485,426.5549,652.9992,333.7788,100.738165,590.3019,290.68457,102.069715,102.069715,208.37216,26.069312,26.069312,7.640322,1.669613,4941.968784,1.92320558487758
-c4,352.4661,1720.485,426.5549,652.9992,333.7788,100.738165,590.3019,290.68457,102.069715,102.069715,208.37216,206.54856,26.069312,7.640322,1.669613,5122.448032,1.8554452701289
-c5,352.4661,1720.485,426.5549,652.9992,333.7788,544.1687,590.3019,49.948052,527.54432,102.069715,208.37216,206.54856,26.069312,7.640322,1.669613,5750.616654,1.65276570576346
-c6,352.4661,1720.485,426.5549,104.457756,333.7788,544.1687,590.3019,49.948052,102.069715,102.069715,208.37216,26.069312,26.069312,7.640322,1.669613,4596.121357,2.06792232253231
-c7,352.4661,1720.485,426.5549,652.9992,333.7788,100.738165,590.3019,290.68457,102.069715,102.069715,208.37216,26.069312,26.069312,7.640322,1.669613,4941.968784,1.92320558487758
-c8,352.4661,1720.485,426.5549,652.9992,333.7788,100.738165,590.3019,290.68457,102.069715,102.069715,208.37216,206.54856,26.069312,7.640322,1.669613,5122.448032,1.8554452701289
-c9,352.4661,1720.485,426.5549,652.9992,333.7788,544.1687,590.3019,49.948052,527.54432,102.069715,208.37216,206.54856,26.069312,7.640322,1.669613,5750.616654,1.65276570576346
-c10,352.4661,1720.485,426.5549,104.457756,333.7788,544.1687,590.3019,49.948052,102.069715,102.069715,208.37216,26.069312,26.069312,7.640322,1.669613,4596.121357,2.06792232253231
-c11,352.4661,1720.485,426.5549,652.9992,333.7788,100.738165,590.3019,290.68457,102.069715,102.069715,208.37216,26.069312,26.069312,7.640322,1.669613,4941.968784,1.92320558487758
-c12,352.4661,1720.485,426.5549,652.9992,333.7788,100.738165,590.3019,290.68457,102.069715,102.069715,208.37216,206.54856,26.069312,7.640322,1.669613,5122.448032,1.8554452701289
-c2,4596.121357
-
-Unpatch Energy
-Configuration,Conv1,Conv2,Conv3,Conv4,Conv5,Conv6,Conv7,Conv8,Conv9,Conv10,Conv11,Conv12,Conv13,FC1,FC2,Total,Improvement
-c0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-c0,0
-
diff --git a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_tensors.txt b/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_tensors.txt
deleted file mode 100644
index 6c6b42b93c0446c298489429261592fe99e2f81b..0000000000000000000000000000000000000000
--- a/llvm/projects/soc_simulator/vgg16_cifar100/vgg16_tensors.txt
+++ /dev/null
@@ -1,64 +0,0 @@
-#Conv1,3
-Conv1,163.415,464.69,310.228,694.841,7.62006,16.0868,83.4767,247.452
-Add1,113.913,376.561,69.7114,211.862,314.261,894.668,83.2799,265.625
-Relu1,75.1381,267.733,63.043,222.775,116.511,379.888,83.4086,300.518
-#Conv2,4
-Conv2,1186.37,5295.79,1399.51,5412.29,109.635,397.898,83.3307,436.945
-Add2,114.041,707.602,69.1905,303.338,336.556,1525.29,83.1178,365.325
-Relu2,74.295,455.493,62.4233,282.215,106.67,463.637,83.0734,372.529
-Pool1,175.393,1012.62,79.7262,360.302,106.496,467.712,20.9488,92.2916
-#Conv3,3
-Conv3,613.82,3267.16,354.47,1585.56,28.5399,124.01,41.6322,203.92
-Add3,51.2818,306.368,40.9358,206.435,52.9835,255.846,41.6412,204.32
-Relu3,36.6237,218.815,31.1491,157.975,53.9219,262.044,41.4745,201.753
-#Conv4,4
-Conv4,982.846,5808.36,543.421,2780.23,52.8973,253.818,41.5238,236.022
-Add4,51.329,344.524,37.6205,200.601,146.069,779.141,41.3499,213.133
-Relu4,36.7139,244.573,31.1126,165.225,52.5946,268.222,41.3761,210.186
-Pool2,88.7044,575.997,40.8451,210.857,52.6109,263.231,10.5892,50.2118
-#Conv5,3
-Conv5,491.91,3047.42,278.988,1427.88,14.8008,70.9043,20.8418,110.25
-Add5,48.2779,312.538,39.1711,216.321,27.4739,144.775,20.8433,109.029
-Relu5,18.3751,115.933,15.6197,87.4008,27.1876,142.132,20.7564,107.244
-#Conv6,3
-Conv6,858.952,5554.16,490.314,2738.69,28.103,144.6,20.8654,122.714
-Add6,48.3038,331.31,38.2773,222.684,78.3804,453.985,20.7551,113.98
-Relu6,18.3515,122.286,15.5774,91.8805,26.9604,148.182,20.7925,111.946
-#Conv7,4
-Conv7,859.425,5760.38,514.632,2922.64,28.2053,152.501,20.8436,124.457
-Add7,48.1751,337.048,38.3104,225.263,81.46,477.128,20.7616,115.225
-Relu7,18.3889,125.084,15.5863,93.2475,27.0202,149.73,20.7296,113.484
-Pool3,45.2833,307.898,21.7732,125.807,27.105,148.004,5.37692,25.667
-#Conv8,3
-Conv8,434.046,2940.43,240.716,1378.49,9.68469,49.5859,10.5597,59.0058
-Add8,46.6813,323.76,42.0846,253.744,14.6636,82.9186,10.5141,57.5583
-Relu8,9.34284,60.6176,7.88397,50.8594,14.4739,80.2487,10.4677,57.0219
-#Conv9,3
-Conv9,801.893,5651.89,477.878,2885.71,16.2347,90.1067,10.5851,63.9065
-Add9,46.6775,338.552,41.8259,265.047,39.8499,247.783,10.4589,60.1816
-Relu9,9.34283,63.4928,7.84042,52.587,14.2726,83.3662,10.4563,59.3499
-#Conv10,4
-Conv10,802.661,5895.34,481.652,3018.22,16.267,94.4725,10.5438,65.7062
-Add10,46.7852,353.064,41.8232,272.933,40.0563,257.396,10.5152,61.7303
-Relu10,9.31493,65.6928,7.8553,54.9037,14.2674,85.1708,10.4698,61.6665
-Pool4,24.6055,179.513,12.2268,81.0192,14.3183,85.3146,2.76689,12.3171
-#Conv11,3
-Conv11,308.859,2238.17,173.774,1077.96,6.79928,37.3658,2.77964,12.8278
-Add11,32.7593,228.974,32.499,205.175,4.84751,25.3885,2.71712,12.3719
-Relu11,2.49349,12.8041,2.09916,16.791,4.48398,23.4935,2.65339,11.6345
-#Conv12,3
-Conv12,308.957,2143.29,172.103,1054.21,6.35668,34.3,2.76929,12.2454
-Add12,32.7648,220.92,32.3458,201.907,4.88662,25.364,2.69777,11.7633
-Relu12,2.50068,12.3579,2.09976,17.1837,4.44727,22.6715,2.64916,11.3108
-#Conv13,4
-Conv13,308.526,2076.4,171.684,1041.08,6.20782,33.1375,2.76826,12.0417
-Add13,32.723,215.055,32.2976,199.983,4.91853,25.4026,2.71496,12.0093
-Relu13,2.48762,12.2284,2.10382,16.7344,4.36041,22.0096,2.72796,11.4339
-Pool5,8.20681,50.2013,4.24587,28.8535,4.30952,21.6535,0.789328,1.12606
-#FC1,3
-Mul1,10.6715,65.8927,5.49296,37.0659,2.18449,8.22985,0.810781,1.37103
-Add14,0.902452,1.91287,0.999932,9.47008,1.88605,7.19323,0.760782,0.960605
-Relu14,0.846016,1.57242,1.14743,10.6248,1.5964,5.26066,0.762322,0.9319
-#FC2,2
-Mul2,0.821147,1.66598,1.31517,8.1931,1.76803,6.49192,0.133188,0.0183427
-Add15,0.226743,0.146973,0.354443,1.81062,0.764484,1.36953,0.120211,0.0438951
diff --git a/llvm/projects/visc-cpu-rt/CMakeLists.txt b/llvm/projects/visc-cpu-rt/CMakeLists.txt
deleted file mode 100644
index 4887abbaf9be21d0fd69811789c0a40a52a23024..0000000000000000000000000000000000000000
--- a/llvm/projects/visc-cpu-rt/CMakeLists.txt
+++ /dev/null
@@ -1,43 +0,0 @@
-add_custom_target(visc-cpu-rt ALL)
-add_custom_command(
- TARGET visc-cpu-rt PRE_BUILD
- COMMAND ${CMAKE_COMMAND} -E copy
- ${CMAKE_CURRENT_SOURCE_DIR}/deviceStatusSwitchIntervals.txt
- ${CMAKE_CURRENT_BINARY_DIR}/deviceStatusSwitchIntervals.txt
- DEPENDS deviceStatusSwitchIntervals.txt
- COMMENT "Copying deviceStatusSwitchIntervals.txt")
-add_custom_command(
- TARGET visc-cpu-rt PRE_BUILD
- COMMAND ${CMAKE_COMMAND} -E copy
- ${CMAKE_CURRENT_SOURCE_DIR}/device_abstraction.h
- ${CMAKE_CURRENT_BINARY_DIR}/device_abstraction.h
- DEPENDS device_abstraction.h
- COMMENT "Copying device_abstraction.h")
-add_custom_command(
- TARGET visc-cpu-rt PRE_BUILD
- COMMAND ${CMAKE_COMMAND} -E copy
- ${CMAKE_CURRENT_SOURCE_DIR}/policy.h
- ${CMAKE_CURRENT_BINARY_DIR}/policy.h
- DEPENDS policy.h
- COMMENT "Copying policy.h")
-add_custom_command(
- TARGET visc-cpu-rt PRE_BUILD
- COMMAND ${CMAKE_COMMAND} -E copy
- ${CMAKE_CURRENT_SOURCE_DIR}/visc-rt.h
- ${CMAKE_CURRENT_BINARY_DIR}/visc-rt.h
- DEPENDS visc-rt.h
- COMMENT "Copying visc-rt.h")
-add_custom_command(
- TARGET visc-cpu-rt PRE_BUILD
- COMMAND ${CMAKE_COMMAND} -E copy
- ${CMAKE_CURRENT_SOURCE_DIR}/visc-rt.cpp
- ${CMAKE_CURRENT_BINARY_DIR}/visc-rt.cpp
- DEPENDS visc-rt.cpp
- COMMENT "Copying visc-rt.cpp")
-add_custom_command(
- TARGET visc-cpu-rt PRE_BUILD
- COMMAND ${CMAKE_COMMAND} -E copy
- ${CMAKE_CURRENT_SOURCE_DIR}/makefile
- ${CMAKE_CURRENT_BINARY_DIR}/makefile
- DEPENDS makefile
- COMMENT "Copying makefile")
diff --git a/llvm/projects/visc-cpu-rt/deviceStatusSwitchIntervals.txt b/llvm/projects/visc-cpu-rt/deviceStatusSwitchIntervals.txt
deleted file mode 100644
index 7069470a1a6f8b1a49eea2824f27204ebdf3fb26..0000000000000000000000000000000000000000
--- a/llvm/projects/visc-cpu-rt/deviceStatusSwitchIntervals.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-10
-10 15 10 16 15 30 15 25 20 15
diff --git a/llvm/projects/visc-cpu-rt/device_abstraction.h b/llvm/projects/visc-cpu-rt/device_abstraction.h
deleted file mode 100644
index 68748c7ab73d316c7bf296e67d88c0114b4cac81..0000000000000000000000000000000000000000
--- a/llvm/projects/visc-cpu-rt/device_abstraction.h
+++ /dev/null
@@ -1,82 +0,0 @@
-#ifndef __DEVICE_ABSTRACTION__
-#define __DEVICE_ABSTRACTION__
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <time.h>
-#include <time.h>
-#include <thread>
-#include <vector>
-#include <iostream>
-#include <fstream>
-
-#define MIN_INTERVAL 2
-#define MAX_INTERVAL 8
-#define NUM_INTERVALS 10
-
-// Device status variable: true if the device is available for use
-volatile bool deviceStatus = true;
-// Intervals at which to change the device status
-std::vector<unsigned> Intervals;
-
-// Set to true when program execution ends and so we can end the device
-// simulation
-volatile bool executionEnd = false;
-
-
-void initializeDeviceStatusIntervals() {
-
- unsigned sz = 0;
- unsigned tmp = 0;
-
- const char *fn =
- "/home/kotsifa2/HPVM/hpvm/build/projects/visc-rt/deviceStatusSwitchIntervals.txt";
- std::ifstream infile;
- infile.open(fn);
- if (!infile.is_open()) {
- std::cout << "Failed to open " << fn << " for reading\n";
- return;
- }
- infile >> sz;
-
- if (sz) {
- // We have data. Read them into the vector
- for (unsigned i = 0; i < sz; i++) {
- infile >> tmp;
- Intervals.push_back(tmp);
- }
- infile.close();
- } else {
- // We have no data. Create random data and write them into the file
- infile.close();
- std::ofstream outfile;
- outfile.open(fn);
- if (!outfile.is_open()) {
- std::cout << "Failed to open " << fn << " for writing\n";
- return;
- }
- sz = 1 + rand()%NUM_INTERVALS;
- outfile << sz;
- for (unsigned i = 0; i < sz; i++) {
- Intervals.push_back(MIN_INTERVAL + rand()%(MAX_INTERVAL - MIN_INTERVAL));
- outfile << Intervals[i];
- }
- outfile.close();
- }
-
- return;
-}
-
-void updateDeviceStatus() {
-
- unsigned i = 0;
- while (!executionEnd) {
- std::this_thread::sleep_for (std::chrono::seconds(Intervals[i]));
- deviceStatus = !deviceStatus;
- std::cout << "Changed device status to " << deviceStatus << "\n";
- i = (i+1) % Intervals.size();
- }
-
-}
-
-#endif // __DEVICE_ABSTRACTION__
diff --git a/llvm/projects/visc-cpu-rt/makefile b/llvm/projects/visc-cpu-rt/makefile
deleted file mode 100644
index a1fd6e1e01721d3be43af7a3aceed1b7a6a50b97..0000000000000000000000000000000000000000
--- a/llvm/projects/visc-cpu-rt/makefile
+++ /dev/null
@@ -1,31 +0,0 @@
-LLVM_SRC_ROOT = ../../../llvm
-LLVM_BUILD_ROOT = ../..
