diff --git a/llvm/test/VISC/DNN_Benchmarks/benchmarks/lenet_dsoc/data/lenet_hpvm/approxhpvm_src.cc b/llvm/test/VISC/DNN_Benchmarks/benchmarks/lenet_dsoc/data/lenet_hpvm/approxhpvm_src.cc
new file mode 100644
index 0000000000000000000000000000000000000000..96b24a0409ae15f708bd7b6897edca08e52d82bb
--- /dev/null
+++ b/llvm/test/VISC/DNN_Benchmarks/benchmarks/lenet_dsoc/data/lenet_hpvm/approxhpvm_src.cc
@@ -0,0 +1,373 @@
+
+#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, 2, 2, 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) {
+ __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_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_convolution(t1, t2, 2, 2, 1, 1);
+ __visc__return(2, r, (size_t) 0);
+}
+
+void var_5_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_6_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_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, 1, 1, 2, 2);
+ __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_mul(t1, t2);
+ __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, 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_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_add(t1, t2);
+ __visc__return(2, r, (size_t) 0);
+}
+
+void var_15_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_16_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* 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(11, input, conv2d_1_w, conv2d_1_b, conv2d_2_w, conv2d_2_b, conv2d_3_w, conv2d_3_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);
+
+ 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, 6, 2, 0);
+ __visc__bindIn(var_4, 7, 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);
+ __visc__bindIn(var_5, 8, 2, 0);
+ __visc__bindIn(var_5, 9, 3, 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);
+ __visc__bindIn(var_13, 18, 2, 0);
+ __visc__bindIn(var_13, 19, 3, 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, 20, 2, 0);
+ __visc__bindIn(var_14, 21, 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);
+
+ 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);
+
+ __visc__bindOut(var_16, 0, 0, 0);
+ __visc__bindOut(var_16, 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* 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("lenet_hpvm/");
+std::string input_path = dir_prefix + std::string("input.bin");
+void* input = readTrainedWeights(input_path.c_str(), 0,10000,1,28,28);
+std::string labels_path = dir_prefix + std::string("labels.bin");
+uint8_t* labels = readLabels(labels_path.c_str(),10000);
+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(), 0,32,1,5,5);
+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(), 0,64,32,5,5);
+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(), 0,1,64,1,1);
+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(), 0,64,64,3,3);
+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 dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
+void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,3136,1024);
+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(), 0,1,1024,1,1);
+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(), 0,1,1,1024,10);
+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(), 0,1,10,1,1);
+
+__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->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();
+ computeAccuracy2(labels, 10000, result);
+return 0;
+
+}
diff --git a/llvm/test/VISC/DNN_Benchmarks/benchmarks/lenet_dsoc/data/lenet_hpvm/conv2d_1_b.bin b/llvm/test/VISC/DNN_Benchmarks/benchmarks/lenet_dsoc/data/lenet_hpvm/conv2d_1_b.bin
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index 0000000000000000000000000000000000000000..2232a48e41b564a7583ef74acfdc0637856a45f7
