diff --git a/llvm/projects/hpvm-tensor-rt/CMakeLists.txt b/llvm/projects/hpvm-tensor-rt/CMakeLists.txt
index ba8d053f5dca4b35a60c37d26062fdca39929b38..bb372be8b2ef2a0705486de8d346e9732abc96db 100644
--- a/llvm/projects/hpvm-tensor-rt/CMakeLists.txt
+++ b/llvm/projects/hpvm-tensor-rt/CMakeLists.txt
@@ -147,11 +147,11 @@ target_link_libraries(resnet18_cifar10 tensor_runtime ${GPU_PROFILER_LIB} ${SOC
add_executable(vgg16_cifar100 dnn_sources/src/vgg16_cifar100.cc)
target_link_libraries(vgg16_cifar100 tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-add_executable(mobilenet_depthwise dnn_sources/src/mobilenet.cc)
-target_link_libraries(mobilenet_depthwise tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
+add_executable(mobilenet dnn_sources/src/mobilenet.cc)
+target_link_libraries(mobilenet tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
-add_executable(mobilenet_shallow_depthwise dnn_sources/src/mobilenet_shallow_depthwise.cc)
-target_link_libraries(mobilenet_shallow_depthwise tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
+add_executable(mobilenet_shallow dnn_sources/src/mobilenet_shallow.cc)
+target_link_libraries(mobilenet_shallow tensor_runtime ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
add_executable(resnet50_imagenet dnn_sources/src/resnet50_imagenet.cc)
target_link_libraries(resnet50_imagenet tensor_runtime_online ${GPU_PROFILER_LIB} ${SOC_SIMULATOR_LIB})
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet2_cifar10.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet2_cifar10.cc
index 14db12f290795dc6edede25cd6cabdb9ba1bc491..c0ca3207132a54f1160e990ff93f37d8112ab5ed 100644
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet2_cifar10.cc
+++ b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/alexnet2_cifar10.cc
@@ -18,40 +18,41 @@ void testCifarNet(){
printf("********* Alexnet2 CIFAR-10 DNN ********** \n");
- std::string dir_prefix = std::string("../model_params/alexnet2_cifar10/");
- std::string input_path = dir_prefix + std::string("norm_cifar_input.bin");
- std::string labels_path = dir_prefix + std::string("test_labels.bin");
-
- void* conv1_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv1.bin",
- float_type, 32, 3, 3, 3);
- void* conv1_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv1_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv2.bin",
- float_type, 32, 32, 3, 3);
- void* conv2_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv2_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv3_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv3.bin",
- float_type, 64, 32, 3, 3);
- void* conv3_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv3_bias.bin",
- float_type, 1, 64, 1, 1);
- void* conv4_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv4.bin",
- float_type, 64, 64, 3, 3);
- void* conv4_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv4_bias.bin",
- float_type, 1, 64, 1, 1);
- void* conv5_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv5.bin",
- float_type, 128, 64, 3, 3);
- void* conv5_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv5_bias.bin",
- float_type, 1, 128, 1, 1);
- void* conv6_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv6.bin",
- float_type, 128, 128, 3, 3);
- void* conv6_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv6_bias.bin",
- float_type, 1, 128, 1, 1);
-
- void* fc1_weights = readTrainedWeights("../model_params/alexnet2_cifar10/fc1.bin",
- float_type, 1, 1, 2048, 10);
- void* fc1_bias = readTrainedWeights("../model_params/alexnet2_cifar10/fc1_bias.bin",
- float_type, 1, 10, 1, 1);
-
+
+ std::string dir_prefix = std::string("../model_params/alexnet2_cifar10_test/");
+ std::string input_path = dir_prefix + std::string("input.bin");
+ std::string labels_path = dir_prefix + std::string("labels.bin");