-
-OPENCL_INC_PATH = /opt/intel/opencl-sdk/include
-
-ifeq ($(NUM_CORES),)
- NUM_CORES=8
-endif
-
-CPP_FLAGS = -I $(LLVM_SRC_ROOT)/include -I $(LLVM_BUILD_ROOT)/include -I $(OPENCL_INC_PATH) -std=c++11 -D__STDC_CONSTANT_MACROS -D__STDC_LIMIT_MACROS
-TARGET:=visc-rt
-
-#LLVM_CC:=$(LLVM_BUILD_ROOT)/bin/clang
-#LLVM_CXX:=$(LLVM_BUILD_ROOT)/bin/clang++
-
-LLVM_CC=clang-4.0
-LLVM_CXX=clang++-4.0
-
-OPTS =
-
-ifeq ($(DEBUG),1)
- OPTS+=-DDEBUG_BUILD
-endif
-
-all: $(TARGET:%=%.ll)
-
-$(TARGET:%=%.ll):%.ll:%.cpp %.h
- $(LLVM_CXX) -DNUM_CORES=$(NUM_CORES) -O3 -S -emit-llvm $(CPP_FLAGS) $(OPTS) $< -o $@
-
-clean :
- rm -f $(TARGET).ll
diff --git a/llvm/projects/visc-cpu-rt/policy.h b/llvm/projects/visc-cpu-rt/policy.h
deleted file mode 100644
index 436ad39295d53b9d28680ff0f436ebb41a7812da..0000000000000000000000000000000000000000
--- a/llvm/projects/visc-cpu-rt/policy.h
+++ /dev/null
@@ -1,103 +0,0 @@
-#ifndef __POLICY__
-#define __POLICY__
-
-#include <string>
-#include "device_abstraction.h"
-
- /************************* Policies *************************************/
-class Policy {
- public:
- virtual int getVersion(const char *, int64_t) = 0;
- virtual ~Policy() {};
-};
-
-class NodePolicy : public Policy {
- virtual int getVersion(const char *name, int64_t it) override {
- std::string s(name);
- //std::string NodeNames[1] = { "_Z9mysgemmNTPfiS_iS_iiff_clonedInternal_level2_cloned" };
- std::string NodeNames[] = {
- "WrapperGaussianSmoothing_cloned",
- "WrapperlaplacianEstimate_cloned",
- "WrapperComputeZeroCrossings_cloned",
- "WrapperComputeGradient_cloned",
- "WrapperComputeMaxGradient_cloned",
- "WrapperRejectZeroCrossings_cloned",
- };
- //if (!s.compare(NodeNames[4])) {
- // std::cout << s << ": CPU" << "\n";
- // return 0;
- //}
- return 2;
- }
-};
-
-class IterationPolicy : public Policy {
- virtual int getVersion(const char *name, int64_t it) override {
- if ((it % 10 == 0) || (it % 10 == 1))
- return 0;
- else
- return 2;
- }
-};
-
-class DeviceStatusPolicy : public Policy {
- virtual int getVersion(const char *name, int64_t it) override {
- if (deviceStatus) {
- //std::cout << "Returning GPU\n";
- return 2;
- }
- else {
- //std::cout << "Returning CPU\n";
- return 0;
- }
- }
-};
-
-/* ------------------------------------------------------------------------- */
-// Added for the CFAR interactive policy demo.
-
-class InteractivePolicy : public Policy {
-private:
- // 0 :for CPU, 1 for GPU, 2 for Vector
- unsigned int userTargetDeviceChoice;
- // Used to end thread execution
- bool end;
- // Thread that will update userTargetDeviceChoice
-
- //std::thread userTargetDeviceChoiceThread;
-
- // Thread function
- void updateUserTargetChoice() {
- while (!end) {
- std::cout << "Select target device (0 for CPU, 1 fpr GPU): ";
-
- std::cin >> userTargetDeviceChoice;
-
- if (userTargetDeviceChoice > 1) {
- std::cout << "Invalid target device. Selecting GPU instead.\n";
- userTargetDeviceChoice = 1;
- }
-
- }
- }
-
-public:
- // Inherited method, erquired for every policy object
- virtual int getVersion(const char *name, int64_t it) {
- return userTargetDeviceChoice;
- }
-
- InteractivePolicy() {
- userTargetDeviceChoice = 1;
- end = false;
- //userTargetDeviceChoiceThread =
- // std::thread(&InteractivePolicy::updateUserTargetChoice, this);
- }
-
- ~InteractivePolicy() {
- end = true;
- //userTargetDeviceChoiceThread.join();
- }
-};
-
-#endif // __POLICY__
diff --git a/llvm/projects/visc-cpu-rt/visc-rt.cpp b/llvm/projects/visc-cpu-rt/visc-rt.cpp
deleted file mode 100644
index b4e9405efb2ee5660124b12e4ca8c07d61eff8f3..0000000000000000000000000000000000000000
--- a/llvm/projects/visc-cpu-rt/visc-rt.cpp
+++ /dev/null
@@ -1,1648 +0,0 @@
-
-#include <iostream>
-#include <string>
-#include <pthread.h>
-#include <cstdlib>
-#include <cstdio>
-#include <cstring>
-#include <cassert>
-#include <map>
-#include <CL/cl.h>
-
-#include <unistd.h>
-
-#if _POSIX_VERSION >= 200112L
-# include <sys/time.h>
-#endif
-#include "visc-rt.h"
-
-#ifndef DEBUG_BUILD
-#define DEBUG(s) {}
-#else
-#define DEBUG(s) s
-#endif
-
-#define BILLION 1000000000LL
-
-using namespace std;
-
-typedef struct {
- pthread_t threadID;
- std::vector<pthread_t>* threads;
- // Map from InputPort to Size
- std::map<unsigned, uint64_t>* ArgInPortSizeMap;
- //std::vector<uint64_t>* BindInSizes;
- std::vector<unsigned>* BindInSourcePort;
- std::vector<uint64_t>* BindOutSizes;
- std::vector<uint64_t>* EdgeSizes;
- std::vector<CircularBuffer<uint64_t>*>* BindInputBuffers;
- std::vector<CircularBuffer<uint64_t>*>* BindOutputBuffers;
- std::vector<CircularBuffer<uint64_t>*>* EdgeBuffers;
- std::vector<CircularBuffer<uint64_t>*>* isLastInputBuffers;
-} DFNodeContext_X86;
-
-typedef struct {
- cl_context clOCLContext;
- cl_command_queue clCommandQue;
- cl_program clProgram;
- cl_kernel clKernel;
-} DFNodeContext_OCL;
-
-cl_context globalOCLContext;
-cl_device_id* clDevices;
-cl_command_queue globalCommandQue;
-
-Policy *policy = NULL;
-MemTracker MTracker;
-vector<DFGDepth> DStack;
-// Mutex to prevent concurrent access by multiple thereads in pipeline
-pthread_mutex_t ocl_mtx;
-
-#define NUM_TESTS 1
-visc_TimerSet kernel_timer;
-
-static inline void checkErr(cl_int err, cl_int success, const char * name) {
- if (err != success) {
- cout << "ERROR: " << name << flush << "\n";
- cout << "ErrorCode: " << err << flush << "\n";
- exit(EXIT_FAILURE);
- }
-}
-
-/************************* Policies *************************************/
-void llvm_visc_policy_init() {
- cout << "Initializing policy object ...\n";
-// policy = new NodePolicy();
-// policy = new IterationPolicy();
-// policy = new DeviceStatusPolicy();
- policy = new InteractivePolicy();
- cout << "DONE: Initializing policy object.\n";
-}
-
-void llvm_visc_policy_clear() {
- if (policy) free(policy);
-}
-
-int llvm_visc_policy_getVersion(const char *name, int64_t i) {
- return policy->getVersion(name, i);
-}
-
-/******************** Device Abstraction ********************************/
-//std::thread deviceStatusThread;
-/*
-void llvm_visc_deviceAbstraction_start() {
- cout << "Starting device status simulation ...\n";
- // Initialize vector with points where ti switch device status
- initializeDeviceStatusIntervals();
- // Create a thread that performs the changes
- deviceStatusThread = std::thread(updateDeviceStatus);
- cout << "Started device status simulation thread ...\n";
- return;
-}
-*/
-
-void llvm_visc_deviceAbstraction_end() {
- cout << "Ending device status simulation thread ...\n";
- // Set the variable that allows the thread to know that execution has ended
- executionEnd = true;
- // Wait for the thread that manages device status to terminate
- //deviceStatusThread.join();
- cout << "Ended device status simulation.\n";
- return;
-}
-
-void llvm_visc_deviceAbstraction_waitOnDeviceStatus() {
- while (!deviceStatus) { };
- return;
-}
-
-/************************* Depth Stack Routines ***************************/
-
-void llvm_visc_x86_dstack_push(unsigned n, uint64_t limitX, uint64_t iX, uint64_t limitY,
- uint64_t iY, uint64_t limitZ, uint64_t iZ) {
- //DEBUG(cout << "Pushing node information on stack:\n");
- //DEBUG(cout << "\tNumDim = " << n << "\t Limit(" << limitX << ", " << limitY << ", "<< limitZ <<")\n");
- //DEBUG(cout << "\tInstance(" << iX << ", " << iY << ", "<< iZ <<")\n");
- //DFGDepth nodeInfo (n, limitX, iX, limitY, iY, limitZ, iZ);
- //DStack.push_back(nodeInfo);
- //DEBUG(cout << "DStack size = " << DStack.size() << flush << "\n");
-}
-
-void llvm_visc_x86_dstack_pop() {
- //DEBUG(cout << "Popping from depth stack\n");
- //DStack.pop_back();
- //DEBUG(cout << "DStack size = " << DStack.size() << flush << "\n");
-}
-
-uint64_t llvm_visc_x86_getDimLimit(unsigned level, unsigned dim) {
- //DEBUG(cout << "Request limit for dim " << dim << " of ancestor " << level <<flush << "\n");
- //unsigned size = DStack.size();
- //DEBUG(cout << "\t Return: " << DStack[size-level-1].getDimLimit(dim) <<flush << "\n");
- //return DStack[size-level-1].getDimLimit(dim);
- return 0;
-}
-
-uint64_t llvm_visc_x86_getDimInstance(unsigned level, unsigned dim) {
- //DEBUG(cout << "Request instance id for dim " << dim << " of ancestor " << level <<flush << "\n");
- //unsigned size = DStack.size();
- //DEBUG(cout << "\t Return: " << DStack[size-level-1].getDimInstance(dim) <<flush << "\n");
- //return DStack[size-level-1].getDimInstance(dim);
- return 0;
-}
-
-/********************** Memory Tracking Routines **************************/
-
-void llvm_visc_track_mem(void* ptr, size_t size) {
- DEBUG(cout << "Start tracking memory: " << ptr << flush << "\n");
- MemTrackerEntry* MTE = MTracker.lookup(ptr);
- if(MTE != NULL) {
- DEBUG(cout << "ID " << ptr << " already present in the MemTracker Table\n");
- return;
- }
- DEBUG(cout << "Inserting ID " << ptr << " in the MemTracker Table\n");
- MTracker.insert(ptr, size, MemTrackerEntry::HOST, ptr);
- DEBUG(MTracker.print());
-}
-
-void llvm_visc_untrack_mem(void* ptr) {
- DEBUG(cout << "Stop tracking memory: " << ptr << flush << "\n");
- MemTrackerEntry* MTE = MTracker.lookup(ptr);
- if(MTE == NULL) {
- cout << "WARNING: Trying to remove ID " << ptr << " not present in the MemTracker Table\n";
- return;
- }
- DEBUG(cout << "Removing ID " << ptr << " from MemTracker Table\n");
- if(MTE->getLocation() == MemTrackerEntry::DEVICE)
- clReleaseMemObject((cl_mem) MTE->getAddress());
- MTracker.remove(ptr);
- DEBUG(MTracker.print());
-}
-
-
-static void* llvm_visc_ocl_request_mem(void* ptr, size_t size, DFNodeContext_OCL* Context, bool isInput, bool isOutput) {
- pthread_mutex_lock(&ocl_mtx);
- DEBUG(cout << "[OCL] Request memory: " << ptr << " for context: " << Context->clOCLContext << flush << "\n");
- MemTrackerEntry* MTE = MTracker.lookup(ptr);
- if (MTE == NULL) {
- MTracker.print();
- cout << "ERROR: Requesting memory not present in Table\n";
- exit(EXIT_FAILURE);
- }
- // If already on device
- if (MTE->getLocation() == MemTrackerEntry::DEVICE &&
- ((DFNodeContext_OCL*)MTE->getContext())->clOCLContext == Context->clOCLContext) {
- DEBUG(cout << "\tMemory found on device at: " << MTE->getAddress() << flush << "\n");
- pthread_mutex_unlock(&ocl_mtx);
- return MTE->getAddress();
- }
-
- DEBUG(cout << "\tMemory found on host at: " << MTE->getAddress() << flush << "\n");
- DEBUG(cout << "\t"; MTE->print(); cout << flush << "\n");
