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diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/approxhpvm_src.cc b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/approxhpvm_src.cc
deleted file mode 100644
index d8985397705cf19d5533a7e4a376a71a9f130fb0..0000000000000000000000000000000000000000
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/approxhpvm_src.cc
+++ /dev/null
@@ -1,430 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/stat.h>
-#include <cstring>
-#include <visc.h>
-#include <tensorTypes.h>
-#include <tensorUtils.h>
-
-void var_0_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_1_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_2_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_3_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_4_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_5_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_6_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_7_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_8_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_9_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_10_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_11_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_12_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_13_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_14_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_mul(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_15_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_16_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_relu(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_17_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_mul(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_18_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(2, t1, t2, 0);
-
- void *r = __visc__tensor_add(t1, t2);
- __visc__return(2, r, (size_t) 0);
-}
-
-void var_19_node(void* t1, size_t bytes_t1) {
- __visc__hint(visc::CUDNN_TARGET);
- __visc__attributes(1, t1, 0);
-
- void* r = __visc__tensor_softmax(t1);
- __visc__return(2, r, (size_t) 0);
-}
-
-void root(void* input, size_t input_bytes,
- void* conv2d_1_w, size_t conv2d_1_w_bytes,
- void* conv2d_1_b, size_t conv2d_1_b_bytes,
- void* conv2d_2_w, size_t conv2d_2_w_bytes,
- void* conv2d_2_b, size_t conv2d_2_b_bytes,
- void* conv2d_3_w, size_t conv2d_3_w_bytes,
- void* conv2d_3_b, size_t conv2d_3_b_bytes,
- void* conv2d_4_w, size_t conv2d_4_w_bytes,
- void* conv2d_4_b, size_t conv2d_4_b_bytes,
- void* dense_1_w, size_t dense_1_w_bytes,
- void* dense_1_b, size_t dense_1_b_bytes,
- void* dense_2_w, size_t dense_2_w_bytes,
- void* dense_2_b, size_t dense_2_b_bytes){
-
-
- __visc__hint(visc::CPU_TARGET);
- __visc__attributes(13, input, conv2d_1_w, conv2d_1_b, conv2d_2_w, conv2d_2_b, conv2d_3_w, conv2d_3_b, conv2d_4_w, conv2d_4_b, dense_1_w, dense_1_b, dense_2_w, dense_2_b, 0);
-
-
- void* var_0 = __visc__createNodeND(0, var_0_node);
-
- __visc__bindIn(var_0, 0, 0, 0);
- __visc__bindIn(var_0, 1, 1, 0);
- __visc__bindIn(var_0, 2, 2, 0);
- __visc__bindIn(var_0, 3, 3, 0);
-
- void* var_1 = __visc__createNodeND(0, var_1_node);
-
- __visc__edge(var_0, var_1, 1, 0, 0, 0);
- __visc__edge(var_0, var_1, 1, 1, 1, 0);
- __visc__bindIn(var_1, 4, 2, 0);
- __visc__bindIn(var_1, 5, 3, 0);
-
- void* var_2 = __visc__createNodeND(0, var_2_node);
-
- __visc__edge(var_1, var_2, 1, 0, 0, 0);
- __visc__edge(var_1, var_2, 1, 1, 1, 0);
-
- void* var_3 = __visc__createNodeND(0, var_3_node);
-
- __visc__edge(var_2, var_3, 1, 0, 0, 0);
- __visc__edge(var_2, var_3, 1, 1, 1, 0);
- __visc__bindIn(var_3, 6, 2, 0);
- __visc__bindIn(var_3, 7, 3, 0);
-
- void* var_4 = __visc__createNodeND(0, var_4_node);
-
- __visc__edge(var_3, var_4, 1, 0, 0, 0);
- __visc__edge(var_3, var_4, 1, 1, 1, 0);
- __visc__bindIn(var_4, 8, 2, 0);
- __visc__bindIn(var_4, 9, 3, 0);
-
- void* var_5 = __visc__createNodeND(0, var_5_node);
-
- __visc__edge(var_4, var_5, 1, 0, 0, 0);
- __visc__edge(var_4, var_5, 1, 1, 1, 0);
-
- void* var_6 = __visc__createNodeND(0, var_6_node);
-
- __visc__edge(var_5, var_6, 1, 0, 0, 0);
- __visc__edge(var_5, var_6, 1, 1, 1, 0);
-
- void* var_7 = __visc__createNodeND(0, var_7_node);
-
- __visc__edge(var_6, var_7, 1, 0, 0, 0);
- __visc__edge(var_6, var_7, 1, 1, 1, 0);
- __visc__bindIn(var_7, 10, 2, 0);
- __visc__bindIn(var_7, 11, 3, 0);
-
- void* var_8 = __visc__createNodeND(0, var_8_node);
-
- __visc__edge(var_7, var_8, 1, 0, 0, 0);
- __visc__edge(var_7, var_8, 1, 1, 1, 0);
- __visc__bindIn(var_8, 12, 2, 0);
- __visc__bindIn(var_8, 13, 3, 0);
-
- void* var_9 = __visc__createNodeND(0, var_9_node);
-
- __visc__edge(var_8, var_9, 1, 0, 0, 0);
- __visc__edge(var_8, var_9, 1, 1, 1, 0);
-
- void* var_10 = __visc__createNodeND(0, var_10_node);
-
- __visc__edge(var_9, var_10, 1, 0, 0, 0);
- __visc__edge(var_9, var_10, 1, 1, 1, 0);
- __visc__bindIn(var_10, 14, 2, 0);
- __visc__bindIn(var_10, 15, 3, 0);
-
- void* var_11 = __visc__createNodeND(0, var_11_node);
-
- __visc__edge(var_10, var_11, 1, 0, 0, 0);
- __visc__edge(var_10, var_11, 1, 1, 1, 0);
- __visc__bindIn(var_11, 16, 2, 0);