+ std::string labels32_path = dir_prefix + std::string("labels32.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 conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
+ void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,128,64,3,3);
+ std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
+ void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,128,1,1);
+ std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
+ void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,128,128,3,3);
+ std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
+ void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,128,1,1);
+ 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(), 0,1,1,2048,10);
+ 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(), 0,1,10,1,1);
+
int conv_mode = 1; // NOTE: using CROSS_CORRELATION
int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
@@ -73,48 +74,47 @@ void testCifarNet(){
int end = (i + 1) * batch_size;
void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
- void* conv1out = tensorConvolution(input, conv1_filter, 1, 1, 1, 1,
+ void* conv1out = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1,
conv_mode, conv_precision);
- tensorAdd(conv1out, conv1_bias);
+ tensorAdd(conv1out, conv2d_1_b);
void* conv1_tanh = tensorTanh(conv1out);
// 2nd Layer
- void* conv2out = tensorConvolution(conv1_tanh, conv2_filter, 1, 1, 1, 1,
+ void* conv2out = tensorConvolution(conv1_tanh, conv2d_2_w, 1, 1, 1, 1,
conv_mode, conv_precision);
- tensorAdd(conv2out, conv2_bias);
+ tensorAdd(conv2out, conv2d_2_b);
void* conv2_tanh = tensorTanh(conv2out);
void* pool2out = tensorPooling(conv2_tanh, 0, 2, 2, 0, 0, 2, 2);
// 3rd Layer
- void* conv3out = tensorConvolution(pool2out, conv3_filter, 1, 1, 1, 1,
+ void* conv3out = tensorConvolution(pool2out, conv2d_3_w, 1, 1, 1, 1,
conv_mode, conv_precision);
- tensorAdd(conv3out, conv3_bias);
+ tensorAdd(conv3out, conv2d_3_b);
void* conv3_tanh = tensorTanh(conv3out);
// 4th Layer
- void* conv4out = tensorConvolution(conv3_tanh, conv4_filter, 1, 1, 1, 1,
+ void* conv4out = tensorConvolution(conv3_tanh, conv2d_4_w, 1, 1, 1, 1,
conv_mode, conv_precision);
- tensorAdd(conv4out, conv4_bias);
+ tensorAdd(conv4out, conv2d_4_b);
void* conv4_tanh = tensorTanh(conv4out);
void* pool4out = tensorPooling(conv4_tanh, 0, 2, 2, 0, 0, 2, 2);
// 5th Layer
- void* conv5out = tensorConvolution(pool4out, conv5_filter, 1, 1, 1, 1,
+ void* conv5out = tensorConvolution(pool4out, conv2d_5_w, 1, 1, 1, 1,
conv_mode, conv_precision);
- tensorAdd(conv5out, conv5_bias);
+ tensorAdd(conv5out, conv2d_5_b);
void* conv5_tanh = tensorTanh(conv5out);
// 6th Layer
- void* conv6out = tensorConvolution(conv5_tanh, conv6_filter, 1, 1, 1, 1,
+ void* conv6out = tensorConvolution(conv5_tanh, conv2d_6_w, 1, 1, 1, 1,
conv_mode, conv_precision);
- tensorAdd(conv6out, conv6_bias);
-
+ tensorAdd(conv6out, conv2d_6_b);
void* conv6_tanh = tensorTanh(conv6out);
void* pool6out = tensorPooling(conv6_tanh, 0, 2, 2, 0, 0, 2, 2);
// final FC Layer
- void* gemm1out = tensorGemmGPU(pool6out, fc1_weights);
- void* gemm1biasout = tensorAdd(gemm1out, fc1_bias);
+ void* gemm1out = tensorGemmGPU(pool6out, dense_1_w);
+ void* gemm1biasout = tensorAdd(gemm1out, dense_1_b);
void* result = tensorSoftmax(gemm1biasout);
uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/alexnet2_cifar10_half.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/alexnet2_cifar10_half.cc
index 0c0c6aaa6291bcd67f3e3bff1eb7b2481bf72f1e..1ad4ba0aaf0cee655a304a7134553821c0885202 100644
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/alexnet2_cifar10_half.cc
+++ b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/alexnet2_cifar10_half.cc
@@ -18,40 +18,39 @@ void testCifarNet(){
printf("********* Alexnet2 CIFAR-10 DNN ********** \n");