- // Else copy and update the latest copy
- cl_mem_flags clFlags;
- cl_int errcode;
-
- if(isInput && isOutput) clFlags = CL_MEM_READ_WRITE;
- else if(isInput) clFlags = CL_MEM_READ_ONLY;
- else if(isOutput) clFlags = CL_MEM_WRITE_ONLY;
- else clFlags = CL_MEM_READ_ONLY;
-
- visc_SwitchToTimer(&kernel_timer, visc_TimerID_COPY);
- //pthread_mutex_lock(&ocl_mtx);
- cl_mem d_input = clCreateBuffer(Context->clOCLContext, clFlags, size, NULL, &errcode);
- //pthread_mutex_unlock(&ocl_mtx);
- checkErr(errcode, CL_SUCCESS, "Failure to allocate memory on device");
- DEBUG(cout<< "\nMemory allocated on device: " << d_input << flush << "\n");
- if(isInput) {
- DEBUG(cout << "\tCopying ...");
- //pthread_mutex_lock(&ocl_mtx);
- errcode = clEnqueueWriteBuffer(Context->clCommandQue,
- d_input,
- CL_TRUE,
- 0,
- size,MTE->getAddress(),
- 0,NULL,NULL);
- //pthread_mutex_unlock(&ocl_mtx);
- checkErr(errcode, CL_SUCCESS, "Failure to copy memory to device");
- }
-
- visc_SwitchToTimer(&kernel_timer, visc_TimerID_NONE);
- DEBUG(cout << " done\n");
- MTE->update(MemTrackerEntry::DEVICE, (void*) d_input, Context);
- DEBUG(cout << "Updated Table\n");
- DEBUG(MTracker.print());
- pthread_mutex_unlock(&ocl_mtx);
- return d_input;
-}
-
-void* llvm_visc_x86_argument_ptr(void* ptr, size_t size) {
- return llvm_visc_request_mem(ptr, size);
-}
-
-void* llvm_visc_request_mem(void* ptr, size_t size) {
- // Ignore objects whose size is 0 - no memory is requested.
- if (size == 0) {
- DEBUG(cout << "[X86] Request memory (ignored): " << ptr << flush << "\n");
- return ptr;
- }
-
- pthread_mutex_lock(&ocl_mtx);
- DEBUG(cout << "[X86] Request memory: " << ptr << flush << "\n");
- MemTrackerEntry* MTE = MTracker.lookup(ptr);
- if(MTE == NULL) {
- cout << "ERROR: Requesting memory not present in Table\n";
- pthread_mutex_unlock(&ocl_mtx);
- exit(EXIT_FAILURE);
- }
- // If already on host
- if(MTE->getLocation() == MemTrackerEntry::HOST) {
- DEBUG(cout << "\tMemory found on host at: " << MTE->getAddress() << flush << "\n");
- pthread_mutex_unlock(&ocl_mtx);
- return MTE->getAddress();
- }
-
- // Else copy from device and update table
- DEBUG(cout << "\tMemory found on device at: " << MTE->getAddress() << flush << "\n");
- DEBUG(cout << "\tCopying ...");
- visc_SwitchToTimer(&kernel_timer, visc_TimerID_COPY);
- //pthread_mutex_lock(&ocl_mtx);
- cl_int errcode = clEnqueueReadBuffer(((DFNodeContext_OCL*)MTE->getContext())->clCommandQue,
- (cl_mem) MTE->getAddress(),
- CL_TRUE,
- 0,
- size,
- ptr,
- 0, NULL, NULL);
- //pthread_mutex_unlock(&ocl_mtx);
- visc_SwitchToTimer(&kernel_timer, visc_TimerID_NONE);
- DEBUG(cout << " done\n");
- checkErr(errcode, CL_SUCCESS, "[request mem] Failure to read output");
- DEBUG(cout << "Free mem object on device\n");
- clReleaseMemObject((cl_mem) MTE->getAddress());
- DEBUG(cout << "Updated Table\n");
- MTE->update(MemTrackerEntry::HOST, ptr);
- DEBUG(MTracker.print());
- pthread_mutex_unlock(&ocl_mtx);
- return ptr;
-}
-
-/*************************** Timer Routines **********************************/
-
-static int is_async(enum visc_TimerID timer)
-{
- return (timer == visc_TimerID_KERNEL) ||
- (timer == visc_TimerID_COPY_ASYNC);
-}
-
-static int is_blocking(enum visc_TimerID timer)
-{
- return (timer == visc_TimerID_COPY) || (timer == visc_TimerID_NONE);
-}
-
-#define INVALID_TIMERID visc_TimerID_LAST
-
-static int asyncs_outstanding(struct visc_TimerSet* timers)
-{
- return (timers->async_markers != NULL) &&
- (timers->async_markers->timerID != INVALID_TIMERID);
-}
-
-static struct visc_async_time_marker_list *
-get_last_async(struct visc_TimerSet* timers)
-{
- /* Find the last event recorded thus far */
- struct visc_async_time_marker_list * last_event = timers->async_markers;
- if(last_event != NULL && last_event->timerID != INVALID_TIMERID) {
- while(last_event->next != NULL &&
- last_event->next->timerID != INVALID_TIMERID)
- last_event = last_event->next;
- return last_event;
- } else
- return NULL;
-}
-
-static void insert_marker(struct visc_TimerSet* tset, enum visc_TimerID timer)
-{
- cl_int ciErrNum = CL_SUCCESS;
- struct visc_async_time_marker_list ** new_event = &(tset->async_markers);
-
- while(*new_event != NULL && (*new_event)->timerID != INVALID_TIMERID) {
- new_event = &((*new_event)->next);
- }
-
- if(*new_event == NULL) {
- *new_event = (struct visc_async_time_marker_list *)
- malloc(sizeof(struct visc_async_time_marker_list));
- (*new_event)->marker = calloc(1, sizeof(cl_event));
- /*
- // I don't think this is needed at all. I believe clEnqueueMarker 'creates' the event
-#if ( __OPENCL_VERSION__ >= CL_VERSION_1_1 )
-fprintf(stderr, "Creating Marker [%d]\n", timer);
- *((cl_event *)((*new_event)->marker)) = clCreateUserEvent(*clContextPtr, &ciErrNum);
- if (ciErrNum != CL_SUCCESS) {
- fprintf(stderr, "Error Creating User Event Object!\n");
- }
- ciErrNum = clSetUserEventStatus(*((cl_event *)((*new_event)->marker)), CL_QUEUED);
- if (ciErrNum != CL_SUCCESS) {
- fprintf(stderr, "Error Setting User Event Status!\n");
- }
-#endif
-*/
- (*new_event)->next = NULL;
- }
-
- /* valid event handle now aquired: insert the event record */
- (*new_event)->label = NULL;
- (*new_event)->timerID = timer;
- //pthread_mutex_lock(&ocl_mtx);
- ciErrNum = clEnqueueMarker(globalCommandQue, (cl_event *)(*new_event)->marker);
- //pthread_mutex_unlock(&ocl_mtx);
- if (ciErrNum != CL_SUCCESS) {
- fprintf(stderr, "Error Enqueueing Marker!\n");
- }
-
-}
-
-static void insert_submarker(struct visc_TimerSet* tset, char *label, enum visc_TimerID timer)
-{
- cl_int ciErrNum = CL_SUCCESS;
- struct visc_async_time_marker_list ** new_event = &(tset->async_markers);
-
- while(*new_event != NULL && (*new_event)->timerID != INVALID_TIMERID) {
- new_event = &((*new_event)->next);
- }
-
- if(*new_event == NULL) {
- *new_event = (struct visc_async_time_marker_list *)
- malloc(sizeof(struct visc_async_time_marker_list));
- (*new_event)->marker = calloc(1, sizeof(cl_event));
- /*
-#if ( __OPENCL_VERSION__ >= CL_VERSION_1_1 )
-fprintf(stderr, "Creating SubMarker %s[%d]\n", label, timer);
- *((cl_event *)((*new_event)->marker)) = clCreateUserEvent(*clContextPtr, &ciErrNum);
- if (ciErrNum != CL_SUCCESS) {
- fprintf(stderr, "Error Creating User Event Object!\n");
- }
- ciErrNum = clSetUserEventStatus(*((cl_event *)((*new_event)->marker)), CL_QUEUED);
- if (ciErrNum != CL_SUCCESS) {
- fprintf(stderr, "Error Setting User Event Status!\n");
- }
-#endif
-*/
- (*new_event)->next = NULL;
- }
-
- /* valid event handle now aquired: insert the event record */
- (*new_event)->label = label;
- (*new_event)->timerID = timer;
- //pthread_mutex_lock(&ocl_mtx);
- ciErrNum = clEnqueueMarker(globalCommandQue, (cl_event *)(*new_event)->marker);
- //pthread_mutex_unlock(&ocl_mtx);
- if (ciErrNum != CL_SUCCESS) {
- fprintf(stderr, "Error Enqueueing Marker!\n");
- }
-
-}
-
-
-/* Assumes that all recorded events have completed */
-static visc_Timestamp record_async_times(struct visc_TimerSet* tset)
-{
- struct visc_async_time_marker_list * next_interval = NULL;
- struct visc_async_time_marker_list * last_marker = get_last_async(tset);
- visc_Timestamp total_async_time = 0;
-
- for(next_interval = tset->async_markers; next_interval != last_marker;
- next_interval = next_interval->next) {
- cl_ulong command_start=0, command_end=0;
- cl_int ciErrNum = CL_SUCCESS;
-
- ciErrNum = clGetEventProfilingInfo(*((cl_event *)next_interval->marker), CL_PROFILING_COMMAND_END, sizeof(cl_ulong), &command_start, NULL);
- if (ciErrNum != CL_SUCCESS) {
- fprintf(stderr, "Error getting first EventProfilingInfo: %d\n", ciErrNum);
- }
-
- ciErrNum = clGetEventProfilingInfo(*((cl_event *)next_interval->next->marker), CL_PROFILING_COMMAND_END, sizeof(cl_ulong), &command_end, NULL);
- if (ciErrNum != CL_SUCCESS) {
- fprintf(stderr, "Error getting second EventProfilingInfo: %d\n", ciErrNum);
- }
-
- visc_Timestamp interval = (visc_Timestamp) (((double)(command_end - command_start)));
- tset->timers[next_interval->timerID].elapsed += interval;
- if (next_interval->label != NULL) {
- struct visc_SubTimer *subtimer = tset->sub_timer_list[next_interval->timerID]->subtimer_list;
- while (subtimer != NULL) {
- if ( strcmp(subtimer->label, next_interval->label) == 0) {
- subtimer->timer.elapsed += interval;
- break;
- }
- subtimer = subtimer->next;
- }
- }
- total_async_time += interval;
- next_interval->timerID = INVALID_TIMERID;
- }
-
- if(next_interval != NULL)
- next_interval->timerID = INVALID_TIMERID;
-
- return total_async_time;
-}
-
-static void
-accumulate_time(visc_Timestamp *accum,
- visc_Timestamp start,
- visc_Timestamp end)
-{
-#if _POSIX_VERSION >= 200112L
- *accum += end - start;
-#else
-# error "Timestamps not implemented for this system"
-#endif
-}
-
-#if _POSIX_VERSION >= 200112L
-static visc_Timestamp get_time()
-{
- struct timespec tv;
- clock_gettime(CLOCK_MONOTONIC, &tv);
- return (visc_Timestamp) (tv.tv_sec * BILLION + tv.tv_nsec);
-}
-#else
-# error "no supported time libraries are available on this platform"
-#endif
-
-void
-visc_ResetTimer(struct visc_Timer *timer)
-{
- timer->state = visc_Timer_STOPPED;
-
-#if _POSIX_VERSION >= 200112L
- timer->elapsed = 0;
-#else
-# error "visc_ResetTimer: not implemented for this system"
-#endif
-}
-
-void
-visc_StartTimer(struct visc_Timer *timer)
-{
- if (timer->state != visc_Timer_STOPPED) {
- // FIXME: Removing warning statement to avoid printing this error
- // fputs("Ignoring attempt to start a running timer\n", stderr);
- return;
- }
-
- timer->state = visc_Timer_RUNNING;
-
-#if _POSIX_VERSION >= 200112L
- {
- struct timespec tv;
- clock_gettime(CLOCK_MONOTONIC, &tv);
- timer->init = tv.tv_sec * BILLION + tv.tv_nsec;
- }
-#else
-# error "visc_StartTimer: not implemented for this system"
-#endif
-}
-
-void
-visc_StartTimerAndSubTimer(struct visc_Timer *timer, struct visc_Timer *subtimer)
-{
-
- unsigned int numNotStopped = 0x3; // 11
- if (timer->state != visc_Timer_STOPPED) {
- fputs("Warning: Timer was not stopped\n", stderr);
- numNotStopped &= 0x1; // Zero out 2^1
- }
- if (subtimer->state != visc_Timer_STOPPED) {
- fputs("Warning: Subtimer was not stopped\n", stderr);
- numNotStopped &= 0x2; // Zero out 2^0
- }
- if (numNotStopped == 0x0) {
- //fputs("Ignoring attempt to start running timer and subtimer\n", stderr);
- return;
- }
-
- timer->state = visc_Timer_RUNNING;
- subtimer->state = visc_Timer_RUNNING;