- __visc__bindIn(var_11, 17, 3, 0);
-
- void* var_12 = __visc__createNodeND(0, var_12_node);
-
- __visc__edge(var_11, var_12, 1, 0, 0, 0);
- __visc__edge(var_11, var_12, 1, 1, 1, 0);
-
- void* var_13 = __visc__createNodeND(0, var_13_node);
-
- __visc__edge(var_12, var_13, 1, 0, 0, 0);
- __visc__edge(var_12, var_13, 1, 1, 1, 0);
-
- void* var_14 = __visc__createNodeND(0, var_14_node);
-
- __visc__edge(var_13, var_14, 1, 0, 0, 0);
- __visc__edge(var_13, var_14, 1, 1, 1, 0);
- __visc__bindIn(var_14, 18, 2, 0);
- __visc__bindIn(var_14, 19, 3, 0);
-
- void* var_15 = __visc__createNodeND(0, var_15_node);
-
- __visc__edge(var_14, var_15, 1, 0, 0, 0);
- __visc__edge(var_14, var_15, 1, 1, 1, 0);
- __visc__bindIn(var_15, 20, 2, 0);
- __visc__bindIn(var_15, 21, 3, 0);
-
- void* var_16 = __visc__createNodeND(0, var_16_node);
-
- __visc__edge(var_15, var_16, 1, 0, 0, 0);
- __visc__edge(var_15, var_16, 1, 1, 1, 0);
-
- void* var_17 = __visc__createNodeND(0, var_17_node);
-
- __visc__edge(var_16, var_17, 1, 0, 0, 0);
- __visc__edge(var_16, var_17, 1, 1, 1, 0);
- __visc__bindIn(var_17, 22, 2, 0);
- __visc__bindIn(var_17, 23, 3, 0);
-
- void* var_18 = __visc__createNodeND(0, var_18_node);
-
- __visc__edge(var_17, var_18, 1, 0, 0, 0);
- __visc__edge(var_17, var_18, 1, 1, 1, 0);
- __visc__bindIn(var_18, 24, 2, 0);
- __visc__bindIn(var_18, 25, 3, 0);
-
- void* var_19 = __visc__createNodeND(0, var_19_node);
-
- __visc__edge(var_18, var_19, 1, 0, 0, 0);
- __visc__edge(var_18, var_19, 1, 1, 1, 0);
-
- __visc__bindOut(var_19, 0, 0, 0);
- __visc__bindOut(var_19, 1, 1, 0);
-
-}
-
-struct ret_t {
- void* tensor;
- size_t bytes;
-};
-
-typedef struct __attribute__((__packed__)) {
- void* input;
- size_t input_bytes;
- void* conv2d_1_w;
- size_t conv2d_1_w_bytes;
- void* conv2d_1_b;
- size_t conv2d_1_b_bytes;
- void* conv2d_2_w;
- size_t conv2d_2_w_bytes;
- void* conv2d_2_b;
- size_t conv2d_2_b_bytes;
- void* conv2d_3_w;
- size_t conv2d_3_w_bytes;
- void* conv2d_3_b;
- size_t conv2d_3_b_bytes;
- void* conv2d_4_w;
- size_t conv2d_4_w_bytes;
- void* conv2d_4_b;
- size_t conv2d_4_b_bytes;
- void* dense_1_w;
- size_t dense_1_w_bytes;
- void* dense_1_b;
- size_t dense_1_b_bytes;
- void* dense_2_w;
- size_t dense_2_w_bytes;
- void* dense_2_b;
- size_t dense_2_b_bytes;
-
- struct ret_t r;
-}
-RootIn;
-
-int main(){
-
-std::string dir_prefix = std::string("hpvm_mio_4/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,64,32,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,64,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1600,256);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,256,1,1);
-std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
-void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,256,5);
-std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
-void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,5,1,1);
-void* input = readTrainedWeights(input_path.c_str(), 0,5000,3,32,32);
-uint32_t* labels = readLabels2(labels_path.c_str(),5000);
-
-__visc__init();
-RootIn* args = static_cast<RootIn*>(malloc(sizeof(RootIn)));
-
-args->input = input;
-args->input_bytes = 0;
-args->conv2d_1_w = conv2d_1_w;
-args->conv2d_1_w_bytes = 0;
-args->conv2d_1_b = conv2d_1_b;
-args->conv2d_1_b_bytes = 0;
-args->conv2d_2_w = conv2d_2_w;
-args->conv2d_2_w_bytes = 0;
-args->conv2d_2_b = conv2d_2_b;
-args->conv2d_2_b_bytes = 0;
-args->conv2d_3_w = conv2d_3_w;
-args->conv2d_3_w_bytes = 0;
-args->conv2d_3_b = conv2d_3_b;
-args->conv2d_3_b_bytes = 0;
-args->conv2d_4_w = conv2d_4_w;
-args->conv2d_4_w_bytes = 0;
-args->conv2d_4_b = conv2d_4_b;
-args->conv2d_4_b_bytes = 0;
-args->dense_1_w = dense_1_w;
-args->dense_1_w_bytes = 0;
-args->dense_1_b = dense_1_b;
-args->dense_1_b_bytes = 0;
-args->dense_2_w = dense_2_w;
-args->dense_2_w_bytes = 0;
-args->dense_2_b = dense_2_b;
-args->dense_2_b_bytes = 0;
-
-void* dfg = __visc__launch(0, root, (void*) args);
-
-__visc__wait(dfg);
-
-void *result = static_cast<RootIn*>(args)->input;
-hpvm_request_tensor(result, 0);
-
-__visc__cleanup();
- computeAccuracy3(labels, result);
-return 0;
-
-}
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/comp_files/compareDenseWeights.py b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/comp_files/compareDenseWeights.py
deleted file mode 100644
index 2a323bb65028b253c8070a3b90c1f82be6f78d70..0000000000000000000000000000000000000000
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/comp_files/compareDenseWeights.py
+++ /dev/null
@@ -1,42 +0,0 @@
-
-
-import numpy as np
-import os
-import sys
-
-
-if __name__ == "__main__":
-
- f_path = "dense_1_w_fp16_trans.bin"
-
- dim1 = 1600
- dim2 = 256
- #dim1 = 256
- #dim2 = 5
-
- print (f_path, dim1, dim2)
- x = np.fromfile(f_path, dtype='float16')
-
- x_reshaped = np.reshape(x, (dim1, dim2))
- x_transposed = np.transpose(x_reshaped, (1, 0))
-
- print ("old = ", x_transposed)
-
- f_path = "dense_1_w_fp16.bin"
- #dim1 = 256
- #dim2 = 5
- dim1 = 1600
- dim2 = 256
-
- print (f_path, dim1, dim2)
-
- x = np.fromfile(f_path, dtype='float16')
- x_reshaped = np.reshape(x, (dim2, dim1))
-
- print ("new = ", x_reshaped)