- std::string dir_prefix = std::string("../model_params/alexnet2_cifar10/");
- std::string input_path = dir_prefix + std::string("norm_cifar_input.bin");
- std::string labels_path = dir_prefix + std::string("test_labels.bin");
-
- void* conv1_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv1.bin",
- float_type, 32, 3, 3, 3);
- void* conv1_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv1_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv2_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv2.bin",
- float_type, 32, 32, 3, 3);
- void* conv2_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv2_bias.bin",
- float_type, 1, 32, 1, 1);
- void* conv3_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv3.bin",
- float_type, 64, 32, 3, 3);
- void* conv3_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv3_bias.bin",
- float_type, 1, 64, 1, 1);
- void* conv4_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv4.bin",
- float_type, 64, 64, 3, 3);
- void* conv4_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv4_bias.bin",
- float_type, 1, 64, 1, 1);
- void* conv5_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv5.bin",
- float_type, 128, 64, 3, 3);
- void* conv5_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv5_bias.bin",
- float_type, 1, 128, 1, 1);
- void* conv6_filter = readTrainedWeights("../model_params/alexnet2_cifar10/conv6.bin",
- float_type, 128, 128, 3, 3);
- void* conv6_bias = readTrainedWeights("../model_params/alexnet2_cifar10/conv6_bias.bin",
- float_type, 1, 128, 1, 1);
-
- void* fc1_weights = readTrainedWeights("../model_params/alexnet2_cifar10/fc1.bin",
- float_type, 1, 1, 2048, 10);
- void* fc1_bias = readTrainedWeights("../model_params/alexnet2_cifar10/fc1_bias.bin",
- float_type, 1, 10, 1, 1);
-
+ std::string dir_prefix = std::string("../model_params/alexnet2_cifar10_test/");
+ std::string input_path = dir_prefix + std::string("input.bin");
+ std::string labels_path = dir_prefix + std::string("labels.bin");
+ std::string labels32_path = dir_prefix + std::string("labels32.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 conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
+ void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,128,64,3,3);
+ std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
+ void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,128,1,1);
+ std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
+ void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,128,128,3,3);
+ std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
+ void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,128,1,1);
+ 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(), 0,1,1,2048,10);
+ 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(), 0,1,10,1,1);
int conv_mode = 1; // NOTE: using CROSS_CORRELATION
int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
@@ -73,48 +72,47 @@ void testCifarNet(){
int end = (i + 1) * batch_size;
void* input = readInputBatch(input_path.c_str(), 0,start,end,3,32,32);
- void* conv1out = tensorHalfConvolution(input, conv1_filter, 1, 1, 1, 1,
+ void* conv1out = tensorHalfConvolution(input, conv2d_1_w, 1, 1, 1, 1,
conv_mode, conv_precision);
- tensorHalfAdd(conv1out, conv1_bias);
+ tensorHalfAdd(conv1out, conv2d_1_b);
void* conv1_tanh = tensorHalfTanh(conv1out);
// 2nd Layer
- void* conv2out = tensorHalfConvolution(conv1_tanh, conv2_filter, 1, 1, 1, 1,
+ void* conv2out = tensorHalfConvolution(conv1_tanh, conv2d_2_w, 1, 1, 1, 1,
conv_mode, conv_precision);
- tensorHalfAdd(conv2out, conv2_bias);
+ tensorHalfAdd(conv2out, conv2d_2_b);
void* conv2_tanh = tensorHalfTanh(conv2out);
void* pool2out = tensorHalfPooling(conv2_tanh, 0, 2, 2, 0, 0, 2, 2);
// 3rd Layer
- void* conv3out = tensorHalfConvolution(pool2out, conv3_filter, 1, 1, 1, 1,