-
-#if _POSIX_VERSION >= 200112L
- {
- struct timespec tv;
- clock_gettime(CLOCK_MONOTONIC, &tv);
-
- if (numNotStopped & 0x2) {
- timer->init = tv.tv_sec * BILLION + tv.tv_nsec;
- }
-
- if (numNotStopped & 0x1) {
- subtimer->init = tv.tv_sec * BILLION + tv.tv_nsec;
- }
- }
-#else
-# error "visc_StartTimer: not implemented for this system"
-#endif
-
-}
-
-void
-visc_StopTimer(struct visc_Timer *timer)
-{
- visc_Timestamp fini;
-
- if (timer->state != visc_Timer_RUNNING) {
- //fputs("Ignoring attempt to stop a stopped timer\n", stderr);
- return;
- }
-
- timer->state = visc_Timer_STOPPED;
-
-#if _POSIX_VERSION >= 200112L
- {
- struct timespec tv;
- clock_gettime(CLOCK_MONOTONIC, &tv);
- fini = tv.tv_sec * BILLION + tv.tv_nsec;
- }
-#else
-# error "visc_StopTimer: not implemented for this system"
-#endif
-
- accumulate_time(&timer->elapsed, timer->init, fini);
- timer->init = fini;
-}
-
-void visc_StopTimerAndSubTimer(struct visc_Timer *timer, struct visc_Timer *subtimer) {
-
- visc_Timestamp fini;
-
- unsigned int numNotRunning = 0x3; // 11
- if (timer->state != visc_Timer_RUNNING) {
- fputs("Warning: Timer was not running\n", stderr);
- numNotRunning &= 0x1; // Zero out 2^1
- }
- if (subtimer->state != visc_Timer_RUNNING) {
- fputs("Warning: Subtimer was not running\n", stderr);
- numNotRunning &= 0x2; // Zero out 2^0
- }
- if (numNotRunning == 0x0) {
- //fputs("Ignoring attempt to stop stopped timer and subtimer\n", stderr);
- return;
- }
-
-
- timer->state = visc_Timer_STOPPED;
- subtimer->state = visc_Timer_STOPPED;
-
-#if _POSIX_VERSION >= 200112L
- {
- struct timespec tv;
- clock_gettime(CLOCK_MONOTONIC, &tv);
- fini = tv.tv_sec * BILLION + tv.tv_nsec;
- }
-#else
-# error "visc_StopTimer: not implemented for this system"
-#endif
-
- if (numNotRunning & 0x2) {
- accumulate_time(&timer->elapsed, timer->init, fini);
- timer->init = fini;
- }
-
- if (numNotRunning & 0x1) {
- accumulate_time(&subtimer->elapsed, subtimer->init, fini);
- subtimer->init = fini;
- }
-
-}
-
-/* Get the elapsed time in seconds. */
-double
-visc_GetElapsedTime(struct visc_Timer *timer)
-{
- double ret;
-
- if (timer->state != visc_Timer_STOPPED) {
- fputs("Elapsed time from a running timer is inaccurate\n", stderr);
- }
-
-#if _POSIX_VERSION >= 200112L
- ret = timer->elapsed / 1e9;
-#else
-# error "visc_GetElapsedTime: not implemented for this system"
-#endif
- return ret;
-}
-
-void
-visc_InitializeTimerSet(struct visc_TimerSet *timers)
-{
- int n;
-
- timers->wall_begin = get_time();
- timers->current = visc_TimerID_NONE;
-
- timers->async_markers = NULL;
-
- for (n = 0; n < visc_TimerID_LAST; n++) {
- visc_ResetTimer(&timers->timers[n]);
- timers->sub_timer_list[n] = NULL;
- }
-}
-
-
-void
-visc_AddSubTimer(struct visc_TimerSet *timers, char *label, enum visc_TimerID visc_Category) {
-
- struct visc_SubTimer *subtimer = (struct visc_SubTimer *) malloc
- (sizeof(struct visc_SubTimer));
-
- int len = strlen(label);
-
- subtimer->label = (char *) malloc (sizeof(char)*(len+1));
- sprintf(subtimer->label, "%s", label);
-
- visc_ResetTimer(&subtimer->timer);
- subtimer->next = NULL;
-
- struct visc_SubTimerList *subtimerlist = timers->sub_timer_list[visc_Category];
- if (subtimerlist == NULL) {
- subtimerlist = (struct visc_SubTimerList *) calloc
- (1, sizeof(struct visc_SubTimerList));
- subtimerlist->subtimer_list = subtimer;
- timers->sub_timer_list[visc_Category] = subtimerlist;
- } else {
- // Append to list
- struct visc_SubTimer *element = subtimerlist->subtimer_list;
- while (element->next != NULL) {
- element = element->next;
- }
- element->next = subtimer;
- }
-
-}
-
-void
-visc_SwitchToTimer(struct visc_TimerSet *timers, enum visc_TimerID timer)
-{
- //cerr << "Switch to timer: " << timer << flush << "\n";
- /* Stop the currently running timer */
- if (timers->current != visc_TimerID_NONE) {
- struct visc_SubTimerList *subtimerlist = timers->sub_timer_list[timers->current];
- struct visc_SubTimer *currSubTimer = (subtimerlist != NULL) ? subtimerlist->current : NULL;
-
- if (!is_async(timers->current) ) {
- if (timers->current != timer) {
- if (currSubTimer != NULL) {
- visc_StopTimerAndSubTimer(&timers->timers[timers->current], &currSubTimer->timer);
- } else {
- visc_StopTimer(&timers->timers[timers->current]);
- }
- } else {
- if (currSubTimer != NULL) {
- visc_StopTimer(&currSubTimer->timer);
- }
- }
- } else {
- insert_marker(timers, timer);
- if (!is_async(timer)) { // if switching to async too, keep driver going
- visc_StopTimer(&timers->timers[visc_TimerID_DRIVER]);
- }
- }
- }
-
- visc_Timestamp currentTime = get_time();
-
- /* The only cases we check for asynchronous task completion is
- * when an overlapping CPU operation completes, or the next
- * segment blocks on completion of previous async operations */
- if( asyncs_outstanding(timers) &&
- (!is_async(timers->current) || is_blocking(timer) ) ) {
-
- struct visc_async_time_marker_list * last_event = get_last_async(timers);
- /* CL_COMPLETE if completed */
-
- cl_int ciErrNum = CL_SUCCESS;
- cl_int async_done = CL_COMPLETE;
-
- ciErrNum = clGetEventInfo(*((cl_event *)last_event->marker), CL_EVENT_COMMAND_EXECUTION_STATUS, sizeof(cl_int), &async_done, NULL);
- if (ciErrNum != CL_SUCCESS) {
- fprintf(stdout, "Error Querying EventInfo1!\n");
- }
-
-
- if(is_blocking(timer)) {
- /* Async operations completed after previous CPU operations:
- * overlapped time is the total CPU time since this set of async
- * operations were first issued */
-
- // timer to switch to is COPY or NONE
- if(async_done != CL_COMPLETE) {
- accumulate_time(&(timers->timers[visc_TimerID_OVERLAP].elapsed),
- timers->async_begin,currentTime);
- }
-
- /* Wait on async operation completion */
- ciErrNum = clWaitForEvents(1, (cl_event *)last_event->marker);
- if (ciErrNum != CL_SUCCESS) {
- fprintf(stderr, "Error Waiting for Events!\n");
- }
-
- visc_Timestamp total_async_time = record_async_times(timers);
-
- /* Async operations completed before previous CPU operations:
- * overlapped time is the total async time */
- if(async_done == CL_COMPLETE) {
- //fprintf(stderr, "Async_done: total_async_type = %lld\n", total_async_time);
- timers->timers[visc_TimerID_OVERLAP].elapsed += total_async_time;
- }
-
- } else
- /* implies (!is_async(timers->current) && asyncs_outstanding(timers)) */
- // i.e. Current Not Async (not KERNEL/COPY_ASYNC) but there are outstanding
- // so something is deeper in stack
- if(async_done == CL_COMPLETE ) {
- /* Async operations completed before previous CPU operations:
- * overlapped time is the total async time */
- timers->timers[visc_TimerID_OVERLAP].elapsed += record_async_times(timers);
- }
- }
-
- /* Start the new timer */
- if (timer != visc_TimerID_NONE) {
- if(!is_async(timer)) {
- visc_StartTimer(&timers->timers[timer]);
- } else {
- // toSwitchTo Is Async (KERNEL/COPY_ASYNC)
- if (!asyncs_outstanding(timers)) {
- /* No asyncs outstanding, insert a fresh async marker */
-
- insert_marker(timers, timer);
- timers->async_begin = currentTime;
- } else if(!is_async(timers->current)) {
- /* Previous asyncs still in flight, but a previous SwitchTo
- * already marked the end of the most recent async operation,
- * so we can rename that marker as the beginning of this async
- * operation */
-
- struct visc_async_time_marker_list * last_event = get_last_async(timers);
- last_event->label = NULL;
- last_event->timerID = timer;
- }
- if (!is_async(timers->current)) {
- visc_StartTimer(&timers->timers[visc_TimerID_DRIVER]);
- }
- }
- }
- timers->current = timer;
-
-}
-
-void
-visc_SwitchToSubTimer(struct visc_TimerSet *timers, char *label, enum visc_TimerID category)
-{
- struct visc_SubTimerList *subtimerlist = timers->sub_timer_list[timers->current];
- struct visc_SubTimer *curr = (subtimerlist != NULL) ? subtimerlist->current : NULL;
-
- if (timers->current != visc_TimerID_NONE) {
- if (!is_async(timers->current) ) {
- if (timers->current != category) {
- if (curr != NULL) {
- visc_StopTimerAndSubTimer(&timers->timers[timers->current], &curr->timer);
- } else {
- visc_StopTimer(&timers->timers[timers->current]);
- }
- } else {
- if (curr != NULL) {
- visc_StopTimer(&curr->timer);
- }
- }
- } else {
- insert_submarker(timers, label, category);
- if (!is_async(category)) { // if switching to async too, keep driver going
- visc_StopTimer(&timers->timers[visc_TimerID_DRIVER]);
- }
- }
- }
-
- visc_Timestamp currentTime = get_time();
-
- /* The only cases we check for asynchronous task completion is
- * when an overlapping CPU operation completes, or the next
- * segment blocks on completion of previous async operations */
- if( asyncs_outstanding(timers) &&
- (!is_async(timers->current) || is_blocking(category) ) ) {
-
- struct visc_async_time_marker_list * last_event = get_last_async(timers);
- /* CL_COMPLETE if completed */
-
- cl_int ciErrNum = CL_SUCCESS;
- cl_int async_done = CL_COMPLETE;
-
- ciErrNum = clGetEventInfo(*((cl_event *)last_event->marker), CL_EVENT_COMMAND_EXECUTION_STATUS, sizeof(cl_int), &async_done, NULL);
- if (ciErrNum != CL_SUCCESS) {
- fprintf(stdout, "Error Querying EventInfo2!\n");
- }
-
- if(is_blocking(category)) {
- /* Async operations completed after previous CPU operations:
- * overlapped time is the total CPU time since this set of async
- * operations were first issued */
-
- // timer to switch to is COPY or NONE
- // if it hasn't already finished, then just take now and use that as the elapsed time in OVERLAP
- // anything happening after now isn't OVERLAP because everything is being stopped to wait for synchronization
- // it seems that the extra sync wall time isn't being recorded anywhere
- if(async_done != CL_COMPLETE)
- accumulate_time(&(timers->timers[visc_TimerID_OVERLAP].elapsed),
- timers->async_begin,currentTime);
-
- /* Wait on async operation completion */
- ciErrNum = clWaitForEvents(1, (cl_event *)last_event->marker);
- if (ciErrNum != CL_SUCCESS) {
- fprintf(stderr, "Error Waiting for Events!\n");
- }
- visc_Timestamp total_async_time = record_async_times(timers);
-
- /* Async operations completed before previous CPU operations:
- * overlapped time is the total async time */
- // If it did finish, then accumulate all the async time that did happen into OVERLAP
- // the immediately preceding EventSynchronize theoretically didn't have any effect since it was already completed.