-
-
-
-
-
-
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/comp_files/compareDenseWeights.py~ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/comp_files/compareDenseWeights.py~
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/comp_files/dense_1_w_fp16.bin b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/comp_files/dense_1_w_fp16.bin
deleted file mode 100644
index 92fa27c1ac651a1430650a3f1ddbaa5665788302..0000000000000000000000000000000000000000
Binary files a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/comp_files/dense_1_w_fp16.bin and /dev/null differ
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/comp_files/dense_1_w_fp16_trans.bin b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/comp_files/dense_1_w_fp16_trans.bin
deleted file mode 100644
index 7e2d4620fdb6e22f8c08e7ca2ad83778b45592d4..0000000000000000000000000000000000000000
Binary files a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/comp_files/dense_1_w_fp16_trans.bin and /dev/null differ
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/comp_files/dense_2_w_fp16.bin b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/comp_files/dense_2_w_fp16.bin
deleted file mode 100644
index 1fd5d45e772c3767c86e524af4260554b49f4eaa..0000000000000000000000000000000000000000
Binary files a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/comp_files/dense_2_w_fp16.bin and /dev/null differ
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/comp_files/dense_2_w_fp16_trans.bin b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/comp_files/dense_2_w_fp16_trans.bin
deleted file mode 100644
index 082b2b57ccd34a11ee353983f6345727e2d561dd..0000000000000000000000000000000000000000
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diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/layers.txt b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/layers.txt
deleted file mode 100644
index c0aecb467775babfd4b5c2873abf287905ee11f8..0000000000000000000000000000000000000000
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/layers.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-Conv1,5000,3,32,32,32,3,3,3
-Conv2,5000,32,30,30,32,32,3,3
-Conv3,5000,32,14,14,64,32,3,3
-Conv4,5000,64,12,12,64,64,3,3
-FC1,5000,1600,1600,256
-FC2,5000,256,256,5
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/promise_src.cc b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/promise_src.cc
deleted file mode 100644
index fd96ab0878269718c58c52115a22b79e2f62ec99..0000000000000000000000000000000000000000
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/promise_src.cc
+++ /dev/null
@@ -1,93 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-int total_runs = 100;
-for (int i = 0 ; i < total_runs; i++){
-
-
-startMemTracking();
-
-int test_input_size = 5000;
-int batch_size = 5000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-
-
-std::string dir_prefix = std::string("hpvm_mio_4/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,64,32,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,64,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1600,256);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,256,1,1);
-std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
-void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,256,5);
-std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
-void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,5,1,1);
-
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = ConvLayer_PROMISE(input, -2.682209e-07, 1.0000002, conv2d_1_w, -1.9097954802513122, 1.849404644250894, conv2d_1_b, -1.4970889, 0.90984344, 0, 0, 1, 1, -1, 0, 1, 0.0, 1.9360680677890976, 9);
-void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 1.9360680677890976, conv2d_2_w, -0.6551046761870384, 0.5357062590122245, conv2d_2_b, -1.2897198, 0.25627556, 0, 0, 1, 1, 0, 2, 1, 0.0, 3.61756298995042, 9);
-void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 3.61756298995042, conv2d_3_w, -0.479531730890274, 0.38338643845919407, conv2d_3_b, -1.9581897, 1.2684464, 0, 0, 1, 1, -1, 0, 1, 0.0, 4.717274737834942, 9);
-void* var_3 = ConvLayer_PROMISE(var_2, 0.0, 4.717274737834942, conv2d_4_w, -0.37545250764489174, 0.3687883540093907, conv2d_4_b, -0.5458527, 0.6755934, 0, 0, 1, 1, 0, 2, 1, 0.0, 6.558154335499082, 9);
-void* var_4 = FCLayer_PROMISE(var_3, 0.0, 6.558154335499082, dense_1_w, -0.19869577795267107, 0.2030584679543994, dense_1_b, -0.1697124, 0.22991186, 1, 0.0, 8.8694415378571, 9);
-void* var_5 = FCLayer_PROMISE(var_4, 0.0, 8.8694415378571, dense_2_w, -0.38784850630164147, 0.387768742352725, dense_2_b, -0.65646386, 0.75299513, -1, -23.875294536590577, 35.08045856094383, 9);
-void* var_6 = tensorSoftmax(var_5);
-
-uint32_t* labels = readLabelsBatch3(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy3(labels, var_6);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-}
-
-dumpExecutionAccuracies();
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/readInput.py b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/readInput.py
deleted file mode 100644