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+++ b/llvm/test/VISC/DNN_Benchmarks/benchmarks/lenet_dsoc/data/lenet_hpvm/conv2d_2_b.bin
@@ -0,0 +1,2 @@
+7Dà<Þå<– ^=À©Š=p =˜<Y-¼ Rž= é<¦:ï;
+cƒ<- =&= "=áx´;Ñu<¼ö·F=t_; 9=ûSO¼²vŸ;G¨»éM8=+¦x½`žŒ<RªŒ:ßPP;/®™<Ûè¼ø®ä; ^Ð<'´9¼43û:ÙË;eø<AÖ#½ Ò»‹±e<••<ó¯==Ÿ¨<À´b<»0à<4È< Óö;\§<#t
<}¨½ƒp¼¬=:s"½p#¨¼v'™¼ð^;@Ç<ë8\¼É‡à<ëþ¾¼`ýa»Î€^¼`‚û;sjÉ<w”L=üÙ«¼
\ No newline at end of file
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diff --git a/llvm/test/VISC/DNN_Benchmarks/benchmarks/lenet_dsoc/data/lenet_hpvm/dense_2_w.bin b/llvm/test/VISC/DNN_Benchmarks/benchmarks/lenet_dsoc/data/lenet_hpvm/dense_2_w.bin
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index 0000000000000000000000000000000000000000..5492117eb22bdaa319de30e4c5247bdcc09cff3d
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diff --git a/llvm/test/VISC/DNN_Benchmarks/benchmarks/lenet_dsoc/data/lenet_hpvm/input.bin b/llvm/test/VISC/DNN_Benchmarks/benchmarks/lenet_dsoc/data/lenet_hpvm/input.bin
new file mode 100644
index 0000000000000000000000000000000000000000..4d2423f74188cfe0364185ccb66837785ccf4c4e
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diff --git a/llvm/test/VISC/DNN_Benchmarks/benchmarks/lenet_dsoc/data/lenet_hpvm/labels.bin b/llvm/test/VISC/DNN_Benchmarks/benchmarks/lenet_dsoc/data/lenet_hpvm/labels.bin
new file mode 100644
index 0000000000000000000000000000000000000000..5e1f3881897f4729d6d90ff208a08ccdabb8fe7c
Binary files /dev/null and b/llvm/test/VISC/DNN_Benchmarks/benchmarks/lenet_dsoc/data/lenet_hpvm/labels.bin differ
diff --git a/llvm/test/VISC/DNN_Benchmarks/benchmarks/lenet_dsoc/data/lenet_hpvm/promise_src.cc b/llvm/test/VISC/DNN_Benchmarks/benchmarks/lenet_dsoc/data/lenet_hpvm/promise_src.cc
new file mode 100644
index 0000000000000000000000000000000000000000..46f4637460485f6d1686901c099d77929099b823
--- /dev/null
+++ b/llvm/test/VISC/DNN_Benchmarks/benchmarks/lenet_dsoc/data/lenet_hpvm/promise_src.cc
@@ -0,0 +1,58 @@
+
+#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("lenet_hpvm/");
+std::string input_path = dir_prefix + std::string("input.bin");
+void* input = readTrainedWeights(input_path.c_str(), 0,10000,1,28,28);
+std::string labels_path = dir_prefix + std::string("labels.bin");
+uint8_t* labels = readLabels(labels_path.c_str(),10000);
+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(), 0,32,1,5,5);
+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(), 0,64,32,5,5);
+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(), 0,1,64,1,1);
+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(), 0,64,64,3,3);
+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 dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
+void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,3136,1024);
+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(), 0,1,1024,1,1);
+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(), 0,1,1,1024,10);
+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(), 0,1,10,1,1);
+
+
+void* var_0 = ConvLayer_PROMISE(input, 0.0, 1.0, conv2d_1_w, -0.23503187, 0.22027442, conv2d_1_b, -0.059452552, 0.05141998, 2, 2, 1, 1, 0, 2, 1, 0.0, 1.4499073, 9);
+void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 1.4499073, conv2d_2_w, -0.16616209, 0.1624253, conv2d_2_b, -0.060705345, 0.07730491, 2, 2, 1, 1, -1, 0, 1, 0.0, 4.394469, 9);
+void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 4.394469, conv2d_3_w, -0.17305803, 0.16375193, conv2d_3_b, -0.01982658, 0.05104504, 1, 1, 2, 2, -1, 0, 1, 0.0, 8.275149, 9);
+void* var_3 = FCLayer_PROMISE(var_2, 0.0, 8.275149, dense_1_w, -0.10478318, 0.10765447, dense_1_b, -0.0300005, 0.03506347, 1, 0.0, 9.308638, 9);