- if(async_done == CL_COMPLETE /*cudaSuccess*/)
- timers->timers[visc_TimerID_OVERLAP].elapsed += total_async_time;
-
- } else
- /* implies (!is_async(timers->current) && asyncs_outstanding(timers)) */
- // i.e. Current Not Async (not KERNEL/COPY_ASYNC) but there are outstanding
- // so something is deeper in stack
- if(async_done == CL_COMPLETE /*cudaSuccess*/) {
- /* Async operations completed before previous CPU operations:
- * overlapped time is the total async time */
- timers->timers[visc_TimerID_OVERLAP].elapsed += record_async_times(timers);
- }
- // else, this isn't blocking, so just check the next time around
- }
-
- subtimerlist = timers->sub_timer_list[category];
- struct visc_SubTimer *subtimer = NULL;
-
- if (label != NULL) {
- subtimer = subtimerlist->subtimer_list;
- while (subtimer != NULL) {
- if (strcmp(subtimer->label, label) == 0) {
- break;
- } else {
- subtimer = subtimer->next;
- }
- }
- }
-
- /* Start the new timer */
- if (category != visc_TimerID_NONE) {
- if(!is_async(category)) {
- if (subtimerlist != NULL) {
- subtimerlist->current = subtimer;
- }
-
- if (category != timers->current && subtimer != NULL) {
- visc_StartTimerAndSubTimer(&timers->timers[category], &subtimer->timer);
- } else if (subtimer != NULL) {
- visc_StartTimer(&subtimer->timer);
- } else {
- visc_StartTimer(&timers->timers[category]);
- }
- } else {
- if (subtimerlist != NULL) {
- subtimerlist->current = subtimer;
- }
-
- // toSwitchTo Is Async (KERNEL/COPY_ASYNC)
- if (!asyncs_outstanding(timers)) {
- /* No asyncs outstanding, insert a fresh async marker */
- insert_submarker(timers, label, category);
- timers->async_begin = currentTime;
- } else if(!is_async(timers->current)) {
- /* Previous asyncs still in flight, but a previous SwitchTo
- * already marked the end of the most recent async operation,
- * so we can rename that marker as the beginning of this async
- * operation */
-
- struct visc_async_time_marker_list * last_event = get_last_async(timers);
- last_event->timerID = category;
- last_event->label = label;
- } // else, marker for switchToThis was already inserted
-
- //toSwitchto is already asynchronous, but if current/prev state is async too, then DRIVER is already running
- if (!is_async(timers->current)) {
- visc_StartTimer(&timers->timers[visc_TimerID_DRIVER]);
- }
- }
- }
-
- timers->current = category;
-}
-
-void
-visc_PrintTimerSet(struct visc_TimerSet *timers)
-{
- visc_Timestamp wall_end = get_time();
-
- struct visc_Timer *t = timers->timers;
- struct visc_SubTimer* sub = NULL;
-
- int maxSubLength;
-
- const char *categories[] = {
- "IO", "Kernel", "Copy", "Driver", "Copy Async", "Compute", "Overlap",
- "Init_Ctx", "Clear_Ctx", "Copy_Scalar", "Copy_Ptr", "Mem_Free",
- "Read_Output", "Setup", "Mem_Track", "Mem_Untrack", "Misc",
- "Pthread_Create", "Arg_Pack", "Arg_Unpack", "Computation", "Output_Pack", "Output_Unpack"
-
- };
-
- const int maxCategoryLength = 20;
-
- int i;
- for(i = 1; i < visc_TimerID_LAST; ++i) { // exclude NONE and OVRELAP from this format
- if(visc_GetElapsedTime(&t[i]) != 0 || true) {
-
- // Print Category Timer
- printf("%-*s: %.9f\n", maxCategoryLength, categories[i-1], visc_GetElapsedTime(&t[i]));
-
- if (timers->sub_timer_list[i] != NULL) {
- sub = timers->sub_timer_list[i]->subtimer_list;
- maxSubLength = 0;
- while (sub != NULL) {
- // Find longest SubTimer label
- if (strlen(sub->label) > (unsigned long) maxSubLength) {
- maxSubLength = strlen(sub->label);
- }
- sub = sub->next;
- }
-
- // Fit to Categories
- if (maxSubLength <= maxCategoryLength) {
- maxSubLength = maxCategoryLength;
- }
-
- sub = timers->sub_timer_list[i]->subtimer_list;
-
- // Print SubTimers
- while (sub != NULL) {
- printf(" -%-*s: %.9f\n", maxSubLength, sub->label, visc_GetElapsedTime(&sub->timer));
- sub = sub->next;
- }
- }
- }
- }
-
- if(visc_GetElapsedTime(&t[visc_TimerID_OVERLAP]) != 0)
- printf("CPU/Kernel Overlap: %.9f\n", visc_GetElapsedTime(&t[visc_TimerID_OVERLAP]));
-
- float walltime = (wall_end - timers->wall_begin)/ 1e9;
- printf("Timer Wall Time: %.9f\n", walltime);
-
-}
-
-void visc_DestroyTimerSet(struct visc_TimerSet * timers)
-{
- /* clean up all of the async event markers */
- struct visc_async_time_marker_list* event = timers->async_markers;
- while(event != NULL) {
-
- cl_int ciErrNum = CL_SUCCESS;
- ciErrNum = clWaitForEvents(1, (cl_event *)(event)->marker);
- if (ciErrNum != CL_SUCCESS) {
- //fprintf(stderr, "Error Waiting for Events!\n");
- }
-
- ciErrNum = clReleaseEvent( *((cl_event *)(event)->marker) );
- if (ciErrNum != CL_SUCCESS) {
- fprintf(stderr, "Error Release Events!\n");
- }
-
- free((event)->marker);
- struct visc_async_time_marker_list* next = ((event)->next);
-
- free(event);
-
- // (*event) = NULL;
- event = next;
- }
-
- int i = 0;
- for(i = 0; i < visc_TimerID_LAST; ++i) {
- if (timers->sub_timer_list[i] != NULL) {
- struct visc_SubTimer *subtimer = timers->sub_timer_list[i]->subtimer_list;
- struct visc_SubTimer *prev = NULL;
- while (subtimer != NULL) {
- free(subtimer->label);
- prev = subtimer;
- subtimer = subtimer->next;
- free(prev);
- }
- free(timers->sub_timer_list[i]);
- }
- }
-}
-
-/**************************** Pipeline API ************************************/
-#define BUFFER_SIZE 1
-
-// Launch API for a streaming dataflow graph
-void* llvm_visc_streamLaunch(void(*LaunchFunc)(void*, void*), void* args) {
- DFNodeContext_X86* Context = (DFNodeContext_X86*) malloc(sizeof(DFNodeContext_X86));
-
- Context->threads = new std::vector<pthread_t>();
- Context->ArgInPortSizeMap = new std::map<unsigned, uint64_t>();
- //Context->BindInSizes = new std::vector<uint64_t>();
- Context->BindInSourcePort = new std::vector<unsigned>();
- Context->BindOutSizes = new std::vector<uint64_t>();
- Context->EdgeSizes = new std::vector<uint64_t>();
- Context->BindInputBuffers = new std::vector<CircularBuffer<uint64_t>*>();
- Context->BindOutputBuffers = new std::vector<CircularBuffer<uint64_t>*>();
- Context->EdgeBuffers = new std::vector<CircularBuffer<uint64_t>*>();
- Context->isLastInputBuffers = new std::vector<CircularBuffer<uint64_t>*>();
-
- DEBUG(cout << "StreamLaunch -- Graph: " << Context << ", Arguments: " << args << flush << "\n");
- LaunchFunc(args, Context);
- return Context;
-}
-
-// Push API for a streaming dataflow graph
-void llvm_visc_streamPush(void* graphID, void* args) {
- DEBUG(cout << "StreamPush -- Graph: " << graphID << ", Arguments: " << args << flush << "\n");
- DFNodeContext_X86* Ctx = (DFNodeContext_X86*) graphID;
- unsigned offset = 0;
- for (unsigned i=0; i< Ctx->ArgInPortSizeMap->size(); i++) {
- uint64_t element;
- memcpy(&element, (char*)args+offset, Ctx->ArgInPortSizeMap->at(i));
- offset += Ctx->ArgInPortSizeMap->at(i);
- for(unsigned j=0; j<Ctx->BindInputBuffers->size();j++) {
- if(Ctx->BindInSourcePort->at(j) == i) {
- // Push to all bind buffers connected to parent node at this port
- //DEBUG(cout << "\tPushing Value " << element << " to buffer\n");
- llvm_visc_bufferPush(Ctx->BindInputBuffers->at(j), element);
- }
- }
- }
- // Push 0 in isLastInput buffers of all child nodes
- for (CircularBuffer<uint64_t>* buffer: *(Ctx->isLastInputBuffers))
- llvm_visc_bufferPush(buffer, 0);
-}
-
-// Pop API for a streaming dataflow graph
-void* llvm_visc_streamPop(void* graphID) {
- DEBUG(cout << "StreamPop -- Graph: " << graphID << flush << "\n");
- DFNodeContext_X86* Ctx = (DFNodeContext_X86*) graphID;
- unsigned totalBytes = 0;
- for(uint64_t size: *(Ctx->BindOutSizes))
- totalBytes+= size;
- void* output = malloc(totalBytes);
- unsigned offset = 0;
- for (unsigned i=0; i< Ctx->BindOutputBuffers->size(); i++) {
- uint64_t element = llvm_visc_bufferPop(Ctx->BindOutputBuffers->at(i));
- //DEBUG(cout << "\tPopped Value " << element << " from buffer\n");
- memcpy((char*)output+offset, &element, Ctx->BindOutSizes->at(i));
- offset += Ctx->BindOutSizes->at(i);
- }
- return output;
-}
-
-// Wait API for a streaming dataflow graph
-void llvm_visc_streamWait(void* graphID) {
- DEBUG(cout << "StreamWait -- Graph: " << graphID << flush << "\n");
- DFNodeContext_X86* Ctx = (DFNodeContext_X86*) graphID;
- // Push garbage to all other input buffers
- for (unsigned i=0; i< Ctx->BindInputBuffers->size(); i++) {
- uint64_t element = 0;
- //DEBUG(cout << "\tPushing Value " << element << " to buffer\n");
- llvm_visc_bufferPush(Ctx->BindInputBuffers->at(i), element);
- }
- // Push 1 in isLastInput buffers of all child nodes
- for (unsigned i=0; i < Ctx->isLastInputBuffers->size(); i++)
- llvm_visc_bufferPush(Ctx->isLastInputBuffers->at(i), 1);
-
- llvm_visc_freeThreads(graphID);
-}
-
-// Create a buffer and return the bufferID