index 6c5d33cc8067b2489392d1dc5c30e9a9dd41ab03..0000000000000000000000000000000000000000
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/readInput.py
+++ /dev/null
@@ -1,18 +0,0 @@
-
-
-import numpy as np
-import os
-import sys
-
-
-if __name__ == "__main__":
-
- f_path = "input.bin"
-
- x = np.fromfile(f_path, dtype='float32')
-
- print (x, np.min(x), np.max(x))
-
-
-
-
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/readInput.py~ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/readInput.py~
deleted file mode 100644
index 2a323bb65028b253c8070a3b90c1f82be6f78d70..0000000000000000000000000000000000000000
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/readInput.py~
+++ /dev/null
@@ -1,42 +0,0 @@
-
-
-import numpy as np
-import os
-import sys
-
-
-if __name__ == "__main__":
-
- f_path = "dense_1_w_fp16_trans.bin"
-
- dim1 = 1600
- dim2 = 256
- #dim1 = 256
- #dim2 = 5
-
- print (f_path, dim1, dim2)
- x = np.fromfile(f_path, dtype='float16')
-
- x_reshaped = np.reshape(x, (dim1, dim2))
- x_transposed = np.transpose(x_reshaped, (1, 0))
-
- print ("old = ", x_transposed)
-
- f_path = "dense_1_w_fp16.bin"
- #dim1 = 256
- #dim2 = 5
- dim1 = 1600
- dim2 = 256
-
- print (f_path, dim1, dim2)
-
- x = np.fromfile(f_path, dtype='float16')
- x_reshaped = np.reshape(x, (dim2, dim1))
-
- print ("new = ", x_reshaped)
-
-
-
-
-
-
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/src.cc b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/src.cc
deleted file mode 100644
index 332d9903e02fa5a218a3117947e9dbf955d07d10..0000000000000000000000000000000000000000
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/src.cc
+++ /dev/null
@@ -1,98 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include <fcntl.h>
-#include <sys/types.h>
-#include <sys/stat.h>
-#include <string.h>
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-int main(){
-
-llvm_hpvm_initTensorRt(0);
-
-
-std::string dir_prefix = std::string("hpvm_mio_4/");
-std::string input_path = dir_prefix + std::string("input.bin");
-std::string labels_path = dir_prefix + std::string("labels.bin");
-std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
-void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,32,3,3,3);
-std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
-void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
-void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,32,32,3,3);
-std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
-void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,32,1,1);
-std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
-void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,64,32,3,3);
-std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
-void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,64,1,1);
-std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
-void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,64,64,3,3);
-std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
-void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,64,1,1);
-std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
-void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,1600,256);
-std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
-void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,256,1,1);
-std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
-void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,256,5);
-std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
-void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,5,1,1);
-
-
-
-startMemTracking();
-
-int test_input_size = 5000;
-int batch_size = 5000;
-int batch_count = test_input_size / batch_size;
-float final_accuracy = 0.0;
-
-for(int i = 0; i < batch_count; i++){
-
-int start = i * batch_size;
-int end = (i + 1) * batch_size;
-
-void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
-
-void* var_0 = tensorConvolution(input, conv2d_1_w, 0, 0, 1, 1, 1, 1);
-void* var_1 = tensorAdd(var_0, conv2d_1_b);
-void* var_2 = tensorRelu(var_1);
-void* var_3 = tensorConvolution(var_2, conv2d_2_w, 0, 0, 1, 1, 1, 1);
-void* var_4 = tensorAdd(var_3, conv2d_2_b);
-void* var_5 = tensorRelu(var_4);
-void* var_6 = tensorPooling(var_5,0,2,2,0,0,2,2);
-void* var_8 = tensorConvolution(var_6, conv2d_3_w, 0, 0, 1, 1, 1, 1);
-void* var_9 = tensorAdd(var_8, conv2d_3_b);
-void* var_10 = tensorRelu(var_9);
-void* var_11 = tensorConvolution(var_10, conv2d_4_w, 0, 0, 1, 1, 1, 1);
-void* var_12 = tensorAdd(var_11, conv2d_4_b);
-void* var_13 = tensorRelu(var_12);
-void* var_14 = tensorPooling(var_13,0,2,2,0,0,2,2);
-void* var_17 = tensorGemmGPU(var_14, dense_1_w);
-void* var_18 = tensorAdd(var_17, dense_1_b);
-void* var_19 = tensorRelu(var_18);
-void* var_21 = tensorGemmGPU(var_19, dense_2_w);
-void* var_22 = tensorAdd(var_21, dense_2_b);
-void* var_23 = tensorSoftmax(var_22);
-
-uint32_t* labels = readLabelsBatch3(labels_path.c_str(),start,end);
-
-float accuracy = computeAccuracy3(labels, var_23);
-final_accuracy += accuracy;
-freeBatchMemory();
-
-}
-
-final_accuracy = final_accuracy / batch_count;
-dumpFinalAccuracy(final_accuracy);