+ void* conv3out = tensorHalfConvolution(pool2out, conv2d_3_w, 1, 1, 1, 1,
conv_mode, conv_precision);
- tensorHalfAdd(conv3out, conv3_bias);
+ tensorHalfAdd(conv3out, conv2d_3_b);
void* conv3_tanh = tensorHalfTanh(conv3out);
// 4th Layer
- void* conv4out = tensorHalfConvolution(conv3_tanh, conv4_filter, 1, 1, 1, 1,
+ void* conv4out = tensorHalfConvolution(conv3_tanh, conv2d_4_w, 1, 1, 1, 1,
conv_mode, conv_precision);
- tensorHalfAdd(conv4out, conv4_bias);
+ tensorHalfAdd(conv4out, conv2d_4_b);
void* conv4_tanh = tensorHalfTanh(conv4out);
void* pool4out = tensorHalfPooling(conv4_tanh, 0, 2, 2, 0, 0, 2, 2);
// 5th Layer
- void* conv5out = tensorHalfConvolution(pool4out, conv5_filter, 1, 1, 1, 1,
+ void* conv5out = tensorHalfConvolution(pool4out, conv2d_5_w, 1, 1, 1, 1,
conv_mode, conv_precision);
- tensorHalfAdd(conv5out, conv5_bias);
+ tensorHalfAdd(conv5out, conv2d_5_b);
void* conv5_tanh = tensorHalfTanh(conv5out);
// 6th Layer
- void* conv6out = tensorHalfConvolution(conv5_tanh, conv6_filter, 1, 1, 1, 1,
+ void* conv6out = tensorHalfConvolution(conv5_tanh, conv2d_6_w, 1, 1, 1, 1,
conv_mode, conv_precision);
- tensorHalfAdd(conv6out, conv6_bias);
-
+ tensorHalfAdd(conv6out, conv2d_6_b);
void* conv6_tanh = tensorHalfTanh(conv6out);
void* pool6out = tensorHalfPooling(conv6_tanh, 0, 2, 2, 0, 0, 2, 2);
// final FC Layer
- void* gemm1out = tensorHalfGemmGPU(pool6out, fc1_weights);
- void* gemm1biasout = tensorHalfAdd(gemm1out, fc1_bias);
+ void* gemm1out = tensorHalfGemmGPU(pool6out, dense_1_w);
+ void* gemm1biasout = tensorHalfAdd(gemm1out, dense_1_b);
void* result = tensorSoftmax(gemm1biasout);
uint8_t* labels = readLabelsBatch(labels_path.c_str(), start, end);
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/backup/conv1.bin b/llvm/projects/hpvm-tensor-rt/model_params/legacy/alexnet_cifar10/backup/conv1.bin
similarity index 100%
rename from llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/backup/conv1.bin
rename to llvm/projects/hpvm-tensor-rt/model_params/legacy/alexnet_cifar10/backup/conv1.bin
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/backup/conv1_bias.bin b/llvm/projects/hpvm-tensor-rt/model_params/legacy/alexnet_cifar10/backup/conv1_bias.bin
similarity index 100%
rename from llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/backup/conv1_bias.bin
rename to llvm/projects/hpvm-tensor-rt/model_params/legacy/alexnet_cifar10/backup/conv1_bias.bin
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/backup/conv2.bin b/llvm/projects/hpvm-tensor-rt/model_params/legacy/alexnet_cifar10/backup/conv2.bin
similarity index 100%
rename from llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/backup/conv2.bin
rename to llvm/projects/hpvm-tensor-rt/model_params/legacy/alexnet_cifar10/backup/conv2.bin
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/backup/conv2_bias.bin b/llvm/projects/hpvm-tensor-rt/model_params/legacy/alexnet_cifar10/backup/conv2_bias.bin
similarity index 100%
rename from llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/backup/conv2_bias.bin
rename to llvm/projects/hpvm-tensor-rt/model_params/legacy/alexnet_cifar10/backup/conv2_bias.bin
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/backup/conv3.bin b/llvm/projects/hpvm-tensor-rt/model_params/legacy/alexnet_cifar10/backup/conv3.bin
similarity index 100%
rename from llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/backup/conv3.bin
rename to llvm/projects/hpvm-tensor-rt/model_params/legacy/alexnet_cifar10/backup/conv3.bin
diff --git a/llvm/projects/hpvm-tensor-rt/model_params/alexnet_cifar10/backup/conv3_bias.bin b/llvm/projects/hpvm-tensor-rt/model_params/legacy/alexnet_cifar10/backup/conv3_bias.bin
similarity index 100%
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