-void* llvm_visc_createBindInBuffer(void* graphID, uint64_t size, unsigned inArgPort) {
- DEBUG(cout << "Create BindInBuffer -- Graph: " << graphID << ", Size: " << size << flush << "\n");
- DFNodeContext_X86* Context = (DFNodeContext_X86*) graphID;
- CircularBuffer<uint64_t> *bufferID = new CircularBuffer<uint64_t>(BUFFER_SIZE, "BindIn");
- DEBUG(cout << "\tNew Buffer: " << bufferID << flush << "\n");
- Context->BindInputBuffers->push_back(bufferID);
- (*(Context->ArgInPortSizeMap))[inArgPort] = size;
- Context->BindInSourcePort->push_back(inArgPort);
- //Context->BindInSizes->push_back(size);
- return bufferID;
-}
-
-void* llvm_visc_createBindOutBuffer(void* graphID, uint64_t size) {
- DEBUG(cout << "Create BindOutBuffer -- Graph: " << graphID << ", Size: " << size << flush << "\n");
- DFNodeContext_X86* Context = (DFNodeContext_X86*) graphID;
- //Twine name = Twine("Bind.Out.")+Twine(Context->BindOutputBuffers->size());
- CircularBuffer<uint64_t> *bufferID = new CircularBuffer<uint64_t>(BUFFER_SIZE, "BindOut");
- DEBUG(cout << "\tNew Buffer: " << bufferID << flush << "\n");
- Context->BindOutputBuffers->push_back(bufferID);
- Context->BindOutSizes->push_back(size);
- return bufferID;
-}
-void* llvm_visc_createEdgeBuffer(void* graphID, uint64_t size) {
- DEBUG(cout << "Create EdgeBuffer -- Graph: " << graphID << ", Size: " << size << flush << "\n");
- DFNodeContext_X86* Context = (DFNodeContext_X86*) graphID;
- //Twine name = Twine("Edge.")+Twine(Context->EdgeBuffers->size());
- CircularBuffer<uint64_t> *bufferID = new CircularBuffer<uint64_t>(BUFFER_SIZE, "Edge");
- DEBUG(cout << "\tNew Buffer: " << bufferID << flush << "\n");
- Context->EdgeBuffers->push_back(bufferID);
- Context->EdgeSizes->push_back(size);
- return bufferID;
-}
-
-void* llvm_visc_createLastInputBuffer(void* graphID, uint64_t size) {
- DEBUG(cout << "Create isLastInputBuffer -- Graph: " << graphID << ", Size: " << size << flush << "\n");
- DFNodeContext_X86* Context = (DFNodeContext_X86*) graphID;
- //Twine name = Twine("isLastInput.")+Twine(Context->EdgeBuffers->size());
- CircularBuffer<uint64_t> *bufferID = new CircularBuffer<uint64_t>(BUFFER_SIZE, "LastInput");
- DEBUG(cout << "\tNew Buffer: " << bufferID << flush << "\n");
- Context->isLastInputBuffers->push_back(bufferID);
- return bufferID;
-}
-
-// Free buffers
-void llvm_visc_freeBuffers(void* graphID) {
- DEBUG(cout << "Free all buffers -- Graph: " << graphID << flush << "\n");
- DFNodeContext_X86* Context = (DFNodeContext_X86*) graphID;
- for(CircularBuffer<uint64_t>* bufferID: *(Context->BindInputBuffers))
- delete bufferID;
- for(CircularBuffer<uint64_t>* bufferID: *(Context->BindOutputBuffers))
- delete bufferID;
- for(CircularBuffer<uint64_t>* bufferID: *(Context->EdgeBuffers))
- delete bufferID;
- for(CircularBuffer<uint64_t>* bufferID: *(Context->isLastInputBuffers))
- delete bufferID;
-}
-
-// Pop an element from the buffer
-uint64_t llvm_visc_bufferPop(void* bufferID) {
- CircularBuffer<uint64_t>* buffer = (CircularBuffer<uint64_t>*) bufferID;
- return buffer->pop();
-}
-
-// Push an element into the buffer
-void llvm_visc_bufferPush(void* bufferID, uint64_t element) {
- CircularBuffer<uint64_t>* buffer = (CircularBuffer<uint64_t>*) bufferID;
- buffer->push(element);
-}
-
-// Create a thread
-void llvm_visc_createThread(void* graphID, void* (*Func)(void*), void* arguments) {
- DEBUG(cout << "Create Thread -- Graph: " << graphID << ", Func: " << Func << ", Args: " << arguments << flush << "\n");
- DFNodeContext_X86* Ctx = (DFNodeContext_X86*) graphID;
- int err;
- pthread_t threadID;
- if((err = pthread_create(&threadID, NULL, Func, arguments)) != 0)
- cout << "Failed to create thread. Error code = " << err << flush << "\n";
-
- Ctx->threads->push_back(threadID);
-}
-
-// Wait for thread to finish
-void llvm_visc_freeThreads(void* graphID) {
- DEBUG(cout << "Free Threads -- Graph: " << graphID << flush << "\n");
- DFNodeContext_X86* Ctx = (DFNodeContext_X86*) graphID;
- for(pthread_t thread: *(Ctx->threads))
- pthread_join(thread, NULL);
-}
-
-/************************ OPENCL & PTHREAD API ********************************/
-
-void* llvm_visc_x86_launch(void* (*rootFunc)(void*), void* arguments) {
- DFNodeContext_X86 *Context = (DFNodeContext_X86*) malloc(sizeof(DFNodeContext_X86));
- //int err;
- //if((err = pthread_create(&Context->threadID, NULL, rootFunc, arguments)) != 0)
- //cout << "Failed to create pthread. Error code = " << err << flush << "\n";
- rootFunc(arguments);
- return Context;
-}
-
-void llvm_visc_x86_wait(void* graphID) {
- DEBUG(cout << "Waiting for pthread to finish ...\n");
- //DFNodeContext_X86* Context = (DFNodeContext_X86*) graphID;
- //pthread_join(Context->threadID, NULL);
- free(graphID);
- DEBUG(cout << "\t... pthread Done!\n");
-}
-
-void* llvm_visc_ocl_initContext(enum visc::Target T) {
- pthread_mutex_lock(&ocl_mtx);
- //DEBUG(std::string Target = T == visc::GPU_TARGET? "GPU" : "SPIR");
- DEBUG(const char* Target = T == visc::GPU_TARGET? "GPU" : "SPIR");
- DEBUG(cout << "Initializing Context for " << Target << " device\n");
- cl_uint numPlatforms;
- cl_int errcode;
- errcode = clGetPlatformIDs(0, NULL, &numPlatforms);
- checkErr(errcode, CL_SUCCESS, "Failure to get number of platforms");
-
- // now get all the platform IDs
- cl_platform_id* platforms = (cl_platform_id*) malloc(sizeof(cl_platform_id)*numPlatforms);
- errcode = clGetPlatformIDs(numPlatforms, platforms, NULL);
- checkErr(errcode, CL_SUCCESS, "Failure to get platform IDs");
-
-
- for(unsigned i=0; i < numPlatforms; i++) {
- char buffer[10240];
- DEBUG(cout << "Device " << i << " Info -->\n");
- clGetPlatformInfo(platforms[i], CL_PLATFORM_PROFILE, 10240, buffer, NULL);
- DEBUG(cout << "\tPROFILE = " << buffer << flush << "\n");
- clGetPlatformInfo(platforms[i], CL_PLATFORM_VERSION, 10240, buffer, NULL);
- DEBUG(cout << "\tVERSION = "<< buffer << flush << "\n");
- clGetPlatformInfo(platforms[i], CL_PLATFORM_NAME, 10240, buffer, NULL);
- DEBUG(cout << "\tNAME = " << buffer << flush << "\n");
- clGetPlatformInfo(platforms[i], CL_PLATFORM_VENDOR, 10240, buffer, NULL);
- DEBUG(cout << "\tVENDOR = " << buffer << flush << "\n");
- clGetPlatformInfo(platforms[i], CL_PLATFORM_EXTENSIONS, 10240, buffer, NULL);
- DEBUG(cout << "\tEXTENSIONS = " << buffer << flush << "\n");
- }
- // set platform property - just pick the first one
- //cl_context_properties properties[] = {CL_CONTEXT_PLATFORM,
- //(long) platforms[0],
- //0};
- //globalOCLContext = clCreateContextFromType(properties, CL_DEVICE_TYPE_GPU,
- //NULL, NULL, &errcode);
- assert(numPlatforms >= 2 && "Expecting two OpenCL platforms");
- // Choose second one which is X86 AVX
- cl_context_properties properties[] = {CL_CONTEXT_PLATFORM,
- (long) platforms[T == visc::GPU_TARGET? 0 : 1],
- 0};
- globalOCLContext = clCreateContextFromType(properties,
- T == visc::GPU_TARGET?
- CL_DEVICE_TYPE_GPU : CL_DEVICE_TYPE_CPU,
- NULL, NULL, &errcode);
- // get the list of OCL devices associated with context
- size_t dataBytes;
- errcode = clGetContextInfo(globalOCLContext, CL_CONTEXT_DEVICES, 0,
- NULL, &dataBytes);
- checkErr(errcode, CL_SUCCESS, "Failure to get context info length");
-
- clDevices = (cl_device_id *) malloc(dataBytes);
- errcode |= clGetContextInfo(globalOCLContext, CL_CONTEXT_DEVICES, dataBytes,
- clDevices, NULL);
- checkErr(errcode, CL_SUCCESS, "Failure to get context info");
- if(false && T == visc::SPIR_TARGET) {
- cl_device_partition_property props[4];
- props[0] = CL_DEVICE_PARTITION_BY_COUNTS;
- props[1] = NUM_CORES;
- props[2] = CL_DEVICE_PARTITION_BY_COUNTS_LIST_END;
- props[3] = 0;
- cl_device_id subdevice_id[8];
- cl_uint num_entries = 8;
-
- cl_uint numDevices;
- clCreateSubDevices(clDevices[0], props, num_entries, subdevice_id, &numDevices);
- //printf("Num of devices = %d\n", numDevices);
- //for(unsigned i =0 ; i< numDevices; i++)
- //printf("Subdevice id %d = %p\n", i, subdevice_id[i]);
- clDevices[0] = subdevice_id[0];
- globalOCLContext = clCreateContext(properties, 1, clDevices, NULL, NULL, &errcode);
- checkErr(errcode, CL_SUCCESS, "Failure to create OCL context");
- }
-
- free(platforms);
- DEBUG(cout << "\tContext " << globalOCLContext << flush << "\n");
- checkErr(errcode, CL_SUCCESS, "Failure to create OCL context");
-
- DEBUG(cout << "Initialize Kernel Timer\n");
- visc_InitializeTimerSet(&kernel_timer);
-
- pthread_mutex_unlock(&ocl_mtx);
- return globalOCLContext;
-}
-
-void llvm_visc_ocl_clearContext(void* graphID) {
- pthread_mutex_lock(&ocl_mtx);
- DEBUG(cout << "Clear Context\n");
- DFNodeContext_OCL* Context = (DFNodeContext_OCL*) graphID;
- // FIXME: Have separate function to release command queue and clear context.