-
-
-llvm_hpvm_cleanupTensorRt();
-
-return 0;
-
-}
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/src/calib.txt b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/src/calib.txt
deleted file mode 100644
index 4fff404f7e9b7beb3ef3dfc19bf1648c68dbe7be..0000000000000000000000000000000000000000
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/src/calib.txt
+++ /dev/null
@@ -1,21 +0,0 @@
-input: 0.007874015748031496
-conv1: 0.041152522047244094
-add1: 0.041152522047244094
-relu1: 0.03673215196850394
-conv2: 0.18990387401574804
-add2: 0.18990387401574804
-relu2: 0.12851757480314963
-pool1: 0.12851757480314963
-conv3: 0.4379456692913386
-add3: 0.4379456692913386
-relu3: 0.13566622047244095
-conv4: 0.3754296456692913
-add4: 0.3754296456692913
-relu4: 0.15238466141732282
-pool2: 0.15238466141732282
-gemm1: 0.27306629921259845
-add5: 0.27306629921259845
-relu5: 0.20020370078740157
-gemm2: 0.6007037007874017
-add6: 0.6007037007874017
-softmax1: 0.007874015748031496
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/src/compile_benchmark.sh b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/src/compile_benchmark.sh
deleted file mode 100644
index 091245e6ab3466872e42544bcc1b9be146d85c15..0000000000000000000000000000000000000000
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/src/compile_benchmark.sh
+++ /dev/null
@@ -1 +0,0 @@
-python3 /home/hsharif3/Gitlab/hpvm_release_nvdla/hpvm/build/tools/hpvm/tools/hpvm-clang/main.py -t nvdla --conf-file data/tuner_confs.txt -DMODEL_PARAMS_DIR=/home/hsharif3/Gitlab/hpvm_release_v2/hpvm/hpvm/test/dnn_benchmarks/model_params/ -fno-exceptions miniera-hpvm.cpp miniera-hpvm
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/src/miniera-hpvm.cpp b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/src/miniera-hpvm.cpp
deleted file mode 100644
index 9c9099cda1afaa4b4ba8812ab5ef84729b12c667..0000000000000000000000000000000000000000
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/src/miniera-hpvm.cpp
+++ /dev/null
@@ -1,451 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <cstring>
-#include <string.h>
-#include <iostream>
-#include <hpvm.h>
-#include <tensorUtils.h>
-//#include <tensorUtils.h>
-
-
-void* readTrainedWeights(const char* file_name, int data_type,
- int dim1_size, int dim2_size,
- int dim3_size, int dim4_size);
-
-
-void var_0_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __hpvm__hint(hpvm::CUDNN_TARGET);
- __hpvm__attributes(2, t1, t2, 0);
-
- void *r = __hpvm__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __hpvm__return(2, r, (size_t) 0);
-}
-
-void var_1_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __hpvm__hint(hpvm::CUDNN_TARGET);
- __hpvm__attributes(2, t1, t2, 0);
-
- void *r = __hpvm__tensor_add(t1, t2);
- __hpvm__return(2, r, (size_t) 0);
-}
-
-void var_2_node(void* t1, size_t bytes_t1) {
- __hpvm__hint(hpvm::CUDNN_TARGET);
- __hpvm__attributes(1, t1, 0);
-
- void* r = __hpvm__tensor_relu(t1);
- __hpvm__return(2, r, (size_t) 0);
-}
-
-void var_3_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __hpvm__hint(hpvm::CUDNN_TARGET);
- __hpvm__attributes(2, t1, t2, 0);
-
- void *r = __hpvm__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __hpvm__return(2, r, (size_t) 0);
-}
-
-void var_4_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __hpvm__hint(hpvm::CUDNN_TARGET);
- __hpvm__attributes(2, t1, t2, 0);
-
- void *r = __hpvm__tensor_add(t1, t2);
- __hpvm__return(2, r, (size_t) 0);
-}
-
-void var_5_node(void* t1, size_t bytes_t1) {
- __hpvm__hint(hpvm::CUDNN_TARGET);
- __hpvm__attributes(1, t1, 0);
-
- void* r = __hpvm__tensor_relu(t1);
- __hpvm__return(2, r, (size_t) 0);
-}
-
-void var_6_node(void* t1, size_t bytes_t1) {
- __hpvm__hint(hpvm::CUDNN_TARGET);
- __hpvm__attributes(1, t1, 0);
-
- void* r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __hpvm__return(2, r, (size_t) 0);
-}
-
-void var_7_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __hpvm__hint(hpvm::CUDNN_TARGET);
- __hpvm__attributes(2, t1, t2, 0);
-
- void *r = __hpvm__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __hpvm__return(2, r, (size_t) 0);
-}
-
-void var_8_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __hpvm__hint(hpvm::CUDNN_TARGET);
- __hpvm__attributes(2, t1, t2, 0);
-
- void *r = __hpvm__tensor_add(t1, t2);
- __hpvm__return(2, r, (size_t) 0);
-}
-
-void var_9_node(void* t1, size_t bytes_t1) {
- __hpvm__hint(hpvm::CUDNN_TARGET);
- __hpvm__attributes(1, t1, 0);
-
- void* r = __hpvm__tensor_relu(t1);
- __hpvm__return(2, r, (size_t) 0);
-}
-
-void var_10_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __hpvm__hint(hpvm::CUDNN_TARGET);
- __hpvm__attributes(2, t1, t2, 0);
-
- void *r = __hpvm__tensor_convolution(t1, t2, 0, 0, 1, 1);
- __hpvm__return(2, r, (size_t) 0);
-}
-
-void var_11_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __hpvm__hint(hpvm::CUDNN_TARGET);
- __hpvm__attributes(2, t1, t2, 0);
-
- void *r = __hpvm__tensor_add(t1, t2);
- __hpvm__return(2, r, (size_t) 0);
-}
-
-void var_12_node(void* t1, size_t bytes_t1) {
- __hpvm__hint(hpvm::CUDNN_TARGET);
- __hpvm__attributes(1, t1, 0);