+void* var_4 = FCLayer_PROMISE(var_3, 0.0, 9.308638, dense_2_w, -0.1958274, 0.14691855, dense_2_b, -0.007710449, 0.039741356, 1, 0.0, 36.96972, 9);
+void* var_5 = tensorSoftmax(var_4);
+
+computeAccuracy2(labels,10000,var_5);
+
+llvm_hpvm_cleanupTensorRt();
+
+return 0;
+
+}
diff --git a/llvm/test/VISC/DNN_Benchmarks/benchmarks/lenet_dsoc/data/lenet_hpvm/src.cc b/llvm/test/VISC/DNN_Benchmarks/benchmarks/lenet_dsoc/data/lenet_hpvm/src.cc
new file mode 100644
index 0000000000000000000000000000000000000000..c26e11316b648481518ee3b41d029e03d6860e91
--- /dev/null
+++ b/llvm/test/VISC/DNN_Benchmarks/benchmarks/lenet_dsoc/data/lenet_hpvm/src.cc
@@ -0,0 +1,68 @@
+
+#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("lenet_hpvm/");
+std::string input_path = dir_prefix + std::string("input.bin");
+void* input = readTrainedWeights(input_path.c_str(), 0,10000,1,28,28);
+std::string labels_path = dir_prefix + std::string("labels.bin");
+uint8_t* labels = readLabels(labels_path.c_str(),10000);
+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(), 0,32,1,5,5);
+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(), 0,64,32,5,5);
+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(), 0,1,64,1,1);
+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(), 0,64,64,3,3);
+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 dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
+void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,3136,1024);
+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(), 0,1,1024,1,1);
+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(), 0,1,1,1024,10);
+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(), 0,1,10,1,1);
+
+
+void* var_0 = tensorConvolution(input, conv2d_1_w, 2, 2, 1, 1, 1, 0);
+void* var_1 = tensorAdd(var_0, conv2d_1_b);
+void* var_2 = tensorRelu(var_1);
+void* var_3 = tensorPooling(var_2,0,2,2,0,0,2,2);
+void* var_4 = tensorConvolution(var_3, conv2d_2_w, 2, 2, 1, 1, 1, 0);
+void* var_5 = tensorAdd(var_4, conv2d_2_b);
+void* var_6 = tensorRelu(var_5);
+void* var_8 = tensorConvolution(var_6, conv2d_3_w, 1, 1, 2, 2, 1, 0);
+void* var_9 = tensorAdd(var_8, conv2d_3_b);
+void* var_10 = tensorRelu(var_9);
+void* var_12 = tensorGemmGPU(var_10, dense_1_w);
+void* var_13 = tensorAdd(var_12, dense_1_b);
+void* var_14 = tensorRelu(var_13);
+void* var_15 = tensorGemmGPU(var_14, dense_2_w);
+void* var_16 = tensorAdd(var_15, dense_2_b);
+void* var_17 = tensorRelu(var_16);
+void* var_18 = tensorSoftmax(var_17);
+
+computeAccuracy2(labels,10000,var_18);
+
+llvm_hpvm_cleanupTensorRt();
+
+return 0;
+
+}
diff --git a/llvm/test/VISC/DNN_Benchmarks/benchmarks/lenet_dsoc/src/lenet.cpp b/llvm/test/VISC/DNN_Benchmarks/benchmarks/lenet_dsoc/src/lenet.cpp
index b6a5f0082170bf5bb806bc63b659cc34f3c5cbe4..eb74dc53feeb887e7a3c0fe02cbd23c851d5a6ec 100644
--- a/llvm/test/VISC/DNN_Benchmarks/benchmarks/lenet_dsoc/src/lenet.cpp
+++ b/llvm/test/VISC/DNN_Benchmarks/benchmarks/lenet_dsoc/src/lenet.cpp
@@ -4,7 +4,7 @@
#include <unistd.h>
#include <fcntl.h>
#include <sys/stat.h>
-#include <cstring>
+#include <string.h>
#include <visc.h>
//#include <tensorTypes.h>
@@ -313,9 +313,10 @@ RootIn;
int main(){
- std::string dir_prefix = std::string("../../../../../../projects/hpvm-tensor-rt/model_params/lenet_hpvm/");
+ std::string dir_prefix = std::string("../data/lenet_hpvm/");
+
std::string input_path = dir_prefix + std::string("input.bin");
- void* input = readTrainedWeights(input_path.c_str(), 0,100,1,28,28);
+ void* input = readTrainedWeights(input_path.c_str(), 0,500,1,28,28);
std::string labels_path = dir_prefix + std::string("labels.bin");
uint8_t* labels = readLabels(labels_path.c_str(),100);
std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");