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
new file mode 100644
index 0000000000000000000000000000000000000000..d8985397705cf19d5533a7e4a376a71a9f130fb0
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/approxhpvm_src.cc
@@ -0,0 +1,430 @@
+
+#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
new file mode 100644
index 0000000000000000000000000000000000000000..2a323bb65028b253c8070a3b90c1f82be6f78d70
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/comp_files/compareDenseWeights.py
@@ -0,0 +1,42 @@
+
+
+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~
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
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
new file mode 100644
index 0000000000000000000000000000000000000000..92fa27c1ac651a1430650a3f1ddbaa5665788302
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diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/input.bin b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/input.bin
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diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/input_fp16.bin b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/input_fp16.bin
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diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/labels.bin b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/labels.bin
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diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/layer_composition.txt b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/layer_composition.txt
new file mode 100644
index 0000000000000000000000000000000000000000..54ef6c9f01517d20355681b1d19c8b865daf514c
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/layer_composition.txt
@@ -0,0 +1,6 @@
+conv add activation
+conv add activation pool
+conv add activation
+conv add activation pool
+dense add activation
+dense add
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
new file mode 100644
index 0000000000000000000000000000000000000000..c0aecb467775babfd4b5c2873abf287905ee11f8
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/layers.txt
@@ -0,0 +1,6 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..fd96ab0878269718c58c52115a22b79e2f62ec99
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/promise_src.cc
@@ -0,0 +1,93 @@
+
+#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
new file mode 100644
index 0000000000000000000000000000000000000000..6c5d33cc8067b2489392d1dc5c30e9a9dd41ab03
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/readInput.py
@@ -0,0 +1,18 @@
+
+
+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~
new file mode 100644
index 0000000000000000000000000000000000000000..2a323bb65028b253c8070a3b90c1f82be6f78d70
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/readInput.py~
@@ -0,0 +1,42 @@
+
+
+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
new file mode 100644
index 0000000000000000000000000000000000000000..332d9903e02fa5a218a3117947e9dbf955d07d10
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/miniera-hpvm/data/weights/src.cc
@@ -0,0 +1,98 @@
+
+#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;
+
+}