-
- void* r = __hpvm__tensor_relu(t1);
- __hpvm__return(2, r, (size_t) 0);
-}
-
-void var_13_node(void* t1, size_t bytes_t1) {
- __hpvm__hint(hpvm::CUDNN_TARGET);
- __hpvm__attributes(1, t1, 0);
-
- void* r = __hpvm__tensor_pool_max(t1, 2, 2, 0, 0, 2, 2);
- __hpvm__return(2, r, (size_t) 0);
-}
-
-void var_14_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __hpvm__hint(hpvm::CUDNN_TARGET);
- __hpvm__attributes(2, t1, t2, 0);
-
- void *r = __hpvm__tensor_mul(t1, t2);
- __hpvm__return(2, r, (size_t) 0);
-}
-
-void var_15_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __hpvm__hint(hpvm::CUDNN_TARGET);
- __hpvm__attributes(2, t1, t2, 0);
-
- void *r = __hpvm__tensor_add(t1, t2);
- __hpvm__return(2, r, (size_t) 0);
-}
-
-void var_16_node(void* t1, size_t bytes_t1) {
- __hpvm__hint(hpvm::CUDNN_TARGET);
- __hpvm__attributes(1, t1, 0);
-
- void* r = __hpvm__tensor_relu(t1);
- __hpvm__return(2, r, (size_t) 0);
-}
-
-void var_17_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __hpvm__hint(hpvm::CUDNN_TARGET);
- __hpvm__attributes(2, t1, t2, 0);
-
- void *r = __hpvm__tensor_mul(t1, t2);
- __hpvm__return(2, r, (size_t) 0);
-}
-
-void var_18_node(void* t1, size_t bytes_t1, void* t2, size_t bytes_t2) {
- __hpvm__hint(hpvm::CUDNN_TARGET);
- __hpvm__attributes(2, t1, t2, 0);
-
- void *r = __hpvm__tensor_add(t1, t2);
- __hpvm__return(2, r, (size_t) 0);
-}
-
-void var_19_node(void* t1, size_t bytes_t1) {
- __hpvm__hint(hpvm::CUDNN_TARGET);
- __hpvm__attributes(1, t1, 0);
-
- void* r = __hpvm__tensor_softmax(t1);
- __hpvm__return(2, r, (size_t) 0);
-}
-
-void root(void* input, size_t input_bytes,
- void* conv2d_1_w, size_t conv2d_1_w_bytes,
- void* conv2d_1_b, size_t conv2d_1_b_bytes,
- void* conv2d_2_w, size_t conv2d_2_w_bytes,
- void* conv2d_2_b, size_t conv2d_2_b_bytes,
- void* conv2d_3_w, size_t conv2d_3_w_bytes,
- void* conv2d_3_b, size_t conv2d_3_b_bytes,
- void* conv2d_4_w, size_t conv2d_4_w_bytes,
- void* conv2d_4_b, size_t conv2d_4_b_bytes,
- void* dense_1_w, size_t dense_1_w_bytes,
- void* dense_1_b, size_t dense_1_b_bytes,
- void* dense_2_w, size_t dense_2_w_bytes,
- void* dense_2_b, size_t dense_2_b_bytes){
-
-
- __hpvm__hint(hpvm::CPU_TARGET);
- __hpvm__attributes(13, input, conv2d_1_w, conv2d_1_b, conv2d_2_w, conv2d_2_b, conv2d_3_w, conv2d_3_b, conv2d_4_w, conv2d_4_b, dense_1_w, dense_1_b, dense_2_w, dense_2_b, 0);
-
-
- void* var_0 = __hpvm__createNodeND(0, var_0_node);
-
- __hpvm__bindIn(var_0, 0, 0, 0);
- __hpvm__bindIn(var_0, 1, 1, 0);
- __hpvm__bindIn(var_0, 2, 2, 0);
- __hpvm__bindIn(var_0, 3, 3, 0);
-
- void* var_1 = __hpvm__createNodeND(0, var_1_node);
-
- __hpvm__edge(var_0, var_1, 1, 0, 0, 0);
- __hpvm__edge(var_0, var_1, 1, 1, 1, 0);
- __hpvm__bindIn(var_1, 4, 2, 0);
- __hpvm__bindIn(var_1, 5, 3, 0);
-
- void* var_2 = __hpvm__createNodeND(0, var_2_node);
-
- __hpvm__edge(var_1, var_2, 1, 0, 0, 0);
- __hpvm__edge(var_1, var_2, 1, 1, 1, 0);
-
- void* var_3 = __hpvm__createNodeND(0, var_3_node);
-
- __hpvm__edge(var_2, var_3, 1, 0, 0, 0);
- __hpvm__edge(var_2, var_3, 1, 1, 1, 0);
- __hpvm__bindIn(var_3, 6, 2, 0);
- __hpvm__bindIn(var_3, 7, 3, 0);
-
- void* var_4 = __hpvm__createNodeND(0, var_4_node);
-
- __hpvm__edge(var_3, var_4, 1, 0, 0, 0);
- __hpvm__edge(var_3, var_4, 1, 1, 1, 0);
- __hpvm__bindIn(var_4, 8, 2, 0);
- __hpvm__bindIn(var_4, 9, 3, 0);
-
- void* var_5 = __hpvm__createNodeND(0, var_5_node);
-
- __hpvm__edge(var_4, var_5, 1, 0, 0, 0);
- __hpvm__edge(var_4, var_5, 1, 1, 1, 0);
-
- void* var_6 = __hpvm__createNodeND(0, var_6_node);
-
- __hpvm__edge(var_5, var_6, 1, 0, 0, 0);
- __hpvm__edge(var_5, var_6, 1, 1, 1, 0);
-
- void* var_7 = __hpvm__createNodeND(0, var_7_node);
-
- __hpvm__edge(var_6, var_7, 1, 0, 0, 0);
- __hpvm__edge(var_6, var_7, 1, 1, 1, 0);
- __hpvm__bindIn(var_7, 10, 2, 0);
- __hpvm__bindIn(var_7, 11, 3, 0);
-
- void* var_8 = __hpvm__createNodeND(0, var_8_node);
-
- __hpvm__edge(var_7, var_8, 1, 0, 0, 0);
- __hpvm__edge(var_7, var_8, 1, 1, 1, 0);
- __hpvm__bindIn(var_8, 12, 2, 0);
- __hpvm__bindIn(var_8, 13, 3, 0);
-
- void* var_9 = __hpvm__createNodeND(0, var_9_node);
-
- __hpvm__edge(var_8, var_9, 1, 0, 0, 0);
- __hpvm__edge(var_8, var_9, 1, 1, 1, 0);
-
- void* var_10 = __hpvm__createNodeND(0, var_10_node);
-
- __hpvm__edge(var_9, var_10, 1, 0, 0, 0);
- __hpvm__edge(var_9, var_10, 1, 1, 1, 0);
- __hpvm__bindIn(var_10, 14, 2, 0);
- __hpvm__bindIn(var_10, 15, 3, 0);
-
- void* var_11 = __hpvm__createNodeND(0, var_11_node);
-
- __hpvm__edge(var_10, var_11, 1, 0, 0, 0);
- __hpvm__edge(var_10, var_11, 1, 1, 1, 0);
- __hpvm__bindIn(var_11, 16, 2, 0);
- __hpvm__bindIn(var_11, 17, 3, 0);
-
- void* var_12 = __hpvm__createNodeND(0, var_12_node);
-
- __hpvm__edge(var_11, var_12, 1, 0, 0, 0);
- __hpvm__edge(var_11, var_12, 1, 1, 1, 0);
-
- void* var_13 = __hpvm__createNodeND(0, var_13_node);
-
- __hpvm__edge(var_12, var_13, 1, 0, 0, 0);
- __hpvm__edge(var_12, var_13, 1, 1, 1, 0);
-
- void* var_14 = __hpvm__createNodeND(0, var_14_node);
-
- __hpvm__edge(var_13, var_14, 1, 0, 0, 0);
- __hpvm__edge(var_13, var_14, 1, 1, 1, 0);
- __hpvm__bindIn(var_14, 18, 2, 0);