- // Would be useful when a context has multiple command queues
- clReleaseKernel(Context->clKernel);
- //clReleaseProgram(Context->clProgram);
- //clReleaseCommandQueue(Context->clCommandQue);
- //clReleaseContext(globalOCLContext);
- //DEBUG(cout << "Released context at: " << globalOCLContext);
- free(Context);
- DEBUG(cout << "Done with OCL kernel\n");
- cout << "Printing VISC Timer: KernelTimer\n";
- visc_PrintTimerSet(&kernel_timer);
- pthread_mutex_unlock(&ocl_mtx);
-
-}
-
-void llvm_visc_ocl_argument_shared(void* graphID, int arg_index, size_t size) {
- pthread_mutex_lock(&ocl_mtx);
- DEBUG(cout << "Set Shared Memory Input:");
- DEBUG(cout << "\tArgument Index = " << arg_index << ", Size = " << size << flush << "\n");
- DFNodeContext_OCL* Context = (DFNodeContext_OCL*) graphID;
- DEBUG(cout << "Using Context: " << Context << flush << "\n");
- DEBUG(cout << "Using clKernel: " << Context->clKernel << flush << "\n");
- //pthread_mutex_lock(&ocl_mtx);
- cl_int errcode = clSetKernelArg(Context->clKernel, arg_index, size, NULL);
- //pthread_mutex_unlock(&ocl_mtx);
- checkErr(errcode, CL_SUCCESS, "Failure to set shared memory argument");
- pthread_mutex_unlock(&ocl_mtx);
-}
-
-void llvm_visc_ocl_argument_scalar(void* graphID, void* input, int arg_index, size_t size) {
- pthread_mutex_lock(&ocl_mtx);
- DEBUG(cout << "Set Scalar Input:");
- DEBUG(cout << "\tArgument Index = " << arg_index << ", Size = " << size << flush << "\n");
- DFNodeContext_OCL* Context = (DFNodeContext_OCL*) graphID;
- DEBUG(cout << "Using Context: " << Context << flush << "\n");
- DEBUG(cout << "Using clKernel: " << Context->clKernel << flush << "\n");
- //pthread_mutex_lock(&ocl_mtx);
- cl_int errcode = clSetKernelArg(Context->clKernel, arg_index, size, input);
- //pthread_mutex_unlock(&ocl_mtx);
- checkErr(errcode, CL_SUCCESS, "Failure to set constant input argument");
- pthread_mutex_unlock(&ocl_mtx);
-}
-
-void* llvm_visc_ocl_argument_ptr(void* graphID, void* input, int arg_index, size_t size, bool isInput, bool isOutput) {
- pthread_mutex_lock(&ocl_mtx);
- DEBUG(cout << "Set Pointer Input:");
- DEBUG(cout << "\tArgument Index = " << arg_index << ", Ptr = " << input << ", Size = "<< size << flush << "\n");
- // Size should be non-zero
- assert(size != 0 && "Size of data pointed to has to be non-zero!");
- DEBUG(cout << "\tInput = "<< isInput << "\tOutput = " << isOutput << flush << "\n");
- DFNodeContext_OCL* Context = (DFNodeContext_OCL*) graphID;
-
- pthread_mutex_unlock(&ocl_mtx);
- // Check with runtime the location of this memory
- cl_mem d_input = (cl_mem) llvm_visc_ocl_request_mem(input, size, Context, isInput, isOutput);
-
- pthread_mutex_lock(&ocl_mtx);
- // Set Kernel Argument
- //pthread_mutex_lock(&ocl_mtx);
- cl_int errcode = clSetKernelArg(Context->clKernel, arg_index, sizeof(cl_mem), (void*)&d_input);
- //pthread_mutex_unlock(&ocl_mtx);
- checkErr(errcode, CL_SUCCESS, "Failure to set pointer argument");
- DEBUG(cout << "\tDevicePtr = " << d_input << flush << "\n");
- pthread_mutex_unlock(&ocl_mtx);
- return d_input;
-}
-
-void* llvm_visc_ocl_output_ptr(void* graphID, int arg_index, size_t size) {
- pthread_mutex_lock(&ocl_mtx);
- DEBUG(cout << "Set device memory for Output Struct:");
- DEBUG(cout << "\tArgument Index = " << arg_index << ", Size = "<< size << flush << "\n");
- DFNodeContext_OCL* Context = (DFNodeContext_OCL*) graphID;
- cl_int errcode;
- //pthread_mutex_lock(&ocl_mtx);
- cl_mem d_output = clCreateBuffer(Context->clOCLContext, CL_MEM_WRITE_ONLY, size, NULL, &errcode);
- //pthread_mutex_unlock(&ocl_mtx);
- checkErr(errcode, CL_SUCCESS, "Failure to create output buffer on device");
- //pthread_mutex_lock(&ocl_mtx);
- errcode = clSetKernelArg(Context->clKernel, arg_index, sizeof(cl_mem), (void*)&d_output);
- //pthread_mutex_unlock(&ocl_mtx);
- checkErr(errcode, CL_SUCCESS, "Failure to set pointer argument");
- DEBUG(cout << "\tDevicePtr = " << d_output << flush << "\n");
- pthread_mutex_unlock(&ocl_mtx);
- return d_output;
-}
-
-void llvm_visc_ocl_free(void* ptr) {
- //DEBUG(cout << "Release Device Pointer: " << ptr << flush << "\n");
- //cl_mem d_ptr = (cl_mem) ptr;
- //clReleaseMemObject(d_ptr);
-}
-
-void* llvm_visc_ocl_getOutput(void* graphID, void* h_output, void* d_output, size_t size) {
- pthread_mutex_lock(&ocl_mtx);
- DEBUG(cout << "Get Output:\n");
- DEBUG(cout << "\tHostPtr = " << h_output << ", DevicePtr = " << d_output << ", Size = "<< size << flush << "\n");
- if(h_output == NULL)
- h_output = malloc(size);
- DFNodeContext_OCL* Context = (DFNodeContext_OCL*) graphID;
- //pthread_mutex_lock(&ocl_mtx);
- cl_int errcode = clEnqueueReadBuffer(Context->clCommandQue, (cl_mem)d_output, CL_TRUE, 0, size,
- h_output, 0, NULL, NULL);
- //pthread_mutex_unlock(&ocl_mtx);
- checkErr(errcode, CL_SUCCESS, "[getOutput] Failure to read output");
- pthread_mutex_unlock(&ocl_mtx);
- return h_output;
-}
-
-void* llvm_visc_ocl_executeNode(void* graphID, unsigned workDim , const size_t*
- localWorkSize, const size_t* globalWorkSize) {
- pthread_mutex_lock(&ocl_mtx);
-
- size_t GlobalWG[3];
- size_t LocalWG[3];
-
- // OpenCL EnqeueNDRangeKernel function results in segementation fault if we
- // directly use local and global work groups arguments. Hence, allocating it
- // on stack and copying.
- for(unsigned i=0; i<workDim; i++) {
- GlobalWG[i] = globalWorkSize[i];
- }
-
- // OpenCL allows local workgroup to be null.
- if(localWorkSize != NULL) {
- for(unsigned i=0; i<workDim; i++) {
- LocalWG[i] = localWorkSize[i];
- }
- }
-
- DFNodeContext_OCL* Context = (DFNodeContext_OCL*) graphID;
- // TODO: Would like to use event to ensure better scheduling of kernels.
- // Currently passing the event paratemeter results in seg fault with
- // clEnqueueNDRangeKernel.
- cl_event* event;
- DEBUG(cout << "Enqueuing kernel:\n");
- DEBUG(cout << "\tCommand Queue: " << Context->clCommandQue << flush << "\n");
- DEBUG(cout << "\tKernel: " << Context->clKernel << flush << "\n");
- DEBUG(cout << "\tNumber of dimensions: " << workDim << flush << "\n");
- DEBUG(cout << "\tGlobal Work Group: ( ");
- for(unsigned i = 0; i<workDim; i++) {
- DEBUG(cout << GlobalWG[i] << " ");
- }
- DEBUG(cout << ")\n");
- if(localWorkSize != NULL) {
- DEBUG(cout << "\tLocal Work Group: ( ");
- for(unsigned i = 0; i<workDim; i++) {
- DEBUG(cout << LocalWG[i] << " ");
- }
- DEBUG(cout << ")\n");
- }
- //pthread_mutex_lock(&ocl_mtx);
- clFinish(Context->clCommandQue);
- //pthread_mutex_unlock(&ocl_mtx);
- visc_SwitchToTimer(&kernel_timer, visc_TimerID_COMPUTATION);
- //for(int i=0 ;i < NUM_TESTS; i++) {
- //cout << "Iteration = " << i << flush << "\n";
- //pthread_mutex_lock(&ocl_mtx);
- cl_int errcode = clEnqueueNDRangeKernel(Context->clCommandQue,
- Context->clKernel, workDim, NULL, GlobalWG, (localWorkSize == NULL)? NULL : LocalWG, 0, NULL, NULL);
- //pthread_mutex_unlock(&ocl_mtx);
- checkErr(errcode, CL_SUCCESS, "Failure to enqueue kernel");
- //}
- //pthread_mutex_lock(&ocl_mtx);
- clFinish(Context->clCommandQue);
- //pthread_mutex_unlock(&ocl_mtx);
- visc_SwitchToTimer(&kernel_timer, visc_TimerID_NONE);
-
- pthread_mutex_unlock(&ocl_mtx);
- return event;
-}
-
-
-//////////////////////////////////////////////////////////////////////////////
-//! Loads a Program binary file.
-//!
-//! @return the source string if succeeded, 0 otherwise
-//! @param Filename program filename
-//! @param szFinalLength returned length of the code string
-//////////////////////////////////////////////////////////////////////////////
-static char* LoadProgSource(const char* Filename, size_t* szFinalLength)
-{
- DEBUG(cout << "Load Prog Source\n");
- // locals
- FILE* pFileStream = NULL;
- size_t szSourceLength;
-
- // open the OpenCL source code file
- pFileStream = fopen(Filename, "rb");
- if(pFileStream == 0)
- {
- return NULL;
- }
-
- // get the length of the source code
- fseek(pFileStream, 0, SEEK_END);
- szSourceLength = ftell(pFileStream);
- fseek(pFileStream, 0, SEEK_SET);
-
- // allocate a buffer for the source code string and read it in
- char* cSourceString = (char *)malloc(szSourceLength + 1);
- if (fread((cSourceString), szSourceLength, 1, pFileStream) != 1)
- {
- fclose(pFileStream);
- free(cSourceString);
- return 0;
- }
-
- // close the file and return the total length of the combined (preamble + source) string
- fclose(pFileStream);
- if(szFinalLength != 0)
- {
- *szFinalLength = szSourceLength;
- }
- cSourceString[szSourceLength] = '\0';
-
- return cSourceString;
-}
-
-void* llvm_visc_ocl_launch(const char* FileName, const char* KernelName) {
- pthread_mutex_lock(&ocl_mtx);
- DEBUG(cout << "Launch OCL Kernel\n");
- // Initialize OpenCL
-
- // OpenCL specific variables
- DFNodeContext_OCL *Context = (DFNodeContext_OCL *) malloc(sizeof(DFNodeContext_OCL));
-
- size_t kernelLength;
- cl_int errcode;
-
- // For a single context for all kernels
- Context->clOCLContext = globalOCLContext;
-
- //Create a command-queue
- //pthread_mutex_lock(&ocl_mtx);
- Context->clCommandQue = clCreateCommandQueue(Context->clOCLContext, clDevices[0], CL_QUEUE_PROFILING_ENABLE, &errcode);
- globalCommandQue = Context->clCommandQue;
- //pthread_mutex_unlock(&ocl_mtx);
- checkErr(errcode, CL_SUCCESS, "Failure to create command queue");
-
- DEBUG(cout << "Loading program binary: " << FileName << flush << "\n");
- char *programSource = LoadProgSource(FileName, &kernelLength);
- checkErr(programSource != NULL, 1 /*bool true*/, "Failure to load Program Binary");
-
- cl_int binaryStatus;
- //pthread_mutex_lock(&ocl_mtx);
- Context->clProgram = clCreateProgramWithBinary(Context->clOCLContext, 1, &clDevices[0],
- &kernelLength,
- (const unsigned char **)&programSource,
- &binaryStatus, &errcode);
- //pthread_mutex_unlock(&ocl_mtx);
- checkErr(errcode, CL_SUCCESS, "Failure to create program from binary");
-
- DEBUG(cout << "Building kernel - " << KernelName << " from file " << FileName << flush << "\n");
- errcode = clBuildProgram(Context->clProgram, 0, NULL, NULL, NULL, NULL);
- // If build fails, get build log from device
- if(errcode != CL_SUCCESS) {
- cout << "ERROR: Failure to build program\n";
- size_t len = 0;
- errcode = clGetProgramBuildInfo(Context->clProgram, clDevices[0] , CL_PROGRAM_BUILD_LOG, 0,
- NULL, &len);
- cout << "LOG LENGTH: " << len << flush << "\n";
- checkErr(errcode, CL_SUCCESS, "Failure to collect program build log length");
- char *log = (char*) malloc(len*sizeof(char));
- errcode = clGetProgramBuildInfo(Context->clProgram, clDevices[0], CL_PROGRAM_BUILD_LOG, len,
- log, NULL);
- checkErr(errcode, CL_SUCCESS, "Failure to collect program build log");
-
- cout << "Device Build Log:\n" << log << flush << "\n";
- free(log);
- pthread_mutex_unlock(&ocl_mtx);
- exit(EXIT_FAILURE);
- }
-
- Context->clKernel = clCreateKernel(Context->clProgram, KernelName, &errcode);
- checkErr(errcode, CL_SUCCESS, "Failure to create kernel");
-
- DEBUG(cout << "Kernel ID = " << Context->clKernel << "\n");
- //free(clDevices);
- free(programSource);
-
- pthread_mutex_unlock(&ocl_mtx);
- return Context;
-}
-
-
-void llvm_visc_ocl_wait(void* graphID) {
- pthread_mutex_lock(&ocl_mtx);
- DEBUG(cout << "Wait\n");
- DFNodeContext_OCL *Context = (DFNodeContext_OCL*) graphID;
- //pthread_mutex_lock(&ocl_mtx);
- clFinish(Context->clCommandQue);
- //pthread_mutex_unlock(&ocl_mtx);
- pthread_mutex_unlock(&ocl_mtx);
-}
-
-void llvm_visc_switchToTimer(void** timerSet, enum visc_TimerID timer) {
- //cout << "Switching to timer " << timer << flush << "\n";
- pthread_mutex_lock(&ocl_mtx);
- //visc_SwitchToTimer((visc_TimerSet*)(*timerSet), timer);
- pthread_mutex_unlock(&ocl_mtx);
-}
-void llvm_visc_printTimerSet(void** timerSet, char* timerName) {
- pthread_mutex_lock(&ocl_mtx);
- cout << "Printing VISC Timer: ";
- if(timerName != NULL)
- cout << timerName << flush << "\n";
- else
- cout << "Anonymous\n";
- visc_PrintTimerSet((visc_TimerSet*) (*timerSet));
- pthread_mutex_unlock(&ocl_mtx);
-}
-
-void* llvm_visc_initializeTimerSet() {
- pthread_mutex_lock(&ocl_mtx);
- visc_TimerSet* TS = (visc_TimerSet*) malloc (sizeof(visc_TimerSet));
- visc_InitializeTimerSet(TS);
- pthread_mutex_unlock(&ocl_mtx);
- return TS;
-}
-
-
-
diff --git a/llvm/projects/visc-cpu-rt/visc-rt.h b/llvm/projects/visc-cpu-rt/visc-rt.h
deleted file mode 100644
index 5e7546f582f09173d5da4f4129218275873e17bb..0000000000000000000000000000000000000000
--- a/llvm/projects/visc-cpu-rt/visc-rt.h
+++ /dev/null
@@ -1,302 +0,0 @@
-/*
- *
- * (c) 2010 The Board of Trustees of the University of Illinois.