- __hpvm__bindIn(var_14, 19, 3, 0);
-
- void* var_15 = __hpvm__createNodeND(0, var_15_node);
-
- __hpvm__edge(var_14, var_15, 1, 0, 0, 0);
- __hpvm__edge(var_14, var_15, 1, 1, 1, 0);
- __hpvm__bindIn(var_15, 20, 2, 0);
- __hpvm__bindIn(var_15, 21, 3, 0);
-
- void* var_16 = __hpvm__createNodeND(0, var_16_node);
-
- __hpvm__edge(var_15, var_16, 1, 0, 0, 0);
- __hpvm__edge(var_15, var_16, 1, 1, 1, 0);
-
- void* var_17 = __hpvm__createNodeND(0, var_17_node);
-
- __hpvm__edge(var_16, var_17, 1, 0, 0, 0);
- __hpvm__edge(var_16, var_17, 1, 1, 1, 0);
- __hpvm__bindIn(var_17, 22, 2, 0);
- __hpvm__bindIn(var_17, 23, 3, 0);
-
- void* var_18 = __hpvm__createNodeND(0, var_18_node);
-
- __hpvm__edge(var_17, var_18, 1, 0, 0, 0);
- __hpvm__edge(var_17, var_18, 1, 1, 1, 0);
- __hpvm__bindIn(var_18, 24, 2, 0);
- __hpvm__bindIn(var_18, 25, 3, 0);
-
- void* var_19 = __hpvm__createNodeND(0, var_19_node);
-
- __hpvm__edge(var_18, var_19, 1, 0, 0, 0);
- __hpvm__edge(var_18, var_19, 1, 1, 1, 0);
-
- __hpvm__bindOut(var_19, 0, 0, 0);
- __hpvm__bindOut(var_19, 1, 1, 0);
-
-}
-
-struct ret_t {
- void* tensor;
- size_t bytes;
-};
-
-typedef struct __attribute__((__packed__)) {
- void* input;
- size_t input_bytes;
- void* conv2d_1_w;
- size_t conv2d_1_w_bytes;
- void* conv2d_1_b;
- size_t conv2d_1_b_bytes;
- void* conv2d_2_w;
- size_t conv2d_2_w_bytes;
- void* conv2d_2_b;
- size_t conv2d_2_b_bytes;
- void* conv2d_3_w;
- size_t conv2d_3_w_bytes;
- void* conv2d_3_b;
- size_t conv2d_3_b_bytes;
- void* conv2d_4_w;
- size_t conv2d_4_w_bytes;
- void* conv2d_4_b;
- size_t conv2d_4_b_bytes;
- void* dense_1_w;
- size_t dense_1_w_bytes;
- void* dense_1_b;
- size_t dense_1_b_bytes;
- void* dense_2_w;
- size_t dense_2_w_bytes;
- void* dense_2_b;
- size_t dense_2_b_bytes;
-
- struct ret_t r;
-}
-RootIn;
-
-
-const int batch_size = 500, input_size = 5000,
- batch_count = input_size / batch_size;
-
-
-int main(){
-
- //std::string input_path = "../../../../../projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/input_fp16.bin";
- std::string labels_path = "../../../../../projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/labels_fp16.bin";
- //char conv2d_1_w_path[] = "../../../../../projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/conv2d_1_w_fp16.bin";
- void* conv2d_1_w = readTrainedWeights("/home/hsharif3/Gitlab/old_hpvm_nvdla/hpvm/llvm/projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/conv2d_1_w_fp16.bin", 0,32,3,3,3);
- //char conv2d_1_b_path[] = "../../../../../projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/conv2d_1_b_fp16_fp16.bin";
- void* conv2d_1_b = readTrainedWeights("/home/hsharif3/Gitlab/old_hpvm_nvdla/hpvm/llvm/projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/conv2d_1_b_fp16.bin", 0,1,32, 1, 1);//30,30);
- //char conv2d_2_w_path[] = "../../../../../projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/conv2d_2_w_fp16_fp16.bin";
- void* conv2d_2_w = readTrainedWeights("/home/hsharif3/Gitlab/old_hpvm_nvdla/hpvm/llvm/projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/conv2d_2_w_fp16.bin", 0,32,32,3,3);
- //char conv2d_2_b_path[] = "../../../../../projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/conv2d_2_b_fp16_fp16.bin";
- void* conv2d_2_b = readTrainedWeights("/home/hsharif3/Gitlab/old_hpvm_nvdla/hpvm/llvm/projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/conv2d_2_b_fp16.bin", 0,1,32, 1, 1);//28,28);
- //char conv2d_3_w_path[] = "../../../../../projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/conv2d_3_w_fp16_fp16.bin";
- void* conv2d_3_w = readTrainedWeights("/home/hsharif3/Gitlab/old_hpvm_nvdla/hpvm/llvm/projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/conv2d_3_w_fp16.bin", 0,64,32,3,3);
- //char conv2d_3_b_path[] = "../../../../../projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/conv2d_3_b_fp16_fp16.bin";
- void* conv2d_3_b = readTrainedWeights("/home/hsharif3/Gitlab/old_hpvm_nvdla/hpvm/llvm/projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/conv2d_3_b_fp16.bin", 0,1,64, 1, 1);//12,12);
- //char conv2d_4_w_path[] = "../../../../../projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/conv2d_4_w_fp16_fp16.bin";
- void* conv2d_4_w = readTrainedWeights("/home/hsharif3/Gitlab/old_hpvm_nvdla/hpvm/llvm/projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/conv2d_4_w_fp16.bin", 0,64,64,3,3);
- //char conv2d_4_b_path[] = "../../../../../projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/conv2d_4_b_fp16_fp16.bin";
- void* conv2d_4_b = readTrainedWeights("/home/hsharif3/Gitlab/old_hpvm_nvdla/hpvm/llvm/projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/conv2d_4_b_fp16.bin", 0,1,64, 1, 1);//10,10);
- //char dense_1_w_path[] = "../../../../../projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/dense_1_w_fp16_fp16.bin";
- void* dense_1_w = readTrainedWeights("/home/hsharif3/Gitlab/old_hpvm_nvdla/hpvm/llvm/projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/dense_1_w_fp16.bin", 0,1,1,1600,256);
- //char dense_1_b_path[] = "../../../../../projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/dense_1_b_fp16_fp16.bin";
- void* dense_1_b = readTrainedWeights("/home/hsharif3/Gitlab/old_hpvm_nvdla/hpvm/llvm/projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/dense_1_b_fp16.bin", 0,1,256,1,1);
- //char dense_2_w_path[] = "../../../../../projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/dense_2_w_fp16_fp16.bin";
- void* dense_2_w = readTrainedWeights("/home/hsharif3/Gitlab/old_hpvm_nvdla/hpvm/llvm/projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/dense_2_w_fp16.bin", 0,1,1,256,5);
- //char dense_2_b_path[] = "../../../../../projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/dense_2_b_fp16_fp16.bin";
- void* dense_2_b = readTrainedWeights("/home/hsharif3/Gitlab/old_hpvm_nvdla/hpvm/llvm/projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/dense_2_b_fp16.bin", 0,1,5,1,1);
-
- //void* input = readTrainedWeights(input_path, 0,1,3,32,32);
- //uint32_t* labels = readLabels3(labels_path, 500);
-
- RootIn* args = static_cast<RootIn*>(malloc(sizeof(RootIn)));
-
- //args->input = input;
- //args->input_bytes = 0;
- args->conv2d_1_w = conv2d_1_w;
- args->conv2d_1_w_bytes = 0;
- args->conv2d_1_b = conv2d_1_b;
- args->conv2d_1_b_bytes = 0;
- args->conv2d_2_w = conv2d_2_w;
- args->conv2d_2_w_bytes = 0;
- args->conv2d_2_b = conv2d_2_b;
- args->conv2d_2_b_bytes = 0;
- args->conv2d_3_w = conv2d_3_w;
- args->conv2d_3_w_bytes = 0;
- args->conv2d_3_b = conv2d_3_b;
- args->conv2d_3_b_bytes = 0;
- args->conv2d_4_w = conv2d_4_w;
- args->conv2d_4_w_bytes = 0;
- args->conv2d_4_b = conv2d_4_b;
- args->conv2d_4_b_bytes = 0;
- args->dense_1_w = dense_1_w;
- args->dense_1_w_bytes = 0;
- args->dense_1_b = dense_1_b;
- args->dense_1_b_bytes = 0;
- args->dense_2_w = dense_2_w;
- args->dense_2_w_bytes = 0;
- args->dense_2_b = dense_2_b;
- args->dense_2_b_bytes = 0;
-
-
- __hpvm__init();
-
-
- startMemTracking();
-#pragma clang loop unroll(disable)
- for (int i = 0; i < batch_count; i++) {
- int start = i * batch_size, end = start + batch_size;
- void* input = readInputBatch("/home/hsharif3/Gitlab/old_hpvm_nvdla/hpvm/llvm/projects/hpvm-tensor-rt/model_params/legacy/hpvm_mio/input.bin", nchw, start, end, 3, 32, 32);
- args->input = input;
- args->input_bytes = 0;
-
- void *dfg = __hpvm__launch(0, root, (void *)args);
- __hpvm__wait(dfg);
- void *result = static_cast<RootIn *>(args)->r.tensor;
- hpvm_request_tensor(result, 0);
-
- llvm_hpvm_invokeRtControl(result, labels_path.c_str(), start, end);
- freeBatchMemory();
- }
- __hpvm__cleanup();
- return 0;
-
-}
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/src/README.md b/hpvm/test/epoch_dnn/README.md
similarity index 100%
rename from hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/src/README.md
rename to hpvm/test/epoch_dnn/README.md
diff --git a/hpvm/test/epoch_dnn/assets/miniera/calib.txt b/hpvm/test/epoch_dnn/assets/miniera/calib.txt
deleted file mode 100644
index 4fff404f7e9b7beb3ef3dfc19bf1648c68dbe7be..0000000000000000000000000000000000000000
--- a/hpvm/test/epoch_dnn/assets/miniera/calib.txt
+++ /dev/null
@@ -1,21 +0,0 @@
-input: 0.007874015748031496
-conv1: 0.041152522047244094
-add1: 0.041152522047244094
-relu1: 0.03673215196850394
-conv2: 0.18990387401574804
-add2: 0.18990387401574804
-relu2: 0.12851757480314963
-pool1: 0.12851757480314963
-conv3: 0.4379456692913386
-add3: 0.4379456692913386
-relu3: 0.13566622047244095
-conv4: 0.3754296456692913
-add4: 0.3754296456692913
-relu4: 0.15238466141732282
-pool2: 0.15238466141732282
-gemm1: 0.27306629921259845
-add5: 0.27306629921259845
-relu5: 0.20020370078740157
-gemm2: 0.6007037007874017
-add6: 0.6007037007874017
-softmax1: 0.007874015748031496
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/calib_AVG.txt b/hpvm/test/epoch_dnn/assets/miniera/scales/calib_AVG.txt
similarity index 100%
rename from hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/calib_AVG.txt
rename to hpvm/test/epoch_dnn/assets/miniera/scales/calib_AVG.txt
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/calib_GAUSS.txt b/hpvm/test/epoch_dnn/assets/miniera/scales/calib_GAUSS.txt
similarity index 100%
rename from hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/calib_GAUSS.txt
rename to hpvm/test/epoch_dnn/assets/miniera/scales/calib_GAUSS.txt
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/calib_LAPLACE.txt b/hpvm/test/epoch_dnn/assets/miniera/scales/calib_LAPLACE.txt
similarity index 100%
rename from hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/calib_LAPLACE.txt
rename to hpvm/test/epoch_dnn/assets/miniera/scales/calib_LAPLACE.txt
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/calib_NONE.txt b/hpvm/test/epoch_dnn/assets/miniera/scales/calib_NONE.txt
similarity index 100%
rename from hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/calib_NONE.txt
rename to hpvm/test/epoch_dnn/assets/miniera/scales/calib_NONE.txt
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/calib_N_STD.txt b/hpvm/test/epoch_dnn/assets/miniera/scales/calib_N_STD.txt
similarity index 100%
rename from hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/calib_N_STD.txt
rename to hpvm/test/epoch_dnn/assets/miniera/scales/calib_N_STD.txt