- */
-#ifndef VISC_RT_HEADER
-#define VISC_RT_HEADER
-
-#include <iostream>
-#include <map>
-#include <ctime>
-#include <vector>
-#include <pthread.h>
-#include <string>
-//#include <condition_variable>
-
-#include "llvm/SupportVISC/VISCHint.h"
-#include "llvm/SupportVISC/VISCTimer.h"
-#include "device_abstraction.h"
-#include "policy.h"
-#ifndef DEBUG_BUILD
-#define DEBUG(s) {}
-#else
-#define DEBUG(s) s
-#endif
-
-
-
-using namespace std;
-
-extern "C" {
-
-/************************* Policies *************************************/
-
-void llvm_visc_policy_init();
-void llvm_visc_policy_clear();
-int llvm_visc_policy_getVersion(const char *, int64_t);
-
-/******************** Device Abstraction ********************************/
-void llvm_visc_deviceAbstraction_start();
-void llvm_visc_deviceAbstraction_end();
-void llvm_visc_deviceAbstraction_waitOnDeviceStatus();
-
-/********************* DFG Depth Stack **********************************/
-class DFGDepth {
- private:
- unsigned numDim;
- unsigned dimLimit[3];
- unsigned dimInstance[3];
- public:
- DFGDepth() {}
- DFGDepth(unsigned n, unsigned dimX = 0, unsigned iX = 0, unsigned dimY = 0, unsigned iY = 0,
- unsigned dimZ = 0, unsigned iZ = 0) {
- assert(n <= 3 && "Error! More than 3 dimensions not supported");
- numDim = n;
- dimLimit[0] = dimX;
- dimLimit[1] = dimY;
- dimLimit[2] = dimZ;
- dimInstance[0] = iX;
- dimInstance[1] = iY;
- dimInstance[2] = iZ;
- }
- unsigned getDimLimit(unsigned dim) {
- assert(dim <= numDim && "Error! Requested dimension limit is not specified");
- return dimLimit[dim];
- }
-
- unsigned getDimInstance(unsigned dim) {
- assert(dim <= numDim && "Error! Requested dimension instance is not specified");
- return dimInstance[dim];
- }
-
- unsigned getNumDim() {
- return numDim;
- }
-};
-
-void llvm_visc_x86_dstack_push(unsigned n, uint64_t limitX = 0, uint64_t iX = 0,
- uint64_t limitY = 0, uint64_t iY = 0, uint64_t limitZ = 0, uint64_t iZ = 0);
-void llvm_visc_x86_dstack_pop();
-uint64_t llvm_visc_x86_getDimLimit(unsigned level, unsigned dim);
-uint64_t llvm_visc_x86_getDimInstance(unsigned level, unsigned dim);
-
-
-/********************* Memory Tracker **********************************/
-class MemTrackerEntry {
-public:
- enum Location {HOST, DEVICE};
- private:
- size_t size;
- Location loc;
- void* addr;
- void* Context;
-
- public:
- MemTrackerEntry(size_t _size, Location _loc, void* _addr, void* _Context):
- size(_size), loc(_loc), addr(_addr), Context(_Context) {
- }
-
- size_t getSize() {
- return size;
- }
-
- Location getLocation() {
- return loc;
- }
-
- void* getAddress() {
- return addr;
- }
-
- void* getContext() {
- return Context;
- }
-
- void update(Location _loc, void* _addr, void* _Context = NULL) {
- loc = _loc;
- addr = _addr;
- Context = _Context;
- }
-
- void print() {
- cout << "Size = " << size << "\tLocation = " << loc << "\tAddress = " << addr << "\tContext = " << Context;
- }
-};
-
-
-class MemTracker {
-
-private:
- std::map<void*, MemTrackerEntry*> Table;
-
-public:
- MemTracker() {
- }
-
- bool insert(void* ID, size_t size, MemTrackerEntry::Location loc, void* addr, void* Context = NULL) {
- MemTrackerEntry* MTE = new MemTrackerEntry(size, loc, addr, Context);
- Table.insert(std::pair<void*, MemTrackerEntry*>(ID, MTE));
- return MTE != NULL;
- }
-
- MemTrackerEntry* lookup(void* ID) {
- if(Table.count(ID) == 0)
- return NULL;
- return Table[ID];
- }
-
- void remove(void* ID) {
- MemTrackerEntry* MTE = Table[ID];
- free(MTE);
- Table.erase(ID);
- }
-
- void print() {
- cout << "Printing Table ... Size = " << Table.size() << flush << "\n";
- for(auto& Entry: Table) {
- cout << Entry.first << ":\t" ;
- Entry.second->print();
- cout << flush << "\n";
- }
- }
-
-};
-
-void llvm_visc_track_mem(void*, size_t);
-void llvm_visc_untrack_mem(void*);
-void* llvm_visc_request_mem(void*, size_t);
-
-/*********************** OPENCL & PTHREAD API **************************/
-void* llvm_visc_x86_launch(void* (void*), void*);
-void llvm_visc_x86_wait(void*);
-void* llvm_visc_ocl_initContext(enum visc::Target);
-
-void* llvm_visc_x86_argument_ptr(void*, size_t);
-
-void llvm_visc_ocl_clearContext(void*);
-void llvm_visc_ocl_argument_shared(void*, int, size_t);
-void llvm_visc_ocl_argument_scalar(void*, void*, int, size_t);
-void* llvm_visc_ocl_argument_ptr(void*, void*, int, size_t, bool, bool);
-void* llvm_visc_ocl_output_ptr(void*, int, size_t);
-void llvm_visc_ocl_free(void*);
-void* llvm_visc_ocl_getOutput(void*, void*, void*, size_t);
-void* llvm_visc_ocl_executeNode(void*, unsigned, const size_t*, const size_t*);
-void* llvm_visc_ocl_launch(const char*, const char*);
-void llvm_visc_ocl_wait(void*);
-
-void llvm_visc_switchToTimer(void** timerSet, enum visc_TimerID);
-void llvm_visc_printTimerSet(void** timerSet, char* timerName = NULL);
-void* llvm_visc_initializeTimerSet();
-
-}
-
-/*************************** Pipeline API ******************************/
-// Circular Buffer class
-unsigned counter = 0;
-template <class ElementType>
-class CircularBuffer {
-private:
- int numElements;
- int bufferSize;
- int Head;
- int Tail;
- pthread_mutex_t mtx;
- pthread_cond_t cv;
- vector<ElementType> buffer;
- std::string name;
- unsigned ID;
-
-public:
- CircularBuffer(int maxElements, std::string _name = "ANON") {
- ID = counter;
- Head = 0;
- Tail = 0;
- numElements = 0;
- name = _name;
- bufferSize = maxElements+1;
- buffer.reserve(bufferSize);
- pthread_mutex_init(&mtx, NULL);
- pthread_cond_init(&cv, NULL);
- counter++;
-
- }
-
- bool push(ElementType E);
- ElementType pop();
-
-};
-
-template <class ElementType>
-bool CircularBuffer<ElementType>::push(ElementType E) {
- //DEBUG(cout << name << " Buffer[" << ID << "]: Push " << E << flush << "\n");
- //unique_lock<mutex> lk(mtx);
- pthread_mutex_lock(&mtx);
- if((Head +1) % bufferSize == Tail) {
- //DEBUG(cout << name << " Buffer[" << ID << "]: Push going to sleep ...\n");
- //cv.wait(lk);
- pthread_cond_wait(&cv, &mtx);
- //DEBUG(cout << name << " Buffer[" << ID << "]: Push woke up\n");
- }
- buffer[Head] = E;
- Head = (Head+1) % bufferSize;
- numElements++;
- //DEBUG(cout << name << " Buffer[" << ID << "]: Total Elements = " << numElements << flush << "\n");
- //lk.unlock();
- pthread_mutex_unlock(&mtx);
- //cv.notify_one();
- pthread_cond_signal(&cv);
- return true;
-}
-
-template <class ElementType>
-ElementType CircularBuffer<ElementType>::pop() {
- //unique_lock<mutex> lk(mtx);
- //DEBUG(cout << name << " Buffer[" << ID << "]: Pop\n");
- pthread_mutex_lock(&mtx);
- if(Tail == Head) {
- //DEBUG(cout << name << " Buffer[" << ID << "]: Pop going to sleep ...\n");
- //cv.wait(lk);
- pthread_cond_wait(&cv, &mtx);
- //DEBUG(cout << name << " Buffer[" << ID << "]: Pop woke up\n");
- }
- ElementType E = buffer[Tail];
- Tail = (Tail + 1) % bufferSize;
- numElements--;
- //DEBUG(cout << name << " Buffer[" << ID << "]: Total Elements = " << numElements << flush << "\n");
- //lk.unlock();
- pthread_mutex_unlock(&mtx);
- //cv.notify_one();
- pthread_cond_signal(&cv);
- return E;
-}
-
-extern "C" {
-// Functions to push and pop values from pipeline buffers
-uint64_t llvm_visc_bufferPop(void*);
-void llvm_visc_bufferPush(void*, uint64_t);
-
-// Functions to create and destroy buffers
-void* llvm_visc_createBindInBuffer(void*, uint64_t, unsigned);
-void* llvm_visc_createBindOutBuffer(void*, uint64_t);
-void* llvm_visc_createEdgeBuffer(void*, uint64_t);
-void* llvm_visc_createLastInputBuffer(void*, uint64_t);
-
-void llvm_visc_freeBuffers(void*);
-
-// Functions to create and destroy threads
-void llvm_visc_createThread(void* graphID, void*(*Func)(void*), void*);
-void llvm_visc_freeThreads(void*);
-
-// Launch API for a streaming graph.
-// Arguments:
-// (1) Launch Function: void* (void*, void*)
-// (2) Push Function: void (void*, std::vector<uint64_t>**, unsgined)
-// (3) Pop Function: void* (std::vector<uint64_t>**, unsigned)
-void* llvm_visc_streamLaunch(void(*LaunchFunc)(void*, void*), void*);
-void llvm_visc_streamPush(void* graphID, void* args);
-void* llvm_visc_streamPop(void* graphID);
-void llvm_visc_streamWait(void* graphID);
-
-}
-
-#endif //VISC_RT_HEADER